Eco-driving technology for sustainable road transport: A review

© 2018 Elsevier Ltd Road transport consumes significant quantities of fossil fuel and accounts for a significant proportion of CO2 and pollutant emissions worldwide. The driver is a major and often overlooked factor that determines vehicle performance. Eco-driving is a relatively low-cost and immediate measure to reduce fuel consumption and emissions significantly. This paper reviews the major factors, research methods and implementation of eco-driving technology. The major factors of eco-driving are acceleration/deceleration, driving speed, route choice and idling. Eco-driving training programs and in-vehicle feedback devices are commonly used to implement eco-driving skills. After training or using in-vehicle devices, immediate and significant reductions in fuel consumption and CO2 emissions have been observed with slightly increased travel time. However, the impacts of both methods attenuate over time due to the ingrained driving habits developed over the years. These findings imply the necessity of developing quantitative eco-driving patterns that could be integrated into vehicle hardware so as to generate more constant and uniform improvements, as well as developing more effective and lasting training programs and in-vehicle devices. Current eco-driving studies mainly focus on the fuel savings and CO2 reduction of individual vehicles, but ignore the pollutant emissions and the impacts at network levels. Finally, the challenges and future research directions of eco-driving technology are elaborated

Predictive energy-efficient motion trajectory optimization of electric vehicles

This work uses a combination of existing and novel methods to optimize the motion trajectory of an electric vehicle in order to improve the energy efficiency and other criteria for a predefined route. The optimization uses a single combined cost function incorporating energy efficiency, travel safety, physical feasibility, and other criteria. Another focus is the optimal behavior beyond the regular optimization horizon

Encouraging eco-driving: the case for vibrotactile information presented through the accelerator pedal

different methods of in-vehicle information presentation to encourage fuel efficient driving behaviours, and to explore the theoretical justifications for the use of in-vehicle haptic stimuli (related to the sense of touch), presented at the site of control (i.e., the accelerator pedal). A review of the literature concerning design, behaviour, and energy use led on to an exploration of Ecological Interface Design, and the Skills, Rules, and Knowledge (SRK) taxonomy of human behaviour, particularly with regard to haptic information presented through the accelerator pedal. Survey and on-road studies served to shed light on the practice of eco-driving more generally, in terms of attitudes, knowledge, behaviour, and cognition. Then followed an analysis of expert eco-drivers’ decision-making processes. This made use of the decision ladder, an analysis tool rooted in the SRK framework. Results of the analysis went on to inform the design of an in-vehicle information system that aimed to support optimum use of the accelerator pedal, both for efficient accelerations, and for maximisation of the coasting phase of the vehicle when approaching deceleration events. A simulator-based experiment served to assess the effects of presenting stimuli in different sensory modes (visual, auditory, vibrotactile), resulting in the conclusion that vibrotactile feedback, being both effective and well received by participants, is indeed suitable for the support of eco-driving. In a second simulator-based study, coasting support provided the sole focus; acceleration behaviours were not investigated. Results suggested that there is a minimum distance away from an event below which stimuli encouraging removal of the foot from the accelerator pedal (in order to coast down to the desired speed)have neither a beneficial effect on driving performance, nor attract positive acceptance ratings from users. Moreover, stimuli presented farther from the event supported greater benefits in terms of efficiency. Overall findings are discussed with regard to the practical aspect of how best to support eco-driving in the private road vehicle, and in relation to the theoretical justifications for accelerator-based haptic feedback in the vehicle

