Eco-driving System Research Articles

Intention to perform eco-driving and acceptance of eco-driving system

Eco-driving is one strategy for reducing transportation sector fuel usage and greenhouse gas emissions. With the advancement of connected-vehicle technology, the dynamic eco-driving concept can utilize real-time vehicle-specific information to optimize vehicle speed, thereby further reducing fuel consumption and emissions. The objective of this research was to determine the elements that influence drivers' intentions to practice eco-driving and their acceptance of eco-driving technology. A theoretical model of technology acceptance for both internal combustion engine vehicle (ICEV) and electric vehicle (EV) drivers was built using a mix of the Theory of Planned Behavior (TPB), the Technology Acceptance Model (TAM), and Goal Framing. Drivers’ acceptance of eco-driving system was hypothesized to be based on their intention to perform eco-driving. The model's validity was verified using a structural equation modeling analysis of data from a survey with 340 replies from ICEV drivers and 315 responses from EV drivers. The findings corroborated the original hypotheses in TAM and TPB, and drivers' intention to practice eco-driving had an indirect effect on their intention to utilize the system via the construct of perceived ease of use. In comparison to ICEV drivers, EV drivers possessed a greater understanding of eco-driving. The four goal framing structures each played a different role in the ICEV and EV models. In the ICEV model, the altruistic goal contributed positively to the social norm construct. By contrast, the social norm was positively influenced by the biospheric and the egoistic goals, and negatively influenced by the hedonic goal in the EV model. This study's framework and results provide theoretical and practical guidelines for researchers, manufacturers, and policy-makers to understand drivers' motivation to perform eco-driving and increase drivers' acceptance of the eco-driving system.

Integration of Second-generation On-board Diagnostics Data via Deep Learning to Develop Eco-driving Analysis System Applicable to Large and Small Cars

Integration of Second-generation On-board Diagnostics Data via Deep Learning to Develop Eco-driving Analysis System Applicable to Large and Small Cars

A Study of Applying Eco-Driving Speed Advisory System on Transit Signal Priority

Transit Signal Priority (TSP) has long been seen as a cost-effective way to reduce bus delays at intersections. With Connected Vehicle (CV) technology, a speed advisory system guides buses to pass intersections in an energy-saving way. The integration of TSP and speed advisory may reduce bus delays and enhance energy consumption performances. This study proposed a system of integrating eco-driving speed advisory on TSP under CV environment. A TSP strategy based on intersections passing probability is designed. In addition to signal priority, this study designed and implemented an eco-driving speed advisory algorithm. A real electric bus route in Tainan City, Taiwan is used for the case study. Intersection layout and traffic related parameters are established in microscopic traffic simulation software SUMO (Simulation of Urban Mobility) to verify the effectiveness of the proposed model. The results provide an insight into how cooperation between signals and vehicles can enhance performances of energy consumption and signal-incurred traffic delays.

Fuzzy-tuned model predictive control for dynamic eco-driving on hilly roads

Existing optimal control systems for vehicles that consider the effect of road slopes use a cost function with fixed weights related to speed deviation, regardless of driving states on slopes. As a result, gravitational potential energy is not efficiently exploited and braking at down-slopes (which wastes energy) becomes unavoidable. Thus, there is still significant scope to improve fuel saving behavior on slopes. To address this opportunity, in this paper, we present a dynamic eco-driving system (EDS) for a (host) vehicle based on model predictive control (MPC) with fuzzy-tuned weights, which helps efficiently utilize the gravitational potential energy. In the proposed EDS, we formulate a nonlinear optimization problem with an appropriate prediction horizon and an objective function based on the factors affecting vehicle fuel consumption. The objective function’s weight is tuned via fuzzy inference techniques using information of the vehicle’s instantaneous velocity and the road slope angle. By considering the vehicle longitudinal dynamics, preceding vehicle’s state, and road slope information (obtained from the digital road map), the optimization generates velocity trajectories for the host vehicle that minimizes fuel consumption and CO2 emission. We also investigate the traffic flow performance of following vehicles (behind the host vehicle) in dense traffic; this was not considered in existing works on hilly roads. The effectiveness of the proposed EDS is evaluated using microscopic traffic simulations on a real road stretch in Fukuoka City, Japan, and the results demonstrate that the fuzzy-tuned MPC EDS significantly reduces fuel consumption and CO2 emission of the host vehicle compared to the traditional driving (human-based) system (TDS) for the same travel time. In dense traffic, the fuel consumption and CO2 emission of following vehicles are noticeably reduced.

