Online Parameter Identification and State of Charge Estimation of Lithium-Ion Batteries Based on Improved Artificial Fish Swarms Forgetting Factor Least Squares and Differential Evolution Extended Kalman Filter

Xiao, Weijia; Wang, Shunli; Yu, Chunmei; Yang, Xiao Guang; Qiu, Jingsong; Fernandez, Carlos

doi:10.1149/1945-7111/acaa5b

Cited by 6 publications

(3 citation statements)

References 43 publications

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“…19 Consider that electrochemical models are complex and not easy to implement in engineering, only the latter two models are utilized here. The lithium-ion battery model in this research takes the first order Thevenin ECM, because first order ECM can provide accurate z E-mail: 13905182179@139.com characteristics of lithium-ion batteries with a reasonable amount of computational burden, 20 as depicted in Fig. 1 and the neural network model for SOC estimation is illustrated in Fig.…”

Section: The Problem Formulationmentioning

confidence: 99%

Multi-Measurement Kalman-Filtering-Based Neural Network Estimator for SOC of Lithium Batteries

Qin,

Hao,

Ding

2023

J. Electrochem. Soc.

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State of charge (SOC) refers to the remaining capacity of the battery, which cannot be measured directly. A multi-measurement Kalman filter which is composed of two sub Kalman filters is constructed to improve the estimation accuracy of SOC. The two sub filters share the same state function but have different measurements, namely the terminal voltage and the SOC estimation from neural network, respectively. Based on minimizing the trace of error covariance, an optimal weighted matrix is computed to fuse the estimates of the two sub filters. The training dataset of neural network is collected from mixed discharging cycles experiment and corresponding charging process. By comparing the results with model-based methods, such as H-infinity filter, unscented Kalman filter, data-driven methods, like neural networks and hybrid method, the multi-measurement Kalman filter is verified by both the root mean square error and mean absolute error that are less than 2% in different drive cycles.

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Section: The Problem Formulationmentioning

confidence: 99%

Multi-Measurement Kalman-Filtering-Based Neural Network Estimator for SOC of Lithium Batteries

Qin,

Hao,

Ding

2023

J. Electrochem. Soc.

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“…Combining the differential evolution algorithm and the extended Kalman filter, Xiao et al. presented a joint algorithm for the state of charge estimation [ 32 ]. This paper took the maximum likelihood fitness in the differential evolution algorithm and thus derived two novel algorithms for model estimation.…”

Section: Introductionmentioning

confidence: 99%

Estimating the Moisture Ratio Model of Cantaloupe Slices by Maximum Likelihood Principle-Based Algorithms

Zhu

Raghavan

2023

Plants

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As an agricultural plant, the cantaloupe contains rich nutrition and high moisture content. In this paper, the estimation problem of the moisture ratio model during a cantaloupe microwave drying process was considered. First of all, an image processing-based cantaloupe drying system was designed and the expression of the moisture ratio with regard to the shrinkage was built. Secondly, a maximum likelihood principle-based iterative evolution (MLP-IE) algorithm was put forward to estimate the moisture ratio model. After that, aiming at enhancing the model fitting ability of the MLP-IE algorithm, a maximum likelihood principle-based improved iterative evolution (MLP-I-IE) algorithm was proposed by designing the improved mutation strategy, the improved scaling factor, and the improved crossover rate. Finally, the MLP-IE algorithm and MLP-I-IE algorithm were applied for estimating the moisture ratio model of cantaloupe slices. The results showed that both the MLP-IE algorithm and MLP-I-IE algorithm were effective and that the MLP-I-IE algorithm performed better than the MLP-IE algorithm in model estimation and validation.

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“…18 Xiao et al introduced an artificial fish swarm (AFS) strategy introduced to optimize the forgetting factor of forgetting factor least squares. 19 Fornaro et al proposed recursive least squares method to identify internal parameters of hybrid energy storage systems for lithium-ion batteries and supercapacitors. 20 Common methods of SOC estimation include time integral, characterization parameter, filter estimation, data-driven, and so on.…”

mentioning

confidence: 99%

State of Charge Estimation of Supercapacitor Under Different Temperatures Using Particle Filter Algorithm Based on Fractional-Order Model

Liu,

Xu,

Zhang

et al. 2023

J. Electrochem. Soc.

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With the rise of new energy vehicles, supercapacitors (SCs) have been used as energy storage components for new energy vehicles due to their high-power density and good low-temperature performance. Accurate modeling and state of charge estimation of SC can ensure the safe operation of new energy vehicles. In order to explore the low-temperature performance of supercapacitors, this paper proposes a dual ZARC fractional-order circuit model to simulate the dynamic characteristics of SC. Using adaptive genetic algorithm for SC parameter identification, the model terminal voltage error is less than 6.5 mV. In addition, the SOC of SC at different temperatures and working conditions is estimated by using the fractional-order particle filter (FOPF) method and compared with the fractional-order extended Kalman filter (FOEKF). The experimental results show that the FOPF method has high estimation accuracy and robustness. Under the temperature of minus 40℃, the maximum mean absolute error and maximum root-mean-square deviation of SOC estimation under different working conditions are less than 2%, showing good low-temperature performance.

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Online Parameter Identification and State of Charge Estimation of Lithium-Ion Batteries Based on Improved Artificial Fish Swarms Forgetting Factor Least Squares and Differential Evolution Extended Kalman Filter

Cited by 6 publications

References 43 publications

Multi-Measurement Kalman-Filtering-Based Neural Network Estimator for SOC of Lithium Batteries

Multi-Measurement Kalman-Filtering-Based Neural Network Estimator for SOC of Lithium Batteries

Estimating the Moisture Ratio Model of Cantaloupe Slices by Maximum Likelihood Principle-Based Algorithms

State of Charge Estimation of Supercapacitor Under Different Temperatures Using Particle Filter Algorithm Based on Fractional-Order Model

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