Integrated energy and thermal management strategy for extended range electric vehicle based on battery temperature and state-of-charge global planning

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There is a coupling relationship between energy and thermal management of extended range electric vehicle (EREV), so developing an integrated energy and thermal management strategy (IETMS) is an effective approach to reduce fuel consumption and battery degradation and further improve vehicle driving economy. Global optimization of EREV energy and thermal management is solved by dynamic programming based on adaptive grid optimization (AGO-DP), which enhances optimization performance by narrowing the feasible domain of battery temperature and state-of-charge (SOC). Meanwhile, within the hierarchical model predictive control (MPC) framework, a novel IETMS based on battery temperature and SOC global planning is proposed in this study. According to the road and weather information, the battery temperature and SOC reference trajectories approximating to the global optimum are generated to guide real-time control. Then MPC is formulated to minimize total operating cost by coordinating APU output power and battery cooling power in the short-term prediction horizon. The simulation results show that the proposed IETMS comprehensively optimizes the vehicle economy from the aspects of tracking reference trajectory, reducing battery peak current and improving fuel efficiency. The total operating cost decreases by 1.77%–14.52% compared with other strategies. Moreover, the proposed IETMS also exhibits desirable versatility and robustness under different driving scenarios, achieving 86.60%—92.18% of the optimal performance.