Abstract
Automotive applications often experience conflicting-objective optimization problems focusing on performance parameters that are catered through precisely developed cost functions. Two such conflicting objectives which substantially affect the working of traction machine drive are maximizing its speed performance and minimizing its energy consumption. In case of an electric vehicle (EV) powertrain, drive energy is bounded by battery dynamics (charging and capacity) which depend on the consumption of drive voltage and current caused by driving cycle schedules, traffic state, EV loading, and drive temperature. In other words, battery consumption of an EV depends upon its drive energy consumption. A conventional control technique improves the speed performance of EV at the cost of its drive energy consumption. However, the proposed optimized energy control (OEC) scheme optimizes this energy consumption by using robust linear parameter varying (LPV) control tuned by genetic algorithms which significantly improves the EV powertrain performance. The analysis of OEC scheme is conducted on the developed vehicle simulator through MATLAB/Simulink based simulations as well as on an induction machine drive platform. The accuracy of the proposed OEC is quantitatively assessed to be 99.3% regarding speed performance which is elaborated by the drive speed, voltage, and current results against standard driving cycles.
Highlights
It can be clearly observed from the figure that even at a higher temperature, the dynamic characteristics of the drive in case of optimized energy control (OEC) scheme in comparison with Higher Order sliding mode control (SMC) are quite close to the characteristics at room temperature at which the resistances are at their nominal values
This paper presents an efficient optimized energy control scheme (OEC) for addresing two conflicting objectives of the electric vehicle (EV) powertrain, which are maximizing its speed performance and minimizing its drive energy consumption
The proposed methodology utilizes an linear parameter varying (LPV) control technique tuned by genetic algorithms to achieve the desired control objectives
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. These algorithms originate from stochastic searching stimulated by a natural variety of species and manipulate optimal results for discontinuous problems by genetic formation [21] These algorithms are implemented in control systems to address the conflicting controller objectives [22]. As per the author’s knowledge, the genetic algorithm-optimized LPV-based control scheme has not been applied in the literature to address the conflicting objectives of reduced drive energy consumption and improved vehicle speed performance for an EV powertrain. There is a necessity for designing and implementing such an optimized energy control scheme that excellently addresses these significant machine drive objectives against standard driving cycles with a wide range of parameter uncertainties to improve the performance capability of EV powertrain.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
