An optimized multi-segment long short-term memory network strategy for power lithium-ion battery state of charge estimation adaptive wide temperatures

Abstract

With the development of intelligentization and network connectivity of new energy vehicles, the estimation of power lithium-ion battery state of charge (SOC) using artificial intelligence methods is becoming a research hotspot. This paper proposes an optimized multi-segment long short-term memory (MSLSTM) network strategy for SOC estimation of powered lithium-ion batteries' adaptive wide temperatures. First, the multi-timescale electrochemical processes during the charging and discharging of power lithium-ion batteries are efficiently analyzed, and the analytically measurable external parameters are classified into subsets based on the analysis. Secondly, the idea of segment long short-term memory (SLSTM) estimation is proposed to enhance the data linkage between the SOC and the nonlinearly varying parameters and to improve the prediction accuracy. Finally, an optimized MSLSTM neural network is proposed for nonlinear regression prediction of SOC in subset intervals through a combination of segmented estimation idea and SLSTM neural network. The proposed algorithm is validated under a variety of temperatures and operating conditions, and the accuracy of the SOC estimation is improved by at least 66.770 % or more. It provides a solution idea for intelligent estimation of power lithium-ion battery SOC.