A Review and Comparison of Lithium-Ion Battery SOC Estimation Methods for Electric Vehicles

2020 ◽  
Author(s):  
Andreas Manthopoulos ◽  
Xiang Wang
Keyword(s):  
Lithium Ion Battery ◽  
Electric Vehicles ◽  
Lithium Ion ◽  
Estimation Methods ◽  
Soc Estimation

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

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.

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

2010 ◽  
Vol 152-153 ◽  
pp. 428-435 ◽  
Author(s):  
Yuan Liao ◽  
Ju Hua Huang ◽  
Qun Zeng
Keyword(s):  
Lithium Ion Battery ◽  
Estimation Method ◽  
Lithium Ion ◽  
State Of Charge ◽  
Estimation Methods ◽  
Counting Method ◽  
Soc Estimation ◽  
Battery Packs ◽  
Novel Method ◽  
Road Data

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.

scholarly journals State of Charge Estimation of Lithium-Ion Battery for Electric Vehicles Using Machine Learning Algorithms

2021 ◽  
Vol 12 (1) ◽  
pp. 38
Author(s):  
Venkatesan Chandran ◽  
Chandrashekhar K. Patil ◽  
Alagar Karthick ◽  
Dharmaraj Ganeshaperumal ◽  
Robbi Rahim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lithium Ion Battery ◽  
Electric Vehicles ◽  
Learning Algorithms ◽  
Gaussian Process Regression ◽  
Lithium Ion ◽  
Machine Learning Algorithms ◽  
State Of Charge ◽  
Support Vector ◽  
Soc Estimation

The durability and reliability of battery management systems in electric vehicles to forecast the state of charge (SoC) is a tedious task. As the process of battery degradation is usually non-linear, it is extremely cumbersome work to predict SoC estimation with substantially less degradation. This paper presents the SoC estimation of lithium-ion battery systems using six machine learning algorithms for electric vehicles application. The employed algorithms are artificial neural network (ANN), support vector machine (SVM), linear regression (LR), Gaussian process regression (GPR), ensemble bagging (EBa), and ensemble boosting (EBo). Error analysis of the model is carried out to optimize the battery’s performance parameter. Finally, all six algorithms are compared using performance indices. ANN and GPR are found to be the best methods based on MSE and RMSE of (0.0004, 0.00170) and (0.023, 0.04118), respectively.

scholarly journals An Overview and Comparison of Online Implementable SOC Estimation Methods for Lithium-Ion Battery

2018 ◽  
Vol 54 (2) ◽  
pp. 1583-1591 ◽  
Author(s):  
Jinhao Meng ◽  
Mattia Ricco ◽  
Guangzhao Luo ◽  
Maciej Swierczynski ◽  
Daniel-Ioan Stroe ◽  
...  
Keyword(s):  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Estimation Methods ◽  
Soc Estimation
Sign in / Sign up

Export Citation Format

Share Document