Time-Efficient Battery Temperature Sensitive Energy Management Strategy for Series Hybrid Electric Vehicle

Hybrid electric vehicles have been recognized as one of the promising countermeasures to realize carbon neutrality and mitigate energy issues, whose performance depends to a certain extent on energy management strategies. On the one hand, the power batteries are highly temperature sensitive, and working in high temperature will damage the battery life. On the other hand, energy management controller should be able to execute computation-intensive optimization task in real-time. Thus, besides fuel economy, battery temperature and algorithmic time efficiency should be of great concern in the development of practicality oriented EMS. In this work, a time-efficient battery temperature sensitive EMS for series hybrid electric vehicle is proposed. First, a multi-objective optimal control problem is mathematically formulated in model predictive control (MPC) framework. Then, the non-convex optimization problem caused by the non-linear battery state of energy and state of temperature models is transformed into convex optimization problem by convex reformulation. Finally, to improve computational efficiency, alternating direction method of multipliers is used to split the optimization problem into small-scale subproblems and solve the energy management problem. The simulation and Hardware-in-loop test results show that the proposed strategy is capable of not only improving the fuel economy but also effectively keeping the battery temperature as close as possible to the thermal comfort zone. It is observed that the proposed method produces a promising computational efficiency, relative to Pontryagin's minimization principle-MPC, close to that produced by rule-based strategy.