Fuel-Saving Servo-Loop Control for an Adaptive Cruise Control System of Road Vehicles With Step-Gear Transmission

Li, Shengbo Eben; Guo, Qiangqiang; Long, Xin; Cheng, Bo; Li, Keqiang

doi:10.1109/tvt.2016.2574740

Cited by 57 publications

(16 citation statements)

References 30 publications

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“…Therefore, eco-driving speed is usually recommended at or safely below the speed limit [17,18,30]. Many studies have been carried out to estimate the optimal driving speed profile under various real-world conditions, such as congestion levels [31], road grades [32,33], car-following scenarios [34], signalized roads [35][36][37][38], and hybrid electric vehicles [39].…”

Section: Driving Speedmentioning

confidence: 99%

Eco-driving technology for sustainable road transport: A review

Huang

Zhou

et al. 2018

Renewable and Sustainable Energy Reviews

290

128

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Road transport consumes significant quantities of fossil fuel and accounts for a significant proportion of CO2 and pollutant emissions worldwide. The driver is a major and often overlooked factor that determines vehicle performance. Eco-driving is a relatively low-cost and immediate measure to reduce fuel consumption and emissions significantly. This paper reviews the major factors, research methods and implementation of eco-driving technology. The major factors of eco-driving are acceleration/deceleration, driving speed, route choice and idling. Eco-driving training programs and in-vehicle feedback devices are commonly used to implement eco-driving skills. After training or using in-vehicle devices, immediate and significant reductions in fuel consumption and CO2 emissions have been observed with slightly increased travel time. However, the impacts of both methods attenuate over time due to the ingrained driving habits developed over the years. These findings imply the necessity of developing quantitative eco-driving patterns that could be integrated into vehicle hardware so as to generate more constant and uniform improvements, as well as developing more effective and lasting training programs and in-vehicle devices. Current eco-driving studies mainly focus on the fuel savings and CO2 reduction of individual vehicles, but ignore the pollutant emissions and the impacts at network levels. Finally, the challenges and future research directions of eco-driving technology are elaborated.

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Section: Driving Speedmentioning

confidence: 99%

Eco-driving technology for sustainable road transport: A review

Huang

Zhou

et al. 2018

Renewable and Sustainable Energy Reviews

290

128

View full text Add to dashboard Cite

show abstract

“…The vehicle-following scenario might become common in urban driving condition, which will lead to driver fatigue and even the rear-end collision. Considering the trend of intelligence and net-connection in automobile industry, energy management issues for plug-in hybrid electric bus (PHEB) during vehicle-following have been studied by many researchers [23]- [25]. Hu et al proposed a look-ahead framework to maximize fuel economy in intelligent transportation, which achieved the desired effect [26].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Stochastic Model Predictive Control Strategy for Plug-in Hybrid Electric Bus During Vehicle-Following Scenario

Jiao

Chao

et al. 2020

IEEE Access

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Vehicle-following operation is a typical scenario in the future intelligent transportation environment. Keeping a safe distance is the most important goal in the vehicle-following scenario. For a plug-in hybrid electric bus (PHEB) running in a specific urban route, the challenge will become how to realize the optimal power split in hybrid powertrain under the premise of maintaining driving safety. Considering the above issues, this paper proposes a stochastic model predictive control (SMPC) strategy for PHEBs during vehicle-following scenario. Firstly, Markov chain-based stochastic driving model is built using real-world bus driving condition data, which is applied to predict future demand torque over a finite receding horizon. And then, a sequential quadratic programming (SQP) algorithm is adopted to solve the rolling optimization problem. Meanwhile, brake specific fuel consumption and electric motor efficiency are fitted offline to match the SMPC strategy. Furthermore, a piecewise function is given to adjust the adaptive factor that balancing fuel economy and vehicle-following in the pre-set cost function. Finally, to verify the control performance of the proposed strategy, a nonlinear model predictive control strategy with dynamic programming optimization (DP-MPC) and a rule-based (RB) strategy are employed for comparison study. Results indicate that the proposed strategy is effectiveness to the given driving condition with excellent fuel economy and vehicle-following performance. Under the driving condition of Chongqing 303 bus line in China and China typical, the fuel consumption is reduced by 20.58% and 37.89% compared with RB strategy, respectively. It is closer to the fuel consumption reduction of 16.77% and 13.11 optimized by DP-MPC. Driving safety during vehicle-following also be demonstrated in the driving condition of Chongqing 303 bus line and China typical. INDEX TERMS Plug-in hybrid electric bus, energy management, vehicle-following, stochastic model predictive control, sequential quadratic programming optimization.

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“…for each Event.Attribute do (3) TransitionStart.InputToken ⟵ Event.Attribute; (4) end (5) for each TransitionStart do (6) if InputToken satisfies Guard then (7) OutputToken ⟵ InputToken; (8) else (9) Clear the Event value, Event pointer ++; (10) end (11) end (12) for each TransitionMid do (13) if TransitionMid.InputToken satisfies Guard then (14) OutputToken ⟵ OutputToken ⊙ InputToken; (15) else (16) Clear the Event value, Event pointer ++; (17) end (18)…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…Second, the direction of the spatial data is not considered. In a complex event processing model for the Internet of ings, the adoption of an appropriate temporal and spatial model can reduce the complexity of the model [14,15].…”

Section: Introductionmentioning

confidence: 99%

STEIM: A Spatiotemporal Event Interaction Model in V2X Systems Based on a Time Period and a Raster Map

Luo

Bao

et al. 2020

Mobile Information Systems

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e Internet of Vehicles (IoV) is an important artificial intelligence research field for intelligent transportation applications. Complex event interactions are important methods for data flow processing in a Vehicle to Everything (V2X) environment. Unlike the classic Internet of ings (IoT) systems, data streams in V2X include both temporal information and spatial information. us, effectively expressing and addressing spatiotemporal data interactions in the IoV is an urgent problem. To solve this problem, we propose a spatiotemporal event interaction model (STEIM). STEIM uses a time period and a raster map for its temporal model and spatial model, respectively. In this paper, first, we provide a spatiotemporal operator and a complete STEIM grammar that effectively expresses the spatiotemporal information of the spatiotemporal event flow in the V2X environment. Second, we describe the design of the operational semantics of the STEIM from the formal semantics. In addition, we provide a spatiotemporal event-stream processing algorithm that is based on the Petri net model. e STEIM establishes a mechanism for V2X event-stream temporal and spatial processing. Finally, the effectiveness of the STEIM-based system is demonstrated experimentally.

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Fuel-Saving Servo-Loop Control for an Adaptive Cruise Control System of Road Vehicles With Step-Gear Transmission

Cited by 57 publications

References 30 publications

Eco-driving technology for sustainable road transport: A review

Eco-driving technology for sustainable road transport: A review

An Adaptive Stochastic Model Predictive Control Strategy for Plug-in Hybrid Electric Bus During Vehicle-Following Scenario

STEIM: A Spatiotemporal Event Interaction Model in V2X Systems Based on a Time Period and a Raster Map

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