针对当前大部分智能算法在求解质子交换膜燃料电池模型参数辨识问题时易陷入局部最优,导致参数辨识精度低、模型泛化能力差等问题,提出一种基于改进鸡群优化算法的质子交换膜燃料电池模型参数辨识方法。首先,引入Tent映射策略初始化种群,提高种群的均匀性和遍历性;其次,设计基于个体进食速度的自适应惯性权重,改善母鸡个体寻优效率,平衡算法的开发与探索能力;然后,利用Levy飞行策略的长短跳跃特点对小鸡位置进行随机更新,增强算法的全局最优搜索能力。最后,通过4组测试函数验证了该算法的优越性,并将算法应用于H-12电堆的参数辨识问题中。结果表明：相比于鲸鱼优化算法、花卉授粉算法等算法,该算法具有更高的参数辨识精度,所辨识出的模型具有更强的泛化能力。
Abstract
Most of the current intelligent algorithms are prone to local optimum when solving the parameter identification problem of proton exchange membrane fuel cells model, which results in low parameter identification accuracy and poor model generalization ability.To solve this problem, a parameter identification method of proton exchange membrane fuel cell based on improved chicken swarm optimization algorithm is proposed in this paper. Firstly, the Tent mapping strategy is introduced to initialize the population, as well as enhance the ergodicity and uniformity of the population. Secondly, the adaptive inertia weight based on individual feeding speed is designed to improve the optimization efficiency of individual hens,which tries to balance the exploitation and exploration ability of the algorithm. In addition, the long and short jump characteristics of Levy flight strategy is used to randomly update the position of chicken,so as to enhance the algorithm's global search ability. Finally, the superiority of the algorithm is verified through the test functions of four groups,and the algorithm is applied to the parameter identification of H-12 stack. According to the results,it indicates that the algorithm has higher parameter identification accuracy and stronger generalization ability in contrast with whale optimization algorithm,flower pollination algorithm and so on.
关键词
质子交换膜燃料电池 /
模型 /
辨识 /
鸡群优化算法 /
Levy飞行策略
Key words
proton exchange membrane fuel cells /
models /
identification /
chicken swarm optimization algorithm /
Levy flight strategy
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基金
上海市技术标准项目基金（21DZ22043）
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