Combined State of Charge and State of Health estimation over lithium-ion battery cell cycle lifespan for electric vehicles

TLDR

A combined SOC and SOH estimation method over the lifespan of a lithium‑ion battery is proposed. The method first models SOC‑dependent nominal parameters of a first‑order RC circuit and quantifies their degradation over time, then applies two EKFs operating on different time scales—real‑time SOC estimation and offline SOH updates—where the SOH estimator’s time scale is set by model accuracy loss. Using extensive lifetime testing data, the approach accurately estimates SOC and SOH across the battery cycle lifespan.

Abstract

A combined SOC (State Of Charge) and SOH (State Of Health) estimation method over the lifespan of a lithium-ion battery is proposed. First, the SOC dependency of the nominal parameters of a first-order RC (resistor-capacitor) model is determined, and the performance degradation of the nominal model over the battery lifetime is quantified. Second, two Extended Kalman Filters with different time scales are used for combined SOC/SOH monitoring: the SOC is estimated in real-time, and the SOH (the capacity and internal ohmic resistance) is updated offline. The time scale of the SOH estimator is determined based on model accuracy deterioration. The SOC and SOH estimation results are demonstrated by using large amounts of testing data over the battery lifetime.