基于MIMO模糊控制的锂离子电池参数自适应等效电路模型及SOC估计
Adaptive Parameters Equivalent-circuit Model of Li-ion Battery Based on MIMO Fuzzy Control and SOC Estimation
查看参考文献17篇
|
文摘
|
针对锂电池等效电路模型无法在荷电状态(SOC)全区间精确反映锂电池内部真实状态的问题,提出了基于多输入多输出(MIMO)模糊控制的参数自适应等效电路模型.该等效电路模型以新一代汽车伙伴关系(PNGV)模型为自适应原型,根据锂离子电池和PNGV模型的外特性参数差异,由MIMO模糊调节器动态实时修正模型参数,达到精确建模、反映电池内部真实状态的目的.实验验证了自适应参数对模型精度和自适应性能的影响及模型在变工况下的模拟效果.通过对比锂电池参数自适应模型和静态参数PNGV模型的扩展卡尔曼滤波算法估计SOC的误差,验证了参数自适应模型的有效性. |
|
其他语种文摘
|
We propose an adaptive parameters equivalent-circuit model of a Li-ion battery based on multi-input multi-output (MIMO) fuzzy control to solve the problem that the normal equivalent-circuit model cannot reflect a Li-ion battery's internal state precisely at all regions of the state of charge (SOC). The model, taking the partnership for a new generation of vehicles (PNGV) model as an adaptive prototype, dynamically updates the parameters in real-time using the MIMO fuzzy regulator. The updates are made according to the difference in the external characteristics between Li-ion battery and the PNGV model, in order to reach the goal of accurate modeling and reflecting the battery's true internal state. Experiments verify the influence of adaptive parameters on the model accuracy and adaptive performance, and show the model-emulating effects under various operating conditions. The comparison of SOC error estimation applying the extended Kalman filter (EKF) between the battery adaptive parameters model and the static parameter PNGV model verifies the effectiveness of the adaptive parameters model. |
|
来源
|
信息与控制
,2015,44(3):263-269 【核心库】
|
|
DOI
|
10.13976/j.cnki.xk.2015.0263
|
|
关键词
|
锂离子电池
;
等效电路模型
;
多输入多输出(MIMO)
;
模糊控制
;
参数自适应
;
荷电状态(SOC)估计
|
|
地址
|
中国科学院沈阳自动化研究所, 中国科学院网络化控制系统重点实验室, 辽宁, 沈阳, 110016
|
|
语种
|
中文 |
|
文献类型
|
研究性论文 |
|
ISSN
|
1002-0411 |
|
学科
|
电工技术 |
|
基金
|
国家863计划
|
|
文献收藏号
|
CSCD:5447497
|
参考文献 共
17
共1页
|
|
1.
Sachin B. Development of improved Li-ion battery model incorporating thermal and rate factor effects.
5th IEEE Vehicle Power and Propulsion Conference,2009:544-550
|
被引
1
次
|
|
|
|
|
2.
Matteo C. A diffusive electro-equivalent Li-ion battery model.
2013 IEEE International Symposium on Circuits and Systems,2013:2976-2979
|
被引
1
次
|
|
|
|
|
3.
Zhou X. State of charge estimation based on improved Li-ion battery model using extended Kalman filter.
2013 IEEE 8th Conference on Industrial Electronics and Applications,2013:607-612
|
被引
1
次
|
|
|
|
|
4.
Chris G. EV/HEV Li-ion battery modelling and state of function determination.
21 st International Symposium on Power Electronics, Electrical Drives, Automation and Motion,2012:353-358
|
被引
1
次
|
|
|
|
|
5.
贾玉健. 电动汽车电池等效电路模型的分类和特点.
电力与能源,2011,32(6):516-521
|
被引
21
次
|
|
|
|
|
6.
魏学哲. 锂离子动力电池参数辨识与状态估计.
同济大学学报:自然科学版,2008,36(2):231-235
|
被引
26
次
|
|
|
|
|
7.
陈全世. 电动汽车用电池性能模型研究综述.
汽车技术,2005(3):1-5
|
被引
36
次
|
|
|
|
|
8.
余修端. 基于控制性能的MIMO非线性系统动态面控制.
信息与控制,2012,41(1):44-60
|
被引
1
次
|
|
|
|
|
9.
徐世许. 不确定MIMO线性系统的全局快速终端滑模控制.
信息与控制,2011,40(5):619-626
|
被引
1
次
|
|
|
|
|
10.
石璞. 基于EKF的AMR锂电池SOC动态估计研究.
仪器仪表学报,2006,27(6):1-3
|
被引
9
次
|
|
|
|
|
11.
Kaypmaz T C. An advanced cell model for diagnosing faults in operation of Li-ion polymer batteries.
7th IEEE Vehicle Power and Propulsion Conference,2011:1-5
|
被引
1
次
|
|
|
|
|
12.
李哲.
纯电动汽车磷酸铁锂电池性能研究,2011
|
被引
40
次
|
|
|
|
|
13.
Thirugnanam K. Mathematical modeling of Li-ion battery for charge/discharge rate and capacity fading characteristics using genetic algorithm approach.
2012 IEEE Transportation Electrification Conference and Expo,2012:1-6
|
被引
1
次
|
|
|
|
|
14.
罗玉涛. 电动汽车锂离子电池组参数辨识与SOC估计.
华南理工大学学报:自然科学版,2012,40(12):79-85
|
被引
9
次
|
|
|
|
|
15.
Gao W G. Research on PNGV model parameter identification of LiFeP04 Li-ion battery based on FMRLS.
2011 6th IEEE Conference on Industrial Electronics and Applications,2011:2294-2297
|
被引
1
次
|
|
|
|
|
16.
张广辉. 基于偏格式线性化的MIMO系统无模型自适应控制.
信息与控制,2011,40(3):338-342
|
被引
3
次
|
|
|
|
|
17.
Hsieh G C. Fuzzy controlled Li-ion battery charge system with active stateofcharge controller.
IEEE Transactions on Industrial Electronics,2001:585-594
|
被引
8
次
|
|
|
|
|
了解气象卫星更多信息，请访问