Method and device for estimating charge state of lithium ion battery

Abstract

The invention relates to a method and device for estimating a charge state of a lithium ion battery. The method comprises the following steps: A, fitting a relation between the open-circuit voltage and the charge state of the lithium ion battery; B, estimating the charge state of the lithium ion battery by utilizing an observer method; C, if the charge state of the lithium ion battery estimated in the step B is larger than a predetermined threshold value, estimating the charge state of the lithium ion battery by using the observer method, and if the charge state of the lithium ion battery estimated in the step B is smaller than the predetermined threshold value, estimating the charge state of the lithium ion battery by using an ampere-hour integral method. Through the method and the device for estimating the charge state of the lithium ion battery, provided by the method, the defects of the ampere-hour integral method and the observer method can be overcome, and the high estimation accuracy is provided in a full life cycle and a full charge state region.

Description

technical field [0001] The invention relates to the technical field of energy storage equipment, in particular to the state detection technology of a rechargeable lithium ion battery. Background technique [0002] The U.S. Advanced Battery Consortium (USABC) defines the state of charge (State of Charge, SOC) of a battery as the percentage of remaining power and actual capacity in its "Electric Vehicle Battery Experiment Manual". The estimation of battery SOC is becoming more and more necessary in the application fields of electric vehicles and smart grids. The SOC of power batteries is used to reflect the remaining available power of the battery, and it plays the role of a fuel gauge for traditional fuel vehicles for electric vehicles. Accurate and reliable SOC estimation can not only enhance the user's handling and comfort of electric vehicles, but also serve as an indispensable decision-making factor for the energy management system of electric vehicles. , Prevent the bat...

AI Technical Summary

Problems solved by technology

In the application of the ampere-hour integration method, if the current measurement is inaccurate, it will cause SOC calculation errors, which will accumulate over a long period of time, and the errors will become larger and larger; in addition, the ampere-hour integration method needs to consider the charging and discharging efficiency of lithium-ion batteries, and in high temperature conditions and severe current fluctuations In the case of the large error

[0005] In addition to the ampere-hour integration method, there are other commonly used lithium-ion battery SOC estimation methods: open circuit voltage method, electrochemical test method, neural network method, impedance spectrum method, Kalman filter method and based on sliding mode observer, dragon Estimation methods based on observers such as Berger observers have limitations: the open circuit voltage method requires the lithium-ion battery to be fully static, which cannot meet the online estimation; the electrochemical method requires the support of special testing equipment; the neural network requires a lot of experiments and Data training, and the adaptability of the model has a certain limit; the impedance analysis method is affected by factors such as temperature and aging; Kalman filtering is difficult to eliminate the error caused by the change of the model and its parameters due to the temperature and aging of the lithium-ion battery

[0006] The observer-based lithium-ion battery SOC estimation method is to estimate the state quantity through the process output, and add the error feedback of the output to correct the estimation of the lithium-ion battery SOC by the ampere-hour integration method, which overcomes the error accumulation of the ampere-hour integration method and the It is necessary to know the shortcomings of the initial value of the SOC of the lithium-ion battery, which greatly improves the estimation accuracy of the SOC of the lithium-ion battery, but the accuracy of the estimation of the method is guaranteed by the accuracy of the model parameters. Online identification of parameters; in addition, due to the characteristics of the lithium-ion battery itself (open circuit voltage-remaining power curve, etc.), the estimation of the SOC of the lithium-ion battery has a large error in a certain range

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