Predictive Energy Management for Dual Motor-Driven Electric Vehicles
Developing pure electric powertrains have become an important way to reduce reliance on crude oil in recent years. This paper concerns energy management of dual motor-driven electric vehicles. In order to obtain a predictive energy management strategy with good performance in computation and energy efficiency, we propose a hybrid algorithm that combines model predictive control (MPC) and convex programming to minimize electrical energy use in real time control. First, few changes are occurred in original component models in order to convert the original optimal control problem into convex programming problem. Then convex optimization algorithm is used in the prediction horizon to optimize torque allocation between two electric motors with different size. To verify the effectiveness of the hybrid algorithm, a real city driving cycle is simulated. Furthermore, different predictive horizons are performed to illustrate the robustness and time efficiency of the proposed method.
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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:
- Li, Yapeng
- Han, Jie
- Tang, Xiaolin
- Hu, Xiaosong
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Conference:
- Vehicle Electrification and Powertrain Diversification Technology Forum Part I
- Location: Shanghai , China
- Date: 2021-11-25
- Publication Date: 2022-2-14
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: Electric vehicles; Energy conservation; Energy consumption; Mathematical models; Motors; Optimization
- Subject Areas: Highways; Vehicles and Equipment;
Filing Info
- Accession Number: 01836399
- Record Type: Publication
- Source Agency: SAE International
- Report/Paper Numbers: 2022-01-7006
- Files: TRIS, SAE
- Created Date: Feb 22 2022 10:43AM