Operating cycle representations for road vehicles
Doctoral thesis, 2019
This thesis discusses different ways to represent road transport operations mathematically. The intention is to make more realistic predictions of longitudinal performance measures for road vehicles, such as the CO2 emissions. It is argued that a driver and vehicle independent description of relevant transport operations increase the chance that a predicted measure later coincides with the actual measure from the vehicle in its real-world application. This allows for fair comparisons between vehicle designs and, by extension, effective product development.
Three different levels of representation are introduced, each with its own purpose and application.
The first representation, called the bird's eye view, is a broad, high-level description with few details. It can be used to give a rough picture of the collection of all transport operations that a vehicle executes during its lifetime. It is primarily useful as a classification system to compare different applications and assess their similarity.
The second representation, called the stochastic operating cycle (sOC) format, is a statistical, mid-level description with a moderate amount of detail. It can be used to give a comprehensive statistical picture of transport operations, either individually or as a collection. It is primarily useful to measure and reproduce variation in operating conditions, as it describes the physical properties of the road as stochastic processes subject to a hierarchical structure.
The third representation, called the deterministic operating cycle (dOC) format, is a physical, low-level description with a great amount of detail. It describes individual operations and contains information about the road, the weather, the traffic and the mission. It is primarily useful as input to dynamic simulations of longitudinal vehicle dynamics.
Furthermore, it is discussed how to build a modular, dynamic simulation model that can use data from the dOC format to predict energy usage. At the top level, the complete model has individual modules for the operating cycle, the driver and the vehicle. These share information only through the same interfaces as in reality but have no components in common otherwise and can therefore be modelled separately. Implementations are briefly presented for each module, after which the complete model is showcased in a numerical example.
The thesis ends with a discussion, some conclusions, and an outlook on possible ways to continue.
Three different levels of representation are introduced, each with its own purpose and application.
The first representation, called the bird's eye view, is a broad, high-level description with few details. It can be used to give a rough picture of the collection of all transport operations that a vehicle executes during its lifetime. It is primarily useful as a classification system to compare different applications and assess their similarity.
The second representation, called the stochastic operating cycle (sOC) format, is a statistical, mid-level description with a moderate amount of detail. It can be used to give a comprehensive statistical picture of transport operations, either individually or as a collection. It is primarily useful to measure and reproduce variation in operating conditions, as it describes the physical properties of the road as stochastic processes subject to a hierarchical structure.
The third representation, called the deterministic operating cycle (dOC) format, is a physical, low-level description with a great amount of detail. It describes individual operations and contains information about the road, the weather, the traffic and the mission. It is primarily useful as input to dynamic simulations of longitudinal vehicle dynamics.
Furthermore, it is discussed how to build a modular, dynamic simulation model that can use data from the dOC format to predict energy usage. At the top level, the complete model has individual modules for the operating cycle, the driver and the vehicle. These share information only through the same interfaces as in reality but have no components in common otherwise and can therefore be modelled separately. Implementations are briefly presented for each module, after which the complete model is showcased in a numerical example.
The thesis ends with a discussion, some conclusions, and an outlook on possible ways to continue.
operating cycle
full vehicle simulation
energy usage
transport operation description
road format
CO2 emissions
SB-H4
Opponent: Stefan Hausberger, Graz University of Technology, Austria
Author
Pär Pettersson
Chalmers, Mechanics and Maritime Sciences (M2), Vehicle Engineering and Autonomous Systems
Comparison of dual and single clutch transmission based on Global Transport Application mission profiles
International Journal of Vehicle Design,;Vol. 77(2018)p. 22-42
Journal article
A proposal for an operating cycle description format for road transport missions
European Transport Research Review,;Vol. 10(2018)p. 1-19
Journal article
A statistical operating cycle description for prediction of road vehicles' energy consumption
Transportation Research Part D: Transport and Environment,;Vol. 73(2019)p. 205-229
Journal article
Pettersson, P., Jacobson, B., Bruzelius, F., Johannesson, P., Fast, L. Intrinsic differences between backward and forward vehicle simulation models
Nowadays, when new vehicles are developed, like personal cars, buses and heavy-duty trucks, much of the development are done using computers. Instead of building new components and concepts as prototypes and testing them physically out in the world, like it used to be, they are built as mathematical models and tested virtually. This is both faster and less resource demanding, and many more concepts can be tested in the same amount of time. In the end, it means that goals and targets that are otherwise very hard to reach, like zero net CO2 emissions or fully automated driving, may become possible to achieve in the long run.
