摘要
Dear Editor,State of health(SOH)estimation is critical for the management of lithium-ion batteries(LIBs).Data-driven estimation methods are appealing with the availability of real-world battery data.However,time-and data-costly training for batteries with different chemistries and models barriers their efficient deployment.
基金
partially supported by the Shenzhen Municipal Science and Technology Innovation Committee(RCBS20210609104423057)
Fujian Key Laboratory of New Energy Generation and Power Conversion(KLIF-202104)
the National Natural Science Foundation of China(52072038)。
