Time-Frequency Hybrid Neuromorphic Computing Architecture Development for Battery State-of-Health Estimation

Abstract

With the rapid adoption of Internet of things (IoT) and artificial intelligence (AI), lithium-ion battery state-of-health (SOH) estimation plays an important role in guaranteeing the secure and stable functioning of various domains. However, the majority of the existing methods are constrained by factors such as transmission latency, computational energy, and computing speed. To address these challenges, we develop a time-frequency hybrid neuromorphic computing architecture for battery SOH estimation. Specifically, an eco-friendly, biodegradable memristor crossbar array is designed, enabling high energy efficiency and high-performance density in the proposed system. To improve the understanding of the designed time-frequency hybrid neuromorphic computing system, a local information extraction module, a time-frequency feature fusion module, and a global information perception module are proposed. Furthermore, the proposed system is validated on two publicly available battery ageing datasets (i.e., the CALCE-CS2 dataset and the NASA dataset). The experimental results show that the system exhibits superior performance to that of the state-of-the-art (SOTA) methods in terms of estimation accuracy (highest estimation accuracy), time consumption (approximately 8 12 times faster), and transmission latency (approximately 10 times faster). This study is expected to promote the advancement and evolution of next-generation computing systems, enabling the realization of low power consumption and high-density information processing in IoT scenarios.