State estimation of lithium-ion batteries based on strain parameter monitored by fiber Bragg grating sensors

Multisensory and artificial intelligence approaches are key tools to achieve intelligent management of future battery systems. Strain monitoring using optical fiber sensors is an important role of multi-sensing in batteries. In this paper, the strain of batteries is monitored by fiber Bragg grating sensors, and the strain data are used to estimate the state of charge (SoC) and state of health (SoH) of batteries. A Kalman filtering (KF) model is proposed for SoC estimation based on strain signal of cells. Moreover, this work employs an artificial neural network (NN) for SoC estimation based on the strain data. The experimental data are acquired from commercial lithium-ion cells under two operating conditions. The KF model is established based on multiple regression between strain and SoC, which shows good performance in estimation for the static cycles. For NN estimators, input variables with strain parameter can enhance the accuracy of SoC estimation. A KF model based on the peak strain is developed to estimate the capacity degradation of battery, and the results show that strain can be used as an indicator to estimate SoH. The results present an encouraging outcome that SoC estimation can be achieved using non-electrical parameters solely, and the strain signal can also be used as an auxiliary parameter to improve the accuracy of SoC estimation. This new exploration provides a basis for multi-parameter cooperative estimation of battery state in the future battery system with a multisensory approach.