Energy Management Strategy of Hybrid Electric Vehicle Using Battery State of Charge Trajectory Information

Heeyun Lee, Jongryeol Jeong, Yeong-il Park, and Suk Won Cha


This paper presents a new energy management control strategy of a hybrid electric vehicle, using dynamic programming with preview driving cycle information. Dynamic programming is a promising solution for energy management problem of the hybrid electric vehicle, yet global optimality can only be achieved with preview driving cycle information due to its non-causal property. As recent driving cycle prediction algorithms facilitate the use of preview driving cycle information, rule-based energy management strategy of the hybrid electric vehicle using dynamic programming optimization is presented in this study, under the assumption that the preview driving cycle information is already given. The strategy is composed of dynamic programming calculation and the rule-based control strategy using that calculation. Dynamic programming analyzes optimal control and battery state of charge trajectory in accordance with the vehicle travel distance, with given predicted driving cycle information. The proposed rule-based strategy distributes vehicle’s demand power into the engine and the electric motor, to follow target battery state of charge trajectory acquired from dynamic programming. To validate the control strategy, simulation is conducted on various standard driving cycles. The energy management strategy shows improved fuel economy performance for diverse driving cycles.