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Eco-driving is a style of driving that minimizes energy consumption, while gamification refers to the use of game techniques to motivate user engagement in non-game contexts. This paper comprises a literature review assessing applying gamification to encourage eco-driving. The Web of Science Core Collection and EBSCO Host platforms were searched in February 2022. Qualifying sources included peer review journal articles, conference proceedings papers, academic book chapters and dissertation reports. The final sample comprised 39 unique publications, of which 34 described gamification adjunct systems used during driving. Most were designed as smartphone apps, but some ran on bespoke in-car feedback displays. Alternatively, using game-based learning, 5 studies described videogames designed to encourage eco-driving. Popular gamification elements were: an eco-driving score; self-comparisons or comparisons with others via leader boards; rewards; challenges, missions or levels; and emotive feedback (e.g., emojis). One system aimed to discourage driving at busy times. While 13 studies assessed the efficacy of the various systems, these were generally of poor quality. This developing literature contains many good ideas for applying gamification to promote eco-driving. However, evidence for efficacy is largely absent and researchers are encouraged to continue to evaluate a wide range of gamification approaches to promote eco-driving. [ABSTRACT FROM AUTHOR]

In the context of automated driving, the connected and automated vehicles (CAVs) technology unlock the energy saving potential. This paper develops an LSTM‐based deep learning framework for eco‐driving adaptive identification on Intelligent vehicle multivariate time series data. The framework can be adapted for Driver Assistance Systems (DAS) to reduce fuel consumption. Specifically, considering overtaking on rural road is a critical maneuver for operation and has potential to reduce consumption, a simulated driving experiment with 30 participants was conducted to collect the multivariate time series data of the overtaking operation behaviors in conditional automation driving. Driving behaviors were classified into eco‐driving operation behaviors and high fuel consumption operation behaviors based on fuel consumption calculated by using vehicle specific power (VSP). Significance analysis based on linear regression was adopted to identify operation behaviors, and an eco‐driving behavior identification model was established with the use of long short‐term memory (LSTM) for multivariate classification theory. Meanwhile, the other four classification algorithms were used to establish identification models for comparison. The results indicated that the gear position, lane position, the acceleration pedal depth, the clutch pedal depth, and the brake pedal depth had a significant influence on fuel consumption. The eco‐driving behavior identification model of overtaking demonstrated a high classification power and robustness with a classification accuracy of 89.16%. According to the simulation results, the developed adaptive identification model is with promising performance. The conclusions provide theoretical support for developing an adaptive strategy for connected eco‐driving. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a re-analysis of the data used in a previously published paper ‘Using on-board data logging devices to study the longer-term impact of an eco-driving course’ by Beusen et al. In this paper the effect of an eco-driving course on fuel consumption was studied. The fuel consumption of 10 drivers was monitored during a year. After half a year they received an eco-driving course. The main conclusion of this paper was that fuel consumption was reduced with 5.8% after the course and that the effect for the group as a whole was permanent up to 6months after the course. The data were analyzed again, including the effect of the ambient temperature on fuel consumption. A higher ambient temperature results in a lower fuel consumption. The main conclusion of our first paper still holds: an eco-driving course results in a significant decrease of the fuel consumption. However, this effect is gradually lost in the months after the course. In the first paper the effect seemed permanent because it was masked by the effect of increasing ambient temperature. [Copyright &y& Elsevier]

In this paper, a comprehensive deterministic Eco-Driving strategy for Connected and Autonomous Vehicles (CAVs) is presented. In this setup, multiple driving modes calculate speed profiles that are ideal for their own set of constraints simultaneously to save fuel as much as possible, while a High-Level (HL) controller ensures smooth and safe transitions between the driving modes for Eco-Driving. This Eco-Driving deterministic controller for an ego CAV was equipped with Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) algorithms. This comprehensive Eco-Driving strategy and its individual components were tested by using simulations to quantify the fuel economy performance. Simulation results are used to show that the HL controller ensures significant fuel economy improvement as compared to baseline driving modes with no collisions between the ego CAV and traffic vehicles, while the driving mode of the ego CAV was set correctly under changing constraints. For the microscopic traffic simulations, a 6.41% fuel economy improvement was observed for the CAV that was controlled by this comprehensive Eco-Driving strategy. [ABSTRACT FROM AUTHOR]

Human driving behavior significantly affects vehicle fuel economy and emissions on hilly roads. This paper presents an ecological (eco) driving scheme (EDS) on hilly roads using nonlinear model predictive control (NMPC) in a mixed traffic environment. A nonlinear optimization problem with a relevant prediction horizon and a cost function is formulated using variables impacting the fuel economy of vehicles. The EDS minimizes vehicle fuel usage and emissions by generating the optimum velocity trajectory considering the longitudinal motion dynamics, the preceding vehicle's state, and slope information from the digital road map. Furthermore, the immediate vehicle velocity and angle of the road slope are used to tune the cost function's weight utilizing fuzzy inference methods for smooth maneuvering on slopes. Microscopic traffic simulations are used to show the effectiveness of the proposed EDS for different penetration rates on a real hilly road in Fukuoka City, Japan, in a mixed traffic environment with the conventional (human-based) driving scheme (CDS). The results show that the fuel consumption and emissions of vehicles are significantly reduced by the proposed NMPC-based EDS compared to the CDS for varying penetration rates. Additionally, the proposed EDS significantly increases the average speed of vehicles on the hilly road. The proposed scheme can be deployed as an advanced driver assistance system (ADAS). [ABSTRACT FROM AUTHOR]

