A Series Regenerative Braking Control Strategy Based on Hybrid-Power

2012 ◽  
Author(s):  
Feng Wang ◽  
Xiaomei Yin ◽  
Huangqiang Luo ◽  
Ying Huang
Keyword(s):  
Control Strategy ◽  
Regenerative Braking ◽  
Hybrid Power ◽  
Braking Control

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

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.

scholarly journals Multi-Objective Optimization Study of Regenerative Braking Control Strategy for Range-Extended Electric Vehicle

2020 ◽  
Vol 10 (5) ◽  
pp. 1789 ◽  
Author(s):  
Hanwu Liu ◽  
Yulong Lei ◽  
Yao Fu ◽  
Xingzhong Li
Keyword(s):  
Control Strategy ◽  
Regenerative Braking ◽  
Multi Objective Optimization ◽  
Braking Performance ◽  
Multi Objective ◽  
Capacity Loss ◽  
Battery Capacity ◽  
Braking Control ◽  
Braking Energy Recovery ◽  
Adhesion Condition

Currently, the researches on the regenerative braking system (RBS) of the range-extended electric vehicle (R-EEV) are inadequate, especially on the comparison and analysis of the multi-objective optimization (MOO) problem. Actually, the results of the MOO problem should be mutually independent and balanced. With the aim of guaranteeing comprehensive regenerative braking performance (CRBP), a revised regenerative braking control strategy (RRBCS) is introduced, and a method of the MOO algorithm for RRBCS is proposed to balance the braking performance (BP), regenerative braking loss efficiency (RBLE), and battery capacity loss rate (BCLR). Firstly, the models of the main components related to the RBS of the R-EEV for the calculation of optimization objectives are built in MATLAB/Simulink and AVL/Cruise. The BP, RBLE, and BCLR are selected as the optimization objectives. The non-dominated sorting genetic algorithm (NSGA-II) is applied in RRBCS to solve the MOO problem, and a group of the non-inferior Pareto solution sets are obtained. The simulation results show a clear conflict that three optimization objectives cannot be optimal at the same time. Then, we evaluate the performance of the proposed method by taking the individual with the optimal CRBP as the final optimal solution. The comparation among BP, RBLE, BCLR, and CRBP before and after optimization are analyzed and discussed. The results illustrate that characteristic parameters of RRBCS is crucial to optimization objectives. After parameters optimization, regenerative braking torque works early to increase braking energy recovery on low tire-road adhesion condition, and to reduce the battery capacity loss rate at the expense of small braking energy recovery on the medium tire-road adhesion condition. In addition, the results of the sensitivity analysis show that after parameter optimization, RRBCS is proved to perform better road adaptability regarding the distribution of solutions. These results thoroughly validate the proposed approach for multi-objective optimization of RRBCS and have a strong directive to optimize the control strategy parameters of RBS.

Analyses of the Relation Between Degree of Mixing and Regenerative Braking in Hybrid Electric Vehicles

2014 ◽  
Vol 926-930 ◽  
pp. 743-746 ◽  
Author(s):  
Jing Ming Zhang ◽  
Jin Long Liu ◽  
Ming Zhi Xue
Keyword(s):  
Electric Vehicles ◽  
Control Strategy ◽  
Recovery Rate ◽  
Hybrid Electric Vehicles ◽  
Front Wheel ◽  
Regenerative Braking ◽  
Theoretical Derivation ◽  
Matlab Simulink ◽  
Hybrid Electric ◽  
Braking Control

The introduction of driving motors brings in the function of regenerative braking for Hybrid Electric Vehicles (HEV). In order to study the further information of regenerative braking, the relation between the degree of mixing in HEV and the recovery rate of regenerative braking was analyzed. The study object was the front-wheel driving HEV with the wire-control composite regenerative braking control strategy. Conclusions were deduced through the theoretical derivation. The braking model was established on the platform in MATLAB/SIMULINK and it was simulated within a HEV. The results indicate that the recovery rate would increase if the degree of mixing rises.

scholarly journals Development of a regenerative braking control strategy for hybridized solar vehicle

2016 ◽  
Vol 49 (11) ◽  
pp. 497-504 ◽  
Author(s):  
M. Grandone ◽  
M. Naddeo ◽  
D. Marra ◽  
G. Rizzo
Keyword(s):  
Control Strategy ◽  
Regenerative Braking ◽  
Braking Control
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