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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.

In marine or stationary engines, consistent engine performance must be guaranteed for long-haul operations. A dual-fuel combustion strategy was used to reduce the emissions of particulates and nitrogen oxides in marine engines. However, in this case, the combustion stability was highly affected by environmental factors. To ensure consistent engine performance, the in-cylinder pressure measured by piezoelectric pressure sensors is generally measured to analyze combustion characteristics. However, the vulnerability to thermal drift and breakage of sensors leads to additional maintenance costs. Therefore, an indirect measurement via a reconstruction model of the in-cylinder pressure from engine block vibrations was developed. The in-cylinder pressure variation is directly related to the block vibration; however, numerous noise sources exist (such as, valve impact, piston slap, and air flowage).