A review of supercapacitors modeling, SoH, and SoE estimation methods: Issues and challenges

Laadjal, Khaled; Cardoso, António J. Marques

doi:10.1002/er.7121

Cited by 36 publications

(13 citation statements)

References 104 publications

(107 reference statements)

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“…Figure 2 shows a three-layer network. In real conditions, the hidden layer [ 10 , 19 , and 20 ] can have many fully connected neural networks.…”

Section: Methodsmentioning

confidence: 99%

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SOC Estimation of Lithium-Ion Battery for Electric Vehicle Based on Deep Multilayer Perceptron

Li¹,

Jiang²,

Guo

et al. 2022

Computational Intelligence and Neuroscience

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The state of charge (SOC) is one of the main indexes of the lithium-ion battery, which affects the practice range of new energy vehicles and the safety of the battery. Nevertheless, the value of SOC cannot be measured directly. At present, the algorithm for estimating the state of charge is not very satisfactory. The multilayer perceptron algorithm designed during this paper encompasses a sensible impact on state estimation. During this paper, the multilayer network is designed to estimate the charged state of lithium batteries from the three-layer artificial neural network to the eleven-layer artificial neural network. After preprocessing the dataset and comparing several activation functions, the ten-layer fully connected neural network is the most efficient to estimate the SOC. In order to prevent over-fitting of the multilayer perceptron algorithm, the two techniques of the BatchNormalization layer and Dropout layer work together to inhibit over-fitting. At the same time, the accuracy of extended Kalman filter, long and short memory network, and recurrent neural network are compared. The multilayer perceptron network designed during this paper has the highest accuracy. Finally, in the open dataset, both the training and test errors achieve good results. The algorithm developed in this paper has made some progress in SOC estimation.

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“…Figure 2 shows a three-layer network. In real conditions, the hidden layer [ 10 , 19 , and 20 ] can have many fully connected neural networks.…”

Section: Methodsmentioning

confidence: 99%

“…However, the estimation error of this method is large, and it cannot be used for fast real-time estimation. The second category is model-based SOC estimation methods [ 10 ]. Due to the internal complexity of the battery, there is no ready-made model for use.…”

Section: Related Workmentioning

confidence: 99%

SOC Estimation of Lithium-Ion Battery for Electric Vehicle Based on Deep Multilayer Perceptron

Li¹,

Jiang²,

Guo

et al. 2022

Computational Intelligence and Neuroscience

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“…7 Laadial et al proposed a generalized model of supercapacitors with good robustness and high accuracy. 8 Allu et al established a generalized threedimensional model considering the charge transfer in the electrode-electrolyte system. 9 Bo et al proposed an artificial intelligence method to predict the thermal behavior of supercapacitors.…”

Section: Introductionmentioning

confidence: 99%

“…Their results indicated that the estimating error of the SOC is less than 3% 7 . Laadial et al proposed a generalized model of supercapacitors with good robustness and high accuracy 8 . Allu et al established a generalized three‐dimensional model considering the charge transfer in the electrode‐electrolyte system 9 .…”

Section: Introductionmentioning

confidence: 99%

“…40 Laadjal et al proposed a generalized supercapacitor model, defined model parameters, and estimated the state of energy and SOH. 8 Liu et al established a databased model and proposed a recurrent neural network to predict the SOH of supercapacitors. 41 Udeogu et al proposed an approach to health indicators on the basis of a deep learning model to predict RUL.…”

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confidence: 99%

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State of charge estimation of supercapacitors based on multi‐innovation unscented Kalman filter under a wide temperature range

Zhang

Yang

et al. 2022

Intl J of Energy Research

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Supercapacitors are characterized by a long service lifetime and high power density, which can meet the instantaneous high-power demand during the acceleration of electric vehicles. In this study, a fractional-order model is developed to simulate the polarization effect and charging/discharging characteristics of supercapacitors, considering the precision of the electrochemical model and the amount of calculation of the equivalent circuit model and using the adaptive genetic algorithm to identify the parameters. The accurate prediction of the state of charge (SOC) can improve efficiency, prolong the service lifetime, and ensure the safety of supercapacitors. This study proposes a multiinnovation unscented Kalman filter algorithm based on the fractional-order model to improve the SOC estimation accuracy. The proposed algorithm is compared with other algorithms and analyzed under different temperatures and operating conditions to verify the accuracy and effectiveness of the proposed algorithm in estimating the SOC and tracking the terminal voltage.Experimental results show that the root mean squared error and mean absolute error of the proposed algorithm are less than those of the other algorithms. The proposed algorithm accurately estimates the SOC and tracks the terminal voltage. The maximum root mean squared error and mean absolute error of SOC estimation error are 1.8% and 1.78%, respectively.

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Strong tracking adaptive window multi‐innovation cubature Kalman filter algorithm for lithium‐ion battery state of energy estimation

Lin,

Wang,

Yang

2024

Circuit Theory & Apps

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SummaryAccurate estimation of lithium‐ion battery state of energy (SOE) is an important prerequisite for prolonging battery life and ensuring battery safety. To achieve a high‐precision estimation of the SOE, while a ternary lithium‐ion battery being the specifically targeted in this study, a novel method for SOE estimation is proposed, which combines limited‐memory recursive least squares (LM‐RLS) with strong tracking adaptive window Multi‐innovation cubature Kalman filtering (STW‐MCKF). In the LM‐RLS algorithm, the model parameters at the current time are updated with a limited dataset to solve the data saturation problem and improve the recognition accuracy of the RLS algorithm. The CKF algorithm is optimized by the STW algorithm in the STW‐CKF algorithm to enhance its robustness under strong disturbances. Additionally, a self‐adaptive window multiple innovation strategy is proposed to improve the accuracy of SOE estimation and the stability of the CKF algorithm, while maintaining a balance between computational complexity and SOE estimation accuracy. To validate the effectiveness of the algorithm, experiments are conducted under DST and BBDST conditions. The results show that the STW‐MCKF algorithm has a maximum convergence time of 4 s and an SOE estimation error within 1.04% under DST conditions. Under BBDST conditions, the maximum convergence time is 3 s, and the SOE estimation error is within 2.34%. Furthermore, the STW‐MCKF algorithm demonstrates good stability under the two conditions, indicating the effectiveness of the proposed method for lithium‐ion battery SOE estimation.

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A review of supercapacitors modeling, SoH, and SoE estimation methods: Issues and challenges

Cited by 36 publications

References 104 publications

SOC Estimation of Lithium-Ion Battery for Electric Vehicle Based on Deep Multilayer Perceptron

SOC Estimation of Lithium-Ion Battery for Electric Vehicle Based on Deep Multilayer Perceptron

State of charge estimation of supercapacitors based on multi‐innovation unscented Kalman filter under a wide temperature range

Strong tracking adaptive window multi‐innovation cubature Kalman filter algorithm for lithium‐ion battery state of energy estimation

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