Joint estimation of State of Charge (SOC) and State of Health (SOH) for lithium ion batteries using Support Vector Machine (SVM), Convolutional Neural Network (CNN) and Long Sort Term Memory Network (LSTM) models

This paper proposes a data-driven method for jointly estimating the State of Charge (SOC) and State of Health (SOH) of batteries, addressing the impact of battery aging on SOC estimation. Initially, Support Vector Machine (SVM) is employed to estimate the SOH of the battery, using the constant voltage charging time and constant voltage discharging time of lithium-ion batteries as inputs, and SOH as the output. By training the SVM model, accurate SOH estimation is achieved. Subsequently, the rated capacity of the battery is adjusted based on the estimated SOH to obtain the current maximum available capacity. This adjustment allows for the coupling of SOH and SOC, resulting in SOC estimation that accounts for aging factors. Leveraging the advantages of Convolutional Neural Networks (CNN) in feature extraction and Long Short-Term Memory (LSTM) neural networks in handling long-term sequential data, a CNN-LSTM model is utilized for SOC estimation. The proposed method utilizes the Oxford Battery Dataset (Cells 1–8) and the NASA Battery Dataset (B0005–B0007) for SOH estimation, and the Oxford Battery Dataset (Cell 8) and the NASA Battery Dataset (B0007) for SOC estimation. The SOH estimation results demonstrate that the Root Mean Square Error (RMSE) is less than 0.81 % and the Mean Absolute Error (MAE) is less than 0.65 % for Cells 1–8, while for B0005 to B0007, the RMSE is less than 1.81 % and the MAE is less than 1.29 %. For SOC estimation, the results show that the average RMSE over the entire lifecycle of Cell 8 is 0.3923 % and the average MAE is 0.3339 %, whereas for B0007, the average RMSE over the entire lifecycle is 0.6123 % and the average MAE is 0.4976 %.