Online Resource Provisioning for Wireless Data Collection

Wireless data collection requires a sequence of resource provisioning decisions due to the limited battery capacity of wireless sensors. The corresponding online resource provisioning problem is challenging. Recently, many prediction methods have been proposed that can be used to benefit the performance of various systems through their incorporation. Therefore, in this article, we focus on online resource provisioning problems with short-term predictions motivated by the wireless data collection problem. Specifically, we design separate online algorithms for systems in which the state evolves in either a stationary manner or an arbitrarily determined manner and prove their performance bounds where their bounds improve as the amount of available predictions increases. Additionally, we design a meta-algorithm that can choose which online algorithm to implement at each point in time, depending on the recent behavior of the system environment. The practical performances of the proposed algorithms are corroborated in trace-driven numerical simulations of data collection of shared bikes. Additionally, we show that the performance of our meta-algorithm in various system environments can be better than that of the single best algorithm chosen in hindsight.

Modeling and control of heat pump system for battery electric buses

Given the continuous tightening of emissions regulations on vehicles, battery-electric buses (BEB) play an essential role in the transition toward cleaner transport technologies, as they represent the most promising solution to replace diesel buses and reduce their environmental impact in the short term. However, heating the bus cabin leads to a considerable increase in energy consumption under cold weather conditions, which significantly reduces the driving range, given the limited battery capacity. Heat pumps (HP) are the primary heating technology used in BEB for their improved consumption performance compared to other technologies. Therefore, this study aims at optimizing the coefficient of performance (COP) of an HP system in a BEB for maximizing the bus electric driving range under cold weather conditions while maintaining satisfactory thermal comfort levels for passengers. Accordingly, an HP model is developed and integrated into an electric bus model using Dymola. A genetic algorithm (GA) based controller is proposed to find the optimal combination of the HP operating parameters, namely the compressor speed, the air mass flow rate at the inlet of the condenser, and the recirculation rate in order to maximize the system’s COP, and extend the BEB driving at different external temperatures, and as a function of the passengers’ occupancy levels. Results are carried under transient and steady-state operating conditions and show that the proposed GA-based controller saves up to 39% of the HP energy consumption as compared to the conventional HP control strategy, and therefore, enhances the BEB driving range up to 17%.

Tertiary Control for Energy Management of EV Charging Station Integrated With PV and Energy Storage

Along with the rapid increase in the number of electric vehicles, more and more EV charging stations tend to have charging infrastructure, rooftop photovoltaic and energy storage all together for energy saving and emission reduction. Compared with individual design for each of the components in such kind of systems, an integrated design can result in higher efficiency, increased reliability, and lower total capital cost. This paper mainly focuses on the tertiary control strategy for dynamic state operation, such as PV generation fluctuation and random arrival/leave of EVs. The tertiary control aims to achieve stable operation under dynamic states, as well as to optimize the energy flow in the station to realize maximal operational benefits with constraints such as peak/valley price of electricity, state of discharge limitation of battery, etc. In this paper, four energy management functions in tertiary control level are proposed, and their performance is verified by simulations. By using prediction of PV power and EV load in the following 72 h, a novel tertiary control logic is proposed to optimize PVC and ESC power flow by changing their droop characteristics, so that minimum operational cost for the station can be achieved. Furthermore, a sensitivity analysis is conducted for three parameters, including ES battery capacity, weather influence, and PV and EV load prediction error. The results from sensitivity analysis indicate that ES battery capacity and weather condition lead to a great impact on the operational cost of the integrated charging station, while a typical prediction error of PV power and EV load will not influence the optimization result significantly.

Optimization of The Energy Efficiency in Smart Internet of Vehicles Assisted By MEC

Smart Internet of Vehicles (IoV) as a promising application in Internet of Things (IoT) emerges with the development of the fifth generation mobile communication (5G). Nevertheless, the heterogeneous requirements of sufficient battery capacity, powerful computing ability and energy efficiency for electric vehicles face great challenges due to the explosive data growth in 5G and the sixth generation of mobile communication (6G) networks. In order to alleviate the deficiencies mentioned above, this paper proposes a mobile edge computing (MEC) enabled IoV system, in which electric vehicle nodes (eVNs) upload and download data through an anchor node (AN) which is integrated with a MEC server. Meanwhile, the anchor node transmitters radio signal to electric vehicles with simultaneous wireless information and power transfer (SWIPT) technology so as to compensate the battery limitation of eletric vehicles. Moreover, the anchor node equips with full-duplex (FD) and multi-input and multi-output (MIMO) technologies for futher improve the spectrum efficiency. Taking into account the issues above, we maximize the average energy efficiency of electric vehicles by jointly optimize the CPU frequency, vehicle transmitting power, computing tasks and uplink rate. In order to solve this nonconvex problem, we propose a novel alternate interior-point iterative scheme (AIIS) under the constraints of computing tasks, energy consumption and time latency. Numerical simulations demonstrate the effectiveness of the proposed scheme comparing with the benchmark schemes.

