State of Charge Estimation of Lithium-Ion Battery for Electric Vehicles Using Machine Learning Algorithms

Venkatesan, Chandran; Patil, Chandrashekhar K.; Karthick, Alagar; Dharmaraj, Ganeshaperumal; Rahim, Robbi; Ghosh, Aritra

doi:10.3390/wevj12010038

Cited by 173 publications

(64 citation statements)

References 62 publications

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“…Deep learning has seen immense work recently in wide variety of field such as malaria detection [29],cervical cancer [30],battery management systems [31], and brain imaging [32]. In our proposed methodology CNN method is employed to extract the discriminative features by effectively improving accuracy in AD classification.…”

Section: Proposed Workmentioning

confidence: 99%

DEMNET: A Deep Learning Model for Early Diagnosis of Alzheimer Diseases and Dementia From MR Images

et al. 2021

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Alzheimer's Disease (AD) is the most common cause of dementia globally. It steadily worsens from mild to severe, impairing one's ability to complete any work without assistance. It begins to outstrip due to the population ages and diagnosis timeline. For classify cases, existing approaches incorporate medical history, neuropsychological testing, and Magnetic Resonance Imaging (MRI), but efficient procedures remain inconstant due to lack of sensitivity and precision. The Convolutional Neural Network (CNN) utilized to create a framework that can exploit to detect specific Alzheimer's disease characteristics from MRI images. By considering four stages of dementia and conducting a particular diagnosis, the proposed model generates high-resolution disease probability maps from the local brain structure to a multilayer perceptron and provides accurate, intuitive visualizations of individual Alzheimer's disease risk. To avoid the problem of class imbalance, the samples should be evenly distributed among the classes. The obtained MRI image dataset from Kaggle has a major class imbalance problem. A DEMentia NETwork (DEMNET) a CNN model is proposed to detect the dementia stages, which is the primary cause of AD. The DEMNET achieves an accuracy of 95.23%, Area Under Curve (AUC) of 97% and Cohen's Kappa value of 0.93 from the Kaggle dataset, which is superior to existing methods. We also used the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset to predict AD classes in order to assess the efficacy of the proposed model.

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Section: Proposed Workmentioning

confidence: 99%

DEMNET: A Deep Learning Model for Early Diagnosis of Alzheimer Diseases and Dementia From MR Images

et al. 2021

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“…Battery monitoring is therefore crucial for these e-PUVs because the safety, operation, and even the life of the passengers depend on the battery system [91]. This feature is exactly the major function of the battery management system (BMS)-to check and control the state of charge and state of health of battery for safe and reliable operating conditions [91][92][93]. The BMS protects the battery from overcharging, overuse, and shortcircuiting [94].…”

Section: Barriers To Sustainable Public Transportmentioning

confidence: 99%

Barriers and Drivers of Transition to Sustainable Public Transport in the Philippines

Guno¹,

Collera

Agaton

2021

WEVJ

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Electrification of public utility vehicles plays a vital role in the transition towards a more sustainable transport system. However, the adoption of electric vehicles (EVs) encounters varying challenges ranging from financing issues, government policies, and public acceptance. Using the Philippines as a case, this research applies political, economic, social, technological, legal, and environmental (PESTLE) analysis to determine how different drivers affect the adoption of EVs in the public transport system from various transport stakeholders’ vantage points. Survey results identified economic and technological factors as the main barriers to the adoption of electric public transport. This includes high investment and operational costs, lack of charging infrastructure, issues in driving range and use in different terrains, and the availability of EV parts and repair stations. On the other hand, the main enabler is the significant public support for the modernization of the public transport system through EVs, backed up by policy and legal drivers. For a zero-emission public transport system, this study recommends that the government should invest in sustainable sources of energy, develop more public infrastructure, diversify the transport sector, fund the development of locally made EVs, and initiate a massive information campaign in educating the public of its advantages.

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“…In the proposed approach, the acquired battery data sets are trained, validated and tested using MATLAB (2019b) by using NN toolbox. The flowchart of the ML algorithm is represented in Fig.9 [45]. It consists of input parameters, feature extraction and ML algorithms to estimate the balancing resistor value.…”

Section: Figure 8 Machine Learning Approaches For Bmsmentioning

confidence: 99%

Machine Learning-Based Optimal Cell Balancing Mechanism for Electric Vehicle Battery Management System

Duraisamy¹,

Deepa

2021

IEEE Access

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Cell balancing is a vital function of battery management system (BMS), which is implemented to extend the battery run time and service life. Various cell balancing techniques are being focused due to the growing requirements of larger and superior performance battery packs. The passive balancing approach is the most popular because of its low cost and easy implementation. As the balancing energy is dissipated as heat by the balancing resistors, an appropriate thermal scheme of the balancing system is necessary, to keep the BMS board temperature under a tolerable limit. In this paper, optimum selection of balancing resistor with respect to degree of cell imbalance, balancing time, C-rate, and temperature rise using machine learning (ML) based balancing control algorithm is proposed to improve the balancing time and optimal power loss management. Variable resistors are utilised in the passive balancing system, in order to optimize the power loss and to obtain optimal thermal characterization. The performance of the proposed system is evaluated using back propagation neural network (BPNN), radial basis neural network (RBNN), and long short term memory (LSTM). Error analysis of the balancing system is done to optimize balancing parameters and the proposed algorithms are compared using performance indices such as mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) to validate the balancing model performance. The possible optimization scope for implementing passive balancing using machine learning algorithms are experimented in the Matlab-Simscape environment.

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State of Charge Estimation of Lithium-Ion Battery for Electric Vehicles Using Machine Learning Algorithms

Cited by 173 publications

References 62 publications

DEMNET: A Deep Learning Model for Early Diagnosis of Alzheimer Diseases and Dementia From MR Images

DEMNET: A Deep Learning Model for Early Diagnosis of Alzheimer Diseases and Dementia From MR Images

Barriers and Drivers of Transition to Sustainable Public Transport in the Philippines

Machine Learning-Based Optimal Cell Balancing Mechanism for Electric Vehicle Battery Management System

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