Optimized designed controlled driver circuit of a three-wheeled electric vehicle and implemented in hardware

Rout, Smitanjali; Samal, Sudhansu Kumar

doi:10.1007/s00202-023-02028-6

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2024

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Cited by 3 publications

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Effect of Ferromagnetic Materials on Vibration of In-wheel Brushless Direct Current Motor for Light Electric Vehicle

Cabuk

2024

J. Electr. Eng. Technol.

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Effect of Ferromagnetic Materials on Vibration of In-wheel Brushless Direct Current Motor for Light Electric Vehicle

Cabuk

2024

J. Electr. Eng. Technol.

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Estimation of SOC for Li-ion battery-powered three-wheeled electric vehicle using machine learning methods

Rout,

Samal,

Mahapatro

2024

Eng. Res. Express

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The Battery Management System (BMS) serves as the heart of the electric vehicle system, in which estimating the state of charge (SOC) is the crucial part of the BMS to ensure the durability, reliability, and sustainability of the battery pack. Due to its nonlinear characteristics, accurately estimating the SOC for a slow degradation of the charge is highly cumbersome. The literature provides a series of machine learning algorithms (MLA) to estimate and predict the SOC of lithium-ion (Li-ion) battery systems for electric vehicle (EV) applications. The literature has proposed various MLA, coulomb counting, and different Kalman filter methods to address this challenge and estimate the SOC of Li-ion battery systems for EV applications. This research looks at the differences and similarities between the coulomb counting method, the unscented Kalman filter method, and a number of machine learning algorithms. These include linear regression, polynomial linear regression, support vector regression, decision trees, random forests, artificial neural networks (ANN), and long short-term memory (LSTM). The goal is to assess the MLAs' accuracy in estimating battery SOC. Analyzing model errors optimizes the battery's performance parameter. We identify ANN and LSTM as the two most efficient methods for accurate SOC estimation in an EV-operated BMS system by evaluating the performance indices of the aforementioned machine learning methodologies. Once again, the LSTM model for SOC estimation has proven to be highly accurate in analyzing the discrepancy between the actual and predicted traveling ranges of the designed prototype. We design a MATLAB/SIMULINK EV powertrain by collecting realtime data from the Li-ion battery pack, analyzing the SOC variation data, and using the previously mentioned MLA in the Python platform to estimate the SOC and its accuracy. It highlights the effectiveness of advanced MLAs in improving SOC estimation, thereby enhancing the performance and reliability of EV battery systems.

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BLDC Motor Speed Control Using Selective Harmonics Elimination (SHE) Inverter

Mistry,

Samal,

Rout

2024

2024 Control Instrumentation System Conference (CISCON)

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Optimized designed controlled driver circuit of a three-wheeled electric vehicle and implemented in hardware

Cited by 3 publications

References 22 publications

Effect of Ferromagnetic Materials on Vibration of In-wheel Brushless Direct Current Motor for Light Electric Vehicle

Effect of Ferromagnetic Materials on Vibration of In-wheel Brushless Direct Current Motor for Light Electric Vehicle

Estimation of SOC for Li-ion battery-powered three-wheeled electric vehicle using machine learning methods

BLDC Motor Speed Control Using Selective Harmonics Elimination (SHE) Inverter

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