<p>This thesis presents a new method for iterative road grade estimation based on sensors that are commonplace in modern heavy duty vehicles. Estimates from multiple passes of the same road segment are merged together to form a road grade map, that is improved each time the vehicle revisits an already traveled route. The estimation algorithm is discussed in detail together with its implementation and experimental evaluation on real vehicles.</p><p> </p><p>An increasing need for goods and passenger transportation drives continuing worldwide growth in road transportation while environmental concerns, traffic safety issues, and cost efficiency are becoming more important. Advancements in microelectronics open the possibility to address these issues through new advanced driver assistance systems. Applications such as predictive cruise control, automated gearbox control, predictive front lighting control and hybrid vehicle state-of-charge control benefit from preview road grade information. Using global navigation satellite systems an exact vehicle position can be obtained. This enables stored maps to be used as a source of preview road grade information. The task of creating such maps is addressed herein by the proposal of a method where the vehicle itself estimates the road grade each time it travels along a road and stores the information for later use.</p><p> </p><p>The presented road grade estimation method uses data from sensors that are standard equipment in heavy duty vehicles equipped with map-based advanced driver assistance systems. Measurements of the vehicle speed and the engine torque are combined with observations of the road altitude from a GPS receiver in a Kalman filter, to form a road grade estimate based on a system model. The noise covariance parameters of the filter are adjusted during gear shifts, braking and poor satellite coverage. The estimated error covariance of the road grade estimate is then used together with its absolute position to update a stored road grade map, which is based on all previous times the vehicle has passed the same location.</p><p> </p><p>Highway driving trials detailed in the thesis demonstrate that the proposed method is capable of accurately estimating the road grade based on few road traversals. The performance of the estimator under conditions such as braking, gear shifting, and loss of satellite coverage is presented. The experimental results indicate that road grade estimates from the proposed method are accurate enough to be used in predictive vehicle control systems to enhance safety, efficiency, and driver comfort of heavy duty vehicles.</p>

An indirect tire pressure monitoring system uses the wheel rolling radius as an indicator of low tire pressure. When extra load is put in the trunk of a car, the load distribution in the car will change. This will affect the rolling radius which in its turn will be confused with a change in the tire pressure. To avoid this phenomenon, the load distribution has to be estimated. In this thesis methods for estimating the pitch angle of a car and an offset in the pitch angle caused by changed load distribution are presented and when an estimate is derived, a load distribution can be derived. Alot of available signals are used but the most important are the longitudinal accelerometer signal and the acceleration at the wheels derived from the velocity of the car. A few ways to detect or compensate for a non-zero road grade are also presented. Based on the estimated offset, a difference between the front and rear axle heights in the vehicle can be estimated and compensating for the changed load distribution in an indirect tire pressure monitoring system will be possible.

An increasing need for goods and passenger transportation drives continued worldwide growth in traffic. As traffic increases environmental concerns, traffic safety, and cost efficiency become ever more important. Advancements in microelectronics open the possibility to address these issues through new advanced driver assistance systems. Applications such as predictive cruise control, automated gearbox control, predictive front lighting control, and hybrid vehicle state-of-charge control decrease the energy consumption of vehicles and increase the safety. These control systems can benefit significantly from preview road grade information. This information is currently obtained using specialized survey vehicles, and is not widely available. This thesis proposes new methods to obtain road grade information using on-board sensors. The task of creating road grade maps is addressed by the proposal of a framework where vehicles using a road network collect the necessary data for estimating the road grade. The estimation can then be carried out locally in the vehicle, or in the presence of a communication link to the infrastructure, centrally. In either case the accuracy of the map increases over time, and costly road surveys can be avoided. This thesis presents a new distributed method for creating accurate road grade maps for vehicle control applications. Standard heavy duty vehicles in normal operation are used to collect measurements. Estimates from multiple passes along a road segment are merged to form a road grade map, which improves each time a vehicle retraces a route. The design and implementation of the road grade estimator are described, and the performance is experimentally evaluated using real vehicles. Three different grade estimation methods, based on different assumption on the road grade signal, are proposed and compared. They all use data from sensors that are standard equipment in heavy duty vehicles. Measurements of the vehicle speed and the engine torque are combined with observations of the road altitude from a GPS receiver, using vehicle and road models. The operation of the estimators is adjusted during gearshifts, braking, and poor satellite coverage, to account for variations in sensor and model reliability. The estimated error covariances of the road grade estimates are used together with their absolute positions to update a stored road grade map. Highway driving trials show that the proposed estimators produce accurate road grade data. The estimation performance improves as the number of road segment traces increases. A vehicle equipped with the proposed system will rapidly develop a road grade map for its area of operation. Simulations show that collaborative generation of the third dimension for a pre-existing large area two-dimensional map is feasible. The experimental results indicate that road grade estimates from the proposed methods are accurate enough to be used in predictive vehicle control systems to enhance safety, efficiency, and driver comfort in heavy duty vehicles. The grade estimators may also be used for on-line validation of road grade information from other sources. This is important in on-board applications, since the envisioned control applications can degrade vehicle performance if inaccurate data are used. / QC 20110408

