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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]

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]

Improving energy efficiency in the transportation system is an important goal in facing the challenges of climate change and limited energy resources. Electric trains are a promising alternative for reducing greenhouse gas emissions and dependence on fossil fuels. Speed profile optimization has been a significant research focus on achieving maximum energy efficiency in electric trains. This paper aims to provide insight into energy efficiency using speed profile optimization. Several issues were discussed in this paper including energy consumption modeling methods, speed profile optimization methods, and integration of speed profile optimization with schedules and regenerative braking. This study concluded that the most frequently used energy consumption modeling is the deterministic model using the Davis equation. There are two classifications in optimization: classical and modern methods (heuristics). Classical optimization methods are often used on problems with simple constraints, and modern methods are often used on problems with more complex constraints or variables.

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]

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]

Eco-driving strategies increasingly emphasize enhancing multi-performance including safety, comfort, durability, and traffic efficiency under optimal energy-saving. This paper introduces a multi-performance enhanced eco-driving strategy for connected fuel cell hybrid electric buses (FCHEBs) based on the stein soft actor-3-critic (SSA3C) algorithm. By leveraging the correlation between the international roughness index (IRI) and weighted root mean square acceleration (WRMSA), energy management system (EMS) and adaptive cruise control (ACC) are integrated the to enhancing multi-performance including vertical comfort and energy efficiency. To address the insufficient action representation and overestimation of Q-values of traditional soft actor-critic (SAC) algorithm, improvements are made to the actor and critic networks using stein variational gradient descent (SVGD) and clipped triple Q-learning (3C), respectively. Composite cycles integrating the standard cycle and IRI are developed to enhance the strategy's generalization. Experimental results indicate that the proposed eco-driving strategy, while maintaining dynamics, safety, durability, and efficiency, efficiently achieves 98.1 % energy savings and equivalent comfort level as dynamic programming (DP). Ablation experiments on vertical comfort demonstrate improvements of 1.24 % in energy efficiency and 2.94 % in WRMSA. Finally, the effectiveness of the network improvements and the adaptability of the proposed strategy are demonstrated through comparative experiments. • An DRL-based eco-driving strategy is developed for connected FCHEVs. • The relationship between road roughness, vertical comfort, and velocity is studied. • Vertical comfort is incorporated to co-optimize eco-driving multi-performances. • A novel Stein Soft Actor-3-Critic with high expressivity and precision is proposed. • Composite driving cycles including IRI are constructed for strategy adaptability. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs), which are a great substitute for gasoline-powered vehicles, have the potential to achieve the goal of reducing energy consumption and emissions. However, the energy consumption of an EV is highly dependent on road contexts and driving behavior, especially at urban intersections. This paper proposes a novel ecological (eco) driving strategy (EDS) for EVs based on optimal energy consumption at an urban signalized intersection under moderate and dense traffic conditions. Firstly, we develop an energy consumption model for EVs considering several crucial factors such as road grade, curvature, rolling resistance, friction in bearing, aerodynamics resistance, motor ohmic loss, and regenerative braking. For better energy recovery at varying traffic speeds, we employ a sigmoid function to calculate the regenerative braking efficiency rather than a simple constant or linear function considered by many other studies. Secondly, we formulate an eco-driving optimal control problem subject to state constraints that minimize the energy consumption of EVs by finding a closed-form solution for acceleration/deceleration of vehicles over a time and distance horizon using Pontryagin's minimum principle (PMP). Finally, we evaluate the efficacy of the proposed EDS using microscopic traffic simulations considering real traffic flow behavior at an urban signalized intersection and compare its performance to the (human-based) traditional driving strategy (TDS). The results demonstrate significant performance improvement in energy efficiency and waiting time for various traffic demands while ensuring driving safety and riding comfort. Our proposed strategy has a low computing cost and can be used as an advanced driver-assistance system (ADAS) in real-time. • A fast PMP method is developed to minimize energy consumption and idling time of EVs. • An energy consumption model considering road context and engine dynamics is presented. • A sigmoid function is used to calculate regenerative braking efficiency. • We validate the scheme via simulations of a signalized intersection in Malaysia. • The optimally controlled EVs significantly reduce energy consumption and idling time. [ABSTRACT FROM AUTHOR]

