Energy management and optimization of PEMFC/battery mobile robot based on hybrid rule strategy and AMPSO

• Published in: Renewable energy 2021-06, Vol.171, p.881-901
• Main Authors: Xueqin Lü, Wu, Yinbo, Lian, Jie, Zhang, Yangyang
• Format: Article
• Language: English
• Subjects: Adaptive mutation particle swarm optimization; Energy consumption minimization; Energy management; Hybrid rule fuzzy state machine; PEMFC/LIB hybrid power system; Power fluctuation
• ISSN: 0960-1481; 1879-0682
• DOI: 10.1016/j.renene.2021.02.135

Due to the flexibility of seam tracking robot in special environment, independent power supply with different characteristics is adopted. Power allocation of hybrid power system composed of proton exchange membrane fuel cell and lithium battery is mainly researched. According to the requirements of economy and safety, hybrid rule fuzzy state machine control is designed to realize the optimal operation and fast response of power supply system. On the basis of energy allocation, further reduction of hydrogen consumption and power fluctuation is considered, which improves the power supply scheme. The sensitivity analysis method is used to screen the optimization variables, which reduces the complexity of the strategy; in addition, an adaptive mutation particle swarm optimization is studied to optimize the strategy, which combines the mutation idea, adaptively changes the weight and learning factor, and realizes the minimization of power fluctuation and equivalent hydrogen consumption. The results show that the stability of fuel cell and the rationality of lithium battery charging and discharging are greatly improved, and the fuel economy and service life of hybrid power system are improved. [Display omitted] •Study power distribution in hybrid power system.•Select the optimal operating range of fuel cell and lithium battery.•Design HRFSM from the perspectives of economy, security and optimal operation.•Select the variables to be optimized by sensitivity analysis method.•Study AMPSO to realize power fluctuation and equivalent consumption minimization.