基于二分法查找-伪循环次数法的动力电池健康状态实时预测
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摘要
针对纯电动汽车动力电池健康状态(SOH)预测过程中算法复杂、编程繁琐、单片机C程序开发困难的问题,以单片机作为主芯片,以电池充放电循环次数为SOH的影响转化为循环次数并将主要影响因素与SOH的非线性关系制成二维数组表,通过二分法查找得到影响程度系数,记录特性因素值及持续时间,获得伪循环次数,进而得到SOH。在国家标准试验条件下验证了该预测方法的有效性、精确性和稳定性。
In order to solve the problems of complex algorithm, complicated programming and difficulty in developing C program of single chip microcomputer in the process of SOH prediction of pure electric vehicle power batteries, a SOH prediction algorithm is proposed, which uses single chip as the main chip and the number of battery charging and discharging cycles as the benchmark index of SOH. The influence on battery SOH is converted into the number of cycles and the non-linear relationship between the main factors and SOH is made into a two-dimensional array table. The influence degree coefficient is obtained by the binary searching method, the value and duration of characteristic factors are recorded, and the number of pseudo-cycles and SOH are obtained. The validity, accuracy and stability of the prediction method are verified under the national standard test conditions.
In order to solve the problems of complex algorithm, complicated programming and difficulty in developing C program of single chip microcomputer in the process of SOH prediction of pure electric vehicle power batteries, a SOH prediction algorithm is proposed, which uses single chip as the main chip and the number of battery charging and discharging cycles as the benchmark index of SOH. The influence on battery SOH is converted into the number of cycles and the non-linear relationship between the main factors and SOH is made into a two-dimensional array table. The influence degree coefficient is obtained by the binary searching method, the value and duration of characteristic factors are recorded, and the number of pseudo-cycles and SOH are obtained. The validity, accuracy and stability of the prediction method are verified under the national standard test conditions.
引文
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[2]张金龙,佟微,孙叶宁,等.锂电池健康状态估算方法综述[J].电源学报,2017(2):128-134.
[3]Berecibar M,Gandiaga I,Villarreal I,et al.Critical Review of State of Health Estimation Methods of Li-Ion Batteries for Real Applications[J].Renewable and Sustainable Energy Reviews,2016,56(3):572-587.
[4]张金,高安同,韩裕生,等.一种基于粒子滤波的锂离子电池健康预测算法[J].电源技术,2015,39(7):1377-1380.
[5]王莉,杨永辉,詹益,等.基于最小二乘支持向量机阀控式铅酸蓄电池寿命预测[J].大连交通大学学报,2017,38(3)116-119.
[6]Xiong R,Sun F,Chen Z,et al.A Data-Driven Multi-Scale Extended Kalman Filtering Based Parameter and State Estimation Approach of Lithium-Ion Olymer Battery in Electric Vehicles[J].Applied Energy,2014,113(1):463-476.
[7]侯恩广,乔昕,刘广敏.基于模糊优化决策的锂电池SOC估计[J].电源技术,2017(6):920-922.
[8]张金国,王小君,朱洁,等.基于MIV的BP神经网络磷酸铁锂电池寿命预测[J].电源技术,2016(1):50-52.
[9]国家发展和改革委员会.电动汽车用锂离子蓄电池QC/T 743-2006[S].北京:国家发展和改革委员会,2006.
[10]Efaz E T,Mamun A A,Khan R.Development of Lithium Ion Battery with Overcharge and Deep Discharge Protection[J].International Journal of Scientific&Engineering Research,2017,8(10):1677-1681.
[11]郑勇.锂离子电池过充及过放电故障诊断研究[D].西安长安大学,2016.
[12]刘文刚,周波,王晓丹,等.18650型锂离子电池的循环容量衰减研究[J].电源技术,2012,36(3):306-309.
[13]桂长清.温度对LiFePO4锂离子动力电池的影响[J].电池,2011(2):88-91.
[14]胡广侠.锂离子电池充放电过程的研究[D].上海:中国科学院上海微系统与信息技术研究所,2002.
[15]崔立丰,高飞,王永武,等.磷酸铁锂动力电池Peukert方程修正模型的研究[C]//第六届中国智能交通年会暨第七届国际节能与新能源汽车创新发展论坛论文集2011:31-42.
[16]Liu X,Chen Z,Zhang C,et al.A Novel Temperature Compensated Model for Power Li-Ion Batteries with DualParticle-Filter State of Charge Estimation[J].Applied Energy,2014,123(3):263-272.
[17]王海涛,朱洪.改进的二分法查找[J].Modified Binary Search,2006(10):60-62+118.
[18]曹铭,于永飞,黄菊花.基于Freescale单片机的电池管理系统设计与实现[J].电源技术,2012(11):1659-1661.
[19]卞启杰,安伟.基于飞思卡尔单片机的SCR控制器开发[J].电子设计工程,2016(22):172-174+179.
[20]Ng K S,Moo C S,Chen Y P,et al.Enhanced Coulomb Counting Method for Estimating State-Of-Charge and State-Of-Health of Lithium-Ion Batteries[J].Applied Energy,2009,86(9):1506-1511.