Dimensioning and configuration of EES systems for electric vehicles with boundary-conditioned adaptive scalarization
Dimensioning and configuration of EES systems for electric vehicles with boundary-conditioned adaptive scalarization
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DOI：
10.1109/codes-isss.2013.6659013
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发表时间：
2013-09
期刊：
影响因子：
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通讯作者：
Wanli Chang;M. Lukasiewycz;S. Steinhorst;S. Chakraborty
中科院分区：
文献类型：
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作者：
Wanli Chang;M. Lukasiewycz;S. Steinhorst;S. Chakraborty
电动汽车（EV）被广泛认为是高效，可持续和智能交通的解决方案。电能存储系统是电动汽车中性能和成本最重要的部件。这项工作提出了一种方法，用于电动汽车的EES系统的最佳尺寸和配置。在参数空间中找到最佳设计点是具有挑战性的，该参数空间随着可用电池类型的数量和可以为每种类型实现的电池的数量而呈指数级扩展。以续驶里程、额定输出功率、安装空间和成本为设计目标，建立了多目标优化问题。我们报告了一种新的边界条件自适应标量化技术来解决凸和凹问题。它提供了一个均匀分布的Pareto点的Pareto曲面，根据汽车制造商的不同具体要求，提出了一组Pareto点，并考虑到在EES系统设计的事实，一个目标的重要性可能是非线性的值。数值和实际实验证明，我们提出的方法是有效的工业使用，并产生最佳的解决方案。
Electric vehicles (EVs) are widely considered as a solution for efficient, sustainable and intelligent transportation. An electrical energy storage (EES) system is the most important component in an EV in terms of performances and cost. This work proposes an approach for optimal dimensioning and configuration of EES systems in EVs. It is challenging to find optimal design points in the parameter space, which expands exponentially with the number of battery types available and the number of cells that can be implemented for each type. A multi-objective optimization problem is formulated with the driving range, rated power output, installation space and cost as design targets. We report a novel boundary-conditioned adaptive scalarization technique to solve both convex and concave problems. It provides a Pareto surface of evenly distributed Pareto points, presents the group of Pareto points according to different specific requirements from automotive manufacturers and also takes the fact in EES system design into account that the importance of an objective could be nonlinear to its value. Numerical and practical experiments prove that our proposed approach is effective for industry use and produces optimal solutions.