韩国电池诊断初创 MONA 在 K-Battery Show 2026 材料·零部件·设备会议上称，将基于1000万颗电芯数据开发可跨电池类型通用的诊断 AI 基础模型，并扩展到 EV、ESS 与梯次利用电池

Battery diagnostics company MONA is developing an artificial intelligence foundation model using data from 10 million battery cells. The goal is to create a universal model that can apply diagnostic capabilities learned from one type of battery to different batteries. The company also plans to expand diagnostics to electric vehicles, energy storage systems (ESS) and second-life batteries.
"We plan to secure data from 10 million battery cells and develop a universal diagnostic model that can be applied across different batteries," MONA CEO Jang Ikhwang said at the K-Battery Show 2026 Materials, Parts and Equipment Conference held Sept. 9 at KINTEX in Ilsan, Gyeonggi Province. MONA has so far collected data from about 8 million battery cells and 1,000 battery packs.
Current battery diagnostic AI models struggle to maintain accuracy when applied to batteries different from those used for training. Materials, designs and production conditions vary by battery, meaning the same abnormality can appear differently in measurement data. MONA is training on large datasets collected from various batteries to develop a universal model capable of determining quality and detecting abnormalities across different products.
MONA's ability to collect large datasets is based on battery diagnostic technology using Electrochemical Impedance Spectroscopy (EIS). EIS applies alternating-current signals to a battery and measures how impedance changes at different frequencies.
The technology can identify latent internal cell defects and quality variations that are difficult to detect through conventional voltage and visual inspections. Even when a battery has normal voltage at shipment, quality problems can emerge during transportation and storage due to differences in factors such as self-discharge.
Applying EIS to mass-production lines requires faster inspection because directly measuring multiple frequency ranges takes time. MONA developed a high-speed diagnostic technology that measures only part of the frequency range and uses generative AI to reconstruct the remaining impedance data. The company has reduced inspection time to about one second per cell, allowing it to accumulate large datasets on mass-production lines.
Fast measurement alone is not enough for accurate quality assessment. Impedance is sensitive to temperature, meaning readings from the same battery can vary depending on the measurement environment. MONA corrects variations caused by temperature and equipment conditions and establishes a normal range based on the statistical distribution of accumulated data. Batteries outside that range are automatically flagged as potential defects.
MONA has also confirmed correlations between its data and actual defects. The company identified batteries in accumulated incoming-inspection data whose internal resistance and charge-transfer characteristics fell outside the normal distribution. Computed tomography (CT) and cross-sectional analysis subsequently found physical defects, including uneven electrode thickness. The abnormalities detected solely from impedance data matched physical defects inside the cells.
MONA is extending data collected from mass-production sites beyond the manufacturing stage. It is expanding the technology to diagnose batteries installed in EVs and determine whether used batteries recovered from vehicles can be reused. The company can collect data throughout the battery lifecycle, from incoming inspection of cells, modules and packs to EV diagnostics and second-life battery reuse.
MONA is also testing the feasibility of using second-life batteries in ESS applications. The company is conducting pilot tests of ESS systems incorporating used battery modules and packs. It is also developing diagnostic solutions for next-generation batteries, including all-solid-state batteries and batteries using silicon anodes.