Full Text:   <973>

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CLC number: TM912.1

On-line Access: 2017-04-05

Received: 2016-03-16

Revision Accepted: 2016-10-12

Crosschecked: 2017-03-09

Cited: 0

Clicked: 1864

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Xi-ming Cheng

http://orcid.org/0000-0001-5933-2630

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Journal of Zhejiang University SCIENCE A 2017 Vol.18 No.4 P.256-267

10.1631/jzus.A1600251


Lithium-ion battery state-of-charge estimation based on deconstructed equivalent circuit at different open-circuit voltage relaxation times


Author(s):  Xi-ming Cheng, Li-guang Yao, Michael Pecht

Affiliation(s):  Collaborative Innovation Center for Electric Vehicles in Beijing, National Engineering Laboratory for Electric Vehicles, Department of Vehicular Engineering, Beijing Institute of Technology, Beijing 100081, China; more

Corresponding email(s):   cxm2004@bit.edu.cn

Key Words:  Lithium-ion batteries, Open-circuit voltage (OCV), State-of-charge (SOC), Recursive least squares (RLS), Extended Kalman filter (EKF)


Xi-ming Cheng, Li-guang Yao, Michael Pecht. Lithium-ion battery state-of-charge estimation based on deconstructed equivalent circuit at different open-circuit voltage relaxation times[J]. Journal of Zhejiang University Science A, 2017, 18(4): 256-267.

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Abstract: 
Equivalent circuit model-based state-of-charge (SOC) estimation has been widely studied for power lithium-ion batteries. An appropriate relaxation period to measure the open-circuit voltage (OCV) should be investigated to both ensure good SOC estimation accuracy and improve OCV test efficiency. Based on a battery circuit model, an SOC estimator in the combination of recursive least squares (RLS) and the extended Kalman filter is used to mitigate the error voltage between the measurement and real values of the battery OCV. To reduce the iterative computation complexity, a two-stage RLS approach is developed to identify the model parameters, the battery circuit of which is divided into two simple circuits. Then, the measurement values of the OCV at varying relaxation periods and three temperatures are sampled to establish the relationships between SOC and OCV for the developed SOC estimator. Lastly, dynamic stress test and federal test procedure drive cycles are used to validate the model-based SOC estimation method. Results show that the relationships between SOC and OCV at a short relaxation time, such as 5 min, can also drive the SOC estimator to produce a good performance.

This paper shows original work towards development of a two stage process to estimate the EC model parameters and determine battery SOC. The circuit deconstruction method was validated with DST and FTP test cycles. The uniqueness of the work lies in the reduction in error in estimation of SOC and also reducing in the OCV test time. The authors have followed a reasonable scientific approach to explain the model development, theory and results.

不同开路电压松弛时间下基于等效电路解构的锂离子电池荷电状态估计

目的:开路电压是基于模型的电池荷电状态估计的必要参数,其测试耗时大、效率低。本文旨在测试各种电压松弛时间的荷电状态-开路电压关系,研究其对开路电压法和等效电路模型的荷电状态估计准确度的影响,提高开路电压测试效率。
创新点:1. 通过电路解构方法,将二阶阻容电路分解为简单路,运用二阶段递推最小二乘法辨识电路模型的参数;2. 基于递推最小二乘法和卡尔曼滤波算法,建立电路参数辨识和荷电状态估计的的联合自适应算法,研究电池电压松弛时间对基于等效电路模型的荷电状态估计的影响。
方法:1. 通过电路解构技术和理论推导,构建辨识二阶阻容等效电路参数的二阶段递推最小二乘法辨识方法(图2和公式(4)~(9));2. 将二阶段递推最小二乘法和扩展卡尔曼滤波器集成,建立适应工况变化的电池模型参数辨识和状态估计的联合算法(图3);3. 通过电池测试,建立多温度和多电压松弛时间的荷电状态与开路电压的关系,驱动自适应联合算法,获得既保证荷电状态估计准确度,又缩短开路电压测试时间的电压松弛时间。
结论:1. 二阶段递推最小二乘法既能简化矩阵计算,又能够保证电路参数的辨识非负性;2. 联合自适应算法能够适应工况变化辨识模型参数和估计荷电状态;3. 联合自适应算法的结果表明, 5 min的电压松弛时间既能保证荷电状态估计性能,又能极大地提高开路电压测试效率。

关键词:锂离子电池;开路电压;荷电状态;递推最小二乘法;扩展卡尔曼滤波器

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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