Integrating Driving Hardware-in-the-Loop Simulator with Large-Scale VANET Simulator for Evaluation of Cooperative Eco-Driving System

Recent advances in information and communication technology (ICT) have enabled interaction and cooperation between components of the transportation system, and cooperative eco-driving systems that apply ICT to eco-driving systems are receiving significant attention. A cooperative eco-driving system is a complex system that requires consideration of the electronic control unit (ECU) and vehicle-to-everything (V2X) communication. To evaluate these complex systems, it is needed to integrate simulators with expertise. Therefore, this study presents an integrated driving hardware-in-the-loop (IDHIL) simulator for the testing and evaluation of cooperative eco-driving systems. The IDHIL simulator is implemented by integrating the driving hardware-in-the-loop simulator and a vehicular ad hoc network simulator to develop and evaluate a hybrid control unit and cooperative eco-driving application for the connected hybrid electric vehicle (CHEV). A cooperative eco-driving speed guidance application is utilized to demonstrate the use of our simulator. The results of the evaluation show the improved fuel efficiency of the CHEV through a calculation of the optimal speed profile and the optimal distribution of power based on V2X communication. Finally, this paper concludes with a description of future directions for the testing and evaluation of cooperative eco-driving systems.

Eco-Driving System for Connected Automated Vehicles: Multi-Objective Trajectory Optimization

This study aims to leverage the advances of connected automated vehicle (CAV) technology to design an eco-driving and platooning system that can improve both fuel and operational efficiency of vehicles on the freeways. The proposed algorithm optimizes CAVs’ trajectories with three objectives, including travel time minimization, fuel consumption minimization, and traffic safety improvement, following a two-stage control logic. The first stage, designed for CAV trajectory planning, is carried out with two optimization models. The first model functions to predict the freeway traffic states in the near future and accordingly optimize CAVs’ desired speed profile to minimize total freeway travel time. Notably, the interactions between CAVs and human-driven vehicles (HVs) are described in the embedded traffic flow model and the optimization can fully account for CAVs’ impact to HVs’ speeds. Then grounded on the obtained speed profile, the second eco-driving model would further update it so as to platoon CAVs and minimize their fuel consumption. The second stage, for real-time control purpose, is developed to ensure the operational safety of CAVs. Particularly, based on the speed profile from the first stage, real-time adaptions would be placed on CAVs to dynamically adjust speeds, in response to local driving conditions. To evaluate the proposed algorithms, this study selects a freeway segment of I-15 in Salt Lake City as the study site. The extensive numerical simulation results confirmed the effectiveness of the proposed framework in both mitigating freeway congestion and reducing vehicles’ fuel consumption.

Cooperative Eco-Driving at Signalized Intersections in a Partially Connected and Automated Vehicle Environment

The emergence of connected and automated vehicle (CAV) technology has the potential to bring a number of benefits to our existing transportation systems. Specifically, when CAVs travel along an arterial corridor with signalized intersections, they can not only be driven automatically using pre-designed control models but can also communicate with other CAVs and the roadside infrastructure. In this paper, we describe a cooperative eco-driving (CED) system targeted for signalized corridors, focusing on how the penetration rate of CAVs affects the energy efficiency of the traffic network. In particular, we propose a role transition protocol for CAVs to switch between a leader and following vehicles in a string. Longitudinal control models are developed for conventional vehicles in the network and for different CAVs based on their roles and distances to intersections. A microscopic traffic simulation evaluation has been conducted using PTV VISSIM with realistic traffic data collected for the City of Riverside, CA, USA. The effects on traffic mobility are evaluated, and the environmental benefits are analyzed by the U.S. Environmental Protection Agency’s MOtor Vehicle Emission Simulator (MOVES) model. The simulation results indicate that the energy consumption and pollutant emissions of the proposed system decrease, as the penetration rate of CAVs increases. Specifically, more than 7% reduction on energy consumption and up to 59% reduction on pollutant emission can be achieved when all vehicles in the proposed system are CAVs.

