Improving Failure Modes and Effects Analysis of a Lithium-Ion battery using fuzzy-based risk assessment

Lithium-ion batteries (LIBs) are extensively used in Industry 4.0 and IoT applications due to their high energy density, long cycle life, and efficiency. However, the increasing integration of LIBs in critical systems raises significant safety and reliability concerns, as failures can result in severe consequences such as thermal runaway, internal short circuits, and performance degradation. In this work, a comprehensive Failure Modes, Effects, and Criticality Analysis (FMECA) is conducted to systematically identify, evaluate, and mitigate potential risks associated with LIBs. To enhance the traditional FMECA approach, a fuzzy logic-based method is employed to refine the calculation of the Risk Priority Number (RPN), enabling a more robust handling of uncertainties in the evaluation of Severity, Occurrence, and Detectability. The analysis highlights the most critical failure modes, including lithium plating, electrode degradation, and mechanical damage, which significantly affect battery performance and safety. The paper shows how fuzzy logic allows for a better definition of the risk levels for each failure mode, with a consequent improvement on risk prioritization. The work also deals with the role of metrological tools and monitoring strategies, such as Battery Management Systems (BMS) and thermal control systems, in mitigating the risk levels of each failure mode. The proposed methodology offers a structured framework to support predictive maintenance and health management strategies, contributing to improved safety, reliability, and lifecycle optimization of LIBs in Industry 4.0 environments.