公开(公告)号：US20230333172A1

公开(公告)日：2023-10-19

申请号：US18300116

申请日：2023-04-13

Applicant: Beijing Institute of Technology

Inventor： Huixing MENG ,  Mengyao GENG ,  Cheng WANG ,  Jinduo XING

IPC: G01R31/387 ,  G01R31/367

CPC classification number: G01R31/387 ,  G01R31/367

Abstract: One or more embodiments of the present specification provide a method and device for capacity degradation prediction of a lithium-ion battery. The method comprises the following steps: acquiring an original battery discharge capacity; decomposing the original battery discharge capacity through a predetermined mode decomposition method to obtain battery discharge capacities composed of a plurality of different frequency signals; inputting the respective frequency signals into a pre-constructed capacity prediction model to obtain capacity prediction results corresponding to the respective frequency signals; selecting capacity prediction result that satisfies a predetermined relevance condition corresponding to the respective frequency signals; and reconstructing the finally predicted battery discharge capacity according to the capacity prediction result that satisfies the predetermined relevance condition.

公开(公告)号：US20240119323A1

公开(公告)日：2024-04-11

申请号：US18318846

申请日：2023-05-17

Applicant: Beijing Institute of Technology

Inventor： Huixing MENG ,  Qiaoqiao YANG ,  Zhiming YIN ,  Cheng WANG ,  Te HAN ,  Jinduo XING

IPC: G06N7/01 ,  G06N20/10

CPC classification number: G06N7/01 ,  G06N20/10

Abstract: One or more embodiments of the present description provide a method and device for risk prediction of thermal runaway in LIB. The method includes: acquiring knowledge of a mechanism for thermal runaway in LIB; describing an evolution process of thermal runaway in LIB by adopting a fault tree; mapping a fault tree structure to a dynamic Bayesian network model for thermal runaway in LIB to obtain quantitative results of a risk of thermal runaway in LIB; and taking the quantitative results of a dynamic Bayesian network as inputs of a machine learning model to obtain prediction results of the risk of thermal runaway. By using the method in the present embodiment, an evolution trend of battery thermal runaway can be predicted by fusing multiple thermal runaway causes and multi-source data, and thus, the prediction results are relatively accurate.