Discharge current steering for battery lifetime optimization
Proceedings of the 2002 international symposium on Low power electronics and design
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
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
Handbook of Transformer Design and Applications
Handbook of Transformer Design and Applications
Dependable, efficient, scalable architecture for management of large-scale batteries
Proceedings of the 1st ACM/IEEE International Conference on Cyber-Physical Systems
Sharing renewable energy in smart microgrids
Proceedings of the ACM/IEEE 4th International Conference on Cyber-Physical Systems
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Battery systems for electric vehicles (EVs) or uninterruptible micro-gridsâ"prototypical cyber-physical systems (CPSs)â"are usually built with several hundreds/thousands of battery cells. How to deal with the inevitable failure of cells quickly and cost-effectively for vehicle warranty or uninterruptible service, for instance, is key to the development of large-scale battery systems. Use of extra (redundant/backup) cells to cope with cell failures must be minimized so as to make the target systems cheaper and lighter, while meeting the reliability requirement that is directly related to, for example, the vehicle warranty. Existing reconfigurable battery systems do not scale well because they incur a long delay in properly setting a large number of switches to bypass faulty cells or adapting to dynamically changing power demands in large battery systems for such applications as EVs. In this paper, we propose a scalable solution, not only to reduce the required number of backup cells and the total cost of a battery system, but also to facilitate recovery from cell failures and adapt to changing power demands while increasing battery utilization. Specifically, we optimize the pack-size by striking a balance between various types of cost in order to reduce the overall cost. We also configure battery packs and optimize their connection topology, reducing delays in failure recovery and power reallocation. Our in-depth evaluation has shown that the time to recover from cell failures remains constant irrespective of the number of cells involved, which is important to scalability. The proposed pack-sizing also reduces the cost and the size of battery systems. Moreover, fast power reallocation is achieved by utilizing prior knowledge of power usage patterns.