Lithium battery SOC estimation method based on adaptive double extended Kalman filtering method

Abstract

The invention discloses an SOC estimation method based on an adaptive double extended Kalman filtering method. The SOC estimation method comprises the following steps: firstly, establishing a second-order RC equivalent circuit model of a lithium battery; then, determining open-circuit voltage and battery equivalent model parameters at different SOC (state of charge) positions of the lithium battery through a pulse charging and discharging experiment; obtaining a function relationship between the open-circuit voltage and the SOC and relationships between other model parameters and different SOCs, the other model parameters including ohmic internal resistance, electrochemical polarization resistance, electrochemical polarization capacitance, concentration difference polarization resistance and concentration difference polarization capacitance values; establishing a state space equation taking the SOC and polarization voltage as state variables and a state space equation taking the ohmicinternal resistance as a state variable; and finally, performing iterative computation to obtain the SOC value of the lithium battery in real time. According to the method, the problem of unknown noise statistical characteristics in the prior art is solved, and meanwhile, the ohmic internal resistance of the battery is estimated by using the Kalman filtering algorithm, so that the model precisionis improved.

Description

technical field [0001] The invention belongs to the technical field of lithium battery state estimation, and in particular relates to an SOC estimation method based on an adaptive double extended Kalman filter method. Background technique [0002] With the development of clean energy, lithium batteries have gained more and more applications in wind, solar energy storage, electric vehicles and other fields. In order to ensure the safe and effective operation of the battery, a battery management system needs to be established to monitor the battery’s voltage, current, temperature and other parameters in real time, and accurately estimate the state of charge (State of Charge, SOC) and health status of the battery. Among them, accurate estimation of SOC is the basis of other state estimation; it can avoid overcharging and overdischarging and prolong battery life; it can also help users make correct product usage plans, which is of great significance. [0003] Currently, methods...

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Problems solved by technology

[0004] The purpose of the present invention is to provide a lithium battery SOC estimation method based on the adaptive double extended Kalman filter method, which solves the problem of unknown statistical characteristics of noise in the prior art, and uses the Kalman filter algorithm to estimate the ohmic internal energy of the battery resistance, improving the accuracy of the model

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