SOC estimation of lithium-ion battery based on new adaptive fading extended Kalman filter

Yang, Yaning; Cui, Naxin; Wang, Chunyu; Liu, Miao; Gao, Ruizhi

doi:10.1109/cac.2017.8243786

Cited by 13 publications

(6 citation statements)

References 8 publications

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“…Based on [37], we can notice that the UIO shows higher accuracy compared to AEKF and EKF methods. The maximum absolute SOC error given under the constant load current is 2.5% for the AEKF and 6% for the EKF.…”

Section: Observer Resultsmentioning

confidence: 96%

“…In reference [35], the authors provided an Adaptive Particle Filter (APF) to estimate the SOC and proved its superiority compared to the conventional PF [36], [34]. Other advanced techniques of adaptive observers are also provided in [37] using a new adaptive fading EKF and in [38] using an adaptive Sliding Mode Observer (SMO). For the adaptive SMO, it was difficult to adjust switching gains to control sliding regime and it was noticed that the convergence time highly depends on the initial SOC.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Unknown input observer design for lithium-ion batteries SOC estimation based on a differential-algebraic model

Snoussi

Elghali

Zerrougui

et al. 2020

Journal of Energy Storage

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An accurate battery management strategy is a crucial need in the developing of reliable and viable plug in and hybrid electric vehicles. This on-board algorithm has the advantages to protect the battery from critical operating conditions and improve its lifetime. However, the effectiveness of the battery management strategy mainly depends on the accuracy of its state of charge (SOC). In this context, this paper proposes a novel technique for the SOC estimation based on the unknown input observer and a new differential-algebraic model of a lithium iron phosphate battery. The proposed observer aims to overcome the unknown value of the initial SOC for on-board batteries using only current and terminal voltage measurements. A reduced-order based unknown input observer is developed to estimate the open circuit voltage and the SOC using the OCV-SOC characteristic offline-determined. The unbiasedness of the estimation error is guaranteed by the parameterization of a set of Sylvester constraints. The performance of the proposed observer is verified by simulations and experiments and the accuracy of the obtained results is analyzed and assessed.

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Section: Observer Resultsmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Unknown input observer design for lithium-ion batteries SOC estimation based on a differential-algebraic model

Snoussi

Elghali

Zerrougui

et al. 2020

Journal of Energy Storage

View full text Add to dashboard Cite

show abstract

“…At present, many improved methods have been generated based on this method [61][62][63][64][65][66]. For example, the Extended Kalman Filtering (EKF) method, which linearizes the nonlinear system; the Unscented Kalman Filtering (UKF) method is obtained by adding the transformation of U to KF which deals with the nonlinear problem with a probability distribution; the Central Difference Kalman Filtering (CDKF) method processes KF using the central difference method., etc.…”

Section: Kalman Filter Methodsmentioning

confidence: 99%

Review on the State of Charge Estimation Methods for Electric Vehicle Battery

Zhang

Fan

2020

WEVJ

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Battery technology has been one of the bottlenecks in electric cars. Whether it is in theory or in practice, the research on battery management is extremely important, especially for battery state-of-charge estimation. In fact, the battery has a strong time-varying and non-linear properties, which are extremely complex. Therefore, accurately estimating the state of charge is a challenging task. This paper reviews various representative patents and papers related to the state of charge estimation methods for an electric vehicle battery. According to their theoretical and experimental characteristics, the estimation methods were classified into three groups: the traditional methods based on the battery experiments, the modern methods based on control theory, and other methods based on the innovative ideas, especially focusing on the algorithms based on control theory. The results imply that the algorithms based on control theory, especially intelligent algorithms, are the focus of research in this field. The future development direction is to establish a rich database, improve hardware technology, come up with a much better battery model, and give full play to the advantages of each algorithm.

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“…In general, well-known adaptive techniques of control theory are combined with the battery model to achieve an online SoC estimate. The familiar Adaptive filter and observer algorithms are Kalman Filter (KF), [85][86][87] Extended Kalman Filter (EKF), [88][89][90][91] Unscented Kalman Filter (UKF), [92][93][94][95][96] Fading Kalman Filter (FKF), 97,98 Cubature Kalman filter, 72,[99][100][101] Particle filter 45 and H∞ observer method. 102,103 In all these methods, each algorithm has some advantages and disadvantages.…”

Section: Adaptive Filter and Observer-based Soc Estimationmentioning

confidence: 99%

Modeling, state of charge estimation, and charging of lithium‐ion battery in electric vehicle: A review

Adaikkappan

Nageswari

2021

Intl J of Energy Research

150

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Extension of driving range and battery run time optimization are necessary key points in the modeling of Electric Vehicle (EV). In this view, Battery Management System (BMS) plays a major role to ensure a safe and trustworthy battery operation, especially when using Lithium-ion (Li-ion) batteries in an electric vehicle. Key function of BMS is State of Charge (SoC) estimation. A well-parameterized battery model is required for accurate state estimation. Consequently, the major factors to be considered in battery modeling are the SoC estimation and charging methodology of an effective BMS development. By focusing on these features, in this paper, the well-known battery models such as the electrochemical model, equivalent circuit model, and data-driven model are comprehensively reviewed along with their strengths and weaknesses. Further, the SoC estimation of a battery is also discussed by using standard methodologies such as direct estimation methods and model-based estimation methods. The comparisons of the three most distinct battery models and the classification of SoC estimation techniques to develop a proper BMS for EV with the focus on accuracy, configuration effort, computational complexity, ease of implementation, and real-time applications are systematically reviewed. In addition to this, convenient battery charging approaches with the consideration of some constraints such as charging time, charging efficiency, state of charge, state of health, charging voltage threshold, capacity fade, power fade, aging effect, capacity utilization, impedance rise, and temperature rise of the battery in EV are presented. Finally, the perspectives of the existing work and the recommended future research work of BMS are summarized.

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SOC estimation of lithium-ion battery based on new adaptive fading extended Kalman filter

Cited by 13 publications

References 8 publications

Unknown input observer design for lithium-ion batteries SOC estimation based on a differential-algebraic model

Unknown input observer design for lithium-ion batteries SOC estimation based on a differential-algebraic model

Review on the State of Charge Estimation Methods for Electric Vehicle Battery

Modeling, state of charge estimation, and charging of lithium‐ion battery in electric vehicle: A review

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