Lithium-ion Open Circuit Voltage (OCV) curve modelling and its ageing adjustment

Lavigne, Loïc; Sabatier, Jocelyn; Francisco, Junior Mbala; Guillemard, Franck; Noury, Agnieszka

doi:10.1016/j.jpowsour.2016.05.121

Cited by 123 publications

(39 citation statements)

References 18 publications

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“…Among The SOC-OCV curve drifting after two calendar years of ageing is shown in Figure 14. In order to overcome the challenges of aging, Lavigne et al [136] proposed a two stage lithium-ion OCV curve model to estimate battery SOC. …”

Section: Lithium-ion Battery Modelmentioning

confidence: 99%

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: Lithium-ion Battery Modelmentioning

confidence: 99%

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

et al. 2018

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“…SoC estimation methods commonly impose a characterization of the OCV curve (mainly through a polynomial or a look-up table), as they use either a direct OCV curve inversion method (if the application permits cell steady state voltage measurement), or a cell model-based methods [42]. Making voltage measurements to determine the SoC for the cell allows us to define the relation:…”

Section: Open Circuit Voltage-based Estimation (Ocv)mentioning

confidence: 99%

“…As this method needs a sinusoidal source, it is The changes of the OCV curve of each cell due to battery aging and performance degradation distort the estimation technique if nothing is done to adjust the curve. Therefore, OCV-SoC curve correction is required and in [42,44] it is implemented by comparing the capacity error with the CC and look-up table methods or by polynomial parametrization for the OCV curve. The OCV method compensates for both parameter and temperature variations in SoC estimation method presented in [20].…”

Section: Impedance Measurement-based Estimationmentioning

confidence: 99%

SoC Estimation for Lithium-ion Batteries: Review and Future Challenges

2017

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Abstract:Energy storage emerged as a top concern for the modern cities, and the choice of the lithium-ion chemistry battery technology as an effective solution for storage applications proved to be a highly efficient option. State of charge (SoC) represents the available battery capacity and is one of the most important states that need to be monitored to optimize the performance and extend the lifetime of batteries. This review summarizes the methods for SoC estimation for lithium-ion batteries (LiBs). The SoC estimation methods are presented focusing on the description of the techniques and the elaboration of their weaknesses for the use in on-line battery management systems (BMS) applications. SoC estimation is a challenging task hindered by considerable changes in battery characteristics over its lifetime due to aging and to the distinct nonlinear behavior. This has led scholars to propose different methods that clearly raised the challenge of establishing a relationship between the accuracy and robustness of the methods, and their low complexity to be implemented. This paper publishes an exhaustive review of the works presented during the last five years, where the tendency of the estimation techniques has been oriented toward a mixture of probabilistic techniques and some artificial intelligence.

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“…Therefore, it is the main indicator of the system state and its knowledge allows the system security level to be increased (avoiding overcharge and over-discharge situations) and the success rate of optimization algorithms oriented to the maximum performance exploitation to be improved [1][2][3]. SoC estimation methods are classified in [4] as direct methods (such as open circuit voltage estimation, Coulomb counting, and electrochemical impedance methods) [5][6][7]) and indirect methods (such as those based on artificial intelligence, adaptive filters, and models [8][9][10][11]). In [4], the advantages and disadvantages of the different methods and the related open issues are also analyzed.…”

Section: Introductionmentioning

confidence: 99%

Li-ion Battery Modeling and State of Charge Estimation Method Including the Hysteresis Effect

et al. 2019

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In this paper, a new approach to modeling the hysteresis phenomenon of the open circuit voltage (OCV) of lithium-ion batteries and estimating the battery state of charge (SoC) is presented. A characterization procedure is proposed to identify the battery model parameters, in particular, those related to the hysteresis phenomenon and the transition between charging and discharging conditions. A linearization method is used to obtain a suitable trade-off between the model accuracy and a low computational cost, in order to allow the implementation of SoC estimation on common hardware platforms. The proposed characterization procedure and the model effectiveness for SoC estimation are experimentally verified using a real grid-connected storage system. A mixed algorithm is adopted for SoC estimation, which takes into account both the traditional Coulomb counting method and the developed model. The experimental comparison with the traditional approach and the obtained results show the feasibility of the proposed approach for accurate SoC estimation, even in the presence of low-accuracy measurement transducers.

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Lithium-ion Open Circuit Voltage (OCV) curve modelling and its ageing adjustment

Cited by 123 publications

References 18 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

SoC Estimation for Lithium-ion Batteries: Review and Future Challenges

Li-ion Battery Modeling and State of Charge Estimation Method Including the Hysteresis Effect

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