In order to solve the problem of inconsistent energy in the charging and discharging cycles of lithium-ion battery packs,a new multilayer equilibrium topology is designed in this paper.The structure adopts a hierarchi...In order to solve the problem of inconsistent energy in the charging and discharging cycles of lithium-ion battery packs,a new multilayer equilibrium topology is designed in this paper.The structure adopts a hierarchical structure design,which includes intra-group equilibrium,primary inter-group equilibrium and secondary inter-group equilibrium.This structure greatly increases the number of equilibrium paths for lithium-ion batteries,thus shortening the time required for equilibrium,and improving the overall efficiency.In terms of control strategy,fuzzy logic control(FLC)is chosen to control the size of the equilibrium current during the equilibrium process.We performed rigorous modeling and simulation of the proposed system by MATLAB and Simulink software.Experiments show that the multilayer equilibrium circuit structure greatly exceeds the traditional single-layer equilibrium circuit in terms of efficacy,specifically,the Li-ion battery equilibrium speed is improved by 12.71%in static equilibrium,14.48%in charge equilibrium,and 11.19%in discharge equilibrium.In addition,compared with the maximum value algorithm,the use of the FLC algorithm reduces the equalization time by about 3.27%and improves the energy transfer efficiency by about 66.49%under the stationary condition,which verifies the feasibility of the equalization scheme.展开更多
为了解决锂电池能量管理系统中均衡速率较慢且控制原理复杂的问题,提高锂电池能量管理系统的性能,文章提出了一种以电池组中单体电池之间的荷电状态(state of charge,SOC)差值为控制目标的神经元PID均衡控制算法。在Matlab/Simulink中...为了解决锂电池能量管理系统中均衡速率较慢且控制原理复杂的问题,提高锂电池能量管理系统的性能,文章提出了一种以电池组中单体电池之间的荷电状态(state of charge,SOC)差值为控制目标的神经元PID均衡控制算法。在Matlab/Simulink中建立充放电反激式变压器电路拓扑结构,并搭建神经元PID控制算法模型,加入到均衡电路拓扑结构中进行仿真验证。仿真验证结果表明:神经元PID控制电路相比于PID控制电路在充电过程中均衡时间提高了约5.9%,放电过程中均衡时间提高了约26.4%,有效地缩短了单体电池能量达到一致所需要的时间;该神经元算法提高了PID控制的自学习能力,在PID均衡的基础上改善了电路拓扑结构的均衡效率,对进一步完善锂电池能量管理系统具有一定的理论指导意义。展开更多
为解决电池组充电不均衡问题,研究了电感对称式均衡电路,根据锂离子电池充电端电压变化特性,制定了分阶段组合式的均衡控制策略,并在此基础上制作了锂离子电池组充电均衡控制系统,成功地将电池组充电结束后端电压差距控制在30 m V之内,...为解决电池组充电不均衡问题,研究了电感对称式均衡电路,根据锂离子电池充电端电压变化特性,制定了分阶段组合式的均衡控制策略,并在此基础上制作了锂离子电池组充电均衡控制系统,成功地将电池组充电结束后端电压差距控制在30 m V之内,消除了电池组充电不均衡现象。展开更多
基金funded by the National Natural Science Foundation of China:Research on the Energy Management Strategy of Li-Ion Battery and Sc Hybrid Energy Storage System for Electric Vehicle(51677058).
文摘In order to solve the problem of inconsistent energy in the charging and discharging cycles of lithium-ion battery packs,a new multilayer equilibrium topology is designed in this paper.The structure adopts a hierarchical structure design,which includes intra-group equilibrium,primary inter-group equilibrium and secondary inter-group equilibrium.This structure greatly increases the number of equilibrium paths for lithium-ion batteries,thus shortening the time required for equilibrium,and improving the overall efficiency.In terms of control strategy,fuzzy logic control(FLC)is chosen to control the size of the equilibrium current during the equilibrium process.We performed rigorous modeling and simulation of the proposed system by MATLAB and Simulink software.Experiments show that the multilayer equilibrium circuit structure greatly exceeds the traditional single-layer equilibrium circuit in terms of efficacy,specifically,the Li-ion battery equilibrium speed is improved by 12.71%in static equilibrium,14.48%in charge equilibrium,and 11.19%in discharge equilibrium.In addition,compared with the maximum value algorithm,the use of the FLC algorithm reduces the equalization time by about 3.27%and improves the energy transfer efficiency by about 66.49%under the stationary condition,which verifies the feasibility of the equalization scheme.
文摘为了解决锂电池能量管理系统中均衡速率较慢且控制原理复杂的问题,提高锂电池能量管理系统的性能,文章提出了一种以电池组中单体电池之间的荷电状态(state of charge,SOC)差值为控制目标的神经元PID均衡控制算法。在Matlab/Simulink中建立充放电反激式变压器电路拓扑结构,并搭建神经元PID控制算法模型,加入到均衡电路拓扑结构中进行仿真验证。仿真验证结果表明:神经元PID控制电路相比于PID控制电路在充电过程中均衡时间提高了约5.9%,放电过程中均衡时间提高了约26.4%,有效地缩短了单体电池能量达到一致所需要的时间;该神经元算法提高了PID控制的自学习能力,在PID均衡的基础上改善了电路拓扑结构的均衡效率,对进一步完善锂电池能量管理系统具有一定的理论指导意义。
