Reliability assessment of performance-based balanced systems with rebalancing mechanisms

Tianzi Tian, Jun Yang, Lei Li and Ning Wang

Reliability Engineering and System Safety, 2023, vol. 233, issue C

Abstract: Performance-based balanced systems (PBSs) are widely used as critical equipment in many fields, so their reliability assessment has become a hot topic. Taking the battery pack PBS as an example, the balance means the performance differences between components are within an acceptable range, where the common bus is used to redistribute the performance for rebalancing, and the redistribution is affected by the transmission loss and transmission capacity limit. To reflect the above rebalancing mechanisms, a reliability evaluation method is proposed for PBSs with common bus performance sharing (PBSs-CBPS) considering balance degree threshold, transmission loss, and transmission capacity limit. Firstly, a continuous-time discrete-state Markov model is built to address the transition behaviors of components. Next, considering the effects of balance degree threshold and transmission loss, the rebalancing process is formulated as a nonlinear programming problem to obtain the performance of components after rebalancing, and the system reliability model is established. Then, the universal generation function (UGF) method combined with nonlinear programming is proposed to calculate system reliability, and Paper-Cut UGF Algorithm (PCUGF) is proposed to improve computational efficiency. Finally, an analytical example and a numerical example of 6S1P lithium-ion battery packs are given to show the effectiveness of the proposed method.

Keywords: Performance-based balanced systems; Reliability assessment; Rebalancing mechanisms; Balance degree threshold; Transmission loss; Universal generating function (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:233:y:2023:i:c:s0951832023000480

DOI: 10.1016/j.ress.2023.109133

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