Journal
IEEE TRANSACTIONS ON POWER ELECTRONICS
Volume 32, Issue 10, Pages 7626-7634Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPEL.2016.2636180
Keywords
Signal denoising; state of charge (SOC); wavelet transform matrix (WTM)
Categories
Funding
- Natural Science Foundation of China [51577089]
- Lite-On Research Funding
- Jiangsu Provincial Cooperative Innovation Fund-Prospective Joint Project [BY2015003-04]
- Fundamental Research Funds for Central Universities (NUAA) [NE2014101]
- Foundation of Graduate Innovation Center in NUAA [KFJJ20160314]
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Due to harsh electromagnetic environment in electric vehicle (EV), the measured current and voltage signals can be seriously polluted, which results in an estimation error of state of charge (SOC). The proposed denoising approach based on wavelet transform matrix (WTM) can analyze and denoise the nonstationary current and voltage signals effectively. This approach reduces the computation burden and is convenient to be programed in microcontroller unit, which is suitable for EV real-time application. The steps of the approach are as follows: 1) decomposition of the current and voltage signals based on WTM; 2) denoising of the wavelet coefficients under the thresholding rule; and 3) reconstruction of the denoised current and voltage signals based on inverse WTM. A battery-management system prototype was built to verify the approach on a Li(NiCoMn)O-2 battery module with nominal capacity of 200 Ah and rated voltage of 3.6 V. SOC estimation error with the proposed denoising approach is limited within 1%. Compared to the maximum error of 2.5% using an adaptive extended Kalman filter without denoising, an estimation error reduction of 1.5% is achieved.
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