Abstract:

In order to enhance the adaptability of the Equivalent Consumption Minimization Strategy (ECMS) for different operating conditions, a Particle Swarm Optimization-based Fuzzy Adaptive Equivalent Consumption Minimization Strategy (PSO-fuzzy A-ECMS) is proposed. This strategy is built upon the method of proportional adjustment of equivalent factors based on State of Charge (SOC) feedback. Addressing the limitations of relying solely on SOC feedback for equivalent factor adjustment, the PSO-fuzzy A-ECMS introduces fuzzy control to adjust the proportional coefficients based on demand power and SOC inputs. Subsequently, the Particle Swarm Optimization algorithm is used to optimize the membership functions of the fuzzy controller, reducing the dependence on expert knowledge in energy management applications.

In this study, by considering demand power and SOC as inputs, the fuzzy controller dynamically adjusts the proportional coefficients of the equivalent factors. This approach better adapts to energy management requirements under different operating conditions, improving fuel economy and battery charge-discharge balance of the system.

To optimize the membership functions of the fuzzy controller, the researchers employed the Particle Swarm Optimization algorithm. This heuristic optimization algorithm simulates the foraging behavior of bird flocks to find the optimal solution. In this research, the Particle Swarm Optimization algorithm is utilized to adjust the parameters of the membership functions in the fuzzy controller, making the energy management strategy of the system more accurate and efficient.

Finally, the performance of the PSO-fuzzy A-ECMS strategy is evaluated through simulation experiments under standard driving cycles. The simulation results demonstrate that compared to the SOC feedback-based equivalent factor correction method, the PSO-fuzzy A-ECMS strategy better maintains SOC within a reasonable range and achieves improved control effectiveness in terms of fuel economy and battery charge-discharge balance.By incorporating fuzzy control and Particle Swarm Optimization, the PSO-fuzzy A-ECMS strategy enhances the adaptability of the Equivalent Consumption Minimization Strategy for different operating conditions, reduces reliance on expert knowledge, and achieves better control performance.