An enhanced temperature‐dependent model and state‐of‐charge estimation for a Li‐Ion battery using extended Kalman filter

Pang, Hui; Guo, Long; Wu, Longxing; Jin, Xinfang

doi:10.1002/er.5435

Cited by 39 publications

(22 citation statements)

References 27 publications

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“…Reference 13 concluded that the best choice for LiNiMnCoO 2 (NMC) LiBs is the Thevenin model while Reference 14 concluded that the DP model performs the best in the Dynamic Stress Test (DST). In addition, different filter algorithms, such as Gaussian process-based filter methods including extended Kalman filter (EKF), 16 adaptive extended Kalman filter (AEKF), 17 unscented Kalman filter (UKF), 18 central difference Kalman filter (CDKF), 18 cubature Kalman filter (CKF) 19 and probability-based filter algorithms including particle filter (PF), 20 unscented particle filter (UPF), 21 and cubature particle filter (CPF), 22,23 have been widely used for online SoC estimation. And the performance of different filter algorithms is usually compared in terms of tracking accuracy, convergence behavior, and computation time.…”

Section: Introductionmentioning

confidence: 99%

A comparative study of the influence of different open circuit voltage tests on model‐based state of charge estimation for lithium‐ion batteries

Ren

et al. 2021

Int J Energy Res

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A predetermined relationship between open circuit voltage (OCV) and state of charge (SoC) is the premise of model-based SoC estimation algorithms. Commonly used OCV tests to obtain the offline OCV-SoC curves include the low current OCV test and the incremental OCV test. In this paper, in addition to comparing the low current OCV test with the incremental OCV test which applies 0.5 C current-rate, the incremental OCV tests which apply 0.2, 0.3, and

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Section: Introductionmentioning

confidence: 99%

A comparative study of the influence of different open circuit voltage tests on model‐based state of charge estimation for lithium‐ion batteries

Ren

et al. 2021

Int J Energy Res

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“…The last one is model-based methods. 16,17 The model-based method is the fusion of battery model and state estimation algorithm. This is a closed-loop SOC estimation method, and has proved to be accurate and implementable online due to the low complexity.…”

Section: Introductionmentioning

confidence: 99%

“…Once the parameters of LIBs are identified online, the SOC of LIBs can be estimated based on various kinds of filter algorithms. Among these filter algorithms, extended Kalman filter (EKF), 17,[29][30][31] unscented Kalman filter (UKF), 32,33 cubature Kalman filter (CKF) 34 and particle filter (PF) 11 are the popular state filter algorithms used for SOC estimation. The filter performance depends on careful selection of the tuning parameters, such as the ICM.…”

Section: Introductionmentioning

confidence: 99%

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State of charge estimation for lithium‐ion battery based on an intelligent adaptive unscented Kalman filter

Sun

Zhang

et al. 2020

Int J Energy Res

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Adaptive unscented Kalman filter (AUKF) has been widely used for state of charge (SOC) estimation of lithium-ion battery. The noise covariance of the conventional AUKF method is updated based on the innovation covariance matrix (ICM), which is estimated using the error innovation sequence (EIS). However, the distribution of EIS changes due to the time-varying noise, load current dynamics and modelling error, which will lead to inaccurate ICM estimation. Therefore, an intelligent adaptive unscented Kalman filter (IAUKF) method is proposed to detect the distribution change of EIS. Then, the ICM is estimated based on the EIS after the distribution change. Results show that the IAUKF method can improve SOC estimation accuracy significantly. Compared with that of the AUKF method, the root mean squared error and the mean absolute error of SOC based on the IAUKF method decrease by 43.70% and 72.37% under random walk discharge condition, respectively. In addition, the computation time of the IAUKF method slightly increases by 6.27% compared with that of AUKF method. Finally, the effect of initial parameters on the SOC estimation accuracy was analysed. The results indicate that proper algorithm tuning, such as initial window length of EIS for ICM update and the threshold value, can further improve the SOC accuracy based on the proposed IAUKF method. The proposed IAUKF method also shows high robustness against initial measurement noise covariance.

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“…In practical applications, the battery temperature dynamically changes within a large range (usually between 10°C and 50°C), thus significantly affecting estimation accuracy of SOC 29 . Thus, it is imperative to establish a temperature‐dependent electrical model to cover the whole operation temperature range, 30 and then the SOC can be estimated robustly with the help of advanced filters 31 …”

Section: Introductionmentioning

confidence: 99%

Stateof charge estimation framework for lithium‐ion batteries based on square root cubature Kalman filter under wide operation temperature range

Shen

Xiong

Shu

et al. 2020

Intl J of Energy Research

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Summary Due to the significant influence of temperature on battery charging and discharging performance, exact evaluation of state of charge (SOC) under complex temperature environment becomes increasingly important. This paper develops an advanced framework to estimate the SOC for lithium‐ion batteries with consideration of temperature variation. First, an accurate electrical model with wide temperature compensation is established, and a series of experiments are carried out under wide range time‐varying temperature from −20°C to 60°C. Then, the genetic algorithm is leveraged to identify the temperature‐dependent model parameters. On this basis, the battery SOC is accurately estimated based on the square root cubature Kalman filter algorithm. Finally, the availability of the proposed method at different temperatures is validated through a complicated mixed working cycle test, and the experimental results manifest that the devised framework can accurately evaluate SOC under wide time‐varying temperature range with the maximum error of less than 2%.

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An enhanced temperature‐dependent model and state‐of‐charge estimation for a Li‐Ion battery using extended Kalman filter

Cited by 39 publications

References 27 publications

A comparative study of the influence of different open circuit voltage tests on model‐based state of charge estimation for lithium‐ion batteries

A comparative study of the influence of different open circuit voltage tests on model‐based state of charge estimation for lithium‐ion batteries

State of charge estimation for lithium‐ion battery based on an intelligent adaptive unscented Kalman filter

Stateof charge estimation framework for lithium‐ion batteries based on square root cubature Kalman filter under wide operation temperature range

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