Analytical optimal solution to the energy managementof hybrid electric vehicles is of interest from theoretical and practical perspectives. Particularly, effort has been made to derive analytical solution to the energy management problem for series hybrid electric vehicles using Pontryagin’s minimum principle (PMP). However, admissibility of the system input was not fully explored in determining the optimal input candidates. In this paper, the analytical solution for the same problem is found by partitioning the positive power demand set into four subsets, where the solution is derived for each case separately according to the corresponding admissible input set. The analytical solution is verified through comparison with numerical solution for a series hybrid electric wheel loader, and two different drive cycles are considered for this purpose. From the proposed solution, effective equivalence factor bounds are found and used to construct an adaptive equivalent consumption minimization strategy. The proposed strategy and the analytical solution are implemented together for the same vehicle to demonstrate their effectiveness in dealing with real-world applications. Simulations are performed for 12 drive cycles, and the results are compared to the one sachieved by PMP-based optimal control where the optimization is done numerically. Simulation results suggest that the proposed methodology is relatively fast and has satisfactory performance in presence of drive cycle uncertainty. It is observed that the proposed method fulfills charge sustenance, and the achieved fuel consumption figures are very close to the optimal benchmarks found by the non-causal method.

This paper presents a multi-objective energy management strategy for hybrid electric vehicles. It aims at reducing fuel consumption and minimizing battery wear simultaneously while fulfilling system’s constraints. A control-oriented differential model is considered to account for battery aging effects, and an algorithm is developed to identify its parameters. The energy management is formulated as an optimal control problem and is solved by Pontryagin’s minimum principle. The controller is then implemented for a hybrid electric wheel loader to demonstrate its effectiveness. In short-term simulations for four drive cycles, behavior of the vehicle is compared to the case where the energy management policy does not encompass battery wear minimization. Long-term simulations suggest that there is a huge potential in extending battery life while the price to pay is a negligible increase in fuel consumption. It is observed that the proposed methodology works best for nonaggressive drive cycles.

A computationally cheap method for computing collision-free trajectories with multiple moving obstacles is proposed here while meeting comfort and safety criteria. By avoiding search in the trajectory calculation and instead using a geometrical set to calculate the trajectory, the calculation time is significantly reduced. The geometrical set is calculated by solving a support vector machine problem and solving the SVM problem characterizes maximum separating surfaces between obstacles and the ego vehicle in the time-space domain. The trajectory on the separating surface might not be kinematically feasible. Therefore, a vehicle model and a Newton-Raphson based procedure is proposed to obtain a safe, kinematically feasible trajectory on the separating surface. A roundabout scenario and two take-over scenarios with different configurations are used to investigate the properties of the proposed algorithm. Robustness properties of the proposed algorithm is investigated by a large number of randomly initiated simulation scenarios.

Simultaneous optimization of fuel consumption and battery lifetime is addressed in this work. A differential capacity degradation model is used to predict capacity loss, and linear time-varying and nonlinear MPC techniques are used to solve the energy management problem. It is shown that penalizing battery power in the MPC cost function can prolong battery lifetime by about 50 percent while achieving small gains in fuel economy compared to when the cost function only aims to minimize fuel consumption. An analysis of robustness against uncertainties in drive-cycle information shows that the controller is well-behaved and has good performance under uncertainty.

Efficient trajectory planning of autonomous vehiclesin complex traffic scenarios is of interest both academically andin automotive industry. Time efficiency and safety are of keyimportance and here a two-step procedure is proposed. First, aconvex optimization problem is solved, formulated as a supportvector machine (SVM), in order to represent the surroundingenvironment of the ego vehicle and classify the search spaceas obstacles or obstacle free. This gives a reduced complexitysearch space and an A* algorithm is used in a state space latticein 4 dimensions including position, heading angle and velocityfor simultaneous path and velocity planning. Further, a heuristicderived from the SVM formulation is used in the A* search anda pruning technique is introduced to significantly improve searchefficiency. Solutions from the proposed planner is compared tooptimal solutions computed using optimal control techniques.Three traffic scenarios, a roundabout scenario and two complextakeover maneuvers, with multiple moving obstacles, are used toillustrate the general applicability of the proposed method.

This paper presents a simulation framework for modeling off-road transport operations at construction worksites using electric construction vehicles. A dynamic model is developed to describe the longitudinal behavior of the electric vehicles, and the outputs from the vehicle dynamic model are fed into a fleet model to evaluate the transport efficiency performance. Discrete event simulation technique is used in the fleet model to represent the logistics of the transport operations and capture the interactions among the vehicles and resources. The simulation framework is applied in a real world quarry operation to study the transport efficiency performance using different number of vehicles. The case study shows that the proposed mechanism can effectively allocate the optimal number of vehicles for the operation and hence serve as an efficient tool in decision-making for construction management.

