Stochastic state of health estimation for lithium-ion batteries with automated feature fusion using quantum convolutional neural network

Abstract

The accurate state of health (SOH) estimation of lithium-ion batteries is crucial for efficient, healthy, and safe operation of battery systems. Extracting meaningful aging information from highly stochastic and noisy data segments while designing SOH estimation algorithms that efficiently handle the large-scale computational demands of cloud-based battery management systems presents a substantial challenge. In this work, we propose a quantum convolutional neural network (QCNN) model designed for accurate, robust, and generalizable SOH estimation with minimal data and parameter requirements and is compatible with quantum computing cloud platforms in the Noisy Intermediate-Scale Quantum. First, we utilize data from 4 datasets comprising 272 cells, covering 5 chemical compositions, 4 rated parameters, and 73 operating conditions. We design 5 voltage windows as small as 0.3 V for each cell from incremental capacity peaks for stochastic SOH estimation scenarios generation. We extract 3 effective health indicators (HIs) sequences and develop an automated feature fusion method using quantum rotation gate encoding, achieving an R² of 96%. Subsequently, we design a QCNN whose convolutional layer, constructed with variational quantum circuits, comprises merely 39 parameters. Additionally, we explore the impact of training set size, using strategies, and battery materials on the model’s accuracy. Finally, the QCNN with quantum convolutional layers reduces root mean squared error by 28% and achieves an R² exceeding 96% compared to other three commonly used algorithms. This work demonstrates the effectiveness of quantum encoding for automated feature fusion of HIs extracted from limited discharge data. It highlights the potential of QCNN in improving the accuracy, robustness, and generalization of SOH estimation while dealing with stochastic and noisy data with few parameters and simple structure. It also suggests a new paradigm for leveraging quantum computational power in SOH estimation.