基于ELM–Takagi Sugeno的车载锂电池优化充电策略的研究
|
摘要
针对目前车载锂电池充电慢、充电效率低以及对电池损害大等问题,提出一种基于ELM–Takagi Sugeno(T–S)模型的锂电池梯级式优化充电策略.首先通过极限学习机(ELM)获得锂电池的最佳充电电流与温度、内阻、极化电压等特征参数之间的数学模型,将离线训练好的参数存储在STM32处理器中,系统每采集1次数据,处理器即调用ELM程序计算当前时刻的最佳充电电流.其次利用T–S模糊模型将恒压、恒流、脉冲3种不同充电方式优化处理实现动态最优充电.仿真结果表明锂电池实际充电电流能够实时跟踪最优充电电流,其充电时间比三阶段充电模式缩短20%,充电效率比CC–CV充电模式提高约25%.
Aiming at the problems of slow charging, low charging efficiency and great damage to the battery,a lithium battery step –optimized charging strategy based on ELM –Takagi Sugeno(T–S) model is proposed.Firstly, lithium is obtained by the extreme learning machine(ELM). The mathematical model between the optimal charging current of the battery and the characteristic parameters such as temperature, internal resistance and polarization voltage, and the offline training parameters are stored in the STM32 processor. Each time the system collects data, the processor calls the ELM program to calculate the optimal charging current at the current moment.Secondly, the T–S fuzzy model is used to optimize the three different charging modes of constant voltage, constant current and pulse to achieve dynamic optimal charging. The simulation results show that the actual charging current of the lithium battery can track the optimal charging current in real time. The charging time is 20% shorter than the three-stage charging mode, and the charging efficiency is increased by about 25% compared with the CC–CV charging mode.
Aiming at the problems of slow charging, low charging efficiency and great damage to the battery,a lithium battery step –optimized charging strategy based on ELM –Takagi Sugeno(T–S) model is proposed.Firstly, lithium is obtained by the extreme learning machine(ELM). The mathematical model between the optimal charging current of the battery and the characteristic parameters such as temperature, internal resistance and polarization voltage, and the offline training parameters are stored in the STM32 processor. Each time the system collects data, the processor calls the ELM program to calculate the optimal charging current at the current moment.Secondly, the T–S fuzzy model is used to optimize the three different charging modes of constant voltage, constant current and pulse to achieve dynamic optimal charging. The simulation results show that the actual charging current of the lithium battery can track the optimal charging current in real time. The charging time is 20% shorter than the three-stage charging mode, and the charging efficiency is increased by about 25% compared with the CC–CV charging mode.
引文
[1]吴海桑,刘伟,陈英杰,等.锂离子动力电池充电优化技术现状[J].电源学报,2017,15(5):144-152.Wu H S,Liu W,Chen Y J,et al.Current situation of technology in charging optimization of Li-ion battery[J].Journal of Power Supply,2017,15(5):144-152.
[2]李文华,王炳龙,马源鸿,等.锂电池五阶段新型快速充电方法研究[J].电源技术,2018,42(6):759-761.Li W H,Wang B L,Ma Y H,et al.Research on a new fast charging method for lithium batteries in five phases[J].Chinese Journal of Power Sources,2018,42(6):759-761.
[3]邓磊,王立欣,葛腾飞,等.基于改进PNGV模型的动力锂电池快速充电优化[J].电源学报,2014(4):10-14.Deng L,Wang L X,Ge T F,et al.Multi-objective optimization of Power Li-ion battery quick charging Based on Improved PNGV Model[J].Journal of Power Supply,2014(4):10-14.
[4]李敬兆.采用神经网络预测和变结构模糊控制的铅酸蓄电池最优充电技术研究[D].合肥:合肥工业大学,2003.Li J Z.Study onthe optimal Lead-acid battery charging with Neural Network Predietionand Variable Structure FuzzyControl[D].Hefei:Hefei University of Technology,2003.