Fault detection and fault isolation performance of a model based diagnosis system mainly depends on the level of model uncertainty and the time allowed for detection. The longer time for detection that can be accepted, the more certain detection can be achieved and the main objective of this paper is to show how the window length relates to a diagnosis performance measure. A key result is an explicit expression for asymptotic performance with respect to window length and it is shown that there exists a linear asymptote as the window length tends to infinity. The gradient of the asymptote is a system property that can be used in the evaluation of diagnosis performance when designing a system. A key property of the approach is that the model of the system is analyzed directly, which makes the approach independent of detection filter design. (C) 2019 Elsevier Ltd. All rights reserved.

Deterministic and real time calculation of safe and comfortable speed profiles is the main topic of this paper. Using vehicle properties and road characteristics, such as friction and road banking, safety limits for rollover and skidding are calculated and applied in the trajectory planning. To satisfy comfort criteria and obtain smooth speed profiles, jerk and acceleration of the vehicle are limited in the speed planning algorithm. For speed planner, an A* based search method is used to calculate a speed profile corresponding to shortest traveling time. In order to avoid stationary and moving obstacles, decoupled prioritized planning is used. A physical model is used to define the behavior of the vehicle in the speed planner, where jerk is main parameter for speed planner. The physical model enables the algorithm to take into account the safety and comfort limitations. The results attained from the search method are compared with optimal solutions in different test scenarios and the comparisons show the properties of the algorithm. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

Utilising optimal control presents an opportunity to increase the fuel efficiency in an off-road transport mission conducted by an articulated hauler. A human machine interface (HMI) instructing the hauler operator to follow the fuel optimal vehicle speed trajectory has been developed and tested in real working conditions. The HMI implementation includes a Dynamic Programming based method to calculate the optimal vehicle speed and gear shift trajectories. Input to the optimisation algorithm is road related data such as distance, road inclination and rolling resistance. The road related data is estimated in a map module utilising an Extended Kalman Filter (EKF), a Rauch-Tung-Striebel smoother and a data fusion algorithm. Two test modes were compared: (1) The hauler operator tried to follow the optimal vehicle speed trajectory as presented in the HMI and (2) the operator was given a constant target speed to follow. The objective of the second test mode is to achieve an approximately equal cycle time as for the optimally controlled transport mission, hence, with similar productivity. A small fuel efficiency improvement was found when the human machine interface was used.

Finding safe and collision free trajectories in an environment with moving obstacles is central for autonomous vehicles but at the same time a complex task. A reason is that the search space in space-time domain is very complex. This paper proposes a two-step approach where in first step, the search space for trajectory planning is simplified by solving a convex optimization problem formulated as a Support Vector Machine resulting in an obstacle free corridor that is suitable for a trajectory planner. Then, in a second step, a basic A* search strategy is used in the obstacle free search space. Due to the physical model used, the comfort and safety criteria are applied while searching the trajectory. The vehicle rollover prevention is used as a safety criterion and the acceleration, jerk and steering angle limits are used as comfort criteria. For simulations, urban environments with intersections and vehicles as moving obstacles are constructed. The properties of the approach are examined by the simulation results. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

To coordinate and optimise an off-road transport mission, on which a wheel loader and two articulated haulers cooperate, a fuel-optimal control algorithm is developed. The control algorithm utilises Pareto fronts of fuel consumption versus cycle time to e

A novel trajectory planning method is proposedin time varying environments for highway driving scenarios.The main objective is to ensure computational efficiency in theapproach, while still ensuring collision avoidance with movingobstacles and respecting vehicle constraints such as comfortcriteria and roll-over limits. The trajectory planning problemis separated into finding a collision free corridor in space-time domain using a support vector machine (SVM), whichmeans solving a convex optimization problem. After that atime-monotonic path is found in the collision free corridor bysolving a simple search problem that can be solved efficiently.The resulting path in space-time domain corresponds to theresulting planned trajectory of the vehicle. The planner is adeterministic search method associated with a cost functionthat keeps the trajectory kinematically feasible and close to themaximum separating surface, given by the SVM. A kinematicmotion model is used to construct motion primitives in thespace-time domain representing the non-holonomic behavior ofthe vehicle and is used to ensure physical constraints on thestates of the vehicle such as acceleration, speed, jerk, steer andsteer rate. The speed limits include limitations by law and alsorollover speed limits. Two highway maneuvers have been usedas test scenarios to illustrate the performance of the proposedalgorithm.

