Adaptive hierarchical energy management strategy for fuel cell mobile robot hybrid power system based on working condition recognition

Energy management of hybrid power is critical to maintain the economical and efficient operation of fuel cell mobile robots. To improve the energy distribution between the proton exchange membrane fuel cell (PEMFC) and battery under different working conditions, this paper proposes an adaptive hierarchical energy management strategy (AHEMS) based on the recognition and management levels. Firstly, the recognition level realizes the identification of different working conditions based on the machine learning (ML) methods including the K-means and KNN. Secondly, the fuel cell hydrogen consumption and efficiency are both optimized by adaptive multi-objective particle swarm optimization (AMOPSO) at the management level. Specifically, an adaptive flight parameter strategy based on the particle dispersity (PD) information is proposed to balance the convergence and diversity of Pareto solutions. Besides, to overcome the parameter uncertainty caused by different working states and improve the system performance, an interval optimization scheme is proposed based on the Pareto solutions. Finally, the fuzzy decision combined with the recognition results and state of charge (SOC) of the battery is performed to find the most appropriate power distribution of the PEMFC and battery. The proposed AHEMS algorithm is compared with different algorithms in the numerical simulation and hardware-in-loop (HIL) experiments. These results demonstrate that the hybrid power system with the proposed optimization scheme performs better than the base model and classical optimization algorithms in terms of the hydrogen consumption and efficiency indexes, revealing the success of this AHEMS approach in solving the energy distribution problem in different working conditions.