In the pursuit of sustainable mobility and the decarbonization of transport systems, public authorities are increasingly scrutinizing the impact of travel speed on emissions within both low-speed and high-speed environments. This study critically examines the evidence concerning emission impacts associated with speed limit changes in different traffic environments by conducting a systematic review of the literature in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 25 studies that met the eligibility criteria were assessed. The results reveal mixed evidence for reducing emissions through speed limit reductions in low-speed areas. However, emerging evidence suggests that reduced urban speeds may abate emissions through enhanced traffic flow and a shift in modal preferences away from personal vehicle use. Additionally, in urban areas, minor observed emission reduction per vehicle can add up to large overall reductions due to the high number of vehicles. In high-speed contexts, the evidence is much clearer, showing that reduced speed limits correlate with significant reductions in NOx, CO2, and particulate matter emissions. The extent of these reductions is highly variable and contingent upon the specific speed limits or limit reductions, the local context, the vehicle type, and the baseline types and levels of pollutants. Notably, there is a lack of research on the effects of speed on emissions, especially in low- and middle-income countries (LMICs), highlighting a critical area for future investigation. The findings of this study underscore the potential environmental benefits of speed management policies and advocate for the promotion of smoother and less aggressive driving behavior to mitigate emissions and enhance sustainable mobility in both low-speed and high-speed settings. [ABSTRACT FROM AUTHOR]

The rapid advancement of Vehicle-to-Everything communication (V2X) technology presents opportunities for enhancing traffic energy efficiency. With V2X, this paper introduces an eco-driving strategy for autonomous electric vehicles to navigate continuous signalized intersections. This strategy incorporates the realistic powertrain's working efficiency and constraints into the vehicle motion optimization. In the domain of the global path, we formulate a co-optimization problem of vehicle motion and powertrain operation to tap the vehicle energy-saving potential and derive an optimal velocity profile, considering constraints related to queue release and limit speed in intersections. In the short rolling time domain, we propose a predictive control approach for vehicle speed tracking and real-time powertrain control. Simulation results under various scenarios show the advantages of the proposed method in terms of energy consumption compared to the baseline and existing methods. • An eco-driving strategy at continuous speed-limit signalized intersections. • The varying speed limits, traffic signal period, and queue effect are considered. • The powertrain efficiency and constraints are incorporated into motion planning. • A long-term co-optimization model of vehicle motion and powertrain operation. • A short-term optimal velocity tracking control for smooth powertrain operation. [ABSTRACT FROM AUTHOR]

Eco-driving has garnered considerable research attention owing to its potential socio-economic impact, including enhanced public health and mitigated climate change effects through the reduction of greenhouse gas emissions. With an expectation of more autonomous vehicles (AVs) on the road, an eco-driving strategy in hybrid traffic networks encompassing AV and human-driven vehicles (HDVs) with the coordination of traffic lights is a challenging task. The challenge is partially due to the insufficient infrastructure for collecting, transmitting, and sharing real-time traffic data among vehicles, facilities, and traffic control centers, and the following decision-making of agents involved in traffic control. Additionally, the intricate nature of the existing traffic network, with its diverse array of vehicles and facilities, contributes to the challenge by hindering the development of a mathematical model for accurately characterizing the traffic network. In this study, we utilized the Simulation of Urban Mobility (SUMO) simulator to tackle the first challenge through computational analysis. To address the second challenge, we employed a model-free reinforcement learning (RL) algorithm, proximal policy optimization, to decide the actions of AV and traffic light signals in a traffic network. A novel eco-driving strategy was proposed by introducing different percentages of AV into the traffic flow and collaborating with traffic light signals using RL to control the overall speed of the vehicles, resulting in improved fuel consumption efficiency. Average rewards with different penetration rates of AV (5%, 10%, and 20% of total vehicles) were compared to the situation without any AV in the traffic flow (0% penetration rate). The 10% penetration rate of AV showed a minimum time of convergence to achieve average reward, leading to a significant reduction in fuel consumption and total delay of all vehicles. [ABSTRACT FROM AUTHOR]

