Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis

Some introduction to our research and the motivations and ideas behind it
Published in Mechanical Engineering
Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis

Share this post

Choose a social network to share with, or copy the shortened URL to share elsewhere

This is a representation of how your post may appear on social media. The actual post will vary between social networks

Related to research

Lithium-ion batteries find applications across various domains of life, garnering increasing attention towards their health status. Presently, a plethora of machine learning-based methods for estimating battery State of Health (SOH) have emerged. However, these methods wholly forsake physical insights, leading to results that are non-interpretable and volatile. Physics-informed machine learning (PIML) represents a novel paradigm for addressing scientific queries employing machine learning techniques, effectively amalgamating physical insights with machine learning models, thereby promising enhanced estimation outcomes. Consequently, our study proposes the utilization of physics-informed neural network (PINN) for estimating battery SOH.


The schematic depiction of our study's framework is illustrated in Figure 1, while detailed technical nuances can be perused in our paper.

Figure 1 The flowchart of the proposed PINN for SOH estimation.


The motivations and ideas behind the research

Throughout our investigation, we observed a profusion of published articles concerning SOH estimation. Nevertheless, the features devised in these articles predominantly cater to specific datasets, rendering them inapplicable when applied to different datasets. Hence, our aim was to devise a more universally applicable feature set. Consequently, we analyzed copious battery degradation data, culminating in the formulation of our own feature design methodology.


On another front, machine learning-based SOH estimation methods grounded in neural networks exhibit considerable prediction error volatility, with model accuracy heavily contingent upon data quality. Hence, we pondered: Could the integration of certain physical insights mitigate these issues to a certain extent? Consequently, we proposed leveraging Physics-Informed Neural Network (PINN) for SOH estimation. Subsequent to extensive analysis and validation, we ascertained the validity of our approach.


In summation, we proffered the PINN for battery SOH estimation, substantiating its superiority through experimentation. Furthermore, we validated its advancements in small-sample learning and transfer learning across batteries with differing chemical compositions and charge/discharge profiles. Physically-informed neural networks hold promise as an efficacious avenue for employing artificial intelligence in addressing real-world engineering quandaries. By harmonizing traditional physical models with neural network models, it becomes feasible to more accurately capture the intricate dynamical behavior of battery systems, thereby fostering more reliable and precise state estimation. However, this nascent domain still necessitates further scholarly exploration. The PINN framework proposed herein serves as an initial exploration of such methodologies, stimulating further research endeavors in this domain.


Fujin Wang1,2, Zhi Zhai1,2, Zhibin Zhao1,2, Yi Di1,2, Xuefeng Chen1,2

1 National and Local Joint Engineering Research Center of Equipment Operation Safety and Intelligent Monitoring, Xi’an Jiaotong University, Xi’an, 710049, Shaanxi, PR China.

2 School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, 710049, Shaanxi, PR China.

Please sign in or register for FREE

If you are a registered user on Research Communities by Springer Nature, please sign in

Subscribe to the Topic

Renewable Energy
Technology and Engineering > Mechanical Engineering > Mechanical Power Engineering > Renewable Energy

Related Collections

With collections, you can get published faster and increase your visibility.

Materials and devices for separation, sensing, and protection

In this Collection, the editors of Nature Communications and Communications Materials welcome the submission of primary research articles that highlight the development and application of functional materials in the areas of separation, sensing, and protection.

Publishing Model: Open Access

Deadline: Jun 30, 2024

Applied Sciences

This collection highlights research and commentary in applied science. The range of topics is large, spanning all scientific disciplines, with the unifying factor being the goal to turn scientific knowledge into positive benefits for society.

Publishing Model: Open Access

Deadline: Ongoing