Reliable lithium-ion battery health assessment is vital for safety. Here, authors present a physics-informed neural network for accurate and stable state-of-health estimation, overcoming challenges of varied battery types and usage conditions.
- Fujin Wang
- Zhi Zhai
- Xuefeng Chen
