Batteries For Electric Vehicle Applications Research Articles

Prediction of the Remaining Useful Life of Lithium-Ion Battery Using Multilayer Perceptron

Cogitating the reliability of the supply and ensuring continuous delivery of power to the loads, especially in the growing demand for Lithium-Ion batteries in electric vehicle applications, prediction of the remaining useful life of Lithium-Ion batteries is crucial for the timely replacement. For prediction of non-linear and chaotic relationship, experience-based approach, physics-based approach and data driven approach are used among which data driven approach is a model free, accurate and reliable approach. Therefore, a driven approach in predicting remaining useful life can be implemented in the battery management system. This research uses a multilayer perceptron to predict the remaining useful life of the battery. The NASA Ames Prognostics Center of Excellence (PCoE) battery dataset is used to test the proposed methodology. The use of multilayer perceptron for remaining life prediction seems promising despite the significant number of jump points, gaps in data and a small quantity of experimental data in the National Aeronautics and Space Administration (NASA) dataset. The predicted result was obtained with 8.52 % mean absolute error and 9.59 % root mean square error. When compared with the predicted results of different literatures, proposed multilayer perceptron with sliding window approach outperforms most of the existing approach. Incorporation of optimization techniques and hybrid algorithm in proposed approach can further enhance the accuracy of the model.

State of Charge Estimation of Lithium-Ion Batteries for Electric Vehicle Application Using Gaussian Process Regression Approach

For the purpose of ensuring a secure, dependable and affordable performancealong with clean energy in electric vehicles, the estimation of the precise state of charge of LIB is very important. In this article, Gaussian Process Regression with different kernel functions-based SOC prediction is proposed and their performance with good health and well-beingare evaluated and analyzed. A useful benefit of employing GPR is the ability to quantify and estimate uncertainties, allowing for the evaluation of the SOC estimate's dependability. The kernel function serves as a crucial hyperparameter that improves GPR performance. GPR considers the temperature and voltage of the battery, which are independent of one another, as their respective input parametersthat relates Industry, innovation and infrastructure where target-dependent variable is battery SOC. Initially, the training process involves determining the ideal hyperparameters of a kernel function to accurately represent the characteristics of the data. The accuracy of predicting SOC of the battery is evaluated using test data. According to the simulation outcomes, the squared exponential kernel function-based GPR estimates SOC with high accuracy and lower RMSE and MAE which ensures energy efficiency and quality education.

Corrigendum: Effect of PVdF Distribution on Properties and Performance of Dry Spray-Coated Graphite Electrodes for Lithium-Ion Batteries for Electric Vehicle Applications

Corrigendum: Effect of PVdF Distribution on Properties and Performance of Dry Spray-Coated Graphite Electrodes for Lithium-Ion Batteries for Electric Vehicle Applications

Effect of PVdF Distribution on Properties and Performance of Dry Spray-Coated Graphite Electrodes for Lithium-Ion Batteries for Electric Vehicle Applications

We used electrostatic dry spray-coating to fabricate graphite/PVdF anodes. We compared the morphological, mechanical, electrical, and electrochemical properties of electrodes fabricated with three different mixing times of dry electrode components. Quantitative and novel relationships between the PVdF distribution and the electrode properties were obtained. Our investigations suggest that our fabrication methods are viable alternatives for producing electrodes with comparable properties to those fabricated using traditional wet solvent-based methods. Overall, our work provides insights into new and promising methods for fabricating high-quality dry-sprayed electrodes (DSEs) with high mass loadings for use in a variety of electrochemical applications such as electric vehicles.

Synthetic ester-based forced flow immersion cooling technique for fast discharging lithium-ion battery packs

