Jason B. Siegel

This code package accompanies the Joule article “Managing silicon burn-out via onboard material diagnostics for durable high-energy density batteries.” If you use this code package, please cite the accompanying Joule article: https://doi.org/10.1016/j.joule.2026.102531 The full dataset is distributed separately on Zenodo and should be downloaded from:https://doi.org/10.5281/zenodo.20259651 After downloading the data archive, unzip it and place the extracted contents in the repository-local data folder before running the notebooks or scripts. The repository contains the analysis and plotting workflows used to identify the effective silicon open-circuit potential (OCP), run material-specific eSOH diagnostics for NMC622/SiO-graphite pouch cells, compute silicon current share and transition state of charge (SoC), evaluate sampling-frequency and SoC-window requirements, and reproduce the main figure-generation workflows. The package includes: 1. Lifetime and C-rate diagnostic workflows: Python notebooks and scripts for running voltage-based eSOH diagnostics, appending transition SoC, and evaluating robustness across C-rate, kinetic compensation, discarded initial charge data, and silicon-OCP treatment. 2. Pathway-dependent silicon-current-share analysis: Workflows for computing material-resolved dQ/dV, silicon current share, and transition SoC for BOL and EOL degradation-pathway examples. 3. Error-bound analysis: CRB-based workflows for evaluating sampling-density and SoC-window requirements. 4. MSMR silicon-OCP identification: Code for reconstructing the effective silicon OCP used by the diagnostic model. 5. Figure-generation scripts: MATLAB and Python workflows for reproducing the figure panels from prepared or generated outputs. The workflows were developed with Python 3.11.5 and MATLAB R2023b. A conda environment file is included as environment.yml. The software is released under the BSD 3-Clause License. For updates and future releases, please see: https://github.com/zhiwen-wan

This dataset accompanies the Joule manuscript “Managing silicon burn-out via onboard material diagnostics for durable high-energy density batteries.” If you use this dataset, please cite the accompanying Joule article: https://doi.org/10.1016/j.joule.2026.102531 Associated code package:https://doi.org/10.5281/zenodo.20259383 The archive contains the data folder required to run the analysis and plotting workflows in the associated code repository. After downloading, users should unzip the archive and place the extracted data folder at the root of the associated code repository. 1. Lifetime data: The dataset includes cycling and calendar-aging data from 60 NMC622/SiO-graphite pouch cells tested under 24 operating conditions, including different pretension, temperature, C-rate, and state-of-charge window protocols. The lifetime data include time-series records such as current, voltage, temperature, capacity/throughput, and protocol labels, together with cycle-level metrics and the experimental test matrix. 2. BOL/EOL C-rate diagnostic data: The dataset includes beginning-of-life (BOL) and end-of-life (EOL) C-rate diagnostic files used to evaluate material-specific diagnostic performance across charging rates, silicon-OCP treatment, discarded initial charge data, and kinetic compensation settings. 3. Electrode OCP reference data: The dataset includes electrode open-circuit-potential (OCP) reference files for graphite, NMC622, silicon, reconstructed silicon, and the composite anode. These files support the MSMR silicon-OCP identification, eSOH diagnostics, transition-SoC calculation, and figure-generation workflows. A SHA256 checksum file is provided to verify the integrity of the downloaded archive. The dataset is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0). The lifetime-aging trends represented by this dataset were previously discussed in:Wan, Zhiwen, et al. “Degradation and expansion of lithium-ion batteries with silicon/graphite anodes: Impact of pretension, temperature, C-rate and state-of-charge window.” eTransportation 24 (2025): 100416. For updates and future releases, please see: https://github.com/zhiwen-wan

This code package accompanies the Joule article “Managing silicon burn-out via onboard material diagnostics for durable high-energy density batteries.” If you use this code package, please cite the accompanying Joule article: https://doi.org/10.1016/j.joule.2026.102531 The full dataset is distributed separately on Zenodo and should be downloaded from:https://doi.org/10.5281/zenodo.20259651 After downloading the data archive, unzip it and place the extracted contents in the repository-local data folder before running the notebooks or scripts. The repository contains the analysis and plotting workflows used to identify the effective silicon open-circuit potential (OCP), run material-specific eSOH diagnostics for NMC622/SiO-graphite pouch cells, compute silicon current share and transition state of charge (SoC), evaluate sampling-frequency and SoC-window requirements, and reproduce the main figure-generation workflows. The package includes: 1. Lifetime and C-rate diagnostic workflows: Python notebooks and scripts for running voltage-based eSOH diagnostics, appending transition SoC, and evaluating robustness across C-rate, kinetic compensation, discarded initial charge data, and silicon-OCP treatment. 2. Pathway-dependent silicon-current-share analysis: Workflows for computing material-resolved dQ/dV, silicon current share, and transition SoC for BOL and EOL degradation-pathway examples. 3. Error-bound analysis: CRB-based workflows for evaluating sampling-density and SoC-window requirements. 4. MSMR silicon-OCP identification: Code for reconstructing the effective silicon OCP used by the diagnostic model. 5. Figure-generation scripts: MATLAB and Python workflows for reproducing the figure panels from prepared or generated outputs. The workflows were developed with Python 3.11.5 and MATLAB R2023b. A conda environment file is included as environment.yml. The software is released under the BSD 3-Clause License. For updates and future releases, please see: https://github.com/zhiwen-wan

This dataset accompanies the Joule manuscript “Managing silicon burn-out via onboard material diagnostics for durable high-energy density batteries.” If you use this dataset, please cite the accompanying Joule article: https://doi.org/10.1016/j.joule.2026.102531 Associated code package:https://doi.org/10.5281/zenodo.20259383 The archive contains the data folder required to run the analysis and plotting workflows in the associated code repository. After downloading, users should unzip the archive and place the extracted data folder at the root of the associated code repository. 1. Lifetime data: The dataset includes cycling and calendar-aging data from 60 NMC622/SiO-graphite pouch cells tested under 24 operating conditions, including different pretension, temperature, C-rate, and state-of-charge window protocols. The lifetime data include time-series records such as current, voltage, temperature, capacity/throughput, and protocol labels, together with cycle-level metrics and the experimental test matrix. 2. BOL/EOL C-rate diagnostic data: The dataset includes beginning-of-life (BOL) and end-of-life (EOL) C-rate diagnostic files used to evaluate material-specific diagnostic performance across charging rates, silicon-OCP treatment, discarded initial charge data, and kinetic compensation settings. 3. Electrode OCP reference data: The dataset includes electrode open-circuit-potential (OCP) reference files for graphite, NMC622, silicon, reconstructed silicon, and the composite anode. These files support the MSMR silicon-OCP identification, eSOH diagnostics, transition-SoC calculation, and figure-generation workflows. A SHA256 checksum file is provided to verify the integrity of the downloaded archive. The dataset is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0). The lifetime-aging trends represented by this dataset were previously discussed in:Wan, Zhiwen, et al. “Degradation and expansion of lithium-ion batteries with silicon/graphite anodes: Impact of pretension, temperature, C-rate and state-of-charge window.” eTransportation 24 (2025): 100416. For updates and future releases, please see: https://github.com/zhiwen-wan

The solid electrolyte interphase (SEI) - a critical passivation layer that governs the longevity, safety, and efficiency of lithium-ion batteries - is created during the last step in cell manufacturing called cell formation. Conventional cell formation protocols are largely empirical, resulting in long processing times and limited control over the SEI growth rate that influences SEI quality and lifetime performance. This paper develops a control-oriented, semi-empirical model to estimate SEI thickness growth from terminal voltage and cell expansion measurements acquired in-operando during manufacturing using low-cost micrometer-precision integrated-sensing fixture. Model parameters are calibrated against cell formation data, and an unscented Kalman filter is employed to estimate the SEI film growth. The results lay the foundation for future closed-loop control of SEI growth, enabling high-quality and more efficient formation processes.

The solid electrolyte interphase (SEI) - a critical passivation layer that governs the longevity, safety, and efficiency of lithium-ion batteries - is created during the last step in cell manufacturing called cell formation. Conventional cell formation protocols are largely empirical, resulting in long processing times and limited control over the SEI growth rate that influences SEI quality and lifetime performance. This paper develops a control-oriented, semi-empirical model to estimate SEI thickness growth from terminal voltage and cell expansion measurements acquired in-operando during manufacturing using low-cost micrometer-precision integrated-sensing fixture. Model parameters are calibrated against cell formation data, and an unscented Kalman filter is employed to estimate the SEI film growth. The results lay the foundation for future closed-loop control of SEI growth, enabling high-quality and more efficient formation processes.

Silicon-graphite (Si/Gr) anodes increase battery energy density, but rapid Si "burn-out" limits lifetime. Yet, battery management systems lack effective tools to diagnose Si aging beyond capacity loss and to manage its degradation during operation. Here, we develop a diagnostic framework that jointly estimates material-specific health and degradation-induced Si open-circuit potential deformation from constant-current charge data. The framework tracks the transition state-of-charge (SoC) below which Si dominates, revealing pathway-dependent Si utilization shifts: from 48% fresh to 73% with lithium loss but 33% with active-Si loss. Transition SoC error remains below 3% up to an effective 0.3C, and error bounds across sampling and charge windows support onboard use. Guided by the finding that elevated temperature improves Si capacity retention in our cells, we propose adaptive thermal management that warms the cell during Si-dominant operation and cools it otherwise, projecting an approximately doubled cycle life without limiting capacity access.