Structural approaches have shown to be useful for analyzing and designing diagnosis systems for industrial systems. In simulation and estimation literature, related theories about differential index have been developed and, also there, structural methods have been successfully applied for simulating large-scale differential algebraic models. A main contribution of this paper is to connect those theories and thus making the tools from simulation and estimation literature available for model based diagnosis design. A key step in the unification is an extension of the notion of differential index of exactly determined systems of equations to overdetermined systems of equations. A second main contribution is how differential-index can be used in diagnosability analysis and also in the design stage where an exponentially sized search space is significantly reduced. This allows focusing on residual generators where basic design techniques, such as standard state-observation techniques and sequential residual generation are directly applicable. The developed theory has a direct industrial relevance, which is illustrated with discussions on an automotive engine example. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

This paper describes a cooperative control method for autonomous vehicles, in order to perform different traffic maneuvers. The problem is formulated as a distributed optimal control problem for a system of multiple autonomous vehicles with an identified model and then solved using nonlinear Model Predictive Control (MPC). The distributed approach has been used in order to make the problem computationally feasible to be solved in real-time. In the proposed method, each vehicle computes its own control inputs using estimated states of neighboring vehicles. The constraints on the control inputs ensure the comfort of passengers. The method allows us to construct a cost function for several different scenarios in which safety and performing the maneuver constitute two terms of the integrated cost of the finite horizon optimization problem. To provide safety, a potential function is introduced for collision avoidance. Simulation results show that the distributed algorithm scales well with increasing number of vehicles. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

In this paper, a cooperative fuel and comfort efficient control for autonomous vehicles is presented in order to perform different traffic maneuvers. The problem is formulated as an optimal control problem in which the cost function takes into account the fuel consumption and passengers comfort, subject to safety and speed constraints. The optimal solution takes into account the comfort and fuel consumption, which is obtained by minimizing a jerk, an acceleration, and a fuel criterion. It is shown that the method can be applied to control different groups of vehicles in different traffic scenarios. Simulation results are used to illustrate the generality property and performance of the proposed approach.

A model predictive controller (MPC) including velocity and path planner is designed for real time calculation of a safe and comfortable velocity and steer angle in a heavy duty vehicle. The calculation time is reduced by finding, based on measurement data, simple roll and motion model. The roll dynamics of the truck is constructed using identification of proposed roll model and it is validated by measurements logged by a heavy duty truck and the suggested model shows good agreement with the measurement data. The safety issues such as rollover prevention and moving obstacle avoidance are taken into account. To increase comfort, acceleration, jerk, steer angle and steer angle rate are limited. The simulation and control algorithm is tested in different scenarios, where the test results show the properties of the algorithm.

This paper presents two conceptual methods, based on dynamic programming, for one-step look-ahead control of a Continuously Variable Transmission (CVT) in a wheel loader. The first method developed, designated Stochastic Dynamic Programming (SDP), uses a statistical load prediction and stochastic dynamic programming for minimizing fuel use. The second method developed, designated Free-Time Dynamic Programming (FTDP), has vehicle speed as a state and introduces a fixed 0.1 s delay in the bucket controls in a combined minimization of fuel and time. The methods are evaluated using a set of 34 measured loading cycles, used in a ‘leave one out’ manner.

The evaluation shows that the SDP method requires about 1/10th of the computational effort of FTDP and has a more transparent impact of differences in the cycle prediction. The FTDP method, on the other hand, shows a 10% lower fuel consumption, which is close to the actual optimum, at the same cycle times, and is able to complete a much larger part of the evaluation cycles.

Wheel loaders often have a highly repetitive pattern of operation, which can be used for creating a rough prediction of future operation. As the present torque converter based transmission is replaced with an infinitely variable device, such as an electric or hydraulic transmission, a freedom in the choice of engine speed is introduced. This choice is far from trivial in the extremely transient operation of these machines, but the availability of a load prediction should be utilized.

In this paper, a predictive engine and generator controller, based on stochastic dynamic programming, is described, implemented and evaluated. The evaluation is performed against non-predictive controllers in the same system, to lift out any possible benefits of utilizing the repetition based prediction. Simulations and field tests show that the controllers are able to handle disturbances introduced from model errors, the machine environment and the human operator, and that the predictive controller gives around 5% lower fuel consumption than the non-predictive reference controllers.

