A spatiotemporal estimation method for temperature distribution in lithium-ion batteries

Effective thermal management is crucial to the optimal operation and health management of lithium-ion batteries. The online estimation of the temperature distribution in vehicle battery systems is not easy, as there are only a few sensors available on site. Furthermore, the thermal behaviors of batteries are difficult to predict, as their dynamics are strongly time-varying. In this paper, a hybrid model is developed for spatiotemporal estimation of temperature distribution in lithium-ion batteries. A simple but effective nominal model is first developed for real-time thermal management using a time/space separation method. Subsequently, a data-based neural model is proposed to compensate the model-plant mismatch caused by the spatial nonlinearity and other model uncertainties. The developed algorithm is simple and can be readily integrated into existing battery management systems. Simulation studies demonstrate the effectiveness of the proposed method.