Rapid advancements in Li-ion battery technology are being made to meet the growing demand for efficient energy storage solutions in electric vehicles and portable electronics. However, heat generation during rapid charging and discharging remains a significant challenge, as it can lead to overheating, fire, and explosion. To address this issue, battery thermal management systems require improvements in cooling strategies. This study aims to optimize the thermal performance of Li-ion battery packs during fast discharge operation by single-phase synthetic ester oil-based forced flow immersion cooling (FFIC) technique. The study analyzes the thermal performance of the FFIC of a 4S2P LIB pack based on various flow rates, pressure drops, and pump power consumption and compares it with various conventional cooling methods and dielectric fluids. The MSMD-based NTGK battery modeling approach is used to analyze the electrical and thermal characteristics of the battery pack at 3C discharge and environmental temperature of 25 °C. The simulation findings revealed that the synthetic ester oil has a superior cooling effect than mineral oil in the 4S2P Li-ion battery pack. The experimental results showed that the synthetic ester-based FFIC Li-ion battery pack achieves an optimal pack temperature of 31.3 °C and uniform temperature distribution at the flow rate of 5 L/min and pressure drop of 228.44 Pa. Moreover, FFIC reduces temperature rise by 51 % compared with natural air convection and 35 % compared with static flow immersion cooling (SFIC) in the 4S2P lithium-ion battery pack. Also, the practical significance of the SEO-based FFIC immersion cooling technique is assessed across extreme climate zones such as −10 °C and 40 °C and determined its superior performance in Li-ion batteries. Hence, this study proved that the FFIC technique is suitable for Li-ion batteries in electric vehicle applications in terms of optimal performance, reliability, and safety during high-current discharge operations.

A comparative well-to-wheel analysis of renewable energy pathways for hydrogen and battery electric vehicles

Renewable hydrogen and batteries are pivotal not only for promoting renewable energy adoption but also for serving as key components in electric vehicles. While multiple hydrogen-based and battery-based pathways exist for linking renewable energy to automotive powertrains, comprehensive evaluations of them are lacking, especially in the context of long-distance energy transmission in China. This study aims to fill this gap by offering an in-depth well-to-wheel (WTW) analysis of costs and CO2 emissions across various pathways. The pathways involve two energy storage mediums - hydrogen and battery, five transport options, and three automotive powertrains - Internal Combustion Engine Vehicle (ICEV), Fuel Cell Electric Vehicle (FCEV), and Battery Electric Vehicle (BEV). How the electricity price, storage duration, and transport distance influence these pathways is also examined. The results show that the battery-based pathway offers the lowest WTW cost and CO2 emissions in BEV applications. Hydrogen-based pathways are not economically feasible for ICEVs. Importantly, the indirect use of hydrogen in BEVs - H2 for power generation - outperforms its direct use in FCEVs and ICEVs in both cost and emissions. The battery-based pathway is significantly affected by storage duration and becomes less feasible for durations exceeding one day. The most cost-efficient hydrogen transport methods are identified for different transport distances and electricity prices. This research may provide valuable insights for the planning of the hydrogen industry, automotive decarbonization and renewable energy development in China.

A Data‐Driven Method based on Discrete Wavelet Transform for online Li‐Ion Battery State‐of‐Health Prediction and Monitoring

AbstractTransportation electrification is accelerating the clean energy transition. Due to high efficiencies and energy density, Li‐ion batteries (LIBs) are used as on‐board energy carrier for battery electric vehicles (BEVs). LIBs are subject to rapid degradation due to fast‐charging, mechanical, electrical and thermal factors. Thus, state‐of‐health (SoH) prediction is required to optimize LIBs exploitation over their lifespan. An online accurate and easy‐of‐implementation battery SoH prediction and monitoring method for BEV applications is here presented. The method implements discrete wavelet transform (DWT) analysis to voltage profiles, measured while driving. Specifically, an extensive cycle aging experimental campaign on NCR 18650 cells was performed, applying two typical US test drives (urban and extra‐urban drive cycle, respectively) to the cells at different SoH. Moreover, tests carried out on LIBs at different temperatures demonstrated that temperature effect on the implemented DWT‐based method can be distinguished and separated from cycle aging effect. The proposed method allows a real‐time SoH estimation showing a good accuracy (MAE, ME and RMSE respectively result in 0.917, 2.897 and 1.32) without requiring high computational efforts. This allows to assess battery SoH during the driving. The method can also be extended to other chemistries requiring a dedicated experimental activity for the parameters tuning.

A CNN-SAM-LSTM hybrid neural network for multi-state estimation of lithium-ion batteries under dynamical operating conditions

Accurately estimating the state of charge (SOC), state of energy (SOE), and state of health (SOH) online is a critical and urgent concern in the management of lithium-ion batteries for electric vehicle applications, particularly in terms of safety and reliability. This paper develops a hybrid neural network, abbreviated as CNN-SAM-LSTM model, which combines a convolutional neural network, self-attention mechanism, and long-short term memory neural network to jointly estimate the state parameters of lithium-ion batteries, including SOC, SOE, and SOH. Additionally, a joint loss function considering homoscedastic uncertainty is developed to optimize weight adjustments for the training losses associated with the three state parameters. Experimental data collected under UDDS, BBDST and CC discharge conditions are employed to showcase the effectiveness of the proposed CNN-SAM-LSTM model. The results demonstrate that the proposed model is capable of simultaneously and accurately estimating SOC, SOE, and SOH for lithium-ion batteries under different dynamical operating conditions. Moreover, when applied to randomly segmented data, the proposed model exhibits robustness, effectively handling deviations from random discharge segments.

