Machine learning-based state of health prediction for battery systems in real-world electric vehicles

Carbon dioxide emission reduction is a significant benefit of electric vehicles. State of health prediction is essential to ensure the safety and reliability of the battery system, which is a key part of electric vehicles. This paper proposes a novel state of health prediction strategy based on ohmic internal resistance and long short-term memory networks. Driving conditions are extracted based on the current and speed of real-world vehicles. Combining the equivalent circuit model and Kalman filter, the parameter identification of the power battery system is performed. Ohmic internal resistance is selected as the health state characterization parameter. According to the ohmic internal resistance results obtained from the identification, Pearson correlation analysis is used to obtain the parameters with the highest correlation to the ohmic internal resistance. These parameters are used as input samples for the long short-term memory network to get the evaluation value of health status. The model is trained and tested using data from different operating conditions and vehicles. The root mean square errors of the model outputs are below 0.02 Ω, demonstrating the method's effectiveness. The proposed method is expected to have considerable application in advanced battery management systems.