5G is the fifth generation of cellular networks and has been used in a lot of different areas. 5G often requires sudden rises in power consumption. To stabilize the power supply, a 5G system requires a lithium-ion battery (LIB) or a mechanism called AC main modernization to provide energy support during the power peak periods. The LIB approach is the best option in terms of simplicity and maintainability. Moreover, a 5G system requires not only high-performance energy but also the ability of tracking and prediction. Therefore, the requirement for a smart power supply for lithium-ion batteries with temporal monitoring and estimation is highly desirable. In this paper, we focus on artificial intelligence (AI) improvements to increase the accuracy of LIB state-of-health prediction. By observing the SeqInSeq nature of the battery data, our approach uses self-attention and fixed-point positional encoding. We also take advantage of autoregression to archive the trainable dependency from a non-linear branch and a linear branch in creating the final output. Compared with the current state-of-the-art (SOTA) method, our experimental results show that we provide better accuracy, compared with the baseline output using the NASA and CALCE datasets. From the same setting, we archive a reduction of 20.08% root mean square error (RMSE) and 29.01% mean absolute percentage error (MAPE) on NASA loss, compared to the SOTA approaches. On CALCE, the numbers are a 5.99% RMSE and 12.59% MAPE decrement, which is significant.

In recent years, lithium-ion batteries (LIB) have been used widely in portable electronic devices because of their advantages of durability, stability, high-capacity, low-cost, light-weight and smallscale. It makes LIB also deployed in various complex systems, in which efficient prediction of battery data, especially state-of-health (SoH), becomes crucial to ensure that the systems work stably without risks of power interruptions. With the recent improvement of Artificial Intelligence (AI) technologies, many works have been reported using deep learning (DL) models to investigate this problem, since such models can potentially increase their performance with more training data. This is also our direction in this research, which introduces a novel data-driven approach so-called Autoregression Nested Sequence (ARNS). On one hand, we come up with a nested sequence model to efficiently aggregate channel-wise and cycle-wise information, both of which are closely related to the operations of LIB. On the other hand, we incorporate relaxation effects into the model operations to handle peak prediction. To the best of our knowledge, ARNS is the first sophisticated deep learning model that combines all those features into a whole predictive system. The experimental results obtained using the NASA and CALCE datasets confirm significant improvement of ARNS, especially when dealing with peak periods in different SoH of multiple cycles.

State-of-Health (SOH) prediction of a Lithium-ion battery is essential for preventing malfunction and maintaining efficient working behaviors for the battery. In practice, this task is difficult due to the high level of noise and complexity. There are many machine learning methods, especially deep learning approaches, that have been proposed to address this problem recently. However, there is much room for improvement because the nature of the battery data is highly non-linear and exhibits higher dependence on multidisciplinary parameters such as resistance, voltage and external conditions the battery is subjected to. In this paper, we propose an approach known as bidirectional sequence-in-sequence, which exploits the dependency of nested cycle-wise and channel-wise battery data. Experimented with real dataset acquired from NASA, our method results in significant reduction of error of approximately up to 32.5%.