Infinitely-Middle State, Probabilistic Health and Interval Energy Forecasting: A Neutrosophic Framework for Electric-Vehicle Electrochemical Energy Storage Technology

This paper presents a new mathematical framework to evaluate the performance of electrochemical storage cells used in electric vehicles. The method applies neutrosophic logic and probability to model uncertainty, inconsistency, and partial truth in cell behavior. Three original components are introduced. First, the law of infinitely many middles is used to describe each battery cell’s operational state using neutrosophic triplets. Second, a statistical health model is developed based on neutrosophic means and deviations. Third, a neutrosophic interval-based forecasting model predicts future energy delivery under uncertain conditions. All concepts are defined within a neutrosophic ring structure to ensure mathematical consistency. The paper includes full equations, definitions, and detailed examples with real-number calculations. Tables and case studies are provided to demonstrate the model’s validity. This approach offers a structured and realistic tool to support energy reliability decisions in electric vehicle systems.