In this paper, a magnetic field sensor with simple fabrication, high sensitivity and wide measurement range is proposed. The sensor consists of fiber Bragg grating, sensitivity enhancement structure (SES), magnetostrictive particles and epoxy resin matrix. Metal tube with length of 5 mm is glued onto both sides of the grating as SES, and the mental tube is covered with grooves. The FBG with mental tubes is coated with the matrix made of magnetostrictive particles and resin. During the curing process of matrix, a uniform magnetic field with 200mT and parallel to the fiber is applied to make the orientation of magnetostrictive particles constant. It also can make particles have uniform spatial distribution. Firstly, by comparing the performances of sensors made of three different resin without mental tube with the sensor made by gluing FBG onto Terfenol-D rod directly, it is found that the sensor with epoxy crystal adhesive has the highest sensitivity reaching 0.58 pm/mT. Secondly the sensor based on epoxy crystal adhesive with SES is evaluated. Compared with the sensor without SES and gluing diretly, the sensitivity increases 5.17 times to 3.58 pm/mT and the measurement expands ranging from 0 mT to 226 mT.
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.