Power battery SOC estimation method and system based on nonlinear observer

Abstract

The invention discloses a power battery SOC estimation method based on a nonlinear observer. The method comprises the steps of: performing an intermittent discharging-standing experiment on a power battery, and obtaining a SOC and OCV expression; establishing a battery equivalent circuit model; determining each parameter value of the model; establishing a discrete state space model of the battery; designing a self-adaptive nonlinear observer; determining an optimizing target function; solving an optimal parameter value; an calculating an estimated value of the battery SOC. A power battery SOC estimation system based on the optimal self-adaptive nonlinear observer comprises a power battery SOC-OCV expression determining module, an equivalent circuit model establishing module, a parameter determining module, a power battery SOC estimation discrete state space model determining module, a self-adaptive nonlinear observer designing module, a parameter optimizing target function determining module, an optimal parameter solving module and a power battery SOC estimation module. According to the invention, the SOC estimation precision is improved, the algorithm calculated quantity is reduced, hardware realization is facilitated, and the power battery SOC estimation method and system based on the nonlinearity observer are applied to the field of electric vehicle power battery managing systems.

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 an optimal adaptive nonlinear observer. Background technique [0002] The power battery is one of the core components of an electric vehicle, which directly affects performance indicators such as the mileage, acceleration and climbing ability of the vehicle. Battery Management System (Battery Management System, BMS), responsible for the management and control of battery status monitoring, power balance, thermal management, energy distribution, etc., to extend battery life, improve battery safety, reduce battery life cycle costs, etc. is of great significance. State of Charge (SOC) is an important indicator that reflects the battery's remaining power and working ability, and is an important basis for battery charge and discharge control, health status monitoring, energy distribution, and pow...

AI Technical Summary

Problems solved by technology

The neural network method requires a large number of training samples. Since it is impossible to obtain sample data that can cover all actual working conditions in practical applications, its accuracy will be affected to a certain extent, and the method has a large amount of calculation and is 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 the calculation amount is relatively large and the hardware implementation is difficult

Therefore, the existing power 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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