Many technical processes and products in the area of mechanical and electrical engineering show increasing integration of mechanics with digital electronics and information processing. This integration is between the components (hardware) and the information-driven functions (software), resulting in integrated systems called mechatronic systems. Their development involves finding an optimal balance between the basic mechanical structure, sensor and actuator implementation, and automatic information processing and overall control. Frequently formerly mechanical functions are replaced by electronically controlled functions, resulting in simpler mechanical structures and increased functionality. The development of mechatronic systems opens the door to many innovative solutions and synergetic effects which are not possible with mechanics or electronics alone. This technical progress has a very strong influence on a multitude of products in the areas of mechanical, electrical, and electronic engineering and is increasingly changing the design, for example, of conventional electromechanical components, machines, vehicles, and precision mechanical devices, and their connection to cloud services. The contribution describes besides of general aspects a procedure for the computer-aided design and specification of mechatronic systems, model-based control system design, and control software development and illustrates mechatronic developments for brake and steering systems of automobiles.

Der landwirtschaftliche Sektor eignet sich besonders für die Nutzung von naturbelassenem Rapsöl als Kraftstoff. Durch dezentrale Ölmühlen bleiben die Transportwege kurz und der Landwirt kann seinen eigenen Kraftstoff herstellen [1]. Diese Selbstversorgung mit erneuerbaren Energien eröffnet den Betrieben zusätzliches Wertschöpfungspotenzial [2]. Die Marktdurchdringung von Rapsölkraftstoff ist jedoch seit Jahrzenten gering. Einer der Gründe hierfür ist die Preisvolatilität von biogenen Kraftstoffen.

A model-based methodology is presented, which allows the estimation of the characteristic phases of diesel combustion using a semi-physical model approach combined with state and parameter estimation through extended Kalman filtering. The physical relation between the fuel injection and the characteristic diesel combustion phases, such as premixed, diffusive combustion and burn-out, are modeled separately by linear dynamic transfer functions formulated in crank angle frequency domain and transformed into state space representation. The resulting state variables are the released burning energy and its derivatives of each combustion phase. Associated crank angle constants determine the dynamics of the combustion phases and represent the rate parameters to be estimated. By incorporating further physical assumptions regarding the fuel path and air–fuel-mixing dynamics, the combustion phase parameters are estimated online for each working cycle. Cylinder pressure signals and online combustion analysis are used to determine the burn rate of the diesel engine at the test bench. Investigations have shown that the estimated rate parameters depend on the current engine operation point. They are estimated during measurements and stored in lookup tables through an online-learning method based on a fast recursive least squares estimation algorithm.