Lithium-ion batteries are developing into a widely used technology in the field of electromobility, defense, and stationary energy storage owing to their high energy density and low associated costs. However, recent incidents such as rapid failure of battery packs, fire-safety issues, and damage to battery packs by fast charging have harmed the brand reputations of cell manufacturers and OEMs. The majority of such incidents are a result of poor cell quality and ineffective quality-detection methods adopted as the industry standard. Real-time electrochemical impedance spectroscopy (RT-EIS) by far supersedes the existing quality-detection methods in the most important metrics - speed, accuracy, robustness, and operational costs.
Combined with the power of intelligent algorithms, machine-learning, and digital twin modeling, the impedance data of batteries can be automatically analyzed to effectively assess the quality of lithium-ion batteries and predict the electrical, thermal, and aging behavior of cells. When RT-EIS is further integrated into a standardized and scalable quality assurance system, cell manufacturers and OEMs can ensure the safety of their battery systems and reduce warranty and maintenance costs, thereby maintaining a stellar brand reputation and smooth operation of a profitable business model.
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