Combined State of Charge and State of Energy Estimation of Lithium-Ion Battery Using Dual Forgetting Factor-Based Adaptive Extended Kalman Filter for Electric Vehicle Applications

Shrivastava, Prashant; Soon, Tey Kok; Idris, Mohd Yamani Idna; Mekhilef, Saad; Adnan, S.B.R.S.

doi:10.1109/tvt.2021.3051655

Cited by 172 publications

(54 citation statements)

References 34 publications

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“…The feedback and correction mechanism are employed in KF to eliminate the noise and approach the real value continuously. Various KF-based extensions have been advanced to account for more complicated and higher nonlinear SOC estimation, and these solution representatives include extended KF (EKF) ( Beelen et al., 2021 ), adaptive EKF (AEKF) ( Shrivastava et al., 2021 ), unscented KF (UKF) ( Marelli and Corno, 2021 ), adaptive UKF (AUKF) ( Li et al., 2020b ), adaptive sigma-point KF (ASPKF) ( Sun et al., 2021 ), cubature KF (CKF) ( Peng et al., 2019 ), adaptive CKF (ACKF) ( Li et al., 2021c ), square root CKF (SRCKF) ( Shu et al., 2021a ), etc. Usually, two essential steps are involved in KF-based estimation, in which the state prediction is firstly implemented to estimate the current output, and then the estimation is updated and corrected to achieve more authentic output.…”

Section: Overview Of Soh Prediction Methodsmentioning

confidence: 99%

State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives

et al. 2021

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Summary Accurate state of health (SOH) prediction is significant to guarantee operation safety and avoid latent failures of lithium-ion batteries. With the development of communication and artificial intelligence technologies, a body of researches have been performed toward precise and reliable SOH prediction method based on machine learning (ML) techniques. In this paper, the conception of SOH is defined, and the state-of-the-art prediction methods are classified based on their primary implementation procedure. As an essential step in ML-based SOH algorithms, the health feature extraction methods reported in the literature are comprehensively surveyed. Next, an exhausted comparison is conducted to elaborate the development of ML-based SOH prediction techniques. Not only their advantages and disadvantages of the application in SOH prediction are reviewed but also their accuracy and execution process are fully discussed. Finally, pivotal challenges and corresponding research directions are provided for more reliable and high-fidelity SOH prediction.

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Section: Overview Of Soh Prediction Methodsmentioning

confidence: 99%

State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives

et al. 2021

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“…Aiming at the joint model parameter and SOC estimation, the iterative calculation schedule is constructed. Comparing the coefficients in two sub formulas illustrated in Equation ( 8), the mathematical calculation relationship for the polarization effect is obtained, as shown in Equation (9).…”

Section: Online Model Parameter Identificationmentioning

confidence: 99%

“…Meanwhile, effective real-time state monitoring and control become crucial to ensure the safety and durability of the power supply systems. [4][5][6][7][8][9][10] In pack applications, balancing and equalization are conducted, and the balancing current is usually not measured. In this case, the current is no longer accurate when identifying the model parameters.…”

Section: Introductionmentioning

confidence: 99%

Improved covariance matching—electrical equivalent modeling for accurate internal state characterization of packing lithium‐ion batteries

Wang

Fan

et al. 2021

Intl J of Energy Research

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As for the cell-to-cell inconsistency of packing lithium-ion batteries, accurate equivalent modeling plays a significant role in the working characteristic monitoring and improving the safety protection quality under complex working conditions. In this work, a novel covariance matching-electrical equivalent circuit modeling method is proposed to realize the adaptive working state characterization by considering the internal reaction features, and an improved adaptive weighting factor correction-differential Kalman filtering model is constructed for the iterative calculation process. A new parameter named state of

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“…Almost all automobile manufacturers are keeping up with the trend of the times and hope to make breakthroughs in key technologies of electric vehicles. However, battery technology is still one of the main factors restricting the development of electric vehicles [2].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Method for SOC Estimation of Power Battery

Xue-chun

Zhang

et al. 2021

Journal of Control Science and Engineering

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The accuracy of battery state of charge (SOC) is crucial for solving the problems such as overcharge, overdischarge, and mileage anxiety of electric vehicle power battery. In this study, an SOC estimation method using a hybrid method (HM) based on threshold switching is proposed, which combines the advantages of the extended Kalman filter (EKF) and the ampere hour integration (AHI) to improve the estimation accuracy and convergence speed. First, the parameters of the second-order RC equivalent model are identified using the least square. Then, the equation of EKF for updating the state variable is reconstructed by using the identified parameters to solve the problem of multiple iterations caused by the uncertainty of the initial value. Finally, the difference between the estimated voltage and the sampling voltage is used as the threshold value for switching between the AHI and the EKF to estimate the SOC of the battery. Simulation results show that the estimated SOC error of the proposed algorithm is less than 1.6% and the convergence time is within 70 s. Experiments under different SOC initial values are carried out to prove the advantages of the proposed method.

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Combined State of Charge and State of Energy Estimation of Lithium-Ion Battery Using Dual Forgetting Factor-Based Adaptive Extended Kalman Filter for Electric Vehicle Applications

Cited by 172 publications

References 34 publications

State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives

State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives

Improved covariance matching—electrical equivalent modeling for accurate internal state characterization of packing lithium‐ion batteries

A Hybrid Method for SOC Estimation of Power Battery

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