Eco-driving Assistance System Research Articles

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 (BEVs) 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. Moreover, the warning alerts during unsafe car-following situations are provided through an auditory 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 eco-driving assistance system, 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.

The need for eco-driving technologies in urban public transport

This article discusses the challenges facing public transport in Ukraine in terms of reducing fuel consumption and emissions. The absence or insufficient development of means and methods for monitoring driver behaviour, as well as high staff turnover, create significant difficulties in controlling drivers and vehicles. A conducted study in Lutsk, the administrative center of the Volyn region, analyzed the driving behavior of passenger buses in the city. Results showed that typical driving modes include idling (40%), acceleration (18%), driving at a constant speed (29%), and braking (13%). The study also revealed average accelerations and decelerations, and these results do not meet the requirements of ecological driving. The correlation between driver behavior and these dynamic acceleration and braking characteristics has been established. Possible causes for this phenomenon are discussed in the study. The article proposes the introduction of modern solutions to solve these problems. These solutions are Eco-Driving Assistance Systems (EDAS) or integrated systems, such as FleetControl from TRIONA, which can help learning operating conditions and reduce fuel consumption and emissions. These programmes can also serve as effective monitoring tools for individual drivers and transport companies. This paper describes these applications and reviews the research related to their use and development. In addition, the article highlights the importance of training drivers in eco-driving as a cost-effective method of improving fuel efficiency in transport companies. The paper concludes by emphasising the need for further research to fully understand the complexities of public transport in Ukraine and the potential benefits of introducing innovative technologies for a more sustainable and efficient future for the industry.

Eco-Driving Strategy Implementation for Ultra-Efficient Lightweight Electric Vehicles in Realistic Driving Scenarios

This paper aims to provide a quantitative assessment of the effect of driver action and road traffic conditions in the real implementation of eco-driving strategies. The study specifically refers to an ultra-efficient battery-powered electric vehicle designed for energy-efficiency competitions. The method is based on the definition of digital twins of vehicle and driving scenario. The models are used in a driving simulator to accurately evaluate the power demand. The vehicle digital twin is built in a co-simulation environment between VI-CarRealTime and Simulink. A digital twin of the Brooklands Circuit (UK) is created leveraging the software RoadRunner. After validation with actual telemetry acquisitions, the model is employed offline to find the optimal driving strategy, namely, the optimal input throttle profile, which minimizes the energy consumption over an entire lap. The obtained reference driving strategy is used during real-time driving sessions at the dynamic driving simulator installed at Politecnico di Milano (DriSMi) to include the effects of human driver and road traffic conditions. Results assess that, in a realistic driving scenario, the energy demand could increase more than 20% with respect to the theoretical value. Such a reduction in performance can be mitigated by adopting eco-driving assistance systems.

Driving Mode Advice for Eco-Driving Assistance System With Driver Reaction Delay Compensation

Current research on eco-driving has focused on solving an optimal control problem (OCP) to generate a speed profile to be tracked by drivers. The optimization variables are often chosen as low level control actions such as engine torque and gear positions, which leads to complex mixed-integer problems. In this brief, we propose an eco-driving assistance system (EDAS) to improve current EDAS in two aspects. First, an adviser with simplified dynamical model is designed using model predictive control (MPC) to provide not only a speed profile but also real-time high level driving mode advice such as cruising and coasting. Second, drivers’ reaction delay to follow the driving mode advice is explicitly compensated, which mitigates the effect of such delay on fuel savings. The time delay is converted to a spatial parameter in the MPC to provide driving advice in advance. To demonstrate the efficiency and effectiveness of the proposed EDAS, a numerical study and a simulation using measured data from a real-life driving test is conducted. Comparisons are made between the proposed EDAS and an eco-driving controller considering both high and low level control inputs without driver’s reaction delay.

