Co-Optimization of Eco-Driving and Energy Management for Connected HEV/PHEVs near Signalized Intersections: A Review

Wang, Ziqing; Dridi, Mahjoub; Moudni, Abdellah El

doi:10.3390/app13085035

Cited by 6 publications

(4 citation statements)

References 108 publications

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“…With the gradual depletion of energy resources, the improvement of energy utilization has received attention from various industries [1]. In the automotive field, the ownership and annual production of internal combustion engine vehicles are growing rapidly worldwide, which leads to increasingly serious problems such as an energy crisis and urban air pollution [2]. Pure electric vehicles (EVs) are constrained by the current technology of power batteries and inadequate infrastructure, which greatly troubles consumers in terms of driving range and charging time [3].…”

Section: Introduction 1pheb Energy Management Strategymentioning

confidence: 99%

Adaptive Equivalent Factor-Based Energy Management Strategy for Plug-In Hybrid Electric Buses Considering Passenger Load Variations

Song,

Meng

et al. 2024

Energies

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Energy management strategies (EMSs) are one of the key technologies for the development of plug-in hybrid electric buses (PHEBs). This paper addresses the issue of optimal energy distribution for PHEBs under significant variations in passenger load at different bus stations, which cannot be solved by a single equivalent factor equivalent fuel consumption minimization energy management strategy (ECMS). An adaptive equivalent factor equivalent fuel consumption minimization energy management strategy (A-ECMS) considering passenger load is proposed. First, the powertrain system of the PHEB is modeled, and the accuracy of the model is verified in a simulation environment. Then, the reference SOC descent trajectory of the battery is obtained using a dynamic programming (DP) algorithm, the recursive least squares (RLS) method is employed to identify the passenger load, and the influence of different loads on the state of charge (SOC) trajectory under a single equivalent factor is analyzed. Finally, a genetic algorithm (GA) is used to establish the correspondence between passenger load, bus station, and equivalent factor, enabling the actual SOC to follow the reference SOC descent trajectory, thereby achieving optimal energy distribution. Simulation results demonstrate that the A-ECMS reduces fuel consumption of the PHEB per 100 km by 2.59% and 10.10% compared to the ECMS and rule-based EMS, respectively, validating the effectiveness of the proposed strategy.

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Section: Introduction 1pheb Energy Management Strategymentioning

confidence: 99%

Adaptive Equivalent Factor-Based Energy Management Strategy for Plug-In Hybrid Electric Buses Considering Passenger Load Variations

Song,

Meng

et al. 2024

Energies

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“…The utilization of the vehicle data can enhance the experience and safety of the driver and passengers, introduce new services, and improve vehicle performance. Some of the applications vehicular data are [1][2][3][4][5]:…”

Section: Introductionmentioning

confidence: 99%

Adaptive Vehicular Communication using ALOHA CSMA with Modified Logistic Function (ALOHAMLF)

2023

IJIES

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The objective of this work is to mathematically model vehicular connectivity using ALOHA base function with modified logistic function as an adaptive control function. The focus of this work is to utilize the ALOHA protocol as a carrier sensing multiple access technique and add to it an intelligent part, which is the modified logistic function (MLF) to enable adaptive communication channel. The work correlates three time based parameters related to vehicle, communication, and human driver, which are associated with connected vehicles (driver response time, machine processing time, and channel communication time). The mathematical model showed that throughput for autonomous and connected vehicles is much higher than that for connected vehicles due to the elimination of human time factor, together with the evident low response time from sensors, electronics, and communication. In addition, a more stable connectivity is established if human element is not part of the model, as this eliminates both uncertainty and lengthy human response time. The ALOHA function role is to enable continuous tries by each node (vehicle) to establish communication with probability related to availability of communication channel and a back off functionality in case of collision. The proposed model provides dynamic and adaptive technique to adjust time slots using the continuous change in the vehicular connectivity environment, which affects the presented time parameters, thus changing frames delivery pattern and time slots utilization. The presented approach, supports better and more efficient use of communication channel, particularly in the case of autonomous and connected vehicles. It also, highlights through comparison the inhibitory effect driver actions can have on safe driving, through time delay, and low communication channel utilization. In addition, the work also focusses on the effect of processing time for sensors and electronics, which also have negative effect on channel usage and effectiveness, if there is a functional problem.

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“…Meanwhile, ecological driving technology [16,17], which is often overlooked, has the potential to significantly improve vehicle fuel consumption. According to research, ecological driving can decrease fuel consumption by 15% to 25% and reduce greenhouse gas emissions by at least 30% [18].…”

Section: Introductionmentioning

confidence: 99%

A Novel Longitudinal Control Method Integrating Driving Style and Slope Prediction for High-Efficiency HD Vehicles

Zhou,

Pan,

Guan

et al. 2023

Applied Sciences

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Developing high-precision vehicle longitudinal control technology guided by ecological driving represents a highly promising yet challenging endeavor. It necessitates the fulfillment of the driver’s operational intentions, precise speed control, and reduced fuel consumption. In light of this challenge, this study presents a novel vehicle longitudinal control model that integrates real-time driving style analysis and road slope prediction. First, it utilizes spectral clustering based on Bi-LSTM automatic encoders to identify driver driving styles. Next, it examines the driving environment and predicts the current slope of the vehicle. Additionally, a fuzzy controller is designed to optimize control performance, adapt to various driving styles and slopes, and achieve better fuel efficiency. The research results indicate that the DS-MPC control model developed in this paper can effectively distinguish various driving modes and has high speed control accuracy while saving 3.27% of fuel.

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Co-Optimization of Eco-Driving and Energy Management for Connected HEV/PHEVs near Signalized Intersections: A Review

Cited by 6 publications

References 108 publications

Adaptive Equivalent Factor-Based Energy Management Strategy for Plug-In Hybrid Electric Buses Considering Passenger Load Variations

Adaptive Equivalent Factor-Based Energy Management Strategy for Plug-In Hybrid Electric Buses Considering Passenger Load Variations

Adaptive Vehicular Communication using ALOHA CSMA with Modified Logistic Function (ALOHAMLF)

A Novel Longitudinal Control Method Integrating Driving Style and Slope Prediction for High-Efficiency HD Vehicles

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