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Title: Scalable Optimal Power Management for Large-Scale Battery Energy Storage Systems
Large-scale battery energy storage systems (BESS) play a pivotal role in advancing sustainability through their widespread applications in electrified transportation, power grids, and renewable energy systems. However, achieving optimal power management for these systems poses significant computational challenges. To address this, we propose a scalable approach that partitions the cells of a large-scale BESS into clusters based on state-of-charge (SoC), temperature, and internal resistance. Each cluster is represented by a model that approximates its collective SoC and temperature dynamics and overall power losses during charging and discharging. Using these clusters, we formulate a receding-horizon optimal power control problem to minimize power losses while promoting SoC and temperature balancing. The optimization determines a power quota for each cluster, which is then distributed among its constituent cells. This clustering approach drastically reduces computational costs by working with a smaller number of clusters instead of individual cells, enabling scalability for large-scale BESS. Simulations show a computational overhead reduction of over 60% for small-scale and 98% for large-scale BESS compared to conventional cell-level optimization. Experimental validation using a 20-cell prototype further underscores the approach's effectiveness and practical utility.  more » « less
Award ID(s):
1847651
PAR ID:
10566965
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
IEEE
Date Published:
Journal Name:
IEEE Transactions on Transportation Electrification
Volume:
10
Issue:
3
ISSN:
2372-2088
Page Range / eLocation ID:
5002 to 5016
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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