Semi-supervised adversarial deep learning for capacity estimation of battery energy storage systems

Abstract

Battery energy storage systems (BESS) play a pivotal role in energy management, and the precise estimation of battery capacity is crucial for optimizing their performance and ensuring reliable power supply. Deep learning methodologies applied to battery capacity estimation have exhibited exemplary performance. However, deep learning methods necessitate supervised training with a significant volume of labeled data, presenting challenges for data collection in industrial scenarios. Moreover, a diverse range of battery types in industrial settings makes it difficult to develop capacity estimation models for different types of batteries from scratch. To address these issues, a semi-supervised adversarial deep learning (SADL) method is proposed for lithium-ion battery capacity estimation. Initially, a subset of labeled lithium-ion battery data, coupled with a subset of unlabeled data, is collected. Voltage and current data are then transformed into capacity increment features. Subsequently, an adversarial training strategy is employed, subjecting labeled and unlabeled data to adversarial training to enhance the performance of SADL. Finally, the effectiveness of the SADL method in estimating the capacity of other lithium-ion batteries is analysed. Experimental results demonstrate that the SADL method accurately estimates the capacity of various battery types, showcasing an RMSE error of approximately 2%, surpassing the performance of other methods. The proposed SADL method emerges as a promising solution for the precise estimation of lithium-ion battery capacity in BESS.