Battery aging-and temperature-aware predictive energy management for hybrid electric vehicles

Lithium-ion battery degradation is one of the most important issues in hybrid electric vehicles. In order to minimize degradation and its equivalent battery life cost, the degradation of Li-ion batteries needs to be considered in energy management strategies. The existing methods mainly consider the battery aging in energy management strategies while ignoring its thermal dynamics. This paper proposes a battery aging-and temperature-aware predictive energy management strategy for parallel hybrid electric vehicles. This method is developed based on model predictive control (MPC) and evaluated in the scenario of urban bus transportation. First, due to the stochastic nature of speed transitions under actual driving conditions, a stochastic speed predictor is built based on the Markov chain model. Then, an optimal control framework for energy management strategy (EMS) is developed based on MPC, and the battery electrical-thermal-aging dynamics are considered in this control framework. The optimization is performed on the receding horizon using Pontryagin's minimization principle (PMP). The newly developed PMP-based MPC method is compared with the rule-based method and the global optimization method. The comparisons show that the PMP method is superior to dynamic programming when the battery electrical-thermal-aging dynamics are considered in the EMS development, and also show that the battery temperature-aware EMS can lower the total energy consumption.