SENSIGA

Overview

Battery cell monitoring using optical fiber sensors, multi-physics instrumentation, and artificial intelligence for degradation estimation and aging prediction.

Dr. Vincent HEIRIES (CEA LETI / CEA LITEN)
Prof. Jean-Marie TARASCON (Collège de France)

The recent convergence of battery science and optical sensor engineering opens up new opportunities for battery diagnostics. We aim to develop ultra-sensitive optical sensors to monitor the physical-thermal and chemical parameters of batteries under real-world conditions. We are also exploring battery monitoring (Li-Ion, Na-Ion, Solid State) using multi-physics sensor systems (thermal, acoustic, mechanical, electrical) to extract observable degradation signatures and enhance the predictive capabilities of battery management systems (BMS). This monitoring could increase the reliability, lifespan, and reduce the cost of batteries. Our research program combines the design of specific sensors with signal processing approaches based on artificial intelligence to diagnose and optimize battery performance.

Keywords

Sensors, optical sensors, BMS, state estimators, algorithms, artificial intelligence

Tasks

Our research

Functional optical sensors integrated into the core of battery cells

The first objective is the in-depth exploration of internal cell instrumentation methods using optical fiber sensors. We will generalize coupled temperature and pressure detection to monitor SEI formation during cell formation, thereby establishing a reliable methodology for manufacturers. We will refine optical calorimetry to determine the specific heat capacity of cells, which is crucial for the design of cooling systems. The birefringence of Bragg gratings will be used to monitor mechanical stresses and cracks in electrodes, with this approach extended to all-solid-state batteries. Optical multiplexing will enable the creation of thermal and stress imaging to identify potential incidents. Chemical compound detection will use sensors exploiting evanescent waves and operando infrared measurements via chalcogenide optical fibers. Finally, multiplexing, which transmits multiple pieces of information simultaneously via a single optical fiber, will significantly enhance the estimation of charge state, health state, and power state of cells.

Artificial intelligence and physics-informed deep learning for exploiting sensor data integrated into battery cells

The second objective focuses on processing data from intra-cell and extra-cell sensors, and the development of signal processing algorithms for battery state diagnostics and aging prognosis. By leveraging the “over-instrumentation” of cells, this project aims to enhance the understanding of internal physicochemical mechanisms and improve the performance, longevity, and safety of batteries. Experimental data from internal and external sensors will help identify the most effective sensors and processing algorithms for each objective and quantify their performance. The artificial intelligence algorithms developed will significantly enhance BMS functionalities and promote optimal battery management.