Eco-Driving at Signalized Intersections: A Multiple Signal Optimization Approach

Consecutive traffic signalized intersections can increase vehicle stops, producing vehicle accelerations on arterial roads and potentially increasing vehicle fuel consumption levels. Eco-driving systems are one method to improve vehicle energy efficiency with the help of vehicle connectivity. In this paper, an eco-driving system is developed that computes a fuel-optimized vehicle trajectory while traversing more than one signalized intersection. The system is designed in a modular and scalable fashion allowing it to be implemented in large networks without significantly increasing the computational complexity. The proposed system utilizes signal phasing and timing (SPaT) data that are communicated to connected vehicles (CVs) together with real-time vehicle dynamics to compute fuel-optimum trajectories. The proposed algorithm is incorporated in the INTEGRATION microscopic traffic assignment and simulation software to conduct a comprehensive sensitivity analysis of various variables, including: system market penetration rates (MPRs), demand levels, phase splits, offsets and traffic signal spacings on the system performance. The analysis shows that at 100\% MPR, fuel consumption can be reduced by as high as 13.8\%. Moreover, higher MPRs and shorter phase lengths result in larger fuel savings. Optimum demand levels and traffic signal spacings exist that maximize the effectiveness of the algorithm. Furthermore, the study demonstrates that the algorithm works less effective when the traffic signal offset is closer to its optimal value. Finally, the study highlights the need for further work to enhance the algorithm to deal with over-saturated traffic conditions.

Design and Implementation of a CANBus-Based Eco-Driving System for Public Transport Bus Services

Driving vehicles according to eco-driving principles and techniques have significant impact on decreasing both fuel consumption and carbon dioxide (CO2) emissions. In addition to some kind of technical and/or mechanical features brought by today's new generation vehicles, driver behavior is also one of the greatest factors affecting the fuel consumption. Many studies show that the effect of eco-driving education on the drivers loses its impact in long term and there needs some sort of continuous monitoring and/or feedback mechanisms. This kind of driver monitoring becomes very critical especially in fleets composed of heavy-duty vehicles, such as municipality buses, truck fleets, etc. Moreover, in order to adapt behavior to drive more economically, information about instant fuel consumption has to be provided to the driver. Hence, in this paper, we introduce an eco-driving system in which data gathered from the controller area network (CANBus) of public transport vehicles are processed for both comparative and fair evaluation of bus drivers' eco-driving performance. Moreover, in-vehicle components of the system guide the drivers during their trips; provide feedbacks and real-time warnings considering the fuel consumption. Developed system was successfully deployed and evaluated in one of the public metrobus systems used by approximately 250000 passengers every day. Based on the 15-months evaluation period, the results are very promising in the sense that both drivers and operators found the system useful and the system provided fuel saving up to approximately 5% even in the short term of monthly comparisons.

Modelling Eco-Driving Support System for Microscopic Traffic Simulation

Microscopic traffic simulation is an ideal tool for investigating the network level impacts of eco-driving in different networks and traffic conditions, under varying penetration rates and driver compliance rates. The reliability of the traffic simulation results however rely on the accurate representation of the simulation of the driver support system and the response of the driver to the eco-driving advice, as well as on a realistic modelling and calibration of the driver’s behaviour. The state-of-the-art microscopic traffic simulation models however exclude detailed modelling of the driver response to eco-driver support systems. This paper fills in this research gap by presenting a framework for extending state-of-the-art traffic simulation models with sub models for drivers’ compliance to advice from an advisory eco-driving support systems. The developed simulation framework includes among others a model of driver’s compliance with the advice given by the system, a gear shifting model and a simplified model for estimating vehicles maximum possible acceleration. Data from field operational tests with a full advisory eco-driving system developed within the ecoDriver project was used to calibrate the developed compliance models. A set of verification simulations used to illustrate the effect of the combination of the ecoDriver system and drivers’ compliance to the advices are also presented.

