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1. Physics-Informed Data-Driven Approaches to Electric Vehicle Battery State-of-Health Prediction: Comparison of Parallel and Series Configurations

   https://doi.org/10.1115/1.4068697  

   Zhao, Yixin; Haapala, Karl R; Natarajan, Arun; Behdad, Sara (September 2025, Journal of Computing and Information Science in Engineering)

   Abstract Battery lifetime and reliability depend on accurate state-of-health (SOH) estimation, while complex degradation mechanisms and varying operating conditions strengthen this challenge. This study presents two physics-informed neural network (PINN) configurations, PINN-parallel and PINN-series, designed to improve SOH prediction by combining an equivalent circuit model (ECM) with a long short-term memory (LSTM) network. PINN-parallel process inputs data through parallel ECM and LSTM modules and combines their outputs for SOH estimation. On the other hand, the PINN-series uses a sequential approach that feeds ECM-derived parameters into the LSTM network to supplement temporal data analysis with physics information. Both models utilize easily accessible voltage, current, and temperature data that match realistic battery monitoring constraints. Experimental evaluations show that the PINN-series outperforms the PINN-parallel and the baseline LSTM model in accuracy and robustness. It also adapts well to different input conditions. This demonstrates that the simulated battery dynamic states from ECM increase the LSTM's ability to capture degradation patterns and improve the model's ability to explain complex battery behavior. However, a trade-off between the robustness and training efficiency of PINNs is identified. The research outcomes show the potential of PINN models (particularly the PINN-series) in advancing battery management systems, although they require considerable computational resources. 

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2. Fast and Simplified Algorithms for SoC and SoH Estimation of Vanadium Redox Flow Batteries

   Khaki, Bahman; Das, Pritam (November 2021, IEEE Green Technologies Conference)

   Abstract— Typically, the electrochemical model and Equivalent Circuit Model-based (ECM) algorithms of Vanadium Redox Flow Batteries (VRFB) are complex and require high computation-time, thus not suitable to be used in the Battery Management Systems (BMS). Therefore, two simplified fast ECM-based estimation algorithms are proposed for the VRFB’s State of Charge (SoC) estimation. The methods are proposed based on two different parameter identification algorithms, namely discharge pulse response and the optimization-based parameter identification for the first-order ECM. The proposed approaches are further extended by an innovative, simplified mathematical model for the capacity fade of VRFBs based on the battery's electrochemical model. The simplified capacity loss model facilitates non-complex and fast estimation of VRFB’s State of Health (SoH), useful for modeling in the BMS. This has been led to a more accurate SoC estimation in the long-term use of the battery when the VRFB’s capacity fades due to electrolyte volume loss. Although the proposed joint estimation of VRFB’s SoC and SoH estimations are simpler to be modeled in the BMS, the proposed estimations are still accurate since the models consider enough electrochemical details of VRFBs. The accuracy, less complexity, reduced computation-time, and lower BMS memory storage highlight the proposed algorithms. Keywords—Battery Management System; Battery Parameter Estimation; Energy Storage Systems; Capacity Fade; State of Charge; State of Health; Vanadium Redox Flow Batteries. 

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3. State of Health Estimation of Electric Vehicle Batteries Using Transformer-Based Neural Network

   https://doi.org/10.1115/1.4065762  

   Zhao, Yixin; Behdad, Sara (October 2024, Journal of Energy Resources Technology)

   Abstract Electric vehicles (EVs) are considered an environmentally friendly option compared to conventional vehicles. As the most critical module in EVs, batteries are complex electrochemical components with nonlinear behavior. On-board battery system performance is also affected by complicated operating environments. Real-time EV battery in-service status prediction is tricky but vital to enable fault diagnosis and prevent dangerous occurrences. Data-driven models with advantages in time-series analysis can be used to capture the degradation pattern from data about certain performance indicators and predict the battery states. The transformer model can capture long-range dependencies efficiently using a multi-head attention block mechanism. This paper presents the implementation of a standard transformer and an encoder-only transformer neural network to predict EV battery state of health (SOH). Based on the analysis of the lithium-ion battery from the NASA Prognostics Center of Excellence website's publicly accessible dataset, 28 features related to the charge and discharge measurement data are extracted. The features are screened using Pearson correlation coefficients. The results show that the filtered features can improve the model's accuracy and computational efficiency. The proposed standard transformer shows good performance in the SOH prediction. 

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4. Battery health management using physics-informed machine learning: Online degradation modeling and remaining useful life prediction

   https://doi.org/10.1016/j.ymssp.2022.109347  

   Shi, Junchuan; Rivera, Alexis; Wu, Dazhong. (November 2022, Mechanical systems and signal processing)

   Wang, Dong (Ed.)

   Lithium-ion batteries have been extensively used to power portable electronics, electric vehicles, and unmanned aerial vehicles over the past decade. Aging decreases the capacity of Lithium-ion batteries. Therefore, accurate remaining useful life (RUL) prediction is critical to the reliability, safety, and efficiency of the Lithium-ion battery-powered systems. However, battery aging is a complex electrochemical process affected by internal aging mechanisms and operating conditions (e.g., cycle time, environmental temperature, and loading condition). In this paper, a physics-informed machine learning method is proposed to model the degradation trend and predict the RUL of Lithium-ion batteries while accounting for battery health and operating conditions. The proposed physics-informed long short-term memory (PI-LSTM) model combines a physics-based calendar and cycle aging (CCA) model with an LSTM layer. The CCA model measures the aging effect of Lithium-ion batteries by combining five operating stress factor models. The PI-LSTM uses an LSTM layer to learn the relationship between the degradation trend determined by the CCA model and the online monitoring data of different cycles (i.e., voltage, current, and cell temperature). After the degradation pattern of a battery is estimated by the PI-LSTM model, another LSTM model is then used to predict the future degradation and remaining useful life (RUL) of the battery by learning the degradation trend estimated by the PI-LSTM model. Monitoring data of eleven Lithium-ion batteries under different operating conditions was used to demonstrate the proposed method. Experimental results have shown that the proposed method can accurately model the degradation behavior as well as predict the RUL of Lithium-ion batteries under different operating conditions. 

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5. Electric Vehicle Battery End-of-Use Recovery Management: Degradation Prediction and Decision Making

   https://doi.org/10.1115/MSEC2022-85536  

   Zhao, Yixin; Behdad, Sara (June 2022, the ASME Manufacturing Science and Engineering Conference (MSEC))

   Electric vehicles (EVs) are spreading rapidly in the market due to their better responsiveness and environmental friendliness. An accurate diagnosis of EV battery status from operational data is necessary to ensure reliability, minimize maintenance costs, and improve sustainability. This paper presents a deep learning approach based on the long short-term memory network (LSTM) to estimate the state of health (SOH) and degradation of lithium-ion batteries for electric vehicles without prior knowledge of the complex degradation mechanisms. Our results are demonstrated on the open-source NASA Randomized Battery Usage Dataset with batteries aging under changing operating conditions. The randomized discharge data can better represent practical battery usage. The study provides additional end-of-use suggestions, including continued use, remanufacturing/repurposing, recycling, and disposal; for battery management dependent on the predicted battery status. The suggested replacement point is proposed to avoid a sharp degradation phase of the battery to prevent a significant loss of active material on the electrodes. This facilitates the remanufacturing/repurposing process for the replaced battery, thereby extending the battery's life for secondary use at a lower cost. The prediction model provides a tool for customers and the battery second use industry to handle their EV battery properly to get the best economy and system reliability compromise. 

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