Challenges and prospects in real-world battery status prediction within Industry 4.0

Abstract

The performance of lithium-ion batteries is critical across a range of applications, including portable devices, electric vehicles, and energy storage systems. Effective diagnostics of these battery systems require evaluating multiple factors such as charge, health, lifespan, and safety. Diagnosing batteries under real-world conditions presents notable challenges, particularly due to dynamic operating environments, inconsistent data quality, and cell-to-cell variations. These challenges complicate diagnostics further when considering the need for model integration, scalability, and managing computational costs. Industry 4.0 introduces new opportunities for intelligent, real-time battery performance evaluation, but also brings its own complexities. This review examines several real-world battery diagnostic scenarios, identifying key obstacles. We provide an in-depth analysis of the integration of intelligent diagnostic technologies in Industry 4.0, with a focus on IoT connectivity, machine learning techniques, and big data analytics. Moreover, we outline promising research directions, such as fostering interdisciplinary collaboration, improving data and model integration, utilizing diverse data patterns, and strengthening partnerships between academia and industry. Cloud-based AI solutions not only enhance diagnostics related to battery lifespan and safety but also align with the Industry 4.0 framework by facilitating automated decision-making and resource management. This review highlights recent advancements and identifies critical challenges that require further exploration. It aims to support sustainable industrial practices and drive the adoption of green technologies within smart, digital and sustainable environments. It aims to promote intelligent industrial practices and accelerate the adoption of battery technologies within smart, digital, and eco-friendly environments.