Adaptive thermal modeling of rechargeable batteries for advanced automotive battery management systems

Proper functioning of secondary (rechargeable) batteries is of primary importance for any electrical vehicle, especially for Full Electrical Vehicles (FEV). One of the challenging problems arising when large battery packs are used in modern FEV and Hybrid Vehicles (HEV) is the thermal management. The Battery Management System (BMS) should be able to predict changes in the temperature of the battery and intervene if necessary. Simple and exact model describing the thermal behaviour of the battery pack is therefore required. However, the performance of the batteries deteriorates during the cycle life, in particular the capacity fades and impedance grows, leading to changes in thermal behaviour. The ageing of batteries is a complex process depending on calendar time, intensity of usage, operation regime and temperature. Forecasting the ageing is complicated and unreliable procedure. Advanced BMS must, therefore, adapt to the ageing of the battery. Algorithms providing adaptive determination of capacity fade and impedance increase have been previously developed by members of Energy Materials and Devices (EMD) group. Combination of these algorithms with classical models of thermal behaviour of the battery lead to concept of adaptive thermal modelling of the battery (packs). BMS equipped with adaptive thermal model is able to predict battery temperature evolution and apply preventive cooling when necessary, thus reducing wear of the battery. Application of such BMS can significantly improve durability and performance of FEV and HEV facilitating their mass production.