Energy management strategy optimization for plug-in hybrid light commercial vehicle based on fuzzy-PMP

Abstract

The distinct structural configuration and load characteristics of P1 + P2.5 plug-in hybrid electric light commercial vehicles (PHEV-LCVs) pose significant challenges to the development of an effective energy management strategy (EMS). To address these challenges, this study proposes a hierarchical two-layer optimization framework that integrates fuzzy logic control (FLC) with Pontryagin’s Minimum Principle (PMP). Initially, a fuzzy logic-based EMS is formulated for the P1 + P2.5 dual-motor configuration, in which a fuzzy controller is employed to allocate torque to the rear axle with enhanced efficiency. To reduce the subjectivity associated with the design of membership functions, the sparrow search algorithm (SSA) is applied offline to optimize both input and output membership parameters. Subsequently, to overcome the difficulty of accurately determining the optimal co-state variable within the PMP framework, a genetic algorithm (GA) is utilized to compute an offline optimal constant co-state. A secondary fuzzy controller is then introduced to perform real-time adjustments of the co-state based on vehicle speed, load variations, and acceleration, thereby enabling the system to adapt dynamically to varying operational conditions. The proposed FLC-PMP strategy is validated through co-simulations using AVL CRUISE and Simulink under three representative driving cycles. Comparative results indicate that the proposed approach exhibits superior adaptability to fluctuating load conditions compared to conventional rule-based control (RB), SSA-optimized fuzzy control (FLC-SSA), and dynamic programming (DP). Specifically, under half-load conditions (3 × WLTC), the proposed method achieves reductions in energy consumption of 9.09% and 6.36% relative to RB and FLC-SSA, respectively, while incurring only a 1.62% increase in comparison to the globally optimal DP strategy. These outcomes substantiate the effectiveness and practical applicability of the proposed hierarchical EMS framework.