磷酸铁锂电池低温性能及放电容量预测研究
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摘要
为研究磷酸铁锂电池的低温放电性能,选取14500和32650型号电池为研究对象,对其低温放电容量及欧姆内阻等性能进行测试。结果表明:随着温度的降低,其容量呈现了不同程度的下降,而欧姆内阻随温度的降低而增加,且增加的幅度越来越大;低温对不同型号电池放电容量的影响存在差异,即随着温度的下降,容量越小的电池衰减越迅速。利用Matlab建立以常温充电容量、温度、欧姆内阻为输入,放电容量为输出的BP神经网络模型,此模型具有较高的准确性,误差在5%以内。
For studying the discharge performance of lithium iron phosphate battery at low temperature, the type14500 and type 32650 batteries were selected as the object of study, and their low temperature discharge capacity and ohm resistance were tested. The results indicate that the discharge capacity decreases as the temperature decreases, while the ohm resistance is increased with the decrease of temperature, and the rate of increase is increasing. There are some differences in which the discharge capacity of different types of batteries was affected by low temperature. As the temperature drops, the smaller the capacity of the battery, the more rapidly it decays. Finally,the BP neural network model was established by using Matlab to take charging capacity at normal temperature, test temperature and ohm resistance as the input and discharge capacity as the output. The model has higher accuracy,and the error is less than 5%.
For studying the discharge performance of lithium iron phosphate battery at low temperature, the type14500 and type 32650 batteries were selected as the object of study, and their low temperature discharge capacity and ohm resistance were tested. The results indicate that the discharge capacity decreases as the temperature decreases, while the ohm resistance is increased with the decrease of temperature, and the rate of increase is increasing. There are some differences in which the discharge capacity of different types of batteries was affected by low temperature. As the temperature drops, the smaller the capacity of the battery, the more rapidly it decays. Finally,the BP neural network model was established by using Matlab to take charging capacity at normal temperature, test temperature and ohm resistance as the input and discharge capacity as the output. The model has higher accuracy,and the error is less than 5%.
引文
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[12]赵钢,孙豪赛,罗淑贞.基于BP神经网络的动力电池SOC估算[J].电源技术, 2016(4):818-819.
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[14]时玮,姜久春,李索宇,等.磷酸铁锂电池SOC估算方法研究[J].电子测量与仪器学报,2010,24(8):769-774.
[15]闻新,李新,张兴旺,等.应用MATLAB实现神经网络[M].北京:国防工业出版社,2015.
[2]刘兴江,肖成伟,余冰,等.混合动力车用锂离子蓄电池的研究进展[J].电源技术,2007, 31(7):509-514.
[3] LEI Z, ZHANG C, LI J, et al. A study on the low-temperature performance of lithium-ion battery for electric vehicles[J]. Automotive Engineering, 2013, 35(10):927-933.
[4]刘英,李秋红,胡悦丽.低温锂离子电池研究现状及发展前景[J].电源技术,2013(2):321-323.
[5]钟其水,李树军,黄波,等.圆柱18650锂离子动力电池放电及温度特性[J].电子科技大学学报,2014, 43(2):311-314.
[6] ZHANG D, HARAN B S, DURAIRAJAN A, et al. Studies on capacity fade of lithium-ion batteries[J]. J Power Sources, 2000, 91:122-129.
[7] ZHANG D, HARAN B S, DURARIRAJAN A, et al. Studies on capacity fade of spinel-based li-ion batteries[J]. J Electrochem Soc,2002,149(1):A5-A60.
[8]李哲,韩雪冰,卢兰光,等.动力型磷酸铁锂电池的温度特性[J].机械工程学报,2011(18):115-120.
[9]吴珍毅,曹龙汉,唐超,等.改进Elman网络在锂离子电池容量预测中的应用[J].西南科技大学学报,2012,27(1):65-69.
[10]冯楠,王振臣,胖莹.基于遗传算法和BP神经网络的电池容量预测[J].电源技术,2011, 35(12):1586-1588.
[11] LIU R H, SUN Y K, JI X F. Battery state of charge estimation for electric vehicle based on neural network[C]//IEEE, International Conference on Communication Software and Networks.USA:IEEE, 2011:493-496.
[12]赵钢,孙豪赛,罗淑贞.基于BP神经网络的动力电池SOC估算[J].电源技术, 2016(4):818-819.
[13]胡晓敏,刘力舟.航空用动力锂离子蓄电池组单体不一致性研究[J].电源世界, 2016(7):28-30.
[14]时玮,姜久春,李索宇,等.磷酸铁锂电池SOC估算方法研究[J].电子测量与仪器学报,2010,24(8):769-774.
[15]闻新,李新,张兴旺,等.应用MATLAB实现神经网络[M].北京:国防工业出版社,2015.