Regenerative Braking Control for High Level Deceleration on Low Mu Surface

2015 ◽  
Vol 4 (1) ◽  
pp. 209-224 ◽  
Author(s):  
Selim Oleksowicz ◽  
Keith Burnham ◽  
Navneesh Phillip ◽  
Phil Barber ◽  
Eddie Curry ◽  
...  
Keyword(s):  
Regenerative Braking ◽  
Braking Control ◽  
High Level

scholarly journals Regenerative braking control strategy based on joint high-efficiency optimization

2016 ◽  
Vol 8 (10) ◽  
pp. 168781401667353 ◽  
Author(s):  
Tao Deng ◽  
Chunsong Lin ◽  
Bin Chen ◽  
Chuanfu Ma
Keyword(s):  
Control Strategy ◽  
High Efficiency ◽  
Regenerative Braking ◽  
Efficiency Optimization ◽  
Braking Control

scholarly journals Research on braking energy recovery strategy of electric vehicle based on ECE regulation and I curve

2019 ◽  
Vol 103 (1) ◽  
pp. 003685041987776 ◽  
Author(s):  
Shengqin Li ◽  
Bo Yu ◽  
Xinyuan Feng
Keyword(s):  
Electric Vehicle ◽  
Control Strategy ◽  
Energy Recovery ◽  
State Of Charge ◽  
Regenerative Braking ◽  
Force Distribution ◽  
Battery State Of Charge ◽  
Braking Force ◽  
Braking Control ◽  
Braking Energy Recovery

Electric vehicles can convert the kinetic energy of the vehicle into electric energy for recycling. A reasonable braking force distribution strategy is the key to ensure braking stability and the energy recovery rate. For an electric vehicle, based on the ECE regulation curve and ideal braking force distribution (I curve), the braking force distribution strategy of the front and rear axles is designed to study the braking energy recovery control strategy. The fuzzy control method is adopted while the charging power limit of the battery is considered to correct the regenerative braking torque of the motor, the ratio of the regenerative braking force of the motor to the front axle braking force is designed according to different braking strengths, then the braking force distribution and braking energy recovery control strategies for regenerative braking and friction braking are developed. The simulation model of combined vehicle and energy recovery control strategy is established by Simulink and Cruise software. The braking energy recovery control strategy of this article is verified under different braking conditions and New European Driving Cycle conditions. The results show that the control strategy proposed in this article meets the requirements of braking stability. Under the condition of initial state of charge of 75%, the variation of state of charge of braking control strategy in this article is reduced by 8.22%, and the state of charge of braking strategy based on I curve reduces by 9.12%. The braking force distribution curves of the front and rear axle are in line with the braking characteristics, can effectively recover the braking energy, and improve the battery state of charge. Taking the using range of 95%–5% of battery state of charge as calculation target, the cruising range of vehicle with braking control strategy of this article increases to 136.64 km, which showed that the braking control strategy in this article could increase the cruising range of the electric vehicle.

scholarly journals Regenerative Braking Control Strategy of Electric-Hydraulic Hybrid (EHH) Vehicle

Energies ◽  
2017 ◽  
Vol 10 (7) ◽  
pp. 1038 ◽  
Author(s):  
Yang Yang ◽  
Chang Luo ◽  
Pengxi Li
Keyword(s):  
Control Strategy ◽  
Regenerative Braking ◽  
Hydraulic Hybrid ◽  
Braking Control

scholarly journals Intelligent Sliding Mode Scheme for Regenerative Braking Control

2018 ◽  
Vol 51 (25) ◽  
pp. 334-339
Author(s):  
Sulakshan Rajendran ◽  
Sarah Spurgeon ◽  
Georgios Tsampardoukas ◽  
Ric Hampson
Keyword(s):  
Sliding Mode ◽  
Regenerative Braking ◽  
Braking Control

Optimization of Regenerative Braking Control Strategy for Pure Electric Vehicle

2017 ◽  
Vol 872 ◽  
pp. 331-336 ◽  
Author(s):  
Zhi Jun Guo ◽  
Dong Dong Yue ◽  
Jing Bo Wu
Keyword(s):  
Electric Vehicle ◽  
Control Strategy ◽  
Energy Recovery ◽  
Pso Algorithm ◽  
Regenerative Braking ◽  
Recovery Efficiency ◽  
Constraint Optimization ◽  
Vehicle Model ◽  
Braking Torque ◽  
Braking Control

The regenerative braking strategy for precursor pure electric vehicle was studied in this paper. Firstly, a constraint optimization model was established for the braking force distribution, in which both braking stability and recovery efficiency of braking energy were taken into account. Secondly, Particle Swarm Optimization (PSO) algorithm was applied to optimize the multi key parameters in the model. Finally, the optimized braking torque of the motor was obtained at different speed, different braking strength and different battery charge state. A vehicle model was built to validate the optimized results through simulation. The results showed that, compared with the original control strategy, the optimized control strategy not only could increase the braking stability effectively, but also improve the energy recovery efficiency in a certain extent.

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