Energy-Efficient and Semi-automated Truck Platooning

This open access book presents research and evaluation results of the Austrian flagship project “Connecting Austria,” illustrating the wide range of research needs and questions that arise when semi-automated truck platooning is deployed in Austria. The work presented is introduced in the context of work in similar research areas around the world. This interdisciplinary research effort considers aspects of engineering, road-vehicle and infrastructure technologies, traffic management and optimization, traffic safety, and psychology, as well as potential economic effects. The book’s broad perspective means that readers interested in current and state-of-the-art methods and techniques for the realization of semi-automated driving and with either an engineering background or with a less technical background gain a comprehensive picture of this important subject. The contributors address many questions such as: Which maneuvers does a platoon typically have to carry out, and how? How can platoons be integrated seamlessly in the traffic flow without becoming an obstacle to individual road users? What trade-offs between system information (sensors, communication effort, etc.) and efficiency are realistic? How can intersections be passed by a platoon in an intelligent fashion? Consideration of diverse disciplines and highlighting their meaning for semi-automated truck platooning, together with the highlighting of necessary research and evaluation patterns to address such a broad task scientifically, makes Energy-Efficient and Semi-automated Truck Platooning a unique contribution with methods that can be extended and adapted beyond the geographical area of the research reported

The Critical Role of Public Charging Infrastructure

Editors: Peter Fox-Penner, PhD, Z. Justin Ren, PhD, David O. JermainA decade after the launch of the contemporary global electric vehicle (EV) market, most cities face a major challenge preparing for rising EV demand. Some cities, and the leaders who shape them, are meeting and even leading demand for EV infrastructure. This book aggregates deep, groundbreaking research in the areas of urban EV deployment for city managers, private developers, urban planners, and utilities who want to understand and lead change

Potencial d'estalvi energètic en sistemes de transport públic urbans mitjançant sistemes d'avís al conductor