Unified understanding and mitigation of detrimental phase transition in cobalt-free LiNiO2

Ni-enriched layered materials are used as electrode materials of Li-ion batteries for electric vehicle applications. Stoichiometric LiNiO2 with cationic Ni3+/Ni4+ redox is the ideal electrode material, but the gradual loss of capacity at the high voltage region, associated with Ni ion migration, hinders its use for practical applications. Therefore, to improve electrode reversibility of LiNiO2, less abundant Co ions and/or other electrochemically inactive ions, e.g., Al3+ ions, are in part substituted for Ni ions. Nevertheless, a unified understanding of improvement by metal substitution is as yet not established. In this study, the origin causing detrimental phase transition in LiNiO2 is discussed through the detailed analysis on LiNiO2 integrated with nanosized Li3PO4 derived from a metastable and rocksalt LiNiO2–Li3PO4 solid solution sample. LiNiO2 derived from the metastable rocksalt oxide has approximately 6 % anti-site defects, and the particle size growth is suppressed by uniformly dispersed nanosized Li3PO4. In low crystallinity LiNiO2 with partial structural disordering, the Ni ion migration to tetrahedral sites is effectively suppressed because of repulsive electrostatic interaction from Ni ions in Li layers. Moreover, from these findings, non-stoichiometric Li0.975Ni1.025O2 with smaller particle size has been directly synthesized without high-energy milling and Li3PO4 integration, and significant improvement of electrode reversibility is achieved. Electrode reversibility of non-stoichiometric Li0.975Ni1.025O2 is further improved through surface stabilization in a highly concentrated electrolyte solution. The unified understanding of deterioration mechanisms for LiNiO2 offers a new criterion to design Co-free LiNiO2 without metal substitution, leading to full utilization of a reversible capacity for layered materials.

Performance analysis of solid-state batteries in Electric vehicle applications

In recent years solid-state batteries, have emerged leading research focus, due to their distinct advantages over conventional batteries. Solid-state batteries, having solid electrolytes, offer higher energy and power density, enhanced safety features and longer lifespan. This makes them ideal in fulfilling demand for energy storage in electric vehicle, and smart grid applications. This study aims to evaluate various types of solid-state batteries, analysing their properties, advantages, and disadvantages, assessing their viability in EV applications. The objective is to identify and recommend the most effective solid-state battery that aligns with the specific demands and operational conditions of electric vehicles and conduct a comprehensive analysis of anode and cathode elements of one of the solid-state batteries in their fresh and damaged state using scanning electron microscopy (SEM).

Estimation of State-of-Charge and Energy Efficiency of Supercapacitors Using a 1-D Electrochemical Model

Supercapacitors (SCs) have found a broad application spectrum due to their unique characteristics, including high power density and long cycle life [1]. Supercapacitors have been extensively studied to be used with Li-ion batteries for electric vehicle applications due to their complementary characteristics [1]. The energy management system of these applications relies on accurate information about the state-of-charge (SOC) and energy efficiency of the storage device at the cell level for effective monitoring and control. In the case of SCs, coulomb counting is the most commonly used SOC identification method; however, it entails certain challenges, including initial SOC information, effects of leakage current, and measurement noise [1]. Alongside improving the capacitive health diagnosis, it is critical to identify utilizable capacitance. During rapid charge/discharge of an SC, the ions in the electrolyte are immediately adsorbed/desorbed within the macro-pores, which leads to achieving the desired terminal voltage level instantaneously. However, this hinders ions’ penetration in the micropores, causing an inhomogeneous distribution of the ions across the electrodes, increasing the electrolyte resistance, resulting in poor utilizable capacitance and a drop in its energy efficiency [2]. Derived from a one-dimensional model [2, 3], a simplified SOC and energy efficiency estimation scheme of the SCs is proposed in this study. In the solid phases, the charge density of the SC is approximated as a function of its electrodes’ specific surface area, double-layer differential capacitance, and the potential drop across the double-layer. The SOC of the SCs can be expressed as a function of the solid phase charge density in the positive electrode or negative electrode, as defined in Eqn. (1). The energy efficiency (Eeff ) of the SCs can be expressed as a function of the ratio of bulk double-layer potential difference of the electrodes to the output terminal voltage change, as defined in Eqn. (2). SOC(t)=(ρ(t)/ρmax)×100 ...............(1); Eeff=(ΔΦ/ΔΦd/ΔV)×CrΔV2/2 ..............(2) where ρ and ρmax are the electrode charge density at t time and a fully charged state, respectively; ΔΦd is the bulk double-layer potential difference; ΔV is the terminal voltage change; and Cr is the rated capacitance.The electrochemical model is used to analyze the effectiveness of the proposed SOC and energy efficiency model considering a galvanostatic charge and discharge. In this limited scope, a concise response of the model compared to their respective standard methods is shown in Fig. 1. A SAFT 3500 F SC cell parameters are considered to perform the simulations [2, 3]. The results show that the SOC estimation scheme identifies the remaining charge without considering the initial SOC information. Similarly, the energy efficiency model calculates the energy received/delivered by the SC effectively. Figure 1

