An optimized long short-term memory-weighted fading extended Kalman filtering model with wide temperature adaptation for the state of charge estimation of lithium-ion batteries. (15th November 2022) - Takyi-Aninakwa, Paul; Wang, Shunli; Zhang, Hongying; Yang, Xiaoyong; Fernandez, Carlos
Highlights: An optimized LSTM model is established to study the temperature effects on the SOC. The model is cross-trained and tested under cold, normal, and hot temperatures. A WFEKF method is proposed to efficiently denoise and optimize the final SOC. The results of the LSTM model show that temperature has distinctive SOC effects. The LSTM-WFEKF model has wide temperature adaptation for real-time BMS applications. Abstract: Accurate state of charge (SOC) estimation at different operating temperatures is essential for the reliable and safe operation of battery management systems (BMS) for lithium-ion batteries in electric vehicles (EVs). In this paper, an optimized long-short-term memory-weighted fading extended Kalman filtering (LSTM-WFEKF) model with wide temperature adaptation is proposed as a temperature-conditioned model for SOC estimation. Firstly, the input datasets are categorized based on the operating temperatures for EVs in the United States Advanced Battery Consortium manual: cold (−10 °C), normal (25 °C), and hot (50 °C) temperatures and optimized with an attention mechanism for faster training of the LSTM model to cross-train and test to specifically study the effects of temperature on the SOC estimation through a transfer learning mechanism. Secondly, the SOC estimated by the LSTM model is input into a WFEKF method, which introduces adaptive weighing and fading factors to correct, denoise, and optimize the final SOC for each temperature variation under complex working conditions. Finally, the results show that the training and testing temperatures have distinctive SOC effects using the LSTM model. Also, the proposed LSTM-WFEKF model estimates the SOC with overall best mean absolute error (MAE), root mean square error (RMSE), and R- squared ( R 2 ) values of 0.0697%, 0.0784%, and 99.9965%, respectively, under different temperatures and complex working conditions, which is optimal compared to other existing models. Based on the MAE, RMSE, and R 2 values under different operating temperatures and complex working conditions, this paper concludes that the 25 °C training dataset ensures a more accurate SOC estimation. Meanwhile, the −10 °C and 50 °C training datasets cause more and less noisy estimates, respectively. The proposed LSTM-WFEKF model has wide temperature and working condition adaptability for real-time BMS applications in EVs.