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A Power Battery System Identification and State Estimation Method for Wide Sampling Interval

A power battery and sampling interval technology, which is applied in the direction of measuring electricity, measuring electrical variables, instruments, etc., can solve the problems of poor stability, large sensor sampling interval, and unusable sensors, etc., achieve strong stability, realize accurate estimation, and realize multiple The effect of scale estimation

Active Publication Date: 2018-10-26
BEIJING INSTITUTE OF TECHNOLOGYGY
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Problems solved by technology

However, the existing methods have extremely poor stability. Under complex working conditions, there will be great errors due to the polarization of the battery. At the same time, they cannot be used when the sensor sampling interval is large.
From an economic point of view, the sensors mounted on real vehicles usually have a relatively large sampling interval, and the existing methods obviously cannot meet the actual needs. There is an urgent need to develop a parameter that can reduce the sampling frequency of the power battery and allow a wide sampling interval. Identification and State Estimation Methods

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  • A Power Battery System Identification and State Estimation Method for Wide Sampling Interval

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Embodiment Construction

[0051] The present invention proposes a power battery system parameter identification and state estimation method for wide sampling intervals, such as figure 1 As shown, it specifically includes the following steps:

[0052] 1). Establishing a SOC-OCV curve, the OCV being the open circuit voltage of the system;

[0053] 2). Online data acquisition, real-time acquisition of voltage and current parameters of power battery cells;

[0054] 3). Establish the state space equation for parameter identification, use the online identification algorithm to update the parameter identification results in real time, and estimate the state of charge;

[0055] 4). When the sampling time k∈{1,2,...,∞}, use the OCV estimated value at time k-1 and the parameter vector estimated value at time k to obtain the current macro time of the power battery system The estimated value of OCV in the scale, based on the SOC-OCV curve, the estimated value of SOC in the current macro time scale of the power b...

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Abstract

The invention relates to a power battery system identification and state estimation method for a wide sampling interval. Based on a power battery equivalent circuit model, a recursive least square algorithm is used, online parameter identification and state estimation on the power battery in multiple scales are realized, and the state of charge of the power battery is thus acquired. Based on an optimal forgetting factor curve in a time scale, accurate state estimation in a wide sampling interval condition is realized. The method has high precision, the stability is strong, and the needed computing power and the storage space are extremely small.

Description

technical field [0001] The invention relates to a management technology of a power battery, in particular to a method for parameter identification and state estimation of a vehicle power battery system. Background technique [0002] As the core component of new energy vehicles, power batteries are widely used in the field of new energy vehicles, and determine many key factors such as service life, safety and economy of new energy vehicles. An excellent battery management system can greatly improve the performance of new energy vehicles in all aspects, and accurately obtaining the state of charge (SOC) of the battery is the primary function of the battery management system. Only by accurately obtaining the SOC of the battery can new energy vehicles be realized. Advanced energy management ensures that the SOC is maintained within a reasonable range, prevents irreversible permanent damage to the battery due to overcharging or overdischarging, and accurately estimates the contin...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01R31/36
CPCG01R31/388
Inventor 熊瑞李治润何洪文田金鹏
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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