☆ 4.8 Review

Review on eco-driving control for connected and automated vehicles

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2024)

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Article Management

E-platooning: Optimizing platoon formation for long-haul transportation with electric commercial vehicles

Joachim Scholl et al.

Summary: Recent technological developments enable the connection of vehicles through digital data transmission, allowing for safe platooning of trucks. This platooning reduces fuel consumption and carbon footprint in road-based freight transportation. Electric trucks, which need to charge their batteries, are particularly suitable for platooning as it reduces energy consumption and increases driving range. This paper presents optimization approaches for scheduling the platoon formation process of electric commercial vehicles, aiming to minimize total energy costs.

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH (2023)

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Article Engineering, Civil

Cooperative Platoon Formation of Connected and Autonomous Vehicles: Toward Efficient Merging Coordination at Unsignalized Intersections

Zhiyun Deng et al.

Summary: This paper proposes a Vehicle-Platoon-Aware Bi-Level Optimization Algorithm for Autonomous Intersection Management (VPA-AIM) to coordinate the merging of Connected and Automated Vehicles at unsignalized intersections. The algorithm aims to balance traffic performance and computational efficiency by incorporating the platoon formation scheme into the upper-level traffic scheduling model and jointly optimizing the passing sequence and timeslots of vehicles with the platoon configuration scheme based on real-time traffic states. A lower-level trajectory planning model is also utilized to generate dynamically-feasible and energy-efficient trajectories, improving space utilization and preventing spillbacks. Numerical experiments demonstrate the superiority of the proposed approach in optimality and stability for real-life applications.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2023)

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Article Engineering, Civil

Energy-Efficient Connected Cruise Control With Lean Penetration of Connected Vehicles

Minghao Shen et al.

Summary: This paper focuses on designing an energy-efficient longitudinal controller for a connected automated truck that operates in mixed traffic conditions. The controller utilizes vehicle-to-everything (V2X) communication to gather information about connected vehicles beyond line of sight. A novel connected cruise control design is proposed, which incorporates delays to account for the finite propagation speed of traffic waves caused by distant connected vehicles. The speeds of non-connected vehicles are modeled as stochastic processes. The paper presents a fundamental theorem linking the spectral properties of motion signals to average energy consumption, and evaluates the controller using a combination of synthetic and real traffic data, demonstrating significant energy savings even with a low penetration of connected vehicles.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2023)

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Article Engineering, Civil

Ecological Cooperative Adaptive Cruise Control for Heterogenous Vehicle Platoons Subject to Time Delays and Input Saturations

Chunjie Zhai et al.

Summary: In this paper, an eco-CACC strategy is proposed for heterogeneous platoons with time delays and input saturations to achieve platoon stability, fuel economy, riding comfort, and driving efficiency.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2023)

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Article Computer Science, Artificial Intelligence

Physics-informed deep reinforcement learning-based integrated two-dimensional car-following control strategy for connected automated vehicles

Haotian Shi et al.

Summary: This study proposes an innovative integrated two-dimensional control strategy for connected automated vehicles, using deep reinforcement learning. The strategy efficiently controls the vehicles in terms of both stability-wise longitudinal control performance and accurate lateral path-tracking performance. The controller utilizes vehicle-to-everything communication and roadway geometry information, and applies a physics-informed DRL state fusion approach and reward function to better utilize the information and borrow the merits of control theory concepts. Simulated experiments validate the controller's accuracy and stability-wise performance in diverse traffic scenarios.

KNOWLEDGE-BASED SYSTEMS (2023)

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Article Energy & Fuels

Cooperative energy management and eco-driving of plug-in hybrid electric vehicle via multi-agent reinforcement learning

Yong Wang et al.

Summary: In this study, a multi-agent reinforcement learning (MARL) based optimal energy-saving strategy is designed for hybrid electric vehicles (HEV), achieving a cooperative control on the powertrain and car-following behaviors to minimize energy consumption and maintain a safe following distance. Compared to the state-of-the-art hierarchical model predictive control (MPC) strategy, the proposed MARL method can reduce fuel consumption by 15.8% while ensuring safety and comfort.

APPLIED ENERGY (2023)

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Article Computer Science, Artificial Intelligence

Hierarchical Velocity Control Considering Traffic Signal Timings for Connected Vehicles

Lulu Guo et al.

