Learning the P2D Model for Lithium-Ion Batteries with SOH Detection
This work addresses the need for efficient battery management systems in applications like electric vehicles, though it is incremental as it applies an existing method (CNN) to a new domain-specific problem.
The paper tackled the computational complexity of the Pseudo Two Dimensional (P2D) electrochemical model for lithium-ion batteries by replacing it with a Convolutional Neural Network (CNN) surrogate model, achieving accurate simulation of lithium-ion concentration profiles and adaptability to State of Health (SOH) changes.
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).