SOC Estimation of Lithium Battery Based on Dual Adaptive Extended Kalman Filter

Zheng, Yongliang; Feng, He; Wang, Wenliang

doi:10.1088/1757-899x/677/3/032077

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…22 Aiming at the problem of battery parameter marginalization and ageing, the authors estimate state of charge accurately by volume Kalman filter combined with recursive least squares. 23 The author focuses on the fractal-order adaptive extended Kalman filter, which can quickly track the unknown variance. 24 An estimation method with an adaptive feedback compensator is proposed, which can improve state of charge estimation performance and fast convergence.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the author proposed a correntropy—weighted least squares—extended Kalman filter algorithm to estimate state of charge, and the error can be effectively reduced under the condition of non‐Gaussian noise 22 . Aiming at the problem of battery parameter marginalization and ageing, the authors estimate state of charge accurately by volume Kalman filter combined with recursive least squares 23 . The author focuses on the fractal‐order adaptive extended Kalman filter, which can quickly track the unknown variance 24 .…”

Section: Introductionmentioning

confidence: 99%

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Collaborative state estimation of lithium‐ion battery based on multi‐time scale low‐pass filter forgetting factor recursive least squares ‐ double extended Kalman filtering algorithm

Long

Wang

Cao

et al. 2022

Circuit Theory & Apps

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For the lithium battery management system and real-time safety monitoring, two issues are of great significance, namely, the ability to accurately update the model parameters in real time and to accurately estimate the state of charge and health. In this context, this thesis adopts the second-order RC equivalent circuit model and the forgetting factor recursive least squares -double extended Kalman filtering (FFRLS-DEKF) algorithm with multi-time scales and low-pass filter. Forgetting factor recursive least squares is applied to conduct online parameter identification, and the traditional double extended Kalman filtering algorithm is optimized to evaluate the state of charge and model parameters in the micro-scale and macro-scale. In this way, the error caused by two different characteristics is reduced, and a low-pass filter is added to optimize the fluctuation problem of the estimated value of the model parameters. According to the experiment results, the maximum error between the model simulation value and the actual value of the terminal voltage is 0.0459 V. If the initial value of the state of charge deviates from the actual value, the maximum errors under BBDST and HPPC conditions record 0.0235 and 0.0048, respectively, the forgetting factor recursive least squares -double extended Kalman filtering algorithm with multi-time scales and low-pass filter is able to track the true value within 40 s. Furthermore, the lithium-ion battery state of health both reaches 98% under the two conditions. In summary, the experimental analysis shows that the algorithm helps reduce the influence of initial values on the results, thereby reducing error accumulation and improving the robustness.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Collaborative state estimation of lithium‐ion battery based on multi‐time scale low‐pass filter forgetting factor recursive least squares ‐ double extended Kalman filtering algorithm

Long

Wang

Cao

et al. 2022

Circuit Theory & Apps

View full text Add to dashboard Cite

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Battery Dual Extended Kalman Filter State of Charge and Health Estimation Strategy for Traction Applications

Duque,

Kollmeyer,

Naguib

et al. 2022

2022 IEEE Transportation Electrification Conference &Amp; Expo (ITEC)

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Machine Learning and Iot-Based Li-Ion Battery Cloud Monitoring System for 5g Base Stations

Guo

et al. 2023

Fractals

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With the accelerated construction of 5G and IoT, more and more 5G base stations are erected. However, with the increase of 5G base stations, the power management of 5G base stations becomes progressively a bottleneck. In this paper, we solve the problem of 5G base station power management by designing a 5G base station lithium battery cloud monitoring system. In this paper, first, the lithium battery acquisition hardware is designed. Second, a new communication protocol is established based on Modbus. Third, the windows desktop upper computer software and the cloud-based monitoring system are designed. Finally, this paper designs the improved ResLSTM algorithm which is fused with ResNet algorithm based on Stacked LSTM. The algorithm designed in this paper is tested in comparison with SVM and LSTM. The performance of the algorithm designed in this paper is better than SVM and LSTM. Furthermore, the communication test, as well as the training and testing of the ResLSTM algorithm are outstanding. The 5G base station lithium-ion battery cloud monitoring system designed in this paper can meet the requirements. It has great significance for engineering promotion. More importantly, the ResLSTM algorithm designed in this paper can better guide the method of lithium-ion battery SOC estimation.

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SOC Estimation of Lithium Battery Based on Dual Adaptive Extended Kalman Filter

Cited by 3 publications

References 9 publications

Collaborative state estimation of lithium‐ion battery based on multi‐time scale low‐pass filter forgetting factor recursive least squares ‐ double extended Kalman filtering algorithm

Collaborative state estimation of lithium‐ion battery based on multi‐time scale low‐pass filter forgetting factor recursive least squares ‐ double extended Kalman filtering algorithm

Battery Dual Extended Kalman Filter State of Charge and Health Estimation Strategy for Traction Applications

Machine Learning and Iot-Based Li-Ion Battery Cloud Monitoring System for 5g Base Stations

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