Design of a Rule-Based Controller and Parameter Optimization Using a Genetic Algorithm for a Dual-Motor Heavy-Duty Battery Electric Vehicle
This paper describes a configuration and controller, designed using Autonomie,1 for dual-motor battery electric vehicle (BEV) heavy-duty trucks. Based on the literature and current market research, this model was designed with two electric motors, one on the front axle and the other on the rear axle. A rule-based control algorithm was designed for the new dual-motor BEV, based on the model, and the control parameters were optimized by using a genetic algorithm (GA). The model was simulated in diverse driving cycles and gradeability tests. The results show both a good following of the desired cycle and achievement of truck gradeability performance requirements. The simulation results were compared with those of a single-motor BEV and showed reduced energy consumption with the high-efficiency operation of the two motors.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/01487191
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Supplemental Notes:
- Abstract reprinted with permission of SAE International.
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Authors:
- Yu, Kyungjin
- Vijayagopal, Ram
- Kim, Namdoo
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Conference:
- WCX SAE World Congress Experience
- Location: Detroit & Online Michigan, United States
- Date: 2022-4-5 to 2022-4-7
- Publication Date: 2022-3-29
Language
- English
Media Info
- Media Type: Web
- Features: References;
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Serial:
- SAE Technical Paper
- Publisher: Society of Automotive Engineers (SAE)
- ISSN: 0148-7191
- EISSN: 2688-3627
- Serial URL: http://papers.sae.org/
Subject/Index Terms
- TRT Terms: Axles; Electric vehicles; Energy consumption; Heavy duty trucks; Market research; Mathematical models; Motors; Optimization; Simulation
- Subject Areas: Highways; Vehicles and Equipment;
Filing Info
- Accession Number: 01841729
- Record Type: Publication
- Source Agency: SAE International
- Report/Paper Numbers: 2022-01-0413
- Files: TRIS, SAE
- Created Date: Apr 6 2022 2:18PM