Li-Ion Battery SOC Estimation Using Non-Linear Estimation Strategies Based on Equivalent Circuit Models McMaster University

Format:

Conference/Event

Author/Creator:

Farag, Farag, author.

Contributor:

Fleckenstein, Matthias

Habibi, Saeid R.

Conference Name:

SAE 2014 World Congress & Exhibition (2014-04-08 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2014

Summary:

AbstractDue to their high energy density, power density, and durability, lithium-ion (Li-ion) batteries are rapidly becoming the most popular energy storage method for electric vehicles. Difficulty arises in accurately estimating the amount of left capacity in the battery during operation time, commonly known as battery state of charge (SOC).This paper presents a comparative study between six different Equivalent Circuit Li-ion battery models and two different state of charge (SOC) estimation strategies. The Battery models cover the state-of-the-art of Equivalent Circuit models discussed in literature. The Li-ion battery SOC is estimated using non-linear estimation strategies id est Extended Kalman filter (EKF) and the Smooth Variable Structure Filter (SVSF).The models and the state of charge estimation strategies are compared against simulation data obtained from AVL CRUISE software. The effectiveness of the models and estimation strategies is then compared through a comprehensive evaluation for model complexity, model accuracy, and root mean squared error in state of charge estimation

Notes:

Vendor supplied data

Publisher Number:

2014-01-1849

Access Restriction:

Restricted for use by site license