Evaluation method of state of charge of vehicle lithium ion power battery

Abstract

The invention provides an evaluation method of SOC of a power lithium ion battery management system. The method includes: firstly, simulating the battery by employing a first-order RC equivalent circuit model according to internal electrical characteristics of a lithium ion power battery; determining a polarized internal resistance R1, a polarized capacitance C1 and an ohmic internal resistance R0 etc. of the power battery as key parameters; performing preliminary identification on model parameters with the combination of a charge and discharge experiment; and on the basis, with the combination of a conventional SOC evaluation method, aiming at the lithium ion power battery, establishing an SOC estimation strategy based on Kalman filtering and with the combination of open-circuit voltage correction and an ampere-hour integration method. The test shows that the SOC estimation precision can reach less than 3%.

Description

technical field [0001] The invention relates to the field of battery management systems for new energy vehicles, in particular to a state of charge (SOC, state of charge) estimation method based on a lithium-ion power battery model. Background technique [0002] As the energy storage unit of the electric vehicle, the power battery directly affects the fuel economy and power of the vehicle. Because the actual operating environment of electric vehicles is very complex and varied, an effective battery management system is necessary to ensure that the power battery pack can work efficiently, reliably and safely. Especially for lithium-ion batteries with strong activity (poor safety), the significance and necessity of the battery management system are even more prominent. [0003] The core task of the battery management system is to estimate the internal state of the battery. Because the internal state of the battery cannot be directly measured, state estimation is also a diffic...

AI Technical Summary

Problems solved by technology

The neural network method is suitable for all kinds of batteries. The disadvantage is that a large amount of reference data is required for training, and the estimation error is greatly affected by the training data and training method.

[0009] It can be seen that the open-circuit voltage method and the ampere-hour integration method are simple in principle and easy to implement, but due to their limited considerations, their scope of application is limited, especially in HEV. Metering SOC

Although the fuzzy reasoning method and the artificial neural network algorithm can solve the nonlinear and multivariable problems of the battery, the algorithm is too complicated and difficult to implement, so it cannot be applied in actual engineering

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