A Novel Smart Energy Management System in Pure Electric Motorcycle Using COA

Todays, Greenhouse Gases (GHG) emissions, densely populated urban areas air quality problems, and depleting fossil fuel concerns motivated motorcycle producers in crowded countries to explore electric propulsion systems. Due to energy storage system constraints, Pure Electric Motorcycles (PEM) range is restricted, so an effective Energy Management System is required to optimize PEM consumed energy. As road curvature is an inevitable part of real roads, this article, proposes a novel Smart Energy Management System (SEMS) based on road geometry which first analyses road data to identify curvature and straight parts. Then, SEMS solves optimization problems separately and optimal speed is calculated in direct and curvature parts independently. The proposed SEMS can communicate to the driver as an eco-driving system by receiving trip time importance and presenting each road point optimal speed. Because of fast response necessity, Cuckoo Optimization Algorithm (COA) is used in this article to solve SEMS optimization problem. For evaluation purposes, three scenarios are defined: The test road is travelled at motorcycle maximum allowable speed in the first scenario. In the 2nd one, PEM speed is also constant but it equals to direct road optimal speed instead of max. speed. Independent optimal speed in direct and curvature parts is considered in 3rd scenario. Simulation results show the significant effect of road curves on consumed energy and SEMS good performance in optimization of PEM consumed energy.

Event-Driven Stochastic Eco-Driving Strategy at Signalized Intersections From Self-Driving Data

Fuel consumption and travel time of a vehicle are significantly influenced by driving behavior, especially when approaching a signalized intersection. Injudicious driving reacting to sudden changes in traffic signal can lead to additional energy consumption and increase of travel time. This paper presents a learning-based event-driven ecological (eco) driving system (EDS) that generates the optimal velocity from self-driving data of a vehicle. Currently, full autonomy of vehicles and proper infrastructure development for vehicle-to-vehicle and infrastructure-to-vehicle communications are not widespread; however, the proposed system can be beneficial for driving scenarios in the existing traffic environment. We design a Gaussian process model using a Bayesian network for naturalistic learning from driving data and traffic signal condition to estimate the probability of a vehicle crossing the intersection within a signal phase. Based on the estimated probability, the optimal velocity is generated and the vehicle (driver) will be advised to either slow down earlier (to avoid aggressive braking) at the red signal or speed up (to cross the intersection) at the green signal. Finally, microscopic simulations are performed to evaluate the performance of the proposed scheme. The results show significant performance improvement in both fuel economy and travel time.

Ecological and safe driving: A model predictive control approach considering spatial and temporal constraints

Road transport plays an important role in the economy but generates huge costs in terms of road trauma and greenhouse gas emissions. The vast majority of previous research efforts have focused on driving behaviours that either minimise fuel consumption, or advise on safe driving behaviours. However, a fuel-optimal behaviour is often difficult to implement due to traffic conditions and may lead to unsafe behaviour without proper constraints. While a range of optimisation techniques are currently available, most lack the ability to both consider time and distance dependent constraints necessary for computationally efficient determination of optimal behaviour. In this paper, a methodology to find an ecological and safe (EcoSafe) driving behaviour by formulating and solving an optimal control problem for the minimum fuel driving behaviour while respecting key safety constraints was developed. We first propose a modified distance-based dynamic programming (MDDP) technique that can treat distance and time inequality constraints while optimising. Then we propose a model predictive control (MPC) structure based on MDDP and Pontryagin’s maximum principle by considering inter vehicle time and time-to-collision as hard safety constraints. In contrast to existing approaches, the proposed MPC method does not require ad hoc tuning of constraint penalty weights in the cost function to ensure safe following distances. Simulation results of the proposed method show that the proposed algorithm produces lower vehicle fuel consumption compared to existing approaches when safety constraints are limiting. Thus, the results demonstrate the importance of considering safety constraints within an eco-driving system.