This paper investigates the effectiveness of gamification on encouraging young drivers to engage in eco-driving using naturalistic driving. Drivers aged 18- to 30-years old (“young drivers”) are over-represented in road traffic accident statistics. Motivating young drivers to engage in safer driving habits is imperative to reduce their risk of being involved in a dangerous road traffic collision (RTC). Additionally, the transportation sector is the highest contributor of greenhouse gas (GHG) emissions. Eco-driving is a driving style that involves less aggressive acceleration and braking, and reduced speed. The literature shows that eco-driving can reduce Carbon Dioxide (CO2) emissions and, due to the calmer driving style, can potentially reduce young drivers’ risk of being involved in an RTC. However, the challenge becomes motivating young drivers to engage in eco-driving. Gamified eco-driving applications have seen positive effects on encouraging eco-driving habits, but very little literature investigates the effectiveness of gamified ecodriving focussed on young drivers. There is also the need to explore additional gamification elements beyond the standard points, badges, and leaderboards. Thirty-three drivers aged 18 to 30 were recruited to test the effectiveness of a gamified eco-driving application using naturalistic driving. Three gamification elements were used: a driving score, a radar plot, and a grid that plots the drivers according to both severity and frequency of aggressive driving events. The grid is new to gamified eco-driving research. The study comprised of three phases: (1) Baseline, (2) Gamification with driving score and radar plot, and (3) Gamification with driving score and grid. A t-test shows a significant increase in eco-driving in phase 2 of the study. This effect size was also large. However, compared to the baseline, eco-driving during phase 3, although better, was not significantly higher than the baseline. [ABSTRACT FROM AUTHOR]

The aim of the paper is to define a limit performance of highly efficient battery electric quadricycles for urban mobility. The vehicle is employed for an energy efficiency competition in which urban concepts compete. A multi-physics (thermo-electro-mechanical) Tank To Wheels (TTW) model has been developed and validated. Given the information on the track route (track map and elevation) and on the throttle input command, the model computes the vehicle power demand and cruising speed. The model is validated by means of both indoor and outdoor experimental tests. The validated model is employed for the optimization of the transmission gear ratio and of the driving strategy to minimize the overall energy consumption on a given track. Design variables are related to the transmission gear ratio and to the throttle command profile. The algorithm aims to minimize the energy consumption, including constraints on the maximum current provided by the battery and on the maximum time available to complete the lap. Comparison with some common driving strategies confirmed the effectiveness of the proposed solution, with a foreseen 9% reduction of the vehicle overall energy demand. The single seater ultra-efficient electric vehicle, compared to other urban quadricycles, has a battery capacity ten times lower and a 50% longer driving range. [ABSTRACT FROM AUTHOR]

Wide usage of vehicle’s onboard navigation system offers vehicles better terms to improve energy efficiency. In this paper, a computationally effective energy management strategy using model predictive control (MPC) is proposed to find the energy optimal torque split, gear shift, and velocity control of a parallel hybrid electric vehicle (HEV). We consider the vehicles in urban driving, where the vehicle trajectory is constrained by the infrastructure (road signs) and other vehicles (traffic). Restricted by the discrete gear ratio, nonlinear dynamics of the vehicles, and especially different time scales between velocity trajectory and torque split optimization, finding these control variables in one optimal problem is quite challenging. Thus, this paper uses bi-level methodology to reduce computational time and simplify the hybrid optimal problem by decoupling its components into two subproblems. In the outer loop, the optimal velocity trajectory is obtained by solving a nonlinear time-varying optimal problem using a Krylov subspace method to improve computational efficiency. In the second subproblem, we provide an explicit solution of the optimal torque split ratio and gear shift schedule by combining Pontryagin’s minimum principle and numerical methods in the framework of MPC. Simulation results on an AMESim model of an HEV with seven-speed automated manual transmission over multiple driving cycles are presented. The results indicate that both energy efficiency and computational speed are improved. [ABSTRACT FROM PUBLISHER]

Traditional car-following models usually prioritize minimizing inter-vehicle distance error when tracking the preceding vehicle, often neglecting crucial factors like driving economy and passenger ride comfort. To address this limitation, this paper integrates the concept of eco-driving and formulates a multi-objective function that encompasses economy, comfort, and safety. A novel eco-driving car-following strategy based on the deep deterministic policy gradient (DDPG) is proposed, employing the vehicle's state, including data from the preceding vehicle and the ego vehicle, as the state space, and the desired time headway from the intelligent driver model (IDM) as the action space. The DDPG agent is trained to dynamically adjust the following vehicle's speed in real-time, striking a balance between driving economy, comfort, and safety. The results reveal that the proposed DDPG-based IDM model significantly enhances comfort, safety, and economy when compared to the fixed-time headway IDM model, achieving an economy improvement of 2.66% along with enhanced comfort. Moreover, the proposed approach maintains a relatively stable following distance under medium-speed conditions, ensuring driving safety. Additionally, the comprehensive performance of the proposed method is analyzed under three typical scenarios, confirming its generalization capability. The DDPG-enhanced IDM car-following model aligns with eco-driving principles, offering novel insights for advancing IDM-based car-following models. [ABSTRACT FROM AUTHOR]

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Recent advances in Connected and Automated Vehicle (CAV) technologies have opened up new opportunities to enable safe, efficient, and sustainable transportation systems. However, developing reliable and rapid speed control algorithms in highly dynamic environments with complex inter‐vehicle interactions and nonlinear vehicle dynamics is still a daunting task. In this paper, we develop a novel speed control method for CAVs to produce optimal speed profiles that minimize the fuel consumption and avoid idling at signalized intersections. To this end, an optimal control problem is formulated using the information of the upcoming traffic signal to adapt vehicles' speeds to avoid frequent stop‐and‐go driving patterns. By applying the pseudospectral discretization method and the sequential convex programming method, the computational efficiency is greatly improved, enabling potential real‐time on‐vehicle applications. In addition, the algorithm is implemented under a model predictive control framework to ensure online control with instant response for collision avoidance and robust vehicle coordination. The proposed algorithm is verified through numerical simulations of three different traffic scenarios. The convergence and accuracy of the proposed approach are demonstrated by comparing with a popular nonlinear solver. Furthermore, the benefit of the proposed method in both traffic mobility and fuel efficiency is validated using the speed profile determined from a traffic following model in a simulation software as the baseline. [ABSTRACT FROM AUTHOR]

