Application research of fuzzy logic control optimized by dynamic programming in combined heat and power system

For the energy management of proton exchange membrane fuel cell-based combined heat and power system (PEMFC-CHP), this study employs a dynamic programming (DP) algorithm to optimize the system's power distribution. The effects of discretization level settings and multi-objective weight configurations are analyzed. Based on the Gaussian mixture model (GMM) and the expectation-maximization (EM) algorithm, rules are extracted from offline optimal data to construct a data-driven fuzzy logic controller (DP-FLC), enabling the online application of the strategy. The results show that selecting N = 200 discrete points can effectively smooth PEMFC output fluctuations while controlling computational complexity. Through multi-objective weight optimization, the system can limit the maximum PEMFC power increment to 8.32 W and achieve a sound balance between energy consumption and system durability. The proposed DP-FLC controller inherits the advantages of offline optimization. Compared to conventional methods, it reduces the standard deviation of PEMFC power fluctuations by approximately 56.7, and the system's energy consumption remains close to the DP-optimized solution. This verifies the effectiveness of the proposed online management strategy in enhancing system stability and energy efficiency.