Year: 2025
Type: article
Abstract: DegradAI is a chemistry-aware deep learning framework designed to predict long-term lithium-ion battery capacity fade using limited early-cycle data (~5 h), enabling robust diagnostics across diverse chemistries and real-world operating conditions. Its novelty lies in explicit cathode chemistry identification (LiNiCoAlO 2 , LiNiMnCoO 2 , LiFePO 4 , and LiCoO 2 ) integrated with a dynamically adaptive architecture. Comprehensive validation across varied temperatures, discharge rates (0.5–3 C), and dynamic cycling profiles demonstrates scalability and transferability. High accuracy is achieved across most chemistries, with a noted variation for LiNiCoAlO 2 due to complex degradation mechanisms. DegradAI can also generate high-fidelity synthetic data; augmenting training sets with an 80:20 synthetic-to-real data mix significantly reduced experimental data needs, improving relative prediction accuracy by up to ~22% while incurring a quantifiable trade-off (~7% increase) in absolute error metrics. This scalable framework is a valuable tool for battery health management in applications like electric vehicles, consumer electronics, and grid storage, adaptable to emerging chemistries.
Source: npj Clean Energy
Institution: Purdue University West Lafayette
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,590
Awards: G4720003262, G6561927106, G8876996369
OpenAlex ID: W4413253963