Battey modelling and SOC estimation with non linear Kalman filters

Lithium ion batteries have been extensively used in portable electronic devices and recently the focus has been shifted to more demanding applications like Hybrid and Electric vehicles. In these applications, the battery pack may consists of hundreds of individual cells, as battery sizes and usage increase, battery control and management strategies need to improve performance, safety, reliability and increase the lifecycle of the battery pack. The battery management system (BMS) needs to have an accurate online estimation of the state of charge (SOC) of the battery pack. In order to overcome this problem, this work addresses SOC estimation of Li-ion cells using both and Extended Kalman Filter and Sigma Points Kalman filter.\newline A battery equivalent circuit model has been chosen since it's a good compromise between complexity and accuracy. Model's parameters has been identified from Hybrid Pulse Power Characterization (HPPC) tests carried out at different temperatures and current rates, in order to obtain a model valid for a wide range of operating conditions. A non linear least square method has been used to fit the dynamic response of the cells after each current pulse. Kalman Filter approach is feasibile for on-board estimation due to its fast response and low computational burden. The SOC estimation strategies are applied to experimental results and validated trough simulations. The results from validation shows that the Sigma Points Kalman filter produces a better estimate of SOC with respect to the Extended Kalman Filter, due to its better capability to deal with system non linearities, with a comparable computational complexity.