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IEEE Transactions on Industrial Informatics
Article . 2012 . Peer-reviewed
License: IEEE Copyright
Data sources: Crossref
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DESA: Dependable, Efficient, Scalable Architecture for Management of Large-Scale Batteries

Authors: Hahnsang Kim; Kang G. Shin;

DESA: Dependable, Efficient, Scalable Architecture for Management of Large-Scale Batteries

Abstract

Conventional battery management systems (BMSs) for electric vehicles (EVs) are designed in an ad hoc way, causing the supply of EVs to fall behind the market demand. A well-designed and combined hardware-software architecture is essential for the efficient management of a large-scale battery pack that may consists of thousands of battery cells as in Tesla Motors and GM Chevy Volt. We propose a Dependable, Efficient, Scalable Architecture (DESA) that effectively monitors a large number of battery cells, efficiently controls, and reconfigures, if needed, their connection arrangement. DESA supports hierarchical, autonomous management of battery cells, where a global BMS orchestrates a group of local BMSs. A local controller on each local BMS autonomously manages an array of battery cells, and the global controller reconfigures the connectivity of such battery-cell arrays in coordination with the local controllers. Also, DESA allows individual arrays and local BMSs to be selectively powered-off for energy savings. The performance of this energy-saving capability is modeled and evaluated using a Markov chain. Our evaluation results show that DESA effectively tolerates battery-cell failures by an order-of-magnitude-while achieving 7.4 × service cost savings-better than a conventional BMS. This superior performance not only extends the battery life significantly, but also provides the flexibility in supporting diverse electric power demands from a growing number of on-board applications.

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
73
Top 1%
Top 1%
Top 10%
bronze
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