System-Level Optimization of Hybrid Excitation Synchronous Machines for a Three-Wheel Electric Vehicle

2020 ◽  
Vol 6 (2) ◽  
pp. 690-702
Author(s):  
Ahmad Shah Mohammadi ◽  
Joao Pedro F. Trovao
2009 ◽  
Vol 58 (5) ◽  
pp. 2137-2149 ◽  
Author(s):  
Y. Amara ◽  
L. Vido ◽  
M. Gabsi ◽  
E. Hoang ◽  
A. Hamid Ben Ahmed ◽  
...  

Author(s):  
Taibi Ahmed ◽  
Hartani Kada ◽  
Allali Ahmed

In high power traction system applications two or more machines are fed by one converter. This topology results in a light, more compact and less costly system. These systems are called multi-machines single-converter systems. The problems posed by different electrical and mechanical couplings in these systems (MMS) affect various stages of the systems and require control strategy to reduce adverse effects. Control of multi-machines single-converter systems is the subject of this paper. The studied MMS is an electric vehicle with four in-wheel PMS motors. A three-leg inverter supplies two permanent magnet synchronous machines which are connected to the front right and rear right wheels, and another inverter supplies the left side. Several methods have been proposed for the control of multi-machines single-inverter systems, the master-slave control structure seems best adapted for our traction system. In this paper, a new control structure based on DTC method is used for the control of bi-machine traction system of an EV. This new control has been implanted in simulation to analyze its robustness in the presence of the various load cases involved in our electric vehicle traction chain. Simulation results indicated that this structure control allowed the stability of the traction system.


Author(s):  
Nehal Doshi ◽  
Drew Hanover ◽  
Sadra Hemmati ◽  
Christopher Morgan ◽  
Mahdi Shahbakhti

Abstract Integrated energy management across system level components in electric vehicles (EVs) is currently an under-explored space. Opportunity exists to mitigate energy consumption and extend usable range of EVs through optimal control strategies which exploit system dynamics via controls integration of vehicle subsystems. Additionally, information available in connected vehicles like driver schedules, trip duration and ambient conditions can be leveraged to predict the operating conditions for a vehicle when a validated model of the vehicle is known. In this study, data-driven and physics-based models for heating, ventilation and air-conditioning (HVAC) are developed and utilized along with the vehicle dynamics and powertrain (VD&PT) models for a hybrid electric vehicle (HEV). The integrated HVAC and VD&PT models are then validated against real world data. Next, an integrated relationship between the internal combustion (IC) engine coolant and the cabin electric heater is established and used to promote potential energy savings in cabin heating when the operating schedule is known. Finally, an optimization study is conducted to establish a control strategy which maximizes the HVAC energy efficiency whilst maintaining occupant comfort levels according to ASHRAE standards and improving usable range of the vehicle relative to its baseline calibration.


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