A Lagrange multiplier and sigma point Kalman filter based fused methodology for online state of charge estimation of lithium-ion batteries

Khan, Hafiz Farhaj; Hanif, Aamir; Ali, Muhammad Umair; Zafar, Amad

doi:10.1016/j.est.2021.102843

Cited by 36 publications

(12 citation statements)

References 37 publications

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“…However, its performance is extremely dependent on the accuracy of the equivalent model used, which directly reflects its complexity. In the paper published by [ 21 ], Lagrange multiplier technique and sigma point Kalman filter (SPKF) is proposed for the lithium-ion battery model identification and state of charge (SoC) estimation, respectively. This model is based on the construction of the state-space model of an RC equivalent circuit.…”

Section: Related Workmentioning

confidence: 99%

State of Charge Estimation of Battery Based on Neural Networks and Adaptive Strategies with Correntropy

Vieira

Villanueva

Flores

et al. 2022

Sensors

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Nowadays, electric vehicles have gained great popularity due to their performance and efficiency. Investment in the development of this new technology is justified by increased consciousness of the environmental impacts caused by combustion vehicles such as greenhouse gas emissions, which have contributed to global warming as well as the depletion of non-oil renewable energy source. The lithium-ion battery is an appropriate choice for electric vehicles (EVs) due to its promising features of high voltage, high energy density, low self-discharge, and long life cycles. In this context, State of Charge (SoC) is one of the vital parameters of the battery management system (BMS). Nevertheless, because the discharge and charging of battery cells requires complicated chemical operations, it is therefore hard to determine the state of charge of the battery cell. This paper analyses the application of Artificial Neural Networks (ANNs) in the estimation of the SoC of lithium batteries using the NASA’s research center dataset. Normally, the learning of these networks is performed by some method based on a gradient, having the mean squared error as a cost function. This paper evaluates the substitution of this traditional function by a measure of similarity of the Information Theory, called the Maximum Correntropy Criterion (MCC). This measure of similarity allows statistical moments of a higher order to be considered during the training process. For this reason, it becomes more appropriate for non-Gaussian error distributions and makes training less sensitive to the presence of outliers. However, this can only be achieved by properly adjusting the width of the Gaussian kernel of the correntropy. The proper tuning of this parameter is done using adaptive strategies and genetic algorithms. The proposed identification model was developed using information for training and validation, using a dataset made available in a online repository maintained by NASA’s research center. The obtained results demonstrate that the use of correntropy, as a cost function in the error backpropagation algorithm, makes the identification procedure using ANN networks more robust when compared to the traditional Mean Squared Error.

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Section: Related Workmentioning

confidence: 99%

State of Charge Estimation of Battery Based on Neural Networks and Adaptive Strategies with Correntropy

Vieira

Villanueva

Flores

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Results indicated that the accuracy of the novel algorithm is increased compared to that of SPKF. Khan et al [39] identified the lithium-ion battery model by the Lagrange multiplier technique and estimated the SOC with SPKF. The comparison results showed that their scheme performed better than recursive least squares RLS-SPKF and forgetting factor RLS-SPKF.…”

Section: Introductionmentioning

confidence: 99%

State of charge estimation for lithium‐ion batteries based on square root sigma point Kalman filter considering temperature variations

Mahboubi

Gavzan

Saidi

et al. 2022

IET Electrical Syst in Trans

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The battery management system (BMS) in electric vehicles monitors the state of charge (SOC) and state of health (SOH) of lithium‐ion battery by controlling transient parameters such as voltage, current, and temperature prevents the battery from operating outside the optimal operating range. The main feature of the battery management system is the correct estimation of the SOC in the broad range of vehicle navigation. In this paper, to estimate real‐time of SOC in lithium‐ion batteries and overcome faults of Extended Kalman Filter (EKF), the Square‐Root Sigma Point Kalman Filter is applied on the basis of numerical approximations rather than analytical methods of EKF. For this purpose, the Hybrid Pulse Power Characterisation tests are combined with the non‐linear least square method that acquired the second‐order equivalent circuit model parameters. Then, the newly developed method is tested with an 18,650 cylindrical lithium‐ion battery with a nominal capacity of 2600 mAh in four different ambient temperatures. Finally, the accuracy and effectiveness of the two proposed methods are verified by comparing with results of pulse discharge and dynamic driving cycle tests. The comparison results indicate the error of the proposed algorithm is about 0.02 under the most test conditions.

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“…The results showed that the proposed approach has good robustness and the maximum RMSE is less than 1%. Khan et al 37 proposed a fused online method to identify parameters and estimate SOC. The effectiveness of the proposed approach was validated using experimental data.…”

Section: Introductionmentioning

confidence: 99%

Online identification of battery model parameters and joint state of charge and state of health estimation using dual particle filter algorithms

Chen

Zhang

et al. 2022

Intl J of Energy Research

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Aiming at the problems of time-varying battery parameters and inaccurate estimations of state of charge (SOC) and state of health (SOH), a joint estimation algorithm of SOC and SOH is proposed. A particle filter algorithm is used to identify the parameters online on the basis of a second-order equivalent circuit model. The algorithm feasibility is verified through the terminal voltage estimation accuracy. Considering that an accurate SOH is one of the foundations to achieve an accurate SOC estimation, a dual particle filter is used to jointly estimate SOC and SOH. Under different test conditions, the effect of different initial values (initial SOC and capacity), temperatures, operation conditions, particle number, and model parameters on the estimation accuracy and robustness is compared and analyzed. The effectiveness of the proposed algorithm is validated by experimental data under different operation conditions. Experimental results show that the online particle filter algorithm can well predict the dynamic battery model parameters. The proposed algorithm has high robustness and a good tracking effect when estimating SOC with a mean absolute error of less than 1.3%, a root mean square error of less than 1%, and a tracking terminal voltage. K E Y W O R D Sonline joint estimation, online parameter identification, particle filter algorithm, state of charge, state of health

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A Lagrange multiplier and sigma point Kalman filter based fused methodology for online state of charge estimation of lithium-ion batteries

Cited by 36 publications

References 37 publications

State of Charge Estimation of Battery Based on Neural Networks and Adaptive Strategies with Correntropy

State of Charge Estimation of Battery Based on Neural Networks and Adaptive Strategies with Correntropy

State of charge estimation for lithium‐ion batteries based on square root sigma point Kalman filter considering temperature variations

Online identification of battery model parameters and joint state of charge and state of health estimation using dual particle filter algorithms

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