The Cloud-End Collaboration Battery Management System with Accurate State-of-Charge Estimation for Large-Scale Lithium-ion Battery System

The lithium-ion battery plays an increasingly im-portant role in energy storage and electric vehicles with the development of renewable energy. To ensure the safe operation of the lithium-ion battery system in application, the battery management system (BMS) is often equipped to monitor the battery cells in real-time. However, due to the inaccuracy and high cost, the traditional BMS is difficult to be adapted to the large-scale lithium-ion battery system, which will be increasingly common in the future. To overcome these two shortcomings, a cloud-end collaboration BMS framework is proposed in this study, which takes advantage of cloud computing and machine learning to monitor the numerous battery cells. In addition, based on this cloud-end collaboration BMS framework, the gated recurrent unit neural network and transfer learning algorithm are used to estimate the state-of-charge of different batteries. And the accuracy and flexibility of the method have been validated through experiments.