Online health diagnosis of lithium-ion batteries based on nonlinear autoregressive neural network

Battery health diagnostics is extremely crucial to guaranty the availability and reliability of the application in which they operate. Data-driven health diagnostics methods, particularly machine learning methods, have gained attention due to their simplicity and accuracy. However, a machine learning method is desired which can cope with the nonlinear behavior of battery cells and yet it avoids high computational complexity to work efficiently in online applications. The accuracy and robustness of machine learning methods strongly depend on the availability of a comprehensive battery degradation dataset that covers a variety of battery aging patterns. While many studies fail to address the aforementioned requirements, this study attempts to address them. Twenty-one nickel manganese cobalt oxide battery cells have been cycled in various operating conditions for more than two years to acquire the data. The partial charging voltage curve is explored to extract the health indicators that describe the health trajectory of the battery. Afterward, a nonlinear autoregressive exogenous (NARX) model is developed to capture the dependency between the health indicators and state of health of battery cells. Finally, the accuracy and robustness of the proposed method are validated. The results demonstrate the ability of NARX to health diagnosis of lithium-ion batteries with a maximum root mean squared error of 0.46 for untrained data. This indicates that the proposed model has high estimation accuracy, low computational complexity, and the ability of battery health estimation regardless of its aging pattern. These features point out the practicability of the proposed technique on online health diagnostics.

Automated summary

Battery health diagnostics is crucial for application availability and reliability. This study develops a NARX model to accurately diagnose lithium-ion battery health by extracting health indicators from partial charging voltage curves, demonstrating high accuracy and low computational complexity.