The Benefit of Assisted and Unassisted Eco-Driving for Electrified Powertrains

Eco-driving assistance systems that encourage drivers to engage in fuel-saving behavior are effective at improving energy-efficiency, with recent research directed towards incorporating predictive models of energy losses in these systems to optimize recommendations. In this article, we evaluate a predictive eco-driving assistance system on three powertrains: a combustion engine-driven vehicle, a parallel hybrid electric vehicle, and a battery electric vehicle. In each case, energy consumption is found by applying a quasi-static model to driving simulator data for a simulated route including urban, rural, and highway sections. We find that both assisted and unassisted eco-driving has a beneficial effect in all cases, with the assistance system leading to reductions in energy usage of 6.1%, 15.9%, and 16.6% for the combustion engine, hybrid electric, and battery electric powertrains, respectively.

Implementation of ECO Driving Assistance System based on IoT

Implementation of ECO Driving Assistance System based on IoT

Incorporating Driver Preferences Into Eco-Driving Assistance Systems Using Optimal Control

Recently there have been several proposals for `eco-driving assistance systems', designed to save fuel or electrical power by encouraging behaviours such as gentle acceleration and coasting to a stop. These systems use optimal control to find driving behaviour that minimises vehicle energy losses. In this paper, we introduce a methodology to account for driver preferences on acceleration, braking, following distances and cornering speed in such eco-driving optimal control problems. This consists of an optimal control model of acceleration and braking behaviour containing several physically-meaningful parameters to describe driver preferences. If used in combination with a model of fuel or energy consumption, this can provide an adjustable trade-off between satisfying those preferences and minimising energy losses. We demonstrate that the model gives comparable performance to existing car-following and cornering models when predicting drivers' speed in these situations by comparison with real-world driving data. Finally, we present an example highway braking scenario for an electric vehicle, illustrating a trade-off between satisfying driver preferences on vehicle speed and acceleration and reducing electrical energy usage by up to 43%.

Real-time Driving Mode Advice for Eco-driving using MPC

This paper proposes an on-line advice eco-driving assistance system (EDAS) for providing the optimal velocity profile to improve fuel economy. The EDAS employs a driver-in-the-loop (DIL) framework, where an adviser is designed to provide high-level driving mode suggestions while the low-level control commands such as throttle and brake, are left to the driver to implement. A simplified dynamic model is developed in the adviser excluding continuous-time control variables such as the engine torque and engine brake torque. The adviser employs an event-triggered model predictive control (MPC) algorithm to provide suggestions in realtime using predictive road and traffic information. On-line computational cost for the MPC has been significantly reduced using an efficient mixed-integer optimal control (MIOC) algorithm. To demonstrate the efficiency and effectiveness of the proposed EDAS, a numerical study and a simulation using measured data from a real-life driving test is conducted. Comparisons are made between the proposed EDAS and an eco-driving controller considering both high and low level control inputs without a driver.

Pedagogical learning supports based on human–systems inclusion applied to rail flow control

The paper presents the new concept on human–systems inclusion for designing, analyzing or evaluating human–machine systems and applied it to two case studies on rail flow control. Human–systems inclusion is related to the development of online learning abilities using pedagogical learning supports and aims at applying both automation-supported human and human-supported automation processes in which a human is supported by automation or automation is supported by a human, respectively. Different learning supports are proposed: accident reports, course supports, internet accesses, simulators, technical systems, etc. People are free to use or not to use them to learn alone, from the other or with the others. The first case study concerns automation-supported driving activities and focuses on the analysis of possible dissonances of use of two automated tools. The second case study consists of designing and validating a human-supported eco-driving assistance system.

Eco-Driving Assistance System for a Manual Transmission Bus Based on Machine Learning

Driving assistance systems (DAS) is a key technology to improve fuel economy for in-use vehicles. This also reduces the operational cost of running a fleet of these vehicles, such as city buses. In this paper, we develop a novel white-box evaluation model using machine learning for a manual transmission bus based on previous research about fuel consumption sensitivity to driving style. Using the proposed evaluation model, an algorithm for learning path planning (LPP) for a driving style is also proposed. The LPP method plans a step-by-step shortest learning path for different driving styles to achieve eco-driving, while increasing the driver’s acceptance and adaptation of DAS. Simulation results based on vehicle and engine physical models show that the proposed evaluation model, a pure data model, can be used as an alternative to physical model for the eco-driving prompt strategy. The results of the verification show that the proposed strategy can progressively guide the driver to improve the fuel consumption by 6.25% with minimal changes to driver’s driving task and driving style.

