On-line monitoring method for self-adaptively correcting lithium ion battery state-of-charge

Abstract

The invention relates to a method for estimating a lithium ion battery state-of-charge (SOC) of a hybrid electric vehicle on line. The method comprises the following steps of: 1, calculating an initiative SOC value of a power battery after the whole vehicle is electrified; 2, calculating an on-line real-time SOC estimated value of the power battery; 3, diagnosing a state of the power battery; and4, self-adaptively correcting the on-line real-time SOC estimated value of the power battery. In the method, an automotive electronic control unit and a time-triggered type controller local area network communication platform are used to realize the functions of performing on-line monitoring, diagnosis and self-adaptive correction on the SOC of a vehicle lithium ion battery, and the battery can be conveniently, quickly and effectively controlled and managed.

Description

technical field [0001] The invention relates to a power battery management system of a new hybrid electric vehicle, in particular to an adaptively corrected lithium-ion battery state-of-charge online monitoring method. Background technique [0002] Vehicle power battery is a key factor affecting the development of electric vehicles. With the rapid development of lithium-ion battery technology, more and more lithium-ion batteries have been used in electric vehicles (mainly divided into hybrid vehicles, fuel cell vehicles, and pure electric vehicles). Therefore, with the continuous development of batteries and their integration technology, how to make good use of batteries has become a key technology. One of its cores is to accurately monitor the state of charge (State of Charge, SOC) of the battery to avoid overcharging and over-discharging of the battery. This is also one of the difficult and hot issues in the research of battery management systems and even electric vehicl...

AI Technical Summary

Problems solved by technology

Therefore, the single ampere-hour integration method is not suitable for hybrid electric vehicles with variable driving conditions, frequent charging and discharging of power batteries, and it is difficult to accurately estimate the initial value of the state of charge.

Measuring the state of charge of the battery with the open circuit voltage method requires the battery to stand still for a long time, and if the characteristic curve of the open circuit voltage of the battery to the state of charge is relatively flat (now applied to a lithium-ion power battery on a fuel cell city bus characteristics), it is difficult to use the relationship curve of open circuit voltage to state of charge to obtain a more accurate state of charge of the power battery

Therefore, the single open circuit voltage method is not suitable for hybrid electric vehicles with variable driving conditions.

If neural network method, electrochemical impedance spectroscopy and Kalman filter method are used, it is necessary to analyze and model battery data offline or online. The method is relatively complicated, not only difficult to implement and apply, but also not suitable for power Battery state of charge online monitoring

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