The supervision of performance in gas turbine applications is crucial in order to achieve: (i) reliable operations, (ii) low heat stress in components, (iii) low fuel consumption, and (iv) efficient overhaul and maintenance. To obtain a good diagnosis of performance it is important to have tests which are based on models with high accuracy. A main contribution is a systematic design procedure to construct a fault detection and isolation (FDI) system for complex nonlinear models. To fulfill the requirement of an automated design procedure, a thermodynamic gas turbine package (GTLib) is developed. Using the GTLib framework, a gas turbine diagnosis model is constructed where component deterioration is introduced. In the design of the test quantities, equations from the developed diagnosis model are carefully selected. These equations are then used to implement a constant gain extended Kalman filter (CGEKF)-based test quantity. The test quantity is used in the FDI-system to supervise the performance and in the controller to estimate the flame temperature. An evaluation is performed using experimental data from a gas turbine site. The case study shows that the designed FDI-system can be used when the decision about a compressor wash is taken. Thus, the proposed model-based design procedure can be considered when an FDI-system of an industrial gas turbine is constructed.

Recent development has renewed the interest in drivetrain concepts which gives a higher degree offreedom by disconnecting the engine and vehicle speeds. This freedom raises the demand for activecontrol, which especially during transients is not trivial, but of which the quality is crucial for the successof the drivetrain concept. In this work the fuel optimal engine operating point trajectories for a naturallyaspirated and a turbocharged diesel engine, connected to a load which does not restrict the engine speed,is derived, analysed and utilized for finding a suboptimal operating point trajectory. The analysis andoptimization is made with dynamic programming, Pontryagin’s maximum principle and a suboptimalstrategy based on the static optimal operating points. Methods are derived for using Pontryagin’smaximum principle for finding the optimal operating point trajectories, for simple load cases. The timeneeded for computation is reduced a factor 1000−100, depending on engine layout, compared to dynamicprogramming. These methods are only applicable to very simple load cases though. Finally, a suboptimalcalculation method which reduce the time needed for computation a factor > 1000 compared to dynamicprogramming, while showing a < 5% increase in fuel consumption compared to the optimal, is presented.

The emerging new idea of lane-keeping electronic stability control is investigated. In a critical situation, such as entering a road curve at excessive speed, the optimal behavior may differ from the behavior of traditional ESC, for example, by prioritizing braking over steering response. The important question that naturally arises is if this has a significant effect on safety. The main contribution here is to give a method for some first quantitative measures of this. It is based on optimal control, applied to a double-track chassis model with wheel dynamics and high-fidelity tire-force modeling. The severity of accidents grows with the square of the kinetic energy for high velocities, so using kinetic energy as a measure will at least not overestimate the usefulness of the new safety system principle. The main result is that the safety gain is significant compared to traditional approaches based on yaw rotation, for several situations and different road-condition parameters.

Three Stochastic Dynamic Programming (SDP) implementations are developed for control of a diesel-electric wheel loader transmission. The implementations each use a stochastic description of the load, with the probabilities either independent of the states, dependent on previous power or on distance driven. Both the cycles used for the controller development and for the evaluation are derived from a measured sequence of cycles.

The evaluation shows that SDP can be used for control of the engine speed and that the resulting trajectories from the three implementations are very similar. The most surprising part is that the method which has constant load probability is able to adjust to the actual load. The combination of the calculation efforts and the outcomes leads to the conclusion that the constant load probability implementation is superior to the other versions.

Supervision of the performance of an industrial gas turbine is important since it gives valuable information of the process health and makes efficient determination of compressor wash intervals possible. Slowly varying sensor faults can easily be misinterpreted as performance degradations and result in an unnecessary compressor wash. Here, a diagnostic algorithm is carefully combined with non-linear state observers to achieve fault tolerant performance estimation. The proposed approach is evaluated in an experimental case study with six months of measurement data from a gas turbine site. The investigation shows that faults in all gas path instrumentation sensors are detectable and isolable. A key result of the case study is the ability to detect and isolate a slowly varying sensor fault in the discharge temperature sensor after the compressor. The fault is detected and isolated before the wash condition of the compressor is triggered, resulting in fault tolerant estimation of compressor health parameters.charge temperature sensor after the compressor. The fault is detected and isolated before the wash condition of the compressor is triggered, resulting in fault tolerant estimation of compressor health parameters.

A method is developed for the minimization of time and fuel required for performing a short loading cycle with a CVT wheel loader. A factor β is used for weighing time to fuel in the optimization. Dynamic programming is used as optimization algorithm, and the developed method is based on the change of independent variable, from time to distance driven. It is shown that a change of states from speeds to kinetic energies in the internal simulations is essential.

