State of Charge Estimation of a Composite Lithium-Based Battery Model Based on an Improved Extended Kalman Filter Algorithm

Ding, Ning; Prasad, K.; Lie, Tek Tjing; Cui, Jinhui

doi:10.3390/inventions4040066

Cited by 11 publications

(19 citation statements)

References 42 publications

(53 reference statements)

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“…A novel composite battery model can easily be made using various known electrochemical battery models. For the composite battery model developed by [24], the State of Charge of the battery was considered as a state variable x in the system, x k denoting the number of the state vectors present in the system. The output voltage of the battery was expressed as y k .…”

Section: Electrochemical Modelling Of a Batterymentioning

confidence: 99%

“…In order to frame Equations (1)-(3), the reactions occurring at the anode and cathode of the battery and the electrolyte ion transfer process are considered and these empirical simplified equations were evaluated, instead of the conventional partial differential equation formation strategy [24]. The electrochemical models of Equations (1)-(3) reflect the relationship between the terminal voltage and the SoC (x k ).…”

Section: Electrochemical Modelling Of a Batterymentioning

confidence: 99%

See 1 more Smart Citation

A Review on Battery Modelling Techniques

Tamilselvi¹,

Gunasundari²,

Karuppiah³

et al. 2021

Sustainability

114

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The growing demand for electrical energy and the impact of global warming leads to a paradigm shift in the power sector. This has led to the increased usage of renewable energy sources. Due to the intermittent nature of the renewable sources of energy, devices capable of storing electrical energy are required to increase its reliability. The most common means of storing electrical energy is battery systems. Battery usage is increasing in the modern days, since all mobile systems such as electric vehicles, smart phones, laptops, etc., rely on the energy stored within the device to operate. The increased penetration rate of the battery system requires accurate modelling of charging profiles to optimise performance. This paper presents an extensive study of various battery models such as electrochemical models, mathematical models, circuit-oriented models and combined models for different types of batteries. It also discusses the advantages and drawbacks of these types of modelling. With AI emerging and accelerating all over the world, there is a scope for researchers to explore its application in multiple fields. Hence, this work discusses the application of several machine learning and meta heuristic algorithms for battery management systems. This work details the charging and discharging characteristics using the black box and grey box techniques for modelling the lithium-ion battery. The approaches, advantages and disadvantages of black box and grey box type battery modelling are analysed. In addition, analysis has been carried out for extracting parameters of a lithium-ion battery model using evolutionary algorithms.

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Section: Electrochemical Modelling Of a Batterymentioning

confidence: 99%

Section: Electrochemical Modelling Of a Batterymentioning

confidence: 99%

A Review on Battery Modelling Techniques

Tamilselvi¹,

Gunasundari²,

Karuppiah³

et al. 2021

Sustainability

114

View full text Add to dashboard Cite

show abstract

“…However, the threshold value of SoC not only impacts the control strategy but also plays a significant role of protecting the battery in the monitoring module. 25 Lower SoC l means a deeper depth of discharge (DOD) and may cause an increase in the internal resistance of the battery. 26 This can lead to a shortened battery life.…”

Section: The Rule-based Control Strategy Designmentioning

confidence: 99%

“…The common optimization method for CS mode is to reduce the value of SoC l as much as possible in order to enlarge the CD mode. However, the threshold value of SoC not only impacts the control strategy but also plays a significant role of protecting the battery in the monitoring module 25 . Lower SoC l means a deeper depth of discharge (DOD) and may cause an increase in the internal resistance of the battery 26 .…”

Section: The Hybrid Ems Designmentioning

confidence: 99%

Design of a hybrid energy management system using designed rule‐based control strategy and genetic algorithm for the series‐parallel plug‐in hybrid electric vehicle

2020

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Summary Electric vehicle (EV) is considered as a critical requirement to the future development of transportation. However, the battery performance in terms of power density and energy density limits the use of EVs. An energy management system (EMS) of plug‐in hybrid electric vehicle (PHEV) is very critical to achieve successful transition from the conventional vehicle to the pure electric vehicle (PEV). This paper proposes a hybrid EMS for the series‐parallel PHEV utilising a rule‐based control strategy and genetic algorithm (GA)‐based optimisation technique to overcome the battery limitations. A mathematical model was developed and verified by conducting simulation studies using the vehicle model from ADVISOR database and the GA Optimization Toolbox (GAOT) in the Matlab Simulink environment. The simulation results show that the GA optimization successfully achieved the sub‐targets set in the fitness function. To show the effectiveness of the proposed technique, the results were compared with the simulation results of a single function of the designed rule‐based control strategy—the proposed EMS achieved a significant improvement in the hydrocarbon (HC) emission and NOx emission.

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“…At present, SOC estimation has attracted widespread attention and many effective SOC estimation methods have been developed by researchers. Generally, the SOC estimation techniques can be classified into three major categories [5]. The first is to establish the relationship between voltage, current and SOC by directly measuring the voltage and current of the battery [6,7].…”

Section: Introductionmentioning

confidence: 99%

State of Charge Estimation of Lithium Battery Based on Improved Correntropy Extended Kalman Filter

Duan

Wang

et al. 2020

Energies

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State of charge (SOC) estimation plays a crucial role in battery management systems. Among all the existing SOC estimation approaches, the model-driven extended Kalman filter (EKF) has been widely utilized to estimate SOC due to its simple implementation and nonlinear property. However, the traditional EKF derived from the mean square error (MSE) loss is sensitive to non-Gaussian noise which especially exists in practice, thus the SOC estimation based on the traditional EKF may result in undesirable performance. Hence, a novel robust EKF method with correntropy loss is employed to perform SOC estimation to improve the accuracy under non-Gaussian environments firstly. Secondly, a novel robust EKF, called C-WLS-EKF, is developed by combining the advantages of correntropy and weighted least squares (WLS) to improve the digital stability of the correntropy EKF (C-EKF). In addition, the convergence of the proposed algorithm is verified by the Cramér–Rao low bound. Finally, a C-WLS-EKF method based on an equivalent circuit model is designed to perform SOC estimation. The experiment results clarify that the SOC estimation error in terms of the MSE via the proposed C-WLS-EKF method can efficiently be reduced from 1.361% to 0.512% under non-Gaussian noise conditions.

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State of Charge Estimation of a Composite Lithium-Based Battery Model Based on an Improved Extended Kalman Filter Algorithm

Cited by 11 publications

References 42 publications

A Review on Battery Modelling Techniques

A Review on Battery Modelling Techniques

Design of a hybrid energy management system using designed rule‐based control strategy and genetic algorithm for the series‐parallel plug‐in hybrid electric vehicle

State of Charge Estimation of Lithium Battery Based on Improved Correntropy Extended Kalman Filter

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