Why Physics-Informed AI is the Future of BMS

Author: Krishna S Hanamaraddi

Developing a generalized AI model for State of Health (SOH) prediction is a “boss level” challenge in battery management.

While standard Neural Networks (LSTMs/GRUs) are excellent at capturing temporal patterns for a specific batch, they often fail to generalize across diverse chemistries (LFP, NMC, NCA), variable C-rates, and fluctuating temperatures. They lack “physical common sense”, sometimes even predicting that a battery’s health can “heal” overnight.

This gap can be filled, by moving toward Physics-Informed Neural Networks (PINNs).

Image credit: Ma, Hongli, Xinyuan Bao, António Lopes, Liping Chen, Guoquan Liu, and Min Zhu. 2024. “State-of-Charge Estimation of Lithium-Ion Battery Based on Convolutional Neural Network Combined with Unscented Kalman Filter” Batteries 10, no. 6: 198.

The Architecture

A Teacher-Student Approach As shown in the framework, we split the complexity into two functional blocks:

1. The Student Net F(·) (Data-Driven): Usually an LSTM or GRU, this network learns directly from sensor features (Voltage, Current, Temp). It’s the “fast learner” that tracks real-time patterns.
2. The Teacher Net G(·) (Physics-Informed): This network acts as the domain expert. It doesn’t just look at raw values; it evaluates gradients (rates of change) to ensure the predictions follow the laws of electrochemistry.

The Triple-Loss concept

The true power of this model lies in its Loss Function. We don’t just tell the AI to “be accurate”; we give it three non-negotiable rules:

• L_data (The Accuracy Rule): Minimizes the error between predicted SOH and the ground truth.
• L_mono (The Monotonicity Rule): Batteries don’t magically recover health. This uses a ReLU penalty to ensure SOH only stays the same or decreases—enforcing physical irreversibility.
• L_PDE (The Physics Rule): Uses Partial Differential Equations to ensure the SOH decay rate matches known degradation dynamics.

The Result

By teaching the AI the laws of physics, we move away from “batch-specific” models toward a generalized domain model. This synergy allows for robust SOH estimation that respects electrochemical reality across any chemistry or condition.

References

1. Wang, F., Zhai, Z., Zhao, Z. et al. Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis. Nat Commun 15, 4332 (2024).

See also: Battery Management System Algorithms, State of Health, State of Charge