Summary: This paper proposes a hierarchical velocity control system considering different drive preferences for connected and automated vehicles to improve overall efficiency under the vehicle-to-X (V2X) environment. Simulation results indicate that energy-saving and computational efficiency are improved using the proposed control system. The solution algorithm is further demonstrated under a hardware-in-the-loop simulation.

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES (2023)

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Article Thermodynamics

Real-time dynamic predictive cruise control for enhancing eco-driving of electric vehicles, considering traffic constraints and signal phase and timing (SPaT) information, using artificial-neural-network-based energy consumption model

Zifei Nie et al.

Summary: This study introduces a real-time dynamic predictive cruise control (PCC) system based on the MPC algorithm to minimize energy consumption for electric vehicles under integrated traffic situations. The system utilizes SPaT-oriented MPC and car-following oriented MPC to calculate optimal control signals and maintain safe interdistances, demonstrating successful validation in real-world tests and robust performance in high-speed driving scenarios.

ENERGY (2022)

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Article Engineering, Civil

Hybrid Reinforcement Learning-Based Eco-Driving Strategy for Connected and Automated Vehicles at Signalized Intersections

Zhengwei Bai et al.

Summary: This study proposes a hybrid reinforcement learning framework to support connected eco-driving in mixed traffic. Through experiments, it shows that the proposed method can reduce energy consumption and save travel time compared to a state-of-the-art model-based approach.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2022)

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Article Chemistry, Physical

Co-optimization method of speed planning and energy management for fuel cell vehicles through signalized intersections

Xiaodong Wei et al.

Summary: This paper proposes a method for improving speed planning and energy management of fuel cell vehicles when passing through multiple signalized intersections, using real-time algorithms to achieve significant fuel savings performance.

JOURNAL OF POWER SOURCES (2022)

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Article Energy & Fuels

A comparative study of energy-efficient driving strategy for connected internal combustion engine and electric vehicles at signalized intersections

Haoxuan Dong et al.

Summary: This paper proposes an energy-efficient driving strategy to improve vehicle energy efficiency and overall traffic throughput in an urban traffic environment. The strategy achieves significant energy saving by considering the queuing effect, especially for electric vehicles.

APPLIED ENERGY (2022)

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Article Energy & Fuels

Energy efficient speed planning of electric vehicles for car-following scenario using model-based reinforcement learning

Heeyun Lee et al.

Summary: This study proposes a reinforcement learning-based energy efficient speed planning strategy for autonomous electric vehicles to minimize energy consumption in diverse driving situations.

APPLIED ENERGY (2022)

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Article Thermodynamics

Co-optimization strategy of unmanned hybrid electric tracked vehicle combining eco-driving and simultaneous energy management

Lingxiong Guo et al.

Summary: This paper proposes an efficient co-optimization strategy for unmanned hybrid electric tracked vehicles, achieving accurate path tracking and optimal energy management simultaneously. The strategy combines deep Q-learning algorithm and explicit model predictive control method to optimize velocity and power distribution in real time, resulting in good fuel economy and high computational efficiency.

ENERGY (2022)

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Article Engineering, Civil

An Efficient On-Ramp Merging Strategy for Connected and Automated Vehicles in Multi-Lane Traffic

Jinqiang Liu et al.

Summary: This study proposes an efficient on-ramp merging strategy to coordinate vehicle merging in multi-lane traffic, which significantly improves traffic efficiency and fuel economy through a lane selection model and vehicle motion planning algorithm.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2022)

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Article Engineering, Civil

Optimized Calculation of the Economic Speed Profile for Slope Driving: Based on Iterative Dynamic Programming

Jun Zhang et al.

Summary: This paper proposes an economic speed planning method based on iterative dynamic programming (IDP) to improve eco-driving performance. By studying the fuel consumption model and establishing a speed planning model, the calculation complexity of the dynamic programming algorithm is analyzed, and an improved algorithm is proposed to enhance calculation efficiency and accuracy.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2022)

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Article Chemistry, Physical

Stochastically predictive co-optimization of the speed planning and powertrain controls for electric vehicles driving in random traffic environment safely and efficiently

Xingyu Zhou et al.

Summary: This study develops a chance constraint stochastic model predictive control (CC-MPC) method for optimizing speed planning and powertrain energy management strategy of electric vehicles in a stochastic driving environment, successfully resolving the conflict between energy economy and driving safety.