[ANGLÈS] Stops of public transport vehicles along a corridor are a source of delays in travel time and an increase of energy consumption. At the chair for Traffic Control Systems and Process Automation at Technische Universität Dresden (TU Dresden) a driver advisory system has been developed, which supports light rail drivers during the approach of the vehicle to traffic lights in a segregated right-of-way (no mixed traffic). The driver is advised the optimal speed so that the vehicle arrives at the traffic light with the “green wave”, avoiding the energy consumption derived not only from a maximum velocity driving but also from braking and accelerating. The aim of this thesis is to estimate the energy that could be saved in light rail lines through this support system, following an optimal energy driving strategy but taking ontime arrivals as first priority. A tool has been created to simulate from theoretical formulation the energy consumption with and without advice and estimate the potential savings that could be reached. Real energy data from Dresden light rail has been used to obtain the consumed energy without advice and calibrate the mentioned tool, giving reliability to the model and the resulting values. Moreover, the idea of creating a line estimation tool has been also developed trying to obtain a rough number on energy savings through the introduction of few trivial data. The results obtained in this thesis show that the application of the presented driver advisory system can lead to savings of 10-20%, when driving on own right-of-way in city center. As it is likely that in some cases the driver shows reluctant to follow the advice, a conservative hypothesis leads to results reduced to a 75% with values of savings around 8-15%. Therefore, the final estimated savings are around 8-20% considering all possible scenarios. As direct consequences to users, in order to reach the 8-20% of savings, time schedule should remain invariable as the little deviation that the application of the system could mean, is to be compensated along the line or rectified by the system itself. The purpose of this thesis has been to use the waiting time at traffic lights as a way to save energy, but always giving priority to arrivals on-time over these possible savings. The system must guarantee a high reliability to ensure these on-time arrivals and avoid mid stops at traffic lights due to bad calculations. A misleading of the system could reduce the trust of the driver, who could refuse following the given advice in the future. Finally, time at stops would be incremented as some of the waiting time at traffic lights would be transferred to stops. Although a certain interrogation around how users would react (possible incomprehension), a longer dwell time allows to take more passengers, saving time to the ones who by arriving late would have missed the tramway; but not incrementing the trip time for other users. Without advice, this extra time would be useless time stopped at a traffic lights.[CASTELLÀ] Las paradas de los vehículos de transporte público a lo largo de un corredor son el origen de demoras en tiempo de viaje y de un aumento del consumo energético. En el departamento de Sistemas de Control de Tráfico y Automatización de Procesos de la Technische Universität Dresden (TU Dresden) se ha desarrollado un sistema inteligente de asesoramiento al conductor que informa a los conductores de tranvías durante la aproximación a un semáforo en vías segregadas (sin tráfico mixto). Así pues, al conductor se le informa de la velocidad óptima de recorrido para llegar al semáforo siguiente dentro de la "ola verde", evitando el consumo de energía derivado, tanto de una conducción a velocidad máxima, como del proceso de frenada y aceleración. El objetivo de esta tesina es estimar la energía que se puede ahorrar en líneas de tranvía a través de este sistema de soporte, siguiendo una conducción óptima en energía y sin provocar llegadas fuera de horario. Con esta finalidad, se ha creado una herramienta para simular a partir de formulación teórica el consumo de energía con y sin asesoramiento y estimar el ahorro potencial que se podría alcanzar. Dadas reales de energía de las líneas de tranvía de Dresde se han utilizado para obtener la cantidad de energía consumida sin sistema de soporte y así, calibrar la herramienta mencionada dando fiabilidad al modelo y a los resultados. Además, también se ha desarrollado la idea de crear una herramienta rápida de estimación para obtener una cifra aproximada de ahorro energético mediante la introducción de datos triviales. Los resultados obtenidos en la tesina demuestran que la aplicación del sistema inteligente de asesoramiento al conductor representa un ahorro de un 10-20%, en vías segregadas y en núcleo urbano. Como es de esperar que en algunos casos el conductor se muestre contrario a seguir las recomendaciones del sistema, aplicando una hipótesis conservadora los resultados se reducen a un 75% con valores del orden de 8-15% de ahorro. Así pues, los valores finales de ahorro estimados son del orden del 8-20% teniendo en cuenta todos los posibles escenarios. Como consecuencia directa hacia los usuarios, para llegar a este 8-20% de ahorro, no se prevé una alteración del tiempo de viaje, ya que las posibles pequeñas desviaciones que podría provocar la aplicación del sistema se compensan a lo largo de la línea o son rectificadas por el propio sistema. El objetivo de esta tesina ha sido utilizar el tiempo en semáforos para ahorrar energía, pero siempre dando prioridad a las llegadas dentro del horario. El sistema ha de garantizar una gran fiabilidad per asegurar estas llegadas a tiempo y evitar paradas intermedias resultado de un mal cálculo. Un asesoramiento erróneo podría reducir la confianza del conductor en el sistema y que este no siguiese las recomendaciones en un futuro. Por último, el tiempo en parada aumentaría proveniente del que antes era tiempo en semáforos. Aunque no está claro como reaccionaría el usuario ante tal efecto, un tiempo en parada más largo permite transportar más pasajeros por viaje, ahorrando tiempo a esos usuarios que hubiesen perdido el tranvía de haber salido antes; pero sin aumentar el tiempo de viaje de los otros usuarios. Sin sistema de asesoramiento al conductor, este tiempo extra sería tiempo perdido en semáforos.[CATALÀ] Les aturades dels vehicles de transport públic al llarg d'un corredor són l'origen de demores en temps de viatge i d'un augment del consum energètic. Al departament de Sistemes de Control de Trànsit i Automatització de Processos de la Technische Universität Dresden (TU Dresden) s’ha desenvolupat un sistema intel·ligent d’assessorament al conductor que informa als conductors de tramvies durant l’aproximació a un semàfor en vies segregades (sense trànsit mix). Així doncs, al conductor se l’informa de la velocitat òptima de recorregut per tal d'arribar al semàfor següent en "l’ona verda", evitant el consum d’energia derivat, tant d’una conducció a velocitat màxima, com del procés de frenada i acceleració. L’objectiu d’aquesta tesina és estimar l’energia que es pot estalviar en línies de tramvia a través d’aquest sistema de suport, seguint una conducció òptima en energia, però sense provocar arribades fora d’horari. Amb aquesta finalitat, s’ha creat una eina per simular a partir de formulació teòrica el consum d’energia amb i sense assessorament i estimar l’estalvi potencial que es podria aconseguir. Dades reals d’energia de les línies de tramvia de Dresden s’han utilitzat per obtenir la quantitat d’energia consumida sense sistema de suport i així, calibrar l’eina esmentada donant fiabilitat al model i als resultats. A més a més, també s’ha desenvolupat la idea de crear una eina ràpida d’estimació per obtenir una xifra aproximada d’estalvi energètic mitjançant la introducció de dades trivials. Els resultats obtinguts en la tesina demostren que l’aplicació del sistema intel·ligent d’assessorament al conductor comporta un estalvi d’un 10-20%, en vies segregades i en nucli urbà. Com és d’esperar que en alguns casos el conductor es mostri contrari a seguir les recomanacions del sistema, aplicant una hipòtesi conservadora els resultats es redueixen a un 75% amb valors de l’ordre de 8-15% d’estalvi. Així doncs, els valors finals d’estalvi estimats són de l’ordre del 8-20% tenint en compte tots els possibles escenaris. Com a conseqüència directa cap als usuaris, per tal d’arribar a aquest 8-20% d’estalvi, no es preveu una alteració del temps de viatge, ja que les possibles petites desviacions que podria provocar l’aplicació del sistema es compensen al llarg de la línea o són rectificades pel propi sistema. L’objectiu d’aquesta tesina ha sigut utilitzar el temps aturat en semàfors per estalviar energia, però sempre donant prioritat a les arribades dins l’horari. El sistema ha de garantir una gran fiabilitat per assegurar aquestes arribades a temps i evitar parades intermèdies resultat d’un mal càlcul. Un assessorament erroni podria reduir la confiança del conductor en el sistema i fer que aquest no seguís les recomanacions en un futur. Per últim, el temps en parada augmentaria provinent del que abans era temps en semàfors. Encara que no és clar com reaccionaria l’usuari davant de tal efecte, un temps en parada més llarg permet prendre més passatgers per viatge, estalviant temps a aquells usuaris que haguessin perdut el tramvia d’haver sortit abans; però sense augmentar el temps de viatge pels altres usuaris. Sense sistema d’assessorament al conductor, aquest temps extra seria temps perdut en semàfors

