SOC Estimation for Li-Ion Battery Using SVM Based on Particle Swarm Optimization

Guo, Gui Fang; Lin, Shu-I; Wu, Xiao Lan; Cao, Bing Gang

doi:10.4028/www.scientific.net/amr.1051.1004

Cited by 13 publications

(9 citation statements)

References 20 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%

“…The inputs taken are voltage, discharge current, impedance and temperature. Prediction error values are smaller between +1.3% and −1.3% [45]. Hu et al (2014) proposed an SoC estimation based on an optimised SVM for regression with a double search optimisation process [46].…”

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

confidence: 99%

“…Under a single driving cycle, such as the New European Driving Cycle (NEDC), the mean absolute error of PLMMWNN was decreased to 0.6% and the maximum absolute error to 5%. A novel hybrid method by fusion of back propagation neural network and improved the ampere hour counting method is proposed for SoC estimation of lithium-ion battery [45]. Five input eigen values (nodes) of the neural network selected are voltage, current, surface temperature of battery, measurement time and number of cycles.…”

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

confidence: 99%

See 2 more Smart Citations

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%

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

confidence: 99%

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

confidence: 99%

See 1 more Smart Citation

A Review on Battery Modelling Techniques

Tamilselvi¹,

Gunasundari²,

Karuppiah³

et al. 2021

Sustainability

143

View full text Add to dashboard Cite

show abstract

“…In the existing studies, the predictive accuracy of the data-driven method is better than that of other methods, although the interpretability is poor, especially in predicting some implicit changes of the vehicle, such as the speed of the vehicle, SOC of the power battery, SOE, SOH. However, the forecast accuracy will be improved greatly over time [23][24][25][26][27][28]. This method requires a lot of historical experience data, and with continuous progress being made in the fields of information and communication technology, more and more data generated during the operation of vehicles can be collected in real-time.…”

Section: Introductionmentioning

confidence: 99%

Method for predicting the remaining mileage of electric vehicles based on dimension expansion and model fusion

Sheng

et al. 2022

IET Intelligent Trans Sys

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Accurately predicting the remaining mileage of electric vehicles (EVs) can effectively alleviate user's mileage anxiety and develop refinement of energy management strategy. However, traditional prediction methods not only consume time and resources, but also accumulate errors and lack interpretability. In this paper, we proposed a model based on dimension expansion and model fusion strategy, which uses the extreme gradient boosting (XGBoost) algorithm to directly predict the remaining mileage of EVs. After pre‐processing the real running data of EVs, we constructed the field of remaining driving range and analyzed the relationship between features and remaining driving range, and then directly predicted the remaining driving mileage. Compared with other machine learning methods, XGBoost model has the highest accuracy. Then dimensional extended data set was obtained based on prior knowledge and symbol conversion, which improved the model performance. Finally, the model fusion strategy was adopted to further improve the generalization ability and stability of the model. The experimental results show that the Bootstrap aggregating (Bagging) fusion model has the highest predictive performance on the test set and outperformed other methods. The maximum RAE is not more than 3.5%, RMSE is less than 3km and MAE is about 2 km.

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“…During their work they checked voltage, amperage and temperature parameters and used the PSO-SVM method for optimization. [23] In [24], which is a study that considers the batteries as complex electro-chemical cells, we can read about their importance. The Relevance Vector Machine and Particle Filters (PF) were used to check the uncertainty properties of the batteries during use.…”

Section: Related Workmentioning

confidence: 99%

Support Vector Machine and Fuzzy Logic

2016

APH

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The article gives a short description of the history of the Support Vector Machine (SVM) method and fuzzy logic and their main parameters. It describes how SVM can be used for classification and regression calculation. In addition, it includes the description of the fuzzy logic and the three main defuzzification methods. Examples will be given to show simultaneous use of the two main Support Vector Machine-methods. It ends with the authors' description of the new possibilites for the use of the SVM and fuzzy logic in evaluation of the parameter deviation in the batteries of electric vehicles as well as those of robots.

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SOC Estimation for Li-Ion Battery Using SVM Based on Particle Swarm Optimization

Cited by 13 publications

References 20 publications

A Review on Battery Modelling Techniques

A Review on Battery Modelling Techniques

Method for predicting the remaining mileage of electric vehicles based on dimension expansion and model fusion

Support Vector Machine and Fuzzy Logic

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