考虑电堆寿命的氢燃料电池汽车能量管理策略研究

Energy Management Strategy for Hydrogen Fuel Cell Vehicle Considering Fuel Cell Stack Lifespan

提出了一种在满足动力性需求并且以氢燃料电池堆作为主要能源的前提下,能有效延长电堆使用寿命的能量管理策略。提出将需求功率SG滤波后再进行规则控制的能量管理策略,将多种循环工况的结果进行手动优化后作为训练数据集,设计三输入一输出的自适应神经模糊推理系统控制器,根据其输出结果再进行一次滤波最终形成基于自适应神经模糊推理系统优化的能量管理策略。使用CLTC-P循环工况对能量管理策略进行仿真验证,结果表明,基于自适应神经模糊推理系统优化的能量管理策略能有效延长氢燃料电池剩余使用寿命,相比滤波加规则策略剩余使用寿命增加了33%,并能保持动力电池SOC处于适宜水平。

An energy management strategy,with a hydrogen fuel cell reactor serving as the primary energy source,is proposed to effectively extend reactor life while satisfying the power demands.Initially,the energy management strategy employing SG filtering followed by regular control is introduced.Then,the results obtained from various cycle conditions are manually optimized and used as training datasets to design an ANFIS(Adaptive-Network-Based Fuzzy Inference System)controller featuring three inputs and one output.The energy management strategy based on ANFIS optimization is finally formed after an additional filtering according to the output results.The simulation results show that the energy management strategy based on ANFIS optimization effectively extends the remaining service life of the hydrogen fuel cells by 33% compared with the filter-plus-rule strategy,and it also maintains the SOC of the power cells at an appropriate level.