Modeling of Back-Propagation Neural Network Based State-of-Charge Estimation for Lithium-Ion Batteries with Consideration of Capacity Attenuation

Zhang, S.; Xu, Guo; Zhang, X.

doi:10.4316/aece.2019.03001

Cited by 28 publications

(10 citation statements)

References 43 publications

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“…Other methods used to estimate SoC include support vector machine [40][41][42], neural network with an extended Kalman filter (EKF) [43,44], support vector machine (SVM) optimised by particle swarm optimisation [45], optimised SVM [46], multi-layered [47] perceptron neural network, fuzzy least square support vector machine [48], time-delayed neural network [49], Levenberg-Marquardt (L-M) algorithm optimised multi-hiddenlayer wavelet neural network (WNN) [50], back propagation neural network [51,52], feedforward artificial neural network [53] and recurrent neural network with gated recurrent unit [54]. Cai et al (2003) developed an adaptive neuro-fuzzy inference system (ANFIS) to estimate SoC [38].…”

Section: Estimation Of Soc Using Black Box Modelling Data-driven Approachmentioning

confidence: 99%

A Review on Battery Modelling Techniques

Tamilselvi¹,

Gunasundari²,

Karuppiah³

et al. 2021

Sustainability

143

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The growing demand for electrical energy and the impact of global warming leads to a paradigm shift in the power sector. This has led to the increased usage of renewable energy sources. Due to the intermittent nature of the renewable sources of energy, devices capable of storing electrical energy are required to increase its reliability. The most common means of storing electrical energy is battery systems. Battery usage is increasing in the modern days, since all mobile systems such as electric vehicles, smart phones, laptops, etc., rely on the energy stored within the device to operate. The increased penetration rate of the battery system requires accurate modelling of charging profiles to optimise performance. This paper presents an extensive study of various battery models such as electrochemical models, mathematical models, circuit-oriented models and combined models for different types of batteries. It also discusses the advantages and drawbacks of these types of modelling. With AI emerging and accelerating all over the world, there is a scope for researchers to explore its application in multiple fields. Hence, this work discusses the application of several machine learning and meta heuristic algorithms for battery management systems. This work details the charging and discharging characteristics using the black box and grey box techniques for modelling the lithium-ion battery. The approaches, advantages and disadvantages of black box and grey box type battery modelling are analysed. In addition, analysis has been carried out for extracting parameters of a lithium-ion battery model using evolutionary algorithms.

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Section: Estimation Of Soc Using Black Box Modelling Data-driven Approachmentioning

confidence: 99%

A Review on Battery Modelling Techniques

Tamilselvi¹,

Gunasundari²,

Karuppiah³

et al. 2021

Sustainability

143

View full text Add to dashboard Cite

show abstract

“…This article aims to study battery cell performance in electric vehicles. The study focuses on lithium-ion accumulators, which have the most promising properties on the market among different battery technologies, particularly in terms of energy and mass power [6][7][8][9][10][11]. It is currently the best candidate for energy storage in electric vehicles [12,13].…”

Section: Introductionmentioning

confidence: 99%

An electric circuit model for a lithium-ion battery cell based on automotive drive cycles measurements

Khalfi

Boumaaz

Soulmani

et al. 2021

IJECE

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The on-board energy storage system plays a key role in electric vehicles since it directly affects their performance and autonomy. The lithium-ion battery offers satisfactory characteristics that make electric vehicles competitive with conventional ones. This article focuses on modeling and estimating the parameters of the lithium-ion battery cell when used in different electric vehicle drive cycles and styles. The model consists of an equivalent electrical circuit based on a second-order Thevenin model. To identify the parameters of the model, two algorithms were tested: Trust-Region-Reflective and Levenberg-Marquardt. To account for the dynamic behavior of the battery cell in an electric vehicle, this identification is based on measurement data that represents the actual use of the battery in different conditions and driving styles. Finally, the model is validated by comparing simulation results to measurements using the mean square error (MSE) as model performance criteria for the driving cycles (UDDS, LA-92, US06, neural network (NN), and HWFET). The results demonstrate interesting performance mostly for the driving cycles (UDDS and LA-92). This confirms that the model developed is the best solution to be integrated in a battery management system of an electric vehicle.

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“…11 Based on advanced machine learning algorithms and without sophisticated battery model, databased methods take selected battery external parameters as model inputs, which include current, voltage and ambient temperature, to realize online battery SOC estimation. 12,13 The machine learning algorithms include Gaussian process regression (GPR), [14][15][16] support vector machine (SVM), [17][18][19] neural network (NN), [20][21][22] fuzzy logic (FL), 23,24 and so on. Sahinoglu et al 14 proposed an original SOC estimation method using recurrent/regular GPR framework, where both simulation and experimental results verified the high estimation accuracy of this method.…”

Section: Introductionmentioning

confidence: 99%

“…A kind of SOC estimation method based on fuzzy least square SVM was adopted in Reference [17] to decrease the effects of the samples with low confidence by successfully employing nonlinear correlation measurement and fuzzy inference. A hybrid method based on the fusion of backpropagation NN and improved ampere‐hour counting method was used in Reference [22] to provide accurate SOC estimation, where the effect of battery capacity attenuation was also taken into consideration. Zheng et al 23 employed the FL controller to establish fuzzy logic sliding mode observer (FLSMO) based on SOC estimation model, where test results showed that the FLSMO algorithm had strong robustness to deal with parameter disturbances and measurement noise.…”

Section: Introductionmentioning

confidence: 99%

Online parameters identification and state of charge estimation for lithium‐ion batteries using improved adaptive dual unscented Kalman filter

Peng

Zhang

et al. 2020

Int J Energy Res

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State of charge (SOC) is a vital parameter which helps make full use of battery capacity and improve battery safety control. In this paper, an improved adaptive dual unscented Kalman filter (ADUKF) algorithm is adopted to realize coestimation of the battery model parameters and SOC. Notably, the covariance matching method that can adapt the system noise covariance and the measurement noise covariance is used to improve the estimation accuracy. Besides, singular value decomposition (SVD) is utilized to deal with the non-positive error covariance matrix in both unscented Kalman filters, further enhancing the stability of estimation algorithm. Verification results under Dynamic Stress test and Federal Urban Driving Schedule test indicate that improved ADUKF can achieve more accurate SOC estimates with error band controlled within 2.8%, while that of traditional dual unscented Kalman filter (DUKF) can only be controlled within 5%. Moreover, robustness analysis is also conducted and the validation results present that the proposed algorithm can still provide precise SOC prediction results under some disturbances, such as erroneous initial SOC, inaccurate battery capacity, and various ambient temperatures.

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Modeling of Back-Propagation Neural Network Based State-of-Charge Estimation for Lithium-Ion Batteries with Consideration of Capacity Attenuation

Cited by 28 publications

References 43 publications

A Review on Battery Modelling Techniques

A Review on Battery Modelling Techniques

An electric circuit model for a lithium-ion battery cell based on automotive drive cycles measurements

Online parameters identification and state of charge estimation for lithium‐ion batteries using improved adaptive dual unscented Kalman filter

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