Abstract
Hydrogen fuel cell water-thermal management systems suffer from slow response time, system vibration, and large temperature fluctuations of load current changes. In this paper, Logistic chaotic mapping, adaptively adjusted inertia weight and asymmetric learning factors are integrated to enhance the particle swarm optimization (PSO) algorithm and combine it with fuzzy control to propose an innovative improved particle swarm optimization-Fuzzy control strategy. The use of chaotic mapping to initialize the particle population effectively enhances the variety within the population, which subsequently improves the ability to search globally and prevents the algorithm from converging to a local optimum solution prematurely; by improving the parameters of learning coefficients and inertia weight, the global and local search abilities are balanced at different stages of the algorithm, so as to strengthen the algorithm’s convergence certainty while reducing the dependency on expert experience in fuzzy control. In this article, a fuel cell experimental platform is constructed to confirm the validity and efficiency of the recommended strategy, and the analysis reveals that the improved particle swarm optimization (IPSO) algorithm demonstrates better convergence performance than the standard PSO algorithm. The IPSO-Fuzzy-PID management approach is capable of providing a swift response and significantly diminishing the overshoot in the system’s performance, to maintain the system’s safe and stable execution.
Published Version
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