No abstract

Based on the movement resistance forces, the vehicle longitudinal dynamics is related to power demand for a specific route. The vehicle gear shifting influences significantly the acceleration performance and fuel consumption because it changes the engine operation point and the powertrain inertia. This paper presents a study based on the US06 velocity profile which involves high speed and high acceleration phases, where the vehicle performance is limited by both the engine power and the tire traction limit. For improving A c c e p t e d M a n u s c r i p t the vehicle performance without increasing fuel consumption, a genetic algorithm (GA) technique is used.

No abstract

The vehicle longitudinal dynamics is responsible for calculating the vehicle power consumption undergone a specific route, by means of the estimation of the forces acting on the system such as aerodynamic drag, rolling resistance, climbing resistance and the driver behavior. The gear shifting tactics influences the vehicle performance and fuel consumption because it changes the powertrain inertia and the engine speed. The literature presents gear-shifting strategies based on the engine power and torque as well as the fuel economy. The last tactics are difficult to determinate, because they depend on a large number of factors like vehicle speed, available transition ratios, engine efficiency, required acceleration, tire-ground traction limit and engine decoupling during the gear shifting process. This paper shows a study based on the Brazilian standard urban driving cycle NBR6601. As there are many factors involved in the vehicle behavior and also in the vehicle dynamics, it was developed an algorithm to optimize the gear shifting process: it makes a choice of the most adequate tactic for each cycle stretch. The analysis were performed by co-simulation between the multibody dynamics software Adams TM and Matlab/Simulink TM , in which is defined the vehicle power demand.

Summary Two big issues involving electric vehicles are energy supply and power management control. To deal with the energy supply problem, this paper proposes the application of a hybrid energy source system, composed of battery pack and ultracapacitor bank. The power management control between the energy supplies was defined by a fuzzy logic with inference rules optimized through genetic algorithm. The genetic algorithm optimizes lower and upper limits of membership functions aiming to reduce the hybrid energy source system total mass while maximizing the electric vehicle drive range and performance. Through the Pareto frontier, we found the best trade‐off solution.