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

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.

Thermal Simulation Analysis of a Lithium-Ion Battery

2021 ◽  
Vol 4 (1) ◽  
Keyword(s):  
Lithium Ion Battery ◽  
Electric Vehicles ◽  
Heat Generation ◽  
Lithium Ion ◽  
High Energy ◽  
Thermal Runaway ◽  
Transport Processes ◽  
High Energy Density ◽  
Battery Cell ◽  
Discharge Rates

Lithium – Ion batteries are now extensively used in electric vehicles (EV) as well as in renewable power generation applications for both on-grid and off grid storage. Some of the major challenges with batteries for electric vehicles are the requirement of high energy density, compatibility with high charge and discharge rates while maintaining high performance, and prevention of any thermal runaway conditions. The objective of this research is to develop a computer simulation model for coupled electrochemical and thermal analysis and characterization of a lithium-ion battery performance subject to a range of charge and discharge loading, and thermal environmental conditions. The electrochemical model includes species and charge transport through the liquid and solid phases of electrode and electrolyte layers along with electrode kinetics. The thermal model includes several heat generation components such as reversible, irreversible and ohmic heating, and heat dissipation through layers of battery cell. Simulation is carried out to evaluate the electrochemical and thermal behavior with varying discharge rates. Results demonstrated a strong variation in the activation and ohmic polarization losses as well as in higher heat generation rates. Results show variation of different modes and order of cell heat generation rates that results in a higher rate of cell temperature rise as battery cell is subjected to higher discharge rates. The model developed will help in gaining a comprehensive insights of the complex transport processes in a cell and can form a platform for evaluating number new candidates for battery chemistry for enhanced battery performance and address safety issues associated with thermal runaway.

scholarly journals Adopting a Conversion Design Approach to Maximize the Energy Density of Battery Packs in Electric Vehicles

Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1939
Author(s):  
Erika Pierri ◽  
Valentina Cirillo ◽  
Thomas Vietor ◽  
Marco Sorrentino
Keyword(s):  
Energy Density ◽  
Electric Vehicles ◽  
Design Procedure ◽  
Lithium Ion ◽  
High Energy ◽  
Battery Pack ◽  
High Energy Density ◽  
Installation Space ◽  
Battery Electric Vehicles ◽  
Battery Packs

Innovative vehicle concepts have been developed in the past years in the automotive sector, including alternative drive systems such as hybrid and battery electric vehicles, so as to meet the environmental targets and cope with the increasingly stringent emissions regulations. The preferred hybridizing technology is lithium-ion battery, thanks to its high energy density. The optimal integration of battery packs in the vehicle is a challenging task when designing e-mobility concepts. Therefore, this work proposes a conceptual design procedure aimed at optimizing the sizing of hybrid and battery electric vehicles. Particularly, the influence of the cell type, physical disposition and arrangement of the electrical devices is accounted for within a conversion design framework. The optimization is focused on the trade-off between the battery pack capacity and weight. After introducing the main features of electric traction systems and their challenges compared to conventional ones, the relevant design properties of electric vehicles are analyzed. A detailed strategy, encompassing the selection of battery format and technology, battery pack design and final assessment of the proposed set-up, is presented and implemented in an exemplary application, assuming an existing commercial vehicle as the reference starting layout. Prismatic, cylindrical and pouch cells are configured aiming at achieving installed battery energy as close as possible to the reference one, while meeting the original installation space constraint. The best resulting configuration, which also guarantees similar peak power performance of the reference battery-pack, allows reducing the mass of the storage system down to 70% of its starting value.

scholarly journals A Novel State of Charge Estimation Algorithm for Lithium-Ion Battery Packs of Electric Vehicles

Energies ◽  
2016 ◽  
Vol 9 (9) ◽  
pp. 710 ◽  
Author(s):  
Zheng Chen ◽  
Xiaoyu Li ◽  
Jiangwei Shen ◽  
Wensheng Yan ◽  
Renxin Xiao
Keyword(s):  
Lithium Ion Battery ◽  
Electric Vehicles ◽  
Lithium Ion ◽  
Estimation Algorithm ◽  
State Of Charge ◽  
Battery Packs ◽  
State Of Charge Estimation

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

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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