The Optimization Of State Of Charge And State Of Health Estimation For Lithium-Ions Battery Using Combined Deep Learning And Kalman Filter Methods

An accurate estimate of the battery state of charge and state of health is critical to ensure the lithium-ion battery's efficiency and safety. The equivalent circuit model-based methods and data-driven models show the potential for robust estimation. However, the state of charge and state of health estimation system's performance with a parallel comparison has been rarely investigated. In this study, the performances of state of charge and state of health with equivalent circuit model-based methods and data-driven estimations are analyzed by different aged and capacity batteries through methods including extended Kalman filters, fully connected deep network with drop methods, and the combination (extended Kalman filters-fully connected deep network with drop methods). Besides the battery state of the voltage and current, the relationship between inner resistance, temperature, and capacity are also considered. Finally, a suggested method is promising for online state estimation of battery working at different temperatures and initial working state. The results indicate that the maximum state of charge estimation errors of the fully connected deep network with drop methods is 0.56% for the fully charged battery. Simultaneously, with an uncertain initial state of charge, the extended Kalman filter shows the lowest maximum state of charge estimation errors (1.4%). For the state of health estimation, the optimized method uses extended Kalman filters to do the monitor first; after 5 testing points, if the state of health drops to lower than 0.95, extended Kalman filters-fully connected deep network with drop methods is suggested. And finally, estimation errors for this method decreased from 30% to 2%.