제품 디자인에서의 컴포넌트 중심 프로세를 이용한 인지감성 정보 시스템

제품 디자인에서의 컴포넌트 중심 프로세를 이용한 인지감성 정보 시스템

Connected Cooperative Ecodriving System Considering Human Driver Error

In recent years, eco-friendly driving or ecodriving technologies are being developed to assist human drivers to achieve maximum fuel/energy efficiency in different driving conditions. Enhanced by V2X wireless communications, connected ecodriving is expected to be very promising in reducing transportation-related fossil fuel consumption as well as pollutant emissions. Besides, the deployment of electric vehicles (EVs) also has great potential in reducing greenhouse gas emissions due to the use of batteries as the sole energy source. Although recent research shows that significant energy savings can be achieved with the aid of ecodriving systems in real-world driving, there have been very few research efforts that consider human driver error, especially for electric vehicle (EV) driving. In this paper, a connected cooperative ecodriving system for energy-efficient driving that considers human driver error is designed and evaluated with an EV energy consumption model. Real-world driving data were collected and used to evaluate system performance in terms of energy consumption. The simulation and numerical analysis shows that an average of 12% energy savings can be achieved by the proposed system that considers human driver error comparing with the conventional ecodriving system without considering driver error.

An eco-driving system for electric vehicles with signal control under V2X environment

The benefit of eco-driving of electric vehicles (EVs) has been studied with the promising connected vehicle (i.e. V2X) technology in recent years. Whereas, it is still in doubt that how traffic signal control affects EV energy consumption. Therefore, it is necessary to explore the interactions between the traffic signal control and EV energy consumption. This research aims at studying the energy efficiency and traffic mobility of the EV system under V2X environment. An optimization model is proposed to meet both operation and energy efficiency for an EV transportation system with both connected EVs (CEVs) and non-CEVs. For CEVs, a stage-wise approximation model is implemented to provide an optimal speed control strategy. Non-CEVs obey a car-following rule suggested by the well-known Intelligent Driver Model (IDM) to achieve eco-driving. The eco-driving EV system is then integrated with signal control and a bi-objective and multi-stage optimization problem is formulated. For such a large-scale problem, a hybrid intelligent algorithm merging genetic algorithm (GA) and particle swarm optimization (PSO) is implemented. At last, a validation case is performed on an arterial with four intersections with different traffic demands. Results show that cycle-based signal control could improve both traffic mobility and energy saving of the EV system with eco-driving compared to a fixed signal timing plan. The total consumed energy decreases as the CEV penetration rate augments in general.

Driver adherence to recommendations from support systems improves if the systems explain why they are given: A simulator study

This paper presents a large-scale simulator study on driver adherence to recommendations given by driver support systems, specifically eco-driving support and navigation support. 123 participants took part in this study, and drove a vehicle simulator through a pre-defined environment for a duration of approximately 10 min. Depending on the experimental condition, participants were either given no eco-driving recommendations, or a system whose provided support was either basic (recommendations were given in the form of an icon displayed in a manner that simulates a heads-up display) or informative (the system additionally displayed a line of text justifying its recommendations). A navigation system that likewise provided either basic or informative support, depending on the condition, was also provided.Effects are measured in terms of estimated simulated fuel savings as well as engine braking/coasting behaviour and gear change efficiency. Results indicate improvements in all variables. In particular, participants who had the support of an eco-driving system spent a significantly higher proportion of the time coasting. Participants also changed gears at lower engine RPM when using an eco-driving support system, and significantly more so when the system provided justifications. Overall, the results support the notion that providing reasons why a support system puts forward a certain recommendation improves adherence to it over mere presentation of the recommendation.Finally, results indicate that participants’ driving style was less eco-friendly if the navigation system provided justifications but the eco-system did not. This may be due to participants considering the two systems as one whole rather than separate entities with individual merits. This has implications for how to design and evaluate a given driver support system since its effectiveness may depend on the performance of other systems in the vehicle.

