Batteries, Vol. 8, Pages twenty nine: Online State-of-Health Estimation of Lithium-Ion Battery Based on Incremental Capacity Curve plus BP Neural Network
Batteries doi: 10. 3390/batteries8040029
Writers: Hongye Lin Longyun Kang Di Xie Jinqing Linghu Jie Li
Lithium-ion batteries (LIBs) have been widely used in numerous fields. In order to ensure the safety associated with LIBs, it is necessary to accurately estimate associated with the state of wellness (SOH) from the LIBs. This particular paper proposes a SOH hybrid estimation method based on incremental capacity (IC) curve and back-propagation neural network (BPNN). The ac electricity and current data of the LIB during the constant current (CC) charging process are used to convert into IC figure. Taking into account the incompleteness of the actual charging process, this paper divides the IC competition into multiple voltage segments for SOH prediction. Related BP neural network is definitely established in multiple voltage segments. The experiment divides the LIBs into five groups to carry out there your aging experiment under different discharge conditions. Aging test data are used in order to establish the non-linear romantic relationship between the decline of SOH and the change of IC curve by BP neural network. Experimental results show that in all voltage segments, the particular maximum mean absolute mistake does not exceed 2%. The SOH estimation technique proposed in this analysis means that we can embed the SOH estimation function in battery power management system (BMS), and can realize high-precision SOH online estimation.