JOURNAL OF POWER SOURCES (2022)

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Article Chemistry, Physical

Predictive co-optimization of speed planning and powertrain energy management for electric vehicles driving in traffic scenarios: Combining strengths of simultaneous and hierarchical methods

Xingyu Zhou et al.

Summary: In this study, a simultaneous method and a hierarchical method are developed for active speed planning and powertrain energy management strategy. A modified hierarchical method is proposed by modifying the decoupling strategy of the hierarchical method, achieving similar energy saving effectiveness and high computational efficiency compared to the simultaneous method.

JOURNAL OF POWER SOURCES (2022)

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Article Thermodynamics

Deep reinforcement learning and reward shaping based eco-driving control for automated HEVs among signalized intersections

Jie Li et al.

Summary: In this study, a deep reinforcement learning-based eco-driving control strategy is proposed to optimize the fuel economy, driving safety, and travel efficiency of automated hybrid electric vehicles in a connected traffic environment with signalized intersections. The method utilizes a twin-delayed deep deterministic policy gradient agent to plan vehicle speed in real-time, and transforms the multi-objective optimization function into the value function of the deep reinforcement learning algorithm. The proposed strategy is verified in a real road traffic environment and demonstrates significant reduction in fuel consumption while satisfying traffic lights and safety rules, showing feasibility for real-time application.

ENERGY (2022)

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Article Thermodynamics

Bi-level convex optimization of eco-driving for connected Fuel Cell Hybrid Electric Vehicles through signalized intersections

Bo Liu et al.

Summary: This paper proposes a bi-level convex approach for eco-driving of connected FCHEVs. By transforming traffic light constraints into linear constraints and using average speed, the upper level solves the speed planning problem while the lower level solves the energy management problem. The results show that this approach greatly reduces computational cost while maintaining high energy efficiency.

ENERGY (2022)

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Article Thermodynamics

Hierarchical eco-driving and energy management control for hydrogen powered hybrid trains

Kai Deng et al.

Summary: This article explores the potential of reducing the operating costs of hydrogen and battery-powered trains by implementing train eco-driving control and energy distribution. The effectiveness of the proposed hierarchical control is demonstrated through simulations.

ENERGY CONVERSION AND MANAGEMENT (2022)

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Article Automation & Control Systems

An Eco-Driving Approach With Flow Uncertainty Tolerance for Connected Vehicles Against Waiting Queue Dynamics on Arterial Roads

Chao Sun et al.

Summary: The study focuses on the impact of incorporating eco-driving at multiple signalized intersections on the energy performance of connected vehicles, with a proposed solution that models and predicts the dynamic variations of waiting queues to optimize the problem.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2022)

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Article Engineering, Civil

Hierarchical Energy-Efficient Control for CAVs at Multiple Signalized Intersections Considering Queue Effects

Shiying Dong et al.

Summary: The paper proposes a hierarchical energy-efficient control strategy to reduce fuel consumption and travel time, including the concept of virtual traffic lights and a distance-based energy-economy velocity optimization problem.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2022)

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Article Engineering, Civil

Comparison of Cooperative Driving Strategies for CAVs at Signal-Free Intersections

Huile Xu et al.

Summary: This study compares the performance of four representative cooperative driving strategies, finding that the Monte Carlo Tree Search-based strategy achieves the best traffic efficiency and fuel consumption performance. Dynamic Resequencing and MCTS strategies both perform well in all metrics. The influence of geometric shape on strategies is more significant than that of arrival rates.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2022)

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Article Computer Science, Information Systems

A survey on security attacks and defense techniques for connected and autonomous vehicles

Minh Pham et al.

Summary: Autonomous Vehicle and Connected and Autonomous Vehicles (CAVs) are transforming transportation systems, but face significant security challenges. Research surveyed attacks and defense techniques for CAVs, revealing a gap between academic research and industry implementation in addressing CAV security issues.

COMPUTERS & SECURITY (2021)

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Article Engineering, Civil

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

Xianfeng Terry Yang et al.

Summary: This study introduces an algorithm to design an eco-driving and platooning system utilizing the advances of connected automated vehicle technology, aiming to optimize trajectories and improve fuel efficiency. The proposed framework includes two stages, with the first focusing on trajectory planning and the second on real-time control to ensure operational safety of CAVs.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2021)

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Article Engineering, Civil

Optimal Eco-Driving of a Heavy-Duty Vehicle Behind a Leading Heavy-Duty Vehicle

Nalin Kumar Sharma et al.

