New Approach of SOC Estimation Method for lithium-ion battery/Ultra-capacity Hybrid system Based on Multi-Model Strategy

In this paper a novel method for estimating state of charge (SOC) of lithium ion battery packs in battery electric vehicle (BEV), based on state of health (SOH) determination is presented. SOH provides information on aging of battery packs and it declines with repeated charging and discharging cycles of battery packs, so SOC estimation depends considerably on the value of SOH. Previously used SOC estimation methods are not satisfactory as they haven’t given enough attention to the decline of SOH. Therefore a novel SOC estimation method based on SOH determination is introduced in this paper; trying to compensate the deficiency for lack of attention to SOH. Real time road data are used to compare the performance of the conventionally often used Ah counting method which doesn’t give any consideration to SOH with the performance of the proposed SOC estimation method, and better results are obtained by the proposed method in comparison with the conventional method.

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SOC Estimation Method of Lithium Ion Battery for Contact Wire and Battery Hybrid Electric Railway Vehicle

Quarterly Report of RTRI ◽

10.2219/rtriqr.54.145 ◽

2013 ◽

Vol 54 (3) ◽

pp. 145-151

Author(s):

Yoshiaki TAGUCHI ◽

Masamichi OGASA

Keyword(s):

Lithium Ion Battery ◽

Estimation Method ◽

Lithium Ion ◽

Railway Vehicle ◽

Contact Wire ◽

Soc Estimation ◽

Electric Railway ◽

Hybrid Electric

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An integrated online adaptive state of charge estimation approach of high-power lithium-ion battery packs

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331217694681 ◽

2017 ◽

Vol 40 (6) ◽

pp. 1892-1910 ◽

Cited By ~ 9

Author(s):

Shunli Wang ◽

Carlos Fernandez ◽

Liping Shang ◽

Zhanfeng Li ◽

Huifang Yuan

Keyword(s):

Lithium Ion Battery ◽

Real Time ◽

High Power ◽

Computational Cost ◽

Estimation Method ◽

Equivalent Circuit Model ◽

Lithium Ion ◽

State Of Charge ◽

The Real ◽

Soc Estimation

A novel online adaptive state of charge (SOC) estimation method is proposed, aiming to characterize the capacity state of all the connected cells in lithium-ion battery (LIB) packs. This method is realized using the extended Kalman filter (EKF) combined with Ampere-hour (Ah) integration and open circuit voltage (OCV) methods, in which the time-scale implementation is designed to reduce the computational cost and accommodate uncertain or time-varying parameters. The working principle of power LIBs and their basic characteristics are analysed by using the combined equivalent circuit model (ECM), which takes the discharging current rates and temperature as the core impacts, to realize the estimation. The original estimation value is initialized by using the Ah integral method, and then corrected by measuring the cell voltage to obtain the optimal estimation effect. Experiments under dynamic current conditions are performed to verify the accuracy and the real-time performance of this proposed method, the analysed result of which indicates that its good performance is in line with the estimation accuracy and real-time requirement of high-power LIB packs. The proposed multi-model SOC estimation method may be used in the real-time monitoring of the high-power LIB pack dynamic applications for working state measurement and control.

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A Battery SOC Estimation Method Based on AFFRLS-EKF

Sensors ◽

10.3390/s21175698 ◽

2021 ◽

Vol 21 (17) ◽

pp. 5698

Author(s):

Ming Li ◽

Yingjie Zhang ◽

Zuolei Hu ◽

Ying Zhang ◽

Jing Zhang

Keyword(s):

Least Squares ◽

Lithium Ion Battery ◽

Estimation Method ◽

Lithium Ion ◽

Recursive Least Squares ◽

Estimation Accuracy ◽

Forgetting Factor ◽

Battery Charge ◽

Estimation Strategy ◽

Soc Estimation

The lithium-ion battery is the key power source of a hybrid vehicle. Accurate real-time state of charge (SOC) acquisition is the basis of the safe operation of vehicles. In actual conditions, the lithium-ion battery is a complex dynamic system, and it is tough to model it accurately, which leads to the estimation deviation of the battery SOC. Recursive least squares (RLS) algorithm with fixed forgetting factor is widely used in parameter identification, but it lacks sufficient robustness and accuracy when battery charge and discharge conditions change suddenly. In this paper, we proposed an adaptive forgetting factor regression least-squares–extended Kalman filter (AFFRLS–EKF) SOC estimation strategy by designing the forgetting factor of least squares algorithm to improve the accuracy of SOC estimation under the change of battery charge and discharge conditions. The simulation results show that the SOC estimation strategy of the AFFRLS–EKF based on accurate modeling can effectively improve the estimation accuracy of SOC.

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Robust Online State of Charge Estimation of Lithium-Ion Battery Pack Based on Error Sensitivity Analysis

Mathematical Problems in Engineering ◽

10.1155/2015/573184 ◽

2015 ◽

Vol 2015 ◽

pp. 1-11 ◽

Cited By ~ 2

Author(s):

Ting Zhao ◽

Jiuchun Jiang ◽

Caiping Zhang ◽

Kai Bai ◽

Na Li

Keyword(s):

Lithium Ion Battery ◽

Estimation Error ◽

Estimation Method ◽

Dynamic Test ◽

Lithium Ion ◽

Vital Role ◽

State Of Charge ◽

Battery Pack ◽

Estimation Accuracy ◽

Soc Estimation

Accurate and reliable state of charge (SOC) estimation is a key enabling technique for large format lithium-ion battery pack due to its vital role in battery safety and effective management. This paper tries to make three contributions to existing literatures through robust algorithms. (1) Observer based SOC estimation error model is established, where the crucial parameters on SOC estimation accuracy are determined by quantitative analysis, being a basis for parameters update. (2) The estimation method for a battery pack in which the inconsistency of cells is taken into consideration is proposed, ensuring all batteries’ SOC ranging from 0 to 1, effectively avoiding the battery overcharged/overdischarged. Online estimation of the parameters is also presented in this paper. (3) The SOC estimation accuracy of the battery pack is verified using the hardware-in-loop simulation platform. The experimental results at various dynamic test conditions, temperatures, and initial SOC difference between two cells demonstrate the efficacy of the proposed method.

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A nonlinear fractional-order H∞ observer for SOC estimation of battery pack of electric vehicles

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1177/0954407021994349 ◽

2021 ◽

pp. 095440702199434

Author(s):

Wei Yue ◽

Cong-zhi Liu ◽

Liang Li ◽

Xiang Chen ◽

Fahad Muhammad

Keyword(s):

Fractional Order ◽

Estimation Method ◽

Equivalent Circuit Model ◽

Lithium Ion ◽

Open Circuit ◽

Design Criterion ◽

Observer Design ◽

Nonlinear Observer ◽

Battery Pack ◽

Soc Estimation

This work is focused on designing a fractional-order [Formula: see text] observer and applying it into the state of charge (SOC) estimation for lithium-ion battery pack system. Firstly, a fractional order equivalent circuit model based on the fractional capacitor is established and identified. Secondly, the SOC estimation method based on the fractional-order [Formula: see text] observer is proposed. The nonlinear intrinsic relationship between the open-circuit voltage and SOC is described as a polynomial function, and its Lipschitz proposition has been discussed. Then, the nonlinear observer design criterion is established based on the Lyapunov method. Finally, the effectiveness of the proposed method is verified with high accuracy and robustness by the experiment results.

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