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The electrification of off-highway machines are increasing significantly. Advanced functionalities, beneficial energy efficiency and effectiveness are only a few advantages of electric propulsion systems. To control these complex systems in varying environments intelligent algorithms at system level are needed. This paper addresses the topic of machine learning algorithms applied to off-highway machines and presents a methodology based on artificial neural networks to identify and recognize recurrent load cycles and work tasks. To gain efficiency and effectiveness benefits the recognized pattern settings are applied to the electric propulsion system to adjust relevant parameters online. A dynamic adaption of the DC-link voltage based on the operating points of the machine processes is identified as such a parameter.

Auxiliary power units (APUs) are used in mobile applications to provide electrical power until approx. 10 kW. It is state of the art that these generators are driven by a diesel engine at constant speed and are selected according to the expected maximal power needed. These systems have a low efficiency and consequently a high fuel consumption particularly when driven at small loads. The system can yield a higher efficiency for partial load conditions by reducing the rotary speed of the driving diesel engine. The optimum in rotary speed of the diesel engine for different loads is pre-programmed (engine mapping) in the diesel control unit. A frequency converter allows a constant frequency of the electricity output at variable speed of the generator. These higher costs for frequency converter and diesel controller demand especially for mobile applications a proof of efficiency, i.e. a proof of economics, which is shown in this paper.

The power density of electric machines is a critical factor in various applications, i.e. like the power train. A major factor to improve the power density is boosting the electric current density, which increases the losses in the limited volume of the electric machine. This results in a need for an optimized thermal design and efficient cooling. The dissipation of heat can be achieved in a multitude of ways, ranging from air cooling to highly integrated cooling solutions. In this paper, this variety is shown and analyzed with a focus on water cooling. Further various structures in electric machines are presented. A planar testbench is built to systematically analyze water cooling geometries. The focus lies in providing different power loss distributions along cooling channels, accurate temperature readings in a multitude of locations, as well as the pressure drop across the channel.

Cooling is a critical factor for improving power density in electrical appliances, especially in integrated drives for mobile applications. However, the issue of distributed losses in electric machines can lead to hotspots and temperature gradients within the electric drive. Traditional cooling jackets use unidirectional flow without or with evenly distributed cooling structures. This often aggravates the issue of hotspots, resulting in thermal derating and thus limiting the operation range. As well, a non-demand oriented distribution of cooling structures leads to unnecessary pressure losses. This problem is addressed with a newly elaborated method for distributing cooling elements, i.e., pin fins with varying density distribution inside the cooling channel. Results from previous work, numerical simulations, and measurement data from a planar test bench are used. The approach segments the cooling channel by using a loss profile.