Lithium ion energy storage battery SOC online estimation method

Abstract

The invention discloses a lithium ion energy storage battery SOC online estimation method, which comprises the steps of obtaining rated parameters of a to-be-detected lithium ion energy storage battery, and establishing an equivalent circuit model of the to-be-detected lithium ion energy storage battery, identifying model parameters of the established equivalent circuit model on line, establishingan improved adaptive extended Kalman filter of the lithium ion energy storage battery according to the established equivalent circuit model and model parameters obtained by online identification, andinputting the model parameters obtained by online identification into an improved adaptive extended Kalman filter of the lithium ion energy storage battery, and carrying out online estimation on theSOC of the lithium ion energy storage battery. Parameters of the first-order RC equivalent circuit model are identified in real time through a recursive least square method with a forgetting factor, and then the parameters are input into the improved adaptive extended Kalman filter, so that accurate estimation of the SOC of the lithium ion energy storage battery is completed.

Description

technical field [0001] The invention belongs to the technical field of state of charge (SOC, State of Charge) estimation of an energy storage lithium battery, and relates to an online estimation method for the SOC of a lithium ion energy storage battery. Background technique [0002] Lithium-ion energy storage batteries are widely used in renewable energy power generation systems as the main energy storage unit due to their high energy density, long service life and high efficiency. Since the lithium-ion energy storage battery is highly non-linear, a reliable battery management system (BMS, Battery Management System) is required to monitor its status to ensure the safe and reliable operation of the energy storage battery. Estimating the state of charge of lithium-ion energy storage batteries is one of the important tasks of BMS. Accurate SOC estimation can avoid abnormal interruption of the system and prevent permanent damage to the internal structure of the battery caused ...

AI Technical Summary

Problems solved by technology

However, the moving window noise adaptive algorithm based on innovation and residual calculation is too complicated, which brings a large amount of calculation to the estimation of SOC, and the selection of the size of the moving window also greatly affects the estimation accuracy of SOC

In addition, the equivalent circuit model parameters of lithium-ion energy storage batteries are very sensitive to factors such as operating temperature, SOC, and aging degree. The model parameters are identified offline by using the Hybrid Pulse Power Characteristic (HPPC, HybridPulse Power Characteristic) experiment. During the SOC estimation process Setting the parameters of the model to fixed values ​​will cause huge errors in the subsequent SOC estimation

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