Researchers have reviewed the challenges and prospects of real-world battery status prediction within Industry 4.0, highlighting how lithium-ion battery diagnostics must adapt to dynamic operating environments, heterogeneous data, and growing demands for intelligent, real-time decision-making. The review focuses on battery charge, health, lifespan, and safety prediction across applications including portable devices, electric vehicles, and energy storage systems.
Battery performance is central to the expansion of electrified transportation and green energy systems. As lithium-ion batteries become more widely deployed, the ability to diagnose their condition accurately becomes increasingly important. Battery management systems must estimate state of charge, state of health, remaining useful life, and safety-related risks, often under operating conditions that differ substantially from controlled laboratory tests.
The review emphasizes that real-world battery diagnostics are difficult because batteries are exposed to complex and changing environments. Temperature, load profile, charging pattern, user behavior, cell-to-cell variation, and data quality can all influence measured battery behavior. These factors make it harder to build diagnostic models that remain reliable outside carefully designed experiments. In practical applications, a model must do more than perform well on one dataset; it must tolerate imperfect data and changing conditions.
Industry 4.0 introduces new opportunities for this problem, but also new complexity. According to the article, intelligent diagnostic technologies can benefit from Internet of Things connectivity, machine learning, and big data analytics. Connected battery systems can generate richer data streams, while cloud-based artificial intelligence can support automated decision-making and resource management. However, these same systems also raise questions about scalability, computational cost, model integration, and data consistency.
A central theme of the review is the need to integrate data and models more effectively in field prognostics. Battery diagnostic research often separates physics-based understanding, laboratory testing, and data-driven prediction. In real-world systems, these elements need to work together. The review discusses the feasibility of integrating heterogeneous data patterns with intelligent models so that diagnostic systems can better reflect how batteries behave in actual operating environments.
The article also identifies interdisciplinary diagnostic tasks as an important direction. Battery status prediction is not only an electrochemical modeling problem or a machine learning problem. It requires knowledge from materials science, control engineering, data science, power electronics, transportation systems, and industrial operations. The review highlights the value of academic and industrial collaboration, particularly because real-world diagnostic problems often depend on data access, deployment constraints, and application-specific requirements.
Another major issue is data heterogeneity. Batteries used in electric vehicles, stationary storage, and portable electronics may experience very different duty cycles and monitoring conditions. Even within one application, sensor quality, sampling frequency, usage history, and cell manufacturing differences can vary. The review suggests that future diagnostic approaches will need to make better use of diverse data sources while remaining interpretable, scalable, and computationally practical.
For smart and sustainable industrial systems, the review points to cloud-based AI as a promising tool. Such systems could help automate battery lifespan and safety diagnostics, support predictive maintenance, and improve resource allocation. If implemented carefully, Industry 4.0 technologies may allow battery systems to move from periodic or reactive assessment toward continuous and intelligent health management.
The review also makes clear that important challenges remain. Future work will need to improve model robustness, strengthen data-model integration, manage computational costs, and validate methods under broader real-world conditions. Even so, the article offers a useful roadmap for battery diagnostics in smart, digital, and sustainable environments. As electrification expands, better real-world battery status prediction could support safer electric vehicles, more reliable energy storage, and more sustainable industrial practices.
Reference
Author:
Xudong Qu a , Jingyuan Zhao b , Hui Pang c , Michael Fowler d , Andrew F. Burke b
Title of original paper:
Challenges and prospects in real-world battery status prediction within Industry 4.0
Article link:
https://www.sciencedirect.com/science/article/pii/S2773153725000489
Journal:
Green Energy and Intelligent Transportation
DOI:
10.1016/j.geits.2025.100298
Affiliations:
a Hubei Longzhong Laboratory, Hubei University of Arts and Science, Xiangyang 441000, China
b Institute of Transportation Studies, University of California Davis, Davis, CA 95616, USA
c School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, Xi'an 710048, China
d Department of Chemical Engineering, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada
Green Energy and Intelligent Transportation
Experimental study
Not applicable
Challenges and prospects in real-world battery status prediction within Industry 4.0
2-Jan-2026