State estimation of lithium polymer battery based on Kalman filter

Li, Jiabo; Min, Yong; Gao, Kangping; Jiao, Shengjie; Xu, Xinxin

doi:10.1007/s11581-021-04165-z

Cited by 7 publications

(3 citation statements)

References 23 publications

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“…22,23 Commonly used SOC estimation methods include the amperehour integration method, Kalman filter and its extended algorithm, and neural network method. 24, 25 Duan et al use extended Kalman filter (EKF) to update model parameters and adaptive unscented Kalman filter (AUKF) to predict battery SOC; the results prove that EKF-AUKF has high estimation accuracy. 26 Yang et al proposed a long short-term memory (LSTM)-cyclic neural network to simulate complex battery behavior at different temperatures and estimate the battery SOC based on voltage, current, and temperature variables, combined with UKF to filter out the noise and further reduce estimation errors.…”

Section: Introductionmentioning

confidence: 99%

“…After establishing an accurate equivalent model and performing parameter identification, relevant algorithms can be used to estimate the SOC 22,23 . Commonly used SOC estimation methods include the ampere‐hour integration method, Kalman filter and its extended algorithm, and neural network method 24,25 . Duan et al use extended Kalman filter (EKF) to update model parameters and adaptive unscented Kalman filter (AUKF) to predict battery SOC; the results prove that EKF‐AUKF has high estimation accuracy 26 .…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%

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

show abstract

“…[10][11][12] The open-circuit voltage method is a common method used in lithium-ion battery testing experiments, this method is mainly based on the fitted OCV-SOC relationship curve to determine the relevant state of the power lithium-ion battery, 13 and it is necessary to ensure that the power battery is in an open-circuit state and resting for too long before estimating the SOC. 14,15 The datadriven method requires a large amount of experimental data with data training, which is too computationally intensive and difficult to meet the real-time nature of powered vehicles. [16][17][18] The model-based method includes an electrochemical model and an equivalent circuit model.…”

mentioning

confidence: 99%

High-Precision State of Charge Estimation of Urban-Road-Condition Lithium-Ion Batteries Based on Optimized High-Order Term Compensation-Adaptive Extended Kalman Filtering

Feng¹,

Wang²,

Yu³

et al. 2023

J. Electrochem. Soc.

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It is significant to improve the accuracy of estimating the state of charge (SOC) of lithium-ion batteries under different working conditions on urban roads. In this study, an improved second-order polarized equivalent circuit (SO-PEC) modeling method is proposed. Accuracy test using segmented parallel exponential fitting parameter identification method. Online parameter identification using recursive least squares with variable forgetting factors(VFFRLS). An optimized higher-order term compensation-adaptive extended Kalman filter (HTC-AEKF) is proposed in the process of estimating SOC. The algorithm incorporates a noise-adaptive algorithm that introduces noise covariance into the recursive process in real-time to reduce the impact of process noise and observation noise on the accuracy of SOC estimation. Multiple iterations are performed for some of the processes in the extended Kalman filter(EKF) to compensate for the accuracy impact of missing higher-order terms in the linearization process. Model validation results show over 98% accuracy. The results after comparing with the EKF algorithm show a 4.1% improvement in SOC estimation accuracy under Hybrid Pulse Power Characterization(HPPC) working conditions. 2.7% improvement in accuracy in Dynamic Stress Test(DST) working conditions. 2.1% improvement in Beijing Bus Dynamic Stress Test(BBDST) working conditions. The superiority of the algorithm is demonstrated.

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State of Charge Estimation of Lithium-Ion Battery Based on Multi-Modal Information Interaction and Fusion

Wang,

Zhang,

et al. 2023

Communications in Computer and Information Science

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State estimation of lithium polymer battery based on Kalman filter

Cited by 7 publications

References 23 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

High-Precision State of Charge Estimation of Urban-Road-Condition Lithium-Ion Batteries Based on Optimized High-Order Term Compensation-Adaptive Extended Kalman Filtering

State of Charge Estimation of Lithium-Ion Battery Based on Multi-Modal Information Interaction and Fusion

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