Method and device for estimating state of charge of battery

Abstract

The invention provides a method and device for estimating a state of charge of a battery. The method comprises the following steps: establishing a multi-parameter fusion mathematical model of a lithium iron phosphate battery; using a fuzzy Kalman filtering algorithm based on an index input membership function to optimize and estimate the state of the charge of the battery, wherein the step of establishing the multi-parameter fusion mathematical model comprises establishing a charge-discharge multiplying power-state of charge sub module, a temperature-state of charge sub module, and a cycle index-state of charge sub module. The method for estimating the state of charge of the battery uses the fuzzy self-adaptive Kalman filtering algorithm based the index input membership function, so that the theoretical value of measurement noise in the Kalman filter is adjusted smoother in a self-adpative manner, thereby improving the matching degree of the system measurement noise, and estimating the state of the charge of the battery more accurately.

Description

technical field [0001] The invention relates to a battery charge state estimation method and device in a battery management system. Background technique [0002] Lithium iron phosphate battery (LiFePO4) has the advantages of small size, light weight, high energy density, good sealing, no leakage, no memory effect, high discharge performance, low self-discharge rate, fast charging, long cycle life, wide operating temperature range, Energy saving and environmental protection, especially suitable for power applications with high voltage, high current and severe load fluctuations. Lithium iron phosphate batteries will have a great impact on the cycle life of the battery under harsh conditions such as short circuit, overcharge, extrusion, and acupuncture. The production process of lithium iron phosphate batteries is relatively complicated, and the consistency difference of single cells will be greater than that of sealed valve-regulated lead-acid batteries. The phenomenon of sh...

AI Technical Summary

Problems solved by technology

However, simple equivalent circuit models such as Rint and RC network models have relatively large calculation errors and cannot meet actual needs; complex equivalent circuit models such as PNGV and GNL models have complex parameter identification and large calculations, which are greatly affected by practical applications. Large limitations; the parameters of the neural network model have no actual physical meaning, and the accuracy is greatly affected by the training samples and training methods

[0006] Traditional methods for measuring SOC usually include open circuit voltage method, current integration method, artificial neural network, Kalman filter algorithm, etc., but in fact, the factors that affect SOC are very complicated, such as operating temperature, charge and discharge rate, number of cycles, internal resistance change, Factors such as self-discharge have a certain impact on SOC. Traditional methods often only consider the two parameters of voltage and current integration. Under current operating conditions, this effect can sometimes be very significant

[0007] In addition, the discharge experiment method takes a lot of measurement time

Only when the entire discharge test is over, the state of charge (SOC) value at each moment before can be calculated, and the real-time estimation of SOC cannot be achieved; the previous work of the battery must be forced to stop and switch to the constant current discharge state

[0008] There is a time problem in the open circuit voltage method. In order to overcome the self-recovery effect, the battery needs to be left standing for a long time to achieve a stable voltage state. Generally, this standing process takes several hours to more than ten hours, which causes a waste of time; In addition, how to correctly determine whether the battery has reached a stable state is also a difficulty in estimating the remaining power.

When the battery is in the mid-discharge platform, the corresponding relationship between the open circuit voltage and the state of charge (SOC) is not very obvious, resulting in a large error in the estimation of the state of charge (SOC)

[0009] The ampere-hour measurement method, the method itself cannot provide the initial value SOC(t0) of the battery state of charge; inaccurate current measurement will increase the estimation error of the state of charge (SOC), and the error will become more and more serious after long-term accumulation. Large; under the condition of high voltage and high current with severe current changes, the estimation error of the state of charge (SOC) will be relatively large; the battery capacity influence coefficient η must be considered when estimating the SOC

Although the accuracy problem of current measurement can be solved by using a high-performance current sensor, this will greatly increase the system cost

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