[5]叶剑晓,于春梅,梁奇.电动汽车用锂电池快速充电技术研究[J].电气传动,2018,48(06):93-96.Ye J X,Yu C M,Liang Q.Research on quick charging technology for electric vehicle lithium battery[J].Electric Drive,2018,48(06):93-96.
[6]Boadu J M A,Guiseppi-Elie A,Sanchez-Sinencio E.The impact of pulse charging parameters on the life cycle of lithium-ion polymer batteries[J].Energies,2018,11:2 162.DOI:10.3390/en11082162.
[7]Klein R,Chaturvedi N A,Christensen J,et al.Optimal charging strategies in lithium-ion battery[C]//American Control Conference.IEEE,2011:382-387.
[8]Huang J W,Liu Y H,Wang S C,et al.Fuzzycontrol-based five-step Li-ion battery charger[C]//International Conference on Power Electronics and Drive Systems.IEEE,2009:1547-1551.
[9]袁臣虎,李海杰,王岁,等.锂电池四阶段智能充电方法的研究[J].电源技术,2019,43(03):402-404.Yuan C H,Li H J,Wang S,et al.Research of four-stage intelligent charging way for lithium battery[J].Chinese Journal of Power Sources,2019,43(03):402-404.
[10]杨柳,赵雪岑,周平,等.多节串并联锂电池智能无线充放电系统[J].云南大学学报:自然科学版,2018,40(02):264-271.Yang L,Zhao X C,Zhou P,et al.On an intelligent wireless charging and discharging system for lithiumion battery packs[J].Journal of Yunnan University:Natural Sciences Edition,2018,40(02):264-271.
[11]牟浩,曹龙汉,李锐,等.锂离子电池最优充电电流预测方法研究[J].计算机工程与应用,2016,52(2):266-270.DOI:10.3778/j.issn.1002-8331.1312-0211.Mu H,Cao L H,Li R,et al.Research on prediction method of optimal charging current of lithium ionbattery[J].Computer Engineering and Applications,2016,52(2):266-270.
[12]虞双吉,苗春生,王新.极限学习机神经网络在短期降水预报中的应用[J].云南大学学报:自然科学版,2013,35(04):507-515.Yu S J,Miao C S,Wang X.Application of extreme learning machine neural network to forecast of shortterm precipitation[J].Journal of Yunnan University:Natural Sciences Edition,2013,35(04):507-515.
[13]Dung L R,Yen J H.ILP-based algorithm for Lithium-ion battery charging profile[C]//IEEE International Symposium on Industrial Electronics.IEEE,2010:2286-2291.
[14]孙增圻,徐红兵.基于T-S模型的模糊神经网络[J].清华大学学报:自然科学版,1997(3):76-80.Sun Z Q,Xu H B.Fuzzy-neural network basedon T-Smodel[J].Journal of Tsinghua University:Science and Technology,1997(3):76-80.
[15]Vries H D,Nguyen T T,Veld B O H.Increasing the cycle life of lithium ion cells by partial state of charge cycling[J].M icroelectronics Reliability,2015,55(11):2 247-2 253.DOI:10.1016/j.microrel.2015.08.014.
[16]石国姣,王公正,黄帅.二维内质量结构三角形晶格声子晶体的带隙研究[J].云南大学学报:自然科学版,2019,41(3):545-550.Shi G J,Wang G Z,Huang S,et al.Study on the band gaps of two dimensional mass-in-mass structure triangular latticephononic crystals[J].Journal of Yunnan University:Natural Sciences Edition,2019,41(3):545-550.
[2]李文华,王炳龙,马源鸿,等.锂电池五阶段新型快速充电方法研究[J].电源技术,2018,42(6):759-761.Li W H,Wang B L,Ma Y H,et al.Research on a new fast charging method for lithium batteries in five phases[J].Chinese Journal of Power Sources,2018,42(6):759-761.
[3]邓磊,王立欣,葛腾飞,等.基于改进PNGV模型的动力锂电池快速充电优化[J].电源学报,2014(4):10-14.Deng L,Wang L X,Ge T F,et al.Multi-objective optimization of Power Li-ion battery quick charging Based on Improved PNGV Model[J].Journal of Power Supply,2014(4):10-14.