Internal short circuits (ISCs) in batteries occur when electrode layers unintentionally come into contact, creating an unanticipated current path that diverts electrical flow from its intended path, which poses critical safety risks. Manufacturing defects, mechanical stress, lithium plating, or exposure to excessive heat may cause soft shorts, which result in self-discharge heating [1]. Soft shorts with a large short-to-cell resistance ratio (R isc /R 0 >1) are difficult to detect via their electrical signature alone (terminal voltage) but could be observable from their thermal signature. Detection of the soft shorts is vital because they could evolve into hard shorts and lead to thermal runaway. Detection of shorted cells in parallel connected groups is even more critical, as the failure of one cell can cause current from adjacent cells to discharge through the faulty cell, accelerating overheating but having a less recognizable faulty electrical response due to the shared voltage across the parallel group. In our study, we develop simple electrical and thermal circuit submodels for parallel-connected lithium-ion battery cells to investigate their behavior under both normal operating conditions and short-circuit scenarios [2]. We recognize that ISCs can occur in various configurations and consider different ISC architectures by connecting a short-circuit resistor (R isc ) parallel to different segments of an equivalent circuit model (EQM), which consists of an open-circuit voltage function and an internal resistance (OCV-R). Leveraging this scalable model, we analyze the complexities arising from an increasing number of parallel-connected cells. We considered two notable ISC scenarios: one involving a short between the Aluminum and Copper current collectors (Al-Cu ISC) and another between the cathode and anode (Ca-An ISC), the latter of which is associated with lithium dendrite formation [3]. While increasing the number of parallel connections tends to reduce both the voltage drop and temperature rise as generally expected, we discovered that the degree of reduction depends on the specific location of the ISC. This variability complicates ISC detection, especially when dealing with a larger network of interconnected cells. We further demonstrate that thermal runaway risk is governed not by the absolute magnitude of the short-circuit resistance but by its ratio to internal resistance, as well as its relationship to the overall capacity of the cell group. By maintaining this ratio while scaling the resistance magnitudes for both types of ISCs, we evaluate its influence on voltage and temperature trends. In our simulations, we consider a pack of up to 10 parallel-connected NMC cells, where one of the cells experiences an ISC. Notably, for a short that occurs between the current collectors (Al-Cu ISC), the voltage drop and temperature rise responses for a pack of parallel-connected cells are nearly identical as resistance values decrease, provided the ratio of R isc /R 0 is the same. In contrast, for a short between the cell’s cathode and anode (Ca-An ISC), higher resistance magnitudes result in reduced voltage drops and temperature rises. These findings highlight threshold tuning and monitoring strategies to mitigate ISC risks, offering insights that can improve threshold selection for conventional detection methods and tuning parameters for model-based algorithms. REFERENCES [1] Chen, Wei, et al. "Defects in Lithium-Ion Batteries: From Origins to Safety Risks." Green Energy and Intelligent Transportation (2024): 100235. [2] Movahedi, Hamidreza, et al. “Interacting Multiple-Model for Fault Detection and Short Resistance Estimation in Parallel Connected Lithium-ion Batteries.” MECC 2025 (submitted) [3] Zhang, Mingxuan, et al. "Internal short circuit trigger method for lithium-ion battery based on shape memory alloy." Journal of The Electrochemical Society 164.13 (2017): A3038. Figure 1: A model showing parallel-connected cells, with one experiencing one of two internal short circuits: Current collector (Al-Cu) and Dendrite (Ca-An). Their OCV-R representation and physical interpretation are also shown. In the center, the time domain results for terminal voltage, c-rate, and temperature are shown for a pack of 2 and 5 parallel-connected cells. Note that t = 0 is where the ISC was induced. At the bottom, the features of voltage drop and temperature rise one second after the ISC occurrence are plotted against the number of parallel-connected cells in a pack (n = 2, 5, 10). To study their impact on these features, the total resistance of the pack, magnitude of the short-circuit resistance (R isc ), and short-to-cell ratio (R isc /R 0 ) were varied for each nP pack. Figure 1

Recent Battery Energy Storage System (BESS) failures highlight the need to detect gases from venting cells as quickly as possible, as well as the vulnerabilities in existing monitoring infrastructure that would alert the occurrence and location of cell venting [1]. Conventional detection strategies, which rely on stationary gas sensors, can fail to identify low-volume gas release from single-cell early stage (first) venting, especially in large-scale BESS that have multiple racks and modules, such as Panel A in Figure 1. Suboptimal or improper fixed sensor placement, gas transport effects (e.g., thermal buoyancy), and dilution below detection thresholds further complicate failure detection. These limitations hinder emergency responders’ ability to accurately assess explosion and/or toxicity risks. To address these challenges, we propose integrating a guided mobile gas sensing platform to complement stationary gas sensors. This system will dynamically monitor spatial and temporal gas evolution during early failure stages by deploying a robotic platform equipped with commercial sensors to detect the most commonly emitted gases (CO2, H2, CO, electrolyte vapor). The robot will patrol accessible areas or be strategically guided to areas of interest to actively sample “suspicious zones” with high concentrations of vent gases. This target navigation will be informed by advanced state-of-health (SOH) diagnostics derived from electrical and thermal measurements typically available in the Battery Management System (BMS) data. A major component of our detection algorithm is to recognize how rapidly a fault is propagating to other cells or the time elapsed from the vent initiation. To this end, we first characterize venting gases in terms of composition, quantity, temperature, and time evolution. A literature review (Figure 1, Panel B) on gas characterization was conducted, and it was found that most prior work focused on global, post-thermal runaway (TR) gas analysis. However, the combustion of vent gases during TR makes their characterization challenging, leaving first venting behavior poorly understood. To address this gap, we aim to develop a systematic way to trigger gas generation without full thermal runaway by inducing controlled overheating and arresting the heat after first venting occurs. We will apply this method to two form factors: cylindrical (2.6 Ah) and prismatic (32 Ah), focusing on LFP cells due to their widespread adoption in grid-scale BESS. After characterizing single-cell behavior, we will construct a representative BESS rack (Figure 1, Panel C) with cells inside modules to study venting propagation from vented cells to the module venting channels and to the rack headspace where the stationary sensors are typically located. Our gas characterization will then inform models and algorithms that can recognize the various stages of an evolving failure, including the location and the number of cells undergoing slow discharge, venting, and/or the propagation of thermal runaway. This will guide the robotic sensor platform to reach the appropriate venting channel, providing a faster response and better guidance for first responders. By integrating robotic mobility with diagnostic and prognostic tools, this will not only improve the detection of first venting events, but also enhance situational awareness for emergency response. It enables high-confidence localization of first venting events, estimates the scale of the fault progression, and quantifies the number of cells involved in or propagating in a thermal runaway event, providing actionable data to guide safer and more rapid containment efforts. ACKNOWLEDGEMENT This project is supported by UL Research Institutes. REFERENCES [1] Srinivasan, L., Shaw, S. and Billaut, E. (2024). Insights from EPRI’s battery energy storage systems (BESS) failure incident database: analysis of failure root cause Figure 1: A typical large-scale Battery Energy Storage System (BESS) with multiple racks and modules (Panel A), an overview of literature showing the lack of vent (pre-thermal runaway) gas characterization for LFP cells (Panel B), and a representative BESS rack with a mockup of the mobile guided gas sensing platform directed to sample from a module vent (Panel C). Figure 1

