Smart Battery Management and Diagnostics: AI, IoT, and Digital Twin Approaches

The accelerating global adoption of electric vehicles, renewable energy solutions, and portable electronics has elevated the importance of battery efficiency, reliability, and safety. While traditional battery management systems (BMS) offer fundamental monitoring and control, they often lack the sophistication required for accurate condition prediction, optimal utilization, and proactive maintenance. Cutting-edge technologies such as artificial intelligence (AI), the Internet of Things (IoT), and digital twins present transformative opportunities to revolutionize battery management. These innovations enable advanced real-time diagnostics, predictive analytics, and holistic system optimization.

The integration of AI and IoT facilitates continuous data acquisition and intelligent, data-driven decision-making, enhancing operational awareness and responsiveness. At the same time, digital twins—virtual replicas of physical battery systems—empower researchers and engineers to conduct comprehensive simulations, forecast performance, and identify faults without intrusive intervention or downtime.

The primary goal of this Research Topic is to develop and showcase an advanced framework for Smart Battery Management and Diagnostics that synergistically combines AI algorithms, IoT connectivity, and digital twin models. Key objectives include:

- Real-time monitoring of battery state and performance
- Precise estimation of battery life, state of health (SoH), and early detection of potential failures
- Implementation of proactive diagnosis and maintenance strategies
- Enhancement of overall system efficiency, safety, and lifespan
- Demonstration of solutions in diverse, real-world applications
- Application of virtual simulations for predictive performance and robust fault diagnosis

This Research Topic welcomes contributions in areas such as:

- AI-driven predictive models for battery diagnostics and health estimation
- IoT-based architectures for real-time data collection and monitoring
- Digital twin frameworks for comprehensive modeling, simulation, and scenario analysis
- Validation and benchmarking of these approaches through both simulation studies and experimental data

Researchers, technologists, and practitioners are invited to present novel methodologies, case studies, and prototype demonstrations highlighting the integration and impact of AI, IoT, and digital twin approaches in next-generation battery management.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• Mini Review
• Original Research
• Perspective
• Review

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• Mini Review
• Original Research
• Perspective
• Review

Keywords: Smart Battery Management, Artificial Intelligence (AI), Internet of Things (IoT), Digital Twin, Battery Diagnostics, Battery Energy Storage System (BESS)

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.