In this work we present a fundamental model‐based analysis of the effect of active material particle size distribution (PSD) on graphite electrodes and their performance. We focused on the determination of the impact of differently shaped and scaled PSDs on the electrode performance, which is mainly influenced by the performance of the individual particles and their interaction. A mathematical electrode model with a distributed particle size is used for analysis to identify the different local current densities and the charging behavior of the particles. The heterogeneity provokes uneven surface overpotentials and reaction rates. Their identification facilitates the investigation of the degradation of such heterogeneous systems. In addition, we present an approach that accounts for the change of a PSD because of the restructuring of the electrode morphology during battery usage into the mathematical model and identify the general impact of particle cracking and agglomeration on the battery performance. Moreover, the importance of PSD in Li‐ion batteries is shown by comparing the results obtained with a single particle model used commonly. This comparison shows that in case of narrow distributions surface‐area‐ and volume‐based mean approximations are sufficient to predict overpotentials and electrode capacity if kinetic losses are dominated either by reaction at the surface or diffusion processes, respectively. This work indicates that the PSD and its change impact the performance and degradation of Li‐ion batteries considerably. We suggest that the PSD and its evolution should be of particular interest in the study of the degradation of particle‐based electrodes.

As multi-core systems transition to the manycore realm, the pressure on the interconnection network is substantially elevated. The Network-on-Chip (NoC) is expected to undertake the expanding demands of the ever-increasing numbers of processing elements, whileat the same time-technological and application constraints increase the pressure for increased performance and efficiency with limited resources. Although NoC research has evolved significantly the last decade, essential questions remain un-answered and call for fresh research ideas and innovative solutions. In this paper, we summarize a selected set of NoC-related research challenges, with the hope to guide future development and trigger high-impact research progress.

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During early design phases performance evaluation becomes increasingly important since major system-level decisions, such as the allocation of hardware resources and the partitioning of functionality onto architecture building blocks, affect the quality of the design significantly. Quantitative analysis is hard to achieve due to growing complexities, heterogeneity, and concurrency of modern embedded systems. We propose the use of multiclass queuing networks during the specification phase of the design flow for modeling data-flow oriented systems. Starting from an executable high-level queuing model our evaluation framework SystemQ 1 enables successive and systematic refinement of behavior and structure towards established TLM and RTL models based on SystemC. We demonstrate why SystemQ's multiclass queuing networks are a natural and feasible abstraction for evaluating network processing platforms. In particular we reveal the impact of scheduling policies on the Quality-of-Service, such as the residence time of network traffic in the system. In our case study, we show how stepwise refinement can reduce memory and latency bounds by up to two orders of magnitude and how the choice of only one queuing discipline can affect these properties. The investigated simulation models run in the range of 1 : 100 to 1 : 1 of real-time on a common off-the-shelf Linux PC. 1 Parts of this paper are based on [26] and are hereby published with permission.Springer 92 S. Sonntag et al.

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