Power battery SOC estimation method and system based on dynamic parameter model

Abstract

The invention discloses a power battery SOC estimation method based on a dynamic parameter model. The method comprises the following steps: performing a discharge-standing experiment to fit an SOC-OCV relational expression; carrying out a pulse discharge-standing experiment to identify a parameter value of a battery equivalent circuit model; establishing a battery system discrete state space model; performing online identification on battery parameters; obtaining the battery dynamic parameter model; and performing online estimation on the SOC of a battery. Further disclosed is a power battery SOC estimation system based on a dynamic parameter model. The system comprises a battery SOC-OCV relational expression determining module, a battery parameter offline identification module, a battery parameter online identification discrete state space model determining module, a battery parameter online identification module; a battery dynamic parameter determining module; a battery SOC estimation discrete state space model determining module and an SOC estimation module. The method and system provided by the invention improve the precision of a battery model, is lower in calculation complexity and can be widely applied to the field of an electric automobile power battery management system.

Description

technical field [0001] The invention relates to the field of electric vehicle power battery management systems, in particular to a power battery SOC estimation method and system based on a dynamic parameter model. Background technique [0002] As the power source of electric vehicles, the power battery is one of the most critical core components of electric vehicles, which directly affects the performance indicators of electric vehicles such as cruising range, acceleration capability and maximum gradeability. The battery management system (Battery Management System, BMS) is responsible for coordinating and managing battery status monitoring, power balance, thermal management, energy distribution, etc., to extend battery life, improve battery safety, and reduce battery life cycle costs. is of great significance. State of Charge (SOC) is an important indicator that reflects the remaining battery power and working ability, and is an important basis for battery charge and disch...

AI Technical Summary

Problems solved by technology

The neural network method requires a large number of training samples. In practical applications, it is impossible for us to obtain sample data that can cover all actual working conditions, so its accuracy will be affected to a certain extent, and the method is computationally intensive and difficult to implement in hardware.

Both the Kalman filter method and the observer method can correct the initial error of the battery SOC very well, and have good anti-noise ability, but they have very high requirements on the model accuracy, and the calculation overhead of the Kalman filter is relatively large

The power battery is a complex nonlinear power system, and the battery parameters are obviously affected by many factors such as temperature, current, aging, etc., so the accuracy of the Kalman filter and the observer method under actual working conditions is difficult to guarantee

[0004] In summary, the existing battery SOC estimation methods have certain inconveniences and defects to varying degrees in practical applications, so it is necessary to make further improvements

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