Inhomogeneity in lithium-ion batteries generally describes nonuniform distributions of materials, properties, and operating conditions that lead to local variation in aging factors (e.g., state-of-charge, current density, and temperature). In extreme cases, inhomogeneities can develop into safety risks, including localized overheating, overcharging, and internal short circuits, making it important for battery management systems (BMS) to characterize inhomogeneity and monitor its evolution. While numerous factors influence inhomogeneity, previous studies have shown that the resulting smoothing of differential voltage (DV) phase transition peaks can be captured by modeling inhomogeneity as a distribution of the state of lithiation or capacity [1,2]. However, there is limited data and discussion about the impact of operating temperature on the evolution of inhomogeneity, especially for cells with significant capacity loss. Our work quantifies inhomogeneity for NMC/graphite cells aged under different temperature and C-rate conditions up to 50% capacity loss and shows how inhomogeneity develops at different temperatures [3]. By applying a combination of algorithms from previous studies [1,2,4], we characterize inhomogeneity by augmenting a differential voltage analysis (DVA) algorithm, which typically assumes invariant half-cell open-circuit potential (OCP) taken from the fresh cell, with a modeled aged negative electrode OCP. The aged electrode OCP model captures the smoothing of graphite phase transition peaks using a Gaussian distribution of electrode capacities, where inhomogeneity (σ Cn ) is characterized as the standard deviation. Overall, the model describes the aged full cell open-circuit voltage (OCV) and DV well, capturing the DV peak smoothing behavior even after the peak corresponding to the Stage 2 transition, or high-SOC peak, is not observable (Fig. 1a). Across all cells, the average root-mean-square errors (RMSE) are low for voltage (RMSE V = 4.2 mV) and differential voltage (RMSE dVdQ = 17.5 mV/Ah). A comparison with the conventional voltage fitting (VF) algorithm shows a significant reduction in the RMSE dVdQ , while maintaining comparably low RMSEV as the cells age (Fig. 1b). Across cells cycled at C/3 and cold (0 o C), room (~25 o C), and hot (45 o C) temperature, we observed different increasing trajectories in inhomogeneity (Fig. 1c). After an initial decrease, σ Cn increases mostly linearly with growth rates in order of hot > cold> room temperature, until the cells transition to an accelerated degradation rate, or “knee” in capacity. After the knee, σ Cn grows notably faster in cold and hot temperature cells. In the cold temperature cells, the sudden knee transition corresponds to a significant increase in σ Cn . In contrast, the knee transition and the corresponding increase in σ Cn in the hot temperature cells are gradual and occur when the N:P ratio<1, indicating electrode saturation. While both cold- and hot-temperature cells have similar σ Cn (0.8 Ah) after 30% capacity loss, cold-temperature cells consumed almost half the amount of Li after the knee compared to hot-temperature cells. While the literature offers suggestions for possible underlying degradation mechanisms, future work will include post-mortem analyses to identify the dominant degradation mechanisms for a subset of cells. REFERENCES [1] Kirst, Cedric, et al. "Non-destructive electrode potential and open-circuit voltage aging estimation for lithium-ion batteries." Journal of Power Sources 602 (2024): 234341. [2] Fath, Johannes Philipp, et al. "Quantification of aging mechanisms and inhomogeneity in cycled lithium-ion cells by differential voltage analysis." Journal of Energy Storage 25 (2019): 100813. [3] Tran, Vivian, et al. “Estimating degradation modes and inhomogeneity in aged Lithium-ion batteries.” (In preparation) [4] Lee, Suhak, et al. "Electrode state of health estimation for lithium ion batteries considering half-cell potential change due to aging." Journal of The Electrochemical Society 167.9 (2020): 090531. Figure 1: Overview of the main modelling and parameterization results, including (a) an example of measured and modeled voltage and DV for a cell aged at C/3 and 45 o C from fresh to 50% SOH; (b) an error comparison to illustrate the impact of adding differential voltage error (V+DV) to the cost function and inhomogeneity (V+DV+σ) to the parameters; and (c) the parameterized degradation modes across temperature for cells cycled at C/3 and 2C. Figure 1

Abstract Detecting internal short circuits (ISCs) in a single cell connected in parallel with others is challenging because unmeasured internal currents can obscure measurable indicators such as charge loss and voltage drop from the faulty cell. In this work, we propose a new method for detecting ISCs based on the interacting multiple model (IMM) estimation technique, which can provide a probability for the occurrence of an ISC and simultaneously estimate the short-circuit resistance, indicating the severity of the ISC. The IMM relies on dynamic electrothermal models of parallel cells (nP), both for the healthy mode and short-circuit mode. The IMM technique is combined with unscented Kalman filters (UKFs) to detect internal short circuits and estimate the short-circuit resistance across various synthetic data sets that are corrupted by Gaussian noise for different values of ISC resistance. Fifty short-circuit scenarios were simulated in which one cell in a 46 P cell group underwent an ISC during a drive cycle. The short-circuit resistances ranged from 0.5 to 100 Ω, tested at ten different states of charge (SOCs). Our simulation outputs included busbar voltage, input current, and cell temperatures, which were then corrupted by Gaussian noise. Our IMM successfully detected and estimated the ISC in all fifty cases, with temperature rise remaining below 6 °C before detection, well before the onset of thermal runaway conditions.

Used electric vehicles are driven and sold across states and countries where there can be a switch in both the driving pattern and environmental temperature in which the vehicle is parked and driven. Predicting battery remaining useful life (RUL) and associated fair resale value under such a switch is challenging as one cannot rely on extrapolating the state-of-health (SOH) trend observed from its first user.This paper presents Gaussian Process (GP) regressions that can predict capacity fade in NMC-graphite cells undergoing a switch in operating temperature (from -5°C to 45°C and vice versa) as they transition from first to second use. The GPs are trained on data collected from three cells cycled until 70% SOH in the laboratory at various temperatures (room: 25°C, cold: -5°C, and hot: 45°C). In addition, the GP is also trained on first-use data (before SOH reaches 80%), after which the operating temperature is switched. Training data consisted of temperature and total Amp-hours throughput collected during slow and full charge/discharge cycles which are performed approximately every 40 cycles, assuming such conditions will rarely (once every year) occur in the field. We compare two versions of GP: a baseline regression with a linear mean function and a domain-knowledge informed regression with nonlinear mean function. The nonlinear mean leverages and retrains a basis function based on an empirical degradation model previously developed by the authors. Our GP predicts RUL in second use after a large temperature swing, with an RMSE of 2.5% for another 3 years of operation (about 100 cycles) in the future.

This study investigates the impact of thermal conditioning (TC) on the energy consumption and driving range of electric vehicles in cold weather, especially for vehicles with low utilization and long parking durations, such as electric school buses (ESBs). Two types of TC are considered during parking. The trickle thermal conditioning (TTC) strategy maintains pack temperature above freezing throughout the parking duration. The fast thermal conditioning (FTC), where the pack is heated fast from potentially subfreezing temperatures depending on the parking time and the environmental temperature. The FTC strategy may require a bigger heater than the TTC, but the TTC strategy may consume more grid energy than the FTC, depending on the parking time. We compare TTC and FTC with no thermal conditioning (NoTC) with respect to the driving range and efficiency.The three strategies (TTC, FTC, and NoTC) are analyzed using an electrical-thermal battery pack model coupled with simple control algorithms to capture the effects of a battery thermal management system (BTMS). The simulated model is validated using real-world winter operating data from three ESBs in three Michigan school districts, covering both the transportation of pupils and extended parking periods.To emulate the BTMS two control approaches are designed: (i) a PI controller for the heating during driving and charging to emulate the heating needed at subfreezing conditions because ESBs drive at low speeds and do not generate enough self-heating and (ii) a thermostatic controller for the TTC and FTC applied to two different size heaters. The simulation results show that FTC can recover 40% of the driving range lost when operating at -10 °C compared to operation at 25 °C, and reduce specific energy consumption by 5% and 3% compared to TTC and NoTC, respectively. These thermal conditioning simulations can be transformed to a digital twin that weighs capital (heater size) and operational cost (TC setpoint) for ESB fleet management in cold regions.

Lithium-ion batteries with silicon/graphite (Si/Gr) anodes achieve higher energy densities but face challenges such as rapid capacity fade, resistance growth, and complex expansion behavior under various cycling conditions. This study systematically addresses these challenges through a comprehensive test matrix to investigate the effects of pressure, temperature, state-of-charge (SoC) windows, and charge rates (C-rates) on the evolution of expansion, resistance, and capacity behavior over the lifetime of the battery. Increasing the applied pressure between 34 and 172 kPa reduced both reversible and irreversible expansion per cycle, as well as resistance growth over time, without significantly impacting capacity fade. Electrochemical Impedance Spectroscopy (EIS) confirmed that increased pressure lowered initial solution resistance and mitigated the further growth of the solution and solid electrolyte interphase (SEI) resistance. Elevated temperature (45°C) extended battery cycle life despite an initial increase in resistance. The lifetime impedance increase under 45°C was dominated by SEI resistance. Consistent with prior studies, operating in a narrow SoC window at high SoC minimized capacity loss. Additionally, charge rates up to 2C had a limited effect on the overall degradation trends. Incremental capacity analysis (ICA) and differential voltage analysis (DVA) identified lithium inventory loss (LLI) as the primary cause of pre-knee degradation, whereas post-knee degradation resulted from a combination of LLI and anode-active material loss, particularly silicon. The deeper understanding of degradation mechanisms in batteries with Si/Gr anodes provided by this work enables the optimal packaging design and selection of operating conditions for the battery management system to extend battery cycle life. • High pressure (34–172 kPa) reduced resistance/expansion with limited capacity impact. • Cycling at 45°C extended battery life but caused more early-life resistance growth. • Irreversible expansion closely aligned with resistance patterns at 25°C and 0°C. • ICA/DVA identified LLI pre-knee and a combined LLI, LAM-Anode (Si) effect post-knee. • EIS revealed SEI resistance as the dominant contributor to kinetic degradation.

