Compute Optimal Travel Duration in Eco-Driving applications

Ciarla, Valentina; Chasse, Alexandre; Moulin, Philippe; Ojeda, Luis Leon

doi:10.1016/j.ifacol.2016.08.076

Cited by 5 publications

(2 citation statements)

References 10 publications

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“…Otherwise, unclear information was found to cause a hard brake and acceleration [116]. In addition, Ciarla et al [117] proposed a model that took into account the effects of intersection, roundabout and other traffic conditions to optimize the travel time. Caulfield et al [93] recently reported on the success of the use of an on-board device in reducing emission.…”

Section: Automated Device To Support Driver's Behaviourmentioning

confidence: 99%

Driving Behaviour and Sustainable Mobility—Policies and Approaches Revisited

et al. 2018

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Climate change is receiving increasing attention in recent years. The transportation sector contributes substantially to increased fuel consumption, greenhouse gas (GHG) emissions, and poor air quality, which imposes a serious respiratory health hazard. Road transport has made a significant contribution to this effect. Consequently, many countries have attempted to mitigate climate change using various strategies. This study analysed and compared the number of policies and other approaches necessary to achieve reduced fuel consumption and carbon emission. Frequency aggregation indicates that the mitigation policies associated with driving behaviours adopted to curtail this consumption and decrease hazardous emissions, as well as a safety enhancement. Furthermore, car-sharing/carpooling was the least investigated approach to establish its influence on mitigation of climate change. Additionally, the influence of such driving behaviours as acceleration/deceleration and the compliance to speed limits on each approach was discussed. Other driving behaviours, such as gear shifting, compliance to traffic laws, choice of route, and idling and braking style, were also discussed. Likewise, the influence of aggression, anxiety, and motivation on driving behaviour of motorists was highlighted. The research determined that driving behaviours can lead to new adaptive driving behaviours and, thus, cause a significant decrease of vehicle fuel consumption and CO 2 emissions.

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Section: Automated Device To Support Driver's Behaviourmentioning

confidence: 99%

Driving Behaviour and Sustainable Mobility—Policies and Approaches Revisited

et al. 2018

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“…(4) Based on various optimization algorithms, an optimal control model is established to calculate the minimum energy consumption driving curve and prompt the driver to drive as required or directly used for vehicle control, such as economic cruise control. This method not only reduces the driving load more thoroughly, but also truly realizes the unity of driving styles, realizing energy-saving driving and reducing energy consumption [10] .…”

Section: Introductionmentioning

confidence: 99%

Research on energy-saving driving recognition and optimization model development based on driving road conditions and driving styles

Li,

gao,

yang

et al. 2024

International Conference on Smart Transportation and City Engineering (STCE 2023)

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The development of intelligent transportation technology and the dual carbon policy have brought new opportunities for energy-saving driving. Energy-saving driving involves two problems, namely, real-time recognition of driving road conditions and driving styles for energy-saving driving evaluation, energy-saving vehicle speed calculation and energysaving driving guidance for non-energy-saving driving. Therefore, this paper designs a real-time recognition model of driving road conditions and driving styles and a global optimization energy-saving vehicle speed calculation model. Firstly, the urban roads in Tianjin and Hefei are selected to carry out the experimental analysis of the influencing factors of energy-saving driving. Secondly, through the selection of characteristic parameters, K-means clustering based on genetic algorithm optimization, and neural network training model based on genetic algorithm, the real-time recognition of driving road conditions and driving styles is realized in road test. Finally, based on the dynamic programming algorithm, a global optimization energy-saving driving strategy model is established to achieve energy-saving driving speed guidance for intense driving. The results show that using energy-saving speed guidance, the maximum reduction of vehicle driving energy consumption is 16.90 %, and the average reduction is 8.34 %.

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