Alleviating Dynamic Model Uncertainty Effects for Improved Battery SOC Estimation of EVs in Highly Dynamic Environments

Wadi, Ali; Abdel–Hafez, Mamoun F.; Hussein, Ala A.; Alkhawaja, Fares

doi:10.1109/tvt.2021.3085006

Cited by 15 publications

(10 citation statements)

References 32 publications

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“…Therefore, the forgetting factor is considered to be added in the identification of the least square method to improve the online estimation ability of the RLS algorithm. The mathematical description expression of the least square method is shown in Equation (3).…”

Section: Improved Optimal Forgetting Factor Least Square Methodsmentioning

confidence: 99%

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A novel adaptive H‐infinity filtering method for the accurate SOC estimation of lithium‐ion batteries based on optimal forgetting factor selection

Liu

Wang

Xie

et al. 2022

Circuit Theory & Apps

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Accurate estimation of the state of charge (SOC) of lithium‐ion batteries is quite crucial to battery safety monitoring and efficient use of energy; to improve the accuracy of lithium‐ion battery SOC estimation under complicated working conditions, the research object of this study is the ternary lithium‐ion battery; the forgetting factor recursive least square (FFRLS) method optimized by particle swarm optimization (PSO) and adaptive H‐infinity filter (HIF) algorithm are adopted to estimate battery SOC. The PSO algorithm is improved with dynamic inertia weight to optimize the forgetting factor to solve the contradiction between FFRLS convergence speed and anti‐noise ability. The noise covariance matrixes of the HIF are improved to realize adaptive correction function and improve the accuracy of SOC estimation. To verify the rationality of the joint algorithm, a second‐order Thevenin model is established to estimate the SOC under three complex operating conditions. The experimental results show that the absolute value of the maximum estimation error of the improved algorithm under the three working conditions is 0.0192, 0.0131, and 0.0111, respectively, which proves that the improved algorithm has high accuracy and offers a theoretical basis for the safe and efficient operation of the battery management system.

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Section: Improved Optimal Forgetting Factor Least Square Methodsmentioning

confidence: 99%

“…With the rapid development of the emerging intelligent industry, the pollution problem facing the world has become more and more severe 1–4 at present. The energy crisis caused by excessive energy consumption has attracted widespread attention from all countries in the world 5–7 .…”

Section: Introductionmentioning

confidence: 99%

A novel adaptive H‐infinity filtering method for the accurate SOC estimation of lithium‐ion batteries based on optimal forgetting factor selection

Liu

Wang

Xie

et al. 2022

Circuit Theory & Apps

View full text Add to dashboard Cite

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“…Remark 1 (EquivalencyofExpectation [34], [35], [44]): From ( 13), (14), and (15), it becomes apparent that…”

Section: Dynamics Reformulation and Iekfmentioning

confidence: 99%

“…These proposed methods can be classified into two main categories, namely, observer-based methods and data-based methods. In the first group, the frequently utilized observer is the extended Kalman filter (EKF) and its variants, [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], while artificial neural network (ANN) based techniques dominate the second group [16], [17], [18], [19], [20], [21], [22], [23], [24], [25]. Both EKFs and ANNs have their own advantages and disadvantages.…”

Section: Introductionmentioning

confidence: 99%

An Invariant Method for Electric Vehicle Battery State-of-Charge Estimation Under Dynamic Drive Cycles

et al. 2023

Self Cite

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This paper proposes a novel invariant extended Kalman filter (IEKF), a modified version of the extended Kalman filter (EKF), for state-of-charge (SOC) estimation of lithium-ion (Li-ion) battery cells. Unlike conventional EKF methods where the correction term used to update the state is linearly proportional to the output error, this paper employs the IEKF where the correction term is independent of the output error, resulting in a significant reduction in the estimation error and improving the estimation accuracy. In contrast to classic method like the EKF and more contemporary ones like the square root variant of the Cubature Kalman Filter (SCKF), the IEKF can successfully mimic the nonlinear dynamics and mitigate measurement noise stochasticity. Moreover, even if the measurement model fails to fully capture the cell's dynamics, the IEKF will still sustain a reasonable performance. Hence, IEKF outperforms the conventional EKF, and even the SCKF, which can diverge if a mismatch between the SOC measurement model and the true SOC measurement occurs. The derivation of the proposed method followed by experimental verification using commercial Li-ion battery cells are presented. INDEX TERMSExtended Kalman filter, invariant extended Kalman filter, EKF, IEKF. ALI WADI (Member, IEEE) received the B.Sc. (cum laude) and M.Sc. degrees in mechanical engineering from the

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“…Compared with the EKF, the SVSF can produce more accurate SOC estimation results. In [25], the extended Kalman smoothing variable structure filter was proposed, which is a new algorithm combining the EKF and SVSF technologies. The experimental results showed that it has strong robustness with respect to inaccurate models and can improve the accuracy of SOC estimation.…”

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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Alleviating Dynamic Model Uncertainty Effects for Improved Battery SOC Estimation of EVs in Highly Dynamic Environments

Cited by 15 publications

References 32 publications

A novel adaptive H‐infinity filtering method for the accurate SOC estimation of lithium‐ion batteries based on optimal forgetting factor selection

A novel adaptive H‐infinity filtering method for the accurate SOC estimation of lithium‐ion batteries based on optimal forgetting factor selection

An Invariant Method for Electric Vehicle Battery State-of-Charge Estimation Under Dynamic Drive Cycles

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

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