ALBERT

Beschreibung: Sustainability, Vol. 10, Pages 2699: A Framework for Regional Ecological Risk Warning Based on Ecosystem Service Approach: A Case Study in Ganzi, China Sustainability doi: 10.3390/su10082699 Authors: Tian Dong Weihua Xu Hua Zheng Yang Xiao Lingqiao Kong Zhiyun Ouyang Worldwide, most ecosystem services have declined. However, the theoretical and analytical frameworks for the ecological risk assessment of ecosystem services are still lacking. Here a framework for the risk assessment of ecosystem services was developed based on the formation, changes, risk, and management of ecosystem services. The framework was tested in Ganzi, the upstream area of the Yangtze River Basin, for the regional ecological warning of ecosystem services. Ecosystem services in the form of soil retention and sandstorm prevention and ecological risks including soil and wind erosion were modelled. The results showed that with the increase in area and quality of natural vegetation (forest and grassland), the soil retention service and sandstorm prevention service increased by 66.92% and 8.59% between 2000 and 2015, respectively. Correspondingly, the ecological risk of soil erosion decreased by 8.8%, and wind erosion remained stable. Despite the negative impacts from agricultural development on sandstorm prevention, the increase in vegetation and improvement in ecological quality led to a decrease in the ecological risks of soil erosion and sandstorm erosion by improvement of ecosystem services. This research provides a new perspective for ecological risk assessment, as well as direct management information on ecological risks, by incorporating ecosystem services.

Beschreibung: Energies, Vol. 11, Pages 1275: A Multiple Legs Inverter with Real Time–Reflected Load Detection Used in the Dynamic Wireless Charging System of Electric Vehicles Energies doi: 10.3390/en11051275 Authors: Yong Tian Jindong Tian Dong Li Shijie Zhou Dynamic wireless power transfer is a potentially effective method to solve issues related to the range anxiety of electric vehicles (EVs) and reduce the cost of on-board batteries. A novel multiple legs inverter topology with a reflected load identification method for dynamic EV charging is proposed in this paper. In the proposed circuit topology, several inductor-capacitor-capacitor (LCC) reactive power compensation resonant networks and primary pads are selectively excited through a sole primary converter. Besides, a high-response and simple method for the reflected load identification is proposed to rapidly and precisely detect the EV’s position, providing accurate power regulation reference to the converter. With the proposed method, the system can realize high-response and closed-loop power control precisely without any additional wireless communication and position detection devices. Simulation and experimental results verified the efficiency of the proposed scheme. Additionally, the cost comparison results reveal that the proposed scheme could reduce costs by nearly 78% in comparison with the conventional scheme.

Beschreibung: Energies, Vol. 11, Pages 59: An Online State of Charge Estimation Algorithm for Lithium-Ion Batteries Using an Improved Adaptive Cubature Kalman Filter Energies doi: 10.3390/en11010059 Authors: Zhibing Zeng Jindong Tian Dong Li Yong Tian An accurate state of charge (SOC) estimation of the on-board lithium-ion battery is of paramount importance for the efficient and reliable operation of electric vehicles (EVs). Aiming to improve the accuracy and reliability of battery SOC estimation, an improved adaptive Cubature Kalman filter (ACKF) is proposed in this paper. The battery model parameters are online identified with the forgetting factor recursive least squares (FRLS) algorithm so that the accuracy of SOC estimation can be further improved. The proposed method is evaluated by two driving cycles, i.e., the New European Driving Cycle (NEDC) and the Federal Urban Driving Schedule (FUDS), and compared with the existing unscented Kalman filter (UKF) and standard CKF algorithms to verify its superiority. The experimental results reveal that comparing with the UKF and standard CKF, the improved ACKF algorithm has a faster convergence rate to different initial SOC errors with higher estimation accuracy. The root mean square error of SOC estimation without initial SOC error is less than 0.5% under both the NEDC and FUDS cycles.