Eco-driving strategies for vehicles with conventional powertrains have been studied for years and attempt to reduce fuel consumption by optimizing the driving velocity profile. For electric vehicles (EVs) with regenerative braking, the speed profile with the best energy efficiency should be different from conventional vehicles. This paper proposes an energy-oriented cruising control strategy for EVs with a hierarchical structure to realize eco-cruising on highways with varying slopes. The upper layer plans the energy-optimized vehicle velocity, and the lower layer calculates the torque allocation between the front and rear axles. However, the resulting speed profile with varying velocity may cause a high charge and discharge rate of the battery, resulting in rapid battery fading. To extend the battery life, we make a tradeoff between the energy consumption and wear of the battery by formulating an optimal control problem, where driving comfort and travel time are also considered. An indirect optimal control method is implemented to derive the optimal control rule. As an extension, the control rule for avoiding rear-end collisions is presented and simulated for driving in the real world. [ABSTRACT FROM AUTHOR]

Traditional ecological driving (Eco-Driving) evaluations often rely on mathematical models that predominantly offer subjective insights, which limits their application in real-world scenarios. This study develops a robust, data-driven Eco-Driving evaluation model by integrating dynamic and distributed multi-source data, including vehicle performance, road conditions, and the driving environment. The model employs a combination weighting method alongside K-means clustering to facilitate a nuanced comparative analysis of Eco-Driving behaviors across vehicles with identical energy consumption profiles. Extensive data validation confirms that the proposed model is capable of assessing Eco-Driving practices across diverse vehicles, roads, and environmental conditions, thereby ensuring more objective, comprehensive, and equitable results. [ABSTRACT FROM AUTHOR]

Finding the optimal speed profile of an autonomous electric vehicle (AEV) for a given route (eco-driving) can lead to a reduction in energy consumption. This energy reduction is even more noticeable when the regenerative braking (RB) capability of AEVs is carefully considered in obtaining the speed profile. In this paper, a new approach for calculating the optimum eco-driving profile of an AEV is formulated using mixed-integer linear programming (MILP) while carefully integrating the RB capability and its limitations in the process of obtaining a driving profile with minimum energy consumption. One of the most important limitations of RB which has been neglected in previous studies is operation below the low-speed boundary (LSB) of electric motors, which impairs the energy extraction capability of RB. The novelty of this work is finding the optimal speed profile given this limitation, leading to a much more realistic eco-driving profile. Python is used to code the MILP problem, and CPLEX is employed as the solver. To verify the results, the eco-driving problem is applied to two scenarios to show the significance of considering a dynamic LSB. It is shown that for the route under study, up to 27% more energy can be harvested by employing the proposed approach. [ABSTRACT FROM AUTHOR]

Eco-driving is one of the most effective control strategies to enable energy management for urban traffic. However, the existing eco-driving strategies still have two shortcomings: (i) these strategies lack the consideration of lateral decision-making; (ii) their performance deteriorates when a controlled vehicle encounters traffic queues at a signalized intersection. To overcome these shortcomings, this paper proposes an innovative eco-driving strategy at intersection approach lanes consisting of the bus-priority lane (BPL) and general-purpose lanes (GPLs). The proposed strategy has the capability of lateral decision-making and allows ego connected and automated vehicles (CAV) to bypass the traffic queue. To enable this capability, the proposed strategy permits the ego CAV to change lanes and share the BPL. Both left-turning-movement CAVs and going-through-movement CAVs are allowed to share the BPL; i.e., the function of the BPL can be switched as per the phases of a traffic signal scheme. Through phase-switch-based bus lane sharing, the proposed eco-driving strategy aims to improve traffic efficiency and sustainability under the partially connected and automated traffic environment. To validate its effectiveness, the proposed strategy is evaluated against the non-control baseline and the state-of-the-art strategy. Sensitivity analysis is conducted under six different demand levels and five different CAV penetration rates. The results show that the proposed eco-driving strategy outperforms and has the benefits of fuel efficiency improvement, throughput improvement, and delay reduction. [ABSTRACT FROM AUTHOR]

Currently, road transport constitutes a considerable proportion of global fossil fuel consumption, as well as CO2 and pollutant emissions. To mitigate transportation energy consumption, two primary approaches have emerged: the large-scale adoption of Hybrid Electric Vehicles (HEVs) and Plug-In Electric Vehicles (PHEVs), as well as the implementation of eco-driving strategies, which present an immediate and low-cost solution. In this context, this paper provides a comprehensive review of these two technologies and their integration for connected HEV/PHEVs. We summarize the framework of recent approaches to incorporate fusion road information in single-vehicle and multi-vehicle scenarios, respectively, wherein we compare their advantages, their disadvantages, and their effectiveness in reducing energy consumption. Additionally, we reflect on the future development directions of cooperative optimization in EMS and eco-driving strategies from various perspectives. This comprehensive review underscores the importance and potential impact of these approaches in addressing environmental challenges in transportation systems, thereby offering useful insights for new researchers and practitioners in this area. [ABSTRACT FROM AUTHOR]

