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Abstract



Optimal Predictive Control of Wheel Loader Transmissions


The transmissions of present heavy wheel loaders are in general based on torque converters. The characteristics of this component suits these machines, especially in that it enables thrust from zero vehicle speed without risk of stalling the engine, without active control. Unfortunately, the component also causes losses which might become large compared to the transmitted power. One approach for mitigating these losses is to switch to a continuously variable transmission. Changing to such a system greatly increases the possibility, and the need, for actively selecting the engine speed, and here a conflict emerges. A low engine speed is desired for high efficiency but a high speed is required for high power. Heavy wheel loaders often operate according to a common repeating pattern known as the short loading cycle. This cycle is extremely transient, which makes the choice of engine operating point both important and difficult. At the same time, the repeating pattern in the operation enables a rough prediction of the future operation. One way to use the uncertain prediction is to use optimization techniques for selecting the best control actions. This requires a method for detecting the operational pattern and producing a prediction from this, to formulate a manageable optimization problem, and for solving this, and finally to actually control the machine according to the optimization results. This problem is treated in the four papers that are included in this dissertation. The first paper describes a method for automatically detecting when the machine is operating according to any of several predefined patterns. The detector uses events and automata descriptions of the cycles, which makes the method simple yet powerful. In the evaluations over 90% of the actual cycles are detected and correctly identified. The detector also enables a quick analysis of large datasets. In several of the following papers this is used to condense measured data sequences into statistical cycles for the control optimization. In the second paper dynamic programming and Pontryagin's maximum principle is applied to a simplified system consisting of a diesel engine and a generator. Methods are developed based on the maximum principle analysis, for finding the fuel optimal trajectories at output power steps, and the simplicity of the system enables a deeper analysis of these solutions. The methods are used to examine and visualize the mechanisms behind the solutions at power transients, and the models form the basis for the models in the following papers. The third paper describes two different concepts for implementing dynamic programming based optimal control of a hydrostatic transmission. In this system one load component forms a stochastic state constraint, and the concepts present two different strategies for handling this constraint. The controller concepts are evaluated through simulations, in terms of implementability, robustness against uncertainties in the prediction and fuel savings. The fourth paper describes the implementation and testing of a predictive controller, based on stochastic dynamic programming, for the engine and generator in a diesel electric powertrain. The controller is evaluated through both simulations and field tests, with several drivers, at a realistic work site, thus including all relevant disturbances and uncertainties. The evaluations indicate a ~5% fuel benefit of utilizing a cycle prediction in the controller.

Tomas Nilsson

2015

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