State-of-health forecasting for lithium-ion batteries using WOA-BiLSTM algorithm
TL;DR
This work develops a Bidirectional Long Short-Term Memory network whose hyperparameters are tuned by the Whale Optimization Algorithm, aiming to achieve accurate State-of- Health estimation for lithium-ion batteries.
Abstract
Lithium-ion batteries act as core energy suppliers for Automated Guided Vehicles. Once the power supply system fails, normal industrial production workflows will be disrupted severely. In practical field applications, it is impossible to directly measure the real health status of such batteries. This work develops a Bidirectional Long Short-Term Memory network whose hyperparameters are tuned by the Whale Optimization Algorithm, aiming to achieve accurate State-of- Health estimation for lithium-ion batteries. To begin with, we screen a set of health indicators closely linked to battery capacity and carry out targeted analysis for these indicators. After feature extraction, two statistical approaches, namely Pearson correlation analysis and Maximal Information Coefficient, are adopted to conduct quantitative evaluation. The analytical results prove that the selected indicators have strong correlations with battery capacity. On this basis, WOA is applied to refine the hyperparameters of the BiLSTM network. A series of comparative tests against mainstream baseline models verify that the proposed hybrid approach delivers higher precision and more stable estimation performance.