Control Strategy of Braking Energy Recovery for Range‐Extended Electric Commercial Vehicles by Considering Braking Intention Recognition and Electropneumatic Braking Compensation. Issue 9 (12th August 2020)

• Record Type: Journal Article
• Authors: Xu, Shiwei; Zhao, Xuan; Yang, Naixing; Bai, Zhifeng
• Is Part Of: Energy technology. Volume 8:Issue 9(2020:Sep.)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-08-12
• Subjects: braking energy recoveries -- braking intention recognitions -- electropneumatic composite braking -- range-extended electric vehicles; Energy development -- Periodicals; Power resources -- Periodicals; 333.79
• DOI: 10.1002/ente.202000407
• Languages: English
• ISSNs: 2194-4288

Abstract

Abstract : Braking energy recovery is a key technology for improving energy efficiency and extending the driving range of electric vehicles. However, there are challenges for maximizing energy recovery and improving braking sense. To solve these problems, a hierarchical control strategy of braking energy recovery that considers braking intention recognition and electropneumatic braking compensation is proposed herein. For the upper controller, Layer Hidden Markov Model‐Dynamic Compensatory Fuzzy Neural Network is applied to recognize the driver's braking intention. For the medium controller, a braking force predistribution strategy based on braking intention recognition results is designed. For the lower controller, an electropneumatic braking compensation method is used to reduce the adverse effects due to the composite braking system. Subsequently, vehicle tests in single‐braking conditions and hardware in loop (HIL) simulation in the World Transient Vehicle Cycle (C‐WTVC) are conducted to verify the performance of the control strategy. The results indicate that the proposed control strategy optimizes the braking force in all the tests and simulations, and the energy recovery rate reaches 12.75% in C‐WTVC, thereby surpassing that of the other three control strategies. Therefore, the results of the study indicate an improvement in the economic performance and ride comfort of electric vehicles. Abstract : A hierarchical control strategy of braking energy recovery that considers braking intention recognition and electropneumatic braking compensation is proposed herein. In detail, Layer Hidden Markov Model‐Dynamic Compensatory Fuzzy Neural Network is applied to recognize the driver's braking intention, and an electropneumatic braking compensation method is used to reduce adverse effects due to the composite braking system.