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Researchers developed a new method using Genetic Algorithms and regression to accurately estimate Electric Vehicle (EV) State of Charge (SOC). This reliable diagnostic tool achieves over 95% accuracy for EVs.

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Automotive Engineering

Background:

  • Growing demand for Electric Vehicles (EVs) necessitates reliable battery management systems.
  • Accurate State of Charge (SOC) estimation is crucial for EV performance and longevity.
  • Current methods face challenges in dynamic operating conditions.

Purpose of the Study:

  • To propose a novel methodology for estimating and modeling the State of Charge (SOC) in Electric Vehicles (EVs).
  • To identify key variables influencing SOC using advanced algorithms.
  • To develop a reliable diagnostic tool for the automotive industry.

Main Methods:

  • Utilized a methodology combining Genetic Algorithms (GA) and multivariate regression.
  • Continuously monitored six load-related variables: vehicle acceleration, speed, battery temperature, motor RPM, motor current, and motor temperature.
  • Evaluated signals to model SOC and minimize Root Mean Square Error (RMSE).

Main Results:

  • Achieved a maximum accuracy of approximately 95.5% in SOC estimation.
  • Successfully identified relevant signals that significantly influence EV State of Charge.
  • Validated the proposed approach using real-world data from a self-assembly EV.

Conclusions:

  • The proposed GA and multivariate regression method provides a reliable approach for EV SOC estimation.
  • This technique can serve as a valuable diagnostic tool in the automotive sector.
  • The findings contribute to enhancing the efficiency and management of electric vehicle batteries.