Cell to Pack Modelling of Li-Ion Batteries for Electric Vehicle Applications: Development and Potential Challenges

Electric vehicles (EVs) and hybrid electric vehicles (HEVs) which are powered by Li-ion batteries (LIBs) have the potential to address the challenges of reducing the dependence on fossil fuels and environmental pollution caused by the transportation sector. However, for the reliable and safe operation of the EVs and HEVs, an accurate estimation of the state of charge (SOC) and state-of-health (SOH) of the battery pack is required. The battery capacity degrades with cycling and calendar ageing over time and usage of the vehicles, which reduces the power capability and driving range of the vehicle. An accurate prediction of the battery SOC and SOH can assist the vehicle user in planning to reach the next charging station or the destination with enough range left before the battery gets fully discharged, hence avoiding annoying circumstances.Battery models have been widely employed in battery health and SOC prediction and can successfully predict the SOC and SOH of individual cells. However, the translation of these models from cell to pack levels adds several unknown critical factors which affect the model prediction capability significantly.Our proposed ECM model is based on the electrical and thermal behavior of individual cells, and it accounts for critical factors such as cell-to-cell variations, temperature gradients, and aging effects. We also incorporate a series-parallel configuration to simulate the behavior of multiple cells at the module and pack levels. By upscaling the model in this way, we can accurately predict the SOC and SOH of the entire battery pack, and provide reliable estimates of the remaining driving range and charging requirements.We anticipate that our proposed ECM model will significantly improve the accuracy of SOC and SOH predictions, which will enable EV/HEV users to better plan their trips, avoid unexpected battery failures, and improve the safety of EV/HEV batteries. Furthermore, our study has the potential to inform the design of future battery technologies and contribute to the transition toward sustainable transportation powered by clean energy sources.

Analysis of lithium diffusion and overpotential in lithium nickle cobalt aluminum oxide based lithium ion cells

Cathode degradation during the charge and discharge process is inevitable due to phase transformations during the de-/lithiation process. Various factors such as temperature, cut-off voltage, and current rates tend to cause irreversibility in the phase transformations during cycling, resulting in the quick aging of lithium-ion batteries for electric vehicle applications. This study aims to evaluate the trends of the lithium-ion diffusion coefficient as a function of the lithium-ion concentration and potential in the lithium nickel cobalt aluminum oxide (NCA) cathode of the VTC5 lithium-ion cell through measurements by the galvanostatic intermittent titration technique (GITT). The lithium-ion cell has shown the lowest diffusion coefficient value of 1.08E−17 m2 s−1 at 0.2C rate at 25 °C temperature and the highest diffusion coefficient of 7.2829E−16 m2 s−1 at the lowest current rate of 0.05C rate and 45 °C temperature during discharging. The trend of diffusion coefficient values for lithium de-/intercalation with changing temperature and current rates is investigated and correlated with the phase transformations in the structure of the cathode and is indicative of a mixed-phase reaction mechanism. The electrochemical impedance spectroscopy data reinforces the dependence on lithium-ion diffusion on temperature, and voltage values and is supported by comparative analysis.

