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State of Charge Estimation Model Based on Genetic Algorithms and Multivariate Linear Regression with Applications in Electric Vehicles

Sensors, ISSN: 1424-8220, Vol: 23, Issue: 6
2023
  • 13
    Citations
  • 0
    Usage
  • 14
    Captures
  • 1
    Mentions
  • 1
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    13
  • Captures
    14
  • Mentions
    1
    • News Mentions
      1
      • 1
  • Social Media
    1
    • Shares, Likes & Comments
      1
      • Facebook
        1

Most Recent News

Recent Studies from Universidad Autonoma de Queretaro Add New Data to Sensor Research (State of Charge Estimation Model Based on Genetic Algorithms and Multivariate Linear Regression with Applications in Electric Vehicles)

2023 MAR 27 (NewsRx) -- By a News Reporter-Staff News Editor at NewsRx Life Science Daily -- New research on sensor research is the subject

Article Description

Nowadays, the use of renewable, green/eco-friendly technologies is attracting the attention of researchers, with a view to overcoming recent challenges that must be faced to guarantee the availability of Electric Vehicles (EVs). Therefore, this work proposes a methodology based on Genetic Algorithms (GA) and multivariate regression for estimating and modeling the State of Charge (SOC) in Electric Vehicles. Indeed, the proposal considers the continuous monitoring of six load-related variables that have an influence on the SOC (State of Charge), specifically, the vehicle acceleration, vehicle speed, battery bank temperature, motor RPM, motor current, and motor temperature. Thus, these measurements are evaluated in a structure comprised of a Genetic Algorithm and a multivariate regression model in order to find those relevant signals that better model the State of Charge, as well as the Root Mean Square Error (RMSE). The proposed approach is validated under a real set of data acquired from a self-assembly Electric Vehicle, and the obtained results show a maximum accuracy of approximately 95.5%; thus, this proposed method can be applied as a reliable diagnostic tool in the automotive industry.

Bibliographic Details

Manriquez-Padilla, Carlos Gustavo; Cueva-Perez, Isaias; Dominguez-Gonzalez, Aurelio; Elvira-Ortiz, David Alejandro; Perez-Cruz, Angel; Saucedo-Dorantes, Juan Jose

MDPI AG

Chemistry; Computer Science; Physics and Astronomy; Biochemistry, Genetics and Molecular Biology; Engineering

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