Model predictive control for power management in a plug-in hybrid electric vehicle with a hybrid energy storage system

The fuel economy performance of plug-in hybrid electric vehicles (PHEVs) strongly depends on the power management strategy. This study proposes an integrated power management for a PHEV with multiple energy sources, including a semi-active hybrid energy storage system (HESS) and an assistance power unit (APU). The HESS consists of battery packs and ultracapacitor packs. In the integrated control strategy, the output power between the battery packs and ultracapacitor packs is regulated by the model predictive control strategy, while the output power between the APU and HESS is allocated by the rule-based strategy. In the model predictive control process, a period of the future velocity will be predicted, and the dynamic programming algorithm will be applied to optimize the control strategy accordingly. The robustness of the proposed approach is verified by three typical driving cycles, including the Manhattan cycle, CBDC cycle and UDDSHDV cycle. The results show that the proposed control strategy can promote fuel economy compared with the original control strategy, especially in the charge sustain mode under the MANHATTAN driving cycle (21.88% improvement).