Adaptive neuro-fuzzy control for dual boost converter in fuel cell electric vehicles

B Panimathi; K. Deepa; S.V. Tresa Sangeetha; T Porselvi; Mohan Lal Kolhe; B Panimathi; K. Deepa; S.V. Tresa Sangeetha; T Porselvi; Mohan Lal Kolhe

doi:10.3934/energy.2025044

AIMS Energy

2025, Volume 13, Issue 5: 1195-1218. doi: 10.3934/energy.2025044

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Adaptive neuro-fuzzy control for dual boost converter in fuel cell electric vehicles

1.
Department of Electrical and Electronics Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Choodasandra, Bengaluru 560035, Karnataka, India
2.
Engineering Department, Electrical and Electronics Section, University of Technology and Applied Science - Al Mussanah, Oman
3.
Department of Electrical and Electronics Engineering, Sri Sairam Engineering College, Sai Leo Nagar, Chennai, India
4.
Faculty of Engineering and Science, University of Agder, 4604 Kristiansand, Norway

Received: 15 May 2025 Revised: 19 July 2025 Accepted: 15 August 2025 Published: 24 September 2025

Electric vehicles powered by fuel cells provide clean energy with zero emissions and are highly sustainable. Increased demand for sustainable transport has rendered hydrogen fuel cell electric vehicles (FCEVs) a feasible choice compared to conventional internal combustion engines. However, efficiency is significantly dependent on the strength of power electronic interfaces and smart control. This research combined an adaptive neuro fuzzy inference system (ANFIS) with two switched boost-converters to manage power from a fuel cell to a battery to deliver a constant voltage and to achieve optimal energy use. This improved the voltage regulation capability, particularly during load change. ANFIS combines the learning ability of neural networks with the decision structure of fuzzy logic. The hybrid approach is conducive to faster response, greater flexibility, and better energy efficiency over varying operating conditions. The ANFIS model recorded a value of root mean square error (RMSE) of 0.0024, confirming high accuracy in dynamic loading handling. Simulation outcomes support the effectiveness of the system and better performance when compared with traditional proportional integral (PI) controllers.
- neuro fuzzy inference system,
- hydrogen fuel cell,
- dual-switch interleaved boost converter,
- dynamic load control,
- energy management,
- power flow optimization,
- voltage regulation and clean energy
Citation: B Panimathi, K. Deepa, S.V. Tresa Sangeetha, T Porselvi, Mohan Lal Kolhe. Adaptive neuro-fuzzy control for dual boost converter in fuel cell electric vehicles[J]. AIMS Energy, 2025, 13(5): 1195-1218. doi: 10.3934/energy.2025044

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Abstract

Electric vehicles powered by fuel cells provide clean energy with zero emissions and are highly sustainable. Increased demand for sustainable transport has rendered hydrogen fuel cell electric vehicles (FCEVs) a feasible choice compared to conventional internal combustion engines. However, efficiency is significantly dependent on the strength of power electronic interfaces and smart control. This research combined an adaptive neuro fuzzy inference system (ANFIS) with two switched boost-converters to manage power from a fuel cell to a battery to deliver a constant voltage and to achieve optimal energy use. This improved the voltage regulation capability, particularly during load change. ANFIS combines the learning ability of neural networks with the decision structure of fuzzy logic. The hybrid approach is conducive to faster response, greater flexibility, and better energy efficiency over varying operating conditions. The ANFIS model recorded a value of root mean square error (RMSE) of 0.0024, confirming high accuracy in dynamic loading handling. Simulation outcomes support the effectiveness of the system and better performance when compared with traditional proportional integral (PI) controllers.

References

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