EconPapers    
Economics at your fingertips  
 

Cooperative optimization of energy recovery and braking feel based on vehicle speed prediction under downshifting conditions

Xiaochuan Zhou, Gang Wu, Chunyan Wang, Ruijun Zhang, Shuaipeng Shi and Wanzhong Zhao

Energy, 2024, vol. 301, issue C

Abstract: Regenerative braking can effectively recover vehicle kinetic energy, but its energy conversion efficiency is low under low-speed conditions, and there is also the problem of premature exit from energy recovery due to insufficient reverse electromotive force. The cooperation of gearbox gears can increase the speed range for the motor to recover energy, but unreasonable shifting will cause fluctuations in braking force and affect the consistency of the braking feel. Therefore, this paper aims to collaboratively optimize energy recovery and braking force fluctuations during gear shifting. Firstly, based on the model of braking system and transmission, the influence of shift strategy on braking impact and energy recovery is studied. In view of the challenge of determining a shift strategy with uncertain target braking speeds, a speed prediction model reconstructed by the support vector regression (SVR) model and the hybrid nonlinear autoregressive neural network (NAR) is proposed. On the basis of NAR-SVR speed prediction, the coupling effect of braking impact force and energy recovery efficiency is considered, and the collaborative optimization of regenerative braking torque and shift time is solved through a multi-objective cuckoo search algorithm. The hardware-in-the-loop test results verified that under high-speed conditions, the braking energy recovery rate of the proposed strategy was increased by 47.06 %, and the peak braking impact was reduced by 61.4 %. This research can provide a reference for the brake downshift optimization strategy and regenerative braking research of vehicles with non-decoupled electro-hydraulic composite braking systems.

Keywords: Regenerative braking; Energy recovery; Braking feel; Cooperative optimization (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544224014725
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:301:y:2024:i:c:s0360544224014725

DOI: 10.1016/j.energy.2024.131699

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().

 
Page updated 2025-03-19
Handle: RePEc:eee:energy:v:301:y:2024:i:c:s0360544224014725