Comprehensive dynamic battery modeling for PHEV applications

With the increasing demand in PHEV safety, performance, etc., the PHEV applications require a battery model which can accurately reflect and predict the battery performance under different dynamic loads, environmental conditions, and battery conditions. An accurate battery model is the basis of the precise battery state (state of charge, state of health and state of function) estimation. And the precise battery state information can be used to enable the optimal control over the battery's charging/discharging process, therefore to manage the battery to its optimal usage, prolong the battery life, and enable vehicle to grid and vehicle to home applications fitting into the future smart grid scenario. One of the challenges in constructing such a model is to accurately reflect the highly nonlinear battery I-V performance, such as the battery's relaxation effect and the hysteresis effect. This paper will mainly focus on the relaxation effect modeling. The relaxation effect will be modeled through series connected RC circuits. Accuracy analysis demonstrates that with more RC circuit the battery model gives better accuracy, yet increases the total computational time. Therefore, to select an appropriate battery model for a certain PHEV application is formulated as a multi-objective optimization problem balancing between the model accuracy and the computational complexity within the constraints set by the minimum accuracy required and the maximum computational time allowed. This multi-objective optimization problem is mapped into a weighted optimization problem to solve.