Novel Machine Learning Control for Power Management Using an Instantaneous Reference Current in Multiple-Source-Fed Electric Vehicles

G. Mathesh, Raju Saravanakumar and Rohit Salgotra ()
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G. Mathesh: School of Electrical Engineering, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India
Raju Saravanakumar: School of Electrical Engineering, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India
Rohit Salgotra: Faculty of Physics and Applied Computer Science, AGH University of Krakw, 30-059 Kraków, Poland

Energies, 2024, vol. 17, issue 11, 1-28

Abstract: Using multiple input power sources increases the reliability of electric vehicles compared to a single source. However, the inclusion of other sources exhibits complexity in the controller system, such as computing time, program difficulty, and switching speed to connect or disconnect the input power to load. To ensure optimal performance and avoid overloading issues, the EV system needs sophisticated control. This work introduces a machine-learning-based controller using an artificial neural network to solve these problems. This paper describes the detailed power management control methodology using multiple sources like solar PV, fuel cells, and batteries. Novel control with an instantaneous reference current scheme is used to manage the input power sources to satisfy the power demand of electric vehicles. The proposed work executes the power split-up operation with standard and actual drive cycles and maximum power point tracking for PV panels using MATLAB Simulink. Finally, power management with a machine learning technique is implemented in an experimental analysis with the LabVIEW software, and an FPGA controller is used to control a 48 V, 1 kW permanent-magnet synchronous machine.

Keywords: ANN; battery; bidirectional DC converter; boost converter; fuel cell; machine learning; solar array (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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