Research on the State of Charge of Lithium-Ion Battery Based on the Fractional Order Model

Zhou, Guangxu; Hu, Dairong; Liu, Yuan; Zhu, Yunhai

doi:10.3390/en14196307

Cited by 12 publications

(6 citation statements)

References 24 publications

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“…In comparison to the method proposed in this paper, although the computational time of SCDPF did not exhibit a significant decrease, our approach markedly enhances the accuracy of SOC estimation. Algorithms RMSE [53] 0.94% [54] 0.79%…”

Section: Algorithm Simulation Analysis Of Accuracy and Convergencementioning

confidence: 99%

Second-Order Central Difference Particle Filter Algorithm for State of Charge Estimation in Lithium-Ion Batteries

Chen,

Huang

2024

WEVJ

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The estimation of the state of charge (SOC) in lithium-ion batteries is a crucial aspect of battery management systems, serving as a key indicator of the remaining available capacity. However, the inherent process and measurement noises created during battery operation pose significant challenges to the accuracy of SOC estimation. These noises can lead to inaccuracies and uncertainties in assessing the battery’s condition, potentially affecting its overall performance and lifespan. To address this problem, we propose a second-order central difference particle filter (SCDPF) method. This method leverages the latest observation data to enhance the accuracy and noise adaptability of SOC estimation. By employing an improved importance density function, we generate optimized particles that better represent the battery’s dynamic behavior. To validate the effectiveness of our proposed algorithm, we conducted comprehensive comparisons at both 25 °C and 0 °C under the new European driving cycle condition. The results demonstrate that the SCDPF algorithm exhibits a high accuracy and rapid convergence speed, with a maximum error which never exceeds 1.30%. Additionally, we compared the SOC estimations with both Gaussian and non-Gaussian noise to assess the robustness of our proposed algorithm. Overall, this study presents a novel approach to enhancing SOC estimation in lithium-ion batteries, addressing the challenges posed by the process itself and measurement noises.

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Section: Algorithm Simulation Analysis Of Accuracy and Convergencementioning

confidence: 99%

Second-Order Central Difference Particle Filter Algorithm for State of Charge Estimation in Lithium-Ion Batteries

Chen,

Huang

2024

WEVJ

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“…The EKF employs Taylor's first-order equation and can operate in real time, but its error rate is larger due to its low SoC precision compared to the adaptive extended Kalman filter (AEKF), which employs the same first-order ECM battery model [23]. In [24], the fractional-order unscented Kalman filter (FO-UKF) is used to estimate SoC. Fractional-order models involve electrochemical impedance spectroscopy (EIS) theory, in which capacity variation is indicated by the constant phase element's (CPE's) impedance.…”

Section: Theoretical Reviewmentioning

confidence: 99%

Tuning Window Size to Improve the Accuracy of Battery State-of-Charge Estimations Due to Battery Cycle Addition

Anggraeni,

Sudiarto,

Fitrianingsih

et al. 2023

WEVJ

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The primary indicator of battery level in a battery management system (BMS) is the state of charge, which plays a crucial role in enhancing safety in terms of energy transfer. Accurate measurement of SoC is essential to guaranteeing battery safety, avoiding hazardous scenarios, and enhancing the performance of the battery. To improve SoC accuracy, first-order and second-order adaptive extended Kalman filtering (AEKF) are the best choices, as they have less computational cost and are more robust in uncertain circumstances. The impact on SoC estimation accuracy of increasing the cycle and its interaction with the size of the tuning window was evaluated using both models. The research results show that tuning the window size (M) greatly affects the accuracy of SoC estimation in both methods. M provides a quick response detection measurement and adjusts the estimation’s character with the actual value. The results indicate that the precision of SoC improves as the value of M decreases. In addition, the application of first-order AEKF has practical advantages because it does not require pre-processing steps to determine polarization resistance and polarization capacity, while second-order AEKF has better capabilities in terms of SoC estimation. The robustness of the two techniques was also evaluated by administering various initial SoCs. The examination findings demonstrate that the estimated trajectory can approximate the actual trajectory of the SoC.

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“…In Ref. [42], particle swarm optimization (PSO) algorithm was used to identify the seven parameters of a fractional-order 2-RC circuit model. The disadvantage of the PSO algorithm is that it tends to fall into local optimal solution.…”

Section: Model Parameter Identificationmentioning

confidence: 99%

State-of-Charge Estimation for Lithium-Ion Batteries Based on Temperature-Based Fractional-Order Model and Dual Fractional-Order Kalman Filter

Wei

Ling

2022

IEEE Access

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Accurate state-of-charge (SOC) estimation of batteries is of great significance for electric vehicles. Ambient temperature influences the relationship between open-circuit voltage (OCV) and SOC as well as model parameter and, accordingly, influences the accuracy of the battery SOC estimation for model-based methods. To address the temperature dependence of battery modeling and SOC estimation, an SOC estimation method for lithium-ion batteries based on a temperature-based fractional first-order RC circuit model and dual fractional-order Kalman filter (DFOKF) was proposed. The OCV-SOC look-up table corresponding to ambient temperature and an offset function regarding to ambient temperature were applied to the developed model for improving modeling accuracy. One of dual filters was used to estimate the SOC, and the other was employed to update the model parameters online for addressing the temperature dependence of model parameter. Comparisons of the SOC estimation results between the developed model and the original model that ignores the influence of ambient temperature under the US06 Highway Driving Schedule and the Federal Urban Driving Schedule (FUDS) tests at eight specified temperatures were performed. The results show that the developed model combined with the DFOKF algorithm improves the accuracy of SOC estimation. For the application of SOC estimation at untested temperature, we proposed to construct the corresponding OCV-SOC look-up table by linear interpolation of the measured OCV-SOC look-up table at the tested temperature. Comparisons of results between using the two kinds of look-up tables to estimate the SOC demonstrate the effectiveness of the proposed approach.INDEX TERMS State-of-charge, lithium-ion batteries, temperature-based model, dual fractional-order Kalman filter.

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Research on the State of Charge of Lithium-Ion Battery Based on the Fractional Order Model

Cited by 12 publications

References 24 publications

Second-Order Central Difference Particle Filter Algorithm for State of Charge Estimation in Lithium-Ion Batteries

Second-Order Central Difference Particle Filter Algorithm for State of Charge Estimation in Lithium-Ion Batteries

Tuning Window Size to Improve the Accuracy of Battery State-of-Charge Estimations Due to Battery Cycle Addition

State-of-Charge Estimation for Lithium-Ion Batteries Based on Temperature-Based Fractional-Order Model and Dual Fractional-Order Kalman Filter

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