Design of a sensitivity-enhanced FBG strain sensor and its application in state estimation for lithium-ion battery

Lithium-ion batteries have become a most promising energy storage candidate in power station and electric vehicles because of its high power capability, high energy-conversion efficiency, and environmental friendliness. It is significant to diagnose the security of battery by monitoring the its state parameters. Wherein, temperature and strain are the two of the important ones. In this work, a sensitivity-enhanced FBG strain sensor was designed for the strain measurement of lithium-ion batteries. This proposed sensor consists of two FBGs and a lever mechanism. The lever mechanism works as a displacement amplifier. The amplified deformation of battery act on the functional FBG and induce the larger wavelength shift. The thermal compensation FBG can eliminate the influence of ambient temperature. The calibration test shows that this sensor has a high sensitivity of 11.55 pm/με and a good linearity. Application test on a battery illustrates that the strain responses of the sensor has a good repeatability in three cycles. Then, artificial neural networks were used for state of charge (SOC) estimation. When the strain and temperature data were set as input parameters, SOC can be well predicted. Therefore, this sensor can monitor the strain on the cell with high sensitivity and accuracy. This research demonstrated a new solution for SOC estimation especially based on strain signals, which can provide more informative data for battery management system.