State of charge estimation in electric vehicles at various ambient temperatures

Guo, Feng; Hu, Guangdi; Zhou, Pengkai; Hu, Jianyao; Sai, Yinghui

doi:10.1002/er.5450

Cited by 27 publications

(20 citation statements)

References 37 publications

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“…by virtue of ESO, F 1 (Á, t) can be approximated in the form of F 0 (Á, t). By fairly accurate estimation of the total disturbances F(Á, t), the system uncertainties in the loop can be actively eliminated through the control rule Equation (16). D can be obtained according to the following calculation formula as:…”

Section: The Eso-assisted Coulomb Counting Methodsmentioning

confidence: 99%

Extended state observer assisted Coulomb counting method for battery state of charge estimation

et al. 2020

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Summary The state of charge (SOC) estimation method for power batteries in engine waste heat recovery system is studied in this article. The nonmodel‐based Coulomb counting (CC) method is among the most popular techniques. However, challenges imposed by inaccurate estimation of initial SOC, current measurement error as well as uncertain system dynamics caused by temperature, and thermal effects will produce an accumulated error. To overcome this error and improve the CC method's disturbance rejection capability, a linear second‐order extended state observer (ESO) is designed to address this problem. A comparative simulation study is carried out to study the estimation performance of the proposed ESO‐assisted CC method and the conventional CC method. Study results of the proposed approach exhibit better estimation performance and adaptability for inaccurate estimation of initial SOC, current measurement error, and uncertain system dynamics. There is only one parameter ωo to tune. The adaptability of estimation performance can be maintained without parameters resetting, which proves the effectiveness and disturbance rejection capability of the proposed ESO‐assisted CC method for SOC estimation.

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Section: The Eso-assisted Coulomb Counting Methodsmentioning

confidence: 99%

Extended state observer assisted Coulomb counting method for battery state of charge estimation

et al. 2020

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“…If there exists a border of the distribution change of the EIS within the detection window, the value ML 2 will be greater than ML 1 ; otherwise, the opposite is true. Therefore, the border of the distribution change of the EIS can be detected according to Equation (38). The parameter Th in Equation 38is the threshold value that can be set through trial and error.…”

Section: Time Update:xmentioning

confidence: 99%

“…One is used for the parameter identification, and the other for the SOC estimation. Typical dual-filters, such as dual EKFs (DEKF), 37,38 dual UKF (DUKF) 39,40 and dual HIF (DHIF), 41 were proposed for the SOC estimation. In comparison with filters based on offline-identified parameters, the dual filters can achieve better SOC estimation accuracy.…”

Section: Introductionmentioning

confidence: 99%

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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“…25 For EECMs, the parameters needed to be identified consist of two parts: (a) the relationship between OCV and SoC and (b) the Ohmic resistance and the resistancecapacitance (RC) parallel network. The commonly used PIM methods to identify the Ohmic resistance and RC parallel network include least squares (LS) method, 19,24 recursive LS method with a forgetting factor (FFRLS), 26,27 KF family algorithms 28,29 and H-∞ algorithm. 30 For the relationship between OCV and SoC, the most commonly used method is to conduct a specific OCV test and analyze the experimental data to obtain an offline OCV-SoC curve.…”

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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State of charge estimation in electric vehicles at various ambient temperatures

Cited by 27 publications

References 37 publications

Extended state observer assisted Coulomb counting method for battery state of charge estimation

Extended state observer assisted Coulomb counting method for battery state of charge estimation

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

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

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