Stochastic Model Predictive Control for Eco-Driving Assistance Systems in Electric Vehicles

Electric vehicles are expected to become one of the key elements of future sustainable transportation systems. The first generation of electric cars are already commercially available but still, suffer from problems and constraints that have to be solved before a mass market might be created. Key aspects that will play an important role in modern electric vehicles are range extension, energy efficiency, safety, comfort as well as communication. An overall solution approach to integrating all these aspects is the development of advanced driver assistance systems to make electric vehicles more intelligent. Driver assistance systems are based on the integration of suitable sensors and actuators as well as electronic devices and software-enabled control functionality to automatically support the human driver. Driver assistance for electric vehicles will differ from the already used systems in fuel-powered cars such as electronic stability programs, adaptive cruise control etc. in a way that they must support energy efficiency while the system itself must also have a low power consumption. In this work, an eco-driving functionality as the first step towards those new driver assistance systems for electric vehicles will be investigated. Using information about the internal state of the car, navigation information as well as advanced information about the environment coming from sensors and network connections, an algorithm will be developed that will adapt the speed of the vehicle automatically to minimize energy consumption. From an algorithmic point of view, a stochastic model predictive control approach will be applied and adapted to the special constraints of the problem. Finally, the solution will be tested in simulations as well as in first experiments with a commercial electric vehicle in the SnT Automation & Robotics Research Group (SnT ARG)