A driving cycle, derived from measurements, representing a short loading cycle is introduced. Optimization is performed against this cycle according to the method presented, using two different values on the time to fuel weighing parameter. It is shown that this parameter can be used to find optimal solutions directed toward short time or low fuel consumption.

A vehicular powertrain is a lightly damped dynamic system that transfers the engine torque to the driving wheels through a number of inertias and elastic elements. Therefore, it is prone to vibrate and emit noise when disturbances are applied. Providing a methodology, for powertrain vibration modeling and simulation, is one of the key steps in various research topics in the field of automobile engineering. Verification of the engine crankshaft torsion and vibration model, as a subsystem of the powertrain, is proposed in this paper. This is achieved by constructing a rotational multi-body system in MATLAB and utilizing nonlinear least squares method for estimation of the model parameters. The simulated engine angular velocity is compared to the measured data, from a car, which shows a good agreement.

A comparative analysis shows how vehicle motion models of different complexity, capturing various characteristics, influence the solution when used in time-critical optimal maneuvering problems. Vehicle models with combinations of roll and pitch dynamics as well as load transfer are considered, ranging from a single-track model to a double-track model with roll and pitch dynamics and load transfer. The optimal maneuvers in a 90◦-turn and a double lane-change scenario are formulated as minimum-time optimization problems, and are solved using numerical optimization software. The results obtained with the different models show that variables potentially important for safety systems, such as the yaw rate, slip angle, and geometric path, are qualitatively the same. Moreover, the numeric differences are mostly within a few percent. The results also indicate that although input torques differ about 50–100 % for certain parts of the maneuver between the most and least complex model considered, the resulting vehicle motions obtained are similar, irrespective of the model. Our main conclusion isthat this enables the use of low-order models when designing the onboard optimization-based safety systems of the future.

For future advanced active safety systems, in road-vehicle applications, there will arise possibilities for enhanced vehicle control systems, due to refinements in, e.g., situation awareness systems. To fully utilize this, more extensive knowledge is required regarding the characteristics and dynamics of vehicle models employed in these systems. Motivated by this, an evaluative study for the lateral dynamics is performed, considering vehicle models of more simple structure. For this purpose, a platform for vehicle dynamics studies has been developed. Experimental data, gathered with this testbed, is then used for model parametrization, succeeded by evaluation for an evasive maneuver. The considered model configurations are based on the single-track model, with different additional attributes, such as tire-force saturation, tire-force lag, and roll dynamics. The results indicate that even a basic model, such as the single-track with tire-force saturation, can describe the lateral dynamics surprisingly well for this critical maneuver.

This paper is focused on structural approaches to study diagnosability properties given a system model taking into account, both simultaneously or separately, integral and differential causal interpretations for differential constraints. We develop a model characterization and corresponding algorithms, for studying system diagnosability using a structural decomposition that avoids generating the full set of system analytical redundancy relations. Simultaneous application of integral and differential causal interpretations for differential constraints results in a mixed causality interpretation for the system. The added power of mixed causality is demonstrated using a Reverse Osmosis Subsystem from the Advanced Water Recovery System developed at the NASA Johnson Space Center. Finally, we summarize our work and provide a discussion of the advantages of mixed causality over just derivative or just integral causality.

This paper focuses on residual generation for model-based fault diagnosis. Specifically, a methodology to derive residual generators when nonlinear equations are present in the model is developed. A main result is the characterization of computation sequences that are particularly easy to implement as residual generators and that take causal information into account. An efficient algorithm, based on the model structure only, which finds all such computation sequences, is derived. Furthermore, fault detectability and isolability performances depend on the sensor configuration. Therefore, another contribution is an algorithm, also based on the model structure, that places sensors with respect to the class of residual generators that take causal information into account. The algorithms are evaluated on a complex highly nonlinear model of a fuel cell stack system. A number of residual generators that are, by construction, easy to implement are computed and provide full diagnosability performance predicted by the model.

Wheel loader transmissions are commonly based on a torque converter and an automatic gearbox. This solution is mechanically robust and well suited for the typical operation of the machine, but the fuel efficiency is low at some modes of operation. One proposed improvement is to replace the present transmission with a multi-mode power-split CVT (MM-CVT). This paper compares the fuel saving potential of the MM-CVT to the potential of the present transmission under different assumptions on the prediction of future loads. A load cycle with a probability distribution is created from a measurement including 34 short loading cycles. Trajectory optimization is performed both against this, probabilistic, and three deterministic load cycles with the two concepts. The optimization shows that the MM-CVT transmission has at least 15% better fuel saving potential than the present transmission, and that this difference is not sensitive to the quality of the prediction or the smoothness or length of the load case.

