A Novel PIML Architecture with Innovative Learning Paradigm Applied in Battery Prognostics

Prognostics and health management (PHM) increasingly play a constructive role throughout the entire lifetime of industrial equipment, significantly benefiting from extensive research in physical modelling and machine learning techniques. This has led to the development of hybrid approaches that seamlessly integrate both domains through physics-informed machine learning (PIML). PIML ensures the generation of cohesive solutions encompassing various aspects of physics knowledge across different stages of the machine-learning pipeline, substantially contributing to detection, diagnostics, and prognostics. However, PIML’s design relies heavily on expert experience and demands rigorous interdisciplinary expertise, requiring a profound understanding of machine learning and physical principles. Inadequate design of PIML often leads to suboptimal outcomes, where the combined effect is less than the sum of its parts. Currently, PIML lacks a scalable and engineered application architecture to effectively utilise its embedded results. To address this challenge, our paper introduces a novel parallel architectural approach that employs pre-training and fine-tuning strategies for optimising the different model parts. Its data-driven branch is first trained with zero output in the PI branch, then fine-tuning the physics-informed branch. It takes the frozen data-driven model as a fixed feature extractor to get physics-consistency prediction. This approach proposes a generic solution for embedding physics knowledge into ML that guarantees performance improvement. The effectiveness of our approach is validated in the context of Remaining Useful Life (RUL) prediction using MIT-Stanford battery data.