The Effects of an Eco-Driving Assistance System for a City Bus on Driving Style

Fuel consumption of a city bus is greatly influenced by driving style, and driving assistance systems (DAS) is a key technology to encourage driving style improvement. In the research reported here, we construct a real-time DAS for a city bus based on a decision tree algorithm. The DAS aims to reduce fuel consumption by encouraging three behaviors: reduced rates of acceleration, reduced depth of acceleration pedal and early upshifting through the gears. Using the proposed DAS, we establish a driver experiment platform with a driving simulator. Then, we design an experiment for 4 non-professional drivers with various driving experience to determine the effects of DAS on their driving style. The driving simulator experiment results show that all the drivers exhibited an observable change in acceleration pedal depth distribution, acceleration pedal positive gradient distribution, and shift timing. These findings indicated that through the real-time guidance on the manipulation of the accelerator pedal position and gear, the drivers could obey the devices from DAS to a certain extent, so the eco-driving device can help drivers change their driving style.

Integrated-Connected Eco-Driving System for PHEVs With Co-Optimization of Vehicle Dynamics and Powertrain Operations

In the past several decades, various types of technologies have been developed to improve vehicle fuel efficiency and reduce tailpipe emissions across different dimensions. For example, powertrain-related technologies improve fuel efficiency by optimizing the powertrain operations in response to different driving conditions (e.g., energy management system for plugin hybrid electric vehicles); another technology dimension lies in intelligent transportation systems (ITS), which improve vehicle fuel efficiency by optimizing the vehicle dynamics or speed under different traffic conditions (e.g., eco-speed harmonization and eco-approach and departure). However, very little effort has been made to investigate the combined benefit of integrating both powertrain and ITS technology dimensions together. In this paper, an integrated and connected eco-driving assistance system with co-optimization of vehicle dynamics and powertrain operations for PHEVs is proposed. To fully evaluate the performance of the proposed system at different vehicle automation levels, real-world driving data for different eco-driving technological stages were collected: uninformed manual driving, eco-driving with an in-vehicle advisory display, and an eco-driving system with automatic longitudinal control. The numerical analysis shows that the co-optimization is able to achieve on average 24% fuel savings for typical urban driving conditions.

Eco-driving command for tram-driver system

In the transport domain, the aim of operators and manufacturers is not only to perform a route as quickly as possible but also to take into account the energy consumption. The scientific advances in the field of solving optimization problems challenge driving habits. Eco-driving assistance systems are then designed to reduce energy consumption. In this paper, achieving an energy effcient driving profile for a tram-driver system is presented. The optimization problem is to achieve inter-station distance in a time t, while respecting the constraints imposed by tram and route models, to minimize energy consumption.

信号交差点におけるエコドライブ支援システムの呈示手法による影響

信号交差点におけるエコドライブ支援システムの呈示手法による影響

The Virtual Driving Coach - design and preliminary testing of a predictive eco-driving assistance system for heavy-duty vehicles

The commercial vehicle sector is characterized by high competitive pressure. Fuel consumption is one major factor that influences the transport efficiency and competitiveness of logistics companies. Therefore, an eco-driving assistance system (EDAS) is developed in order to support the driver in sustainably maintaining an efficient driving style—the Virtual Driving Coach (ViDCo). In this paper, we describe the design and development process of ViDCo as well as results of the first steps of evaluation and preliminary testing. An EDAS is developed that uses knowledge of infrastructure based on digital maps in order to proactively and predictively provide the driver with driving advice. The system’s algorithms are structured within the modules “situation detection”, “driving error detection”, and “message filtering and prioritization”. The evaluation of ViDCo comprises preliminary field-testing on public roads as well as a driving simulator experiment. Driving tests show that the Virtual Driving Coach is capable of enhancing fuel efficiency for commercial vehicles in real-world scenarios. The results of the driving simulator experiment indicate a positive level of user acceptance and system safety. Furthermore, the results point towards a positive correlation between user acceptance and the subjects’ judgment of learning. The Virtual Driving Coach’s concept is a promising approach for efficient and environmentally friendly road transport.

