Title

Design of a simplified hierarchical bayesian network for residential energy storage degradation

Document Type

Article

Publication Date

1-30-2020

Department

Department of Electrical and Computer Engineering, Department of Mechanical Engineering-Engineering Mechanics

Abstract

In this paper a simplified hierarchical Bayesian network (BN) is developed to estimate residential energy storage degradation in terms of capacity fade. The BN is trained using experimental results of lithium iron phosphate batteries. Residential energy storage capacity fade was estimated for multiple cases. These cases originated from a smart home energy management system (SHEMS). The cases reflect that capacity fade of the residential energy storage depends on SHEMS architecture, power consumption limits and electric vehicle schedule.

Publication Title

2019 IEEE Power & Energy Society General Meeting (PESGM)

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