State of charge estimation of lithium-ion battery under time-varying noise based on Variational Bayesian Estimation Methods

Yun, Zhonghua; Qin, Wenhu; Shi, Weipeng

doi:10.1016/j.est.2022.104916

Cited by 13 publications

(3 citation statements)

References 29 publications

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“…The process included tests in conduction cycles within the Matlab software. Yun et al [65] used something similar: a variable bayesian unscented Kalman filter coupled with a variable bayesian square-root cubature Kalman filter to estimate battery SoC. Liu et al [66] proposed an adaptive square root unscented Kalman filter that overcame the EKF and UKF and proved to be more accurate and stable, and they presented a better self-adaptive response to the system.…”

Section: Methods Based On Filtering Algorithmsmentioning

confidence: 99%

An Overview of Methods and Technologies for Estimating Battery State of Charge in Electric Vehicles

et al. 2023

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Recently, electric vehicles have gained enormous popularity due to their performance and efficiency. The investment in developing this new technology is justified by the increased awareness of the environmental impacts caused by combustion vehicles, such as greenhouse gas emissions, which have contributed to global warming and the depletion of oil reserves that are not renewable energy sources. Lithium-ion batteries are the most promising for electric vehicle (EV) applications. They have been widely used for their advantages, such as high energy density, many cycles, and low self-discharge. This work extensively investigates the main methods of estimating the state of charge (SoC) obtained through a literature review. A total of 109 relevant articles were found using the prism method. Some basic concepts of the state of health (SoH); a battery management system (BMS); and some models that can perform SoC estimation are presented. Challenges encountered in this task are discussed, such as the nonlinear characteristics of lithium-ion batteries that must be considered in the algorithms applied to the BMS. Thus, the set of concepts examined in this review supports the need to evolve the devices and develop new methods for estimating the SoC, which is increasingly more accurate and faster. This review shows that these tools tend to be continuously more dependent on artificial intelligence methods, especially hybrid algorithms, which require less training time and low computational cost, delivering real-time information to embedded systems.

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Section: Methods Based On Filtering Algorithmsmentioning

confidence: 99%

An Overview of Methods and Technologies for Estimating Battery State of Charge in Electric Vehicles

et al. 2023

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“…193 Bayesian algorithm is based on probability theory and statistics knowledge to complete classication, which requires the efficient sample data that independent of each other. 194 It has the characteristics of high classication accuracy and fast speed. Hierarchical Bayesian models (HBMs) provide rapid prediction of LFP performance under high-rate charging.…”

Section: Supervised Learningmentioning

confidence: 99%

Towards interactional management for power batteries of electric vehicles

Xie

et al. 2023

RSC Adv.

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“…When the system is more certain, the estimated state is more accurate, and vice versa. The variation idea is employed in the variational Bayesian estimation, by which the state and the system parameters are estimated simultaneously by fixing a portion of the variables within the state and the system parameters [25]. As a consequence, the variation idea is an iteration process with heavy computational pressure to arrive at an accurate estimation performance within the Bayesian framework.…”

Section: Introductionmentioning

confidence: 99%

An Extended Kalman Filter Design for State-of-Charge Estimation Based on Variational Approach

Zhou,

Zhang

2023

Batteries

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State of charge (SOC) is a very important variable for using batteries safely and reliably. To improve the accuracy of SOC estimation, a novel variational extended Kalman filter (EKF) technique based on least square error method is herein provided by establishing a second-order equivalent circuit model for the battery. It was found that when SOC decreased, resistance polarization occurred in the electrochemical model, and the parameters in the equivalent RC model varied. To decrease the modeling error in the equivalent circuit model, the system parameters were identified online depending on the SOC’s estimated result. Through the SOC-estimation process, the variation theorem was introduced, which enabled the system parameters to track the real situations based on the output measured. The experiment results reveal the comparison of the SOC-estimation results of the variational EKF algorithm, the traditional EKF algorithm, the recursive least square (RLS) EKF algorithm, and the forgotten factor recursive least square (FFRLS) EKF algorithm based on different indices, including the mean square error (MSE) and the mean absolute error (MAE). The variational EKF algorithm provided in this paper has higher estimation accuracy and robustness than the traditional EKF, which verifies the superiority and effectiveness of the proposed method.

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State of charge estimation of lithium-ion battery under time-varying noise based on Variational Bayesian Estimation Methods

Cited by 13 publications

References 29 publications

An Overview of Methods and Technologies for Estimating Battery State of Charge in Electric Vehicles

An Overview of Methods and Technologies for Estimating Battery State of Charge in Electric Vehicles

Towards interactional management for power batteries of electric vehicles

An Extended Kalman Filter Design for State-of-Charge Estimation Based on Variational Approach

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