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

Li, Jie; Liu, Yonggang; Zhang, Hongjie; Lei, Zhenzhen; Chen, Zheng; Li, Guang

doi:10.1016/j.jpowsour.2021.229916

Cited by 45 publications

(10 citation statements)

References 42 publications

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“…Secondly, the method is able to perform lane changes with the goal of saving energy. Most of the previous studies only considered eco-cruise control on a single lane [32,43,44]. However, the traffic flow conditions in different lanes have a large impact on the speed and acceleration of the vehicle, which affects the energy efficiency.…”

Section: Discussionmentioning

confidence: 99%

An Energy-Efficient Driving Method for Connected and Automated Vehicles Based on Reinforcement Learning

et al. 2023

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The development of connected and automated vehicles (CAV) technology not only helps to reduce traffic accidents and improve traffic efficiency, but also has significant potential for energy saving and emission reduction. Using the dynamic traffic flow information around the vehicle to optimize the vehicle trajectory is conducive to improving the energy efficiency of the vehicle. Therefore, an energy-efficient driving method for CAVs based on reinforcement learning is proposed in this paper. Firstly, a set of vehicle trajectory prediction models based on long and short-term memory (LSTM) neural networks are developed, which integrate driving intention prediction and lane change time prediction to improve the prediction accuracy of surrounding vehicle trajectories. Secondly, an energy-efficient driving model is built based on Proximity Policy Optimization (PPO) reinforcement learning. The model takes the current states and predicted trajectories of surrounding vehicles as input information, and outputs energy-saving control variables while taking into account various constraints, such as safety, comfort, and travel efficiency. Finally, the method is tested by simulation on the NGSIM dataset, and the results show that the proposed method can save energy consumption by 9–22%.

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Section: Discussionmentioning

confidence: 99%

An Energy-Efficient Driving Method for Connected and Automated Vehicles Based on Reinforcement Learning

et al. 2023

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“…Obviously, calculating reinforcement signal according to energy consumption can further promote fuel economy. In our previous work [15], a data-driven energy consumption cost model is established to approximate the optimal energy consumption cost considering the nonlinear characteristics of the hybrid powertrain. Unlike the common ACC methods based on ADP, the data-driven energy consumption model is applied to generate the reinforcement signal during the offline training of the critic-actor controller.…”

Section: A Approximate Dynamic Programmingmentioning

confidence: 99%

“…where ( ) r k is reinforcement signal, which is comprised of fuel economy index and dynamics performance index, ˆenergy cost is the estimated energy consumption cost generated by the datadriven energy consumption model, and NN C f represents the data-driven energy consumption model, of which the detailed construction can be found in our previous study [15]. From eq.…”

Section: B Ecological Adaptive Cruise Control Based On Adpmentioning

confidence: 99%

“…In [14], Pontryagin's minimum principle (PMP) is employed to generate ecological driving speed in a cruise condition. In [15], a datadriven based eco-driving control strategy is illustrated to achieve co-optimization of velocity in a high computational efficiency way. Recently, machine learning methods are leveraged to realize model-free control of eco-driving.…”

Section: Introductionmentioning

confidence: 99%

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Cooperative Ecological Adaptive Cruise Control for Plug-In Hybrid Electric Vehicle Based on Approximate Dynamic Programming

Liu

Fotouhi

et al. 2023

IEEE Trans. Veh. Technol.

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Eco-driving control generates significant energysaving potential in car-following scenarios. However, the influence of preceding vehicle may impose unnecessary velocity waves and deteriorate fuel economy. In this research, a learning-based method is exploited to achieve satisfied fuel economy for connected plug-in hybrid electric vehicles (PHEVs) with the advantage of vehicle-to-vehicle communication system. A data-driven energy consumption model is leveraged to generate reinforcement signals for approximate dynamic programming (ADP) with the consideration of nonlinear efficiency characteristics of hybrid powertrain system. An advanced ADP scheme is designed for connected PHEVs driving in car-following scenarios. Additionally, the cooperative information is incorporated to further improve the fuel economy of the vehicle under the premise of driving safety. The proposed method is mode-free and showcases acceptable computational efficiency as well as adaptability. The simulation results demonstrate that the fuel economy during car-following processes is remarkably improved through cooperative driving information, thereby partially paving the theoretical basis for energy-saving transportation.

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“…This information can be imported to facilitate the planning of optimal paths and velocity trajectories, so as to improve energy utilization efficiency and traffic efficiency [2]. The scheme behind this inspiration is referred to as eco‐driving [1], which has been widely accepted as an ideal solution to achieve a reasonable allocation of space resources and ultimately promote energy utilization efficiency and traffic mobility [3]. On the other hand, the vigorous development of connected and automated vehicle (CAV) technologies inspires us to further explore more advanced eco‐driving strategies in the urban road to avoid unnecessary decelerations and accelerations, and consequently reduce idling waiting time when approaching signalized intersections [4].…”

Section: Introductionmentioning

confidence: 99%

Hierarchical cooperative eco‐driving control for connected autonomous vehicle platoon at signalized intersections

Chen

Shen

et al. 2023

IET Intelligent Trans Sys

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Vehicles in the platoon can sufficiently incorporate the information via V2X communication to plan ecological speed trajectories and pass the intersection smoothly. Most existing eco‐driving studies mainly focus on the optimal control of a single vehicle at an individual signalized intersection, while rarely involving the cooperative optimization of a group of vehicles at successive signalized intersections. In this study, a hierarchical cooperative eco‐driving control for a connected autonomous vehicle (CAV) platoon is proposed to enhance traffic mobility and energy efficiency, wherein the velocity trajectory of the leading vehicle at each isolated signalized intersection is planned using the pseudo‐spectral method, and then the cooperative optimization of following vehicles in the platoon is conducted via rolling optimization, with the aim of improving driving comfort, safety and energy economy for the platoon. The simulation results highlight that the proposed hierarchical cooperative eco‐driving strategy can lead to preferable vehicle‐following behaviours and platoon driving performance, and the overall energy consumption and trip time of vehicle platoon are respectively reduced by 26.10% and 2.83%, compared with that under manual driving. Furthermore, the overall energy economy is promoted by 4.95% and 4.60%, compared with cooperative adaptive cruise control and intelligent driver model‐based platoon control strategies.

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

Cited by 45 publications

References 42 publications

An Energy-Efficient Driving Method for Connected and Automated Vehicles Based on Reinforcement Learning

An Energy-Efficient Driving Method for Connected and Automated Vehicles Based on Reinforcement Learning

Cooperative Ecological Adaptive Cruise Control for Plug-In Hybrid Electric Vehicle Based on Approximate Dynamic Programming

Hierarchical cooperative eco‐driving control for connected autonomous vehicle platoon at signalized intersections

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