A method for estimating the state of charge and state of health of power batteries with multiple time scales

Abstract

The present invention relates to a method for estimating the state of charge and the state of health of a power battery by using multi-time scale IAPF (Improved Adaptive Particle Filtering). According to the method, preparatory work is performed, namely, a capacity-SOC(state of charge)-OCV (open-circuit voltage) three-dimensional response surface is established; the multi-time scale IAPF algorithm is adopted to obtain the parameter estimation values of a power battery system based on a macroscopic time scale and to estimate of the state of the system based on a microscopic time scale; and the characterization quantities of an SOC (state of charge) and an SOH (health state) are proposed to estimate the state of charge and health state of the power battery; and a multi-time scale-based power battery parameter and state combined estimation method can be formed; and therefore, precise combined estimation of the charge of state and available capacity of the power battery under uncertain application environments can be realized, estimation results are more stable and reliable in a full work cycle, and the calculation quantity of a BMS (battery management system) can be decreased.

Description

Technical field: [0001] The invention relates to the technical field of power battery management, in particular to the field of vehicle power battery system parameter identification, charge state and health state estimation. Background technique: [0002] In the previous patent application CN201610375853.4, a joint estimation method for jointly estimating the state of charge SOC and parameters of the power battery was proposed for the first time. The HF algorithm was used for online parameter identification, and the UKF algorithm and the results of the aforementioned online parameter identification were used for online SOC estimation. The implementation estimation method targets the real-time parameter update and state estimation of the system. [0003] However, in the above method, the parameters are updated in real time according to the collected data, but the parameters are a slowly changing process, while the state can be considered as a fast real-time changing process, ...

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Problems solved by technology

[0003] However, in the above method, the parameters are updated in real time according to the collected data, but the parameters are a slowly changing process, while the state can be considered as a fast real-time changing process, so although the HF-UKF joint estimation method can be used in real time according to the working conditions of the battery Update the parameters of the battery in order to obtain a more accurate state of charge SOC, but the real-time update of slowly changing parameters is relatively computationally intensive for the on-board battery management system (hereinafter referred to as BMS), and a more reasonable method is to use different time scales to Update battery parameters and state of charge

In addition, the HF-UKF joint estimation method cannot obtain the health status of the battery

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