This paper presents two fuel-prioritized feedback controllers, which are called the estimated minimum principle (EMP) and kinetic energy conversion (KEC), to realize eco-cruising on varying slopes for vehicles with conventional powertrains. The former is derived from the minimum principle with an estimated Hamiltonian, and the latter is designed based on the equivalent conversion between the kinetic-energy change of vehicle body and the fuel consumption of the engine. They are implemented with analytical control laws and rely on current road slope information only without look-ahead prediction. This feature results in a very light computing load, with the average computing time of each step less than one millisecond. Their fuel-saving performances are quantitatively studied and compared with a model predictive control and a constant speed control. As an expansion, the control rule for avoiding rear-end collision is also designed by using a safety-guaranteed car-following model to constrain the high-risk behaviors. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a driving assistant that makes recommendations in order to reduce the fuel consumption. The solution only requires a smartphone and an OBD/Bluetooth device. Eco-driving advices try to avoid situations that cause an increase in the fuel consumption such as inappropriate speed or slow reaction to the detection of traffic signs and traffic incidents. The main contribution of this paper is the use of artificial intelligence techniques in order to issue the eco-driving tips that are best adapted to the user profile, the characteristics of the vehicle, and the road state conditions. This is very important because the driver may lose the interest due to the high requirements that tend to be provided by general use eco-driving assistants. In order to properly assess and validate the proposed solution, it has been implemented on several Android mobile devices and has been validated using a dataset of 2,250 driving tests using three different models of vehicles with 25 different drivers on three distinct routes. The results show that the system reduces the fuel consumption by 11.04 percent on average and even, in certain cases, the fuel saving is greater than 15 percent. [ABSTRACT FROM PUBLISHER]

Signalized intersections can increase the vehicle stops and consequently increase the energy consumption by forcing stop-and-go dynamics on vehicles. Eco-driving with the help of connectivity is a solution that could avoid multiple stops and improve energy efficiency. In this paper, an eco-driving framework is developed, which finds the energy-efficient speed profile both up- and downstream of a signalized intersection in free-flow situations (eco-FF). The proposed framework utilizes the signal phasing and timing (SPaT) data that are communicated to the vehicle. The energy consumption model used in this framework is a combination of vehicle dynamics and time-dependent auxiliary consumption, which implicitly incorporates the travel time into the function and is validated with real-world test data. It is shown that, by using the proposed eco-FF framework, the vehicle's energy consumption is notably reduced. [ABSTRACT FROM AUTHOR]

Eco-driving through multiple intersections can have significant fuel benefit for road transportation. Many existing studies assume that a vehicle runs at a constant speed between intersections, which can lead to a large error in fuel prediction and trajectory optimization. By directly taking powertrain dynamics into consideration, this paper studies generic operating rules of fuel-optimal operation for connected vehicles traveling between arbitrary two red-signalized intersections. An optimal control problem is formulated to minimize engine fuel consumption, which is numerically solved by the Legendre pseudospectral algorithm. It was found that the optimal driving operation between two red-signalized intersections takes the form of either a two-stage solution, i.e., accelerating and decelerating, or a three-stage solution, i.e., accelerating, constant speed cruising, and decelerating, depending on the distance between the intersections and the speed limit. A quasi-optimal operating rule is proposed to approximate the optimal solutions, achieving much faster computational speed and less than ±1.5% fuel consumption error compared to the numerical method. The effectiveness of the quasi-optimal rule is demonstrated by applying it to selected multiintersection passing scenarios. [ABSTRACT FROM AUTHOR]

Long queues of vehicles are often found at signalized intersections, which increases the energy consumption of all the vehicles involved. This paper proposes an enhanced eco-approach control (EEAC) strategy with consideration of the queue ahead for connected electric vehicles (EVs) at a signalized intersection. The discharge movement of the vehicle queue is predicted by an improved queue discharge prediction method (IQDP), which takes both vehicle and driver dynamics into account. Based on the prediction of the queue, the EEAC strategy is designed with a hierarchical framework: the upper-stage uses dynamic programming to find the general trend of the energy-efficient speed profile, which is followed by the lower-stage model predictive controller to computes the explicit solution for a short horizon with guaranteed safe inter-vehicular distance. Finally, numerical simulations are conducted to demonstrate the energy efficiency improvement of the EEAC strategy. Besides, the effects of the queue prediction accuracy on the performance of the EEAC strategy are also investigated. [ABSTRACT FROM AUTHOR]

With the emerging connected automated vehicles, 5G and Internet of Things (IoT), vehicles and road infrastructure become connected and cooperative, enabling Cooperative Intelligent Transport Systems (C-ITS). C-ITS are transport system of systems that involves many stakeholders from different sectors. While running their own systems and providing services independently, stakeholders cooperate with each other for improving the overall transport performance such as safety, efficiency and sustainability. Massive information on road and traffic is already available and provided through standard services with different protocols. By reusing and composing the available heterogeneous services, novel value-added applications can be developed. This paper introduces a choreography-based service composition platform, i.e. the CHOReVOLUTION Integrated Development and Runtime Environment (IDRE), and it reports on how the IDRE has been successfully exploited to accelerate the reuse-based development of a choreography-based Urban Traffic Coordination (UTC) application. The UTC application takes the shape of eco-driving services that through real-time eco-route evaluation assist the drivers for the most eco-friendly and comfortable driving experience. The eco-driving services are realized through choreography and they are exploited through a mobile app for online navigation. From specification to deployment to execution, the CHOReVOLUTION IDRE has been exploited to support the realization of the UTC application by automatizing the generation of the distributed logic to properly bind, coordinate and adapt the interactions of the involved parties. The benefits brought by CHOReVOLUTION IDRE have been assessed through the evaluation of a set of Key Performance Indicators (KPIs). [ABSTRACT FROM AUTHOR]

Emissions from road traffic contribute to climate change. One approach to reducing the carbon footprint is providing eco-driving feedback so that drivers adapt their driving style. Research about the impact of eco-feedback on energy consumption is the basis for designing a mobile eco-driving feedback information system that supports drivers in reducing fuel consumption. This work develops design knowledge from existing knowledge. Subsequently, we implement a prototypical instantiation based on the derived knowledge. Insights from a field study suggest that our design artifact allows most drivers to decrease fuel consumption by 4% on average. The paper's theoretical contribution is a set of design principles and an architecture of the proposed mobile eco-driving feedback information system. One recommendation is to provide normative feedback that compares drivers with each other. This feedback appears to encourage drivers to decrease their fuel consumption additionally. The design knowledge may support researchers and practitioners in implementing efficient eco-driving feedback information systems. [ABSTRACT FROM AUTHOR]