Summary: The study proposes an eco-driving technique for heavy-duty ego vehicles, which predicts the future speed of a leading vehicle based on its power capability observation, and optimizes the speed of the ego vehicle to minimize fuel consumption and maintain a safe distance. By using a leading vehicle observer, fuel savings of up to 8% are achieved compared to assuming constant speed of the leading vehicle.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2021)

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Article Engineering, Electrical & Electronic

Real-time predictive eco-driving assistance considering road geometry and long-range radar measurements

James Fleming et al.

Summary: The eco-driving assistance system incorporating predictive or feedforward information is effective in increasing energy-efficiency and reducing CO2 emissions. Studies have shown a 6.09% reduction in fuel consumption and improvements in travel time, demonstrating the feasibility of real-time implementation of the system.

IET INTELLIGENT TRANSPORT SYSTEMS (2021)

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Article Transportation Science & Technology

Hybrid deep reinforcement learning based eco-driving for low-level connected and automated vehicles along signalized corridors

Qiangqiang Guo et al.

Summary: The paper introduces a hybrid reinforcement learning-based Eco-Driving algorithm that reduces fuel consumption by learning longitudinal acceleration/deceleration and lane-changing operations. Numerical experiments on multi-lane urban signalized corridors demonstrate that the algorithm can significantly reduce fuel consumption.

TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES (2021)

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Article Engineering, Civil

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

Hao Yang et al.

Summary: The study focuses on an eco-driving system that optimizes vehicle fuel consumption while traversing consecutive signalized intersections. By implementing the system in large networks and conducting a comprehensive analysis of various variables, optimal demand levels and traffic signal spacings were identified to maximize the algorithm's effectiveness.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2021)

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Article Computer Science, Information Systems

Cooperative- and Eco-Driving: Impact on Fuel Consumption for Heavy Trucks on Hills

Juergen Hauenstein et al.

Summary: Greenhouse gas emissions cause climate change, impacting people and the environment negatively. Reducing fuel consumption of conventional engines helps achieve climate protection goals and eco-driving reduces fuel costs. However, cooperative driving must be considered to avoid increased fuel consumption.

ELECTRONICS (2021)

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Article Transportation Science & Technology

Automated eco-driving in urban scenarios using deep reinforcement learning

Marius Wegener et al.

Summary: Implementing eco-driving strategies for automated vehicles in urban settings is challenging due to limited information on the preceding vehicle pulk and low penetration of vehicle-to-vehicle communication. This study used Reinforcement Learning to develop energy-saving driving strategies for scenarios with limited traffic data, leading to energy savings of up to 19% compared to human drivers and up to 11% compared to a fine-tuned GLOSA algorithm in a probabilistic traffic scenario reflecting real-world conditions.

TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES (2021)

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Article Engineering, Multidisciplinary

Cooperative optimization of velocity planning and energy management for connected plug-in hybrid electric vehicles

Yonggang Liu et al.

Summary: This paper proposes a cooperative optimization strategy for velocity planning and energy management of intelligent connected plug-in hybrid electric vehicles, by converting driving cycles from time based profiles to distance based speed values using a mathematical analytical method, and utilizing iterative dynamic programming for collaborative optimization of speed planning and power allocation.

APPLIED MATHEMATICAL MODELLING (2021)

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Article Engineering, Electrical & Electronic

Enhanced Eco-Approach Control of Connected Electric Vehicles at Signalized Intersection With Queue Discharge Prediction

Haoxuan Dong et al.

Summary: This study introduces an enhanced eco-approach control strategy to improve energy efficiency at signalized intersections by predicting the movement of vehicle queues. Through a hierarchical framework and numerical simulations, it is shown that the EEAC strategy can effectively enhance energy utilization efficiency.

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2021)

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Article Chemistry, Physical

Data-driven based eco-driving control for plug-in hybrid electric vehicles

Jie Li et al.

Summary: This paper proposes a data-driven eco-driving control strategy for plug-in hybrid electric vehicles, which can improve fuel economy and computational efficiency through neural network models.

JOURNAL OF POWER SOURCES (2021)

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Article Engineering, Mechanical

Prediction of vehicle driving conditions with incorporation of stochastic forecasting and machine learning and a case study in energy management of plug-in hybrid electric vehicles

Yonggang Liu et al.

