Physics-Encoded Neural Networks for Enhanced Li-Ion EV Battery Prognostics with EMA Rectification

As Lithium-ion battery applications transition into mission-critical aerospace and autonomous sectors, the requirement for high-precision prognostics has surpassed the capabilities of standard deep learning. Current state-of-the-art models, such as the AccuCell Prodigy, exhibit a precision ceiling at the 10^-2 Mean Absolute Error scale and suffer from temporal lag during the electrochemical knee-point. This paper proposes the Physics-Encoded Neural Network with Exponential Moving Average, a framework that embeds a 5th-order non-linear degradation manifold directly into the neural architecture. By utilizing a dual-stage Exponential Moving Average rectification suite, the model suppresses stochastic sensor jitter to achieve a rectified 10^-5 precision floor. Validated against the NASA Prognostics Data Repository, the PENN-EMA demonstrates a three-order-of-magnitude improvement in accuracy and successfully eliminates recursive error drift. Results indicate an R² score of 0.9912, proving that coupling electrochemical constraints with gradient rectification provides the mathematical reliability necessary for zero-fault Battery Management Systems.