Optimal Speed on Small Gradients - Consequences of a Non-Linear Fuel Map

Ivarsson, Maria; Åslund, Jan; Nielsen, Lars

doi:10.3182/20080706-5-kr-1001.00572

Cited by 6 publications

(4 citation statements)

References 6 publications

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“…Monastyrsky et al [11] implemented an algorithm based on a dynamic programming approach that finds a global optimum but can be used only for limited route lengths due to the complexity of dynamic programming. Ivarsson et al [8] used an analytical approach that is appropriate only for routes with small gradients. In addition, this approach requires a lot of knowledge about the vehicle engine, e.g., specific fuel consumption diagram, which is usually unknown.…”

Section: Related Workmentioning

confidence: 99%

A multiobjective optimization algorithm for discovering driving strategies

Dovgan

Gams

Filipič

2011

Proceedings of the 13th Annual Conference Companion on Genetic and Evolutionary Computation

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This paper presents a deterministic multiobjective optimization algorithm for discovering driving strategies. The goal is to find a set of nondominated driving strategies with respect to two conflicting objectives: time and fuel consumption. The presented multiobjective algorithm is based on the breadth-first search algorithm and Nondominated Sorting Genetic Algorithm (NSGA-II). Experiments on a 10-km route show that the results significantly depend on the discretization of the search space.

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Section: Related Workmentioning

confidence: 99%

A multiobjective optimization algorithm for discovering driving strategies

Dovgan

Gams

Filipič

2011

Proceedings of the 13th Annual Conference Companion on Genetic and Evolutionary Computation

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“…Model-based techniques are analytical and require knowledge about the applied vehicle model. They optimize either a weighted sum of the fuel consumption and the traveling time (see, e.g., Huang et al 2008;Ivarsson, Aslund, and Nielsen 2008), or the fuel consumption only while considering the traveling time as a constraint (see, e.g., Khmelnitsky 2000;Howlett, Pudney, and Vu 2009;Melnik 2009). Black-box techniques are usually numerical and use the vehicle models without any knowledge of vehicle operation.…”

Section: Introductionmentioning

confidence: 99%

A Real-Time Multiobjective Optimization Algorithm for Discovering Driving Strategies

Dovgan

Gams

Filipič

2019

Transportation Science

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“…Professor Nielsen, from Linkoping University, applied numerous numerical algorithms. Many attempts have been made to simplify the engine model [9,10]. The nonlinearity of the engine has been analyzed in detail [11].…”

Section: Introductionmentioning

confidence: 99%

Method for Switching between Traction and Brake Control for Speed Profile Optimization in Mountainous Situations

2018

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Making full use of front road grade information to achieve the best fuel efficiency is important for intelligent vehicles. Normal theoretical studies pay too much attention to engine continuous feedback control. The theoretical foundation of switching between traction and brake control has been ignored. In mountainous terrain, both the engine and road slopes are energy sources. Switching between traction and brake control is the key point. This research focuses on broadening the normal control range. The comprehensive objective function that contains traction and brake control is built, and then the analytical switching control law is derived based on Pontryagin's maximum principle (PMP). Analytical switching control laws express the mechanism of switching between traction and brake control for economic cruise control (ECC). Simulation results show that the model can solve the switch time and the entire speed profile precisely. Brake control is very important in downhill situations. The parameters in the objective function influence not only the switch time but also the switch process. This research offers a theoretical foundation for ECC with road slopes and can make onboard control more precise and efficient.

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Optimal Speed on Small Gradients - Consequences of a Non-Linear Fuel Map

Cited by 6 publications

References 6 publications

A multiobjective optimization algorithm for discovering driving strategies

A multiobjective optimization algorithm for discovering driving strategies

A Real-Time Multiobjective Optimization Algorithm for Discovering Driving Strategies

Method for Switching between Traction and Brake Control for Speed Profile Optimization in Mountainous Situations

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