In the rapidly evolving landscape of energy storage, the management of retired lithium-ion batteries has become a critical challenge and opportunity. As electric vehicles (EVs) and energy storage systems proliferate, the volume of retired lithium-ion batteries (LiBs) is set to skyrocket, with estimates suggesting 5 million tons annually by 2030. The environmental and economic implications of this surge are profound, driving the need for innovative solutions to monitor and repurpose these batteries effectively. Enter Yu He, a researcher from the School of Engineering at King Mongkut’s Institute of Technology Ladkrabang (KMITL) in Bangkok, Thailand, who has developed a groundbreaking model to estimate the State of Health (SoH) of retired LiBs.
He’s model, published in the journal Energies, leverages a multi-input metabolic gated recurrent unit (MM-GRU) to predict the SoH of retired batteries with unprecedented accuracy. Traditional methods for SoH estimation, such as experimental and model-based approaches, often fall short due to their time-consuming nature and complexity. Data-driven methods, while more efficient, typically require large datasets and fixed input windows, limiting their robustness and generalization.
He’s MM-GRU model addresses these limitations by dynamically updating input data through a metabolic mechanism, requiring only four cycles of historical data to predict subsequent cycles accurately. The model uses constant-current charging time, charging current area, and the 1800 s voltage drop as key health indicators, offering a more comprehensive and adaptable approach to SoH estimation.
“The MM-GRU model not only enhances the breadth and precision of SoH monitoring for retired batteries but also offers robust technical support for their safe deployment and asset optimization in energy storage systems,” He explains. This breakthrough could revolutionize the way retired LiBs are managed, extending their lifespan and maximizing their economic value.
The implications for the energy sector are vast. Accurate SoH estimation enables safer utilization, efficient management, and intelligent scheduling of retired batteries. This translates to significant economic and social benefits, including reduced environmental pollution and optimized resource utilization. He’s research demonstrates that the MM-GRU model outperforms traditional machine learning algorithms such as SVM, BPNN, and GRU, achieving a root mean square error of less than 1.2% and a mean absolute error of less than 1%.
As the demand for clean energy and electric transportation continues to grow, the ability to repurpose retired LiBs becomes increasingly vital. He’s MM-GRU model offers a promising solution, paving the way for more sustainable and efficient energy storage systems. By enabling precise SoH estimation across various operating conditions and battery types, this innovative approach could reshape the future of battery management in the energy sector.