Movement of a Solar Electric Vehicle Controlled by ANN-based DTC in Hot Climate Regions

*Asma Benayad  -  Department of Renewable Energy and Hydrocarbure, African University Ahmed Draia - Adrar, Adrar, 01000,, Algeria
Brahim Gasbaoui  -  Faculty of Sciences and Technology, University of Bechar, Algeria
Said Bentouba  -  Department of Renewable Energy and Hydrocarbure, African University Ahmed Draia - Adrar, Adrar, 01000,, Algeria
Mohammed Amine Soumeur  -  Laboratory of Smart Grid Renewable Energy (SGRE), Faculty of Technology, Department of Electrical Engineering, Bechar University, B.P. 417, 0800, Algeria
Received: 20 Apr 2018; Revised: 14 Sep 2020; Accepted: 3 Oct 2020; Published: 1 Feb 2021; Available online: 6 Oct 2020.
Open Access Copyright (c) 2021 The Authors. Published by CBIORE
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
Vehicle autonomy presents the most complex problem for modern commercialized solar electric vehicle (SEV) propulsion systems. The power supplied by electric vehicles’ batteries is limited by the state of charge, the type of battery, and its level of technological development. This study’s aim was to resolve the problem of energy variation at several velocities and under different road topology conditions. Several works related to the use of fuzzy logic confirm that classical regulators have such advantages over fuzzy regulators as short processing times and mathematical precision. Therefore, the hybrid power source is presented as the best solution for energy management, and it is composed of a solar panel (PV) and a nickel metal hydride battery. The PV system is connected to the SEV via a boost converter that is controlled using the maximum power point tracking technique. In this paper, we used an intelligent PI regulator for direct torque control, which introduced a certain degree of intelligence into the regulation strategy. Indeed, this approach of associating the PI regulator with the fuzzy rules-composed supervisor allowed us to take advantage of both the PI’s mathematical precision and the adaptability, flexibility, and simplicity of fuzzy linguistic formalism. Because of its dynamic capabilities, an adaptive PI regulator was substituted to achieve high speeds and a satisfactorily vigorous performance while quickly compensating for the disturbances that were expected to possibly take place on the regulation chain. The present study’s results confirm that the proposed control approach increased the utility of SEV autonomy under several speed variations. Moreover, the industry’s future offerings must take the option of hybrid power management into consideration during this type of vehicle’s manufacturing phase
Keywords: Solar Photovoltaic (PV); MMPT; Artificial Neural network; Buck Boost; DC-DC converter

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