This paper presents two nonlinear model predictive control (MPC) methods for the integrated propulsion and cabin-cooling management of electric vehicles. An air-conditioning (AC) model, which has previously been validated on a real system, is used to accomplish system-level optimization. To investigate the optimal solution for the integrated optimal control problem (OCP), we first build an MPC, referred to as a joint MPC, in which the goal is to minimize battery energy consumption while maintaining cabin-cooling comfort. Second, we divide the integrated OCP into two small-scale problems and devise a co-optimization MPC (co-MPC), where speed planning on hilly roads and cabin-cooling management with propulsion power information are addressed successively. Our proposed MPC methods are then validated through two case studies. The results show that both the joint MPC and co-MPC can produce significant energy benefits while maintaining driving and thermal comfort. Compared to regular constant-speed cruise control that is equipped with a proportion integral (PI)-based AC controller, the benefits to the battery energy earned by the joint MPC and co-MPC range from 2.09% to 2.72%. Furthermore, compared with the joint MPC, the co-MPC method can achieve comparable performance in energy consumption and temperature regulation but with reduced computation time. [ABSTRACT FROM AUTHOR]

This paper studies the fuel-optimal cruising strategies of parallel hybrid electric vehicles (HEVs) and their underlying mechanisms. To achieve fuel-optimal operations, a discontinuous nonlinear optimal control problem is formulated and solved using the Legendre pseudospectral method and the knotting technique. Three optimal cruising strategies in free/fixed-speed cruising scenarios are proposed: vehicle speed pulse-and-glide strategy, state-of-charge (SoC) pulse-and-glide (PnG) strategy, and constant-speed strategy. The performance and optimal behavior of the engine and the motor are presented, and their fuel-saving mechanisms are explained. Finally, two principles to compromise between fuel economy and ride comfort are proposed and studied. [ABSTRACT FROM PUBLISHER]

Eco-driving at signalized intersections has significant potential for energy saving. In this paper, we focus on eco-departure operations of connected vehicles equipped with an internal combustion engine and a step-gear automatic transmission. A Bolza-type optimal control problem (OCP) is formulated to minimize engine fuel consumption. Due to the discrete gear ratio, this OCP is a nonlinear mixed-integer problem, which is challenging to handle by most existing optimization methods. The Legendre pseudospectral method combining the knotting technique is employed to convert it into a multistage interconnected nonlinear programming problem, which then solves the optimal engine torque and transmission gear position. The fuel-saving benefit of the optimized eco-departing operation is validated by a passenger car with a five-speed transmission. For real-time implementation, a near-optimal departing strategy is proposed to quickly determine the behavior of the engine and transmission. When a string of vehicles are departing from an intersection, the acceleration of the leading vehicle(s) should be considered to control the following vehicles. This issue is also addressed in this paper. [ABSTRACT FROM PUBLISHER]

Previous research has identified that fuel consumption and emissions can be considerably reduced if drivers engage in eco-driving behaviours. However, the literature suggests that individuals struggle to maintain eco-driving behaviours without support. This paper evaluates an in-vehicle visual interface system designed to support eco-driving through recommendations based on both feedforward and feedback information. A simulator study explored participants' fuel usage, driving style, and cognitive workload driving normally, when eco-driving without assistance and when using a visual interface. Improvements in fuel-efficiency were observed for both assisted (8.5%) and unassisted eco-driving (11%), however unassisted eco-driving also induced a significantly greater rating of self-reported effort. In contrast, using the visual interface did not induce the same increase of reported effort compared to everyday driving, but itself did not differ from unassisted driving. Results hold positive implications for the use of feedforward in-vehicle interfaces to improve fuel efficiency. Accordingly, directions are suggested for future research. Practitioner Summary: Results from a simulator study comparing fuel usage from normal driving, engaging in unassisted eco-driving, or using a novel speed advisory interface, designed to reduce fuel use, are presented. Whilst both unassisted and assisted eco-driving reduced fuel use, assisted eco-driving did not induce workload changes, unlike unassisted eco-driving. Abbreviations: CO-2: carbon dioxide; NASA-TLX: NASA task load index; RMS: root-mean-square; MD: mean difference [ABSTRACT FROM AUTHOR]

Dynamiceco-driving is a well-known umbrella term describing speed control schemes that utilize connected and automated vehicle technology for the purpose of saving fuel. If dynamic eco-driving is to be widely prescribed as an integral part of widespread fuel-saving endeavors, its expected performance as part of the overall traffic system must be analyzed. Specifically, it must be determined to what extent this type of control remains effective in the presence of dense traffic. This paper presents a series of multi-vehicle traffic simulations, which begin to answer important questions surrounding the effects of dynamic eco-driving on traffic and its potential for fuel savings in a mixed traffic environment. Three representative methods of dynamic eco-driving are tested in various high traffic scenarios and the estimated fuel economy, trip time, and average speed results are compared. Independent variables include technology penetration rate and amount of traffic, quantified by the delay level of service of the road network’s traffic light facility. It is shown that, for the given test cases, average mpg increases linearly with technology penetration rate and dynamic eco-driving causes an average increase in mpg regardless of traffic amount. Overall results are promising for the usefulness of this clever class of fuel-saving technologies, in high traffic as well as low. [ABSTRACT FROM AUTHOR]

Eco-driving is considered as one of the effective ways to reduce energy consumption. The objective of this study is to establish an eco-driving behaviour identification model and analyse the eco-driving behaviour characteristics. First, this study built a driving simulation platform and conduct an experiment for research. Then, based on significant driving behaviours related to fuel consumption, Piecewise Linear Representation (PLR) method was used to fit multivariate time series consisting of Significant driving behaviour variables. At last, the features of each time series segment were extracted as the input of a Random Forest (RF) model to recognize the eco-driving behaviour. The results indicated that the depth of the acceleration pedal, the depth of the clutch pedal, the depth of the brake pedal, steering wheel angle, and gear position had significant effects on fuel consumption. The eco-driving behaviour identification model demonstrated a high predictive power with a prediction accuracy of 0.718. The graph of eco-driving behaviours during lane change was established. The conclusions provide theoretical support for developing eco-driving intervenes. (Received in March 2022, accepted in May 2022. This paper was with the authors 2 weeks for 1 revision.) [ABSTRACT FROM AUTHOR]

