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 un-equivocally 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.