Monitoring of an industrial gas turbine is important since it gives valuable information for the customer about maintenance, performance and process health. The objective of the paper is to develop a monitoring system for an industrial gas turbine application with a model based diagnosis approach. A constant gain extended Kalman observer is developed. The observer compensates for different ambient conditions such as pressure, temperature and relative humidity, due to the amount of water in the atmosphere. The developed observer, extended with seven health parameters, is automatically constructed from the diagnosis model. These health parameters shall capture deviations in some of the gas path performance parameters such as efficiency, mass flow, turbine inlet area and head loss. The constructed observer is evaluated through a simulation study where the ambient conditions are changed. The considered observer capture the change in different ambient conditions nearly perfect. An observer that does not compensate for different ambient conditions gives an error for about 1–2% for the considered health parameters for the given test case. The constructed observer is also evaluated on measurement data from a mechanical drive site. A degradation in efficiency and mass flow for the compressor due to fouling can be seen in the estimations. After the compressor wash is performed, the degradations for the compressor are partially restored by about 2% which can be seen in the considered health parameters.

A straightforward approach is presented to investigate the ride dynamic system for a typical rear-drive passenger car. The procedure is based on introducing two main ride excitation sources, i.e., engine/driveline and road inputs, which reduce passengers’ comfort. The measured engine fluctuating torques are applied on the coupled model of the driveline and the suspension, to obtain the vehicle body longitudinal vibration. Further, the body vertical response to an average road roughness, is found by employing the quarter-car model. Through the frequency analysis done in this paper, it is shown that we can fastly determine the transfer functions of the systems and also their forced responses at the desired positions, without guessing any initial conditions for the states. The results illustrate that the high frequency inputs, from the engine, are appropriately damped by the current suspension. Hence, the associated vehicle body longitudinal acceleration meets the \acISO criteria. This is not the case for the low frequency disturbances, from the road surface irregularities, where the vehicle body vertical acceleration is above the \acISO criteria.

Complex transmission concepts may enable high fuel efficiency but require much effort in controller development. This effort should only be spent if the potential of the concept if high, a potential which can be determined using optimization techniques. This paper examine the use of stochastic dynamic programming for transmission potential evaluation, applied on a wheel loader. The concepts evaluated is the present automatic gearbox and a multi-mode CVT (MM-CVT). A probabilistic driving cycle is created from a measurement including 34 loading cycles. Trajectory optimization is performed both against probabilistic and deterministic cycles. The paper shows that the introduction of a probabilistic load highly affect the application of optimization. It is also shown that the MM-CVT has approximately 20% lower minimum fuel requirement than the present transmission, and that this number is not sensitive to the quality of the prediction.

Complex transmission concepts may enable high fuel efficiency but require much effort in controller development. This effort should only be spent if the potential of the concept if high, a potential which can be determined using optimization techniques. This paper examine the use of stochastic dynamic programming for transmission potential evaluation, applied on a wheel loader. The concepts evaluated is the present automatic gearbox and a multi-mode CVT (MM-CVT). A probabilistic driving cycle is created from a measurement including 34 loading cycles. Trajectory optimization is performed both against probabilistic and deterministic cycles. The paper shows that the introduction of a probabilistic load highly affect the application of optimization. It is also shown that the MM-CVT has approximately 20% lower minimum fuel requirement than the present transmission, and that this number is not sensitive to the quality of the prediction.

Recent development has renewed the interest in drivetrain concepts which give a higher degree of freedom by disconnecting the engine and vehicle speeds. This freedom raises the demand for active control, which especially during transients is not trivial but of which the quality is crucial for the success of the drivetrain concept. In this work the fuel optimal solution for a turbocharged diesel engine connected to a load which does not restrict the engine speed is derived, analysed and utilized for finding a suboptimal operating point trajectory. We use a Willan s efficiency model for the engine, expanded with a first order delay dependent torque reduction representing the turbocharger pressure, and study different output power transients. The analysis is made with dynamic programming, Pontryagin’s maximum principle and a suboptimal strategy based on the static optimal operating points. We present a method for using Pontryagin’s maximum principle for deriving the optimal operating point trajectory. The time needed for computation was reduced a factor >100 compared to dynamic programming, but this method is only applicable to load cases with steps between different high output powers. We also present a suboptimal method which shows a <1% increase in fuel consumption compared to the optimal, while reducing the time needed for computation a factor >1000 compared to dynamic programming.

