A Battery Digital Twin From Laboratory Data Using Wavelet Analysis and Neural Networks

Abstract

Lithium-ion (Li-ion) batteries are the preferred choice for energy storage applications. Li-ion performances degrade with time and usage, leading to a decreased total charge capacity and to an increased internal resistance. In this article, the wavelet analysis is used to filter the voltage and current signals of the battery to estimate the internal complex impedance as a function of state of charge (SoC) and state of health (SoH). The collected data are then used to synthesize a battery digital twin (BDT). This BDT outputs a realistic voltage signal as a function of SoC and SoH inputs. The BDT is based on feedforward neural networks trained to simulate the complex internal impedance and the open-circuit voltage generator. The effectiveness of the proposed method is verified on the dataset from the prognostics data repository of NASA.

A Digital Twin Framework for Simulating Distributed Energy Resources in Distribution Grids

“…Additionally, DTs allow for real-time analysis and testing of LIB characteristics, offering a versatile approach to testing different configurations and control strategies without physical prototypes [36]. Moreover, DT models electrode structures at a microscopic level [37] and simulates battery behavior with AI [38,39]. It can also be applied to simulate lithium-air battery electrodes under varying electrolyte saturations [40], simulate mesostructures of electrodes [41], and model thermal behaviors [42].…”