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

能效越来越成为人机系统的设计标准。对于车辆控制这样的动态环境，迫切需要对人在环中设置的能效的建模、优化和表示进行跨学科研究。为此，该项目整合了两条研究主线：(1)驾驶员和情景自适应能源相关优化策略研究(面向控制的建模和能源效率优化)和(2)与能源相关的人车交互研究(心理模型和与能源相关的行为规则优化)。在该项目中，将这两个研究方向结合在一起，形成了基于工程心理学和控制工程的电池电动汽车(BEV)中与能源相关的驾驶员-车辆相互作用的建模和优化，通过动作集成的能效表示。重点在于(1)作为这一优化的一部分的驱动程序的表示，以及(2)关于不同的能效表示方法对(A)人机系统的综合性能和(B)用户在行动调节期间的体验的影响的实证调查，其中特别注意多样性特征(用户多样性)的作用。该项目考虑了一种控制概念，其中司机直接参与能量最优控制，并在控制策略(司机在回路中)中明确考虑。人的行动控制(例如，在确保安全的同时，对时间效率、能源效率和舒适性等目标进行情景权衡)不会被自动车辆控制取代，而是得到能量动态的综合行动表示(通知系统)的辅助。因此，该项目研究了一种与能源相关的人机耦合的控制工程方法，到目前为止，几乎没有任何研究工作。[项目第一阶段]追求三个研究目标：(1)开发和校准综合驾驶-能源模拟，(2)发展理论-方法基础，(3)与能源相关的驾驶情景的分析、综合和测试。[项目第二阶段]在此基础上，通过(1)面向感觉运动、(2)面向效率和(3)基于规则的最佳行为表示，研究了通过能量显示实现人机耦合的3种结构化方法。该项目的关键假设是，与传统的基于能量/距离度量的能源效率表示相比，这些基于优化的能源效率表示可以在能源效率和行动监管方面获得用户体验方面的优势。

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.