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Intelligent Deceleration Energy-Saving Control Strategy for Electric Vehicle
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 06, 2021 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Event: SAE WCX Digital Summit
In order to improve the vehicle economy of electric vehicles, this paper first analyzes the energy-saving mechanism of electric vehicles. Taking the energy consumption of the deceleration process as a starting point, this paper deeply analyzes the energy consumption of the deceleration process under several different control modes by the test data, so as to obtain two principles that should be followed in energy-saving control strategy. Then, an intelligent deceleration energy-saving control strategy by getting the forward vehicle information is developed. The overall architecture of the control strategy consists of three parts: information processing, target calculation and torque control. The first part is mainly to obtain the forward vehicle information from the perception systems, and the user's habits information from big data, and this information is processed for the next part. The second part mainly determines the entry and exit timing of the intelligent deceleration energy-saving control function, and calculates the target deceleration at the same time. The third part is to use PID control algorithm to control the torque, and filter the torque when the function enters and exits. Finally, the function test and road test have been carried out on an electric vehicle, and the results show that the proposed intelligent deceleration energy-saving control strategy can realize automatic deceleration control. And considering the driver's daily driving habits, it could better meet the deceleration expectations of drivers. This automatic control can improve vehicle economy and reduce fatigue caused by frequent braking during driving.
CitationZhao, Y., Zhang, Q., Pang, E., Li, J. et al., "Intelligent Deceleration Energy-Saving Control Strategy for Electric Vehicle," SAE Technical Paper 2021-01-0123, 2021, https://doi.org/10.4271/2021-01-0123.
Data Sets - Support Documents
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- Laiqing , J. , Donghao , Z. , Yugong , L. , Rui , C. , and Keqiang , L. Radar Sharing Energy-Saving Control Strategy for Intelligent Hybrid Electric Vehicle J Tsinghua Univ (Sci & Technol) 58 358 286 297 291 2018 https://doi.org/10.4271/10.16511
- Lin , P. , Enming , L. , Qianbo , Z. , and Jing , H. 一种利用毫米波雷达的电动车能量回收系统及智能电动车 2019
- Wei , Z. 奔驰EQC纯电动新技术剖析(二) Auto Mechtenance 12 26 30 2019
- Moran , K. , Foley , B. , Fastenrath , U. , and Raimo , J. Digital Maps, Connectivity and Electric Vehicles-Enhancing the EV/PHEV Ownership Experience SAE Int. J. Passeng. Cars-Electron. Electr. Syst. 3 2 76 83 2010 https://doi.org/10.4271/2010-01-2316
- Boehme , T. , Held , F. , Rollinger , C. , Rabba , H. et al. Application of an Optimal Control Problem to a Trip-Based Energy Management for Electric Vehicles SAE Int. J. Alt. Power. 2 1 2013 https://doi.org/10.4271/2013-01-1465
- Sun , B. , He , R. , Deng , W. , Wu , J. et al. Personalized Eco-Driving for Intelligent Electric Vehicles SAE Technical Paper 2018-01-1625 2018 https://doi.org/10.4271/2018-01-1625