A novel fuzzy adaptive cubature Kalman filtering method for the state of charge and state of energy co-estimation of lithium-ion batteries

Yang, Xiao Guang; Wang, Shunli; Xu, Wenhua; Qiao, Jialu; Yu, Chunmei; Takyi‐Aninakwa, Paul; Jin, Siyu

doi:10.1016/j.electacta.2022.140241

Cited by 40 publications

(4 citation statements)

References 50 publications

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“…The SOH value of lithiumion batteries is predicted using the ANN. Empirical mode decomposition is also used in the prediction process [79]. The improved ELM method is used to predict SOH, but the DNN method can also be used.…”

Section: Application Analysis From Other Studiesmentioning

confidence: 99%

A Critical Review of Improved Deep Convolutional Neural Network for Multi-Timescale State Prediction of Lithium-Ion Batteries

et al. 2022

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Lithium-ion batteries are widely used as effective energy storage and have become the main component of power supply systems. Accurate battery state prediction is key to ensuring reliability and has significant guidance for optimizing the performance of battery power systems and replacement. Due to the complex and dynamic operations of lithium-ion batteries, the state parameters change with either the working condition or the aging process. The accuracy of online state prediction is difficult to improve, which is an urgent issue that needs to be solved to ensure a reliable and safe power supply. Currently, with the emergence of artificial intelligence (AI), battery state prediction methods based on data-driven methods have high precision and robustness to improve state prediction accuracy. The demanding characteristics of test time are reduced, and this has become the research focus in the related fields. Therefore, the convolutional neural network (CNN) was improved in the data modeling process to establish a deep convolutional neural network ensemble transfer learning (DCNN-ETL) method, which plays a significant role in battery state prediction. This paper reviews and compares several mathematical DCNN models. The key features are identified on the basis of the modeling capability for the state prediction. Then, the prediction methods are classified on the basis of the identified features. In the process of deep learning (DL) calculation, specific criteria for evaluating different modeling accuracy levels are defined. The identified features of the state prediction model are taken advantage of to give relevant conclusions and suggestions. The DCNN-ETL method is selected to realize the reliable state prediction of lithium-ion batteries.

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Section: Application Analysis From Other Studiesmentioning

confidence: 99%

A Critical Review of Improved Deep Convolutional Neural Network for Multi-Timescale State Prediction of Lithium-Ion Batteries

et al. 2022

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“…Due to the high nonlinearities of lithium‐ion batteries, other state parameters such as state of health (SOH), state of energy (SOE), are state of power (SOP) are studied. Yang et al 41 proposed a novel fuzzy adaptive cubature KF method for the co‐estimation of the SOC and SOE. However, the inaccuracies and instabilities associated with the KF and EKF methods are not eliminated.…”

Section: Introductionmentioning

confidence: 99%

A strong tracking adaptive fading‐extended Kalman filter for the state of charge estimation of lithium‐ion batteries

Takyi‐Aninakwa

Wang

Zhang

et al. 2022

Intl J of Energy Research

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“…[27][28][29][30] Accurate estimation of SOE is beneficial to enhance the management and control of power battery systems and improve the utilization efficiency of power batteries. [31][32][33][34][35] The literature 36 proposed a Particle Filter-Extended Kalman Filter (PF-EKF) algorithm to estimate SOE to improve the accuracy and robustness of SOE estimation; the literature 37 combined the equivalent circuit model with the Unscented Particle Filter algorithm to deal with model nonlinearity. The literature 38 used two interrelated particle filters to simultaneously implement the equivalent model parameter identification and SOE estimation, which partially attenuated the impact of the time-varying equivalent parameters on the SOE estimation accuracy; the literature 39 proposed a dual filtering algorithm based on the extended Kalman filter and particle filter to establish an online energy state estimator based on the model.…”

mentioning

confidence: 99%

An Improved Compression Factor Particle Swarm Optimization-Unscented Particle Filter Algorithm for Accurate Lithium-Ion Battery State of Energy Estimation

Hao

Wang

Fan

et al. 2023

J. Electrochem. Soc.

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Accurate prediction of the remaining range remains a challenge for electric vehicles. The state of energy (SOE) is a state parameter representing the remaining mileage and remaining charge of a lithium-ion battery, which is related to the prediction of the remaining range of electric vehicles. To obtain the mathematical description and SOE parameters of lithium-ion batteries with high accuracy, a parameter identification method using an improved particle swarm optimization algorithm with compression factor is proposed. For the estimation of energy state, a particle filter (PF) is constructed in this paper, and the unscented particle filtering (UPF) algorithm with particle swarm optimization (PSO) is used to achieve the estimation of energy state, which can solve the problems of particle degradation and insufficient particle diversity of particle filtering. The experimental results show that the SOE estimation error is within 0.97% at 25 degrees for all three operating conditions and within 1.29% at 5 degrees for all three operating conditions. Therefore, the proposed algorithm has high accuracy and strong robustness at different temperatures and different working conditions, and the estimation results prove the validity of energy state estimation.

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A novel fuzzy adaptive cubature Kalman filtering method for the state of charge and state of energy co-estimation of lithium-ion batteries

Cited by 40 publications

References 50 publications

A Critical Review of Improved Deep Convolutional Neural Network for Multi-Timescale State Prediction of Lithium-Ion Batteries

A Critical Review of Improved Deep Convolutional Neural Network for Multi-Timescale State Prediction of Lithium-Ion Batteries

A strong tracking adaptive fading‐extended Kalman filter for the state of charge estimation of lithium‐ion batteries

An Improved Compression Factor Particle Swarm Optimization-Unscented Particle Filter Algorithm for Accurate Lithium-Ion Battery State of Energy Estimation

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