Distributed robust $${{\varvec{H}}}_{\varvec{\infty }}$$ H ∞ control of connected eco-driving system with time-varying delay and external disturbances in the vicinity of traffic signals

In order to improve traffic efficiency and reduce fuel consumption and CO $$_{2}$$ emission, most existing models are considered as an optimization problem. However, these models cannot provide a satisfactory solution when nonlinear time-varying system and external disturbances are investigated for connected vehicles in the vicinity of traffic signals. In this paper, connected eco-driving system with time-varying delay and external disturbances in the vicinity of traffic signals is proposed from cyber-physical perspective firstly. Second, distributed robust $$H_\infty $$ control of connected eco-driving system is investigated by adopting Lyapunov–Krasovskii stability theory and the feedback controller is designed to attenuate that closed-loop system is robustly stable with disturbance attenuation level. Third, the sufficient conditions for the state feedback controller are given via employing linear matrix inequality. Finally, numerical simulations are executed to verify the effectiveness and feasibility of the proposed control strategies. The results indicate that the proposed control strategies for connected eco-driving system in the vicinity of traffic signals could not only improve traffic efficiency and make traffic flow smooth, but also reduce fuel consumption and CO $$_{2}$$ emission.

Eco-Approach and Departure System for Left-Turn Vehicles at a Fixed-Time Signalized Intersection

This research proposed an eco-approach and departure system for left-turn vehicles at a fixed-time signalized intersection. This system gives higher priority to enhancing traffic safety than improving mobility and fuel efficiency, and optimizes the entire traffic consisted of connected and automated vehicles (CAVs) and conventional human-driven vehicles by providing ecological speed trajectories for left-turn CAVs. All the ecological speed trajectories are offline optimized before the implementation of system. The speed trajectory optimization is constructed in Pontryagin’s Minimum Principle structure. The before and after evaluation of the proposed system shows the percentage of vehicles that drive pass the intersection at safe speed increases by 2.14% to 45.65%, fuel consumption benefits range 0.53% to 18.44%, emission benefits range from 0.57% to 15.69%, no significant throughput benefits is observed. The proposed system significantly enhances the traffic safety and improves the fuel efficiency and emission reduction of left-turn vehicles with no adverse effect on mobility, and has a good robustness against the randomness of traffic. The investigation also indicates that the computation time of proposed system is greatly reduced compared to previous eco-driving system with online speed optimization. The computation time is up to 0.01 s. The proposed system is ready for real-time application.

A Distance-Based Two-Stage Ecological Driving System Using an Estimation of Distribution Algorithm and Model Predictive Control

This paper proposes a distance-based two-stage ecological (eco-) driving scheme by using estimation of distribution algorithms (EDA) and model-based prediction of traffic conditions. Before departure, the optimal speed profile for an entire route is generated by an EDA in combination with speedup approaches for a faster computing time, which can optimize the complex cost function of ecodriving without simplification within a reasonably short computing time. This optimization is performed in a distance domain for localizing changes in the optimal speed profile due to traffic conditions while driving. After departure, by taking the optimal speed profile and actual traffic conditions into consideration, the speed profile for a short term—to only the next location—is adapted. In order to reliably react to actual traffic conditions, additional points are interpolated into the long-term distance step and fine control of speeds at the additional points is established, which is based on a predictive model for estimating the spacing to the preceding vehicle. The proposed ecodriving system is evaluated in two types of route conditions, and its results are compared with the optimization result by the quadratic programming method. This comparison shows that an EDA can generate a speed profile with better optimization results in terms of fuel efficiency and driving time within a shorter computing time.