Capacity Estimation of Lithium-ion Battery with Multi-task Autoencoder and Empirical Mode Decomposition

Abstract

Capacity estimation of lithium-ion batteries is a commonly used method in health diagnosis and management. Its mainstream method involves using data-driven time series forecasting models to learn the patterns of changes in capacity. However, capacity regeneration poses a challenge for training time series forecasting models. Therefore, we propose a hybrid method that applies empirical mode decomposition and a multi task autoencoder. In detail, empirical mode decomposition is applied to decompose the time series of capacity into intrinsic mode functions and a residual. Then, a multi-task autoencoder based on diagonal state space models is applied to estimate intrinsic mode functions while support vector regression is utilized for the residual. Experimental results show that the method outperforms seven baselines on three datasets, with an average root mean square error of 0.0103, 0.0111, and 0.0004. Furthermore, it is capable of performing an inference on the CPU in 3.57 ms with 0.69MB of memory usage.

Qi Sun

Master of Cyberspace Security

My research interests include natural language processing, time series forecasting and big data analysis.