Eco-driving aims to enhance vehicle efficiency by optimizing speed profiles and driving patterns. However, ensuring safe following distances during eco-driving can lead to excessive use of lithium-ion batteries (LIBs), causing accelerated battery wear and potential safety concerns. This study addresses this issue by proposing a novel, multi-physics-constrained cruise control strategy for intelligently connected electric vehicles (EVs) using deep reinforcement learning (DRL). Integrating a DRL framework with an electrothermal model to estimate unmeasurable states, this strategy simultaneously manages battery degradation and thermal safety while maintaining safe following distances. Results from hardware-in-the-loop simulation testing demonstrated that this approach reduced overall driving costs by 18.72%, decreased battery temperatures by 4 °C to 8 °C in high-temperature environments, and reduced state-of-health (SOH) degradation by up to 46.43%. These findings highlight the strategy's superiority in convergence efficiency, battery thermal safety, and cost reduction compared to existing methods. This research contributes to the advancement of eco-driving practices, ensuring both vehicle efficiency and battery longevity. [ABSTRACT FROM AUTHOR]

Battery electric vehicles (BEVs) face challenges like their limited all-electric range, the discrepancy between promised and actual energy efficiency, and battery health degradation, despite their environmental benefits. This article proposes an optimal adaptive cruise control (OACC) framework by leveraging ideal vehicle-to-vehicle communication to address these challenges. In a connected vehicle environment, where it is assumed that the Ego vehicle's vehicle control unit (VCU) accurately knows the speed and position of the Leading vehicle, the VCU can optimally plan the acceleration trajectory for a short-term future time window through a model predictive control (MPC) framework tailored to BEVs. The primary objective of the OACC is to reduce the energy consumption and battery state-of-health degradation of a BEV. The Chevrolet Spark 2015 is chosen as the BEV platform used to validate the effectiveness of the proposed OACC. Simulations conducted under urban and highway driving conditions, as well as under communication delay and infused noise, resulted in up to a 3.7% reduction in energy consumption and a 9.7% reduction in battery state-of-health (SOH) degradation, demonstrating the effectiveness and robustness of the proposed OACC. [ABSTRACT FROM AUTHOR]

Electric vehicles have been considered into effective solutions to address energy problems in urban traffic systems for their remarkable performance in energy costs and carbon emissions reduction. However, the energy-saving effect of electric vehicles in urban traffic systems is restricted due to the complexities arising from mixed traffic conditions. To improve energy efficiency, this paper proposes an eco-driving-based mixed vehicular platoon control model for successive signalized intersections with connected and autonomous vehicles (CAVs), connected vehicles (CVs) and manned vehicles (MVs). Firstly, the dynamics model and spacing policy are analyzed, then the optimal platoon size and speed are calculated according to different traffic scenarios. Secondly, the mixed vehicular platoon control algorithm is established by considering traffic timing, energy consumption and string stability. Thirdly, an actual eco-driving-based platoon control system for tested electric vehicles is designed. The field test results demonstrate that all platoons can pass through intersections during green phase with minimum energy consumption, meanwhile the string stability can be guaranteed. It is noteworthy that the proposed model can greatly improve urban traffic capacity and energy efficiency. • A novel dynamical model of mixed vehicular platoon is explored by integrating different characteristics of CAV, CV, and MV. • An optimal speed algorithm of the leading vehicle to pass through successive signalized intersections is proposed according to signal timing, vehicular speed, and entry time. • The eco-driving-based mixed vehicular platoon control model is established, and the multi-objective function considering traffic environmental constraints are formulated to minimize the travel time and energy consumption. [ABSTRACT FROM AUTHOR]