Lithium-ion batteries (LIBs) degrade the least compared to other battery chemistries but are sensitive to operational limits corresponding to physical degradation processes in each electrode. As LIBs age, their capacity fades and this is observable from the decrease in their range when they operate within fixed maximum and minimum terminal voltage limits (V min and V max ). There is rich literature on a variety of physics-based, data-driven and empirical models that have been developed to track degradation indicators like capacity fade and resistance growth. However, capacity and resistance alone do not provide insights into individual electrodes' internal health. Therefore, it is valuable to track any four out of the six “electrode state-of-health” eSOH parameters = [C p , C n , x 100 , y 100 , x 0 , y 0 ] as defined in [1,2,3] that allow us to limit the operation of each electrode within safe limits and slow down degradation. In this study, the eSOH parameters are the electrode capacities – C p for cathode/positive and C n for anode/negative – and lithium stoichiometric windows – x 0 , x 100 in Li x C 6 and y 0 , y 100 in Li y NiMnCO – in the negative and positive electrodes, respectively. Note that the capacity C can be derived using four independent eSOH parameters. Due to aging caused by the loss of cyclable lithium (due to SEI formation, plating, and cathode metal (Mn) dissolution) and loss of active material (LAM, as a result of particle fracture), the electrodes' stoichiometric windows shrink and shift (see Figure 1 panel (a)) with time along with a reduction in their cyclable capacities. These intrinsic degradation mechanisms at the electrode particle level govern the observable capacity fade that is achieved when the cell terminal voltage is between V min and V max . When fresh, operating between V min /V max protects the electrodes from reaching damaging overpotentials such as anode overpotential which can lead to plating. However, as the eSOH shrinks and shifts, the V min and V max limits might force one or both electrodes to operate at damaging overpotentials, making it necessary to apply individual eSOH limits in order to achieve fast charging against plating and Mn dissolution in an electric vehicle as shown in Figure 1 panel (b). Estimating eSOH and programming the battery management system (BMS) to impose operational limits as shown in Figure 1 panel (b) can avoid unsafe operation and/or accelerated degradation: The BMS should limit the maximum anode stoichiometry to prevent lithium plating. Plating usually occurs at low temperatures and high charging C-rates when the battery's average particle state of charge is already high. The BMS should limit the minimum of the cathode stoichiometry as high cell voltages can also cause cathode metal dissolution when the cathode has low stoichiometry. Forecasting the four eSOH parameters [C p , C n , x 100 , y 0 ] leverages Gaussian process regression (GPR) on positive and negative eSOH parameters and thermodynamic capacity to predict future capacity fade for up to 80 cycles. To ensure accurate extrapolation, domain knowledge is integrated into GPR modeling by selecting a suitable prior mean function. Two representative cells have been selected from the experimental dataset presented in [4] to demonstrate the performance of the GP model. Results for one cell depicted in Figure 1 panel (c) show that capacity forecasted using positive and negative eSOH parameters is consistent with that of thermodynamic capacity, with RMSE being less than 1.6%. References: [1] Mohtat, P., et al. "On identifying the aging mechanisms in li-ion batteries using two points measurements," American Control Conference (ACC) , 2017. [2] Lee, S., et al. (2020). “Electrode state of health estimation for lithium ion batteries considering half-cell potential change due to aging”. J-ECS . [3] Lopetegi, I., et al . (2024). “A new battery SOC/SOH/eSOH estimation method using a PBM and interconnected SPKFs: Part ii. SOH and eSOH estimation”. J-ECS . [4] Mohtat, P., et al (2022). “Comparison of expansion and voltage differential indicators for battery capacity fade”. Journal of Power Sources . Figure 1

Lithium-ion batteries are projected to reach cost targets making them competitive with internal combustion engines for light-duty transportation within the decade [1], which will help drive demand for EVs. Upcoming regulations in Europe, China, and the United States for automotive safety and durability will drive the design of battery systems, and the algorithms for battery management over the next decade. The Global Technical Regulation 20 (GTR20) describes the safety requirements for electric vehicle battery systems. In particular, the proposed regulation addresses the need for early detection and warning of occupants about battery faults and failures. The warning should allow for egress 5 minutes prior to the presence of a hazardous situation inside the passenger compartment caused by a battery cell thermal runaway. However, detecting the failure with enough time can be difficult using the current production battery sensor suites. Durability assessment for Lithium-ion batteries is also tricky because these systems are designed to live for more than 10-15 years, so testing under representative conditions could exceed multiple product development cycles. Accelerated aging tests can shorten the testing time to reach the product's end-of-life conditions, typically defined as 70 or 80% state of health, but a deep physical understanding of the degradation modes that are accelerated by the testing is required to project these results back to the real-world use case and warranty period. Due to the many possible definitions of battery State of Health (SoH) which can be related to both capacity loss and internal resistance growth, the Global Technical Regulation 22 (GTR22) defines their durability requirements in terms of two new related metrics: the State of Certified Energy (SOCE) and the State of Certified Range (SOCR). Both metrics represent a percentage of the certified battery energy or electric range remaining at a given point in time. The SOCE is based on the Society of Automotive Engineers (SAE) metric of Usable Battery Energy (UBE). The GTR22 will also require a way for the consumer to read battery health information and usage data from the vehicle. Physics-based battery models which include degradation mechanisms have great promise in addressing many of these challenges due to the relationship between modeled design parameters and the resulting device performance and durability. The models can enable virtual engineering to assess design tradeoffs before building the first prototype, however, parameterizing these models can be difficult due to the large number of parameters. In this presentation, we will explore the challenges of parameterizing physics-based battery models using conventional current, voltage, and temperature measurements. We will show how the measurement of the cell expansion in the laboratory setting and the fixtures that support the testing [2]. The augmentation of expansion data can inform model development and parameterization of a Single Particle Model with electrolyte dynamics (SPMe) implemented in the PyBaMM (Python Battery Mathematical Modelling) framework [3]. Next, we will demonstrate how the sensor data could be paired with the models to augment diagnostic algorithms to infer electrode-level aging phenomena, improve the state of charge estimation, and provide an early warning of gas generation before cell venting. Finally, we will discuss the practical implementation issues in packaging temperature and strain sensors in automotive battery modules and packs. [1] C. Shen, P. Slowik, A. Beach. “INVESTIGATING THE U.S. BATTERY SUPPLY CHAIN AND ITS IMPACT ON ELECTRIC VEHICLE COSTS.” Feb 2024, ICCT REPORT [2] S. Pannala, A. Weng, I. Fischer, J.B. Siegel, and A.G. Stefanopoulou, “Low-cost inductive sensor and fixture kit for measuring battery cell thickness under constant pressure.” 2022, IFAC-PapersOnLine, 55(37), 712-717. [3] Sravan Pannala et al 2024 J. Electrochem. Soc. 171 010532

With the increase in electrification, addressing safety concerns from emergency responders and the reverse logistics teams who handle Li-ion battery (LIB) packs at the end of life is increasingly urgent. Battery failures and thermal runaway (TR) present several risks, including fire, fire reignitions, explosions, and toxic gas. Moreover, in dense areas with electric vehicles (EV), e.g. parking lots and charging stations, thermal runaway (TR) risks can quickly spread to adjacent vehicles. A combination of early detection and controlled fast discharge can mitigate the hazards by limiting TR propagation and removing otherwise stranded energy from the battery pack to prevent fire reignitions. However, the effectiveness of the discharge depends on several factors including the risk of first venting, which releases flammable electrolytes and can be avoided by detecting and limiting cell expansion from gas generation at high temperatures. Understanding the impact of model mismatch due to parameter identification errors and unmodeled behavior will be important for managing venting risks and informing thresholds for control and detection, especially at high temperatures where gas generation is highly nonlinear [1]. This work uses a pouch cell venting model to represent the two-staged expansion behavior observed during an external short circuit (ESC) in a fixed-displacement fixture, enforcing similar boundary conditions to those found in an EV pack. As illustrated in Figure 1, the model considers active material thermal expansion and gas generation due to electrolyte vaporization and solid-electrolyte interphase (SEI) decomposition. Venting occurs when the internal pressure overcomes the critical venting pressure. In this work, we will first identify parameters in the venting model that relate normal operation to LIB mechanical behavior during failure and quantify the sensitivity of vent timing to the parameters identified. The preload force is one example where the model predicts that increased preload leads to earlier vent timing, which was demonstrated by Jia et al. [2]. Additionally, previous work showed that aging at different temperatures affects the onset temperature of SEI decomposition [4]. Aging at cold temperatures leads to reduced SEI thermal stability due to plated/mossy Li, while aging at high temperatures leads to increased stability due to increased thickness [4]. The SEI decomposition reaction rate can be modeled as a self-limiting, Arrhenius reaction rate such that dx SEI /dt=-A SEI x SEI exp(-E SEI k b T), where x SEI is the mole fraction of Li in SEI to negative electrode active material [3]. The impact of cell degradation on vent timing can be modeled through the parameters related to thermal stability and SEI quantity, which include the frequency factor (A SEI ) and initial x SEI (x SEI,0 ). The simulations show that the largest sensitivities of vent timing are to parameter errors in A SEI and x SEI,0 , changing the vent timing during an ESC by up to 30 seconds. The SEI decomposition parameters are theoretically related to quantities captured during aging, including electrode-specific state-of-health (eSOH), electrochemical impedance spectroscopy (EIS) resistances, and ir/reversible expansion. Together, the aging data informs an appropriate range for the parameter sensitivity study. Representative results are shown for x SEI,0 in Figure 1, where the simulations illustrate how vent timing can be affected by the initial amount of SEI. Increased SEI quantity corresponds to more gas generation and faster venting, for example, in aged cells whose resistances can double over life, assuming a proportional relationship. Ultimately, studying the parameter sensitivity of cell venting to real-world operating factors increases the feasibility of health-aware failure mitigation responses. By leveraging models, we can better understand how to set detection thresholds and manage venting risks during fast discharge as cells age to minimize the safety risks associated with end-of-life EV pack failures. REFERENCES [1] Tran, Vivian, Jason B. Siegel, and Anna G. Stefanopoulou. “Extending a Multiphysics Li-ion Battery Model from Normal Operation to Short Circuit and Venting.” Journal of The Electrochemical Society. (Submitted) [2] Jia, Zhuangzhuang, et al. "The preload force effect on the thermal runaway and venting behaviors of large-format prismatic LiFePO4 batteries." Applied Energy 327 (2022): 120100. [3] Hatchard, T. D., et al. "Thermal model of cylindrical and prismatic lithium-ion cells." Journal of The Electrochemical Society 148.7 (2001): A755. [4] Börner, M., et al. "Correlation of aging and thermal stability of commercial 18650-type lithium-ion batteries." Journal of Power Sources 342 (2017): 382-392. Figure 1: Parameter sweep of x SEI,0 , where Δσ cell is the change in fixture compression stress due to cell expansion with contributions from gas generation (Δσ gas ) and thermal expansion (Δσ thermal ). Venting occurs when the cell’s internal pressure overcomes the critical venting pressure. Figure 1

