Announcements

All papers published in 2024 in Batteries (ISSN 2313-0105) were considered for the Batteries 2024 Best Paper Award.

After a thorough evaluation of the originality and significance of the papers, citations, and downloads, the following winner was selected:

“Review on Modeling and SOC/SOH Estimation of Batteries for Automotive Applications”
by Pierpaolo Dini, Antonio Colicelli and Sergio Saponara
Batteries 2024, 10(1), 34; https://doi.org/10.3390/batteries10010034
Available online: https://www.mdpi.com/2313-0105/10/1/34

Information about authors:

The following is an interview with Dr. Pierpaolo Dini:

Background and Inspiration

1. Could you introduce yourself or your research group?

I am a Researcher and Assistant Professor of Electronics at the Department of Information Engineering of the University of Pisa, where my research focuses on the design of advanced electronic systems for automotive, industrial, and energy applications. My work mainly covers battery management systems, power electronics, electric drives, embedded systems, model-based design, and the integration of artificial intelligence for monitoring, diagnostics, and predictive maintenance. A distinctive aspect of my research is the combination of rigorous system modeling with practical implementation on resource-constrained embedded platforms, aiming to bridge the gap between theoretical developments and real industrial applications.

This award-winning review was developed within the framework of a European collaborative research project involving universities, research centers, and industrial partners. Our research group strongly believes in multidisciplinary collaboration, bringing together expertise in electronics, control engineering, embedded systems, battery technologies, and artificial intelligence. Working in such an international environment allowed us to continuously compare different perspectives and practical requirements, ultimately helping us produce a review that is not only scientifically comprehensive but also highly relevant for researchers and engineers working on next-generation Battery Management Systems.

2. Can you please share what inspired your research?

The inspiration for this review emerged naturally from our involvement in a European collaborative research project focused on electrified mobility and advanced battery management systems. During the project, we continuously interacted with researchers, industrial partners, and technology developers working on different aspects of battery modeling, state estimation, and system integration. We realized that although a vast amount of scientific literature was available, the knowledge was often fragmented across different disciplines, ranging from electrochemical and equivalent circuit models to artificial intelligence and data-driven estimation techniques.

Our objective was therefore not simply to summarize the literature, but to provide a structured framework capable of connecting modeling approaches with state of charge (SOC) and state of health (SOH) estimation methods, highlighting their respective advantages, limitations, and application domains. We wanted to create a reference that could support both newcomers entering the field and experienced researchers seeking a comprehensive overview of the rapidly evolving battery research landscape.

3. In your career of battery research, which mentor or predecessor has had the greatest influence on your scientific thinking? How does this influence reflect on the writing style of this paper or the choice of research path?

Prof. Sergio Saponara has undoubtedly been the person who has had the greatest influence on my scientific development. Throughout my PhD and subsequent research career, he has been much more than a supervisor. He has taught me to approach research with scientific rigor while always keeping a strong engineering perspective, ensuring that theoretical developments ultimately address real industrial challenges.

One of the most valuable lessons I learned from him is the importance of combining analytical thinking with practical applicability. Rather than pursuing elegant theoretical solutions alone, he encouraged me to develop methodologies that could eventually be implemented, validated, and transferred into real engineering systems.

This philosophy is clearly reflected in our review. Instead of presenting battery modeling and estimation techniques as isolated academic topics, we organized the paper from a system-level engineering perspective, discussing how different approaches fit specific automotive applications and Battery Management System requirements. I believe this practical viewpoint has been one of the key strengths appreciated by the research community.

Publishing Experience

4. Why did you choose to publish with Batteries, and how was your experience?

We chose Batteries because it has established itself as an important international forum dedicated to battery science, technologies, and applications, attracting contributions from both academia and industry. Since our review aimed to address researchers working across multiple disciplines—including electronics, electrochemistry, control engineering, artificial intelligence, and automotive systems—we considered Batteries the most appropriate venue to reach this diverse audience.

Our publishing experience was extremely positive. The editorial process was well organized, and the peer-review comments were constructive and technically valuable. The reviewers encouraged us to further improve the clarity and completeness of the manuscript, ultimately strengthening its quality. Receiving the Best Paper Award is particularly meaningful because it confirms that the paper has generated interest within the scientific community and that the effort invested in producing a comprehensive and balanced review has been appreciated.

Research Process and Challenges

5. What was the biggest challenge you faced while writing this paper, and how did you overcome it?

The greatest challenge was not collecting the literature itself, but organizing an extremely broad and heterogeneous body of knowledge into a coherent and useful framework. Battery research spans multiple disciplines, including electrochemistry, electrical engineering, control theory, embedded systems, and artificial intelligence, each with its own terminology, evaluation criteria, and research priorities.

