Reducing the complexity of defect level modeling using the clustering effect
DATE '00 Proceedings of the conference on Design, automation and test in Europe
Discharge Current Steering for Battery Lifetime Optimization
IEEE Transactions on Computers
Computing Battery Lifetime Distributions
DSN '07 Proceedings of the 37th Annual IEEE/IFIP International Conference on Dependable Systems and Networks
On Dynamic Reconfiguration of a Large-Scale Battery System
RTAS '09 Proceedings of the 2009 15th IEEE Symposium on Real-Time and Embedded Technology and Applications
Scheduling of Battery Charge, Discharge, and Rest
RTSS '09 Proceedings of the 2009 30th IEEE Real-Time Systems Symposium
An analytical model for predicting the remaining battery capacity of lithium-ion batteries
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Balanced reconfiguration of storage banks in a hybrid electrical energy storage system
Proceedings of the International Conference on Computer-Aided Design
Pack Sizing and Reconfiguration for Management of Large-Scale Batteries
ICCPS '12 Proceedings of the 2012 IEEE/ACM Third International Conference on Cyber-Physical Systems
Real-time prediction of battery power requirements for electric vehicles
Proceedings of the ACM/IEEE 4th International Conference on Cyber-Physical Systems
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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 management of a large-scale battery pack that 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 is monarchy-based and 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. Configuration of a battery system is controlled by three types of switch---called P-, S-, and B-switches---and an algorithm that changes the setting of these switches. Our evaluation results show that DESA effectively tolerates battery-cell failures by order of magnitude---while achieving service cost savings 7.4 times---more than a conventional BMS. This superior performance not only extends the battery life signifcantly, but also provides the flexibility in supporting diverse electric power demands from a growing number of on-board applications.