This thesis presents a new method for iterative road grade estimation based on sensors that are commonplace in modern heavy duty vehicles. Estimates from multiple passes of the same road segment are merged together to form a road grade map, that is improved each time the vehicle revisits an already traveled route. The estimation algorithm is discussed in detail together with its implementation and experimental evaluation on real vehicles.  An increasing need for goods and passenger transportation drives continuing worldwide growth in road transportation while environmental concerns, traffic safety issues, and cost efficiency are becoming more important. Advancements in microelectronics open the possibility to address these issues through new advanced driver assistance systems. Applications such as predictive cruise control, automated gearbox control, predictive front lighting control and hybrid vehicle state-of-charge control benefit from preview road grade information. Using global navigation satellite systems an exact vehicle position can be obtained. This enables stored maps to be used as a source of preview road grade information. The task of creating such maps is addressed herein by the proposal of a method where the vehicle itself estimates the road grade each time it travels along a road and stores the information for later use.  The presented road grade estimation method uses data from sensors that are standard equipment in heavy duty vehicles equipped with map-based advanced driver assistance systems. Measurements of the vehicle speed and the engine torque are combined with observations of the road altitude from a GPS receiver in a Kalman filter, to form a road grade estimate based on a system model. The noise covariance parameters of the filter are adjusted during gear shifts, braking and poor satellite coverage. The estimated error covariance of the road grade estimate is then used together with its absolute position to update a stored road grade map, which is based on all previous times the vehicle has passed the same location.  Highway driving trials detailed in the thesis demonstrate that the proposed method is capable of accurately estimating the road grade based on few road traversals. The performance of the estimator under conditions such as braking, gear shifting, and loss of satellite coverage is presented. The experimental results indicate that road grade estimates from the proposed method are accurate enough to be used in predictive vehicle control systems to enhance safety, efficiency, and driver comfort of heavy duty vehicles. / QC 20101119

This Master's thesis presents a method for on-line estimation of vehicle mass and road grade using Kalman filter. Many control strategies aiming for better fuel economy, drivability and safety in today's vehicles rely on precise vehicle operating information. In this context, vehicle mass and road grade are crucial parameters. The method is based on an extended Kalman filter (EKF) and a longitudinal vehicle model. The main advantage of this method is its applicability on drivelines with continuous power output during gear shifts and cost effectiveness compared to hardware solutions. The performance has been tested on both simulated data and on real measurement data, collected with a truck on road. Two estimators were developed; one estimates both vehicle mass and road grade and the other estimates only vehicle mass using an inclination sensor as an additional measurement. Tests of the former estimator demonstrate that a reliable mass estimate with less than 5 % error is often achievable within 5 minutes of driving. Furthermore, the root mean square error of the grade estimate is often within 0.5°. Tests of the latter estimator show that this is more accurate and robust than the former estimator with a mass error often within 2 %. A sensitivity analysis shows that the former estimator is fairly robust towards minor modelling errors. Also, an observability analysis shows under which circumstances simultaneous vehicle mass and road grade is possible.

Refuse trucks, commonly referred to as garbage trucks are a critical component of a municipality’s waste management industry. Their primary purpose is to collect, transport and deposit waste from households or businesses to designated transfer sites or dumps. Historically, refuse trucks have been powered by diesel fuel. The consumption of diesel fuel paired with the frequent accelerations or decelerations between each residential household along a route attribute to high amounts of tailpipe emissions and noise pollution within neighbourhoods. There is significant opportunity to explore avenues of powertrain electrification in refuse trucks to reduce their emissions and improve energy efficiency. To rapidly test promising powertrains, vehicle software models were developed. To accurately model the energy usage and power requirements of refuse trucks, environments for the models to operate were created. The environments were created using on-board diagnostic and positional data collected from refuse trucks in the City of Hamilton in Ontario, Canada. The data collection was done under a research collaboration between the City of Hamilton and the McMaster Automotive Resource Centre. The approaches used to develop the drive and duty cycles for the vehicle models offer some innovative approaches without the need for invasive devices to be installed. The powertrains that were modelled includes an all-electric, ranged extended electric and conventional refuse trucks. A comparative analysis of the pump-to-wheel powertrain efficiencies were completed looking at metrics such as fuel economy, payload capacity and fuel costs. Lastly, a look at truck emissions from a well-to-wheel perspective were completed to investigate the impact of each powertrain on greenhouse gasses and the effect on air quality of their immediate surroundings. / Thesis / Master of Applied Science (MASc)

NVDB contains information of Sweden’s roads and a large number of properties concerning them. This information is provided as open data and is commonly usedin routing. For an optimal route optimization, several attributes are needed based on the road properties. At the time that this report was written, there was no attribute for slopes. The purpose of this report was, in collaboration with the Swedish transport Administration, to develop a new property “slope” for route optimization based on fuel consumption. The development of the slope phenomenon was built with FME where a script was created that calculated inclination on road links.The script calculated the inclination using a variation of the linear equation. Where x, y and z coordinates and attributes from NVDB were used to obtain values for the equation.The testing and validation of the generated slope property was performed with route optimization in the ArcMap application. The results from route optimization and previous research show a significant reduction in fuel consumption within routes with regard to slope. / NVDB innehåller information om Sveriges vägar och ett stort antal egenskaper för vägarna. Informationen tillhandahålls som öppen data och används ofta inom ruttning. För en optimal ruttoptimering behövs det ett flertal attribut baserat på vägegenskaper. Under tiden den här rapporten skapades så saknades det ett attribut för lutning. Syftet med den här rapporten var att i samarbete med Trafikverket ta fram en ny egenskap “lutning” vid ruttoptimering baserat på bränsleförbrukning. Framtagandet av lutningsföreteelsen var uppbyggt med hjälp av FME där ett skript skapades som beräknade lutningen i väglänkarna. §Skriptet beräknade lutning med hjälp av en variation av räta linjens ekvation. Därx, y och z koordinater samt attribut från NVDB användes för att få fram värden för ekvationen. Testandet och valideringen av den framtagna lutningsegenskapen framfördes med ruttoptimering i programmet ArcMap. Resultatet av ruttoptimeringen och tidigare forskning visar markant reduktion av bränsleförbrukning inom rutter med hänsyntill lutning.