IRO Journals
Journal of Electrical Engineering and Automation

SVM-Based EV Battery Monitoring with Dual Active Bridge Converter

Open Access
Published: 25 April, 2026
Pages: 73-87
  • Hariharan S. , Hariharan S.
    Department of Electronic Communication Engineering, Dr.M.G.R Educational and Research Institute, Maduravoyal, Chennai, India
    Department of Electronic Communication Engineering, Dr.M.G.R Educational and Research Institute, Maduravoyal, Chennai, India
  • Prithiviraj A. , Prithiviraj A.
    Department of Electronic Communication Engineering, Dr.M.G.R Educational and Research Institute, Maduravoyal, Chennai, India
    Department of Electronic Communication Engineering, Dr.M.G.R Educational and Research Institute, Maduravoyal, Chennai, India
  • Balaji D.C. , Balaji D.C.
    Department of Electronic Communication Engineering, Dr.M.G.R Educational and Research Institute, Maduravoyal, Chennai, India
    Department of Electronic Communication Engineering, Dr.M.G.R Educational and Research Institute, Maduravoyal, Chennai, India
  • Vignesh A. Vignesh A.
    Department of Electronic Communication Engineering, Dr.M.G.R Educational and Research Institute, Maduravoyal, Chennai, India
    Department of Electronic Communication Engineering, Dr.M.G.R Educational and Research Institute, Maduravoyal, Chennai, India
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How to Cite

S., Hariharan, Prithiviraj A., Balaji D.C., and Vignesh A. 2026. “SVM-Based EV Battery Monitoring With Dual Active Bridge Converter”. Journal of Electrical Engineering and Automation 8 (2): 73-87. https://doi.org/10.36548/jeea.2026.2.001.
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Keywords

Electric Vehicle (EV)
Battery Health Prognostics
State of Health (SOH)
Remaining Useful Life (RUL)
Multiport DC–DC Converter
Support Vector Machine (SVM)
Space Vector Modulation
dsPIC30F4011 Controller

Abstract

Estimating the health status of batteries used in electric vehicles is essential in ensuring safe operation and maximizing their life expectancy and reliability. Precise lifespan prediction reduces expenses and increases confidence. However, existing prediction techniques lack sufficient real-world battery degradation data, suffer from non-linear degradation patterns due to differences in usage cases, and require heavy computational capacity. To solve the above issues, this research work introduces a battery prognostics algorithm that uses the SVM algorithm to predict key parameters of lithium batteries' health state, including their SOH and RUL. The developed prognostics system is combined with a DC–DC converter. Thus, it allows transferring power in both directions between the converter and the vehicle battery. The power transfer control unit is based on the PWM generator and the digital signal controller dsPIC30F4011 that controls the operation of the system by controlling the power distribution. Besides, the system has voltage and current sensors that monitor the battery's parameters continuously. Finally, the developed system enables not only to predict but also charge the EV battery. TLP250 drivers help generate gate pulses to control the switching process, while the web-based monitoring service created using the ESP32 microcontroller helps to visualize data.

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