CPSO-Based Parameter-Identification Method for the Fractional-Order Modeling of Lithium-Ion Batteries

Yu, Zhihao; Huai, Ruituo; Li, Hongyu

doi:10.1109/tpel.2021.3073810

Cited by 32 publications

(6 citation statements)

References 45 publications

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“…The method estimates the SOH by identifying the model parameters. After modeling, Kalman filter and other methods are usually used to achieve parameter fitting [20,21]. In [22], a second-order RC equivalent circuit model is established and the adaptive unscented Kalman filter algorithm is used to estimate the ohmic internal resistance of the battery in real time.…”

Section: Open Accessmentioning

confidence: 99%

Online estimation of SOH for lithium-ion battery based on SSA-Elman neural network

Guo

Yang

Zhang

et al. 2022

Prot Control Mod Power Syst

114

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The estimation of state of health (SOH) of a lithium-ion battery (LIB) is of great significance to system safety and economic development. This paper proposes a SOH estimation method based on the SSA-Elman model for the first time. To improve the correlation rates between features and battery capacity, a method combining median absolute deviation filtering and Savitzky–Golay filtering is proposed to process the data. Based on the aging characteristics of the LIB, five features with correlation rates above 0.99 after data processing are then proposed. Addressing the defects of the Elman model, the sparrow search algorithm (SSA) is used to optimize the network parameters. In addition, a data incremental update mechanism is added to improve the generalization of the SSA-Elman model. Finally, the performance of the proposed model is verified based on NASA dataset, and the outputs of the Elman, LSTM and SSA-Elman models are compared. The results show that the proposed method can accurately estimate the SOH, with the root mean square error (RMSE) being as low as 0.0024 and the mean absolute percentage error (MAPE) being as low as 0.25%. In addition, RMSE does not exceed 0.0224 and MAPE does not exceed 2.21% in high temperature and low temperature verifications.

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Section: Open Accessmentioning

confidence: 99%

Online estimation of SOH for lithium-ion battery based on SSA-Elman neural network

Guo

Yang

Zhang

et al. 2022

Prot Control Mod Power Syst

114

View full text Add to dashboard Cite

show abstract

“…The disadvantage of the PSO algorithm is that it tends to fall into local optimal solution. To address the issue, Yu et al [43] presented the coevolutionary particle swarm optimization (CPSO) to identify the FOM parameter. In Ref.…”

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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“…The performance of the PSO depends on the preset parameters, and it often suffers the problem of being trapped in local optima [25]. If the PSO falls into the local extremum, the velocities of all particles are easy to rapidly decrease to zero and stop flying, which causes the premature convergence [26]. Howerver, characterized as ergodicity, randomicity and regularity [27], the chaotic search can experience all positions in a specific area without repeat.…”

Section: Cpso Algorithmmentioning

confidence: 99%

Shearer Reliability Prediction Using Support Vector Machine Based on Chaotic Particle Swarm Optimization Algorithm

Zhang

2022

Preprint

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Shearer reliability is considered as one of the most important indexes in longwall mining production. However, the traditional reliability methods are based on the specific distribution of the failure parameters, which are incongruent in the actual practice. Therefore, a novle shearer reliability prediction method based on support vector machine (SVM) with chaotic particle swarm optimization (CPSO) is proposed. It combines the advantages of the high accuracy of SVM and the fast convergence of CPSO, where the chaos idea is introduced to particle swarm optimization for the particle initialization, inertia weight coefficient optimizing and premature convergence treatment. Then this CPSO is used to select and optimize the important parameters of SVM. Ultimately, the optimized parameters are used to obtain a superior CPSO-SVM method for reliability prediction. To show the effectiveness of the proposed method, two numerical comparisons are designed respectively using the literature data and the actual shearer data from the coal mine enterprise. The research results reveal the prediction accuracy and validity of the proposed method.

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CPSO-Based Parameter-Identification Method for the Fractional-Order Modeling of Lithium-Ion Batteries

Cited by 32 publications

References 45 publications

Online estimation of SOH for lithium-ion battery based on SSA-Elman neural network

Online estimation of SOH for lithium-ion battery based on SSA-Elman neural network

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

Shearer Reliability Prediction Using Support Vector Machine Based on Chaotic Particle Swarm Optimization Algorithm

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