Intelligent GPS-Based Vehicle Control for Improved Fuel Consumption and Reduced Emissions

Lee, S. H.; Walters, Simon; Howlett, Robert J.

doi:10.1007/978-3-540-85567-5_87

Cited by 8 publications

(9 citation statements)

References 5 publications

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“…预测能量管理 (predictive energy management) 是以混合动力汽车能量管理为基础, 参考道路与交通信息而形成的新的能量管理策略. 全球定位系统 (global positioning system, GPS) 及地理信息系统 (geographic information system, GIS) 在车辆上的应用, 使得预测能量管理系统获取前方交通流、道路坡度、路段长度、限速等信息 [26] , 通过发动机/电机的转矩或功率分配以及挡位的配合, 提高车辆的燃油经济性 [27∼29] .…”

Section: 考虑道路和交通信息的单车行驶优化unclassified

Optimal ecodriving control of vehicles

Gao¹,

Chen²,

Guo³

2016

Sci. Sin.-Inf.

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Optimal ecodriving is considered an efficient approach to reduce fuel consumption of vehicles by optimizing velocity and implementing energy management. Essentially, the idea is that there are different routes to choose from to complete a specific journey, which are not equivalent from an energy point of view. The objective is to find the optimal one with the highest energy efficiency. This paper summarizes optimal ecodriving and related technologies, including history, state of the art, and major strategies. Furthermore, the optimal ecodriving intelligent transportation system is mentioned, as well as its direction of development and theoretical challenges. Keywords optimal ecodriving, driving assistance, intelligent transportation system, fuel economy Lulu GUO received his B.S. degree from Jilin University, Changchun, China in 2014, where he is currently working toward his Ph.D. degree in Control Theory and Engineering. His current research interests include advanced vehicle powertrain control and energy management of HEVs. Bingzhao GAO received his B.S. and M.S. degrees from Jilin University of Technology, China in 1998 and Jilin University, China in 2002, respectively. He earned his Ph.D. degrees in Mechanical Engineering from Yokohama National University, Japan, and Control Engineering from Jilin University, China in 2009. He is currently a professor at Jilin University. His research interests include vehicle powertrain control and vehicle stability control.

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Section: 考虑道路和交通信息的单车行驶优化unclassified

Optimal ecodriving control of vehicles

Gao¹,

Chen²,

Guo³

2016

Sci. Sin.-Inf.

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“…The European Position System (Galileo) is expected to provide measurements that will range from 3 to 5 m (Lee et al, 2008) or even centimetre-level accuracy. A concise but comprehensive description of the GPS system and its applications can be found in Mintsis et al (2004), while a brief history of the evolution of GPS, its operation and potential applications may be found in Theiss et al (2005).…”

Section: Space Segmentmentioning

confidence: 99%

“…In their qualitative survey, Baumgertner et al (2008) found that major telematics services like combination of automatic routing systems and GPS, semi-automated route optimisation, monitoring of loading space utilisation and comparison between planned and actual trips are all beneficial for the reduction of greenhouse gas emissions. In the same context, Lee et al (2008) designed a real-time GPS-based vehicle control system to alter the control of the engine, aiming to reduce fuel consumption and greenhouse gas emissions. Examining the application of GPS in commercial bus routes, Hickman (2004) states that GPS has direct impacts on air quality due to reduction in pollutant emissions and an indirect impact due to changes in passengers' travelling habits as well.…”

Section: Application Of Telematics In Freight Transport and Distributionmentioning

confidence: 99%

Telematics for Efficient Transportation and Distribution of Agrifood Products

Marentakis

2011

Intelligent Agrifood Chains and Networks

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For the agrifood industry the issue of product traceability over the supply chain is of great importance. Agrifood product companies use various methods and technologies to safeguard product quality and their market reputation. Moreover, management of contemporary complex and rapidly changing logistics systems require high availability of real-time information about the position and state of a company's fleet and freight. Supply-chain management, and more specifically the freight transportation channel, is currently supported by technological innovations in information and communication technologies (ICT) in many ways, providing valuable information for carriers and shippers to manage fleets and goods effectively, and at the same time enhancing their capability of responding to changing customer requirements. ICT applications allow decision-makers and planners to effectively handle problems related to the daily operation of distribution centres, freight forwarding, urban distribution, long-haul freight transport, loading and unloading planning, route scheduling etc. The growing volume of data, coupled with the need for flexibility in daily operations, requires the support of suitable information technology. Recent advances in microelectronics and computing technology have enhanced the capability of electronic devices, for example display size, interfaces, portability and energy consumption. In addition, new portable devices (personal data assistants and personal navigation systems have been developed recently and do not require fixed installation in vehicles.Coherent combinations of ICT technologies and devices constitute specific classes of sophisticated information systems (typically referred to as 'telematics' or automatic vehicle location systems). These offer monitoring and interaction capabilities between fleet trucks, drivers and freight stakeholders at almost any level of management, covering the planning, the trip and route monitoring, and reporting needs. The need for ICT support in freight transport and distribution becomes even more important when transported goods are of high value, perishable, time-dependent, hazardous or related to just-in-time services for those companies aiming at supplying products in complex and continuously growing distribution networks. Typical examples of these are agricultural and food products. Such networks aim to deliver products to stores ranging from small retailers (offering a range of 1000-3000 stock keeping units (SKUs), 90% of which are agrifood products) to supermarkets (offering a range of 20 000-30 000 SKUs, 75-90% of which are agrifood products) and hypermarkets (offering Intelligent Agrifood Chains and Networks, First Edition. Edited by Michael Bourlakis, Ilias Vlachos and Vasileios Zeimpekis.

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“…Huang et al [14] used a threshold logic control strategy that is optimized continuously during operation to control a PHEV. Lee et al [15] developed neuro-fuzzy technique to analyze and learn GPS data and control the hybrid vehicle to improve fuel economy and reduce emissions. Fu [16] developed a methodology for real-time prediction of torque availability in interior permanent magnet motors.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Energy Management Control for Hybrid Electric Powertrains

Zaher

Cetinkunt

2013

Journal of Control Science and Engineering

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This paper focuses on embedded control of a hybrid powertrain concepts for mobile vehicle applications. Optimal robust control approach is used to develop a real-time energy management strategy. The main idea is to store the normally wasted mechanical regenerative energy in energy storage devices for later usage. The regenerative energy recovery opportunity exists in any condition where the speed of motion is in the opposite direction to the applied force or torque. This is the case when the vehicle is braking, decelerating, the motion is driven by gravitational force, or load driven. There are three main concepts for energy storing devices in hybrid vehicles: electric, hydraulic, and mechanical (flywheel). The real-time control challenge is to balance the system power demands from the engine and the hybrid storage device, without depleting the energy storage device or stalling the engine in any work cycle. In the worst-case scenario, only the engine is used and the hybrid system is completely disabled. A rule-based control algorithm is developed and is tuned for different work cycles and could be linked to a gain scheduling algorithm. A gain scheduling algorithm identifies the cycle being performed by the work machine and its position via GPS and maps both of them to the gains.

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Intelligent GPS-Based Vehicle Control for Improved Fuel Consumption and Reduced Emissions

Cited by 8 publications

References 5 publications

Optimal ecodriving control of vehicles

Optimal ecodriving control of vehicles

Telematics for Efficient Transportation and Distribution of Agrifood Products

Real-Time Energy Management Control for Hybrid Electric Powertrains

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