Health-Aware Adaptive Energy Management Strategy for Fuel Cell Vehicles Based on Variable Horizon Driving Pattern Recognition

Energy management strategies (EMSs) are essential to minimize hydrogen consumption and mitigate the degradation of power sources for fuel cell vehicles. Effective driving pattern recognition (DPR) can enhance the adaptability of these strategies to varying driving cycles. However, existing methods typically rely on constant horizon DPR, leading to reduced recognition accuracy and suboptimal power allocation. This article proposes a variable horizon DPR-based adaptive equivalent consumption minimization strategy (VHDPR-AECMS) to minimize the total driving cost. First, a realistic driving cycle, the Changchun driving cycle (CCDC), is constructed based on real-world driving data. Then, optimal horizons for different driving patterns are investigated, and the VHDPR strategy based on short-term speed prediction is developed. Furthermore, the equivalent factor of the VHDPR-AECMS is adjusted according to VHDPR results, with further fine-tuning based on the battery’s state of charge (SOC) feedback. Results show that VHDPR achieves an accuracy of 93.62% under the CCDC, outperforming optimal constant horizon DPR. Moreover, VHDPR-AECMS outperforms benchmark strategies and ensures faster SOC convergence. Compared with the rule-based strategy, VHDPR-AECMS reduces total driving cost by 34.59%, mitigating fuel cell system and battery degradation by 37.70% and 11.99%, respectively. Finally, hardware-in-the-loop (HIL) testing demonstrates the real-time applicability of the proposed strategy on actual vehicle controllers.