Journal
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
Volume 69, Issue 12, Pages 14150-14159Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVT.2020.3004010
Keywords
Battery design; constrained optimization problem; constraint satisfaction problem; electric vehicles; multi-objective optimization
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Funding
- Canada Research Chairs Program [950-230672]
- Natural Sciences and Engineering Research Council of Canada [RDCJP 490763]
- European Regional Development Fund through the Operational Program for Competitiveness and Internationalization - COMPETE 2020 Program
- Portuguese funding agency (FCT - Fundacao para a Ciencia e a Tecnologia) [POCI-01-0145-FEDER-016434, UIBD/00308/2020]
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The design of electric vehicle batteries is a challenge requiring significant computational, human and material resources. This paper proposes a new framework to design a battery pack that allows quick iterations between designs and computes optimized configurations to give more information about the best available solutions. The battery pack is represented as a series/parallel configuration in a Constraint Satisfaction Problem (CSP) that is solved to exploit every possible configuration for specific vehicle requirements. The result is then used in a multi-objective Constrained Optimization Problem (COP) to determine the non-dominated (Pareto optimal) solutions according to selected objectives functions. Non-dominated solutions are computed by optimizing weighted-sum scalar functions as well as minimizing different types of distances to a design reference. A case study is explored to design a battery pack for an electric motorcycle. A 24s/6p battery module designed with this methodology has been validated by experiments and the battery pack has successfully been used at MotoStudent in 2018.
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