Construction of state of charge estimation method for automotive ternary batteries

In this chapter, we focus on the estimation of lithium-ion battery SOC (state of charge) by combining the basic structure analysis, operation characteristics analysis, equivalent modeling study, and iterative algorithm exploration for model parameter identification of on-board ternary lithium-ion battery of electric vehicles. First, the Kalman filter algorithm is investigated and the basic state prediction principle and algorithm steps are optimized to obtain a better derivative algorithm, and then a unique pairwise online algorithm with Fading Memory Recursive Least Square algorithm and Square Root Unscented Kalman Filter algorithm is established through the process analysis of the square root traceless Kalman algorithm. Through a series of improvements and optimizations, the stability and accuracy of SOC estimation for ternary lithium-ion batteries are achieved.