3 research outputs found
Evaluation of the Driving Performance and User Acceptance of a Predictive Eco-Driving Assistance System for Electric Vehicles
In this work, a predictive eco-driving assistance system (pEDAS) with the
goal to assist drivers in improving their driving style and thereby reducing
the energy consumption in battery electric vehicles while enhancing the driving
safety and comfort is introduced and evaluated. pEDAS in this work is equipped
with two model predictive controllers (MPCs), namely reference-tracking MPC and
car-following MPC, that use the information from onboard sensors, signal phase
and timing (SPaT) messages from traffic light infrastructure, and geographical
information of the driving route to compute an energy-optimal driving speed. An
optimal speed suggestion and informative advice are indicated to the driver
using a visual feedback. pEDAS provides continuous feedback and encourages the
drivers to perform energy-efficient car-following while tracking a preceding
vehicle, travel at safe speeds at turns and curved roads, drive at
energy-optimal speed determined using dynamic programming in freeway scenarios,
and travel with a green-wave optimal speed to cross the signalized
intersections at a green phase whenever possible. Furthermore, to evaluate the
efficacy of the proposed pEDAS, user studies were conducted with 41
participants on a dynamic driving simulator. The objective analysis revealed
that the drivers achieved mean energy savings up to 10%, reduced the speed
limit violations, and avoided unnecessary stops at signalized intersections by
using pEDAS. Finally, the user acceptance of the proposed pEDAS was evaluated
using the Technology Acceptance Model (TAM) and Theory of Planned Behavior
(TPB). The results showed an overall positive attitude of users and that the
perceived usefulness and perceived behavioral control were found to be the
significant factors in influencing the behavioral intention to use pEDAS.Comment: Submitted to Transportation Research Part C: Emerging Technologies
Journa
Eco-Driving planification profile for electric motorcycles
Los perfiles de Eco-Driving son algoritmos capaces de utilizar información adicional para crear
recomendaciones o limitaciones sobre las capacidades del conductor. Aumentan la autonomía del
vehículo, pero actualmente su uso no está relacionado con la autonomía requerida por el
conductor. Por esta razón, en este trabajo, el desafío de la conducción ecológica se traduce en
un controlador óptimo de dos capas diseñado para vehículos eléctricos puros. Este controlador
está orientado a asegurar que la energía disponible sea suficiente para completar un viaje
demandado, agregando límites de velocidad para controlar la tasa de consumo de energía. Se
exponen y analizan los modelos mecánicos y eléctricos requeridos. La función de costo está
optimizada para corresponder a las necesidades de cada viaje de acuerdo con el comportamiento
del conductor, el vehículo y la información de la trayectoria. El controlador óptimo propuesto en
este trabajo es un controlador predictivo de modelo no lineal (NMPC) asociado a una optimización
unidimensional no lineal. La combinación de ambos algoritmos permite aumentar alrededor de un
50% la autonomía con una limitación del 30% de las capacidades de velocidad y aceleración.
Además, el algoritmo es capaz de asegurar una autonomía final con un 1,25% de error en presencia
de ruido de sensor y actuador.The Eco-Driving profiles are algorithms capable to use additional information in order to create
recommendations or limitation over the driver capabilities. They increase the autonomy of the
vehicle but currently its usage is not related to the autonomy required by the driver. For this
reason, in this paper, the Eco-Driving challenge is translated into two layers optimal controller
designed for pure electric vehicles. This controller is oriented to ensure that the energy available
is enough to complete a demanded trip, adding speed limits to control the energy consumption
rate. The mechanical and electrical models required are exposed and analyzed. The cost function
is optimized to correspond to the needs of each trip according to driver behavior, vehicle and
trajectory information. The optimal controller proposed in this paper is a nonlinear model
predictive controller (NMPC) associated to a nonlinear unidimensional optimization. The
combination of both algorithms lets to increase around 50% the autonomy with a limitation of the
30% of the speed and acceleration capabilities. Also, the algorithm is capable to ensure a final
autonomy with a 1.25% of error in the presence of sensor and actuator noise.Doctor en IngenieríaDoctorad