Interface design considerations for an in-vehicle eco-driving assistance system

This high-fidelity driving simulator study used a paired comparison design to investigate the effectiveness of 12 potential eco-driving interfaces. Previous work has demonstrated fuel economy improvements through the provision of in-vehicle eco-driving guidance using a visual or haptic interface. This study uses an eco-driving assistance system that advises the driver of the most fuel efficient accelerator pedal angle, in real time. Assistance was provided to drivers through a visual dashboard display, a multimodal visual dashboard and auditory tone combination, or a haptic accelerator pedal. The style of advice delivery was varied within each modality. The effectiveness of the eco-driving guidance was assessed via subjective feedback, and objectively through the pedal angle error between system-requested and participant-selected accelerator pedal angle. Comparisons amongst the six haptic systems suggest that drivers are guided best by a force feedback system, where a driver experiences a step change in force applied against their foot when they accelerate inefficiently. Subjective impressions also identified this system as more effective than a stiffness feedback system involving a more gradual change in pedal feedback. For interfaces with a visual component, drivers produced smaller pedal errors with an in-vehicle visual display containing second order information on the required rate of change of pedal angle, in addition to current fuel economy information. This was supported by subjective feedback. The presence of complementary audio alerts improved eco-driving performance and reduced visual distraction from the roadway. The results of this study can inform the further development of an in-vehicle assistance system that supports ‘green’ driving.

Machine‐learning methodology for energy efficient routing

Eco-driving assistance systems encourage economical driving behaviour and support the driver in optimising his/her driving style to achieve fuel economy and consequently, emission reductions. Energy efficiency is also one of the most pertinent issues related to the autonomy of fully electric vehicles. This study introduces a novel methodology for energy efficient routing, based on the realisation of dependable energy consumption predictions for the various road segments constituting an actual or potential vehicle route, performed mainly by means of machine-learning functionality. This proposed innovative methodology, the functional architecture implementing it, as well as demonstrative experimental results are presented in this study.

Perceived usefulness of eco-driving assistance systems in Europe

Assessing the perceived usefulness of eco-driving assistance systems is of high interest for system developers and researchers in the development stage of such systems. This applies especially to systems that try to influence the driver's behaviour in everyday situations and generating their greatest benefit if regularly used. This study presents results of a survey simultaneously conducted in 11 European countries with more than 5000 respondents aiming at collecting needs and requirements for future eco-driving assistance systems. Such systems have the potential of changing driving behaviour in the long run for supporting a more fuel-efficient driving and reducing emissions caused by motorised traffic. Three different driver assistance systems are considered covering inefficiencies in pre-trip, on-trip and post-trip situations. Each system is assessed according to the factors of perceived usefulness, the expected environmental impact and the user's willingness to pay for it. The study will show that in general car drivers welcome the deployment of eco-driving assistance systems and rate them as useful. It likewise demonstrates that the acceptance of additional costs for the user is very low.

Eco-Driving Performance Assessment With in-Car Visual and Haptic Feedback Assistance

In this experiment, 28 participants completed an urban driving task in a highly immersive driving simulator at Renault’s Technical Centre for Simulation. This simulator provides a 150 deg field of view in a fully instrumented cockpit. Two different eco-driving assistance devices were added: a visual display on the midconsole and a force feedback system on the gas pedal, in order to apply an additionnal reaction torque on drivers’ foot. The feedback information was computed by comparing the car’s instant acceleration with an optimal acceleration level based on a proprietary consumption model of a Renault diesel engine. This experiment has three main goals: I. Assess the contribution of verbal instructions to eco-driving performance; II. Quantify the additional contribution generated by two eco-driving assistance systems (visual and haptic); III. Measure drivers’ acceptance of haptic eco-driving assistance system. Basic eco-driving instructions, such as changing gears under 2000 Rpm, yield significant decrease of polluting emissions. Assisting drivers with visual, haptic, or visual-haptic on-board devices, in addition to low engine speed verbal instructions, lead to supplementary significant savings of polluting emissions. There is no significant difference between assistance feedback type; suggesting that the haptic feedback provides the same ecoperformance as visual feedback. In particular, subjects show good adaptation to the haptic feedback pedal at first utilization of the system. They apparently relied more on haptic modality to achieve the eco-driving task, when they used both visual and haptic assistance.