This paper presents a real-time vehicle speed planning system called the real-time energy-optimal deceleration planning system (RT-EDPS). Connectivity and autonomous driving technologies that provide map and navigation data, traffic light information, and front detection sensor data are exploited to perceive and forecast upcoming deceleration events. A parameterized polynomial-based deceleration model is employed as the deceleration strategy. Real driving test data that characterize the physical limits of regenerative braking are used to model the objective function and constraints of the parameterized deceleration commands. The proposed RT-EDPS involves two speed-planning strategies with different scales of the planning horizon. Within a deceleration event horizon ascertained by the signal phase and timing data of traffic signals and 3D geographic map data, a dynamic programming-based energy-efficient deceleration strategy is used to generate long-sighted speed profiles. While the scheduled vehicle speed is being tracked, if a preceding vehicle is detected within a predefined threshold range, then a model predictive control-based energy-efficient deceleration strategy that incorporates the traffic status ahead of the ego vehicle and exploits V2V communication with the preceding vehicles is activated to re-plan the speed trajectory of the vehicle to guarantee a collision-free distance for the preceding vehicle. In virtual driving experiments, we record energy-recuperation efficiency gains of over 40% as compared to human drivers. [ABSTRACT FROM AUTHOR]

Eco-driving algorithms optimize the speed profile to reduce the energy consumption of a vehicle. This paper presents an eco-driving algorithm for battery electric powertrains that applies a split loss integration approach to incorporate the component losses. The algorithm consistently uses loss models to overcome the drawbacks of efficiency maps, which cannot represent no-load losses at zero torque. The use of loss models is crucial since the optimal solution includes gliding, during which there are no-load losses. An analysis shows, that state-of-the-art nonlinear programming algorithms cannot represent these no-load losses at zero torque with a small modeling error. To effectively compute the powertrain losses with only a small error in comparison to the measurement data, we introduce a tailored combination of nonlinear inequality constraints that interleave two polynomial fits. This approach can properly represent reality. We parameterize the algorithm and validate the vehicle model used with real-world measurement data. Furthermore, we investigate the influence of the proposed interleaved fits by comparing them to a single continuous high-order polynomial fit and to the state of the art. The algorithm is published open source. [ABSTRACT FROM AUTHOR]

Eco-driving is a keystone in energy reduction in railways and a fundamental tool to contribute to the Sustainable Development Goals in the transport sector. However, its results in real applications are subject to uncertainties such as climatological factors that are not considered in the train driving optimisation. This paper aims to develop an eco-driving model to design efficient driving commands considering the uncertainty of climatological conditions. This uncertainty in temperature, pressure, and wind is modelled by means of fuzzy numbers, and the optimisation problem is solved using a Genetic Algorithm with fuzzy parameters making use of an accurate railway simulator. It has been applied to a realistic Spanish high-speed railway scenario, proving the importance of considering the uncertainty of climatological parameters to adapt driving commands to them. The results obtained show that the energy savings expected without considering climatological factors account for 29.76%, but if they are considered, savings can rise up to 34.7% in summer conditions. With the proposed model, a variation in energy of 5.32% is obtained when summer and winter scenarios are compared while punctuality constraints are fulfiled. In conclusion, the model allows the operator to estimate better energy by obtaining optimised driving adapted to the climate. [ABSTRACT FROM AUTHOR]

This paper presents a computationally efficient algorithm for eco-driving along horizons of over 100 km. The eco-driving problem is formulated as a bi-level program, where the bottom level is solved offline, pre-optimising gear as a function of longitudinal velocity (kinetic energy) and acceleration. The top level is solved online, optimising a nonlinear dynamic program with travel time, kinetic energy and acceleration as state variables. To further reduce computational effort, the travel time is adjoined to the objective by applying necessary Pontryagin’s Maximum Principle conditions, and the nonlinear program is solved using real-time iteration sequential quadratic programming scheme in a model predictive control framework. Compared to average driver’s driving cycle, the energy savings of using the proposed algorithm is up to 11.60%. [ABSTRACT FROM AUTHOR]

The main contribution of this paper is the formulation of a predictive optimal velocity-planning algorithm that uses probabilistic traffic-signal phase and timing (SPAT) information to increase a vehicle's energy efficiency. We introduce a signal-phase prediction model that uses historically averaged timing data and real-time phase data to determine the probability of green for upcoming traffic lights. In an optimal control framework, we then calculate the best velocity trajectory that maximizes the chance of going through green lights. The case study results from a multisignal simulation indicating that energy efficiency can be increased with probabilistic timing data and real-time phase data. Monte Carlo simulations are used to confirm that the case study results are valid, on average. Finally, simulated vehicles are driven through a series of traffic signals, using recorded data from a real-world set of traffic-adaptive signals, to determine the applicability of these predictive models to various types of traffic signals. [ABSTRACT FROM PUBLISHER]