Summary: Predicting short-term driving conditions is crucial for energy management and fuel economy improvement of plug-in hybrid electric vehicles. A fused model combining stochastic forecasting and machine learning techniques is established, incorporating Markov chain for transition probability calculation and neural network for learning driving information. Optimizing network parameters with genetic algorithm, the proposed fusion algorithm outperforms single models in prediction precision and difference distribution evaluation.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2021)

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Proceedings Paper Automation & Control Systems

Cooperative Eco-Driving of Electric Vehicle Platoons for Energy Efficiency and String Stability

Vinith Kumar Lakshmanan et al.

Summary: This paper discusses cooperation applied to a platoon of electric vehicles using an Eco-Driving Optimal Controller (EDOC). The simulation results show that a platoon with cooperative EDOC performs better in terms of energy efficiency and string compactness than without cooperation.

IFAC PAPERSONLINE (2021)

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Article Automation & Control Systems

Leveraging Multiple Connected Traffic Light Signals in an Energy-Efficient Speed Planner

Jihun Han et al.

Summary: Connecting automated vehicles to traffic lights for energy-efficient driving, a hierarchical speed planner is proposed for optimal energy-saving path at intersections. It consists of two modules for green window selection and reference trajectory generation, leveraging information from multiple connected traffic lights. The planner aims to balance energy and traffic flow perspectives, demonstrated through a high-fidelity simulation framework for real-world urban route scenarios.

IEEE CONTROL SYSTEMS LETTERS (2021)

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Article Engineering, Electrical & Electronic

Eco-Driving Operation of Connected Vehicle With V2I Communication Among Multiple Signalized Intersections

Qingfeng Lin et al.

Summary: This article focuses on eco-driving operation through multiple signalized intersections considering more realistic powertrain dynamics, and proposes an open-loop optimal control problem and operation rules to approximate its optimal solution, achieving improved fuel economy.

IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE (2021)

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Review Green & Sustainable Science & Technology

Thorough state-of-the-art analysis of electric and hybrid vehicle powertrains: Topologies and integrated energy management strategies

Dai-Duong Tran et al.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2020)

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Article Engineering, Multidisciplinary

Integrated energy-oriented cruising control of electric vehicle on highway with varying slopes considering battery aging

Zhuang WeiChao et al.

SCIENCE CHINA-TECHNOLOGICAL SCIENCES (2020)

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Jointly dampening traffic oscillations and improving energy consumption with electric, connected and automated vehicles: A reinforcement learning based approach

Xiaobo Qu et al.

APPLIED ENERGY (2020)

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Co-optimization of speed trajectory and power management for a fuel-cell/battery electric vehicle

Youngki Kim et al.

APPLIED ENERGY (2020)

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Efficient energy management strategy for hybrid electric vehicles/plug-in hybrid electric vehicles: review and recent advances under intelligent transportation system

Chao Yang et al.

IET INTELLIGENT TRANSPORT SYSTEMS (2020)

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Safety, Energy, and Emissions Impacts of Adaptive Cruise Control and Cooperative Adaptive Cruise Control

Iman Mahdinia et al.

TRANSPORTATION RESEARCH RECORD (2020)

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Optimal Eco-Driving Control of Connected and Autonomous Vehicles Through Signalized Intersections

Chao Sun et al.

IEEE INTERNET OF THINGS JOURNAL (2020)

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Aerodynamic-aware coordinated control of following speed and power distribution for hybrid electric trucks

Shaobo Xie et al.

ENERGY (2020)

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Eco-Driving for Energy Efficient Cornering of Electric Vehicles in Urban Scenarios

G. P. Padilla et al.

IFAC PAPERSONLINE (2020)

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Predictive kinetic energy management for an add-on driver assistance eco-driving of heavy vehicles

DoHyun Daniel Yoon et al.

IET INTELLIGENT TRANSPORT SYSTEMS (2020)

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A Minimum Principle-Based Algorithm for Energy-Efficient Eco-Driving of Electric Vehicles in Various Traffic and Road Conditions

Daliang Shen et al.

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES (2020)

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Variable Step-Size Discrete Dynamic Programming for Vehicle Speed Trajectory Optimization

Ziqi Ye et al.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2019)

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Real-Time Predictive Cruise Control for Eco-Driving Taking into Account Traffic Constraints

Hong Chen et al.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2019)

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Ecological Cooperative Look-Ahead Control for Automated Vehicles Travelling on Freeways With Varying Slopes

Chunjie Zhai et al.

