Lithium-Ion Battery Health Estimation Using an Adaptive Dual Interacting Model Algorithm for Electric Vehicles

To ensure reliable operation of electrical systems, batteries require robust battery monitoring systems (BMSs). A BMS’s main task is to accurately estimate a battery’s available power, referred as the state charge (SOC). Unfortunately, SOC cannot be measured directly due its structure, and so must estimated using indirect measurements. In addition, methods used are highly dependent on capacity, known health (SOH), which degrades used, resulting in complex problem. this paper, novel adaptive estimation method proposed. The proposed uses dual-filter architecture conjunction with interacting multiple model (IMM) algorithm. dual filter strategy allows for model’s parameters updated while IMM access different degradation rates. well-known Kalman (KF) relatively new sliding innovation (SIF) implemented SOC. dual-KF-IMM dual-SIF-IMM, respectively. As demonstrated both algorithms show accurate SOH lithium-ion under cycling conditions. results strategies will interest safe particular focus electric vehicles.