During the last decade, many variants of the original particle swarm optimization （PSO） algorithm have been proposed for global numerical optimization, hut they usually face many challenges such as low solution qual...During the last decade, many variants of the original particle swarm optimization （PSO） algorithm have been proposed for global numerical optimization, hut they usually face many challenges such as low solution quality and slow convergence speed on multimodal function optimization. A composite particle swarm optimization （CPSO） for solving these difficulties is presented, in which a novel learning strategy plus an assisted search mechanism framework is used. Instead of simple learning strategy of the original PSO, the proposed CPSO combines one particle＇s historical best information and the global best information into one learning exemplar to guide the particle movement. The proposed learning strategy can reserve the original search information and lead to faster convergence speed. The proposed assisted search mechanism is designed to look for the global optimum. Search direction of particles can be greatly changed by this mechanism so that the algorithm has a large chance to escape from local optima. In order to make the assisted search mechanism more efficient and the algorithm more reliable, the executive probability of the assisted search mechanism is adjusted by the feedback of the improvement degree of optimal value after each iteration. According to the result of numerical experiments on multimodal benchmark functions such as Schwefel, Rastrigin, Ackley and Griewank both with and without coordinate rotation, the proposed CPSO offers faster convergence speed, higher quality solution and stronger robustness than other variants of PSO.展开更多
锂离子电池力学特性参数可作为电池循环充放电导致的内部结构变化、损伤与性能衰退的评估依据,然而现有检测方法难以无损获取实际工况下的电池力学特性参数。超声波形特征能够反演电池内部结构的微小变化,与电池力学特性参数变化密切相...锂离子电池力学特性参数可作为电池循环充放电导致的内部结构变化、损伤与性能衰退的评估依据,然而现有检测方法难以无损获取实际工况下的电池力学特性参数。超声波形特征能够反演电池内部结构的微小变化,与电池力学特性参数变化密切相关,可准确表征电池力学特性参数。该文提出一种基于锂离子电池声学均一化模型与粒子群优化算法的电池力学特性参数估计方法。基于均匀化理论对电池多层结构进行简化,建立电池声学均一化模型。搭建电池超声检测平台,获取超声实验数据,为力学特性参数估计提供数据支撑。基于粒子群优化算法联合声学均一化模型进行仿真优化,利用仿真波形逼近实验波形,不断迭代优化模型,反推出此时电池整体有效杨氏模量与整体有效密度等力学特性参数。最后,通过高斯过程回归算法建立融合力学特征的锂离子电池荷电状态(state of charge,SOC)估计模型,对比融合不同特征对SOC估计精度的影响。实验结果表明,融合力学特征可以有效提高SOC估计精度。在动态工况下,SOC估计值的均方根误差为1.92%,平均绝对误差在1.68%,验证了利用力学特性参数进行SOC估计的可靠性与准确性。展开更多
基金Projects(50275150,61173052)supported by the National Natural Science Foundation of China
文摘During the last decade, many variants of the original particle swarm optimization （PSO） algorithm have been proposed for global numerical optimization, hut they usually face many challenges such as low solution quality and slow convergence speed on multimodal function optimization. A composite particle swarm optimization （CPSO） for solving these difficulties is presented, in which a novel learning strategy plus an assisted search mechanism framework is used. Instead of simple learning strategy of the original PSO, the proposed CPSO combines one particle＇s historical best information and the global best information into one learning exemplar to guide the particle movement. The proposed learning strategy can reserve the original search information and lead to faster convergence speed. The proposed assisted search mechanism is designed to look for the global optimum. Search direction of particles can be greatly changed by this mechanism so that the algorithm has a large chance to escape from local optima. In order to make the assisted search mechanism more efficient and the algorithm more reliable, the executive probability of the assisted search mechanism is adjusted by the feedback of the improvement degree of optimal value after each iteration. According to the result of numerical experiments on multimodal benchmark functions such as Schwefel, Rastrigin, Ackley and Griewank both with and without coordinate rotation, the proposed CPSO offers faster convergence speed, higher quality solution and stronger robustness than other variants of PSO.
文摘锂离子电池力学特性参数可作为电池循环充放电导致的内部结构变化、损伤与性能衰退的评估依据,然而现有检测方法难以无损获取实际工况下的电池力学特性参数。超声波形特征能够反演电池内部结构的微小变化,与电池力学特性参数变化密切相关,可准确表征电池力学特性参数。该文提出一种基于锂离子电池声学均一化模型与粒子群优化算法的电池力学特性参数估计方法。基于均匀化理论对电池多层结构进行简化,建立电池声学均一化模型。搭建电池超声检测平台,获取超声实验数据,为力学特性参数估计提供数据支撑。基于粒子群优化算法联合声学均一化模型进行仿真优化,利用仿真波形逼近实验波形,不断迭代优化模型,反推出此时电池整体有效杨氏模量与整体有效密度等力学特性参数。最后,通过高斯过程回归算法建立融合力学特征的锂离子电池荷电状态(state of charge,SOC)估计模型,对比融合不同特征对SOC估计精度的影响。实验结果表明,融合力学特征可以有效提高SOC估计精度。在动态工况下,SOC估计值的均方根误差为1.92%,平均绝对误差在1.68%,验证了利用力学特性参数进行SOC估计的可靠性与准确性。
