Parameters identification of Thevenin model for lithium-ion batteries using self-adaptive Particle Swarm Optimization Differential Evolution algorithm to estimate state of charge

Wang, Chuan; Xu, Minyi; Zhang, Qinjin; Feng, Jinhong; Jiang, Ruizheng; Wei, Yi; Liu, Yancheng

doi:10.1016/j.est.2021.103244

Cited by 32 publications

(11 citation statements)

References 68 publications

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“…For the simulation purposes, the initial b is randomly generated within the sample space whose lower bound is 0 and higher bound is 250 mA, s b is equal to 10 À3 and the initial p b is set to 10 À3 . Db is calculated using equation (43). True b is then calculated in every calculation step by adding Db to the previous b.…”

Section: Simulations and Resultsmentioning

confidence: 99%

“…41,42 The first-order Thevenin model is revealing superiority over other ECMs due to its simplicity but accuracy in representing the battery dynamics. 43–45,46 Figure 1 shows the ECM used in this work. The ECM has an ideal OCV source ( V oc ) , an ohmic resistance ( R 0 ) and an RC branch consisting of a polarisation resistor R p and a polarisation capacitor C p .…”

Section: Adaptive Battery Model Identificationmentioning

confidence: 99%

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Mitigation of state of charge estimation error due to noisy current input measurement

Kadem

Kim

2023

Proceedings of the Institution of Mechanical Engineers, Part I:

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The key indicator to assess the performance of a battery management system is the state of charge (SoC). Although various SoC estimation algorithms have been developed to increase the estimation accuracy, the effect of the current input measurement error on the SoC estimation has not been adequately considered in these algorithms. The majority of SoC estimation algorithms are based on noiseless current measurement models in the literature. More realistic battery models must include the current measurement modelled with the bias noise and the white noise. We present a novel method for mitigating noise in current input measurements to reduce the SoC estimation error. The proposed algorithm is validated by computer simulations and battery experiments. The results show that the proposed method reduces the maximum SoC estimation error from around 11.3% to 0.56% in computer simulations and it is reduced from 1.74% to 1.17% in the battery experiment.

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Section: Simulations and Resultsmentioning

confidence: 99%

Section: Adaptive Battery Model Identificationmentioning

confidence: 99%

Mitigation of state of charge estimation error due to noisy current input measurement

Kadem

Kim

2023

Proceedings of the Institution of Mechanical Engineers, Part I:

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“…It mainly contains convolutional layer, pooling layer and full connected layer. The inputs are three-dimensional vectors constructed of charge current, voltage, and cell capacity and finally transformed into a single output that is used to represent the cell's SOH [4] . For migration learning, first by pretraining to get an initial network model, and then adjusting the parameters of the weights in the network according to the specific training data, we can greatly reduce the training time and the requirement for the number of data in the training set [5] .…”

Section: Data-driven Methodsmentioning

confidence: 99%

State-of-Health Estimation of Lithium-ion Battery Based on Data-driven

Gao¹,

Li²,

Yan³

et al. 2022

Proceedings of the International Conference on Information Economy, Data Modeling and Cloud Computing, ICIDC 2022, 17-19 June 2

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With the increase of the output of electric vehicles, it is of great significance to predict the health status of lithium-ion batteries for the safe operation of electric vehicles. In this paper, some common data-driven methods for health state estimation of lithium-ion batteries are reviewed. First of all, this paper introduces the charge and discharge principle of lithium-ion battery. Then four common SOH prediction methods are introduced, and their advantages and disadvantages are summarized and reviewed. In the part of introducing the data-driven research on the health status of lithium-ion battery, it focuses on the application of machine learning and deep neural network. Finally, the research prospect of health state estimation of lithium-ion battery is explained.

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“…The accurate correspondence between SOC and OCV is the most important and basic guarantee of model accuracy. 41 However, the mapping relation between SOC and OCV is nonlinear, and conventional PF methods cannot guarantee accurate fitting over the complete SOC range. After considering the ambient temperature, the three-dimensional PF will inevitably lead to a further decrease in fitting accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…Second, appropriate intelligent algorithms are selected to identify the other parameters of the ECM. The accurate correspondence between SOC and OCV is the most important and basic guarantee of model accuracy 41 . However, the mapping relation between SOC and OCV is nonlinear, and conventional PF methods cannot guarantee accurate fitting over the complete SOC range.…”

Section: Introductionmentioning

confidence: 99%

Considering the temperature influence state‐of‐charge estimation for lithium‐ion batteries based on a back propagation neural network and improved unscented Kalman filtering

Lian

Wang

et al. 2022

Intl J of Energy Research

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Summary Obtaining an accurate mapping relationship between the state‐of‐charge (SOC) and open‐circuit voltage (OCV) of lithium‐ion batteries at different ambient temperatures is of great significance for realizing accurate lithium‐ion battery SOC estimation considering the ambient temperature influence. However, the desired OCV‐SOC relationship is highly nonlinear, and the conventional polynomial fitting method is likely to result in relatively large fitting errors. To solve this problem, a method based on a backpropagation neural network (BPNN) to improve the OCV‐SOC fitting accuracy is proposed, and the SOC estimation of lithium‐ion batteries considering ambient temperature influence is completed. First, the relationship between the SOC and OCV of a lithium‐ion battery at different ambient temperatures is obtained by establishing a BPNN fitting model. Second, by optimizing the covariance decomposition process, a diagonalization of matrix unscented Kalman filtering (UKF) is proposed, which improves the accuracy and stability of the filtering algorithm. Then, the forgetting factor recursive least squares algorithm is combined to accomplish the online update of battery model parameters. Finally, under three working conditions, the effectiveness and robustness of the proposed method are verified. The simulation results show that the proposed method can obtain the most accurate SOC estimation results at each temperature, and the root mean square error (RMSE) and mean absolute error (MAE) under all three working conditions are less than 1.1%. Even if there is a certain error in the initial SOC, the proposed method can ensure that the RMSE and MAE of the SOC estimation results at each temperature do not exceed 1.5%.

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Parameters identification of Thevenin model for lithium-ion batteries using self-adaptive Particle Swarm Optimization Differential Evolution algorithm to estimate state of charge

Cited by 32 publications

References 68 publications

Mitigation of state of charge estimation error due to noisy current input measurement

Mitigation of state of charge estimation error due to noisy current input measurement

State-of-Health Estimation of Lithium-ion Battery Based on Data-driven

Considering the temperature influence state‐of‐charge estimation for lithium‐ion batteries based on a back propagation neural network and improved unscented Kalman filtering

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