SOC estimation based on online parameter identification and AUKF CATARC

Format:

Book

Conference/Event

Author/Creator:

Huang, Denggao, author.

Contributor:

Li, Cheng

Liu, Zhihong

Conference Name:

WCX SAE World Congress Experience (2020-04-21 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2020

Summary:

The SOC plays an important role in vehicle energy management, power battery capacity utilization, battery charge and discharge protection. Battery model accuracy and noise variance will greatly affect the result of SOC estimation algorithm. In order to solve this problems, this paper builds Second-order equivalent circuit model, and applys the recursive least squares algorithm to identify the battery parameters online, and consequently the adaptive unscented Kalman method is proposed to estimation SOC. In order to verify the performance of the proposed algorithm, this paper uses experimental data of lithium battery to build a simulation model and test environment. The results show that compared with the current three algorithms, the proposed method in this paper has high estimation accuracy and minimum root mean square error

Notes:

Vendor supplied data

Publisher Number:

2020-01-1183

Access Restriction:

Restricted for use by site license