In advanced road vehicle safety systems it is imperative to have a model describing the vehicle motions and behaviors with sufficient precision. Often a model incorporating a higher level of complexity generates more accurate data, with the disadvantage of demanding additional calculation power. This study will therefore focus on investigating how models of different detail level represents the vehicle behavior, for maneuvers going from moderate to more aggressive. The characteristics in particular investigated are tire saturation, tire force lag and the effect of load transfers. A vehicle testbed has also been developed, making model validations towards experimental data available.

Recent development has renewed the interest in drivetrain concepts which give a higher degree of freedom by disconnecting the engine and vehicle speeds. This freedom raises the demand for active control, which especially during transients is not trivial but of which the quality is crucial for the success of the drivetrain concept. This work attempts to analyze and explain the fuel optimal solution for the simplest drivetrain setup, which is an engine connected to a load which does not restrict the engine speed. This is made by using a Willan's model for the engine and deriving the fuel optimal solution during output power transients. The analysis is made with dynamic programming, Pontryagin's maximum principle and backward simulation under a static optimal line restriction. The analysis show that the optimal transients can be explained, visualized and, in simple cases, derived from phase planes of the engine speed and the Lagrange multiplier. In these cases the time needed for computation was reduced a factor >; 1000 compared to dynamic programming. Restricting the engine to the static optimal line turns out to be very close to optimal, even during highly transient operation, while reducing the time needed for computation a factor ≫ 1000.

This work is focused on structural approaches to studying diagnosability properties given a system model taking into account, both simultaneously or separately, integral and differential causal interpretations for differential constraints. We develop a model characterization and corresponding algorithms, for studying system diagnosability using a structural decomposition that avoids generating the full set of system ARRs. Simultaneous application of integral and differential causal interpretations for differential constraints results in a mixed causality interpretation for the system. The added power of mixed causality is demonstrated using a case study. Finally, we summarize our work and provide a discussion of the advantages of mixed causality over just derivative or just integral causality. © 2011 IFAC.

Structural methods have previously been used to perform isolability analysis and finding testable sub-models, so called Minimal Structurally Overdetermined (MSO) sets, Analytical Redundancy Relations (ARR), or Possible Conflicts (PC). The number of MSO sets grows exponentially in the degree of redundancy making the task of computing MSO sets intractable for systems with high degree of redundancy. This paper describes an efficient graph-theoretical algorithm for computing a similar, but smaller, set of testable submodels called Test Equation Supports (TES). A key difference, compared to an MSO based approach, is that the influence of faults is taken into account and the resulting number of testable models as well as the computational complexity of finding them can be reduced significantly without reducing the possible diagnosis performance. It is shown that the TESs in a direct way characterize the complete multiple fault isolability property of a model and thus extends previous structural approaches from the single-fault case.

A fuel-optimal control algorithm is developed for a heavy diesel truck that utilizes information about the road topography ahead of the vehicle when the route is known. A prediction model is formulated where special attention is given to properly include gear shifting. The aim is an algorithm with sufficiently low computational complexity. To this end, a dynamic programming algorithm is tailored, and complexity and numerical errors are analyzed. It is shown that it is beneficial to formulate the problem in terms of kinetic energy in order to avoid oscillating solutions and to reduce linear interpolation errors. A residual cost is derived from engine and driveline characteristics. The result is an on-board controller for an optimal velocity profile and gear selection.

Model based diagnosis and supervision of industrial gas turbines are studied. Monitoring of an industrial gas turbine is important as it gives valuable information for the customer about service performance and process health. The overall objective of the paper is to develop a systematic procedure for modelling and design of a model based diagnosis system, where each step in the process can be automated and implemented using available software tools. A new Modelica gas media library is developed, resulting in a significant model size reduction compared to if standard Modelica components are used. A systematic method is developed that, based on the diagnosis model, extracts relevant parts of the model and transforms it into a form suitable for standard observer design techniques. This method involves techniques from simulation of DAE models and a model reduction step. The size of the final diagnosis model is 20% of the original model size. Combining the modeling results with fault isolation techniques, simultaneous isolation of sensor faults and fault tolerant health parameter estimation is achieved.

Recent studies from several authors show that it is possible to lower the fuel consumption for heavy trucks by utilizing information about the road topography ahead of the vehicle. The approach in these studies is receding horizon control where horizon length and residual cost are main topics. To approach these topics, fuel equivalents previously introduced based on physical intuition are given a mathematical interpretation in terms of Lagrange multipliers. Measures for the suboptimality, caused by the truncated horizon and the residual cost approximation, are defined and evaluated for different routes and parameters.

