incomparable advantages over other rechargeable batteries, including a higher energy density, fewer maintenance requirements, a higher nominal voltage, and lower self-discharge rate; thus, the LIB is regarded as one of the most promising energy solutions. However, its performance deteriorates with time because of impacts from aging and its operating environment. Battery failure may result in serious, sometimes catastrophic, consequences. Therefore, it is desirable to estimate the state of health (SOH) of a battery to predict how soon it will fail or void the guarantee of satisfactory performance.The aging of lithium (Li)-ion cells is a critical factor in all applications that employ this type of battery. Aging impacts battery performance [1] and, hence, modifies considerably the output parameters such as the SOH and state of charge (SOC). Electrochemical and electrical researchers use electrochemical impedance spectroscopy (EIS) measurements to examine aging. The EIS measurements provide electrochemical researchers with information about the kinetics in the electrodes (e.g., Li + diffusion rate) [2], information that assists the electrical researcher to ensure an accurate equivalent circuit of the battery. This common measurement technique has been studied for almost all battery chemistries, not only for the SOC but also for SOH determination [3,4].Typical methods of SOC estimation include amperehour counting (e.g., Coulomb counting), inverse nonlinear mapping from the open circuit voltage to the SOC, which includes the Kalman filter (KF) [5] and its extensions [6-8], methods based on artificial neural networks [9], and methods based on fuzzy logic [10]. Because the KF method cannot be used directly for the state prediction of a nonlinear system, methods based on the extended Kalman filter (EKF) [6,7] and unscented Kalman filter (UKF) [11] are the most widely used. However, the EKF must compute the Jacobian matrix and is generally not suitable for highly nonlinear systems with non-Gaussian noise. Similarly, the Abstract This study involved developing a meter for rapidly evaluating the state of health (SOH) of lithium-ion batteries (LIBs). The key component was a magnetic induction antenna (MIA) module with an interdigital pattern fabricated using a 355-nm pulsed ultraviolet laser system. For inducing high voltage from the LIB to the MIA module for evaluating the SOH, interdigital patterns of four line widths (i.e., 0.25, 0.5, 0.75, and 1.0 mm) were designed. The experimental results indicate that patterns with wider lines induce higher voltages. However, patterns using the 0.75-mm line offer optimal SOH measurements, because those with the 1.0-mm line yielded lower power storage. The optimal operating frequency for enhancing SOH identification was found to be 700 kHz. In addition, this study established an empirical equation for expressing the relationship between the induced voltage and SOH of LIBs.