Interacting Multiple Model Strategy for Electric Vehicle Batteries State of Charge/Health/ Power Estimation

States estimation of lithium-ion batteries is an essential element Battery Management Systems (BMS) to meet the safety and performance requirements electric hybrid vehicles. Accurate estimations battery's State Charge (SoC), Health (SoH), Power (SoP) are for safe effective operation vehicle. They need remain accurate despite changing characteristics battery as it ages. This paper proposes online adaptive strategy high accuracy SoC, SoH SoP be implemented onboard a BMS. A third-order equivalent circuit model structure considered with its state vector augmented two more variables including internal resistance SoC bias. An Interacting Multiple Model (IMM) Smooth Variable Structure Filter (SVSF) then employed determine resistance, bias battery. The IMM results in generation mode probability that related aging. combined SoH. estimated used which provides complete states condition. efficacy proposed condition-monitoring tested validated using experimental data obtained from accelerated aging tests conducted on Lithium Polymer automotive cells.