A fuzzy model was established to estimate the state of charge（SOC） of a lithium-ion battery for electric vehicles.The robust Gustafson-Kessel（GK） clustering algorithm based on clustering validity indices was appli...A fuzzy model was established to estimate the state of charge（SOC） of a lithium-ion battery for electric vehicles.The robust Gustafson-Kessel（GK） clustering algorithm based on clustering validity indices was applied to identify the structure and antecedent parameters of the model.The least squares algorithm was utilized to determine the consequent parameters.Validation results show that this model can provide accurate SOC estimation for the lithium-ion battery and satisfy the requirement for practical electric vehicle applications.展开更多
To monitor the tool wear states in turning, a new way based on the wavelet transformation to get the signal characters, which can reflect the tool wear states, was proposed. Using discrete dyadic wavelet transform, th...To monitor the tool wear states in turning, a new way based on the wavelet transformation to get the signal characters, which can reflect the tool wear states, was proposed. Using discrete dyadic wavelet transform, the acoustic emission(AE) signal of cutting process was decomposed; the root mean square(RMS) values of the decomposed signals at different scales were taken as the feature vector; the technique of fuzzy pattern identification was used to real time monitor the tool wear states. Based on choosing the suitable standard samples, this method can correctly identify the tool wear states. Experiments showed that the technique based on wavelet analysis is suitable for real time implementation in manufacturing application.展开更多
基金Sponsored by the National High Technology Research and Development Program of China("863"Program)(2003AA501800)
文摘A fuzzy model was established to estimate the state of charge（SOC） of a lithium-ion battery for electric vehicles.The robust Gustafson-Kessel（GK） clustering algorithm based on clustering validity indices was applied to identify the structure and antecedent parameters of the model.The least squares algorithm was utilized to determine the consequent parameters.Validation results show that this model can provide accurate SOC estimation for the lithium-ion battery and satisfy the requirement for practical electric vehicle applications.
文摘To monitor the tool wear states in turning, a new way based on the wavelet transformation to get the signal characters, which can reflect the tool wear states, was proposed. Using discrete dyadic wavelet transform, the acoustic emission(AE) signal of cutting process was decomposed; the root mean square(RMS) values of the decomposed signals at different scales were taken as the feature vector; the technique of fuzzy pattern identification was used to real time monitor the tool wear states. Based on choosing the suitable standard samples, this method can correctly identify the tool wear states. Experiments showed that the technique based on wavelet analysis is suitable for real time implementation in manufacturing application.
