The freight transport industry faces big challenges as the demand for transport and fuel prices are steadily increasing, whereas the environmental impact needs to be significantly reduced. Heavy-duty vehicle (HDV) platooning is a promising technology for a sustainable transportation system. By semi-autonomously governing each platooning vehicle at small inter-vehicle spacing, we can effectively reduce fuel consumption, emissions, and congestion, and relieve driver tension. Yet, it is not evident how to synthesise such a platoon control system and how constraints imposed by the road topography affect the safety or fuel-saving potential in practice. This thesis presents contributions to a framework for the design, implementation, and evaluation of HDV platooning. The focus lies mainly on establishing fuel-efficient platooning control and evaluating the fuel-saving potential in practice. A vehicle platoon model is developed together with a system architecture that divides the control problem into manageable subsystems. Presented results show that a significant fuel reduction potential exists for HDV platooning and it is favorable to operate the vehicles at a small inter-vehicle spacing. We address the problem of finding the minimum distance between HDVs in a platoon without compromising safety, by setting up the problem in a game theoretical framework. Thereby, we determine criteria for which collisions can be avoided in a worst-case scenario and establish the minimum safe distance to a vehicle ahead. A systematic design methodology for decentralized inter-vehicle distance control based on linear quadratic regulators is presented. It takes dynamic coupling and engine response delays into consideration, and the structure of the controller feedback matrix can be tailored to the locally available state information. The results show that a decentralized controller gives good tracking performance and attenuates disturbances downstream in the platoon for dynamic scenarios that commonly occur on highways. We also consider the problem of finding a fuel-efficient controller for HDV platooning based on road grade preview information under road and vehicle parameter uncertainties. We present two model predictive control policies and derive their fuel-saving potential. The thesis finally evaluates the fuel savings in practice. Experimental results show that a fuel reduction of 3.9–6.5 % can be obtained on average for a heterogenous platoon of HDVs on a Swedish highway. It is demonstrated how the savings depend on the vehicle position in the platoon, the behavior of the preceding vehicles, and the road topography. With the results obtained in this thesis, it is argued that a significant fuel reduction potential exists for HDV platooning. / <p>QC 20140527</p>

<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>

<p>Knowledge about the current road slope can improve several applications in a heavy-duty vehicle such as predictive cruise control and automated gearbox control. In this thesis the possibility of estimating the road slope based on signals from a vehicles air suspension system has been studied. More specifically, the measurement consists of a pressure signal measuring the axle load, and a vertical distance sensor.</p><p>A variety of suspension systems can be mounted on a Scania truck. During this thesis, two discrete-time models based on two different rear axle air suspension systems have been proposed. The models use the effect of alternating axle load during a change in the road slope and the estimates are computed using an extended Kalman filter.</p><p>The first model is based on a rear axle suspension known as the 2-bellow system. This type of suspension is strongly affected by the driveshaft torque, which results in a behaviour where the rear end is pushed upwards and thus decreasing the rear axle load during uphill driving. A model was developed in order to compensate for this behaviour. Unfortunately, the estimates showed less promising results and all attempts to determine the error was unsuccessful.</p><p>The latter model is based on the 4-bellow system. This suspension system is not affected by the driveshaft torque and a less complex model could be derived. The experimental results indicated that road slope estimation was possible and with a fairly accurate result. However, more work is needed since the estimate is affected by road surface irregularities and since the algorithm requires knowledge about the vehicles mass and the location of the centre of gravity.</p><p>All the presented results have been estimated based on real data from a test track at Scania Technical Centre in Södertälje.</p>

Knowledge about the current road slope can improve several applications in a heavy-duty vehicle such as predictive cruise control and automated gearbox control. In this thesis the possibility of estimating the road slope based on signals from a vehicles air suspension system has been studied. More specifically, the measurement consists of a pressure signal measuring the axle load, and a vertical distance sensor. A variety of suspension systems can be mounted on a Scania truck. During this thesis, two discrete-time models based on two different rear axle air suspension systems have been proposed. The models use the effect of alternating axle load during a change in the road slope and the estimates are computed using an extended Kalman filter. The first model is based on a rear axle suspension known as the 2-bellow system. This type of suspension is strongly affected by the driveshaft torque, which results in a behaviour where the rear end is pushed upwards and thus decreasing the rear axle load during uphill driving. A model was developed in order to compensate for this behaviour. Unfortunately, the estimates showed less promising results and all attempts to determine the error was unsuccessful. The latter model is based on the 4-bellow system. This suspension system is not affected by the driveshaft torque and a less complex model could be derived. The experimental results indicated that road slope estimation was possible and with a fairly accurate result. However, more work is needed since the estimate is affected by road surface irregularities and since the algorithm requires knowledge about the vehicles mass and the location of the centre of gravity. All the presented results have been estimated based on real data from a test track at Scania Technical Centre in Södertälje.

