| Lithium-ion battery as an energy carrier for new energy applications,how to accurately estimate its State of Health(SOH)is one of the key and core technologies in the field of lithium-ion battery research.However,how to correctly evaluate the performance status and health status of Li-ion battery is not mature,which directly restricts the large-scale and deep application of Li-ion battery.This paper mainly studied the method for estimating the lithium-ion battery state of health,the specific research contents include:(1)Analyzed the lithium-ion battery state of charge,state of health and the estimate has been clear about the current state of health of the basic methods and research progress,pointed out the direction for the paper work.(2)Studied the lithium-ion battery state of charge estimation method.Based on electrochemical single particle model,the introduction of particles in liquid phase diffusion equations describing motion and simplified,combined with the extended Kalman filter and the model parameters are identified,the online estimation of lithium-ion battery state of charge and the estimate value as input of state of health,auxiliary state of health estimation.(3)Studied the method for estimating the lithium-ion battery state of health.Lithium-ion battery cycle aging experiments,using experimental data offline training Elman neural network.The trained network model used on-line to estimate the lithium-ion battery state of health.(4)Set the battery charge and discharge experiment platform.The platform allows maximum voltage 30 V,current 30 A and any condition for battery charge and discharge to simulate the actual working condition.In this paper,the experimental data in addition to the NASA public data,all collected by this platform. |
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