A Mathematical Model for Capacity Loss Optimization of Electrical Vehicles Iron-Phosphate-Based Supercharged Batteries Using Bees Algorithm

D. Mourad, A. Yousef, M. Sammany, Z. Shawa, A. Steef, A. Atalla
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Abstract

The recent orientation towards using Electrical Vehicles (EVs), as an alternative to fossil-fuelled- powered vehicles, led to increasing the interest in producing super charged batteries, which is the critical component of EVs and the key of its development and rapid spread. Iron-Phosphate-Based Supercharged Battery (IP-BSBs) has proved its efficiency as a competitor to lead and lithium batteries. Now, it became necessary to increase its efficiency, by the optimum design, to appropriately fit its correspondent vehicle. However, conventional calibration models used to obtain the optimal design parameters often lead to a dramatic waste of time, effort, and resources (cost), without any guarantee to reach the optimal solution. In this paper, a mathematical model is proposed to optimize the capacity loss of IP-BSBs under real manufacturing conditions. The proposed model was solved using meta-heuristic search algorithm represented by Bees Algorithm (BA). Simulation results have shown the precision of our model and the possibility of obtaining an optimal design of IP-BSBs compared to its counterparts of widely existing types.
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基于蜜蜂算法的电动汽车磷酸铁基增压电池容量损失优化数学模型
近年来,电动汽车作为化石燃料动力汽车的替代品,引起了人们对生产超级充电电池的兴趣,超级充电电池是电动汽车的关键部件,也是电动汽车发展和迅速普及的关键。磷酸铁基增压电池(IP-BSBs)已经证明了其作为铅电池和锂电池的竞争对手的效率。现在,有必要通过优化设计来提高其效率,以适当地适应相应的车辆。然而,用于获得最优设计参数的传统校准模型往往会导致大量的时间、精力和资源(成本)浪费,并且无法保证达到最优解。本文提出了在实际制造条件下优化IP-BSBs容量损失的数学模型。采用以Bees算法(BA)为代表的元启发式搜索算法对模型进行求解。仿真结果表明,与现有的IP-BSBs相比,该模型的精度和优化设计的可能性。
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