Wavelet-Based Identification Method of Li-Ion Battery Model for Electric Vehicles

Abstract:

Online parameters identification is one of the major functions of model-based battery management system (BMS), which can be used to monitor the working status of battery, such as state of charge (SOC) and state of health (SOH). This paper proposed a wavelet-based identification method of Li-ion battery model for Electric Vehicles (EVs). The main idea is to decompose the measured terminal voltage and current data at multiple scales, and then recursive least squares (RLS) algorithm is used to extract the model parameters at a suitable scale. The proposed method is shown to have good robustness to measured noise and thus enhances the estimation accuracy by taking advantage of the noise removal ability and signal approximation properties of wavelet decomposition.

• Periodical: Advanced Materials Research (Volumes 608-609)
• Pages: 1529-1532
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.608-609.1529
• Online since: December 2012
• Authors: Da Zhong Mu, Jiu Chun Jiang
• Keywords: Battery Model, Multiscale Representation, Parameter Estimation, System Identification, Wavelet Decomposition

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