A comparative study of lumped equivalent circuit models of a lithium battery for state of charge prediction

Ren, Hongbin; Zhao, Yuzhuang; Chen, Sizhong; Yang, Lin

doi:10.1002/er.4759

Cited by 20 publications

(22 citation statements)

References 22 publications

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“…The ICM H k is updated online based on Equation 23. Then, the noise covariance R k and Q k are updated based on Equations (24) and (25) in the AUKF algorithm. When the AUKF algorithm is applied for SOC estimation…”

Section: Time Update:xmentioning

confidence: 99%

“…where L k follows the adaptive rule described by Equation (39). With the obtained ICM H k , the covariance of the measurement R k and process noise Q k can be updated according to Equations (24) and (25).…”

Section: Noise Estimator Based On the Selected Eismentioning

confidence: 99%

“…Thevenin model is one of the typical ECMs used for the SOC estimation. 24 It consists of an OCV, an internal resistor and a RC network. Different ways have been proposed to describe the OCV-SOC relationship, such as polynomial function, logarithmic function and exponential function.…”

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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Section: Time Update:xmentioning

confidence: 99%

Section: Noise Estimator Based On the Selected Eismentioning

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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“…And Figure shows the relationship between OCV and SOC, which is obtained by the static experiments . The parameters R 0 , R 1 , C 1 , R 2 and C 2 in Equations () and () are identified by hybrid pulse power characteristic (HPPC) test . The HPPC test is to discharge the battery for 10s with 1C current, then stand the battery for 30s, and finally charge the battery for 10s with 1C current.…”

Section: State Of Charge Estimation Based On Bpnnmentioning

confidence: 99%

Equalization of series connected lithium‐ion batteries based on back propagation neural network and fuzzy logic control

Wang

Qin²,

Zhao

et al. 2020

Int J Energy Res

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Summary In this article, a nondissipative equalization scheme is proposed to reduce the inconsistency of series connected lithium‐ion batteries. An improved Buck‐Boost equalization circuit is designed, in which the series connected batteries can form a circular energy loop, equalization speed is improved, and modularization is facilitated. This article use voltage and state of charge (SOC) together as equalization variables according to the characteristics of open‐circuit voltage (OCV)‐SOC curve of lithium‐ion battery. The second‐order RC equivalent circuit model and back propagation neural network are used to estimate the SOC of lithium‐ion battery. Fuzzy logic control (FLC) is used to adjust the equalization current dynamically to reduce equalization time and improve efficiency. Simulation results show that the traditional Buck‐Boost equalization circuit and the improved Buck‐Boost equalization circuit are compared, and the equalization time of the latter is reduced by 34%. Compared with mean‐difference algorithm, the equalization time of FLC is decreased by 49% and the energy efficiency is improved by 4.88% under static, charging and discharging conditions. In addition, the proposed equalization scheme reduces the maximum SOC deviation to 0.39%, effectively reducing the inconsistency of batteries.

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“…However, the regulation of the state‐of‐charge (SOC) of the storage devices was not considered, which is necessary to allow an increased availability of the HESS. Different methods to estimate the SOC of an ESS were proposed in References and . Nevertheless, the performance of the ESS was not evaluated, nor was the ESS integrated under a more complex hybrid energy system.…”

Section: Introductionmentioning

confidence: 99%

Predictive energy management for a wind turbine with hybrid energy storage system

et al. 2019

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Summary Hybrid energy storage systems (HESSs) help mitigating the fluctuations and variable availability of certain renewable sources, such as wind power, as they can provide support in different time scales. Therefore, regulating their state‐of‐charge (SOC) becomes crucial to ensure that the hybrid system complies with generation commitments agreed in time‐ahead markets despite subsequent unexpected wind speed variations. So far, research has been mainly targeted at avoiding extreme SOC situations in the storage devices, whereas the regulation of this parameter to specific values has often been disregarded. A novel approach is proposed in this work, where model predictive control (MPC) is used to regulate the SOC of a HESS under variable wind and grid demand scenarios. The MPC‐based supervisory controller developed for the hybrid system has been implemented and simulated under different situations. This controller monitors the future variation of the SOC with the aim of having the HESS available to develop its assigned functions successfully. The results show that a proper regulation of the SOC in the HESS increases the capacity to manage the active power supplied to the grid by the hybrid system based on wind power, as well as the level of compliance with generation commitments established time ahead.

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A comparative study of lumped equivalent circuit models of a lithium battery for state of charge prediction

Cited by 20 publications

References 22 publications

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

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

Equalization of series connected lithium‐ion batteries based on back propagation neural network and fuzzy logic control

Predictive energy management for a wind turbine with hybrid energy storage system

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