Our objective was to develop a review that would go beyond a simple collection of existing publications. We carefully analyzed the relationships between battery models and SOC/SOH estimation algorithms, identifying common principles, practical trade-offs, computational requirements, and typical application scenarios. Maintaining objectivity throughout this process required extensive discussion among the authors and multiple revisions of the manuscript.

In the end, we believe this systematic organization transformed the review into a practical reference rather than merely a bibliographic survey.

6. How did feedback during your research influence your direction?

Feedback played a fundamental role throughout both the research activities that inspired this review and the manuscript preparation itself. Working within an international collaborative project meant continuously discussing ideas with researchers from different scientific backgrounds as well as engineers from industrial partners. These interactions often challenged our initial assumptions and encouraged us to consider practical constraints alongside theoretical developments.

The peer-review process provided an additional opportunity to refine the manuscript. The reviewers’ comments motivated us to improve the organization of several sections, clarify comparisons between different methodologies, and better highlight the practical implications of the reviewed techniques.

Overall, these different forms of feedback reinforced one important lesson: high-quality research is rarely the result of individual work alone but rather emerges through continuous scientific discussion, constructive criticism, and interdisciplinary collaboration.

7. What are the current challenges in the battery research field, and how can they be addressed?

Battery technologies are advancing at an extraordinary pace, creating new challenges that extend far beyond electrochemistry alone. One of the major issues is the development of accurate, robust, and computationally efficient battery management systems capable of operating reliably under highly dynamic real-world conditions throughout the battery lifetime.

Another important challenge concerns the integration of physics-based models with artificial intelligence. While data-driven approaches have demonstrated remarkable capabilities, they often face limitations regarding interpretability, robustness, and generalization under operating conditions not represented in the training data. Future research should therefore focus on hybrid methodologies that combine physical knowledge with machine learning, leveraging the strengths of both paradigms.

Finally, stronger collaboration between academia and industry will be essential to validate new methodologies using realistic datasets and practical operating conditions, accelerating the transfer of scientific advances into commercial battery systems.

Teamwork and Collaboration

8. What role did you play in your research team, and how did teamwork affect the paper’s outcome?

My primary responsibility was coordinating the scientific development of the review, including the definition of its overall structure, the critical analysis of the literature, and the integration of the various technical contributions into a coherent manuscript. I also led much of the writing process, ensuring consistency across the different sections and maintaining a system-level perspective throughout the paper.

This work greatly benefited from teamwork. Professor Sergio Saponara continuously provided scientific guidance and strategic direction, helping us maintain both technical rigor and a broader engineering vision. Antonio Colicelli contributed through his experience in managing the collaborative research activities within the European project and supported the preparation of the manuscript by ensuring alignment with the project’s technical objectives.

The combination of complementary expertise allowed us to produce a review that is technically comprehensive while remaining closely connected to the practical challenges encountered in industrial and research environments.

Future Insights

9. What trends and technologies do you see shaping the future of battery technology?

I believe the future of battery technology will be increasingly driven by the convergence of advanced modeling, artificial intelligence, and digitalization. Battery management systems will evolve from passive monitoring platforms into intelligent systems capable of continuously estimating battery states, predicting degradation, optimizing charging strategies, and supporting predictive maintenance throughout the battery lifecycle.

Hybrid approaches combining physics-based models, data-driven techniques, and digital twins are likely to become increasingly important, enabling more accurate and explainable battery diagnostics while maintaining computational efficiency suitable for embedded implementation.

At the same time, the availability of connected vehicles and cloud infrastructures will facilitate continuous data collection and fleet-level analysis, allowing battery models to be continuously updated and refined. These developments will contribute not only to improving battery safety and reliability but also to extending battery lifetime and enhancing the sustainability of electrified transportation.

Advice and Impact

10. What impact do you hope your research will have, and what key innovation do you see in your paper?

I hope this review will serve as a valuable reference for researchers, engineers, and graduate students working on battery technologies, particularly those entering the field of battery management systems. Rather than promoting a specific modeling or estimation technique, our objective was to provide a structured and balanced framework that helps readers understand when different approaches are most appropriate, what assumptions they rely on, and which trade-offs they involve.

The main contribution of the paper lies in its system-level perspective. We integrated battery modeling techniques and SOC/SOH estimation methodologies into a unified framework while emphasizing their practical implications for automotive applications. By connecting theoretical developments with engineering implementation aspects, we aimed to facilitate informed design decisions and stimulate future research toward more reliable, explainable, and computationally efficient battery management solutions.

Ultimately, I hope this work encourages stronger collaboration between academia and industry, contributing to the development of safer, more efficient, and more sustainable electrified mobility systems.