Online estimation method and device for state of charge and state of health of vehicle-mounted power battery and storage medium

Abstract

The invention discloses an online estimation method and device for the state of charge and the state of health of a power battery and a storage medium. The online estimation method comprises the steps of 1, collecting the current, the voltage and the temperature of a single battery in real time in the charging and discharging process of the power battery, 2, in the discharging process, identifying parameters of each single battery on line through a recursive least square method with a forgetting factor, 3, estimating the terminal voltage of each single battery by using a Thevenin equivalent circuit model in the charging and discharging process, 4, estimating the SOC of each single battery by using a self-adaptive extended Kalman filtering algorithm, 5, calculating the capacity of each single battery in a charging process, and 6, calculating the battery pack charge state, the battery pack capacity and the battery pack health state, and 7, applying the calculated capacity of each single battery to the capacity parameter of each single battery in the Thevenin equivalent circuit model to realize joint estimation of the state of charge and the state of health of the battery subjected to online closed-loop feedback correction. The method can be used for accurately evaluating the inconsistency state between the battery pack monomers, and provides a basis for formulating a power battery equalization control strategy.

Description

technical field [0001] The invention relates to the technical field of power battery management systems, and is suitable for online state estimation of vehicle power battery management systems. Background technique [0002] The estimation accuracy of power battery state estimation based on the equivalent circuit model largely depends on the accuracy of the parameters of the equivalent circuit model. The 0th-order equivalent circuit model ignores the polarization effect of the power battery, and the estimation results cannot reflect the real state of the power battery. The 2nd-order and above equivalent circuit models can more accurately reflect the real state of the power battery, but there are many parameters to be identified. In practical application, it is limited by working conditions. [0003] In the actual use of power batteries, there are inconsistencies such as individual capacity, state of charge, and ohmic internal resistance among groups of individual batteries. ...

AI Technical Summary

Problems solved by technology

The main problems: 1. There are certain limitations in the method of evaluating the battery health status through the internal resistance of the battery. There is a strong correlation between the internal resistance of the battery and factors such as temperature, SOC state, charge-discharge rate, etc., and the relevant factors are not mentioned in the article The impact on the internal resistance of the battery; 2. The battery state parameters change as the battery ages. Using offline parameter identification and online application, the estimation error increases with aging

Compared with the Thevenin equivalent circuit model, its method requires more parameters to be identified in the actual implementation process, and the online implementation of parameter identification is complicated and the identification results are unstable, which will lead to the disadvantage of divergence of the Kalman filter estimation results

[0006] Patent document CN105301509B discloses a joint estimation method of state of charge, state of health and power state of lithium-ion batteries. The SOH calculation method is based on the Rint equivalent circuit model combined with the least square method parameter identification results and the OCV-SOC relationship to obtain △SOC The method has the following limitations: 1. The least square method parameter identification adopted by this method can only achieve better identification results under dynamic conditions, and the parameters OCV and R0 identified at the same time have a trade-off relationship , the accuracy of OCV estimation cannot be fully guaranteed, that is, the stability and accuracy of △SOC calculation during the SOH calculation process cannot be guaranteed; The lower current fluctuates greatly. To obtain a more accurate △Ah, there is a higher requirement for the acquisition accuracy of the current sensor.

This method uses the least squares method to identify parameters and can achieve better results under dynamic discharge conditions, but it is difficult to ensure the estimation accuracy of SOC in the process of obtaining SOC through the identification parameter OCV through table lookup in the charging process, which can only reach about 5%.

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