A Novel Method for Estimating State of Charge of Lithium Ion Battery Packs

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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State of Charge Estimation Techniques for Lithium-ION Batteries Used in Electric Vehicle Applications

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.37859 ◽

2021 ◽

Vol 9 (8) ◽

pp. 2750-2760

Author(s):

Nikhil P

Keyword(s):

Lithium Ion Battery ◽

Electric Vehicles ◽

Lithium Ion ◽

High Energy ◽

State Of Charge ◽

High Energy Density ◽

Estimation Methods ◽

Estimation Techniques ◽

Soc Estimation ◽

Battery Packs

Abstract: Lithium-ion battery packs constitute an important part of Electric vehicles. The usage of Lithium-ion based chemistries as the source of energy has various advantages like high efficiency, high energy density, high specific energy, longevity among others. However, the management of lithium-ion battery packs require a Battery Management System (BMS). The BMS deals with functions like safety, prevention of abusive usage of battery pack, overcharging & over-discharging protection, cell balancing and others. One of the prominent features of the BMS is the estimation of State of charge (SOC). SOC is like a fuel gauge in automobile, it indicates how much more the battery can be used before charging it again. SOC is also required for other functions of BMS like State of Health (SOH) tracking, Range calculation, power & energy availability calculations. However, there is no means of measuring it directly (at least not on-board a vehicle) or estimating it easily. Various techniques should be used to estimate SOC indirectly. This paper starts from classical techniques that have existed since long time and reviews some of the modern & developing methods for SOC estimation. It contains a brief review about most of these SOC estimation methods, thus highlighting the methodology, advantages & disadvantages of each of these techniques. A brief review of other developing SOC estimation techniques is also provided. Keywords: State of Charge, SOC, Lithium-ion battery packs, Electric vehicles, Kalman Filter.

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State-of-charge estimation for second-life lithium-ion batteries based on cell difference model and adaptive fading unscented Kalman filter algorithm

International Journal of Low-Carbon Technologies ◽

10.1093/ijlct/ctab019 ◽

2021 ◽

Author(s):

Banghua Du ◽

Zhang Yu ◽

Shuhao Yi ◽

Yanlin He ◽

Yulin Luo

Keyword(s):

Lithium Ion Batteries ◽

Lithium Ion Battery ◽

Unscented Kalman Filter ◽

Lithium Ion ◽

Economic Benefits ◽

Operating Conditions ◽

State Of Charge ◽

Difference Model ◽

Soc Estimation ◽

Battery Packs

Abstract Lithium-ion batteries retired from electric vehicles can provide considerable economic benefits when they are retired for secondary use. However, retired batteries after screening and restructuring still face the problem of inaccurate battery pack state-of-charge (SOC) estimation due to the existence of extreme inconsistency. To solve this problem, an adaptive fading unscented Kalman filtering (AFUKF) algorithm based on the cell difference model (CDM) is proposed in this paper for improving the accuracy of SOC estimation of retired lithium-ion battery packs. Firstly, an improved CDM based on a hypothetical Rint model is developed based on a second-order resistor/capacitor equivalent circuit model. Secondly, an AFUKF algorithm is developed to improve the adaptability and robustness of local state estimation against process modelling errors. Finally, characteristic data are obtained by conducting discharge tests on the screened retired lithium-ion batteries under specific operating conditions. The proposed method can improve the accuracy of SOC estimation of retired lithium-ion battery packs and provide a new idea for SOC estimation of retired lithium-ion battery packs, as shown by the simulated real experimental data.

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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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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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State-of-Charge Uncertainty of Lithium-Ion Battery Packs Considering the Cell-to-Cell Variability

ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg ◽

10.1115/1.4042847 ◽

2019 ◽

Vol 5 (2) ◽

Author(s):

Modjtaba Dahmardeh ◽

Zhimin Xi

Keyword(s):

Lithium Ion Battery ◽

Lithium Ion ◽

Uncertainty Modeling ◽

State Of Charge ◽

Soc Estimation ◽

Propagation Analysis ◽

Battery Packs ◽

The One ◽

Definition Of ◽

Cell To Cell Variability

Accurate state-of-charge (SOC) estimation of lithium-ion battery packs is technically challenging because of the cell-to-cell variability due to the manufacturing tolerance. In addition, there is no unanimous definition of the pack SOC since each cell has its own SOC and the pack can be configured in different ways. This study first adopts a suitable pack SOC definition among existing ones, then proposes uncertainty modeling and propagation analysis for pack SOC estimation considering the cell-to-cell variability, and finally conducts the SOC estimation for one serially connected battery pack using the one state hysteresis model with the extended Kalman filter (EKF). The results reveal that pack SOC variability is inevitable due to the cell-to-cell variability and accurate pack SOC estimation is challenging considering both the cell-level SOC accuracy and the pack-level estimation efficiency.

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