Method and device for estimating power battery charge state on line

Abstract

The invention discloses a method and device for estimating a power battery charge state on line. The method comprises the steps that a capacity correction factor of a power battery is calculated according to the accumulated released energy of the power battery, the internal resistance of the power battery is calculated, a state space model of the power battery is established, and the state space model of the power battery is substituted into an unscented Kalman filter to obtain the estimated value of the battery charge state. According to the method and device for estimating the power battery charge state on line, the capacity correction factor of the power battery is calculated according to the accumulated released energy of the power battery, the accumulated released energy of the power battery is the sum of energy consumed by load acting and energy consumed by the internal resistance from the first time up to now, the accumulated released energy of the power battery can reflect the using history of the battery like the cycle index and is more beneficial to accurate measuring in numerical value, and therefore the method of correcting the related parameters of the battery through the accumulated released energy of the power battery is better in operability and is more accurate in estimated value.

Description

technical field [0001] The invention relates to the field of online estimation of the state of charge of a power battery, in particular to an online estimation method and device for the state of charge of a power battery. Background technique [0002] The state of charge (State of Charge, SOC) is an important indicator that reflects the remaining power of the battery and its working ability. An important basis for functions such as monitoring and thermal management. Therefore, the estimation of SOC is the most important and core technology in BMS development. However, as the internal state of the power battery, SOC cannot be directly measured, and can only be estimated by detecting external parameters such as battery voltage, current, internal resistance, and temperature. However, the relationship between the above external parameters and SOC not only changes with the aging process of the battery, but also is closely related to the working state of the battery and the oper...

AI Technical Summary

Problems solved by technology

Among them, although the ampere-hour integration method (also known as the coulomb measurement method) is simple to implement, it has several outstanding defects: first, the cumulative estimation error caused by the current measurement error is large; Any SOC value starts to work, so it is difficult to determine its initial SOC value, and thus brings a large estimation error

However, the neural network method is used in data fitting, which results in a large amount of calculation and is not easy to implement in hardware; in addition, only relying on the open circuit voltage method to determine the initial SOC value of the battery has a large error; moreover, the actual use of the power battery , it often occurs that the battery is recharged before it is fully discharged, and it is discharged again before it is fully charged, that is, "charge and use as you go", which makes it impossible to accurately count the number of full cycle charge and discharge of the battery. Therefore, the number of cycles corrected The method is not easy to implement

[0007] Looking at the existing technology, the main problems are: the influence of temperature, SOC and cycle life on the battery capacity, internal resistance, etc. is not considered, and the error is relatively large; or although the actual capacity and internal resistance of the battery are considered The correction of parameters, but the method adopted has a large amount of calculation, and the actual operability is poor

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