Power battery system parameter and state of charge joint estimation method

Abstract

The invention relates to a power battery system parameter and stage of charge joint estimation method, which comprises the steps of firstly performing online data acquisition, and acquiring voltage, current and temperature of a battery in real time; then establishing an HF (H infinity filter) state-space equation, updating an HF state vector of the battery in real time by using an HF algorithm, wherein the HF state vector comprises ohmic internal resistance, polarization internal resistance and polarization capacitance of the battery; and finally establishing a UKF (Unscented Kalman Filter) state-space equation, updating the HF state vector in real time by combining the HF algorithm, updating a UKF state vector of the battery in real time by using a UKF algorithm, wherein the UKF state vector comprises a state of charge of the battery. Therefore, model parameters are identified online by using the HF algorithm, and the model parameters are transferred to the UKF algorithm to perform online real-time SoC (State of Charge) estimation, thereby tracking model parameter variations of the battery in real time according to different battery operating environments, and thus improving the SoC estimation precision.

Description

technical field [0001] The invention relates to the field of parameter estimation in power battery management, in particular to power battery system parameters and charge state estimation. Background technique [0002] Estimation of the state of charge (SoC) of a battery is the main function of a battery management system (BMS). The present invention is mainly aimed at the joint estimation of battery parameter identification and state of charge SoC, wherein the battery system parameters correspond to the ohmic internal resistance R of the battery system 0 , polarization internal resistance R p and polarized capacitance C p . [0003] SoC describes the amount of remaining battery power and is an important parameter during battery use. SoC estimation is the basic function in the battery management system. Relying on accurate battery SoC, BMS can accurately formulate various strategies such as charge and discharge control, balance management, safety management and fault dia...

AI Technical Summary

Problems solved by technology

At present, mainstream researches all use model-based SoC estimation methods, but most of the model-based SoC estimation methods in the prior art are simulated and experimentally verified under a certain dynamic working condition, temperature and other conditions. And the SoC estimation results under the full working environment often deviate from the objective situation. This is because the parameters of the battery vary greatly under different working conditions such as temperature and aging, and the parameters and states of the battery model are coupled, so the identification under a single condition The parameters cannot be applied to the SoC estimation under the full life cycle of the battery and the full working environment

On the other hand, the reliability of power battery SoC estimation results is significantly reduced due to the uncertainty of the initial value of SoC.

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