Investigating the error sources of the online state of charge estimation methods for lithium-ion batteries in electric vehicles

Zheng, Yuejiu; Ouyang, Minggao; Han, Xiao; Lu, Languang; Li, Jianqiu

doi:10.1016/j.jpowsour.2017.11.094

Cited by 352 publications

(146 citation statements)

References 276 publications

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“…The unknown initial SOC, capacity fading, self-discharge rate, and current sensor errors are the error sources for the Ah counting method. In order to improve the accuracy of the Ah method, the initial capacity and SOC value of the battery, and the current sensor drift can be corrected and adjusted regularly [83].…”

Section: Conventional Methodsmentioning

confidence: 99%

“…(2) OCV method OCV is the method that uses the stable battery electromotive force in the open circuit state and SOC relationship to estimate the SOC value [83]. Although there is an approximate linear relationship between SOC and OCV, this relationship is not exactly the same for different batteries.…”

Section: Conventional Methodsmentioning

confidence: 99%

“…Although the SOC-OCV relationship of lithium ion batteries is relatively stable, it will change according to the environmental temperature and cycle life of the battery. In addition, in order to have a reliable SOC-OCV relationship, researchers and engineers may need to conduct massive experiments at different temperatures and cycle lives [83].…”

Section: Conventional Methodsmentioning

confidence: 99%

“…But it is very difficult to measure online electrical impedance spectroscopy (EIS), because sinusoidal alternating current (AC) may be required, the SOC and impedance relationship is not stable, and the cost is expensive [83]. To obtain the internal resistance, it needs direct current (DC) and the value of the voltage and current at a small time interval.…”

Section: Conventional Methodsmentioning

confidence: 99%

“…However, internal resistance changes slowly and is hard to observe for SOC estimation. In general, SOC estimation based on the impedance and internal resistance method is not suitable for use in EVs [83].…”

Section: Conventional Methodsmentioning

confidence: 99%

See 4 more Smart Citations

State of the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles

et al. 2018

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Sate of charge (SOC) accurate estimation is one of the most important functions in a battery management system for battery packs used in electrical vehicles. This paper focuses on battery SOC estimation and its issues and challenges by exploring different existing estimation methodologies. The key technologies of lithium-ion battery state estimation methodologies of the electrical vehicles categorized under five groups, such as the conventional method, adaptive filter algorithm, learning algorithm, nonlinear observer, and the hybrid method, are explored in an in-depth analysis. Lithium-ion battery characteristic, battery model, estimation algorithm, and cell unbalancing are the most important factors that affect the accuracy and robustness of SOC estimation. Finally, this paper concludes with the challenges of SOC estimation and suggests other directions for possible research efforts.

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Section: Conventional Methodsmentioning

confidence: 99%

Section: Conventional Methodsmentioning

confidence: 99%

Section: Conventional Methodsmentioning

confidence: 99%

Section: Conventional Methodsmentioning

confidence: 99%

Section: Conventional Methodsmentioning

confidence: 99%

See 3 more Smart Citations

State of the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles

et al. 2018

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A graphical model for evaluating the status of series-connected lithium-ion battery pack

Feng

et al. 2018

Int J Energy Res

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Summary State evaluation of battery pack is essential for battery management but laborious when dealing with massive information of cells within the pack. A graphical model for evaluating the status of series‐connected Li‐ion battery pack is established to release the burden. The model is founded by a 2D diagram, with the electric quantity “E” and the capacity “Q” as its axes, therefore called by the “E‐Q diagram.” The new graphical diagram presents the dynamics of cell variations in a linear way, thereby benefiting the design and management of battery pack, including (1) quantifying the cell variations by region, (2) illustrating the evolution of cell variations during aging, (3) guiding the estimation of pack states considering algorithm error in cell states, and (4) solving the balancing problem. The experimental results conform to the theoretical analysis, indicating that the E‐Q diagram will be pervasively applied in the design and management of series‐connected battery pack. Moreover, the E‐Q diagram is suitable for education on the basics of a battery pack, because it is a graphical model.

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A novel fast estimation and regroup method of retired lithium‐ion battery cells

et al. 2020

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With the commercialization of the electric vehicles, the large-scale lithium-ion cells as the power of electric vehicles are to be retired. The second-use of retired cells is of great significance to improve the battery economy. A fast classification and regroup evaluation method of the retired lithium-ion cells are proposed in this paper to improve the classification efficiency of retired lithium-ion cells and adapt to the regroup under different conditions. The lithium-ion cells after being balanced in parallel are charged in series with a constant current. A support vector regression (SVR) model with the parameters optimized by the particle swarm optimization (PSO) algorithm is built for the fast capacity estimation and the error will not exceed 0.3%. Different cells regrouped means different performance. In order to improve the consistency of retired cells and satisfy different using conditions, a Weighted-K-means algorithm is proposed in this paper to regroup the cells with the known capacity and internal resistance. The classification method is evaluated by the voltage consistency of cells using different working conditions, which indicates capacity occupied a large proportion can meet the requirement of energy condition meanwhile keep a good consistency. But the resistance will dominate in algorithm under conditions which have requirement for instantaneous power.

show abstract

Investigating the error sources of the online state of charge estimation methods for lithium-ion batteries in electric vehicles

Cited by 352 publications

References 276 publications

State of the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles

State of the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles

A graphical model for evaluating the status of series-connected lithium-ion battery pack

A novel fast estimation and regroup method of retired lithium‐ion battery cells

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