A hybrid machine learning framework for joint SOC and SOH estimation of lithium-ion batteries assisted with fiber sensor measurements

Accurate State of Charge (SOC) and State of Health (SOH) estimation is crucial to ensure safe and reliable operation of battery systems. Considering the intrinsic couplings between SOC and SOH, a joint estimation framework is preferred in real-life applications where batteries degrade over time. Yet, it faces a few challenges such as limited measurements of key parameters such as strain and temperature distributions, difficult extraction of suitable features for modeling, and uncertainties arising from both the measurements and models. To address these challenges, this paper first uses Fiber Bragg Grating (FBG) sensors to obtain more process related signals by attaching them to the cell surface to capture multi-point strain and temperature variation signals due to battery charging/discharging operations. Then a hybrid machine learning framework for joint estimation of SOC and capacity (a key indicator of SOH) is developed, which uses a convolutional neural network combined with the Gaussian Process Regression method to produce both mean and variance information of the state estimates, and the joint estimation accuracy is improved by automatic extraction of useful features from the enriched measurements assisted with FBG sensors. The test results verify that the accuracy and reliability of the SOC estimation can be significantly improved by updating the capacity estimation and utilizing the FBG measurements, achieving up to 85.58% error reduction and 42.7% reduction of the estimation standard deviation.

Automated summary

This study utilized FBG sensors to capture more process-related signals of batteries, and developed a hybrid machine learning framework for joint estimation of SOC and capacity, achieving significant improvements in estimation accuracy.