ACGNet: An interpretable attention crystal graph neural network for accurate oxidation potential prediction

LiNixCoyMn1-x-yO2 (NCM) is one of the most critical cathode materials for high energy density lithium-ion batteries in electric vehicle applications. Nevertheless, capacity degradation and long-term cycle instability due to the aging of cathode/electrolyte interfaces remain significant challenges for NCM materials. Various surface stabilization techniques, including doping and coating, can be employed for NCM modifications. Traditionally, new coating materials are identified by chemical intuition or trial-and-error synthesis, which hinders the discovery speed of high-performance coating materials. A novel neural network model named Attention Graph Convolutional Neural Network (ACGNet) has been developed to predict crystals’ electrochemical stability windows from atom and bonding features and exhibits remarkable predictive performance with a mean absolute error of 0.586 V. Then, the developed model is utilized to conduct high-throughput screening of 13,943 candidate compounds for their coating potential. Among the candidates, LiPO3 exhibits prominent potential as a coating material due to its high oxidation voltage and low preparation cost. The subsequent battery assembly experiment and electrochemical characterization reveal that the incorporation of LiPO3 significantly enhances the cycle stability of NCM batteries. In summary, our model exhibits exceptional accuracy in predicting material properties and serves as a compelling example for machine learning applications in battery materials.

A comparison study between the lithium sulphur battery and fuel cell in new energy vehicle applications

In recent years, the development of new energy vehicles has been rapid to solve the emerging energy depletion and carbon emission problems. Lithium-sulfur batteries and fuel batteries have become the most competitive two development directions in the new generation of power batteries for electric vehicle applications. Lithium-sulfur battery has a high specific capacity, low production cost and is environmentally friendly. However, some problems include low coulomb efficiency and short life span in the process of lithium-sulfur battery production. Fuel cells are also environmentally friendly, energy-efficient and drive efficiently. But fuel cells are extremely expensive, and their longevity problems regarding the membranes and electrode materials and supporting infrastructure, such as the hydrogen and oxygen storage obstacles, must also be overcome. This paper focuses on analyzing the two new energy vehicle power resource principles, advantages and disadvantages of two kinds of typical batteries, the current development bottleneck, and respectively put forward suggestions for future development.

Antioxidant layer enables chemically stable cathode-electrolyte interface towards durable and safe Li-ion batteries

Although nickel-rich layered lithium transition metal oxides are one of the most promising candidates for high energy-density Li-ion batteries in electric vehicle applications, they yet suffer from irreversible capacity fading and poor safety properties due to the unstable cathode-electrolyte interphase (CEI), especially at high voltage and high temperature. This instability is mainly caused by the attack of free radicals generated from electrolyte decomposition and active oxygen species (especially singlet oxygen) released from the surface lattice. Here, we propose a novel modification method to construct a protective antioxidant layer on the surface of LiNi0.8Co0.1Mn0.1O2 (NCM). By scavenging free radicals and singlet oxygen, the antioxidant layer greatly reduces the interfacial side reactions and significantly suppresses irreversible rock-salt phase transitions and the associated oxygen species release, leading to the stabilization of the interface. As a result, superior electrochemical performance and enhanced thermal stabilities are achieved. Specifically, the modified NCM exhibits a capacity retention of 92.0% over 1000 cycles in full cells and a dramatic increase of onset temperature (T1) from 75.2 °C to 114.2 °C. This antioxidant layer modification by scavenging free radicals and singlet oxygen provides a new strategy for addressing challenges of CEI design, which is theoretically applicable to all layered transition metal oxide cathode materials.

Thermal performance analysis and experimental verification of lithium-ion batteries for electric vehicle applications through optimized inclined mini-channels

Power units (i.e., batteries) of electric vehicles (EVs) generate heat while being charged or discharged, which deteriorates their performance and reliability over time. This paper investigates a comprehensive spectrum of geometric and thermo-fluidic parameters of a liquid coolant flowing through mini-channels. These are embedded in the surface of an EV battery to curtail overheating. Design parameters such as aspect ratio and angular orientation of the mini-channels were varied randomly to investigate several geometric configurations that are scarcely intuitive. The coolant mass flow rate and the fluid inlet temperature were also varied through a large dataset of randomly distributed values. A real-time EV driving cycle was implemented alongside an experimentally validated model to evaluate the battery operation, which evidenced the complex dependence of the battery’s thermal state with different levels of cooling retrofitting. The study also analyzed the parasitic power consumption arising from the pumping and cooling energy demands to drive the coolant system to achieve an optimally designed retrofit for a reliable battery performance. It was found that the mini-channel parameters considerably affect the thermal performance of the battery. However, the optimized case was found to have a minimum temperature difference in the battery and a minimum power requirement. The case with a fluid inlet velocity of 0.13 m/s, a fluid inlet temperature of 312.9 K, an aspect ratio of 1.7, and an inclination angle of 4.9° was found to be the most suitable, leading to a refrigeration power requirement of 0.85 W only. The battery temperature after the end of the driving cycle was maintained at 313 K.

