Manufacturer of industry batteries are obliged to take back used batteries and initiate the classification process for reuse or recycling. In order to realize this, within the framework of the publicly funded project ReCycle, three diagnostic stages have been defined and evaluated. The initial stage takes place in the field during usage, where the current condition like State of Health and anomaly detection of the battery is made possible by collected data. This stage aims to detect battery malfunctions at an early stage to enable predictive maintenance and extend the first life. The second stage of the diagnostic approach is used for maintenance, aiming to keep the service deployment as short as possible. Therefore, a tester is developed combining data driven approaches with established measurement methods. During dismantling of the system, the third diagnostic stage is utilized, which is intended to enable the identification of reusable components. Here, in addition to functional testing, optical and olfactory recognition are carried out. These results support the decision process whether to reuse, redesign, refurbish or recycle the used components.
In the field, the first stage of the diagnostic approach involves data driven approaches for monitoring and evaluating the performance, health, and charge state of industrial batteries under real-world conditions. In the case of maintenance on site, the second stage of the diagnostic approach involves generating test sequences using onboard diagnostic results and existing symptoms through a tester device. This enables the determination of the cause of the error. Additionally, the device includes safety evaluation methods such as gas classification and structure recognition, contributing to a comprehensive understanding of the battery’s operational safety.
As batteries progress to the workshop environment for maintenance, the final phase of the diagnostic approach, remanufacturing, and eventual recycling takes place. Implementing effective diagnostic strategies is crucial for maximizing resource recovery and minimizing environmental impact. This step focuses on techniques for sorting, characterizing, and reusing battery components. Particularly, the integration of technologies such as structure recognition, voltage and current profiling, electrochemical impedance spectroscopy (EIS), and data-driven approaches plays a critical role in providing insights into the battery’s condition and remaining useful life.
By providing a comprehensive overview of the industrial battery diagnosis throughout its entire lifecycle, from field operations to recycling processes, this paper aims to contribute to the development of more reliable, cost-effective, and environmentally sustainable energy storage solutions for diverse industrial applications. Moreover, the study underscores the synergy between field and workshop diagnostics, emphasizing the importance of real-time data transfer and remote monitoring systems. This integration of diagnostic phases not only extends the lifespan of industrial batteries but also enhances sustainability through the implementation of efficient recycling practices.