Long Short-Term Memory Networks for State of Charge and Average Temperature State Estimation of SPMeT Lithium–Ion Battery Model

Abstract

Lithium–ion batteries are the dominant battery type for emerging technologies in the efforts to slow climate change. Accurate and quick estimations of state of charge (SOC) and internal cell temperature are vital to battery-management systems to enable the effective operation of portable electronics and electric vehicles. Therefore, a long short-term memory (LSTM) recurrent-neural network is proposed which completes the state estimation of SOC and internal average cell temperature (Tavg) of lithium–ion batteries under varying current loads. The network is trained and evaluated using data compiled from a newly developed extended single-particle model coupled with a thermal dynamic model. Results are promising, with root mean square values typically under 2% for SOC and 1.2 K for Tavg, while maintaining quick training and testing times. In addition, we examined a comparison of a single-feature versus multi-feature network, as well as two different approaches to data partitioning.