A rear loading refuse truck was simulated with a conventional and hydraulic hybrid configuration. Models for the hydraulic hybrid components were developed to simulate the system. A control algorithm was developed using a stochastic dynamic programming approach. The results did not match those that are advertised by the commercially available systems, but reasons for this deviation are discussed. The predicted improvement in fuel economy ranged from 1% to 15% depending on variance in drive cycle and vehicle weight. A brief analysis of the cost of the hybrid system was also conducted based on an estimated drive cycle. This analysis showed that, at current fuel prices of about $4.00/gallon, the system may not make financial sense for a 10 year period of ownership. / text

Fuel consumption is one of the factors that are considered when deciding a vehicle’s optimal specification. In order to swiftly estimate the fuel consumed during real world driving scenarios, a simulation tool has been developed that is well suited for vehicle configuration exploration applications. The simulation method described in this paper differs from the static calculation method currently in use at Scania cv since the dynamic translation of the vehicle is considered, yet the simulation time is kept low. By adopting a more dynamic approach, the estimation accuracy is increased and simulation of fuel saving technology, e.g. intelli- gent driver support system, is enabled. In this paper, the modeling and implementation process is described. Different approaches is discussed and the choices made during the development is presented. In order to achieve a low simulation time and obtain a good compatability with Scania’s current software application, some of the influencial factors have been omitted from the model or described using simple relations. The validation of the fuel consumption estimation indicates an accuracy within three percent for motorway driving. Utilizing the newly devised simulation tool, a look-ahead cruise controller has been implemented and simulated. Instead of continuously finding the optimal control signals during the driving scenario like most look-aheadcontrollers, a dynamic programming algorithm is used to find a fuel efficient speed profile for the entire route. The speed profile is used as the reference speed for a conventional cruise controller and comparison with another simulation tool indicates that this is a fast and accurate way to emulate a real look-ahead controller.

Typically the combustion in engines are open-loop controlled. By using an in-cylinder pressure sensor it is possible to create virtual sensors for closed-loop combustion control (CLCC). With CLCC it is possible to counteract dynamic effects as component ageing, fuel type and cylinder variance. A virtual sensor system was implemented based on a one-zone heat-release analysis, including signal processing of the pressure sensor input. A parametrisation of the heat-release based on several Vibe functions was implemented with good results. The major focus of the virtual sensor system was to perform a tolerance analysis on experimental data, where typical error sources in a production heavy-duty vehicle were identified and their effect on the estimates quantified. It could be concluded that estimates are very much dependent on the choice of heat-release and specific heat ratio models. Especially crank angle phasing has a large impact on estimation performance, stressing the importance of accounting for crankshaft torsion in production vehicles. Biodiesel advances the combustion angle and give a lower IMEP and total heat amount compared to standard diesel. However, error sensitivity is not affected. Further investigations must be made on improving the signal processing in terms of gain error compensation and filtering. Also a better understanding of how errors propagate between subsystems in a CLCC system is required for successful implementation.

No description available.

The aim of this thesis is to investigate the Air Conditioning system in a heavy duty truck and to develop a control strategy for the case when low cooling capacity is needed from the AC-system. A model of the AC-system was developed in order for an efficient controller to be designed. The model was designed to comprise of all the basic behaviours that the AC- system has, rather than to be an exact model of the system. As the AC-system showed to be very complex, a number of limitations in the model had to be made. The AC-system has two temperature sensors and is actuated by turning the AC- compressor on or off. Two different control strategies were tested for the control of the AC-compressor. The first was to use a controller to directly control the compressor clutch and the second one utilised a pulswidth modulated control structure were the controller stated the pulswidth to be used. Both control structures were implemented in the computer model, the AC-rig and in a truck in a climate chamber. Both control strategies showed to fulfil the demands on the system in the somewhat idealistic circumstances during which they were tested.

Platooning has the potential to significantly reduce fuel consumption, but with heavy duty vehicles scattered on roads driving alone, there is a need for coordination. One solution is for a vehicle to increase its speed to catch up and platoon with a preceding vehicle. This could reduce the fuel consumption of a mission, but it could also increase it if too much fuel is spent catching up. By finding the fuel consumption of catching up and platooning and comparing it to driving alone the decision of whether or not to catch up can be made. This thesis proposes a fuel-optimal algorithm based on a look-ahead controller taking future road topography into account to find the optimal trajectory and merge point when catching up to a preceding vehicle. By weighting time against fuel in the objective function, the addition of a state to keep track of time can be avoided and thus the algorithm can remain low in complexity, making it suitable for dynamic programming (DP). The DP algorithm is iterated in a forward fashion keeping track of the time-to-come for each state until it catches up to the preceding vehicle, then the platooning is simulated with a constant time gap, making it easy and fast to simulate. The algorithm is tested on real-world road topography data where it showed that taking road topography into account when choosing the merge point can have a significant fuel reduction.