Min Xu

Publications

2 Road Vehicle Recognition Using Magnetic Sensing Feature Extraction and Classification

Authors: Min Xu, Xiao Chen, Xiaoying Kong

Abstract:

This paper presents a road vehicle detection approach for the intelligent transportation system. This approach mainly uses low-cost magnetic sensor and associated data collection system to collect magnetic signals. This system can measure the magnetic field changing, and it also can detect and count vehicles. We extend Mel Frequency Cepstral Coefficients to analyze vehicle magnetic signals. Vehicle type features are extracted using representation of cepstrum, frame energy, and gap cepstrum of magnetic signals. We design a 2-dimensional map algorithm using Vector Quantization to classify vehicle magnetic features to four typical types of vehicles in Australian suburbs: sedan, VAN, truck, and bus. Experiments results show that our approach achieves a high level of accuracy for vehicle detection and classification.

Keywords: Signal Processing, Magnetic Sensing, vehicle classification, road traffic model

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 855
1 Offline Parameter Identification and State-of-Charge Estimation for Healthy and Aged Electric Vehicle Batteries Based on the Combined Model

Authors: Saeid Habibi, Xiaowei Zhang, Min Xu, Fengjun Yan, Ryan Ahmed

Abstract:

Recently, Electric Vehicles (EVs) have received extensive consideration since they offer a more sustainable and greener transportation alternative compared to fossil-fuel propelled vehicles. Lithium-Ion (Li-ion) batteries are increasingly being deployed in EVs because of their high energy density, high cell-level voltage, and low rate of self-discharge. Since Li-ion batteries represent the most expensive component in the EV powertrain, accurate monitoring and control strategies must be executed to ensure their prolonged lifespan. The Battery Management System (BMS) has to accurately estimate parameters such as the battery State-of-Charge (SOC), State-of-Health (SOH), and Remaining Useful Life (RUL). In order for the BMS to estimate these parameters, an accurate and control-oriented battery model has to work collaboratively with a robust state and parameter estimation strategy. Since battery physical parameters, such as the internal resistance and diffusion coefficient change depending on the battery state-of-life (SOL), the BMS has to be adaptive to accommodate for this change. In this paper, an extensive battery aging study has been conducted over 12-months period on 5.4 Ah, 3.7 V Lithium polymer cells. Instead of using fixed charging/discharging aging cycles at fixed C-rate, a set of real-world driving scenarios have been used to age the cells. The test has been interrupted every 5% capacity degradation by a set of reference performance tests to assess the battery degradation and track model parameters. As battery ages, the combined model parameters are optimized and tracked in an offline mode over the entire batteries lifespan. Based on the optimized model, a state and parameter estimation strategy based on the Extended Kalman Filter (EKF) and the relatively new Smooth Variable Structure Filter (SVSF) have been applied to estimate the SOC at various states of life.

Keywords: Lithium-Ion Batteries, genetic algorithm optimization, battery aging test, and parameter identification

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 908

Abstracts

3 Road Vehicle Recognition Using Magnetic Sensing Feature Extraction and Classification

Authors: Min Xu, Xiao Chen, Xiaoying Kong

Abstract:

This paper presents a road vehicle detection approach for the intelligent transportation system. This approach mainly uses low-cost magnetic sensor and associated data collection system to collect magnetic signals. This system can measure the magnetic field changing, and it also can detect and count vehicles. We extend Mel Frequency Cepstral Coefficients to analyze vehicle magnetic signals. Vehicle type features are extracted using representation of cepstrum, frame energy, and gap cepstrum of magnetic signals. We design a 2-dimensional map algorithm using Vector Quantization to classify vehicle magnetic features to four typical types of vehicles in Australian suburbs: sedan, VAN, truck, and bus. Experiments results show that our approach achieves a high level of accuracy for vehicle detection and classification.

