改进锂离子电池主动均衡充电控制方法
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摘要
电动汽车的主要驱动动力是锂离子电池,当前电池充电均衡技术的不成熟成为制约电动汽车普及化的最大难题,从而严重影响了电动汽车的性能。设计一种以双向Buck-Boost拓扑为主电路的主动均衡控制系统,采用神经网络与无迹卡尔曼滤波器(UKF)相结合的估算方法估计荷电状态(SOC),并以SOC作为主要的均衡判据,提出了一种改进主动均衡控制策略,实现了锂电池组在充电过程中的主动均衡,并通过MATLAB仿真分析。实验结果表明:电池组能够较好地完成各单体电池间的能量均衡,所改进的主动均衡控制方法可以更加有效快速地达到均衡目标,同时能量损耗较少,证明了该方案的可行性。
The main driving force of electric vehicles is lithium-ion batteries. The current battery charge balance technology has become the biggest problem of restricting the popularity of electric vehicles, and has seriously affected the performance of electric vehicles. An active equalization control system based on bi-directional Buck-Boost topology was designed. The SOC method was used to estimate the state of charge(SOC) by using the neural network and the unexplained Kalman filter(UKF). With SOC as the main equilibrium criterion, an improved active balance control strategy was proposed to realize the active equilibrium of the lithium batteries in the charging process. The proposed active balance control strategy was simulated by MATLAB. The experimental results show that the energy balance between the individual cells can be well completed, the improved active equalization control method can achieve the equilibrium goal accurately and efficiently, and the energy loss is less, proving the feasibility of the program.
The main driving force of electric vehicles is lithium-ion batteries. The current battery charge balance technology has become the biggest problem of restricting the popularity of electric vehicles, and has seriously affected the performance of electric vehicles. An active equalization control system based on bi-directional Buck-Boost topology was designed. The SOC method was used to estimate the state of charge(SOC) by using the neural network and the unexplained Kalman filter(UKF). With SOC as the main equilibrium criterion, an improved active balance control strategy was proposed to realize the active equilibrium of the lithium batteries in the charging process. The proposed active balance control strategy was simulated by MATLAB. The experimental results show that the energy balance between the individual cells can be well completed, the improved active equalization control method can achieve the equilibrium goal accurately and efficiently, and the energy loss is less, proving the feasibility of the program.
引文
[1]安志胜,孙志毅,何秋生.车用锂离子电池管理系统综述[J].电源技术,2013,37(6):1069-1071.
[2]符晓玲,商云龙,崔纳新.电动汽车电池管理系统研究现状及发展趋势[J].电力电子技术,2011,45(12):27-30.
[3]熊永华,杨艳.基于SOC的锂动力电池多层双向自均衡方法[J].电子学报,2014,42(4):766-773.
[4]胡志坤,刘斌,林勇,等.电池SOC的自适应平方根无极卡尔曼滤波估计算法[J].电机与控制学报,2014,18(4):111-116.
[5]刘晓悦,么舜禹.双通道ELM在锂离子电池SOC估算的应用[J].辽宁工程技术大学学报,2016,35(8):878-844.
[6] LOZANO J G,CADAVAL E R,MILANES M M I,et al. Batteryqualization active methods[J]. Journal of Power Sources,2014,246(8):934-949.
[7] BARONTI F,RONCELLA R,ROBERTO S. Performance compari-son of active balancing techniques for lithium-ion batteries[J].Jour-nal of Power Sources,2014,267(6):603-609.
[2]符晓玲,商云龙,崔纳新.电动汽车电池管理系统研究现状及发展趋势[J].电力电子技术,2011,45(12):27-30.
[3]熊永华,杨艳.基于SOC的锂动力电池多层双向自均衡方法[J].电子学报,2014,42(4):766-773.
[4]胡志坤,刘斌,林勇,等.电池SOC的自适应平方根无极卡尔曼滤波估计算法[J].电机与控制学报,2014,18(4):111-116.
[5]刘晓悦,么舜禹.双通道ELM在锂离子电池SOC估算的应用[J].辽宁工程技术大学学报,2016,35(8):878-844.
[6] LOZANO J G,CADAVAL E R,MILANES M M I,et al. Batteryqualization active methods[J]. Journal of Power Sources,2014,246(8):934-949.
[7] BARONTI F,RONCELLA R,ROBERTO S. Performance compari-son of active balancing techniques for lithium-ion batteries[J].Jour-nal of Power Sources,2014,267(6):603-609.