Pulse and glide (PnG) is a longitudinal control strategy that operates vehicles in a more fuel-efficient way than driving at a constant speed. The pulse phase is an acceleration phase, followed by a deceleration, or glide, phase. Former research on PnG had in common that the acceleration profile during the pulse phase is chosen to operate the engine along the course of optimal engine efficiency. However, this optimal strategy will not be accepted by human drivers due to high acceleration values and sudden changes in acceleration. The purpose of this paper is to assess the driveability of the PnG based on the subjective evaluations from test drives and, as a result, to define a PnG operation that is noticeable only to the skeptical customers. The fuel savings of this PnG operation are simulated afterward. The results of this paper reveal that a PnG operation that is noticeable only to skeptical customers is reached if the acceleration value and the absolute values of the jerk are kept below the specific thresholds. Also, the duration of deceleration needs to be limited. Furthermore, the selection of the target gear after exiting sailing needs to be calibrated specifically for PnG to cope with the engine vibrations. Within the identified thresholds, the fuel savings of PnG are larger than 18% below 48 km/h and decrease gradually with increasing speed. Consequently, PnG can be implemented to the drivers’ satisfaction, leading to significant fuel savings. Moreover, as we provide a general link between the acceleration and jerk levels and the corresponding ride comfort, the results can directly be implemented in the trajectory planning of further automated longitudinal vehicle control schemes to attain the desired ride comfort. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Driving is a daily encountered task for many. Unlike the majority of life's daily hassles, however, the act of driving has significant environmental repercussions. Supporting the development of environmentally conscious driving techniques and developing tools and interfaces to reduce the environmental impact of driving is warranted to minimise the negative impact of these actions. The current paper documents the development of a complete cognitive work analysis (CWA) to support environmentally conscious driving. The paper proposes that the use of the CWA approach-enabled examination of the fuel-efficient driving task and consideration of the role numerous objects, agents and skills can play in facilitating fuel-efficient driving. In addition to the traditional CWA process, this paper shows how the revealed finding can be used as the basis for developing specifications that can be taken forward to allow for the development of novel in-vehicle interfaces to support fuel-efficient driving. [ABSTRACT FROM AUTHOR]

This paper proposes an eco-driving framework for electric connected vehicles (CVs) based on reinforcement learning (RL) to improve vehicle energy efficiency at signalized intersections. The vehicle agent is specified by integrating the model-based car-following policy, lane-changing policy, and RL policy, to ensure the safe operation of a CV. Subsequently, a Markov Decision Process (MDP) is formulated, which enables the vehicle to perform longitudinal control and lateral decisions, jointly optimizing the car-following and lane-changing behaviours of the CVs in the vicinity of intersections. Then, the hybrid action space is parameterized as a hierarchical structure and thereby trains the agents with two-dimensional motion patterns in a dynamic traffic environment. Finally, our proposed methods are evaluated in SUMO software from both a single-vehicle-based perspective and a flow-based perspective. The results show that our strategy can significantly reduce energy consumption by learning proper action schemes without any interruption of other human-driven vehicles (HDVs). [ABSTRACT FROM AUTHOR]

Fuel consumption of fossil-based road vehicles is significantly affected by the way vehicles are driven. The same is true for automated vehicles with longitudinal control. This paper presents a periodic servo-loop longitudinal control algorithm for an adaptive cruise control (ACC) system to minimize fuel consumption in car-following scenarios. The fuel-saving mechanism of pulse-and-glide (PnG) operation is first discussed for the powertrain with internal combustion engine and step-gear transmission. The servo-loop controller is then designed based on a periodic switching map for real-time implementation and adjusted with a range-bounded feedback regulator to enhance the robustness to model mismatch. Simulations in both uniform and natural traffic flows demonstrate that this algorithm achieves a significant fuel-saving benefit in automated car-following scenarios up to 8.9% in naturalistic traffic flow (when coasting at neutral gear), compared with a linear quadratic (LQ) controller. Meanwhile, its intervehicle range is preferably bounded so that the negative impact on safety and traffic smoothness is contained. [ABSTRACT FROM PUBLISHER]

The rising concerns about global warming have motivated the international community to take remedial actions to lower greenhouse gas emissions. The transportation sector is believed to be one of the largest air polluters. The quantity of greenhouse gas emissions is directly linked to the fuel consumption of vehicles. Eco-driving is an emergent driving style that aims at improving gas mileage. Real-time fuel estimation is a critical feature of eco-driving and eco-routing. There are numerous approaches to fuel estimation. The first approach uses instantaneous values of speed and acceleration. This can be accomplished using either GPS data or direct reading through the OBDII interface. The second approach uses the average value of the speed and acceleration that can be measured using historical data or through web mapping. The former cannot be used for route planning. The latter can be used for eco-routing. This paper elaborates on a highly pipelined VLSI architecture for the fuel estimation algorithm. Several high-level transformation techniques have been exercised to reduce the complexity of the algorithm. Three competing architectures have been implemented on FPGA and compared. The first one uses a binary search algorithm, the second architecture employs a direct address table, and the last one uses approximation techniques. The complexity of the algorithm is further reduced by combining both approximated computing and precalculation. This approach helped reduce the floating-point operations by 30% compared with the state-of-the-art implementation. [ABSTRACT FROM AUTHOR]

Recently developed efficient driving schemes usually solve a predictive optimization problem or determining the vehicle control input, and at the expense of high computational cost, they improve the overall traffic flows and individual driving performances on urban roads. This paper presents a more practical technique for automated vehicles’ predictive driving by extending the existing adaptive cruise control (ACC) scheme with a look-ahead functionality. Such a look-ahead driving scheme (LDS) predicts the states of the preceding vehicle at an adaptive look-ahead time step and, with negligible computation costs, computes the vehicle control input more circumspectly for efficient driving in urban traffic. The proposed LDS is evaluated in typical urban traffic at the signalized intersections by observing the intersection utilization, flowing characteristics, and individual vehicles’ fuel efficiency. Furthermore, we also evaluate the influences of the LDS-vehicles’ penetration rates on overall traffic performances at various traffic volumes. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper proposes a predictive cruise control based on eco-driving for a passage car that uses the information of upcoming traffic limits and the preceding vehicle to realize better fuel economy. To fully exploit the inherent potential of the powertrain system to reduce fuel consumption, the velocity is obtained by optimizing the engine torque, the brake force, and the gearshift while ensuring safe distance separation and traffic speed limits. The problem is described as a nonlinear mixed-integer problem and solved by the concept of combining Pontryagin’s minimum principle and bisection method. The simulation results show a significant improvement in computational efficiency compared with traditional numerical methods, and the simulation results also show that the computational time increases linearly with prediction horizon. It is shown that an improvement of 8% in fuel is achieved in a realistic scenario compared with a basic vehicle using a standard adaptive cruise control. [ABSTRACT FROM AUTHOR]

