Proton exchange membrane fuel cells face rapid operation temperature shifts and changes in membrane water content during startup and dynamic operations, which can impede the performance of hybrid maximum power point tracking algorithms, leading to slower tracking speeds, reduced accuracy, and efficiency losses. To tackle the problems of flawed switching logic and inadequate optimization in current hybrid algorithm, this paper introduces an improved hybrid maximum power point tracking algorithm. First, a dual-mode intelligent switching mechanism that employs a power variation increment threshold alongside a fixed iteration count as dual constraints, thereby boosting dynamic switching stability and minimizing false switching risks. Second, an adaptive step-size optimization strategy during the incremental conductance tracking phase to enhance both convergence speed and steady-state accuracy. MATLAB/Simulink simulations for a PEMFC system show that the proposed improved hybrid algorithm reduces dynamic tracking time by 58.88 %–75.42 % under startup and dynamic conditions, increases tracking accuracy by 0.40 %–1.76 %, and ultimately boosts tracking efficiency by 0.96 %–5.50 %. When applied to fuel cell systems, this algorithm can significantly enhance clean power output.
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