scholarly journals Non-Fragile H∞ Nonlinear Observer for State of Charge Estimation of Lithium-Ion Battery Based on a Fractional-Order Model

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4771
Author(s):  
Zhongwei Zhang ◽  
Dan Zhou ◽  
Neng Xiong ◽  
Qiao Zhu
Keyword(s):  
Lithium Ion Battery ◽  
Fractional Order ◽  
Lipschitz Condition ◽  
Nonlinear Function ◽  
Lithium Ion ◽  
State Of Charge ◽  
Nonlinear Observer ◽  
Estimation Accuracy ◽  
Distributed Model ◽  
Operation Conditions

This paper deals with the state of charge (SOC) estimation of lithium-ion battery (LIB) in electric vehicles (EVs). In order to accurately describe the dynamic behavior of the battery, a fractional 2nd-order RC model of the battery pack is established. The factional-order battery state equations are characterized by the continuous frequency distributed model. Then, in order to ensure the effective function of nonlinear function, Lipschitz condition and unilateral Lipschitz condition are proposed to solve the problem of nonlinear output equation in the process of observer design. Next, the linear matrix (LMIS) inequality based on Lyapunov’s stability theory and H∞ method is presented as a description of the design criteria for non-fragile observer. Compared with the existing literature that adopts observers, the proposed method takes the advantages of fractional-order systems in modeling accuracy, the robustness of H∞ method in restricting the unknown variables, and the non-fragile property for tolerating slow drifts on observer gain. Finally, The LiCoO2 LIB module is utilized to verify the effectiveness of the proposed observer method in different operation conditions. Experimental results show that the maximum estimation accuracy of the proposed non-fragile observer under three different dynamic conditions is less than 2%.

scholarly journals A Physics-Based Electrochemical Model for Lithium-Ion Battery State-of-Charge Estimation Solved by an Optimised Projection-Based Method and Moving-Window Filtering

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 2120 ◽  
Author(s):  
Wei He ◽  
Michael Pecht ◽  
David Flynn ◽  
Fateme Dinmohammadi
Keyword(s):  
Lithium Ion Battery ◽  
Solid Phase ◽  
Chemical Properties ◽  
Lithium Ion ◽  
State Of Charge ◽  
Critical Parameters ◽  
Estimation Accuracy ◽  
Moving Window ◽  
Battery Management ◽  
Electrochemical Model

State-of-charge (SOC) is one of the most critical parameters in battery management systems (BMSs). SOC is defined as the percentage of the remaining charge inside a battery to the full charge, and thus ranges from 0% to 100%. This percentage value provides important information to manufacturers about the performance of the battery and can help end-users identify when the battery must be recharged. Inaccurate estimation of the battery SOC may cause over-charge or over-discharge events with significant implications for system safety and reliability. Therefore, it is crucial to develop methods for improving the estimation accuracy of battery SOC. This paper presents an electrochemical model for lithium-ion battery SOC estimation involving the battery’s internal physical and chemical properties such as lithium concentrations. To solve the computationally complex solid-phase diffusion partial differential equations (PDEs) in the model, an efficient method based on projection with optimized basis functions is presented. Then, a novel moving-window filtering (MWF) algorithm is developed to improve the convergence rate of the state filters. The results show that the developed electrochemical model generates 20 times fewer equations compared with finite difference-based methods without losing accuracy. In addition, the proposed projection-based solution method is three times more efficient than the conventional state filtering methods such as Kalman filter.

scholarly journals Comparisons of Modeling and State of Charge Estimation for Lithium-Ion Battery Based on Fractional Order and Integral Order Methods

Energies ◽  
2016 ◽  
Vol 9 (3) ◽  
pp. 184 ◽  
Author(s):  
Renxin Xiao ◽  
Jiangwei Shen ◽  
Xiaoyu Li ◽  
Wensheng Yan ◽  
Erdong Pan ◽  
...  
Keyword(s):  
Lithium Ion Battery ◽  
Fractional Order ◽  
Lithium Ion ◽  
State Of Charge ◽  
State Of Charge Estimation ◽  
Integral Order

scholarly journals State of Charge Estimation for Lithium-Ion Battery Based on Nonlinear Observer: An H∞ Method

Energies ◽  
2017 ◽  
Vol 10 (5) ◽  
pp. 679 ◽  
Author(s):  
Qiao Zhu ◽  
Neng Xiong ◽  
Ming-Liang Yang ◽  
Rui-Sen Huang ◽  
Guang-Di Hu
Keyword(s):  
Lithium Ion Battery ◽  
Lithium Ion ◽  
State Of Charge ◽  
Nonlinear Observer ◽  
State Of Charge Estimation

scholarly journals A comparative study of fractional order models on state of charge estimation for lithium ion batteries

2020 ◽  
Author(s):  
Jinpeng Tian ◽  
Rui XIONG ◽  
Weixiang Shen ◽  
Ju Wang
Keyword(s):  
Lithium Ion Batteries ◽  
Fractional Order ◽  
Unscented Kalman Filter ◽  
Critical Role ◽  
Lithium Ion ◽  
State Of Charge ◽  
Identification Accuracy ◽  
Estimation Accuracy ◽  
Ambient Temperatures ◽  
Soc Estimation

Abstract State of charge (SOC) estimation for lithium ion batteries plays a critical role in battery management systems for electric vehicles. Battery fractional order models (FOMs) which come from frequency-domain modelling have provided a distinct insight into SOC estimation. In this article, for the first time, we compare five state-of-the-art FOMs in terms of SOC estimation. Firstly, characterisation tests on lithium ion batteries are conducted, and the experimental results are used to identify FOM parameters. Parameter identification results show that increasing the complexity of FOMs cannot always improve accuracy. The model R(RQ)W shows superior identification accuracy than the other four FOMs. Secondly, the SOC estimation based on a fractional order unscented Kalman filter is conducted to compare model accuracy and computational burden under different profiles, memory lengths, ambient temperatures, cells and voltage/current drifts. The evaluation results reveal that the SOC estimation accuracy does not necessarily positively correlate to the complexity of FOMs. Although more complex models can have better robustness against temperature variation, R(RQ), the simplest FOM, can overall provide satisfactory accuracy and generalisation ability. It can also maintain high accuracy even under the occurrence of sensor drift.

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