A Brain-Inspired Spiking Network Framework Based on Multi-Time-Step Self-Attention for Lithium-Ion Batteries Capacity Prediction

• Published in: IEEE transactions on consumer electronics Vol. 70; no. 1; pp. 3008 - 3017
• Main Authors: Wang, Huan, Sun, Muxia, Li, Yan-Fu
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; battery health monitoring; Biological system modeling; Bionics; Brain; Brain modeling; Computational modeling; Convolution; Encoding; Energy consumption; Lithium-ion batteries; Lithium-ion battery; Neural networks; Neurons; Power consumption; Rechargeable batteries; self-attention; Spiking; spiking neural network

Lithium-ion (Li-ion) batteries have gained widespread usage in numerous consumer electronic products and have significantly contributed to the growth of related industries. Due to the instability issues which might cause explosion or fire, it is critical to ensure the safety and reliability of Li-io...