Comparative study of granular computing and RKCCA-based feature fusion for early lithium-ion battery RUL estimation.

Efficient prediction of the remaining useful life (RUL) of lithium ion batteries is essential in ensuring safety, reliability and energy efficiency of new energy-storage and mobility systems. This paper suggests a hybrid machine-learning model that combines regularized kernel canonical correlation analysis (RKCCA) with random-forest (RF) regression, hence allowing one to predict RUL accurately at early stages. In this context, the battery-degradation data can be separated in interpretable information granules by two complementary base models, Granular Model1 (GM1), which uses a small bunch of polynomies to employ degradation patterns on a low-order basis; and Granular Model2 (GM2), which utilizes deep neural networks to implement degradation movements on a higher-order platform. In the RKCCA component, fusion of nonlinear features is through the projection of both heterogeneous sensor measurements, such as voltage, current, and capacity, to an assumed common space in the latent features. This is a transformation that improves the correlation between features and also alleviates effects of noise and the dimensionality in the data. The resulted fused latent representations are used as inputs to the RF regression model to give the final RUL estimate. The application of the experimental analysis to the dataset of MIT Battery Degradation (A123 LFP/Graphite cells) reveals that the suggested RKCCA + RF model significantly exceeds GM1 and GM2. The model outputs a mean relative error of 3.45 cycles, root-mean-square error of 4.75 cycles and coefficient of determination ([Formula: see text]) of 0.9995. Overall, the offered results are the testimony to the fact that the model is a high-ability framework that is able to model nonlinear dynamics of degradation and provide high-accuracy predictions, and that can be implemented effectively in the sphere of data-driven prognostics and predictive maintenance and real-time battery-management systems.