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.

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.

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.

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.

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.

Ce document présente deux méthodes de conception, basées sur la programmation dynamique, pour la commande à un pas de prédiction d’une transmission continûment variable (Continuously Variable Transmission, CVT) d’un chargeur sur pneus. La première méthode développée, appelée programmation dynamique stochastique (Stochastic Dynamic Programming, SDP) utilise une prédiction statistique de la charge et la programmation dynamique stochastique pour minimiser l’utilisation de carburant. La seconde méthode développée, appelée programmation dynamique à temps libre (Free-Time Dynamic Programming, FTDP), établit la vitesse du véhicule en tant qu’état et introduit un retard de 0:1 s dans les commandes du godet pour minimiser à la fois l’utilisation de carburant et le temps nécessaire à l’opération.

Les méthodes sont évaluées en s’appuyant sur 34 cycles de chargement mesurés, utilisés selon la méthode de validation croisée « leave-one-out ».

L’évaluation montre que la méthode SDP requiert environ 1 dixième de l’effort de calcul de la méthode FTDP, et qu’elle a un impact plus transparent sur les écarts dans la prédiction du cycle. D’un autre côté, avec la méthode FTDP on obtient une réduction de 10% de la consommation de carburant, ce qui est proche de l’optimum réel, pour les mêmes durées de cycle, et elle permet de réaliser une plus grande partie des cycles d’évaluation.

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.