Modeling a Digital Twin to Predict Battery Deterioration with Lower Prediction Error in Smart Devices: From the Internet of Things Sensor Devices to Self-Driving Cars

Abstract

The complete life cycle management of complex equipment is seen as critical to the smart transformation and upgrading of today’s industrial industry. In recent years, digital twin (DT) technology and machine learning (ML) have arisen as emerging technologies. Developing technologies like DT technology and ML in entire battery life cycle management may make each stage of the life cycle more predictable and proactive. We propose a hybrid DT model based on ML that can enhance the performance of an existing DT mathematical model formulated to simulate lithium-ion battery deterioration behavior using DT technology. Firstly, we develop a long short-term memory (LSTM)-based model to forecast the error term of battery capacity enumerated for each charge and discharge cycle from the existing DT model. In this work, we use 18,650 lithium-ion battery discharge data from NASA Ames’ prognostics data repository as our experimental data. The LSTM model is configured with Adam optimizer and the mean absolute error (MAE) loss function. The early stopping criterion is also employed as a regularization technique to overcome model overfitting. Secondly, we develop our proposed hybrid DT by integrating both the existing DT and the LSTM model. Thirdly, we formulate an empirical mathematical model, which allows us to better replicate behavior of battery degradation of any lithium-ion battery. Finally, we evaluate the performance of the proposed hybrid DT in terms of the MAE metric. Compared with the existing model, our proposed model reduces the error of battery capacity during the entire degradation period by 68.42%.