However, there are some issue with testing things in a virtual world, that does not exist in the physical counterpart. Namely the world itself. Not only must the vehicle (or component, if working with parts and subsystems) be modelled mathematically, but the surroundings and their effects must be considered too. In the real world, the laws of physics are already there. When a vehicle drives around it is affected by gravitation, aerodynamic resistance, slippery surfaces, rolling resistance, bumps, potholes and a million other things, all the time. In addition, there is a driver at the helm, with his or her ideas about how to manoeuvre, where to go and how fast to get there.
In a virtual setting, all these things must be described manually, otherwise they will not show up. It is important to get them right, so that the virtual vehicle is used in a realistic way. If this is fulfilled, then there is a good chance that the technical designs and solutions that are developed, work in the manner that they are supposed to, for example by lowering the CO2 emissions in the predicted way. On the other hand, if the virtual vehicle is not used in realistically, then it becomes very difficult to develop effective solutions, because there is a high risk they do not work as expected. In this thesis, we look at a couple of ways that the road, the surroundings, the traffic and the transport mission can be described mathematically.
One particularly interesting way of doing this, is to pretend that the road consists of a bunch of properties that behave randomly, much like the roll of a die. The different properties, like the hills and the valleys, the curves, the unevenness, and the road signs, behave in their own way - the dice are different - so the randomness can be fitted to the individual properties. When this randomness is measured, we can then start to predict what a road should look like (on average and something about how it varies) and model it realistically in the virtual world.
However, there are some issue with testing things in a virtual world, that does not exist in the physical counterpart. Namely the world itself. Not only must the vehicle (or component, if working with parts and subsystems) be modelled mathematically, but the surroundings and their effects must be considered too. In the real world, the laws of physics are already there. When a vehicle drives around it is affected by gravitation, aerodynamic resistance, slippery surfaces, rolling resistance, bumps, potholes and a million other things, all the time. In addition, there is a driver at the helm, with his or her ideas about how to manoeuvre, where to go and how fast to get there.
In a virtual setting, all these things must be described manually, otherwise they will not show up. It is important to get them right, so that the virtual vehicle is used in a realistic way. If this is fulfilled, then there is a good chance that the technical designs and solutions that are developed, work in the manner that they are supposed to, for example by lowering the CO2 emissions in the predicted way. On the other hand, if the virtual vehicle is not used in realistically, then it becomes very difficult to develop effective solutions, because there is a high risk they do not work as expected. In this thesis, we look at a couple of ways that the road, the surroundings, the traffic and the transport mission can be described mathematically.
One particularly interesting way of doing this, is to pretend that the road consists of a bunch of properties that behave randomly, much like the roll of a die. The different properties, like the hills and the valleys, the curves, the unevenness, and the road signs, behave in their own way - the dice are different - so the randomness can be fitted to the individual properties. When this randomness is measured, we can then start to predict what a road should look like (on average and something about how it varies) and model it realistically in the virtual world.
Operating cycle energy management (OCEAN)
Swedish Energy Agency (2013-006720), 2014-01-01 -- 2017-12-31.
COVER – Real world CO2 assessment and Vehicle enERgy efficiency
VINNOVA (2017-007895), 2018-01-01 -- 2021-12-31.
Swedish Energy Agency (2017-007895), 2018-01-01 -- 2021-12-31.
Areas of Advance
Transport
Subject Categories
Transport Systems and Logistics
Vehicle Engineering
Infrastructure
ReVeRe (Research Vehicle Resource)
ISBN
978-91-7905-210-2
Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 4677
Publisher
Chalmers
SB-H4
Opponent: Stefan Hausberger, Graz University of Technology, Austria