A modular approach for cooperative energy management of hybrid electric vehicles considering predictive information

Energy management strategies (EMSs) for hybrid vehicles have been extensively studied to achieve high system efficiency. EMSs usually focus on the torque split between the electric motor and the main power source. Other powertrain components, such as the gearbox or battery management system, are optimized individually. However, the cooperation between different powertrain components has been studied for specific hybrid architectures and demonstrated to be highly beneficial. A modular EMS that ensures the cooperation of multiple components with different characteristics, shared constraints and objectives, while taking advantage of predictive information will be highly beneficial. To address this research gap, a modular cooperative EMS is proposed using parametric controllers with parameter updates realized in the background using available predictive information. The strategy emphasizes modularity, feasibility, and systematically takes advantage of any available predictive information to improve the overall vehicle objectives, hence considering all the components playing a role in the EMS. The proposed cooperative strategy is first detailed for a generic EMS and then demonstrated for the control of the torque split and gear selection of a hybrid electric vehicle. A numerical study is presented to compare the proposed method with the optimal strategy derived from dynamic programming. The results are detailed for different available predictive information, both in terms of quantity and quality. The proposed method is revealed to be robust against incomplete predictive information and guarantees feasibility with low computational effort, making it real-time capable.