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Prof. Peng Wu
Shanghai Universitiy of Engineering Science

Basic Info

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Research Keywords & Expertise

0 Power electronic control
0 power electrical engineering
0 Power system planning and optimization
0 power electronic systems
0 Renewable and clean energy

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Short Biography

Peng Wu received the Ph.D. degree from Shanghai Jiao Tong University, China, in 2009. He is current an associate professor with the School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, China. His research interests are optimization and planning of integrated energy system, operation of power system.

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Journal article
Published: 09 June 2021 in Energies
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Bidirectional coupling systems for electricity and natural gas composed of gas units and power-to-gas (P2G) facilities improve the interactions between different energy systems. In this paper, a combined optimization planning method for an electricity-natural gas coupling system with P2G was studied. Firstly, the characteristics of the component model of the electricity-natural gas coupling system were analyzed. The optimization planning model for the electricity-natural gas coupling system was established with the goal of minimizing the sum of the annual investment costs and the annual operation costs. Based on the established model, the construction statuses for different types of units, power lines, and pipelines and the output distribution values for gas units and P2G stations were optimized. Then, the immune algorithm was proposed to solve the optimization planning model. Finally, an electricity-natural gas coupling system composed of a seven-node natural gas system and a nine-node power system was taken as an example to verify the rationality and effectiveness of the model under different scenarios.

ACS Style

Jie Xing; Peng Wu. Optimal Planning of Electricity-Natural Gas Coupling System Considering Power to Gas Facilities. Energies 2021, 14, 3400 .

AMA Style

Jie Xing, Peng Wu. Optimal Planning of Electricity-Natural Gas Coupling System Considering Power to Gas Facilities. Energies. 2021; 14 (12):3400.

Chicago/Turabian Style

Jie Xing; Peng Wu. 2021. "Optimal Planning of Electricity-Natural Gas Coupling System Considering Power to Gas Facilities." Energies 14, no. 12: 3400.

Journal article
Published: 30 April 2021 in Sustainability
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State of charge (SOC) of the lithium-ion battery is an important parameter of the battery management system (BMS), which plays an important role in the safe operation of electric vehicles. When existing unknown or inaccurate noise statistics of the system, the traditional unscented Kalman filter (UKF) may fail to estimate SOC due to the non-positive error covariance of the state vector, and the SOC estimation accuracy is not high. Therefore, an improved adaptive unscented Kalman filter (IAUKF) algorithm is proposed to solve this problem. The IAUKF is composed of the improved unscented Kalman filter (IUKF) that is able to suppress the non-positive definiteness of error covariance and Sage–Husa adaptive filter. The IAUKF can improve the SOC estimation stability and can improve the SOC estimation accuracy by estimating and correcting the system noise statistics adaptively. The IAUKF is verified under the federal urban driving schedule test, and the SOC estimation results are compared with IUKF and UKF. The experimental results show that the IAUKF has higher estimation accuracy and stability, which verifies the effectiveness of the proposed method.

ACS Style

Jie Xing; Peng Wu. State of Charge Estimation of Lithium-Ion Battery Based on Improved Adaptive Unscented Kalman Filter. Sustainability 2021, 13, 5046 .

AMA Style

Jie Xing, Peng Wu. State of Charge Estimation of Lithium-Ion Battery Based on Improved Adaptive Unscented Kalman Filter. Sustainability. 2021; 13 (9):5046.

Chicago/Turabian Style

Jie Xing; Peng Wu. 2021. "State of Charge Estimation of Lithium-Ion Battery Based on Improved Adaptive Unscented Kalman Filter." Sustainability 13, no. 9: 5046.