In this presentation, we will explore the coupling between active material volume change, mechanics, transport, and various degradation modes for lithium-ion battery pouch cells. An experimental aging study was designed to probe the impact of temperature, SOC cycling window, and preload pressure. Over one hundred pouch cells were manufactured and cycle-aged in custom build fixtures which enable the application of a nearly constant spring-loaded surface pressure while continuously measuring the cell thickness during charging and discharging. A Single Particle Model with electrolyte dynamics (SPMe) implemented in the PyBaMM (Python Battery Mathematical Modelling) framework was augmented with particle cracking, SEI growth, and Lithium plating degradation mechanisms [1]. This work extends the model with the theory from [2] to address the impact of preloading and external boundary conditions on porosity. We then considered the mechanical impacts of SEI growth and accumulation of dead lithium [3] on the electrode volume change equation. Since the cells were built on the UM Battery Lab pilot-scale manufacturing line the geometric dimensions, electrode loadings, material properties, and half-cell potentials were readily accessible to populate the physics-based model. The remaining kinetic model parameters were tuned to match the beginning of life performance on a modified HPPC profile. Finally, the rates of various aging mechanisms were tuned to match the individual cell capacity and thickness growth at each reference performance test in this unique and rich experimental dataset. The reversible thickness change (during each charge-discharge cycle) is directly captured by the predicted changes in the positive and negative electrode stoichiometry range based on the model's predicted Loss of Active Material (LAMn,p). The predicted electrode capacity retention and remaining cyclable lithium feed to the model calculation of reversible active material volumetric expansion. In addition, the irreversible thickness growth (across many cycles) is parameterized for different aging mechanisms with a linear relationship on SEI growth, quadratic on the Lithium plating, and linear on the particle fracture. The model is trained with data from slow and fast charge and discharge rates at a full SOC cycling window and its accuracy is evaluated at asymmetric charge-discharge and partial SOC cycling window. By matching both the cell voltage and thickness change over life with a single parameter set we can achieve higher confidence in the model parameters. This is a key step in understanding the impact of degradation mechanisms on the irreversible changes in electrode thickness. The expected benefit of the proposed simulation tool will aid in the design process by allowing researchers to rapidly assess the implications of cell design choices (for example N/P ratio, electrode, separator and fixture compressibility ratio, and external pressure and dimensions) on performance over life and to understand the implications for packaging and potential performance tradeoffs reducing the need for physical testing. Sravan Pannala et al 2024 J. Electrochem. Soc. 171 010532 Taylor R. Garrick et al 2017 J. Electrochem. Soc. 164 E3592 Shanshan Xu et al 2019 J. Electrochem. Soc. 166 A3456

Electric vehicle (EV) batteries are often underutilized. Vehicle-to-grid (V2G) services can tap into this unused potential, but increased battery usage may lead to more degradation and shorter battery life. This paper substantiates the advantages of providing load-shifting V2G services when the battery is aging, primarily due to calendar aging mechanisms (active degradation mechanisms while the battery is not used). After parameterizing a physics-based digital-twin for three different dominant degradation patterns within the same chemistry (NMC), we introduce a novel metric for evaluating the benefit and associated harm of V2G services: throughput gained versus days lost (TvD) and show its strong relationship to the ratio of loss of lithium inventory (LLI) due to calendar aging to the total LLI ( LLI Cal / LLI ). Our results which focus systematically on degradation mechanisms via lifetime simulation of digital-twins significantly expand prior work that was primarily concentrating on quantifying and reducing the degradation of specific cells by probing their usage and charging patterns. Examining various cell chemistries and conditions enables us to take a broader view and determine whether a particular battery pack is appropriate for load-shifting V2G services. Our research demonstrates that the decision ”to V2G or not to V2G” can be made by merely estimating the portion of capacity deterioration caused by calendar aging. Specifically, TvD is primarily influenced by the importance of aging while EV is at rest and the environmental temperature where the car is parked, while the usage intensity and charging patterns of EVs play a lesser role. • Physics-based simulation of V2G for three distinct aging patterns • A new metric to quantify V2G impact: throughput gained versus days lost (TvD) • Qualification of the popular belief: “Use it or lose it” • The fraction of calendar aging to overall aging is the key factor for V2G impact • Charging protocols and driving behaviors are secondary factors

Challenges such as mechanical degradation and limited cycle life persist for high energy density lithium-ion batteries with silicon/graphite composite anodes. In this research work, the patterns of degradation of cells with silicon/graphite composite and NMC622 cathode are examined at varied cycling conditions and applied external pressure or pretension. The most notable outcome of this analysis is that cells cycled between 0 and 100 State-of-Charge (SoC) exhibit the most accelerated aging process. Increasing the pretension force effectively restrains the irreversible expansion of the cells, and has a positive effect on capacity retention. In this research, a comprehensive experiment was conducted involving 46 cells subjected to diverse cycling conditions, voltage windows, pretension forces, and temperatures. Reference performance tests (e.g. HPPC and 1/20 C-rate charge tests) are conducted regularly for analysis of degradation mechanisms. The capacity fade, resistance growth, and thickness increase are correspondingly shown in Figures 1, 2, and 3 with ampere hour throughput as the x-axis. The legend columns indicate test conditions, including C-rate, SoC window during cycling, temperature (in degrees Celsius), and pretension force (in psi). As is shown in all figures, there is a substantial dependence on the SoC window for cycling. To be specific, a rapid rate of capacity loss, resistance increase, and thickness increase occurs in the cell group cycled over the full SoC window (plotted in blue). Cells cycled under full SoC windows also exhibit an early accelerated fading (knee [1]) of capacity and accelerated increase (elbows [2]) of resistance and thickness. According to [3], this accelerated aging could be a consequence of side reactions and increased mechanical stress within the silicon particle when operating across a broad potential range. Meanwhile, for the cell group with restrained cycle windows (plotted in gray) the cells have not yet reached any knee, and have a relatively linear capacity loss. It should be noted that, within the range of partial cycling windows examined, cells subjected to a cycling range of 50-100 exhibit the most rapid degradation, which aligns with the conclusions presented in [4] due to the time at elevated potential. In Figure 2, the elevated temperature (depicted in red) exhibits a significant influence on the increase in resistance, potentially attributed to the growth of the solid electrolyte interface (SEI), but minimal impact on capacity loss. Maintaining other conditions constant and comparing cells under 25 psi and 15 psi (marked with hollow circles and filled circles, respectively), it is evident that a higher pretension force has a positive effect on cell capacity loss. Simultaneously, in Figure 3, the pretension force at 25 psi (marked with hollow circles) effectively restrains the irreversible expansion of the cells. The results are similar to the degradation pattern outlined in [5], it is observed that employing a high pretension force facilitates the mitigation of both degradation and expansion. As highlighted in [6], heightened temperatures lead to accelerated resistance growth. Nevertheless, the impact of various C-rates on degradation remains inconclusive [6]. This research systematically analyzed cell-level degradation through an extensive array of experiments, providing valuable insights into the intricate dynamics of capacity fade, resistance increase, and thickness growth. The study's revelation that the cycle window exerts a pronounced impact on battery health could offer crucial guidance for the design of Battery Management Systems (BMS). Moreover, the work establishes a foundational basis for future research, particularly in exploring electrode-level degradation patterns. These contributions collectively enhance the understanding of energy storage systems, offering practical implications for optimizing battery performance and longevity in various applications. [1]Attia, Peter M., et al. "“Knees” in lithium-ion battery aging trajectories." Journal of The Electrochemical Society 169.6 (2022): 060517. [2] Strange, Calum, et al. "Elbows of internal resistance rise curves in Li-ion cells." Energies 14.4 (2021): 1206. [3] Verbrugge, Mark, et al. "Fabrication and characterization of lithium-silicon thick-film electrodes for high-energy-density batteries." Journal of The Electrochemical Society 164.2 (2016): A156. [4] Xu, Bolun, et al. "Modeling of lithium-ion battery degradation for cell life assessment." IEEE Transactions on Smart Grid 9.2 (2016): 1131-1140. [5] Mohtat, Peyman, et al. "Reversible and irreversible expansion of lithium-ion batteries under a wide range of stress factors." Journal of The Electrochemical Society 168.10 (2021): 100520. [6] Pannala, Sravan, et al. "An Experimental Correlation of Degradation with Cell Reversible and Irreversible Expansion Measurement in Pouch Cells." Electrochemical Society Meeting Abstracts 243. No. 2. The Electrochemical Society, Inc., 2023. Figure 1

