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<p>At present, three critical problems exist in the energy management system of proton exchange membrane fuel cell / lithium-ion battery hybrid systems. First, the system fails to adapt to complicated and variable vehicle driving conditions. Second, the system cannot achieve optimal power distribution rapidly. Third, the system still cannot balance the reduction of hydrogen consumption, the suppression of power fluctuations and the stabilization of the state of charge (SOC) of lithium-ion batteries. To address the above problems, a multi-objective fuzzy energy management strategy based on an Improved Newton-Raphson Based Optimization (INRBO) algorithm is proposed in this paper. The INRBO algorithm is an improved version of the NRBO algorithm presented in this study. Three improvements are implemented in this algorithm. Chaotic mapping is used in initialization to expand the solution space coverage. An adaptive exploration-exploitation balance strategy is designed for dynamic search intensity adjustment. A stagnation detection and perturbation mechanism is introduced to escape local optima. The membership functions of the fuzzy logic controller are optimized offline with the INRBO algorithm in this paper. The comprehensive objectives of minimizing equivalent hydrogen consumption, suppressing PEMFC power fluctuations and stabilizing the SOC of lithium-ion batteries are realized by applying the INRBO fuzzy energy management strategy. The INRBO algorithm is compared with five high-performance intelligent algorithms under static and dynamic conditions with different training durations. The INRBO-fuzzy energy management strategy is compared with two classical energy management strategies under the China Light-Duty Vehicle Test Cycle-Passenger (CLTC-P). Simulation results show that INRBO is characterized by fast convergence speed and high optimization accuracy. Under the CLTC-P driving cycle, the proposed INRBO-fuzzy control strategy achieves rapid optimal power distribution, consequently reducing hydrogen consumption, smoothing power fluctuations of the PEMFC, and stabilizing the state of charge of the lithium-ion battery. Specifically, compared with the power-following strategy, the proposed strategy reduces total equivalent hydrogen consumption by 31.44% and decreases the standard deviation of PEMFC output power by 19.96%. Compared with the expert-experience-based fuzzy control strategy, it achieves additional reductions of 13.35% in hydrogen consumption and 20.62% in power standard deviation. The lithium-ion battery SOC is maintained within the range of 0.67 to 0.85, ensuring safe and stable operation without overcharging or over-discharging.</p>

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Energy management strategy for PEMFC hybrid power system based on INRBO-fuzzy control

  • 期刊论文

  • 发表日期：

    2026年7月29日

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  • Jun Zhao,
  • Hang Shang,
  • Yongpeng Shen,
  • Pu Liu,
  • Xiaoliang Yang,
  • Zhiwei Chen

摘要

At present, three critical problems exist in the energy management system of proton exchange membrane fuel cell / lithium-ion battery hybrid systems. First, the system fails to adapt to complicated and variable vehicle driving conditions. Second, the system cannot achieve optimal power distribution rapidly. Third, the system still cannot balance the reduction of hydrogen consumption, the suppression of power fluctuations and the stabilization of the state of charge (SOC) of lithium-ion batteries. To address the above problems, a multi-objective fuzzy energy management strategy based on an Improved Newton-Raphson Based Optimization (INRBO) algorithm is proposed in this paper. The INRBO algorithm is an improved version of the NRBO algorithm presented in this study. Three improvements are implemented in this algorithm. Chaotic mapping is used in initialization to expand the solution space coverage. An adaptive exploration-exploitation balance strategy is designed for dynamic search intensity adjustment. A stagnation detection and perturbation mechanism is introduced to escape local optima. The membership functions of the fuzzy logic controller are optimized offline with the INRBO algorithm in this paper. The comprehensive objectives of minimizing equivalent hydrogen consumption, suppressing PEMFC power fluctuations and stabilizing the SOC of lithium-ion batteries are realized by applying the INRBO fuzzy energy management strategy. The INRBO algorithm is compared with five high-performance intelligent algorithms under static and dynamic conditions with different training durations. The INRBO-fuzzy energy management strategy is compared with two classical energy management strategies under the China Light-Duty Vehicle Test Cycle-Passenger (CLTC-P). Simulation results show that INRBO is characterized by fast convergence speed and high optimization accuracy. Under the CLTC-P driving cycle, the proposed INRBO-fuzzy control strategy achieves rapid optimal power distribution, consequently reducing hydrogen consumption, smoothing power fluctuations of the PEMFC, and stabilizing the state of charge of the lithium-ion battery. Specifically, compared with the power-following strategy, the proposed strategy reduces total equivalent hydrogen consumption by 31.44% and decreases the standard deviation of PEMFC output power by 19.96%. Compared with the expert-experience-based fuzzy control strategy, it achieves additional reductions of 13.35% in hydrogen consumption and 20.62% in power standard deviation. The lithium-ion battery SOC is maintained within the range of 0.67 to 0.85, ensuring safe and stable operation without overcharging or over-discharging.