Multi-depot Electric Bus Scheduling Considering Operational Constraint and Partial Charging: A Case Study in Shenzhen, China

Electric buses (e-buses) demonstrate great potential in improving urban air quality thanks to zero tailpipe emissions and thus being increasingly introduced to the public transportation systems. In the transit operation planning, a common requirement is that long-distance non-service travel of the buses among bus terminals should be avoided in the schedule as it is not cost-effective. In addition, e-buses should begin and end a day of operation at their base depots. Based on the unique route configurations in Shenzhen, the above two requirements add further constraint to the form of feasible schedules and make the e-bus scheduling problem more difficult. We call these two requirements the vehicle relocation constraint. This paper addresses a multi-depot e-bus scheduling problem considering the vehicle relocation constraint and partial charging. A mixed integer programming model is formulated with the aim to minimize the operational cost. A Large Neighborhood Search (LNS) heuristic is devised with novel destroy-and-repair operators to tackle the vehicle relocation constraint. Numerical experiments are conducted based on multi-route operation cases in Shenzhen to verify the model and effectiveness of the LNS heuristic. A few insights are derived on the decision of battery capacity, charging rate and deployment of the charging infrastructure.

A Sensor-Based Drone for Pollutants Detection in Eco-Friendly Cities: Hardware Design and Data Analysis Application

The increase in produced waste is a symptom of inefficient resources usage, which should be better exploited as a resource for energy and materials. The air pollution generated by waste causes impacts felt by a large part of the population living in and around the main urban areas. This paper presents a mobile sensor node for monitoring air and noise pollution; indeed, the developed system is installed on an RC drone, quickly monitoring large areas. It relies on a Raspberry Pi Zero W board and a wide set of sensors (i.e., NO2, CO, NH3, CO2, VOCs, PM2.5, and PM10) to sample the environmental parameter at regular time intervals. A proper classification algorithm was developed to quantify the traffic level from the noise level (NL) acquired by the onboard microphone. Additionally, the drone is equipped with a camera and implements a visual recognition algorithm (Fast R-CNN) to detect waste fires and mark them by a GPS receiver. Furthermore, the firmware for managing the sensing unit operation was developed, as well as the power supply section. In particular, the node’s consumption was analysed in two use cases, and the battery capacity needed to power the designed device was sized. The onfield tests demonstrated the proper operation of the developed monitoring system. Finally, a cloud application was developed to remotely monitor the information acquired by the sensor-based drone and upload them on a remote database.

Influence of Wind Speed Forecasting Error on the Choice of the Number of Balancing System Batteries

In the energy balancing system of distributed generation systems with RES (renewable energy sources), in particular with wind turbines, the effective use of the battery of the balancing system depends on the charge-discharge modes that are implemented. To be effectively used in an energy balancing system, the RES control system should coordinate the processes of energy generation and accumulation in the system through the implementation of operational management with forecasting. Depending on the characteristics of the battery and the accuracy of the measurement or prediction of the energy the battery capacity (or the number of batteries) that will provide the specified control range (controlled operation area) needs to be chosen. Empirical relations (equations) devoted to the dependence of the battery capacity on the discharge current and to the change of voltage at the terminals of the battery during direct current discharge were listed. Among the equations Peukert’s law was chosen. A general view of the dependence of the battery capacity on the discharge current was shown. The formula for Peukert's constant (coefficient) was given. 5 Packert's law limitations were listed including the fact that the effect of temperature on the battery is not taken into account. The influence of depth charge-discharge and the number of discharge cycles on the capacitance was shown. In the process of using the battery and increasing the number of charge-discharge cycles, the capacity decreases. Peukert’s formula was extended to be influenced by temperature: both the Peukert’s capacity and the Peukert’s coefficient depend on the temperature because the Peukert’s coefficient depends on the capacity. For further calculations, a rechargeable battery HZB12-180FA from manufacturer HAZE Battery Campany Ltd was chosen. The temperature was taken into account by empirical dependences from the manufacturer and then they were approximated by 3rd order polynomials. Graphical results of the approximation were shown. The formula of dependency between the power of the wind turbine and the wind speed was shown. The connection between wind speed prediction error, amount of power that could not be obtained because of that and the number of batteries that would provide the specified control range (controlled operation area) was shown. Thus, for calculation of the number of batteries the depth of discharge, temperature and prediction (measurement) error were taken into account. Example dependences of the number of batteries on the wind speed error at temperatures of -20 °C, 0 °C and 20 °C were shown. Curves of dependence of the number of batteries of the balancing system on the ambient temperature and the error of wind speed forecasting was constructed. As an example, when the prediction error increases from 10% to 15%, the number of batteries needs to be increased by 1.17 times, and when the temperature decreases from 20 °C to 0 °C, the number of batteries needs to be increased by 1.48 times. The results of the work can be used at the stage of planning the wind turbine when choosing the number and capacity of the batteries to be installed. Possible areas of further research are using Peukert's formulas, generalized for other or different types of batteries, using other formulas, except for Peukert’s one, for taking into account the dependence of battery capacity on discharge current, using a non-empirical approach to include dependency on temperature.