Estimation of Lithium-Ion Battery SOC Model Based on AGA-FOUKF Algorithm

Fang, Chi; Jin, Zhiyang; Wu, Jingjin; Liu, Chenguang

doi:10.3389/fenrg.2021.769818

Cited by 12 publications

(6 citation statements)

References 30 publications

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“…However, the forgetting factor of the least squares is fixed, which is not capable of accurately describing the complex working conditions, and it will cause a major bias in the identification results. In addition, Fang et al 23 proposed the fractional‐order unscented Kalman filter (FOUKF) method to approximate the power battery SOC using FOM, which proved the effectiveness of the FOM model combined with the UKF algorithm in comparison with other algorithms. Yuan et al 24 combined the multinomial holography theory with Kalman filtering and proposed the multi‐innovations unscented Kalman filter (MIUKF) method, which successfully alleviates the issue of poor consistency in the UKF algorithm.…”

Section: Introductionmentioning

confidence: 99%

Online estimation of lithium battery SOC based on fractional order FOUKF‐FOMIUKF algorithm with multiple time scales

Xing,

Ren,

Luo

et al. 2024

Energy Science & Engineering

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Aiming at the matter of poor precision in predicting the charge of lithium battery by applying conventional integer‐order models and offline parameter identification, this paper proposes a joint fractional‐order multi‐innovations unscented Kalman filter (FOUKF‐FOMIUKF) algorithm for predicting the cells' state of charge (SOC) online and uses the theory of singular‐value decomposition to tackle the issue of failure of the traceless transformation. Initially, the circuitry model of fractional order is built. The parameters of the model are recognized online by fractional‐order unscented Kalman filtering (FOUKF), and the obtained parameters are then transmitted to the method known as the fractional order multi‐innovations unscented Kalman filter (FOMIUKF) to calculate the SOC of the cell. The algorithm was validated under four working conditions such as FUDS (US Federal Urban Driving Distance), BJDST (Beijing Dynamic Stress Test), DST (Dynamic Stress Test), and US06 (Highway Driving Distance Test), respectively, and compared with the FOMIUKF, MIUKF, and FOUKF algorithms for offline identification. The conclusions demonstrate that the SOC estimated by the FOUKF‐FOMIUKF method is controlled within 0.5% of the mean absolute error under the four conditions and the root‐mean‐square error is controlled within 0.8%. It is not difficult to find that the FOUKF‐FOMIUKF algorithm estimates SOC with higher accuracy and robustness.

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Section: Introductionmentioning

confidence: 99%

Online estimation of lithium battery SOC based on fractional order FOUKF‐FOMIUKF algorithm with multiple time scales

Xing,

Ren,

Luo

et al. 2024

Energy Science & Engineering

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“…The electrochemical impedance spectroscopy method is only used for laboratory research [7]. In addition, the adaptive filtering method has a complex algorithm and long calculation cycle, which includes nonlinear Kalman filter, particle filter, specifically including extended Kalman filter, traceless Kalman filter, and other methods [8][9][10][11][12]. In neural network methods [13][14][15] and support vector machine methods [16][17][18][19], the SOC estimation of a battery is viewed as a regression problem, using multiple inputs (e.g., voltage, current, and environmental variables) to predict the SOC.…”

Section: Introductionmentioning

confidence: 99%

SOC Estimation Methods for Lithium-Ion Batteries without Current Monitoring

Zhang,

Shao,

et al. 2023

Batteries

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State of charge (SOC) estimation is an important part of a battery management system (BMS). As for small portable devices powered by lithium-ion batteries, no current sensor will be configured in BMS, which presents a challenge to traditional current-based SOC estimation algorithms. In this work, an electrochemical model is developed for lithium batteries, and three methods, including the incremental seeking method, dichotomous method, and extended Kalman filter algorithm (EKF), are separately developed to establish the framework of current and SOC estimation simultaneously. The results show that the EKF algorithm performs better than the other two methods in terms of estimation accuracy and convergence speed. In addition, the estimation error of the EKF algorithm is within ±2%, which demonstrates its feasibility.

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“…25,26 Therefore, in recent years, researchers have put forward some improved algorithms based on the traditional algorithms to solve the problems of online estimation of lithium-ion batteries and low estimation accuracy. [27][28][29] Fang et al 30 proposed a model-based SOC estimation method for lithium-ion batteries based on the state estimation error caused by inaccurate parameter estimation of the battery model. The results show that the proposed algorithm can effectively improve the accuracy of SOC estimation.…”

Section: Introductionmentioning

confidence: 99%

“…Fang et al 30 proposed a model‐based SOC estimation method for lithium‐ion batteries based on the state estimation error caused by inaccurate parameter estimation of the battery model. The results show that the proposed algorithm can effectively improve the accuracy of SOC estimation.…”

Section: Introductionmentioning

confidence: 99%

A novel fuzzy‐extended Kalman filter‐ampere‐hour (F‐EKF‐Ah) algorithm based on improved second‐order PNGV model to estimate state of charge of lithium‐ion batteries

Liu

Wang

Fan

et al. 2022

Circuit Theory & Apps

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Aiming at the problem that it is difficult to accurately estimate the state of charge (SOC) of lithium‐ion batteries in the strongly nonlinear interval, a novel algorithm based on a fuzzy control strategy is proposed. It integrates extended Kalman filter (EKF) and ampere‐hour (Ah) integration accurately estimate the SOC of lithium‐ion batteries. First, the algorithm uses the advantage that the EKF algorithm has high estimation accuracy in the nonlinear interval and can solve the problem of the large error caused by the inaccurate initial value of the Ah integral algorithm. Then the fuzzy‐EKF‐Ah (F‐EKF‐Ah) is used to fuse the two algorithms of EKF and Ah integral. The fused algorithm can effectively solve the problems of the cumulative error caused by the sampling accuracy of the Ah integral algorithm and the large estimation error of the EKF algorithm in the strong nonlinear interval. Finally, the equivalent circuit model is used for analysis. The experimental results show that the improved algorithm can achieve high estimation accuracy under three experimental conditions.

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Estimation of Lithium-Ion Battery SOC Model Based on AGA-FOUKF Algorithm

Cited by 12 publications

References 30 publications

Online estimation of lithium battery SOC based on fractional order FOUKF‐FOMIUKF algorithm with multiple time scales

Online estimation of lithium battery SOC based on fractional order FOUKF‐FOMIUKF algorithm with multiple time scales

SOC Estimation Methods for Lithium-Ion Batteries without Current Monitoring

A novel fuzzy‐extended Kalman filter‐ampere‐hour (F‐EKF‐Ah) algorithm based on improved second‐order PNGV model to estimate state of charge of lithium‐ion batteries

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