Co-estimation of state of charge and state of health for lithium-ion batteries based on fractional-order model with multi-innovations unscented Kalman filter method

Ma, Lili; Xu, Yonghong; Zhang, Honggang; Yang, Fubin; Wang, Xu; Cheng, Li

doi:10.1016/j.est.2022.104904

Cited by 35 publications

(8 citation statements)

References 47 publications

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“…The multi‐innovation algorithm 33–37 updates the state variables in such a way that the residuals of the end‐voltage observations

{y}_{k}

and end‐voltage predictions

{\hat{y}}_{k}

at multiple moments of time are utilized to be extended into a multi‐innovation matrix, which increases the traceless Kalman filter's prediction performance through the information matrix. The algorithm takes full advantage of the residual information generated by the multiple time scales to correct the current a posteriori estimates, improving the reliability and stability of the method.…”

Section: Fomiukf Algorithm Soc Estimationmentioning

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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“…The multi‐innovation algorithm 33–37 updates the state variables in such a way that the residuals of the end‐voltage observations

{y}_{k}

and end‐voltage predictions

{\hat{y}}_{k}

Section: Fomiukf Algorithm Soc Estimationmentioning

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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“…e main parameters monitored through the BMS is the state of charge (SOC) and state of health (SOH) [73]. e monitored parameters are communicated to the on-board or off-board control system.…”

Section: Battery Management Systemsmentioning

confidence: 99%

An Extensive Critique on Electric Vehicle Components and Charging Systems

Iqubal

Sathiyan

Stonier³

et al. 2022

International Transactions on Electrical Energy Systems

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Nowadays the demand for electric vehicles has been increasing due to their dropping price range and zero emission of carbon. This article initially discusses the development of various electric vehicles from the past to the present. Since the electric motor plays a significant role in EVs, various motors suitable for EVs have been identified and surveyed in this work. The battery storage system is a critical component of EVs; hence, the article goes over all the different types of batteries, from lead acid to lithium ion. Additionally, the other vehicle components such as converters required for charging the batteries, intelligent controllers, electric vehicle charging process, power management, and battery energy management concepts that are available are discussed in detail. Moreover, we bring our work to a conclusion by outlining the research opportunities that remain for the academic and industrial groups. Therefore, the proposed work intends to assist as a state-of-the-art reference for researchers in the field of various electric vehicle configurations, storage systems, converter configurations, charging techniques, control methods, and modulation methods.

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“…Xiong et al [26] proposed real-time estimation of battery capacity was achieved using a multi-scale EKF (MIEKF), and finally the SOC was estimated. Ma et al [27] proposed estimated the SOC using a multi-innovations UKF (MIUKF) and then used the UKF for SOH prediction to update the actual capacity of the SOC estimator, and the experiments showed that the estimated SOC remained highly accurate over multiple tests cycles.…”

Section: Introductionmentioning

confidence: 99%

SOC Estimation for Lithium Batteries Based on Fractional Order Model and Robust Unscented Kalman Filter

Xing¹,

Luo²,

Liu³

et al. 2023

Preprint

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Lithium batteries are widely used due to their advantages such as high energy density, stable performance, low pollution, and long recyclable life. Accurate SOC estimation is important for the use of lithium batteries. To address the problem of low accuracy of SOC estimation by traditional methods, this paper proposes a joint method of fractional order robust unscented Kalman filter (FORUKF) and robust unscented Kalman filter (RUKF) to estimate SOC. The method is based on a fractional order model (FOM) that combines the unscented transformation (UT) technique, the H∞ observer and the joint estimator. Specifically, an adaptive genetic algorithm (AGA) was first used to identify the parameters of the FOM for lithium batteries and to verify the accuracy of the model. Estimation and updating of the ohmic resistance R0 and the capacity QN in the model in real-time by RUKF and then estimation of the SOC by FORUKF. Finally, the accuracy of FORUKF-RUKF was verified under the Federal Urban Driving Schedule (FUDS), US06 Highway Driving Schedule and Beijing Dynamic Stress Test (BJDST). According to the results, the FORUKF-RUKF estimated SOC was found to have better accuracy and robustness than the RUKF, FOUKF and EKF.

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Co-estimation of state of charge and state of health for lithium-ion batteries based on fractional-order model with multi-innovations unscented Kalman filter method

Cited by 35 publications

References 47 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

An Extensive Critique on Electric Vehicle Components and Charging Systems

SOC Estimation for Lithium Batteries Based on Fractional Order Model and Robust Unscented Kalman Filter

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