Mu Synthesis Robust Control on Regenerative Braking of Hybrid Power System Electric Vehicle

2013 ◽  
Author(s):  
Yan Qun ◽  
Xu Peng ◽  
Yu Da-Tai ◽  
Tang Yong-Xin
Keyword(s):  
Robust Control ◽  
Power System ◽  
Electric Vehicle ◽  
Regenerative Braking ◽  
Hybrid Power System ◽  
Hybrid Power ◽  
Mu Synthesis

scholarly journals Optimal Control for Hybrid Energy Storage Electric Vehicle to Achieve Energy Saving Using Dynamic Programming Approach

Energies ◽  
2019 ◽  
Vol 12 (4) ◽  
pp. 588 ◽  
Author(s):  
Chaofeng Pan ◽  
Yanyan Liang ◽  
Long Chen ◽  
Liao Chen
Keyword(s):  
Dynamic Programming ◽  
Energy Consumption ◽  
Power System ◽  
Energy Saving ◽  
Electric Vehicle ◽  
System Efficiency ◽  
Rule Based ◽  
Hybrid Power System ◽  
Hybrid Power ◽  
Simulation Results

In this paper, the efficiency characteristics of battery, super capacitor (SC), direct current (DC)-DC converter and electric motor in a hybrid power system of an electric vehicle (EV) are analyzed. In addition, the optimal efficiency model of the hybrid power system is proposed based on the hybrid power system component’s models. A rule-based strategy is then proposed based on the projection partition of composite power system efficiency, so it has strong adaptive adjustment ability. Additionally. the simulation results under the New European Driving Cycle (NEDC) condition show that the efficiency of rule-based strategy is higher than that of single power system. Furthermore, in order to explore the maximum energy-saving potential of hybrid power electric vehicles, a dynamic programming (DP) optimization method is proposed on the basis of the establishment of the whole hybrid power system, which takes into account various energy consumption factors of the whole system. Compared to the battery-only EV based on simulation results, the hybrid power system controlled by rule-based strategy can decrease energy consumption by 13.4% in line with the NEDC condition, while the power-split strategy derived from the DP approach can reduce energy consumption by 17.6%. The results show that compared with rule-based strategy, the optimized DP strategy has higher system efficiency and lower energy consumption.

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