Modeling abusive battery operation before thermal runaway (TR) can reduce the testing requirements and facilitate the development of effective and reliable early failure detection and fast discharge strategies [1] which require multiple types of sensor signals, including current, voltage, temperature, and pressure. We present a multi-physics model that can predict the rapidly evolving discharge behavior of cells undergoing an external short circuit, including hazards like first venting. First venting models account for gas generation due to SEI decomposition and electrolyte vaporization at high temperatures to calculate the build-up of internal cell pressure under constant-displacement conditions [2]. Modeling gas generation requires accurate temperature prediction, which subsequently requires accurate Joule heating calculated from current and voltage predictions. From the cascading chain of model input dependencies, the first challenge is managing error propagation through the submodels with appropriate parameterization. Previous modeling work parameterized a venting model for a cell undergoing an external short circuit (ESC) but did not include a thermal or electrical model [2]. The second challenge is modeling the diffusion-limited discharge behavior with currents approaching 50-80 C with a fast and accurate model that is feasible for detection and control applications. Diffusion-limited behavior occurs when the local Li concentration saturates, suddenly causing large concentration overpotentials that are observable in the terminal voltage and current (see Fig. 1) as well as numerical instability when solving the Butler-Volmer (BV) equation. Previous ESC modeling works either used the Doyle-Fuller-Newman (DFN) model with a limiting current density in the BV equation [3] or an equivalent circuit model (Eq-CM) [1]. However, solving the DFN is computationally intensive for a control application, while Eq-CMs cannot predict the diffusion-limited electrical behavior beyond the data they were parameterized on, requiring apriori experiments to be accurate. To balance the trade-offs, we instead look towards reduced-order modeling. In this work, we present a control-oriented, reduced-order, multi-physics model that captures the electrochemical, thermal, and venting behavior of four 4.6 Ah NMC pouch cells undergoing an external short circuit with different initial state-of-charge (SOC). The multi-physics model couples a first venting model with a lumped thermal model driven by Joule heating calculated through the overpotentials from the Single-Particle Model with electrolyte (SPMe). The combined model was parameterized through four experiments by fitting five key parameters in the submodels related to the SEI decomposition rate, cell thermal behavior, and solid electrode diffusion to capture the first venting timing, peak temperature, and diffusion-limited current-voltage drops. Additionally, two resistances were tuned to match the initial current and voltage with all other parameters were taken from the literature. Applying the same parameter set consisting of the average of the fitted values on all four cells, the combined multi-physics model correctly predicted that the fully-charged cells would vent and conservatively predicted the cell venting timing up to about 40 s before it occurred in the experiment. For high initial SOC cells, the SPMe also accurately predicted the current and voltage drops associated with diffusion limitations and SOC up until the cell vented. This is the first work that systematically parameterizes and couples a reduced-order, physics-based model to capture the electrical, thermal, and venting behavior under battery abuse conditions. Future work will explore applications for the model in early detection and response to critical safety events. REFERENCES [1] Tran, Vivian, Jason Siegel, and Anna Stefanopoulou. "Emergency Li-ion Battery Discharge using Nonlinear Model Predictive Control with Temperature and Venting Pressure Constraints." 2023 American Control Conference (ACC). IEEE, 2023. [2] Cai, Ting, et al. "Modeling li-ion battery first venting events before thermal runaway." IFAC-PapersOnLine 54.20 (2021): 528-533. [3] Rheinfeld, Alexander, et al. "Quasi-isothermal external short circuit tests applied to lithium-ion cells: Part ii. modeling and simulation." Journal of The Electrochemical Society 166.2 (2019): A151. Fig. 1: 15-min simulation of an external short circuit for cells at different initial SOC. Model results and experimental measurements are shown for four cells that were externally shorted. The measured current, voltage, temperature, and expansion force were measured at 10 Hz and plotted versus log time to highlight the dynamic current-voltage behavior in the first few minutes. The diffusion-limited current and voltage behavior are captured by the model. The two cells at 100% SOC, experienced venting and higher peak temperatures, which are well captured by the model. The cell venting timing was predicted to be 43 s and 7 s early for Cell 100A and 100B, respectively. The cells that were shorted at lower SOC did not vent. Figure 1

The intelligent design of durable, next-generation lithium-ion batteries can be facilitated through the use of computationally efficient models that capture battery physics. In this work, we demonstrate a single particle model with electrolyte dynamics (SPMe), which accounts for battery capacity fade caused by mechanisms such as solid electrolyte interface (SEI) growth, lithium plating, and stress-induced particle cracking. The model is trained using both experimental data and data generated using a higher-fidelity Doyle-Fuller-Newman model so the ground truth parameters are known. To quantify and reduce the uncertainty in the unknown model parameters, we are developing a Bayesian experimental design approach that, with the aid of multiple models with varying fidelity, can rapidly identify optimal experimental input conditions leading to the greatest expected information gain.

We present a battery lifetime model that predicts Irreversible battery thickness change under constant pressure operation as the battery ages, along with capacity loss and resistance growth. These metrics are very important for predicting lithium-ion battery state of health (SOH) and remaining useful life due to their interdependence with battery packaging and safety. To design and operate battery storage systems safely, we must be able to predict and detect changes in all three SOH metrics over life accurately and simultaneously. Traditionally, these three SOH metrics have been modeled separately. However, we developed a single model that simultaneously predicts battery capacity, resistance, and expansion over its lifetime. In this work, battery degradation is modeled using the single particle model (SPM) framework, including mechanical damage in the electrodes, which results in loss of active material (LAM), and the side reactions for SEI growth and Li plating, which result in loss of lithium inventory (LLI). We introduce an equivalent stress and concentration dependence of stress to the mechanical damage model. Commonly used fatigue models assume symmetric cycles (zero-mean stress) [1]. To account for cycling conditions with different currents on charge and discharge, an equivalent stress that adjusts for non-zero mean stress is needed. The concentration dependency of strain and the resulting stress allows us to capture different degradation rates for cells cycled at different depths of discharge (DOD). Lithium plating at the separator interface is often described as a spatially non-uniform process using the pseudo-2D (P2D) model. This phenomenon can be captured in the SPM framework using a current-dependent dynamic term to account for spatial-nonuniformity of electrolyte dynamics at higher currents. Experimental data from cells cycling with periodic reference performance tests were used to tune the degradation model. In addition to the standard I, V, and T measurements, continuous measurements of the thickness change of the battery pouch cells were recorded. From this data, we extracted the capacity, electrode state of health (eSOH), resistance, and reversible and irreversible expansion. The test conditions were designed to excite different dominant degradation mechanisms (e.g. mechanical damage) by cycling the cells at different conditions (C-rate, temperature, preload pressure, and DOD). The tuning, which uses accelerated aging simulations [2] (to speed up the tuning process), results in a single set of parameters that predicts capacity loss, resistance growth, and irreversible thickness change as the battery ages, even for conditions that were not used for parameter tuning. The tuning is performed in two sequential steps. First, the parameters related to the electrochemical and mechanical degradation rates, which result in LLI and LAM, are tuned to match the extracted eSOH variables at each Reference Performance Test (RPT). The first tuning gives us the amount of plated Li, SEI growth, and mechanical damage in the particles. We then relate these quantities to irreversible expansion using a linear relationship in the case of SEI and mechanical damage and quadratic in the case of Li plating [3]. The same relationship is used for all cells, and this represents the 2nd tuning step. Our data set and calibration are unique in that the dominant degradation mode is the loss of lithium inventory due to the loss of active anode material. Most prior work on lifetime predictive models relied on SEI growth as the dominant degradation mode. Figure 2 shows the tuned model prediction of the three performance metrics over the cycle life of cells for various cycling conditions. The markers represent the measurement at each reference performance test, and the lines correspond to the model. References: [1] R. Burger and Y.-L. Lee, “Assessment of the mean-stress sensitivity factor method in stress-life fatigue predictions,” Journal of Testing and Evaluation , 2013 [2] S. Pannala et al. Methodology for Accelerated Inter-Cycle Simulations of Li-ion Battery Degradation with Intra-Cycle Resolved Degradation Mechanisms, American Controls Conferenc e, 2022 [3] S. Pannala et al. Consistently Tuned Battery Lifetime Predictive Model of Capacity Loss, Resistance Increase, and Irreversible Thickness Growth, Journal of the Electrochemical Society , 2023 Figure 1

