A Novel ANFIS-Dynamic Programming Fusion Strategy for Real-Time Energy Management Optimization in Fuel Cell Electric Commercial Vehicles

The present study proposes an integrated real-time energy management strategy (EMS) that combines an adaptive neuro-fuzzy inference system (ANFIS) with dynamic programming (DP) to enhance the energy efficiency and system durability of fuel cell electric commercial vehicles (FCECVs). Firstly, a comprehensive DP framework was established to optimize the EMS offline, which simultaneously considers power allocation and automated manual transmission (AMT) gear-shifting to minimize hydrogen consumption (HC). Then, the DP framework was employed to determine optimal power allocation patterns of the FCECVs under various initial state-of-charge (SOC) battery conditions. Based on the DP results, a novel real-time EMS integrating ANFIS with DP solution was developed to formulate an efficient fuzzy inference system (FIS), where the ANFIS model was trained using the particle swarm optimization (PSO) algorithm. The proposed ANFIS-DP EMS was evaluated through extensive simulations under stochastic driving cycles, with performance comparisons against both the DP method and conventional charge-depleting and charge-sustaining (CD-CS) strategies. The experimental results demonstrate that the ANFIS-DP maintains efficient FCS operation across diverse driving conditions while effectively controlling the rate of power change within optimal ranges. Compared to the CD-CS strategy, the proposed method achieves a substantial 14.98% reduction in HC, approaching the performance of DP (only 5.40% higher). Most notably, the ANFIS-DP strategy demonstrates remarkable computational efficiency improvements, outperforming DP by 96.13% and CD-CS by 22.05%. These findings collectively validate the effectiveness of our proposed approach in achieving real-time energy management optimization for FCECVs.