Developing an Online Data-Driven State of Health Estimation of Lithium-Ion Batteries Under Random Sensor Measurement Unavailability

Abstract

Data-driven approaches have demonstrated remarkable accuracy in battery’s state of health (SOH) estimation; however, they are susceptible to data quality and quantity. Therefore, an accurate data-based battery health estimation method is highly desirable in an unreliable industry environment when sensors’ random measurements unavailability is ubiquitous. Successful training under random data unavailability becomes a difficult task to undertake. Therefore, the main challenge is how an offline trained model can be reliable and accurate under random sensors’ measurements unavailability. This article develops an accurate SOH estimation model based on nonlinear autoregressive with exogenous inputs recurrent neural network for lithium-ion batteries whose features’ measurements are subjected to different random missing observations. To evoke the uncertainty of sensors’ measurements in online health diagnostic, missing observation occurrence is addressed by randomly eliminating sample data and then evaluating the model on the available measurements. Therefore, it does not require any imputation strategy for missing values. The accuracy of the estimator model is guaranteed when extracted underlying features are fused by adding their exponential moving average as the health features. The experimental results on two different datasets, Oxford and Toyota, under different battery chemistry and working operations demonstrate that the mean absolute errors (MAEs) and RMSs are well bounded below 2.70% and 3.10% for different random data missing rates of 1%–30%. It is a promising prediction model for numerous industrial applications with a high probability of random data unavailability.