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1. 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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2. Physics-Informed Machine Learning for Degradation Diagnostics of Lithium-Ion Batteries

   https://doi.org/10.1115/DETC2021-71407  

   Thelen, Adam; Lui, Yu Hui; Shen, Sheng; Laflamme, Simon; Hu, Shan; Hu, Chao (November 2021, 47th Design Automation Conference (DAC))

   State of health (SOH) estimation of lithium-ion batteries has typically been focused on estimating present cell capacity relative to initial cell capacity. While many successes have been achieved in this area, it is generally more advantageous to not only estimate cell capacity, but also the underlying degradation modes which cause capacity fade because these modes give further insight into maximizing cell usage. There have been some successes in estimating cell degradation modes, however, these methods either require long-term degradation data, are demonstrated solely on artificially constructed cells, or exhibit high error in estimating late-life degradation. To address these shortfalls and alleviate the need for long-term cycling data, we propose a method for estimating the capacity of a battery cell and diagnosing its primary degradation mechanisms using limited early-life degradation data. The proposed method uses simulation data from a physics-based half-cell model and early-life degradation data from 16 cells cycled under two temperatures and C rates to train a machine learning model. Results obtained from a four-fold cross validation study indicate that the proposed physics-informed machine learning method trained with only 60 early life data (five data from each of the 12 training cells) and 30 high-degradation simulated data can decrease estimation error by up to a total of 9.77 root mean square error % when compared to models which were trained only on the early-life experimental data. 

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

   Zhao, Yixin; Behdad, Sara (August 2023, Proceedings of the ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE2023)

   Electric vehicles (EVs) are considered an environmentally friendly option 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 aid in the prevention of 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 is capable of capturing 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 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 effectively improve the accuracy of the model as well as the computational efficiency. The proposed standard Transformer shows good performance in SOH prediction. 

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5. On the Error of Li-ion Battery Parameter Estimation Subject to System Uncertainties

   https://doi.org/10.1149/1945-7111/acbc9c  

   Fogelquist, Jackson; Lai, Qingzhi; Lin, Xinfan (March 2023, Journal of The Electrochemical Society)

   Emerging lithium-ion battery systems require high-fidelity electrochemical models for advanced control, diagnostics, and design. Accordingly, battery parameter estimation is an active research domain where novel algorithms are being developed to calibrate complex models from input-output data. Amidst these efforts, little focus has been placed on the fundamental mechanisms governing estimation accuracy, spurring the question, why is an estimate accurate or inaccurate? In response, we derive a generalized estimation error equation under the commonly adopted least-squares objective function, which reveals that the error can be represented as a combination of system uncertainties (i.e., in model, measurement, and parameter) and uncertainty-propagating sensitivity structures in the data. We then relate the error equation to conventional error analysis criteria, such as the Fisher information matrix, Cramér-Rao bound, and parameter sensitivity, to assess the benefits and limitations of each. The error equation is validated through several uni- and bivariate estimations of lithium-ion battery electrochemical parameters using experimental data. These results are also analyzed with the error equation to study the error compositions and parameter identifiability under different data. Finally, we show that adding target parameters to the estimation without increasing the amount of data intrinsically reduces the robustness of the results to system uncertainties. 

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