To develop a high-density and long-life lithium-ion battery, a technology is needed that allows non-destructive visualization of the spatial distribution of deteriorated parts after cycle test. In the present study, we measured the distribution of the magnetic field leaking from the lithium-ion battery during its operation. Based on the measurement results, we evaluated the spatial distribution of electric current density that corresponds to the reaction rate of the active material and the ion diffusion rate in the electrolyte solution inside a battery using the electric current reconstruction process. With respect to the changes in the internal state of the lithium-ion battery associated with cycle deterioration, we successfully visualized the part where the electrical conductivity has changed that is the deteriorated part causing the battery capacity to decrease inside the lithium-ion battery.

The aim of this study is to observe the spatial inhomogeneity of a rechargeable battery's electric conductivity distribution. Therefore, we have developed a system that uses the measurement results of a minute magnetic field that leaks from the cell to visualize, in real time, the cell's electric conductivity distribution. This system has a magnetic detection capability of 30 pT/Hz 0.5 (at 1 Hz); it measures the magnetic field distribution in the 240 × 240-mm range. This system has the ability to detect the 500-μA electric current that flows in a rechargeable battery 5 mm away from the sensor module. Because the magnetic signals are detected at the frequency synchronized with the alternating current flowing in the cell, this system is not affected by environmental magnetic field noise. Using this system, we have successfully visualized the short-circuit spot in a cell with significant self-discharge. Furthermore, we observe that the magnetic field distribution changes continually when the short circuit is being generated. The coordinate where the magnetic field distribution changed and the coordinate where metal precipitates were confirmed significant agreement.

With the rapid spread of electric vehicles and hybrid vehicles in recent years, the energy density of lithium-ion batteries, which are the power sources of them, tends to increase.This means that the risk of ignition is further increased as represented by some fire accident case of an EV vehicle that had taken long time to extinguish, and thus eventually makes the requirement of safety management in the battery manufacturing process be expected stricter.To prevent ignition due to internal short circuit of battery, and eliminate a short-life battery product, we have been developing a technology to non-destructively visualize the self-discharged part inside the storage battery. In this technology, on the first, electric current is applied to the battery from external power source, and the leakage magnetic field that generated due to the internal electric current in the battery is sensed on outside of the battery. Next, the electric current density distribution inside the battery can be reconstructed and visualized three-dimensionally by using the spatial distribution of the measured magnetic field as the boundary condition and solving the inverse problem analytically. To measure leakage magnetic field with high sensitivity, it is necessary to take steps against disturbances as like DC magnetic field derived from magnetism or magnetic materials (often Ni is used for the collector foil), and AC magnetic fields generated from external circuits. In this method, two steps are used in combination for detection sensitivity of the pico-tesla order; (1) An AC current is applied to the battery, and the AC magnetic field generated in synchronization is phase-detected. (2) Install a small coil near the sensor and generate a DC cancel magnetic field by feedback control. By this method, it is possible to detect only the AC magnetic field corresponding to the applied current with high sensitivity in the linear operating region of the magnetic sensor. In actual detection of the leakage magnetic field, a DC voltage that balances with the output voltage of the battery is applied to the AC output of the AC current source connected to the battery so that the SOC of the storage battery is kept constant voltage. In addition to the above, the frequency of the AC output is usually set to 10 Hz or less to avoid the shielding effects of electrodes and the metallic package of the battery.By these steps and scanning two-dimensional of magnetic sensors or using a sensor array in which magnetic sensors are arranged two-dimensionally, the spatial distribution of the two-dimensional magnetic field outside the storage battery can be obtained. Since the sensor detects only one direction component of magnetic field, changing the direction of the sensor by 90 ° is needed. On data processing, some realistic structural assumptions are required to solve the inverse problem. (1) Defining that the plane parallel to the storage battery electrode plane is the XY plane, the Z component of the magnetic field vector is zero around the battery, and only X component and Y component remains as magnetic field vector components generated from internal current of the battery. (2) The thickness of the battery is sufficiently smaller than the size of the battery in the plane direction, that is, the three-dimensional current in the battery is confined in the thin two-dimensional plane. In the first step of the reconstruction, under the fact of (1), Laplace's equation is analytically solved in the "free space from the measurement surface to just above the electrode surface in the battery, without a magnetic source", and derive the two-dimensional magnetic field distribution directly above the electrode surface. In the next step, it is possible to obtain a three-dimensional current distribution inside the battery by analytically solving the Poisson-type equation and the electric current continuity equation under the realistic structural assumption of (2) with first calculation result as the boundary condition. In the presentation, we will introduce in detail the experiment and data processing for identifying the self-discharge point with this technology, including examples of visualizing the magnetic field distribution and current density distribution inside a battery after aging test.