The energy efficiency of a hybrid electric vehicle is dictated by the topology (coupling option of power sources/sinks), choice (technology), and control of components. The first design area among these, the topology,...
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The energy efficiency of a hybrid electric vehicle is dictated by the topology (coupling option of power sources/sinks), choice (technology), and control of components. The first design area among these, the topology, has the biggest flexibility of them all, yet, so far in the literature, the topology design is limited investigated due to its high complexity. In practice, a predefined small set of topologies is used to optimize their energy efficiency by varying the power specifications of the main components (sizing). By doing so, the complete design of the vehicle is, inherently and to a certain extent, suboptimal. Moreover, various complex topologies appear on the automotive market and no tool exists to optimally choose or evaluate them. To overcome this design limitation, in this paper, a novel framework is presented that deals with the automatic generation of possible topologies given a set of components (e.g., engine, electric machine, batteries, or transmission elements). This paper uses a platform (library of components) and a hybrid knowledge base (functional and cost-based principles) to set up a constraintlogicprogramming problem, and outputs a set of feasible topologies for hybrid electric vehicles. These are all possible topologies that could be built considering a fixed, yet large, set of components. Then, by using these results, insights are given on what construction principles are mostly critical for simulations time, and what topologies could be selected as candidate topologies for sizing and control studies. Such a framework can be used for any powertrain application;it can offer the topologies to be investigated in the design phase and can provide insightful results for optimal design analyses.
Feature modeling has been found very effective for modeling and managing variability in Software Product Lines. The nature of feature models invites, sometimes even requires, the use of global constraints. This paper ...
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ISBN:
(纸本)9783642153952
Feature modeling has been found very effective for modeling and managing variability in Software Product Lines. The nature of feature models invites, sometimes even requires, the use of global constraints. This paper lays the groundwork for the inclusion of global constraints in automated reasoning on feature models. We present a mapping from extended feature models to constraint logic programming over finite domains, and show that this mapping enables using global constraints on feature attributes, as well as features, for a variety of analysis operations on feature models. We also present performance test results and discuss the benefits of using global constraints.
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