In battery manufacturing, the formation process is both paramount and problematic. It is paramount since all batteries undergo formation charging and aging steps to build a resilient solid electrolyte interphase (SEI) and to screen for defects. It is problematic because the formation process is expensive to operate, remains a major source of factory energy demand, requires larger factory footprints, and takes an order of magnitude longer than nearly every other manufacturing step. Despite the centrality of the formation process in battery manufacturing, steps taken to optimizing formation protocols remain ad-hoc in the absence of design principles and physical models. We highlight recent progress in developing a principled understanding of the SEI formation process applied towards industrial battery formation process (i.e. in the context of a full cell). We review a reduced-order electrochemical model of the formation process that captures first cycle charge dynamics and subsequent cycling and aging degradation behavior [1]. The model predicts measurable quantities such as first cycle efficiency (FCE) and irreversible thickness growth under multiple formation protocols. We discuss opportunities to integrate the electrochemical formation model with differential voltage analysis (DVA) to visualize the connection between first cycle charge dynamics from the perspective of shifting electrode stoichiometries [2] and comment on applications towards physics-informed battery lifetime prediction [3]. We finally share recent data elucidating the effect of formation temperature, pressure, and protocol on cycle life. References: Weng, Andrew, Everardo Olide, Iaroslav Kovalchuk, Jason B. Siegel, and Anna Stefanopoulou. 2023. “Modeling Battery Formation: Boosted SEI Growth, Multi-Species Reactions, and Irreversible Expansion.” Journal of the Electrochemical Society 170 (9): 090523. Weng, Andrew, Jason B. Siegel, and Anna Stefanopoulou. 2023. “Differential Voltage Analysis for Battery Manufacturing Process Control.” Frontiers in Energy Research 11. https://doi.org/10.3389/fenrg.2023.1087269. Weng, Andrew, Peyman Mohtat, Peter M. Attia, Valentin Sulzer, Suhak Lee, Greg Less, and Anna Stefanopoulou. 2021. “Predicting the Impact of Formation Protocols on Battery Lifetime Immediately after Manufacturing.” Joule 5 (11): 2971–92. Figure 1: SEI reaction and full cell thickness expansion dynamics during the first formation charge cycle as predicted by the formation model. (A) Full cell voltage, electrode potentials, and SEI reaction potentials. (B) Applied current and SEI reaction currents. (C) Full cell thickness expansion, including both reversible expansion from electrode intercalation and irreversible expansion from SEI components. EC: ethylene carbonate. VC: vinylene carbonate. (1) Negligible SEI growth occurs before the first charge cycle due to reaction limitations. (2) VC reduces first, followed by (3) EC reduction. (4) Expansion rate of lithiated graphite slows down at mid-SOCs, causing a (5) decrease in the EC reduction current as a result of the SEI growth boosting/de-boosting mechanism. Figure 1

Vehicle-to-grid (V2G) services, utilizing electric vehicle (EV) batteries for grid support, enhance reliability and reduce peak energy demand. We present a physics-based model tuned for three different cell chemistry families and examine the influence of bulk V2G services on EV battery life. We consider various duty cycles, cell chemistries with nickel content (NMCxyz and various anode graphites), and associated dominant degradation mechanisms. To quantify the impact of offering V2G services, we introduce the V2G benefit-to-harm ratio (B2H) . We define the benefit as the Ah gained with V2G and the harm as the life lost in days due to V2G, both compared to the baseline noV2G. B2H=(normalized Ah gained by 70% capacity fade) divided by (time in days reduction by the time that capacity fade has reached 70%). Note that the 70% capacity fade can be changed for various vehicle models depending on the warranty definition of the % capacity or usable battery energy (UBE) at the end of the warranty. Our findings indicate that the dominant degradation mechanism plays a crucial role in the benefit-to-harm (B2H) ratio of V2G or vehicle-to-building (V2B) [1]. Specifically, we clarify that the relative contribution of calendar aging to overall capacity loss is pivotal in determining the degradation due to V2G and possible associated benefits. Our model takes into account four degradation mechanisms based on a single-particle model. SEI growth and cathode transition metal dissolution are part of the calendar aging loss of lithium inventory (LLI Cal ). The remaining LLI is attributed to Li-plating and mechanical degradation caused by particle cracking. We calibrate the model for three families of cell chemistries and conditions. The primary degradation mechanism for one cell family is anode mechanical degradation (LAM Neg ). The second family of cells predominantly ages due to SEI layer growth, and the third family is degrading due to both mechanisms [2]. We show that, in cases with a higher contribution of calendar aging to degradation (LLI Cal /LLI), V2G can be potentially more beneficial (in Ah gained by 70% capacity fade) than harmful (lifetime reduction in days by the time that capacity fade has reached 70%). Furthermore, we have evidence that the V2G charging pattern can have a secondary effect on the B2H ratio. For example, being a risk taker and charging the battery late or being cautious and charging it as soon as possible can lead to different degrees of degradation depending on the chemistry and conditions of the battery [3]. With our multiphysics reduced order model of battery lifetime degradation, we fully explain why previous studies [4] have shown that the impact on EV battery degradation varies from inconsequential [5] to projecting a need for early battery replacement [6]. Our results clarify that battery chemistry and usage patterns are important factors in determining whether or not to utilize a vehicle for grid support, as well as the overall financial impact on the owner and OEM warranty. References : Nazari, Shima, Francesco Borrelli, and Anna Stefanopoulou. "Electric vehicles for smart buildings: A survey on applications, energy management methods, and battery degradation." Proceedings of the IEEE 109.6 (2020): 1128-1144. Movahedi, Hamidreza, Jason Siegel, and Anna Stefanopoulou. “Predictive Lifetime Battery Simulations of Intra- and Inter-Cycle Degradation for V2G Use: Final report for CRC project SM-4/8”, Coordinated Research Council (under review). Movahedi, Hamidreza, Sravan Pannala, Jason Siegel, and Anna Stefanopoulou. “Assessing the Viability of Bulk V2G Operations for Different Battery Conditions Using Physics-based Degradation Models”, (manuscript submitted). Petit, Martin, Eric Prada, and Valérie Sauvant-Moynot. "Development of an empirical aging model for Li-ion batteries and application to assess the impact of Vehicle-to-Grid strategies on battery lifetime." Applied Energy 172 (2016): 398-407. Uddin, Kotub, et al. "On the possibility of extending the lifetime of lithium-ion batteries through optimal V2G facilitated by an integrated vehicle and smart-grid system." Energy 133 (2017): 710-722. Dubarry, Matthieu, Arnaud Devie, and Katherine McKenzie. "Durability and reliability of electric vehicle batteries under electric utility grid operations: Bidirectional charging impact analysis." Journal of Power Sources 358 (2017): 39-49. Figure 1. Capacity retention wrt.(a) days (b) normalized Ah throughput for different scenarios and operational conditions. Discharging to the grid reduces the battery life substantially and increases Ah throughput minimally in NMC111 cells. NMC622-45 case reacts the opposite, and NMC622-25C is somewhere between these two cases. (c) Comparison of the V2G benefit-to-harm ratio wrt. the portion of LLI that is caused by SEI growth. Figure 1

Electric vehicle (EV) batteries are often underutilized. Vehicle-to-grid (V2G) services can tap into this unused potential, but increased battery usage may lead to more degradation and shorter battery life. This paper substantiates the advantages of providing load-shifting V2G services when the battery is aging, primarily due to calendar aging mechanisms (active degradation mechanisms while the battery is not used). After parameterizing a physics-based digital-twin for three different dominant degradation patterns within the same chemistry (NMC), we introduce a novel metric for evaluating the benefit and associated harm of V2G services: \textit{throughput gained versus days lost (TvD)} and show its strong relationship to the ratio of loss of lithium inventory (LLI) due to calendar aging to the total LLI ($\text{LLI}_\text{Cal}/\text{LLI}$). Our results that focus systematically on degradation mechanisms via lifetime simulation of digital-twins significantly expand prior work that was primarily concentrating on quantifying and reducing the degradation of specific cells by probing their usage and charging patterns. Examining various cell chemistries and conditions enables us to take a broader view and determine whether a particular battery pack is appropriate for load-shifting (V2G) services. Our research demonstrates that the decision "to V2G or not to V2G" can be made by merely estimating the portion of capacity deterioration caused by calendar aging. Specifically, TvD is primarily influenced by the chemistry of cells and the environmental temperature where the car is parked, while the usage intensity and charging patterns of EVs play a lesser role.

Automotive manufacturers are working to improve individual cell, module, and overall pack design by increasing the performance, range, and durability, while reducing cost. One key piece to consider during the design process is the active material volume change, its linkage to the particle, electrode, and cell level volume changes, and the interplay with structural components in the rechargeable energy storage system. As the time from initial design to manufacture of electric vehicles decreases, design work needs to move to the virtual domain; therefore, a need for coupled electrochemical-mechanical models that take into account the active material volume change and the rate dependence of this volume change need to be considered. In this study, we illustrated the applicability of a coupled electrochemical-mechanical battery model considering multiple representative particles to capture experimentally measured rate dependent reversible volume change at the cell level through the use of an electrochemical-mechanical battery model that couples the particle, electrode, and cell level volume changes. By employing this coupled approach, the importance of considering multiple active material particle sizes representative of the distribution is demonstrated. The non-uniformity in utilization between two different size particles as well as the significant spatial non-uniformity in the radial direction of the larger particles is the primary driver of the rate dependent characteristics of the volume change at the electrode and cell level.

