Modeling and Real-Time Scheduling of Large-Scale Batteries for Maximizing Performance

Modern electric vehicles are equipped with an advanced battery management system, responsible for providing the necessary power efficiently from batteries to electric motors while maintaining the batteries within an operational condition. Because discharge-rate and temperature of batteries affect their health and efficiency significantly, batteries are managed to mitigate their discharge and thermal stresses. In this paper, we develop a real-time, efficient integrated management system for discharge-rate and temperature of batteries. To achieve this objective, we first construct a prognosis system predicting the likely states of batteries' capacity and capability. Based on prognostic estimates of the impact of temperature and discharge-rate on the performance, we solve an optimization problem to search for efficient discharging and cooling scheduling. Our experimentation and simulation demonstrate that the proposed management enhances system performance up to 85.3%.