Journal of Guangxi Normal University(Natural Science Edition) ›› 2021, Vol. 39 ›› Issue (3): 1-10.doi: 10.16088/j.issn.1001-6600.2020061109
WANG Rui, SONG Shuxiang*, XIA Haiying
CLC Number:
[1]潘海鸿,吕治强,李君子,等. 基于灰色扩展卡尔曼滤波的锂离子电池荷电状态估算[J]. 电工技术学报,2017,32(21):1-8. DOI:10.19595/j.cnki.1000-6753.tces.160837. [2]刘欣博,王乃鑫,李正熙. 基于扩展卡尔曼滤波法的锂离子电池荷电状态估算方法研究[J]. 北方工业大学学报,2016,28(1):49-56. [3]谷苗,夏超英,田聪颖. 基于综合型卡尔曼滤波的锂离子电池荷电状态估算[J]. 电工技术学报,2019,34(2):419-426. DOI:10.19595/j.cnki.1000-6753.tces.171560. [4]韦振汉,宋树祥,夏海英. 基于随机森林的锂离子电池荷电状态估算[J]. 广西师范大学学报(自然科学版),2018,36(4):27-33. DOI:10.16088/j.issn.1001-6600.2018.04.004. [5]聂文亮,谭伟杰,邱刚,等. 基于ARX模型的锂离子电池荷电状态在线估算[J]. 中国电机工程学报,2018,38(18):5415-5424. DOI:10.13334/j.0258-8013.pcsee.172196. [6]付浪,杜明星,刘斌,等. 基于开路电压法与卡尔曼滤波法相结合的锂离子电池SOC估算[J]. 天津理工大学学报,2015,31(6):9-13. [7]冯飞,宋凯,逯仁贵,等. 磷酸铁锂电池组均衡控制策略及荷电状态估计算法[J]. 电工技术学报,2015,30(1):22-29. DOI:10.19595/j.cnki.1000-6753.tces.2015.01.004. [8]DU L B,CHENG X M,YANG L. The research on battery SOC estimation within first-order Markov process[C]// 2015 34th Chinese Control Conference. Piscataway, NJ: IEEE Press, 2015:7849-7854. [9]高明煜,何志伟,徐杰.基于采样点卡尔曼滤波的动力电池SOC估计[J]. 电工技术学报,2011,26(11):161-167. DOI:10.19595/j.cnki.1000-6753.tces.2011.11.024. [10]CHANG K C,CHEN H D. Efficient inference algorithms for hybrid dynamic bayesian networks(HDBN)[J]. Proceedings of SPIE,2004,5429(1):402-409. DOI:10.1117/12.544060. [11]MASTALI M, VAZQUEZ-ARENAS J, FRASER R, et al. Battery state of the charge estimation using Kalman filtering[J]. Journal of Power Sources,2013,239:294-307. DOI:10.1016/j.jpowsour.2013.03.131. [12]杨世春,麻翠娟. 基于 PNGV 改进模型的 SOC 估计算法[J]. 汽车工程,2015,37(5):582-586,598. [13]魏克新,陈峭岩. 基于自适应无迹卡尔曼滤波算法的锂离子动力电池状态估计[J]. 中国电机工程学报,2014,34(3):445-452. DOI:10.13334/j.0258-8013.pcsee.2014.03.016. [14]WOLFF N,HARTING N,HEINRICH M,et al. Nonlinear frequency response analysis on lithium-ion batteries: a model-based assessment[J]. Electrochim Acta,2018,260:614-622. DOI:10.1016/j.electacta.2017.12.097. [15]赵天意,彭喜元,彭宇,等. 改进卡尔曼滤波的融合型锂离子电池 SOC 估计方法[J]. 仪器仪表学报,2016,37(7):1441-1448. DOI:10.19650/j.cnki.cjsi.2016.07.001. [16]CHEN J,OUYANG Q,XU C F,et al. Neural network-based state of charge observer design for lithium-ion batteries[J]. IEEE Transactions on Control Systems Technology,2018,26(1):313-320. [17]YE M,GUE H,CAO B G. A model-based adaptive state of charge estimator for a lithium-ion battery using an improved adaptive particle filter[J]. Applied Energy,2017,190:740-748. DOI:10.1016/j.apenergy.2016.12.133. [18]王惠娟,郭利健.电化学阻抗谱在锂电池状态检测中的应用[J]. 电源技术,2014,38(1):73-74. [19]李晓宇,朱春波,魏国,等.基于分数阶联合卡尔曼滤波的磷酸铁锂电池简化阻抗谱模型参数在线估计[J]. 电工技术学报,2016,31(24):141-149. DOI:10.19595/j.cnki.1000-6753.tces.2016.24.017. [20]DONG G Z,WEI J W,CHEN Z H. Constrained bayesian dual-filtering for state of charge estimation of lithium-ion batteries[J]. International Journal of Electrical Power & Energy Systems,2018,99:516-524. DOI:10.1016/j.ijepes.2018.02.005. [21]GUO P Y,CHENG Z,YANG L. A data-driven remaining capacity estimation approach for lithium-ion batteries based on charging health feature extraction[J]. Journal of Power Sources,2019,412:442-450. DOI:10.1016/j.jpowsour.2018.11.072. [22]XU J,MI C C,CAO B G,et al. A new method to estimate the state of charge of lithium-ion batteries based on the battery impendence model[J]. Journal of Power Sources,2013,233:277-284. DOI:10.1016/j.jpowsour.2013.01.094. [23]ZHANG L,HU X S,WANG Z P,et al. Fractional-order modeling and state-of-charge estimation for ultracapacitors[J]. Journal of Power Sources,2016,314:28-34. DOI:10.1016/j.jpowsour.2016.01.066. |
[1] | WEI Zhenhan, SONG Shuxiang, XIA Haiying. State-of-charge Estimation Using Random Forest for Lithium Ion Battery [J]. Journal of Guangxi Normal University(Natural Science Edition), 2018, 36(4): 27-33. |
|