Lithium battery SOC online estimation method

Abstract

The invention discloses a lithium battery SOC online estimation method comprising the following steps that 1) the open-circuit voltage of a battery is measured, and the state-of-charge initial value of the battery is obtained according to an OCV-SOC curve; 2) the second-order RC equivalent model of the battery is established and the parameter initial value of the battery equivalent model is estimated; 3) the estimation program is started, and the matching coefficient initial value of a state equation is set according to the battery state-of-charge initial value and the parameter initial value of the battery equivalent model; 4) the current battery state-of-charge value is obtained by using an adaptive unscented Kalman filtering algorithm, and the current open-circuit voltage is obtained according to the OCV-SOC curve; 5) the least square method with the forgetting factor is started to identify the parameters of the current battery equivalent model, the matching coefficient of the state equation is updated by the identified parameters and the battery state-of-charge value of the next moment is solved; and 6) the steps 4) and 5) are repeated so that the battery state-of-charge value of each moment is obtained. Compared with the conventional unscented Kalman filtering algorithm, the method has higher accuracy and higher error convergence.

Description

technical field [0001] The invention relates to the field of electric vehicle battery management, in particular to an online estimation method for lithium battery SOC. Background technique [0002] In recent years, with the deteriorating air quality and the scarcity of oil resources, new energy vehicles, especially pure electric vehicles, have become the development hotspots of major automobile companies in the world today. The power battery pack is a key component of an electric vehicle. The power battery SOC is used to directly reflect the remaining power of the battery. It is an important basis for the vehicle control system to formulate an optimal energy management strategy. Accurate estimation of the power battery SOC value is important for improving battery safety and reliability. It is of great significance to improve battery energy utilization and prolong battery life. [0003] At present, the commonly used SOC estimation methods mainly include open circuit voltage ...

AI Technical Summary

Problems solved by technology

The ampere-hour integration method has the advantages of low cost and convenient measurement, but it also has the following problems in the application of electric vehicles: it needs to use other methods to obtain the initial value of SOC; the accuracy of current measurement has a decisive impact on the accuracy of SOC estimation; The error cannot be eliminated. If the charging and discharging time is too long during a calculation, the accumulated error may lead to unreliable estimation results.

[0006] The neural network method has good nonlinear mapping ability. In theory, the nonlinear characteristics of the power battery can be better mapped by the neural network, but it requires a large amount of data for training, and the use is complicated. The impact of training data and training methods on the estimation accuracy larger

[0007] The core idea of ​​the Kalman filter method is to make an optimal estimate of the state of the dynamic system in the sense of the least mean square. The advantage of the Kalman filter lies in its strong error correction ability, but the disadvantage is that the estimation accuracy is highly dependent on the accuracy of the battery model.

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