[4]李敬兆.采用神经网络预测和变结构模糊控制的铅酸蓄电池最优充电技术研究[D].合肥:合肥工业大学,2003.Li J Z.Study onthe optimal Lead-acid battery charging with Neural Network Predietionand Variable Structure FuzzyControl[D].Hefei:Hefei University of Technology,2003.
[5]叶剑晓,于春梅,梁奇.电动汽车用锂电池快速充电技术研究[J].电气传动,2018,48(06):93-96.Ye J X,Yu C M,Liang Q.Research on quick charging technology for electric vehicle lithium battery[J].Electric Drive,2018,48(06):93-96.
[6]Boadu J M A,Guiseppi-Elie A,Sanchez-Sinencio E.The impact of pulse charging parameters on the life cycle of lithium-ion polymer batteries[J].Energies,2018,11:2 162.DOI:10.3390/en11082162.
[7]Klein R,Chaturvedi N A,Christensen J,et al.Optimal charging strategies in lithium-ion battery[C]//American Control Conference.IEEE,2011:382-387.
[8]Huang J W,Liu Y H,Wang S C,et al.Fuzzycontrol-based five-step Li-ion battery charger[C]//International Conference on Power Electronics and Drive Systems.IEEE,2009:1547-1551.
[9]袁臣虎,李海杰,王岁,等.锂电池四阶段智能充电方法的研究[J].电源技术,2019,43(03):402-404.Yuan C H,Li H J,Wang S,et al.Research of four-stage intelligent charging way for lithium battery[J].Chinese Journal of Power Sources,2019,43(03):402-404.
[10]杨柳,赵雪岑,周平,等.多节串并联锂电池智能无线充放电系统[J].云南大学学报:自然科学版,2018,40(02):264-271.Yang L,Zhao X C,Zhou P,et al.On an intelligent wireless charging and discharging system for lithiumion battery packs[J].Journal of Yunnan University:Natural Sciences Edition,2018,40(02):264-271.
[11]牟浩,曹龙汉,李锐,等.锂离子电池最优充电电流预测方法研究[J].计算机工程与应用,2016,52(2):266-270.DOI:10.3778/j.issn.1002-8331.1312-0211.Mu H,Cao L H,Li R,et al.Research on prediction method of optimal charging current of lithium ionbattery[J].Computer Engineering and Applications,2016,52(2):266-270.
[12]虞双吉,苗春生,王新.极限学习机神经网络在短期降水预报中的应用[J].云南大学学报:自然科学版,2013,35(04):507-515.Yu S J,Miao C S,Wang X.Application of extreme learning machine neural network to forecast of shortterm precipitation[J].Journal of Yunnan University:Natural Sciences Edition,2013,35(04):507-515.
[13]Dung L R,Yen J H.ILP-based algorithm for Lithium-ion battery charging profile[C]//IEEE International Symposium on Industrial Electronics.IEEE,2010:2286-2291.
[14]孙增圻,徐红兵.基于T-S模型的模糊神经网络[J].清华大学学报:自然科学版,1997(3):76-80.Sun Z Q,Xu H B.Fuzzy-neural network basedon T-Smodel[J].Journal of Tsinghua University:Science and Technology,1997(3):76-80.
[15]Vries H D,Nguyen T T,Veld B O H.Increasing the cycle life of lithium ion cells by partial state of charge cycling[J].M icroelectronics Reliability,2015,55(11):2 247-2 253.DOI:10.1016/j.microrel.2015.08.014.
[16]石国姣,王公正,黄帅.二维内质量结构三角形晶格声子晶体的带隙研究[J].云南大学学报:自然科学版,2019,41(3):545-550.Shi G J,Wang G Z,Huang S,et al.Study on the band gaps of two dimensional mass-in-mass structure triangular latticephononic crystals[J].Journal of Yunnan University:Natural Sciences Edition,2019,41(3):545-550.