A Real-Time Fuel-Optimal Cruise Controller for Heavy Trucks Using Road Topography Information

Hellström, Erik; Fröberg, Anders; Nielsen, Lars

doi:10.4271/2006-01-0008

Cited by 64 publications

(51 citation statements)

References 5 publications

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“…Our optimization strategy obtains fuel savings lower than those given for strategies such as [25,26] that do not include the travel time in the fuel minimization process, but optimal speed profiles produced by our DP algorithm produce slightly higher fuel savings than other similar systems [28,66] that involve travel time within the fuel consumption optimization process. Furthermore, the adaptation to unexpected traffic situations in real time, both when receiving information in advance or under sudden deviations, is another of the main advantages of the proposed algorithm that improves energy consumption savings.…”

Section: Discussionmentioning

confidence: 66%

“…In [65], the operational costs are divided into monetary factors per fuel quantity and time units. A similar approach is made by [66,67] where the evaluation is realized by the arithmetic sum of the spent fuel and travel time. The travel time importance is modulated using weighting factors.…”

Section: Objective Functionmentioning

confidence: 99%

“…In [66] simulations are performed of how long the road stretch taken into account should be in the dynamic programming strategy and indicated that fuel consumption savings tend to be constant with distances of over 2 km.…”

Section: Overlap Of Dp Solutions Of Road Stretchesmentioning

confidence: 99%

“…Tests have been made varying overlapping distances for a trip of 20 km, and the results are shown in Table 2. Therefore, we have chosen overlap stretches of 5 km, longer than the 2 km stretches proposed in [66], because this value provides a short computational time and results accuracy is significantly improved. This process makes the DP algorithm be performed every D meters, so it is possible to adjust the weighting factors in each run to ensure that the destination is reached on time taking into account the deviations from the optimal speed profile and modifications in speed limits or the appearance of unforeseen traffic congestion situations.…”

Section: Overlap Of Dp Solutions Of Road Stretchesmentioning

confidence: 99%

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System for Road Vehicle Energy Optimization Using Real Time Road and Traffic Information

Jiménez

Cabrera-Montiel

2014

Energies

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Nowadays, reducing the energy and fuel consumption of road vehicles is a key issue. Different strategies have been proposed. One of them is to promote Eco-driving behaviour among drivers. Most Eco-driving tips take into account only the road stretch where the vehicle is located. However, larger improvements could be achieved if information from subsequent stretches is used. The main objective of this work is to develop a system to warn the driver in real time of the optimal speed that should be maintained on every road segment in order to optimize the energy used and the fuel consumed while observing a time schedule. The system takes into account the road vertical profile, the fixed and variable speed limits and the traffic information retrieved using V2V and V2I communications. The system has been tested on real road sections with satisfactory results in fuel savings.

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Section: Discussionmentioning

confidence: 66%

Section: Objective Functionmentioning

confidence: 99%

Section: Overlap Of Dp Solutions Of Road Stretchesmentioning

confidence: 99%

Section: Overlap Of Dp Solutions Of Road Stretchesmentioning

confidence: 99%

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System for Road Vehicle Energy Optimization Using Real Time Road and Traffic Information

Jiménez

Cabrera-Montiel

2014

Energies

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“…Low-order models are often called control-oriented model because they are built for control design approaches, such as Pontryagin's minimum principle (Chang and Morlok, 2005) and dynamic programming (Mensing et al, 2013;Hellström et al, 2006;Liamas et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

An optimal gear-shifting strategy for heavy trucks with trade-off study between trip time and fuel economy

Zhao

Ing

Azad

et al. 2015

IJHVS

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Advances in vehicle technology have improved driving performance and fuel efficiency. Further increases in fuel efficiency of a heavy mining truck can be achieved by optimizing gear shifting strategy. Using characteristic tests of the diesel engine, a high-fidelity model of a mining truck was built in MapleSim and a consistent low-order model was developed in Matlab. The dynamic programming approach has been applied to the low-order model of the specialized off-road 30-tonne mining truck over a fixed route in a mining area. There were two competing objectives: fuel use and trip time. Weighting coefficients were used to combine these objective functions to a single value, and a Pareto curve was created to analyse the effect of the weights on the fuel use and trip time. Using the control strategy obtained from dynamic programming on the high-fidelity model, it is estimated that forty-thousand litres of fuel can be saved annually based on a mine which produces 110 kilotons of coal per day.

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Exploiting Terrain Information for Enhancing Fuel Economy of Cruising Vehicles by Supervised Training of Recurrent Neural Optimizers

Abou-Nasr

Michelini

Filev

2014

Annals of Information Systems

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A Real-Time Fuel-Optimal Cruise Controller for Heavy Trucks Using Road Topography Information

Cited by 64 publications

References 5 publications

System for Road Vehicle Energy Optimization Using Real Time Road and Traffic Information

System for Road Vehicle Energy Optimization Using Real Time Road and Traffic Information

An optimal gear-shifting strategy for heavy trucks with trade-off study between trip time and fuel economy

Exploiting Terrain Information for Enhancing Fuel Economy of Cruising Vehicles by Supervised Training of Recurrent Neural Optimizers

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