Multiple Indicators-Based Health Diagnostics and Prognostics for Energy Storage Technologies Using Fuzzy Comprehensive Evaluation and Improved Multivariate Grey Model

Abstract

Precise health diagnostics and prognostics for batteries, which can improve the reliability and efficiency of energy storage technologies are significant. It is still a challenge to predict and diagnose state-of-health (SOH) of batteries due to the complicated and unobservable electrochemical reaction inside the batteries. In this article, a novel battery health estimation framework based on an optimized multiple health indicators (MHIs) system using fuzzy comprehensive evaluation (FCE) and improved multivariate grey model (IMGM) is proposed. The proposed MHIs system, which considers different characteristics of batteries is introduced. Health indicators (HIs) including partial incremental capacity curve peak area (PICA) and partial charge time period are extracted and optimized based on the Box-Cox transformation method. On the basis of the MHIs system, the FCE method is proposed for SOH diagnosis, which decreases the impact of dispersion of different batteries. In addition, an IMGM method is proposed for battery health prognostics considering the coupling relationship between MHIs and battery aging. The MHIs work together on the health prognostics, reducing the impact of the error of any HI on the overall prediction result. The experiments results indicate that the proposed methods show good performance on battery online health diagnostics and prognostics.