_A dual-model battery health assessment framework analyzes real-world voltage data from retired EV batteries in grid storage. Using incremental capacity and probability density methods, it improves aging classification accuracy, supports strategic cell replacement, and enhances storage performance and economic viability under dynamic operating conditions._
New York, NY, United States, February 26, 2026 — As global energy storage expands toward 741 GWh by 2030, operators face challenges managing retired electric vehicle batteries for grid applications. Traditional state-of-health assessment methods developed under laboratory constant-current conditions fail to address real-world complexity, including variable power demands, fluctuating temperature, and dynamic grid requirements. These limitations result in premature failures, reduced capacity, degraded performance, and safety concerns, necessitating robust analytical frameworks validated under actual operating conditions.
The study introduces dual modeling approaches, analyzing charging voltage data from 20 kW/100 kWh systems with retired bus batteries. Incremental capacity analysis employs differential processing, extracting ΔQ/ΔV peaks near 3.33V correlating with degradation, establishing linear relationships with R²=0.9584. Probability density function analysis processes voltage frequencies optimized fora 3.20-3.40V range. Both methods enable aging classification through peak height grading without capacity calibration.
Implementation validation tested 216 cells across 27 modules in commercial facilities operating at a constant 18 kW power. The result demonstratedthat incremental capacity achieved a maximum absolute error 2.37%, outperforming the probability density function methods at 3.58% error. System analysis revealed a state-of-health spanning 60-85%, identifying strategic replacement of four cells increased capacity from 120 Ah to 130 Ah, delivering 8% improvement in storage capability and economic benefits.
Contributing to this research is Xue Li, R&D Engineer at Shanghai Electric Distributed Energy Technology Co., Ltd., holding a Master of Science in Electrical Engineering from Southeast University and a Bachelor’s from Xi’an Jiaotong University. Xue’s technical expertise spans C++, Python, and SQL for energy management systems. Academic achievements include the Journal of Power Sources publication as second author, six first-author publications, and ten first-author patents, the Shanghai Electric Youth Innovation Award, and the Energy Equipment Innovation competition Second Place.
Xue Li’s Professional engineering encompasses grid-side energy storage control for the Gansu Jinchang project, implementing AGC, AVC, and primary frequency regulation, achieving millisecond-level real-time response for large-scale systems. Leadership of retired battery consistency research established machine learning models achieving 7% accuracy while improving storage 5%, resulting in SCI publication and patent. Project management of the GEF6 Global Environment Facility microgrid earned United Nations technical guideline recognition. Additional contributions include PMS1000A controller development, Guinea off-grid microgrid control, and photovoltaic prediction using BP neural networks.
The integration of state-of-health research with energy management systems demonstrates how analytical methods translate into operational improvements. By establishing battery aging assessment frameworks for real operating conditions while developing control strategies for distributed energy systems, this work bridges theoretical analysis with measurable impact, addressing battery management challenges through systematic approaches, delivering improvements in energy storage performance and economic viability.
Contact Info:
Name: Xue Li
Email: [Send Email](https://send.releasecontact.com/89184376)
Organization: Xue Li
Website: [https://scholar.google.com/citations?hl=zh-CN&user=ECbz2rUAAAAJ](https://scholar.google.com/citations?hl=zh-CN&user=ECbz2rUAAAAJ)
Release ID: 89184376
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