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Electric vehicles (EVs) are promising alternatives to carbonized propulsion-based vehicles. They are capable of reducing environmental degradation without compromising driving performance. Power management strategies (PMS) are particularly essential for electrified vehicles to ensure optimal power split between on-board energy storage sources and to meet operational requirements of each source. However, optimization concept in PMS, have been constantly addressed in literature to achieve optimal power handling decisions in real-time, particularly under unknown driving conditions. In this contribution, an intelligent rule-based PMS with embedded offline-optimized control parameters and online driving state recognition is proposed to achieve optimal power handling decisions for EVs situatively and adaptively. A set of characteristic variables defining driving states have been extracted from representative segments of several driving cycles, to which optimized control strategies are tuned offline. Three different driving cycles representing urban, highway, and mixed trip conditions have been implemented for comparative investigation of achieved results. The analysis of results reveals the potential of proposed PMS to reduce the energy consumption by 13.6 – 30.9 %.

Current research and development in recognizing and predicting driving behaviors plays an important role in the development of Advanced Driver Assistance Systems (ADAS). For this reason, many machine learning approaches have been developed and applied. Hidden Markov Model (HMM) is a suitable algorithm due to its ability to handle time series data and state transition descriptions. Therefore, this contribution will focus on a review of HMM and its applications. The aim of this contribution is to analyze the current state of various driving behavior models and related HMM-based algorithms. By examining the current available approaches, a review is provided with respect to: i) influencing factors of driving behaviors corresponding to the research objectives of different driving models, ii) summarizing HMM related methods applied to driving behavior studies, and iii) discussing limitations, issues, and future potential works of the HMM-based algorithms. Conclusions with respect to the development of intelligent driving assistant system and vehicle dynamics control systems are given.

With respect to planning capabilities, a model is developed to be used for the evaluation of human operator's cognitive performance. Plans are goal-directed interaction sequences cognitively simulated before appearing in dynamic human-machine systems. In this context, human operator's actions have long-term effects as well as different consequences depending on the time of their activation. The proposed model allows the evaluation of cognitive task performance by comparing implemented decisions to the available options represented by plans. In this paper, situation operator modeling is applied to describe operator's behavior and the planning process based on the system modeled using colored Petri Nets. The Petri Net is applied for the prediction of future system states. The planning model uses a set of rules that describe the normative behavior of operators as definitions of problems and actions required to avoid these. The rules are derived systematically from the operator's objectives and from the available actions. The proposed method allows the calculation and, therefore, the prediction of future states as well as possible upcoming conflicts within the system. Corresponding rules may modify and, therefore, affect the generated plan. The method is demonstrated using a simplified air traffic control simulation. This method extends earlier methods published by the authors for the determination of available options based on Petri Nets. It can be concluded that the calculation of sequences of actions for interaction with dynamic systems with constant change over time is possible and realized for the first time.

This contribution outlines the idea, concept and realisation of learning abilities based on a Situation-Operator-Modelling (SOM) technique. The approach of modelling the human-machine-interaction is applied to autonomous technical systems. The idea is to obtain the already modelled human flexibility also for use within algorithmic and/or technical approaches. The proposed modelling technique is used for the conception of an autonomous system and realises a cognitive-based behaviour. The cognitive functions of the system are divided into the learning, testing, and exploration module and the planning and plan supervision module. The steps to realise the proposed architecture are illustrated and detailed for a mobile robot, as an example. Furthermore, first experiences with this approach in a real world environment are shown.