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

Abstract

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.

DOI: 10.5281/zenodo.15927264