A novel fusion model for enhanced fault diagnosis in lithium-ion batteries for electric vehicle safety

To achieve carbon neutrality, the transportation industry is transitioning toward electrification, with electric vehicles (EVs) playing a key role in reducing emissions. As a result, EV adoption has surged in recent years due to advancements in battery technology, particularly lithium-ion batteries, which are favored for their high energy density and environmental benefits. However, safety concerns have also emerged, especially thermal runaway, which could potentially lead to catastrophic failures like fires and explosions. Despite substantial research into battery safety and failure mechanisms, accurate diagnosis remains challenging. This study focuses on improving fault diagnosis by considering both internal battery characteristics and operational behaviors, and develops a fusion model that leverages electrochemical model parameters in conjunction with risk accumulation outcomes, enabling a comprehensive assessment of battery safety. The proposed methodology has been validated on two vehicle models, demonstrating its accuracy and broad applicability, as well as its potential in advancing the EV industry, particularly in the areas of fault diagnosis and the design of high-safety EVs.