Electric vehicles (EV) powered by batteries will play a significant role in the road traffic of the future. The unique characteristics of such EVs-limited cruising range, long recharge times, and the ability to regain energy during deceleration-require novel routing algorithms, since the task is now to determine the most economical route rather than just the shortest one. This paper proposes extensions to general shortestpath algorithms that address the problem of energy-optimal routing. Specifically, we (i) formalize energy-efficient routing in the presence of rechargeable batteries as a special case of the constrained shortest path problem (CSP) with hard and soft constraints, and (ii) present an adaption of a general shortest path algorithm (using an energy graph, i.e., a graph with a weight function representing the energy consumption) that respects the given constraints and has a worst case complexity of O(n 3). The presented algorithms have been implemented and evaluated within a prototypic navigation system for energy-efficient routing.

Automated problem-solving for engineered devices is based on models that capture the essential aspects of the behavior. In this paper, we deal with the problem of automatically abstracting behavior models such that their level of granularity is as coarse as possible, but still sufficiently detailed to carry out a given behavioral prediction or diagnostic task. A task is described by a behavior model, as composed from a library, a specified granularity of the possible observations, and a specified granularity of the desired results. The goal of task-dependent qualitative domain abstraction is to determine maximal partitions for the variables' domains (termed qualitative values) that retain all the necessary distinctions. We present a formalization of this problem within a relational (constraintbased) framework, and devise solutions to automatically determine qualitative values for a device model. The results enhance the ability to use a behavior model of a device as a common basis to support different tasks along its life cycle.

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Testing is the process of stimulating a system with inputs in order to reveal hidden parts of the system state. In the case of nondeterministic systems, the difficulty arises that an input pattern can generate several possible outcomes. Some of these outcomes allow to distinguish between different hypotheses about the system state, while others do not. In this paper, we present a novel approach to find, for non-deterministic systems modeled as constraints over variables, tests that allow to distinguish among the hypotheses as good as possible. The idea is to assess the quality of a test by determining the ratio of distinguishing (good) and not distinguishing (bad) outcomes. This measure refines previous notions proposed in the literature on model-based testing and can be computed using model counting techniques. We propose and analyze a greedy-type algorithm to solve this test optimization problem, using existing model counters as a building block. We give preliminary experimental results of our method, and discuss possible improvements.

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