scholarly journals Application of Super Capacitor in HEV Regenerative Braking System

2015 ◽  
Vol 8 (1) ◽  
pp. 581-586 ◽  
Author(s):  
Haoming Zhang ◽  
Yinghai Wang ◽  
Peh Lian Soon
2014 ◽  
Vol 1049-1050 ◽  
pp. 586-589
Author(s):  
Ying Hai Wang ◽  
Hao Ming Zhang ◽  
Lian Soon Peh

Much energy was created when hybrid electric vehicle braked down, Battery storage system can not absorb the energy efficiently due to its limitation, which caused energy waste. Regenerative braking system with super capacitor based on TMS320F2812 was brought forward in order to solve the problem, ADVSOR simulation results prove the system can improve battery’s performance and reduce engine emission pollution greatly.


2012 ◽  
Vol 157-158 ◽  
pp. 149-153 ◽  
Author(s):  
Qing Sheng Feng ◽  
Hong Li

The necessity of the electric vehicle using energy regenerative braking system is described in this paper. Then the design process of the braking system with the super capacitor was discussed and the determination method and the control mode of the main technical parameters was analyzed. Using the system the electric automobile can realize regenerative braking in any common condition especially when the conventional regenerative braking can not be achieved. Under these circumstances the electric energy which is generated by braking process and stored in the super capacitor can output to the traction inverter DC link when the automobile is in the traction mode .So it is a good way to significantly reduce the electric vehicle energy consumption.


2011 ◽  
Vol 383-390 ◽  
pp. 5729-5737
Author(s):  
Jiang Hong ◽  
De Wang Zhang ◽  
Guang Pin Wang ◽  
Ni Sui

The pure electric vehicles (PEV) research is mainly focus on regenerative braking. How to improve the efficiency of battery power utilization and increase vehicles’ driving range is a crucial problem. Based on the analysis of braking feeling, super capacitor characteristics and the efficiency of regenerative braking energy recovery, the control strategy of regenerative braking system is firstly established, which has two objective functions. One is to control the regenerative braking force. The other is to improve the recovery efficiency of regenerative braking energy. Then, the main operating mode of regenerative braking system is presented. On this basis, regenerative braking controller that is based on DC-DC controller is designed and implemented in simulink software. The results show that the regenerative braking control strategy can effectively control the regenerative braking force during braking and increase driving range of electric vehicles


Author(s):  
Rafael Rivelino da Silva Bravo ◽  
Artur Tozzi C Gama ◽  
Amir Antonio Martins Oliveira ◽  
Victor Juliano De Negri

Author(s):  
Lingying Zhao ◽  
Min Ye ◽  
Xinxin Xu

To address the comfort of an electric vehicle, a coupling mechanism between mechanical friction braking and electric regenerative braking was studied. A cooperative braking system model was established, and comprehensive simulations and system optimizations were carried out. The performance of the cooperative braking system was analyzed. The distribution of the braking force was optimized by an intelligent method, and the distribution of a braking force logic diagram based on comfort was proposed. Using an intelligent algorithm, the braking force was distributed between the two braking systems and between the driving and driven axles. The experiment based on comfort was carried out. The results show that comfort after optimization is improved by 76.29% compared with that before optimization by comparing RMS value in the time domain. The reason is that the braking force distribution strategy based on the optimization takes into account the driver’s braking demand, the maximum braking torque of the motor, and the requirements of vehicle comfort, and makes full use of the braking torque of the motor. The error between simulation results and experimental results is 5.13%, which indicates that the braking force’s distribution strategy is feasible.


2013 ◽  
Author(s):  
Junzhi Zhang ◽  
Chen Lv ◽  
Xiaowei Yue ◽  
Mingzhe Qiu ◽  
Jinfang Gou ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document