Keywords: Signal Processing, Magnetic Sensing, vehicle classification, road traffic model

Procedia PDF Downloads 179
2 The Comparison Study of Methanol and Water Extract of Chuanxiong Rhizoma: A Fingerprint Analysis

Authors: Min Xu, Li Chun Zhao, Zhi Chao Hu, Xi Qiang Liu, Man Lai Lee, Chak Shing Yeung, Man Fei Xu, Yuen Yee Kwan, Alan H. M. Ho, Nickie W. K. Chan, Bin Deng, Zhong Zhen Zhao

Abstract:

Background: Chuangxiong Rhizoma (Chuangxion, CX) is one of the most frequently used herbs in Chinese medicine because of its wide therapeutic effects such as vasorelaxation and anti-inflammation. Aim: The purposes of this study are (1) to perform non-targeted / targeted analyses of CX methanol extract and water extract, and compare the present data with previously LC-MS or GC-MS fingerprints; (2) to examine the difference between CX methanol extract and water extract for preliminarily evaluating whether current compound markers of methanol extract from crude CX materials could be suitable for quality control of CX water extract. Method: CX methanol extract was prepared according to the Hong Kong Chinese Materia Medica Standards. DG water extract was prepared by boiling with pure water for three times (one hour each). UHPLC-Q-TOF-MS/MS fingerprint analysis was performed by C18 column (1.7 µm, 2.1 × 100 mm) with Agilent 1290 Infinity system. Experimental data were analyzed by Agilent MassHunter Software. A database was established based on 13 published LC-MS and GC-MS CX fingerprint analyses. Total 18 targeted compounds in database were selected as markers to compare present data with previous data, and these markers also used to compare CX methanol extract and water extract. Result: (1) Non-targeted analysis indicated that there were 133 compounds identified in CX methanol extract, while 325 compounds in CX water extract that was more than double of CX methanol extract. (2) Targeted analysis further indicated that 9 in 18 targeted compounds were identified in CX methanol extract, while 12 in 18 targeted compounds in CX water extract that showed a lower lose-rate of water extract when compared with methanol extract. (3) By comparing CX methanol extract and water extract, Senkyunolide A (+1578%), Ferulic acid (+529%) and Senkyunolide H (+169%) were significantly higher in water extract when compared with methanol extract. (4) Other bioactive compounds such as Tetramethylpyrazine were only found in CX water extract. Conclusion: Many new compounds in both CX methanol and water extracts were found by using UHPLC Q-TOF MS/MS analysis when compared with previous published reports. A new standard reference including non-targeted compound profiling and targeted markers functioned especially for quality control of CX water extract (herbal decoction) should be established in future. (This project was supported by Hong Kong Baptist University (FRG2/14-15/109) & Natural Science Foundation of Guangdong Province (2014A030313414)).

Keywords: Quality Control, Fingerprint analysis, targeted analysis, Chuanxiong rhizoma

Procedia PDF Downloads 238
1 Offline Parameter Identification and State-of-Charge Estimation for Healthy and Aged Electric Vehicle Batteries Based on the Combined Model

Authors: Saeid Habibi, Xiaowei Zhang, Min Xu, Fengjun Yan, Ryan Ahmed

Abstract:

Recently, Electric Vehicles (EVs) have received extensive consideration since they offer a more sustainable and greener transportation alternative compared to fossil-fuel propelled vehicles. Lithium-Ion (Li-ion) batteries are increasingly being deployed in EVs because of their high energy density, high cell-level voltage, and low rate of self-discharge. Since Li-ion batteries represent the most expensive component in the EV powertrain, accurate monitoring and control strategies must be executed to ensure their prolonged lifespan. The Battery Management System (BMS) has to accurately estimate parameters such as the battery State-of-Charge (SOC), State-of-Health (SOH), and Remaining Useful Life (RUL). In order for the BMS to estimate these parameters, an accurate and control-oriented battery model has to work collaboratively with a robust state and parameter estimation strategy. Since battery physical parameters, such as the internal resistance and diffusion coefficient change depending on the battery state-of-life (SOL), the BMS has to be adaptive to accommodate for this change. In this paper, an extensive battery aging study has been conducted over 12-months period on 5.4 Ah, 3.7 V Lithium polymer cells. Instead of using fixed charging/discharging aging cycles at fixed C-rate, a set of real-world driving scenarios have been used to age the cells. The test has been interrupted every 5% capacity degradation by a set of reference performance tests to assess the battery degradation and track model parameters. As battery ages, the combined model parameters are optimized and tracked in an offline mode over the entire batteries lifespan. Based on the optimized model, a state and parameter estimation strategy based on the Extended Kalman Filter (EKF) and the relatively new Smooth Variable Structure Filter (SVSF) have been applied to estimate the SOC at various states of life.

Keywords: Lithium-Ion Batteries, parameter identification, genetic algorithm optimization, battery aging test

Procedia PDF Downloads 104