In this paper, the computation of eco-driving cycles for electric, conventional, and hybrid vehicles using receding horizon and optimal control is studied. The problem is formulated as consecutive-optimization problems aiming at minimizing the vehicle energy consumption under traffic and speed constraints. The impact of the look-ahead distance and the optimization frequency on the optimal speed computation is studied to find a tradeoff between the optimality and the computation time of the algorithm. For the three architectures considered, simulation results show that in urban driving conditions, a look-ahead distance of 300–500 m leads to a sub-optimality less than $1\%$ in the energy consumption compared to the global solution. For highway driving conditions, a look-ahead distance of 1–1.5 km leads to a sub-optimality less than  $2\%$ compared to the global solution. [ABSTRACT FROM AUTHOR]

Velocity profile optimization of on-road vehicles is one of the main eco-driving techniques, which has great potential to extend the capability of powertrain and automatic longitudinal control by minimizing the energy consumption. Due to the multi factors affecting the driving trajectory and longer prediction horizon comparing with other traditional control, the calculation of a velocity profile optimization often requires a large number of computations. In this paper, a hierarchical control (HC) strategy of velocity optimization is proposed to reduce computation burden with little accuracy loss. In the HC strategy, a specific driving task is divided into several operation of modes as acceleration (A), constant speed (C), deceleration (D), and braking (B). The shift timing of the driving modes are optimized by formulating a nonlinear programming problem in a master controller. Then, engine torque, gear position, and brake force are optimized in each driving mode. Results indicate that the computation time of velocity profile optimization using the proposed HC strategy is reduced by 90% of the ones using the basic centralized optimal controller while the resulting velocities are similar. It is also shown that an improvement of 30% in fuel economy is achieved compared with the real-life human-driven velocity profiles. [ABSTRACT FROM AUTHOR]

The horizontal curvature and a driver’s behavior on curved roads significantly affect vehicle fuel economy. This paper describes how optimal speed profiles can minimize fuel consumption for a vehicle travelling on a curve. Previous studies suggested that sustaining a constant speed throughout a level road is the optimal measure for conserving fuel, within certain bounds. Based on the established vehicle dynamic model and an instantaneous fuel consumption model, the optimal constant speeds corresponding to circular curves with different radii can be derived. When entering or departing a curve for additional study, a dynamic programming algorithm is tailored to obtain the optimal speed profile in the vicinity of the curve. The algorithm is verified by using co-simulation of CarSim and Matlab/Simulink, and the results show the algorithm can save approximately 5.46% to 17.64% of fuel compared with the deceleration and acceleration modes of typical driver model controlled by a proportional integral controller. This technology can not only improve the conventional vehicle fuel economy by taking into account the horizontal curvature but also provide decision-making reference for the autonomous vehicle speed control. [ABSTRACT FROM AUTHOR]

This paper describes the design and preliminary test of a virtual reality driving simulator capable of conveying haptic and visual messages to promote eco-sustainable driving behavior. The driving simulator was implemented through the Unity game engine; a large street environment, including high-speed and urban sections, was created to examine different driving behaviors. The hardware setup included a gaming driving seat, equipped with a steering wheel and pedals; the virtual scenarios were displayed through an Oculus Rift headset to guarantee an immersive experience. Haptic stimulation (i.e., vibrations) was delivered to the driver through the accelerator pedal, while visual stimuli (i.e., icons and colors) were shown on a virtual head-up display. The sensory feedbacks were presented both alone and in combination, providing information about excessive acceleration and speed. Four different virtual scenarios, each one including a distracting element (i.e., navigator, rain, call, and traffic), were also created. Ten participants tested the simulator. Fuel consumption was evaluated by calculating a mean power index (MPI) in reference to the sensory feedback presentation; physiological reactions and responses to a usability survey were also collected. The results revealed that the haptic and visuo-haptic feedback were responsible for an MPI reduction, respectively, for 14% and 11% compared with a condition of no feedback presentation; while visual feedback alone resulted in an MPI increase of 11%. The efficacy of haptic feedback was also accompanied by a more relaxing physiological state of the users, compared with the visual stimulation. The system's usability was adequate, although haptic stimuli were rated slightly more intrusive than the visual ones. Overall, these preliminary results highlight how promising the use of the haptic channel can be in communicating and guiding the driver toward a more eco-sustainable behavior. [ABSTRACT FROM AUTHOR]

Recent improvements in vehicle-to-everything (V2X) communication and onboard computing power have enabled the development of control algorithms that jointly optimize the vehicle velocity and powertrain control in Connected and Automated Vehicles (CAVs), commonly referred to as the Eco-Driving problem. This paper presents a novel and computationally efficient algorithm to optimize the velocity planning and energy management in a CAV with a hybrid electric powertrain. The Eco-Driving problem is formulated as a dynamic, constrained optimization problem in the spatial domain, where information about the upcoming speed limits and road topography is assumed known. This problem is solved by embedding an Equivalent Consumption Minimization Strategy (ECMS) into a Dynamic Programming (DP) optimization to obtain a sub-optimal solution that provides results close to the global optimum at a fraction of the computational cost. Further, a multi-layer hierarchical control architecture is proposed as a path to a causal, real-time implementation. The DP-ECMS algorithm is converted into a Model Predictive Control (MPC) framework by using principles of Approximate Dynamic Programming (ADP). This causal implementation is finally benchmarked to a global optimal solution obtained with DP for different scenarios. [ABSTRACT FROM AUTHOR]