Machine Learning a Million Cycles as 2D Images from Practical Batteries for Electric Vehicle Applications

Machine Learning a Million Cycles as 2D Images from Practical Batteries for Electric Vehicle Applications

Charging and Discharging Control of Li-Ion Battery for Electric Vehicle Applications

Charging and Discharging Control of Li-Ion Battery for Electric Vehicle Applications

Battery calendar aging and machine learning

Eric J. Dufek, PhD, is the department manager for the Energy Storage and Electric Transportation Department at Idaho National Laboratory. His research interests span from understanding battery material degradation to electric vehicle infrastructure. Recently he has focused on the use of advanced analysis technigues, including machine learning, to significantly reduce the time needed to make life and failure mode predictions and classifications. By applying these advanced techniques, he hopes to reduce the time needed to transition high-energy and fast-charge battery technologies from the benchtop to consumer adoption. Tanvir R. Tanim, PhD, is an R&D engineer and the group lead for the Energy Storage Technology Group in the Energy Storage and Electric Transportation Department at Idaho National Laboratory. His research focuses on enabling next-generation high-energy and power lithium-ion batteries, developing advanced algorithms for reliable life estimation, and expanding and/or verifying advanced diagnostics and prognostics of these high-energy and power batteries for electric vehicle applications. Between 2011 and 2022, he authored or co-authored 38 peer-reviewed scientific articles and patents. Bor-Rong Chen, PhD, is a staff scientist at Idaho National Laboratory (INL) in the Energy Storage and Electric Transportation Department. She earned a PhD in materials science and engineering from Northwestern University in the US. Her research interest is the early identification of degradation mechanisms in Li-ion batteries and the development of advanced materials for high-energy batteries. Sangwook Kim, PhD, is a staff engineer in the Energy Storage and Electric Transportation Department at Idaho National Laboratory (INL). He obtained his MS and PhD from the Department of Mechanical Engineering at North Carolina State University in 2015 and 2019. During his PhD program, he was awarded a 2018 INL Graduate Fellowship. His current research focuses on understanding aging modes and the development of diagnostic/prognostics tools for advanced lithium-ion and lithium metal batteries. Eric J. Dufek, PhD, is the department manager for the Energy Storage and Electric Transportation Department at Idaho National Laboratory. His research interests span from understanding battery material degradation to electric vehicle infrastructure. Recently he has focused on the use of advanced analysis technigues, including machine learning, to significantly reduce the time needed to make life and failure mode predictions and classifications. By applying these advanced techniques, he hopes to reduce the time needed to transition high-energy and fast-charge battery technologies from the benchtop to consumer adoption. Tanvir R. Tanim, PhD, is an R&D engineer and the group lead for the Energy Storage Technology Group in the Energy Storage and Electric Transportation Department at Idaho National Laboratory. His research focuses on enabling next-generation high-energy and power lithium-ion batteries, developing advanced algorithms for reliable life estimation, and expanding and/or verifying advanced diagnostics and prognostics of these high-energy and power batteries for electric vehicle applications. Between 2011 and 2022, he authored or co-authored 38 peer-reviewed scientific articles and patents. Bor-Rong Chen, PhD, is a staff scientist at Idaho National Laboratory (INL) in the Energy Storage and Electric Transportation Department. She earned a PhD in materials science and engineering from Northwestern University in the US. Her research interest is the early identification of degradation mechanisms in Li-ion batteries and the development of advanced materials for high-energy batteries. Sangwook Kim, PhD, is a staff engineer in the Energy Storage and Electric Transportation Department at Idaho National Laboratory (INL). He obtained his MS and PhD from the Department of Mechanical Engineering at North Carolina State University in 2015 and 2019. During his PhD program, he was awarded a 2018 INL Graduate Fellowship. His current research focuses on understanding aging modes and the development of diagnostic/prognostics tools for advanced lithium-ion and lithium metal batteries.