Estimating battery states such as State of Charge (SOC) and State of Health (SOH) is an essential component in developing energy storage technologies, which require accurate estimation of complex and nonlinear systems. A significant challenge is extracting pertinent spatial and temporal features from original battery data, which is crucial for efficient battery management systems. The emergence of digital twin (DT) technology offers a novel opportunity for performance monitoring and management of lithium-ion batteries, enhancing collaborative capacity among different battery state estimation techniques and enabling optimal operation of battery storage units. In this study, we propose a DT-supported battery state estimation method, in collaboration with the temporal convolutional network (TCN) and the long short-term memory (LSTM), to address the challenge of feature extraction. Firstly, we introduce a 4-layer hierarchical DT to overcome computational and data storage limitations in conventional battery management systems. Secondly, we present an online algorithm, TCN-LSTM for battery state estimation. Compared to conventional methods, TCN-LSTM outperforms other cyclic networks in various sequence modelling tasks and exhibits reduced reliance on the initial state conditions of the battery. Our methodology employs transfer learning to dynamically adjust the neural network parameters based on fresh data, ensuring real-time updating and enhancing the DT’s accuracy. Focusing on SOC, SOH and Remaining Useful Life (RUL) estimation, our model demonstrates exceptional results. When testing with 90 cycle data, the average root mean square error (RMSE) values for SOC, SOH, and RUL are 1.1%, 0.8%, and 0.9% respectively, significantly outperforming traditional CNN’s 2.2%, 2.0% and 3.6% and others. These results unequivocally demonstrate the contribution of the DT model to battery management, highlighting the outstanding robustness of our proposed method, showcasing consistent performance across various conditions and superior adaptability compared to other models.

Numerous performance indicators exist for semiconductor manufacturing systems. Several studies have been conducted regarding the performance optimization of semiconductor manufacturing systems. However, because of the complex manufacturing processes, potential complementary or inhibitory correlations may exist among performance indicators, which are difficult to demonstrate specifically. To analyze the correlation between the performance indicators, this study proposes a performance evaluation system based on the mathematical significance of each performance indicator to design statistical schemes. Several samples can be obtained by conducting simulation experiments through the performance evaluation system. The Pearson correlation coefficient method and canonical correlation analysis are used on the received samples to analyze linear correlations between the performance indicators. Through the investigation, we found that linear and other complex correlations exist between the performance indicators. This finding can contribute to our future studies regarding performance optimization for the scheduling problems of semiconductor manufacturing.