Pack Sizing and Reconfiguration for Management of Large-Scale Batteries

Jin, Fangjian; Shin, Kang G.

doi:10.1109/iccps.2012.22

Cited by 44 publications

(29 citation statements)

References 11 publications

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“…However, the voltage regulators introduce additional energy loss when converting voltages, and the energy loss on regulators increases as the difference between the supplied and required voltages increases. This fact is also reported in [23], [34]. Thus, to optimize the system energy efficiency, it is key to match the supplied voltage with the load's required voltage as much as possible.…”

Section: ) Matching Supplied and Required Voltagesmentioning

confidence: 59%

“…For many of these applications, the load requirement on the battery system is dynamic and could significantly change over time [3], [28]. For example, depending on the driving states, the required voltage output of electric vehicles may vary from around 70 V to more than 700 V [3], [4], [23]. Such dynamic loads make the problem of optimizing the energy efficiency of large-scale battery systems even more critical and challenging, which is attracting increasing attentions of funding agencies [2], [8] (e.g., ARPA-E has awarded USD 43 million to 19 energy storage projects in 2012 [2]), and research efforts [25], [27], [29].…”

Section: Introductionmentioning

confidence: 99%

“…Unfortunately, the energy efficiency of voltage regulators may degrade significantly under two scenarios: (i) the difference between the supplied and required voltages is large [23], [29], [34], and (ii) the load is light and the system operates in a low power modes [41].…”

Section: Introductionmentioning

confidence: 99%

“…The adaptive reconfiguration not only avoids the low efficiency issue of the traditional regulator-based approaches, but also increases the system robustness in that failed batteries can be by-passed without significantly degrading the system performance [23], [26]. Much research has been conducted to design battery systems that offer higher configuration flexibility with fewer supplementary electronic components such as connectors and switches [10], [20], [24], which has already been implemented in many off-the-shelf battery packs [8], [19], [24], [26], [39].…”

Section: Introductionmentioning

confidence: 99%

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Exploring Adaptive Reconfiguration to Optimize Energy Efficiency in Large-Scale Battery Systems

Kong

et al. 2013

2013 IEEE 34th Real-Time Systems Symposium

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Abstract-Large-scale battery packs with hundreds/thousands of battery cells are commonly adopted in many emerging cyberphysical systems such as electric vehicles and smart micro-grids. For many applications, the load requirements on the battery systems are dynamic and could significantly change over time. How to resolve the discrepancies between the output power supplied by the battery system and the input power required by the loads is key to the development of large-scale battery systems. Traditionally, voltage regulators are often adopted to convert the voltage outputs to match loads' required input power. Unfortunately, the efficiency of utilizing such voltage regulators degrades significantly when the difference between supplied and required voltages becomes large or the load becomes light. In this paper, we propose to address this problem via an adaptive reconfiguration framework for the battery system. By abstracting the battery system into a graph representation, we develop two adaptive reconfiguration algorithms to identify the desired system configurations dynamically in accordance with real-time load requirements. We extensively evaluate our design with empirical experiments on a prototype battery system, electric vehicle driving trace-based emulation, and battery discharge trace-based simulations. The evaluation results demonstrate that, depending on the system states, our proposed adaptive reconfiguration algorithms are able to achieve 1× to 5× performance improvement with regard to the system operation time.

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Section: ) Matching Supplied and Required Voltagesmentioning

confidence: 59%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Exploring Adaptive Reconfiguration to Optimize Energy Efficiency in Large-Scale Battery Systems

Kong

et al. 2013

2013 IEEE 34th Real-Time Systems Symposium

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“…• In smart power grid, researchers have i) developed models based on measurement from phaser measurement units to solve the wide area control problem of large-scale power systems [6], [7], [8], ii) investigated the integration of renewable energy into power grid [9,14,16,17,27], and iii) optimized the packing size of large scale batteries to improve battery utilization in microgrids [11]. Our work builds on previous works, but concentrates on minimizing on minimizing energy sharing loss over DC line at the small community level.…”

Section: Related Workmentioning

confidence: 99%

Sharing renewable energy in smart microgrids

Zhu

Huang

Sharma

et al. 2013

Proceedings of the ACM/IEEE 4th International Conference on Cyber-Physical Systems

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Renewable energy harvested from the environment is an attractive option for providing green energy to homes. Unfortunately, the intermittent nature of renewable energy results in a mismatch between when these sources generate energy and when homes demand it. This mismatch reduces the efficiency of using harvested energy by either i) requiring batteries to store surplus energy, which typically incurs ∼20% energy conversion losses; or ii) using net metering to transmit surplus energy via the electric grid's AC lines, which severely limits the maximum percentage of possible renewable penetration. In this paper, we propose an alternative structure wherein nearby homes explicitly share energy with each other to balance local energy harvesting and demand in microgrids. We develop a novel energy sharing approach to determine which homes should share energy, and when, to minimize system-wide efficiency losses. We evaluate our approach in simulation using real traces of solar energy harvesting and home consumption data from a deployment in Amherst, MA. We show that our system i) reduces the energy loss on the AC line by 60% without requiring large batteries, ii) scales up performance with larger battery capacities, and iii) is robust to changes in microgrid topology.

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