Electrical Engineering and Systems Science > Systems and Control
[Submitted on 7 Jun 2021 (v1), last revised 9 Sep 2022 (this version, v3)]
Title:Lite-Sparse Hierarchical Partial Power Processing for Second-Use Battery Energy Storage Systems
View PDFAbstract:The explosive growth of electric vehicles (EVs) is leading to a surge in retired EV batteries, which are typically recycled despite having nearly 80% available capacity. Repurposing automotive batteries for second-use battery energy storage systems (2-BESS) has both economical and environmental benefits. The challenge with second-use batteries is the heterogeneity in their state of health. This paper introduces a new strategy to optimize 2-BESS performance despite the heterogeneity of individual batteries while reducing the cost of power conversion. In this paper, the statistical distribution of the power heterogeneity in the supply of batteries is used to optimize the choice of power converters and design the power flow within the battery energy storage system (BESS) to optimize power capability. By leveraging a new lite-sparse hierarchical partial power processing (LS-HiPPP) approach, we study how a hierarchy in partial power processing (PPP) partitions power converters to significantly reduce converter ratings, process less power to achieve high system efficiency with lower cost (lower efficiency) converters, and take advantage of economies of scale by requiring only a minimal number of sets of identical converters. Our results demonstrate that LS-HiPPP architectures offer the best tradeoff between battery utilization and converter cost and have higher system efficiency than conventional partial power processing (C-PPP) in all cases.
Submission history
From: Xiaofan Cui [view email][v1] Mon, 7 Jun 2021 00:36:20 UTC (2,768 KB)
[v2] Mon, 14 Mar 2022 03:48:20 UTC (9,192 KB)
[v3] Fri, 9 Sep 2022 07:35:19 UTC (14,109 KB)
Current browse context:
eess.SY
References & Citations
Bibliographic and Citation Tools
Bibliographic Explorer (What is the Explorer?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)
Code, Data and Media Associated with this Article
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)
Demos
Recommenders and Search Tools
Influence Flower (What are Influence Flowers?)
Connected Papers (What is Connected Papers?)
CORE Recommender (What is CORE?)
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.