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The majority of goods are transported by trucks and ships, which are propelled by heavy-duty powertrains. These powertrains are mainly powered by internal combustion engines. In the coming decades, internal combustion engines will remain the primary power source. To support the transition to climate neutral mobility, the complexity of future powertrains will increase due to electrification and the introduction of waste heat recovery and highly efficient combustion concepts.Control systems are the brain of the powertrain. These systems play an essential role in minimizing fuel consumption, in making vehicle performance robust for real-world driving conditions, and in enabling the use of a wide range of sustainable fuels. As powertrain development time and costs will reach unacceptable levels with conventional control methods, industry is facing a turning point in the near future. Development of self-learning powertrains is crucial to deal with the complexity and diversity of future ultra-clean and efficient vehicles and to minimize development time and costs. This requires integration of energy and emission management strategies at system level. My research concentrates on the development of self-learning control concepts, in which the energy efficiency of the total powertrain is optimized online by the application of smart sensors and route information.
To support future green transport, Reactivity Controlled Compression Ignition (RCCI) is a promising combustion concept. It enables ultra-high efficiencies and the use of a wide range of sustainable fuels. After multi-cylinder engine demonstration of transient RCCI control, current focus is on achieving thermal efficiencies beyond 50%. Two new concepts are studied: an electrically assisted-turbocharger concept and self-learning engine control. It is shown that, with these concepts, the desired in-cylinder conditions can be realized, energy can be recovered from exhaust gasses and the heat release process can be optimally shaped, such that fuel consumption is minimized. In addition, AI-based methods are applied for engine control design. These methods dramatically reduce the control calibration effort.In addition, a self-learning control strategy for hybrid-electric vehicles is developed for operation in future zero emission zones around cities. This strategy has to guarantee sufficient full electric driving range in the zero emission zone by energy management and low nitrogen oxides emissions outside the zone using advanced emission management. By applying this novel integrated energy and emission strategy, it is demonstrated that this is a feasible solution, which should be considered in future legislation.