Lithium ion battery SOC estimation method based on intelligent adaptive extended Kalman filtering

Abstract

The invention discloses an LIB SOC estimation method based on intelligent adaptive extended Kalman filtering, belongs to the field of power battery management. The LIB SOC estimation method comprisesthe following specific steps of establishing a state equation and a measurement equation according to a RC equivalent circuit, and discretizing to obtain an LIB state space model; constructing a polynomial open-circuit voltage model according to open-circuit voltage and SOC test data and obtaining model parameters; substituting an open-circuit voltage model into a measurement equation, obtaining current and terminal voltage through HPPC test, aiming at minimizing the error between the actually measured terminal voltage and an estimated value, and obtaining equivalent circuit model parameters based on a genetic algorithm; estimating the SOC of the LIB based on intelligent adaptive extended Kalman filtering, wherein the method adopts a dynamic window length innovation sequence to estimate noise and can adapt to the measurement noise change caused by dynamic working conditions; and the comparison result shows that the method has higher estimation precision and better robustness.

Description

technical field [0001] The invention relates to a LIB SOC estimation method, in particular to an IAEKF-based LIB SOC estimation method. Background technique [0002] Lithium-ion batteries (LIBs) have been widely used in consumer electronics, electric vehicles and other fields due to their high energy density, long life, high efficiency and low self-discharge rate. SOC is an important evaluation index of LIB, which reflects the remaining power of the battery. For smart phones, accurate SOC estimation can avoid sudden power failure of mobile phones; for electric vehicles, accurate SOC estimation can avoid car breakdown. In addition, accurate SOC estimation can avoid battery overcharge and overdischarge phenomena. Overestimating SOC can easily lead to LIB overdischarge, and underestimating SOC can easily lead to LIB overcharge. Whether it is overcharge or overdischarge, it will cause damage to the battery and even cause thermal runaway. Therefore, it is necessary to study t...

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Problems solved by technology

In view of the above shortcomings, the researchers proposed a LIB SOC estimation method based on the intelligent adaptive extended Kalman filter (AEKF) based on the covariance matching principle, but the covariance matching principle uses a constant window length error innovation sequence to estimate the noise, The impact of operating conditions on the system is ignored, thus affecting the accuracy of noise estimation

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