Dynamic estimation and intelligent correction method of remaining capacity of vehicle mounted lithium battery system

Abstract

The invention relates to a dynamic estimation and intelligent correction method of remaining capacity of vehicle mounted lithium battery system. The method comprises the following steps: incorporating the time integral method, the open circuit voltage method and the lookup table method into the Kalman filter system so as to optimize the Kalman filtering as an extended Kalman algorithm; obtaining the estimated power capacity through the extended Kalman algorithm; measuring the remaining power capacity of the battery resting through the open circuit voltage method; controlling the configuration strategy of the module algorithm through the control of a strategy state machine; and through the algorithm control module, configuring two different basic modules for each working condition so as to realize the dynamic operation of estimation and correction. According to the invention, the method reduces the errors generated from the use of a single method to estimate the remaining power capacity of a battery and the accumulated errors. Therefore, it is possible to provide an optimal error correction scheme based on the different states of the battery as well as the different working conditions of an entire vehicle.

Description

technical field [0001] The invention relates to a method for dynamically estimating and intelligently correcting the remaining power of a battery system, in particular to a method for dynamically estimating and intelligently correcting the remaining power of a vehicle-mounted lithium battery system. Background technique [0002] Accurately estimating and correcting the remaining capacity (SOC) of the on-board lithium battery system can help avoid overcharging and overdischarging of the battery, ensure the normal use of the battery, and prolong the service life of the battery. [0003] When lithium materials are used as energy carriers, the available remaining power of the load is not a fixed constant, but varies with many factors such as temperature, charge and discharge rate, self-voltage, resting time, battery service time (battery aging), etc. dynamic changes. Even a small error in a single estimation will accumulate in the repeated charge and discharge cycle of the batt...

AI Technical Summary

Problems solved by technology

[0005] 1. Ampere-hour integration method: It does not consider the influence of temperature, standing time, and battery aging on the remaining power. Although its error rate is very small, it will produce cumulative errors

[0006] 2. Open-circuit voltage method: It does not consider the impact of charge-discharge rate and rest time on the remaining power, and is usually only used in relatively static remaining power correction, battery full-full discharge, and battery balancing strategies

[0007] 3. Look-up table method: The data obtained are theoretical data, without considering the actual deviation of each battery cell in the battery system of the vehicle under dynamic and complex working conditions

[0008] 4. Kalman filter method: lack of dynamic adjustment and correction of the accuracy of battery remaining power estimation in different working conditions of the vehicle

[0009] In addition, the first three methods do not take into account the factor of battery aging capacity decay (SOH) in estimating the remaining capacity of the battery. When the total capacity of the battery drops, if it is still estimated with reference to the electrochemical parameters of the new battery, the error will become larger and larger. Big

[0010] From the above description, it can be seen that the current estimation of the remaining power of the vehicle lithium battery system is still estimated by a single method, which will generate errors and cumulative errors, and cannot provide an error correction solution, so a better solution must be sought

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