Learning the P2D Model for Lithium-Ion Batteries with SOH Detection

TL;DR

This paper introduces a CNN surrogate model for the P2D electrochemical battery model, enabling efficient SOH detection and accurate lithium-ion concentration predictions, simplifying complex battery simulations.

Contribution

It presents a novel CNN-based surrogate for the P2D battery model that accurately predicts battery behavior and adapts to SOH changes, reducing computational complexity.

Findings

CNN accurately captures lithium-ion concentration profiles

Surrogate model reduces simulation complexity

Model adapts to battery SOH variations

Abstract

Lithium ion batteries are widely used in many applications. Battery management systems control their optimal use and charging and predict when the battery will cease to deliver the required output on a planned duty or driving cycle. Such systems use a simulation of a mathematical model of battery performance. These models can be electrochemical or data-driven. Electrochemical models for batteries running at high currents are mathematically and computationally complex. In this work, we show that a well-regarded electrochemical model, the Pseudo Two Dimensional (P2D) model, can be replaced by a computationally efficient Convolutional Neural Network (CNN) surrogate model fit to accurately simulated data from a class of random driving cycles. We demonstrate that a CNN is an ideal choice for accurately capturing Lithium ion concentration profiles. Additionally, we show how the neural network model can be adjusted to correspond to battery changes in State of Health (SOH).