Research on eco-driving optimization of hybrid electric vehicle queue considering the driving style

Wang, Shaohua; Yu, Ping; Shi, Dehua; Yu, Chengquan; Yin, Chengliang

doi:10.1016/j.jclepro.2022.130985

Cited by 32 publications

(7 citation statements)

References 27 publications

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“…Wang et al [22]collected longitudinal acceleration, relative distance and relative speed to the vehicle in front to study the behavior preference of different drivers in car-following scenario. Wang et al [23]selected 9 parameters to investigate the ecodriving optimization of the hybrid electric vehicle queue in urban road conditions considering the driving behavior features.…”

Section: ) Data Typementioning

confidence: 99%

Vehicle Directional Driving Behavior Segmentation Based on Cornering Strength

Bo,

Jia,

Guan

et al. 2024

IEEE Access

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With the rapid development in recent years, the autonomous driving system comes to the stage of commercial deployment now. However, it is not well accepted by society. This is due to the fact that the driving behavior features of autonomous driving vehicles are currently different from those of human drivers. It is important to understand how people drive for increasing acceptance. And the primary task of that is driving behavior identification. In this study, we proposed a segmentation method for vehicle driving process into basic directional driving behaviors. In order to clarify the relationship between vehicle motion state and driver operation, we rebuilt the vehicle motion state in the natural coordinate system. We innovatively proposed the concept of cornering strength as a metric of directional driving behavior intensity. To verify the validity of our approach, 22 participants were asked to maneuver a car through some common driving scenarios including turn, lane change, overtaking and U-turn. As a result, our method can extract directional driving behaviors with an average accuracy of 93.33%. In addition, the cornering strength of participants with different driving styles exhibit a significant difference though in the same driving scenario. Driving age has a significant effect on CS stability of participants. The variance of CS reaches a maximum of 0.22. The directional driving behavior features of the same participant can be different influenced by psychological or physiological aspects. Road adhesion condition and traffic congestion condition also have an effect on the CS values of participants.INDEX TERMS Autonomous driving system, cornering strength, driving behavior segmentation, maneuvering target tracking, motion state rebuilding.

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Section: ) Data Typementioning

confidence: 99%

Vehicle Directional Driving Behavior Segmentation Based on Cornering Strength

Bo,

Jia,

Guan

et al. 2024

IEEE Access

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“…However, such anticipations are hardly possible for a human driver. A viable technique to address such human driving issues is eco-driving , which encourages energy-efficient driving behavior by preventing excessive acceleration and braking, and by optimizing the vehicle speed via anticipating surrounding road traffic conditions and driving states [7] , [8] . It has been demonstrated that adopting an eco-driving system can improve fuel consumption by about 4%–25% [9] , [10] .…”

Section: Introductionmentioning

confidence: 99%

Optimal eco-driving scheme for reducing energy consumption and carbon emissions on curved roads

Bakibillah,

Kamal,

Tan

et al. 2024

Heliyon

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“…This performance is evaluated by the capability to energy regenerate up to the engine's regeneration limit while respecting maximum speed preferences [43]. To manage standard control strategies, an adaptive method will be extremely important [44], [45]. Thus, this paper has two major contributions: (i) This research predicts the power regenerated by drivers over a long horizon (30 seconds), utilizing LSTM and NARX models.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Regenerative Braking Strategy Design Based on Naturalistic Regeneration Performance for Intelligent Vehicles

Ziadia,

Kelouwani,

Amamou

et al. 2023

IEEE Access

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The effectiveness of regenerative braking strategies plays an important role in extending the driving range of electric vehicles. Since the driver is still an essential factor in levels 3 and 4 of intelligent electric vehicles, improving user acceptance and adoption of the braking control strategy is crucial. This paper puts forward a new regenerative braking strategy to find a compromise between optimal braking control performance and naturalistic regeneration performance while satisfying the maximum speed preference when driving between two-stop events. Unlike other similar works that only maximize regenerative braking energy while satisfying the physical limits of an electrified powertrain, this paper considers naturalistic regeneration performance. To achieve this, firstly, the power regenerated by three drivers is predicted with a long-horizon (30 seconds), using long-short-term memory networks (LSTM) and non-linear autoregressive exogenous model (NARX). Subsequently, an estimation of the energy recovery maximization rate is performed to give a perception of the naturalistic regeneration performance. As this performance varies, the deceleration planning employs three horizon scales of long, medium, and short, determined by the energy recovery maximization rate. Finally, dynamic programming (DP) is utilized to optimize a deceleration profile. The study utilizes real data of inverter efficiency, transmission efficiency, and motor-to-battery efficiency map. The outcome of this study shows that the proposed regeneration braking strategy is adaptive, improving regeneration efficiency by 39,6% for driver 1, 16% for driver 2, and 26% for driver 3, and forecasting the optimality of some deceleration behaviors.

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Research on eco-driving optimization of hybrid electric vehicle queue considering the driving style

Cited by 32 publications

References 27 publications

Vehicle Directional Driving Behavior Segmentation Based on Cornering Strength

Vehicle Directional Driving Behavior Segmentation Based on Cornering Strength

Optimal eco-driving scheme for reducing energy consumption and carbon emissions on curved roads

An Adaptive Regenerative Braking Strategy Design Based on Naturalistic Regeneration Performance for Intelligent Vehicles

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