Abstract

Hybrid Electric Vehicles (HEVs) enable fuel savings by re-using kinetic and potential energy that was recovered and stored in a battery during braking or driving down hill. Besides, the vehicle itself can be seen as a storage device, where kinetic energy can be stored and retrieved by changing the forward velocity. It is beneficial for fuel consumption to optimize the velocity trajectory in two ways; i) to assist the driver in tracking an optimal velocity trajectory (e.g. input to an Adaptive Cruise Controller); ii) to estimate the future power request trajectory which can be used to optimize the hybrid components use. Taking advantage of satellite navigation, together with the vehicles current mass and road load parameters, an optimization problem is formulated, and solved for a driver defined time constraint. Despite tight velocity constraints, this can result in 5% fuel saving compared to a Cruise Controller with constant velocity setpoint. The benefit of velocity trajectory optimization is indicated with experimental results. © 2010 AACC.

Abstract

This paper presents an integrated vehicle model to simulate simultaneously the driver, powertrains, chassis, body, road condition, vehicle dynamics and the Active Suspension (AS) system with/without an energy harvesting module. The developed model is used to investigate the ride comfort and influences of energy harvesting AS system on the total energy consumption of battery Electric Vehicles (EVs) relative to EVs with a passive suspension system. Preliminary simulation results show that compared to EVs with a passive suspension system, the ones with AS system improve ride comfort, up to 31% reduction of the vehicle body acceleration RMS value, with an expense of higher energy consumption. This expense can be reduced to about 2.8% when using an energy harvesting AS system. Simulation results also demonstrate that the available energy for recuperation during the AS system operation is significant in relation to the regenerative braking energy of the propulsion system, up to approx. 70% on bumpy road surfaces.

Abstract

For conventional buses, based on the decades of their operational knowledge, public transport companies are able to optimize their cost of operation. However, with recent trend in the usage of electric buses, cost optimal operation can become challenging. In this paper an offline optimal charging strategy is developed to minimize the energy cost. This is done by exploiting the periodicity and predictable operation of the city buses. For effective usage of the developed offline strategy, the actual energy demand of the electric bus must be known a-priori, which can be demanding. In order to address this issue, a predictor is designed. The neural network based predictor is able to estimate the energy demand for the next day. Using this, three different optimal charging strategies are implemented. Initially, only the operational constraints are considered to ensure the completion of a trip, later, a more involved problems consisting of battery state of charge (SoC) constraints and temperature constraints are included for the second and third optimization problems, respectively. All the three approaches result in significant energy cost minimization when compared to the non-optimal strategy of charging the electric bus to the full capacity at every available opportunity. Additionally, for the second and third formulations, namely, SoC and temperature constraints, by using a qualitative aging approach, some enhancements in the battery health is observed when compared to the non-optimal charging strategy.

Abstract

In this paper TNO-Advance is presented. It is built in a modular way, therefore allowing quick assembling of specific vehicle configurations and easy integration of new component models and future evolutions of existing models. Its entensive library is built in Matlab/Simulink, taking advantage of its flexible and open architecture, the effective mathematical libraries and real-time application possibilities. TNO-Advance allows not only to develop, analyze and evaluate both chassis and powertrain systems, but also the interactions between them. All car suspensions can be represented and conventional as well as electric and hybrid electric vehicle powertrains. By applying the fast control options of Matlab/Simulink, chassis and powertrain controllers can be developed, as well as tested on robustness. Even interactions between vehiclesq, i.e. platooning, or new road transport systems, i.e. Intelligent Transport Systems and automatically guided vehicles, can be studied effectively.