Online parameter identification and SOC joint estimation method based on variable forgetting factor

Abstract

The invention discloses an online parameter identification and SOC joint estimation method based on a forgetting factor. The method comprises the following steps: establishing a lithium battery second-order equivalent circuit model; determining a function relationship between each parameter of the circuit and the SOC, and establishing a state-space equation of the lithium battery; initializing an SOC state variable and a parameter state variable, and estimating the SOC of the lithium battery by using an extended Kalman filtering algorithm under a microscopic time scale; when the SOC estimation of the lithium battery reaches a preset time, switching to a macroscopic time scale, identifying equivalent circuit parameters by using a variable forgetting factor recursive least square method, and finally updating the equivalent circuit parameters and the state-space equation of the lithium battery to carry out a next round of calculation. According to the method, online parameter identification is performed on the lithium battery model through the variable forgetting factor recursive least square method, and the SOC of the lithium battery is estimated in combination with the extended Kalman filtering algorithm, so that the problem that the forgetting factor is fixed in the forgetting factor recursive least square method is solved, online updating of the parameters of the lithium battery is realized, and the SOC estimation precision of the lithium battery is improved.

Description

technical field [0001] The invention relates to the field of state of charge estimation of lithium batteries, in particular to an online parameter identification and SOC joint estimation method based on variable forgetting factors. Background technique [0002] With the advancement of science and technology, in order to comply with the concept of sustainable development, it has become a global consensus to seek clean and green energy instead of traditional fossil energy; therefore, electric vehicles have developed rapidly, and lithium batteries are the core of energy for electric vehicles. Ensure the safety of the car, and can effectively improve the battery life. [0003] At present, lithium battery SOC estimation mainly includes traditional methods based on battery characteristics, data-driven methods, methods based on battery models and observer technology, and methods based on model and observer technology are the most widely studied, mainly using the equivalent of lithi...

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Problems solved by technology

[0003] At present, lithium battery SOC estimation mainly includes the traditional method based on battery characteristics, data-driven method, method based on battery model and observer technology, and the method based on model and observer technology is the most widely studied, and the equivalent of lithium battery is mainly used. The circuit model is combined with the Kalman filter technology to estimate the battery SOC. The battery model parameters of this method are usually identified by an optimization algorithm in an offline situation. The model obtained by the offline parameter identification has a high accuracy, but it does not conform to the characteristics of the lithium battery working process. Under complex working conditions, with the drastic change of the current, the battery parameters will change, and if the offline identification model is used to estimate the SOC error, the error will increase, so online parameter identification is essential

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