Hybridization and velocity management are two important techniques for energy efficiency that mainly have been treated separately. Here they are put in a common framework that from the hybridization perspective can be seen as an extension of the equivalence factor idea in the well known strategy ECMS. From the perspective of look-ahead control, the extension is that energy can be stored not only in kinetic energy, but also electrically. The key idea is to introduce more equivalence factors in a way that enables efficient computations, but also so that the equivalence factors have a physical interpretation. The latter fact makes it easy to formulate a good residual cost to be used at the end of the look-ahead horizon. The formulation has different possible uses, but it is here applied on an evaluation of the size of the electrical system. Previous such studies, for e.g. ECMS, have typically used a driving cycle, i.e. a fixed velocity profile, but here the extra freedom to choose an optimal driving pattern is added.

In systems using only single-component tests, the fault status of a component is ready if a test only supervising the component has been evaluated. However, if plausibility tests that supervise multiple components are used, then a component can be ready before all tests supervising the component have been evaluated. Based on test results, this paper contributes with conditions on when a component is ready. The conditions on readiness are given for both centralized and distributed systems and are here applied to the distributed diagnostic system in an automotive vehicle.

A fuel optimal gear shift control has been studied, when look ahead information is available, and the impact of the automated manual transmission (AMT) gear-shifting process is analayzed. For a standard discrete heavy truck transmission, answers are found on when to shift gears, prior to or when in an uphill slope. The gear-shifting process of a standard AMT is modeled, not considering the comfort details, in order to capture the fuel and time aspects of the gear shift. A numerical optimization is performed by dynamic programming, minimizing fuel consumption and time by controlling fuel injection and gear. Since a standard AMT does not have look ahead information, it sometimes gears down unnecessarily and thus gives a significantly higher fuel consumption compared to the optimal control. However, if gearing down is inevitable, the AMT gear-shifting strategy, based on engine thresholds, is a well-functioning gear control so that the optimal control only gives marginal additional savings. To attain the possible fuel reductions it is shown that the reduced propulsion of an AMT gear-shifting process, and the resulting vehicle retardation, must be considered. The point of shifting gears must be chosen to ensure an adequate engine speed in order to get a sufficient engine power after the gear shift, even as the truck is decelerated during gear shift.

Consequences of non-linearities in specific fuel consumption (SFC) of a heavy truck combustion engine are studied with focus on such small road gradients that a constant speed is optimal if the engine torque has an affine relation to fuelling. A quasi-static analysis gives valuable insights into the intrinsic properties of minimization of fuel consumption. Two objective functions are shown to give different optimal velocity trajectories on a constant road gradient, when the non-linearity in SFC is significant, a notation which is quantified. For a significant non-linearity, when a constraint is set to keep a final time, switching between twocharacteristic speeds is optimal. Alternatively, if consumed time, in addition to fuel consumption, is part of the objective function, then keeping to one constant speed is optimal also for significant non-linearities. However, the different optimal solutions still show similarities, since for a certain significant non-linearity a specific speed range determined by the characteristic velocities is shown to be unobtainable for both optimality criteria. Similarresults are obtained for a full dynamic model including a realistic fuel map and other realistic constraints.

The scenario studied is a drive mission for a heavy diesel truck. With aid of an on board road slope database in combination with a GPS unit, information about the road geometry ahead is extracted. This look-ahead information is used in an optimization of the velocity trajectory with respect to a criterion formulation that weighs trip time and fuel consumption. A dynamic programming algorithm is devised and used in a predictive control scheme by constantly feeding the conventional cruise controller with new set points. The algorithm is evaluated with a real truck on a highway, and the experimental results show that the fuel consumption is significantly reduced.

This work develops a methodology to solve the sensor placement problem for fault detection and isolation. The proposed methodology is suitable to handle highly non-linear and large scale systems since it is based on structural models. Furthermore, causality is assigned in those variable-equation relations that the variable can be computed from the equation in order to guarantee the computability of the unknown variables in the residual generation design. Finally, the developed methodology is applied on an air compressor model.

This work develops a methodology to solve the sensor placement problem for fault detection and isolation. The proposed methodology is suitable to handle highly non-linear and large scale systems since it is based on structural models. Furthermore, causality is assigned in those variable-equation relations that the variable can be computed from the equation in order to guarantee the computability of the unknown variables in the residual generation design. Finally, the developed methodology is applied on an air compressor model.