Energy-Efficient and Semi-automated Truck Platooning

This open access book presents research and evaluation results of the Austrian flagship project “Connecting Austria,” illustrating the wide range of research needs and questions that arise when semi-automated truck platooning is deployed in Austria. The work presented is introduced in the context of work in similar research areas around the world. This interdisciplinary research effort considers aspects of engineering, road-vehicle and infrastructure technologies, traffic management and optimization, traffic safety, and psychology, as well as potential economic effects. The book’s broad perspective means that readers interested in current and state-of-the-art methods and techniques for the realization of semi-automated driving and with either an engineering background or with a less technical background gain a comprehensive picture of this important subject. The contributors address many questions such as: Which maneuvers does a platoon typically have to carry out, and how? How can platoons be integrated seamlessly in the traffic flow without becoming an obstacle to individual road users? What trade-offs between system information (sensors, communication effort, etc.) and efficiency are realistic? How can intersections be passed by a platoon in an intelligent fashion? Consideration of diverse disciplines and highlighting their meaning for semi-automated truck platooning, together with the highlighting of necessary research and evaluation patterns to address such a broad task scientifically, makes Energy-Efficient and Semi-automated Truck Platooning a unique contribution with methods that can be extended and adapted beyond the geographical area of the research reported

Belief State Planning for Autonomous Driving: Planning with Interaction, Uncertain Prediction and Uncertain Perception

This thesis presents a behavior planning algorithm for automated driving in urban environments with an uncertain and dynamic nature. The uncertainty in the environment arises by the fact that the intentions as well as the future trajectories of the surrounding drivers cannot be measured directly but can only be estimated in a probabilistic fashion. Even the perception of objects is uncertain due to sensor noise or possible occlusions. When driving in such environments, the autonomous car must predict the behavior of the other drivers and plan safe, comfortable and legal trajectories. Planning such trajectories requires robust decision making when several high-level options are available for the autonomous car. Current planning algorithms for automated driving split the problem into different subproblems, ranging from discrete, high-level decision making to prediction and continuous trajectory planning. This separation of one problem into several subproblems, combined with rule-based decision making, leads to sub-optimal behavior. This thesis presents a global, closed-loop formulation for the motion planning problem which intertwines action selection and corresponding prediction of the other agents in one optimization problem. The global formulation allows the planning algorithm to make the decision for certain high-level options implicitly. Furthermore, the closed-loop manner of the algorithm optimizes the solution for various, future scenarios concerning the future behavior of the other agents. Formulating prediction and planning as an intertwined problem allows for modeling interaction, i.e. the future reaction of the other drivers to the behavior of the autonomous car. The problem is modeled as a partially observable Markov decision process (POMDP) with a discrete action and a continuous state and observation space. The solution to the POMDP is a policy over belief states, which contains different reactive plans for possible future scenarios. Surrounding drivers are modeled with interactive, probabilistic agent models to account for their prediction uncertainty. The field of view of the autonomous car is simulated ahead over the whole planning horizon during the optimization of the policy. Simulating the possible, corresponding, future observations allows the algorithm to select actions that actively reduce the uncertainty of the world state. Depending on the scenario, the behavior of the autonomous car is optimized in (combined lateral and) longitudinal direction. The algorithm is formulated in a generic way and solved online, which allows for applying the algorithm on various road layouts and scenarios. While such a generic problem formulation is intractable to solve exactly, this thesis demonstrates how a sufficiently good approximation to the optimal policy can be found online. The problem is solved by combining state of the art Monte Carlo tree search algorithms with near-optimal, domain specific roll-outs. The algorithm is evaluated in scenarios such as the crossing of intersections under unknown intentions of other crossing vehicles, interactive lane changes in narrow gaps and decision making at intersections with large occluded areas. It is shown that the behavior of the closed-loop planner is less conservative than comparable open-loop planners. More precisely, it is even demonstrated that the policy enables the autonomous car to drive in a similar way as an omniscient planner with full knowledge of the scene. It is also demonstrated how the autonomous car executes actions to actively gather more information about the surrounding and to reduce the uncertainty of its belief state