With the popularity of electric vehicles, the ever-increasing demand for high-capacity batteries highlights the need for monitoring the health status of batteries. In this article, we proposed a magnetic imaging technique (MIT) to investigate the health status of power batteries nondestructively. This technique is based on a magnetic sensor array, which consists of a 16-channel high-performance magnetoelectric sensor, and the noise equivalent magnetic induction (NEB) of each channel reaches 3–5 pT/Hz^1/2@10 Hz. The distribution of the magnetic field is imaged by scanning the magnetic field variation of different positions on the surface. Therefore, the areas of magnetic anomalies are identified by distinguishing different magnetic field abnormal results. and it may be possible to classify the battery failure, so as to put forward suggestions on the use of the battery. This magnetic imaging method expands the application field of this high-performance magnetoelectric sensor and contributes to the battery’s safety monitoring. Meanwhile, it may also act as an important role in other nondestructive testing fields.

The application of high-performance magnetic sensors and the parameter estimation of ferromagnetic target from magnetic anomaly data are two important parts in magnetic anomaly detection (MAD) technique. In this work, we proposed a detection approach using a self-designed high-sensitivity three-axis magnetoelectric (ME) magnetic sensor based on a hybrid algorithm of particle swarm optimization (PSO) and simulated annealed (SA) algorithm. The magnetic dipole model is utilized as a universal model in the parameter estimation. Combining the strong global exploration ability and fast convergence of PSO algorithm with the probabilistic acceptance criterion in SA algorithm, the hybridization entitled PSO-SA algorithm for magnetic anomaly detection is expounded in detail. The ME sensor was applied to construct a test platform with an equivalent magnetic noise (EMN) lower than 26.08pT $/ \surd $ Hz at 1Hz. The proposed method was evaluated by three controlled experiments performed on the test platform using the ME sensor. Based on the observed results for the designed experiments, a high fitting level has been demonstrated by the goodness indexes of the proposed algorithm as high as 0.9182, 0.9287 and 0.9320 for the three experimental data, respectively. Moreover, the characteristics of the estimated parameters are in good agreement with the experimental design, which proves the reliability of the proposed algorithm and reveals its universality in magnetic anomaly detection as well.

In recent years, magnetic sensors based on the magnetoelectric (ME) coupling effect have made remarkable progress in the detection of an ac magnetic field, but the detection of the dc magnetic field is still a challenging issue. Therefore, our work aims to develop a highly sensitive device for measuring the dc magnetic field. The designed ME device is based on a pair of highly consistent ME sensors and an intelligent differential configuration. By applying a strong driving ac magnetic field, the even-order harmonic components of the output voltage can be eliminated, and the odd-order harmonic components are superimposed concurrently. Due to the high responsivity (149 mV/ $\mu \text{T}$ ) of the first harmonic component to the dc magnetic field, we obtained a low detection limit of 0.8 nT for the dc magnetic field. These results further advance the research of ME composites used in dc field detection. This ME device may play an important role in many fields of magnetic field detection, such as magnetic localization, magnetic navigation, and so on.