An Invariant Method for Electric Vehicle Battery State-of-Charge Estimation Under Dynamic Drive Cycles

This paper proposes a novel invariant extended Kalman filter (IEKF), modified version of the (EKF), for state-of-charge (SOC) estimation lithium-ion (Li-ion) battery cells. Unlike conventional EKF methods where correction term used to update state is linearly proportional output error, this employs IEKF independent resulting in significant reduction error and improving accuracy. In contrast classic method like more contemporary ones square root variant Cubature Filter (SCKF), can successfully mimic nonlinear dynamics mitigate measurement noise stochasticity. Moreover, even if model fails fully capture cell’s dynamics, will still sustain reasonable performance. Hence, outperforms EKF, SCKF, which diverge mismatch between SOC true occurs. The derivation proposed followed by experimental verification using commercial Li-ion cells are presented.