Comparative analysis of proactive tracking strategies for mobile PV systems: From discrete brute-force to continuous metaheuristic optimization
DOI:
https://doi.org/10.18686/cest822Keywords:
solar electric vehicles; particle swarm optimization (PSO); proactive tracking; GPS trajectory; computational efficiency; renewable energyAbstract
The efficiency of energy harvesting in Solar Electric Vehicles (SEVs) is significantly constrained by dynamic vehicle movement and the limited area of the roof. Conventional tracking techniques are often reactive, thus requiring substantial computing resources and causing high energy losses associated with constant changes. A new idea of a solar tracking system with a proactive approach, using both GPS trajectory data and Particle Swarm Optimization for the sake of optimizing the total net energy gain while minimizing computation, has been introduced in this study. In comparison with the exhaustive search approach, the journey is divided into proactive segments with the use of Particle Swarm Optimization within the dynamically varying search domain. In a simulation process done under the Python programming environment and considering the real trajectory of the path within a period of 10 h from Karak, Jordan, the PSO technique showed total energy gain amounting to 1,701.9 Wh. Even though there was only a minor improvement in terms of the total energy gained as compared with the benchmark values of energy gain (0.27%), the biggest advantage in this case can be seen in a computational decrease of 90% and 96% of the energy used by the embedded control device.
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