Comparison of robustness of different state of charge estimation algorithms

Ren, Lichao; Zhu, Guorong; Wang, Jing V.; Luo, Bingyang; Kang, Jianqiang

doi:10.1016/j.jpowsour.2020.228767

Cited by 21 publications

(9 citation statements)

References 22 publications

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“…It is composed of some traditional electronic components, such as resistance, capacitance, and voltage source. Plett first proposed six ECMs, and then the lumped parameter ECMs with resistors and capacitors as the core were successively built to increase prediction accuracy, including the first-order RC model ,,, and second-order RC model. ,− To avoid system failure caused by a single circuit structure, various circuit structure fusion methods based on the H Infinity Filter algorithm have been established by Lin et al to improve the accuracy and reliability of the estimation results, whereas this method also enhances the computational complexity . Hu et al systematically compared 12 ECM structures starting from complexity, accuracy, and robustness and found that the circuit structure can be generalized to adjacent cells.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Various Offline and Online ECM Parameter Identification Methods of Lithium-Ion Batteries in Underwater Vehicles

Chen

Mao

et al. 2022

ACS Omega

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For underwater vehicles, the state of charge (SOC) of battery is often used to guide the optimal allocation of energy. An accurate SOC estimation can improve work efficiency and reliability of underwater vehicles. Model-based SOC estimation methods are still mainstream routes used in practical applications. Hence, accurate battery models are highly desirable, which depends not only on the circuit structure but also on the circuit parameters. Four-parameter identification algorithms, offline mechanism-based and least squared (LS) methods, as well as online recursive least-squares with forget factor (FFRLS) and extended Kalman filter (EKF) methods were analyzed in terms of SOC estimation under three different conditions. The results revealed that in the case without any disturbance, the predicted SOCs based on four-parameter identification circuits fitted well with the reference. Moreover, it is remarkable that the LS offline methods work better than the FFRLS online routes. In addition, the robustness has also been accessed through the other two conditions, i.e., measurement data with disturbance and initial SOC value with deviation. The results showed that maximum errors of SOC estimation based on the EKF approach are significantly lower than those of the other methods, and the values are 0.51% and 0.20%, respectively. Thus, the circuit model based on the EKF parameter identification approach possessed a stronger anti-interference performance during the SOC estimation process. This research can provide corresponding theoretical support on ECM parameter identification for lithium-ion batteries in underwater vehicles.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Various Offline and Online ECM Parameter Identification Methods of Lithium-Ion Batteries in Underwater Vehicles

Chen

Mao

et al. 2022

ACS Omega

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“…At the same time, Zhao et al [28] proposed using the extended model to address the reduction in the accuracy of SOC estimation when errors occur in sensors and pointed out that when errors occur in voltage and current sensors, the method used may not accurately estimate the SOC and bias at the same time. In [29,30], the state estimation problem was studied when noise and bias occur in voltage sensors or current sensors. The DEKF was used to estimate the battery SOC, and the results showed that the proposed estimation method has good convergence and robustness.…”

Section: Introductionmentioning

confidence: 99%

A Robust Algorithm for State-of-Charge Estimation under Model Uncertainty and Voltage Sensor Bias

Guo

2022

Energies

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Accurate estimation of the state of charge (SOC) of zinc–nickel single-flow batteries (ZNBs) is an important problem in battery management systems (BMSs). A nonideal electromagnetic environment will usually cause the measured signal to contain nonnegligible noise and bias. At the same time, due to the influence of battery ageing, environmental temperature changes, and a complex reaction mechanism, it is difficult to establish a very accurate system model that can be applied to various complex working conditions. The unscented Kalman filter (UKF) is a widely used SOC estimation algorithm, but the UKF will reduce the estimation accuracy and divergence under the influence of inaccurate model and sensor errors. To improve the performance of the UKF, a robust desensitized unscented Kalman filter (RDUKF) is proposed to realize an accurate SOC estimation of batteries in the context of different disturbances. Then, the proposed method is applied to cases of error interference, such as Gaussian noise, voltage sensor drift, an unknown initial state, and inaccurate model parameters. The simulation and experimental results show that compared with the standard UKF algorithm, the proposed estimation algorithm can effectively suppress the influence of various errors and disturbances and achieve higher accuracy and robustness.

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“…29,30 Therefore, an estimator based on an efficient numerical calculation method is needed to achieve accurate SOC estimation. Simple SOC estimation methods such as the ampere-hour (Ah) integration 31 and the OCV 32 methods are the earliest to be applied to the BMS. Nonetheless, due to the drawbacks of open-loop estimation, these methods cannot adapt to changes in hysteresis voltage and varying ambient temperatures.…”

Section: Introductionmentioning

confidence: 99%

Battery hysteresis compensation modeling and state‐of‐charge estimation adaptive to time‐varying ambient temperature conditions

Shi

Wang

Fernandez

et al. 2022

Intl J of Energy Research

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Temperature and cell hysteretic effects are two major factors that influence the reliability and safety in long-term management of battery-integrated systems.In this paper, a hysteresis-compensated electrical characteristic model is established to track the terminal voltage of batteries with the uncertain hysteretic effect of the open-circuit voltage. Then, an autoregressive exogenous model with multi-feature coupling is employed for the identification of the parameters to make them adaptive to the uncertainties of the temperature and hysteretic effects. After that, a novel method for state-of-charge (SOC) estimation based on an adaptive moving window-square root unscented Kalman filter is constructed to avoid the filtering divergence problem caused by the negative error covariance matrix. Multiple constraints, such as Coulombic efficiency, varying ambient temperatures, and hysteresis voltage, are considered for the SOC estimation. Experimental results show that the root-mean-square error for SOC calculation can be limited to 0.0211 when the temperature varied up to 40 C and the root-mean-square error of the voltage measurement noise up to 61.9 mV. The proposed method provides an effective way for batteryintegrated management of electric vehicles. K E Y W O R D Sadaptive moving window-square root unscented Kalman filter, adaptive noise matching, hysteresis-compensated modeling, lithium-ion battery, state-of-charge

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Comparison of robustness of different state of charge estimation algorithms

Cited by 21 publications

References 22 publications

Evaluation of Various Offline and Online ECM Parameter Identification Methods of Lithium-Ion Batteries in Underwater Vehicles

Evaluation of Various Offline and Online ECM Parameter Identification Methods of Lithium-Ion Batteries in Underwater Vehicles

A Robust Algorithm for State-of-Charge Estimation under Model Uncertainty and Voltage Sensor Bias

Battery hysteresis compensation modeling and state‐of‐charge estimation adaptive to time‐varying ambient temperature conditions

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