Evaluation of Drive Cycle-Based Traction Motor Design Strategies Using Gradient Optimisation

Author:

Pastellides StavrosORCID,Gerber StiaanORCID,Wang Rong-JieORCID,Kamper MaartenORCID

Abstract

In this paper, two design optimisation methods are evaluated using gradient-based optimisation for electric vehicle traction applications. A driving cycle-based approach is used to evaluate specific operational points for the design optimisation procedure. To determine the operational points, an energy centre of gravity (ECG) approach is used. Both optimisation methods are described, namely the point based method and the flux mapping method, with a focus on the flux mapping procedure. Within the flux mapping approach, an inner optimisation loop is defined in order to maintain the stability of gradient calculation for the gradient-based optimisation. An emphasis is placed on the importance of how the optimisation problem is defined, in terms of the objective function and constraints, and how it affects a gradient based optimisation. Based on a design case study conducted in the paper, it is found that the point-based strategy realised motor designs with a slightly lower overall cost (5.66% lower than that of the flux mapping strategy with 8 ECG points), whereas the flux mapping strategy found motor designs with a lower input energy (1.48% lower than that of the point-based strategy with 8 ECG points). This may be attributed to the difference in the definition and interpretation of constraints between these two methods. It is also shown that including more operational points from the driving cycle in the design optimisation leads to designs with reduced total input energy and thus better drive-cycle energy efficiency. This paper further illustrates the significant computational advantages of a gradient-based optimisation over a global optimisation method as it can be completed within a fraction of the time while still finding a global optimum, as long as the problem definition is correctly determined.

Publisher

MDPI AG

Subject

Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous)

Cited by 9 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Optimal sizing and cost analysis of hybrid energy storage system for EVs using metaheuristic PSO and firefly algorithms;Results in Engineering;2024-09

2. Baseline Determination for Drive Cycle Performance Analysis of Induction Motors;2023 IEEE Transportation Electrification Conference and Expo, Asia-Pacific (ITEC Asia-Pacific);2023-11-28

3. Baseline Determination for Drive Cycle Performance Analysis of Permanent Magnet Synchronous Motors;2023 IEEE Transportation Electrification Conference and Expo, Asia-Pacific (ITEC Asia-Pacific);2023-11-28

4. Multiobjective Optimization of a Traction Motor in Driving Cycles Using a Coupled Electromagnetic–Thermal 1D Simulation;International Journal of Energy Research;2023-11-17

5. IPM Machine Design Using K-Means Data Clustering Technique for Automotive Applications;2023 9th International Conference on Control, Decision and Information Technologies (CoDIT);2023-07-03

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