Action-integrated modeling and optimization of energy-related driver-vehicle interaction

Energy efficiency is increasingly becoming a design criterion for human-machine systems. For dynamic contexts such as vehicle control, there is a great need for interdisciplinary research on the modeling, optimization, and representation of energy efficiency in human-in-the-loop settings. To this end, the project integrates two central lines of research: (1) research on driver- and situation-adaptive energy-related optimization strategies (control-oriented modeling & optimization of energy efficiency) and (2) research on energy-related driver-vehicle interaction (psychological modeling & optimization of energy-related action regulation). In this project, both lines of research are combined into an engineering psychology and control engineering based modeling and optimization of energy-related driver-vehicle interactions in battery electric vehicles (BEV) via action-integrated energy efficiency representations. The focus lies on (1) the representation of drivers as part of this optimization and (2) the empirical investigation of the effect of different representation approaches for energy efficiency on (a) the integrated performance of the human-machine system and (b) the experience of users during action regulation, with special attention paid to the role of diversity characteristics (user diversity). The project considers a control concept in which drivers are directly involved in the energy-optimal control and explicitly considered in the control strategy (driver-in-the-loop). Human action regulation (e.g. situational weighing of goals such as time efficiency, energy efficiency and comfort, while ensuring safety) is not replaced by automated vehicle control, but assisted by action-integrated representations of energy dynamics (informing system). The project thus investigates a control engineering approach to energy-related human-machine couplings, on which hardly any research work has been done so far. [Project Phase-1] pursues 3 research goals: (1) the development and calibration of an integrated driving-energy simulation, (2) the development of theoretical-methodological foundations, and (3) the analysis, synthesis, and testing of energy-relevant driving scenarios. [Project Phase-2] investigates on this basis 3 structural approaches to human-machine coupling via energy displays through (1) sensorimotor-oriented, (2) efficiency-oriented, and (3) rule-based representations of optimal behavior. The key hypothesis of the project is that these optimization-based representations of energy efficiency can achieve advantages in terms of energy efficiency and user experience in action regulation when compared to conventional representations of energy efficiency according to energy/distance metrics.