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2019)

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Field implementation and testing of an automated eco-cooperative adaptive cruise control system in the vicinity of signalized intersections

Mohammed Hamad Almannaa et al.

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Reducing time headway for platooning of connected vehicles via V2V communication

Yougang Bian et al.

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A Two-Level MPC for Energy Management Including Velocity Control of Hybrid Electric Vehicles

Stephan Uebel et al.

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2019)

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Energy management strategies of connected HEVs and PHEVs: Recent progress and outlook

Fengqi Zhang et al.

PROGRESS IN ENERGY AND COMBUSTION SCIENCE (2019)

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Ecological Adaptive Cruise Control and Energy Management Strategy for Hybrid Electric Vehicles Based on Heuristic Dynamic Programming

Guoqiang Li et al.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2019)

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Intelligent energy-saving control strategy for electric vehicle based on preceding vehicle movement

Laiqing Xie et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2019)

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Predictive Cruise Control with Private Vehicle-to-Vehicle Communication for Improving Fuel Consumption and Emissions

Xueru Zhang et al.

IEEE COMMUNICATIONS MAGAZINE (2019)

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Cooperative Game Approach to Optimal Merging Sequence and on-Ramp Merging Control of Connected and Automated Vehicles

Shoucai Jing et al.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2019)

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Article Energy & Fuels

High efficient internal combustion engine using partially premixed combustion with multiple injections

Lianhao Yin et al.

APPLIED ENERGY (2019)

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Predictive energy-saving optimization based on nonlinear model predictive control for cooperative connected vehicles platoon with V2V communication

Fangwu Ma et al.

ENERGY (2019)

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Series Hybrid Electric Vehicle Simultaneous Energy Management and Driving Speed Optimization

Boli Chen et al.

IEEE-ASME TRANSACTIONS ON MECHATRONICS (2019)

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Computation of eco-driving cycles for Hybrid Electric Vehicles: Comparative analysis

D. Maamria et al.

CONTROL ENGINEERING PRACTICE (2018)

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A Distributed Reference Governor Approach to Ecological Cooperative Adaptive Cruise Control

Bijan Sakhdari et al.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2018)

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On the Optimal Speed Profile for Eco-Driving on Curved Roads

Feng Ding et al.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2018)

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Real-Time Energy-Efficient Control for Fully Electric Vehicles Based on an Explicit Model Predictive Control Method

Shuwei Zhang et al.

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2018)

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Eco-driving technology for sustainable road transport: A review

Yuhan Huang et al.

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A platoon based cooperative eco-driving model for mixed automated and human-driven vehicles at a signalised intersection

Weiming Zhao et al.

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Energy saving potentials of connected and automated vehicles

Ardalan Vahidi et al.

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Eco-driving advisory strategies for a platoon of mixed gasoline and electric vehicles in a connected vehicle system

Xiaozheng He et al.

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Article Computer Science, Artificial Intelligence

Safe- and Eco-Driving Control for Connected and Automated Electric Vehicles Using Analytical State-Constrained Optimal Solution

Jihun Han et al.

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An economic model predictive cruise controller for electric vehicles using Gaussian Process prediction

Florian Morlock et al.

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Predictive Energy Management Strategy for Fully Electric Vehicles Based on Preceding Vehicle Movement

Shuwei Zhang et al.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2017)

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Instantaneous Feedback Control for a Fuel-Prioritized Vehicle Cruising System on Highways With a Varying Slope

Shaobing Xu et al.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2017)

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An adaptive cruise control for connected energy-saving electric vehicles

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A Review of Communication, Driver Characteristics, and Controls Aspects of Cooperative Adaptive Cruise Control (CACC)

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2016)

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2016)

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2015)

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Steven E. Shladover et al.

TRANSPORTATION RESEARCH RECORD (2015)

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Eco-Driving Versus Green Wave Speed Guidance for Signalized Highway Traffic: A Multi-Vehicle Driving Simulator Study

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A comprehensive review of the development of adaptive cruise control systems

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VEHICLE SYSTEM DYNAMICS (2010)

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Longitudinal control of heavy trucks in mixed traffic: Environmental and fuel economy considerations

JL Zhang et al.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2006)

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Research advances in intelligent collision avoidance and adaptive cruise control

A Vahidi et al.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2003)

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