A Method to Optimize 3D Microbatteries

Abstract

The rapid development of information technology (IT) since the end of the 20th century and the technological progress of sensors and devices have familiarized the concept of the Internet of Things (IoT). So far, the miniaturization of the sensors and devices has significantly contributed to the rapid growth of the IoT ecosystem. However, compared with such technological progress on the sensors and devices, the miniaturization technology of the power sources for the off-grid operation of the electronics has not been well established, which hinders further prevalence of the IoT. Especially, for the integration with microelectronics, the high areal energy density is recognized as one of the key properties for such power sources.As one of the promising candidates for such IoT-enabled power sources, lithium-ion microbatteries have attracted attention. Especially, the three-dimensional design of the microbattery at full cell level has been investigated in order to improve the areal energy density while maintaining high power density. The benefit of the geometrical modification is the lowering of the internal resistance due to the reduction of the ion transport distance without reducing the battery capacity. Indeed, the recent advancement of a variety of 3D manufacturing technologies has allowed the realization of the 3D microbattery. However, the 3D electrode structure is limited to simple shapes such as interdigitated plate or cylindrical electrodes and, therefore, the battery performance is restricted by such electrode shapes. To design the 3D battery architecture beyond the interdigitated configuration, the 3D battery optimization method at the full cell level is vital. Also, the 3D battery optimization method is crucial from the point of view of industrial demand since there are a variety of IoT devices that have different functions, and it is demanding to optimize the battery following the requirements of each IoT device by hand. However, there are no reports regarding the 3D battery optimization methods at the full cell level.In this talk, we propose a novel battery optimization system at the full cell level, which consists of an automatic geometry generator and performance simulators. The geometry generator designs the full cell based on the random sampling by only receiving the size and the resolution of the 3D battery cell as the input data. For compatibility with the geometry generator, as one of the performance simulators, we newly propose the transmission line model, the so-called 3D porous electrode model, to compute the internal resistance of the 3D battery quickly. To demonstrate the effectiveness of our approach, the tradeoff frontier for the internal resistance and capacitance is created by plotting 200,000 data points as shown in Fig. 1 (a). From the figure, we successfully find new battery geometry (Geometry D in Fig. 1 (b)), which has both higher power and energy densities than the interdigitated plate configuration (Geometry C in Fig. 1 (b)). Figure 1