—This paper proposes two real-time energy-oriented driving strategies to minimize the energy consumption for electric vehicles on highways with varying slopes. First, a novel strategy, called normalized-energy consumption minimization strategy (NCMS), adopts a designed kinetic energy conversion factor to convert the vehicle kinetic energy change into the equivalent battery energy consumption. By minimizing the total normalized energy consumption, the energy-orientated vehicle control sequence is calculated. In addition, a logic car-following algorithm is developed to enhance NCMS for avoiding collisions with the potential preceding vehicle on the journey. Second, an improved model predictive control (MPC) is developed with a hierarchical framework, which achieves a balance between optimization and computational efficiency. In the upper level, a global, coarse-grained, iterative dynamic programming is employed to penalize the MPC terminal state, while the lower level performs online rolling optimization of the vehicle within a moderate time step. Thirdly, the performance of the proposed driving strategies is verified through a traffic simulation to evaluate the energy efficiency improvement and processor computation time compared to dynamic programming and constant speed strategy. Finally, a vehicle-in-the-loop test is carried out to validate the feasibility of the proposed two novel driving strategies. • A normalized energy consumption model is presented, combining two energy usage. • A novel eco-driving strategy is developed based on the proposed energy consumption. • The algorithm suits both free traffic and car-following scenarios with safety. • The characteristics of the four eco-driving strategies are compared. • The proposed strategy is verified by both simulation and vehicle-in-the-loop tests. [ABSTRACT FROM AUTHOR]

Fossil fuel vehicles significantly contribute to CO 2 emissions due to their high consumption of fossil fuels. Accurate estimation of vehicular fuel consumption and the associated CO 2 emissions is crucial for mitigating these emissions. Although driving behavior is a vital factor influencing fuel consumption and CO 2 emissions, it remains largely unaddressed in current CO 2 emission estimation models. This study incorporates novel driving behavior data, specifically counts of occurrences of dangerous driving behaviors, including speeding, sudden accelerating, and sudden braking, as well as driving time and driving distances on expressways, national highways, and local roads, respectively, into monthly fuel consumption estimation models for individual gasoline and hybrid vehicles. The CO 2 emissions are then calculated as a secondary parameter based on the estimated fuel consumption, assuming a linear relationship between the two. Using regression algorithms, it has been demonstrated that all the proposed driving behavior data are relevant for monthly CO 2 emission estimation. By integrating the driving behavior data of various vehicle categories, a generalizable CO 2 estimation model is proposed. When utilizing all the proposed driving behavior data collectively, our random forest regression model achieves the highest prediction accuracy, with R 2 , RMSE, and MAE values of 0.975, 13.293 kg, and 8.329 kg, respectively, for monthly CO 2 emission estimation of individual vehicles. This research offers insights into CO 2 emission reduction and energy conservation in the road transportation sector. [ABSTRACT FROM AUTHOR]

Abstract 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.

This paper presents a control strategy to optimize both the vehicle speed and gearbox position on curved roads with the aim of maximizing fuel economy. Previous studies have focused only on the vehicle speed optimization during cornering. Combining vehicle speed and gearbox position optimization can significantly maximize the energy-saving for an Internal combustion engine (ICE) vehicle. The problem is formulated as two successive optimization problems. In the first one, based on the road map and traffic data, the vehicle’s optimal speed profile is calculated by minimizing the energy spent for the entire curve. In the second one, the gearbox position is optimized for fuel consumption minimization using realistic engine map. The presented control structure’s effectiveness was evaluated through co-simulation of Matlab/Simulink and CarSim in various driving scenarios. The results show that approximately 5.25% to 11.44 % of fuel savings can be achieved compared with a typical driver model.

Energy consumption and emissions of a vehicle are highly influenced by road contexts and driving behavior. Especially, driving on horizontal curves often necessitates a driver to brake and accelerate, which causes additional fuel consumption and emissions. This paper proposes a novel optimal ecological (eco) driving scheme (EDS) using nonlinear model predictive control (MPC) considering various road contexts, i.e., curvatures and surface conditions. Firstly, a nonlinear optimization problem is formulated considering a suitable prediction horizon and an objective function based on factors affecting fuel consumption, emissions, and driving safety. Secondly, the EDS dynamically computes the optimal velocity trajectory for the host vehicle considering its dynamics model, the state of the preceding vehicle, and information of road contexts that reduces fuel consumption and carbon emissions. Finally, we analyze the effect of different penetration rates of the EDS on overall traffic performance. The effectiveness of the proposed scheme is demonstrated using microscopic traffic simulations under dense and mixed traffic environment, and it is found that the proposed EDS substantially reduces the fuel consumption and carbon emissions of the host vehicle compared to the traditional (human-based) driving system (TDS), while ensuring driving safety. The proposed scheme can be employed as an advanced driver assistance system (ADAS) for semi-autonomous vehicles.

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).