Diagnosing imbalances in capacity and resistance within parallel-connected cells in battery packs is critical for battery management and fault detection, but it is challenging given that individual currents flowing into each cell are often unmeasured. This work introduces a novel method useful for identifying imbalances in capacity and resistance within a pair of parallel-connected cells using only voltage and current measurements from the pair. Our method utilizes differential voltage analysis (DVA) when the pair is under constant cur-rent discharge and demonstrates that features of the pair's differential voltage curve (dV/dQ), namely its mid-to-high SOC dV/dQ peak's height and skewness, are sensitive to imbalances in capacity and resistance. We analyze and explain how and why these dV/dQ peak shape features change in response to these imbalances, highlighting that the underlying current imbalance dynamics resulting from these imbalances contribute to these changes. Ultimately, we demonstrate that dV/dQ peak shape features can identify the product of capacity imbalance and resistance imbalance, but cannot uniquely identify the imbalances. This work lays the groundwork for identifying imbalances in capacity and resistance in parallel-connected cell groups in battery packs, where commonly only a single current sensor is placed for each parallel cell group.

Automotive manufacturers are working to improve individual cell, module, and overall pack design by increasing the performance, range, and durability, while reducing cost. One key piece to consider during the design process is the active material volume change, its linkage to the particle, electrode, and cell level volume changes, and the interplay with structural components in the rechargeable energy storage system. As the time from initial design to manufacture of electric vehicles decreases, design work needs to move to the virtual domain; therefore, a need for coupled electrochemical-mechanical models that take into account the active material volume change and the rate dependence of this volume change need to be considered. In this study, we illustrated the applicability of a coupled electrochemical-mechanical battery model considering multiple representative particles to capture experimentally measured rate dependent reversible volume change at the cell level through the use of an electrochemical-mechanical battery model that couples the particle, electrode, and cell level volume changes. By employing this coupled approach, the importance of considering multiple active material particle sizes representative of the distribution is demonstrated. The non-uniformity in utilization between two different size particles as well as the significant spatial non-uniformity in the radial direction of the larger particles is the primary driver of the rate dependent characteristics of the volume change at the electrode and cell level.

Mitigation of Li-ion battery system fires consists of reliable fault detection and proactive, fast discharge control. Both require modeling of failure modes due to high temperatures and currents between normal operation and thermal runaway. In this work, we present a control-oriented, reduced-order, multiphysics model that captures the electrochemical, thermal, gas generation, mechanical expansion, and venting behavior of NMC pouch cells undergoing an external short circuit (ESC) from different initial state-of-charge (SOC). The model is parameterized through experiments by fitting the solid-electrolyte interphase (SEI) decomposition rate, the cell’s thermal parameters, and the particle solid-phase diffusion parameters to capture the first venting timing, peak temperature, and diffusion-limited electrical behavior at high currents. Using a single parameter set, the multiphysics model can capture behavior during an ESC to predict whether a cell will generate gas and vent, predict the vent timing within 10 seconds of it occurring in the experiment, and maximum cell expansion pressure within 10 kPa for cells that did not vent. The model can also predict the SOC trajectory for cells with a high initial SOC within 6% SOC for the 15-minute discharge or until the cell vents.

Diagnosing imbalances in capacity and resistance within parallel-connected cells in battery packs is critical for battery management and fault detection, but it is challenging given that individual currents flowing into each cell are often unmeasured. This work introduces a novel method useful for identifying imbalances in capacity and resistance within a pair of parallel-connected cells using only voltage and current measurements from the pair. Our method utilizes differential voltage analysis (DVA) when the pair is under constant current discharge and demonstrates that features of the pair's differential voltage curve (dV/dQ), namely its mid-to-high SOC dV/dQ peak's height and skewness, are sensitive to imbalances in capacity and resistance. We analyze and explain how and why these dV/dQ peak shape features change in response to these imbalances, highlighting that the underlying current imbalance dynamics resulting from these imbalances contribute to these changes. Ultimately, we demonstrate that dV/dQ peak shape features can identify the product of capacity imbalance and resistance imbalance, but cannot uniquely identify the imbalances. This work lays the groundwork for identifying imbalances in capacity and resistance in parallel-connected cell groups in battery packs, where commonly only a single current sensor is placed for each parallel cell group.

Mitigation of Li-ion battery system fires consists of reliable fault detection and proactive, fast discharge control. Both require modeling of failure modes due to high temperatures and currents between normal operation and thermal runaway. In this work, we present a control-oriented, reduced-order, multi-physics model that captures the electrochemical, thermal, gas generation, mechanical expansion, and venting behavior of NMC pouch cells undergoing an external short circuit (ESC) from different initial state-of-charge (SOC). The model is parameterized through experiments by fitting the solid-electrolyte interphase (SEI) decomposition rate, the cell’s thermal parameters, and the particle solid-phase diffusion parameters to capture the first venting timing, peak temperature, and diffusion-limited electrical behavior at high currents. Using a single parameter set, the multi-physics model can capture behavior during an ESC to predict whether a cell will generate gas and vent, predict the vent timing within 10 s of it occurring in the experiment, and maximum cell expansion pressure within 10 kPa for cells that did not vent. The model can also predict the SOC trajectory for cells with a high initial SOC within 6% SOC for the 15-minute discharge or until the cell vents.

The battery state of health (SOH) based on capacity fade and resistance increase is not sufficient for predicting Remaining Useful life (RUL). The electrochemical community blames the path-dependency of the battery degradation mechanisms for our inability to forecast the degradation. The control community knows that the path-dependency is addressed by full state estimation. We show that even the electrode-specific SOH (eSOH) estimation is not enough to fully define the degradation states by simulating infinite possible degradation trajectories and remaining useful lives (RUL) from a unique eSOH. We finally define the deepSOH states that capture the individual contributions of all the common degradation mechanisms, namely, SEI, plating, and mechanical fracture to the loss of lithium inventory. We show that the addition of cell expansion measurement may allow us to estimate the deepSOH and predict the remaining useful life.

<div class="section abstract"><div class="htmlview paragraph">The US EPA and the California Air Resources Board (CARB) require electric vehicle range to be determined according to the Society of Automotive Engineers (SAE) surface vehicle recommended practice J1634 - Battery Electric Vehicle Energy Consumption and Range Test Procedure. In the 2021 revision of the SAE J1634, the Short Multi-Cycle Test (SMCT) was introduced. The proposed testing protocol eases the chassis dynamometer test burden by performing a 2.1-hour drive cycle on the dynamometer, followed by discharging the remaining battery energy into a battery cycler to determine the Useable Battery Energy (UBE). Opting for a cycler-based discharge is financially advantageous due to the extended operating time required to fully deplete a 70-100kWh battery commonly found in Battery Electric Vehicles (BEVs). This paper provides a review of the communication protocols enabling V2X (Vehicle to X, where X can be grid, vehicle, building, etc.) power transfer and the tools required to initiate, control, and terminate the vehicle testing procedure as per SAE J1634. The primary focus is on the series of ISO 15118 standards for road vehicles - vehicle to grid communication interface and SAE J2847/2 (Surface vehicle standard for communication between plug-in vehicles and off-board DC chargers).</div></div>

The battery state of health (SOH) based on capacity fade (SOH-C), and resistance increase (SOH-R) is not sufficient for predicting the Remaining Useful life (RUL). The electrochemical community blames the path dependency of the battery degradation mechanisms for our inability to forecast future degradation. The control community knows that the path dependency is addressed by full state estimation. In this work, we demonstrate the inadequacy of popular definitions of State of Health (SOH). We demonstrate that even electrode-specific SOH (eSOH) and resistance estimations are not sufficient to completely characterize degradation even in a simplified model based on a single particle model (SPM) which includes SEI, plating, and mechanical fracture. We illustrate that it is possible to simulate different possible degradation trajectories and RULs from the same eSOH and second-life duty cycles. Therefore, the lifetime battery degradation of more complex models will be even less predictable, let alone in the real world. We finally define the deepSOH (states of the degradation mechanisms) that capture the individual contributions of each degradation mechanism to the total loss of lithium inventory. We show that adding other battery measurements, such as cell expansion, allows us to identify the deepSOH and, therefore, predict the RUL.

Hybrid electric vehicles (HEV) enable reduction of emissions without sacrificing consumer expected range and drivability. The diversification of the powertrain with multiple power sources allows downsizing the internal combustion engine and implementing optimal energy management strategies. The interaction among components of an HEV are key to the overall efficiency. Therefore, efficiency potential is lost if this interdependence is neglected during the powertrain design by focusing on individual optimization of component specifications. This letter formulates and solves a co-design problem by integrating the energy management with the optimal powertrain and drivetrain component sizing for a hybrid powertrain equipped with an opposed piston (OP) engine in a series architecture. Our novel approach develops a model for an OP engine and integrates battery capacity degradation into the co-design problem. The optimal solution allows for a minimally sized engine that accounts for the average power requirements, and a large enough battery to provide fast power dynamics.