A driving-cycle predictive control approach for energy consumption optimization of hybrid electric vehicle

This paper proposes an adaptive control strategy of fuel consumption optimization for hybrid electric vehicles (HEVs). The strategy combines a moving-horizon-based nonlinear autoregressive (NAR) algorithm, a backpropagation (BP) neural network algorithm, and an equivalent consumption minimization strategy (ECMS) method to reduce energy consumption. The moving-horizon-based NAR algorithm is applied to predict the short future driving cycle. The BP neural network algorithm is employed to recognize the driving cycle types, which provides the basis for the adaptive ECMS. Based on the abovementioned approach, the power split of the fuel and electric system is determined in advance, and the optimal control of energy efficiency is achieved. A driving experiment platform is established, taking a synthetic driving cycle composed of several standard driving cycles as the target cycle, and the control strategy is tested by the driver’s real operation. The results indicate that, compared with the basic ECMS, the A-ECMS with moving-horizon-based driving cycle prediction and recognition has better SOC (state of charge) retention and reduces the fuel consumption of the engine by 3.31%, the equivalent fuel consumption of the electric system by 0.9 L/100 km and the total energy consumption by 1 L/100 km. Adaptive ECMS based on driving cycle prediction and recognition is an effective method for the energy management of HEVs.

Real-time monitoring of the prototype design of electric system by the ubidots platform

<span>In this paper, a prototype DC electric system was practically designed. The idea of the proposed system was derived from the microgrid concept. The system contained two houses each have a DC generator and load that consists of four 12 V DC lamps. Each house is controlled fully by Arduino UNO microcontroller to work in Island mode or connected it with the second house or main electric network. House operating mode depends on the power generated by its source and the availability of the main network. Under all operating cases, the minimum price of electricity consumption should satisfy as possible. Information between the houses about the operating mode and the main network state was exchanging wirelessly with the help of the RF-HC12. This information uploaded to the Ubidots platform by the Wi-Fi-ESP8266 included in the node MCU microcontroller. This platform has several advantages such as capture, visualization, analysis, and management of data. The system was examined for different cases to verify its working by varying the load in each building. All tested states showed that the houses transfer from one mode to another automatically with high reliability and minimum energy cost. The information about the main grid states and the sources of the houses were monitored and stored at the Ubidots platform.</span>

DC Communities: Transformative Building Blocks of the Emerging Energy Infrastructure

Serious environmental concerns call for revolutionary solutions to cope with the harmful effects of the conventional energy landscape. Therefore, residential and commercial customers require cleaner and more reliable energy sources as they become more dependent on energy for daily and critical needs. In this case, transitioning to a cleaner energy economy is of paramount importance for both the environment and the utilities as well as the end-users. The desired transformation will require the deployment of massive amounts of clean energy sources. Many of these resources, such as solar photovoltaic (PV), provide electricity in the form of direct current (DC) that enables the return of DC grids to the electric power arena. The electric system has slowly transitioned to DC, mainly on the demand side. In recent years, modern electronic devices, lighting systems, and an increased number of appliances (≈22% of the residential and commercial loads) have adopted DC systems. Studies suggest that DC loads would account for more than 50% of the available loads in the next few years. Furthermore, the growing proliferation of electric vehicles influx is another example of a successful DC application. From this perspective, the viability of returning to the DC distribution system in the form of DC community grids is explored. We start by defining the DC community grid, which is followed by introducing the benefits of adopting DC at the distribution level. Finally, a summarizing outlook of successful pilot cases, projections of DC community deployment, barriers and concerns, strategies to address barriers and concerns, and suggestions for future research directions are presented. This perspective could shed new light on the building blocks of the transformed energy landscape for various stakeholders.

Power Management of Hybrid Grid System With Battery Deprivation Cost Using Artificial Neural Network

Continuous power supply in an integrated electric system supplied by solar energy and battery storage can be optimally maintained with the use of diesel generators. This article discusses the optimum setting-point for isolated wind, photo-voltaic, diesel, and battery storage electric grid systems. Optimal energy supply for hybrid grid systems means that the load is sufficient for 24 h. This study aims to integrate the battery deprivation costs and the fuel price feature in the optimization model for the hybrid grid. In order to count charge–discharge cycles and measure battery deprivation, the genetic algorithm concept is utilized. To solve the target function, an ANN-based algorithm with genetic coefficients can also be used to optimize the power management system. In the objective function, a weight factor is proposed. Specific weight factor values are considered for simulation studies. On the algorithm actions, charging status, and its implications for the optimized expense of the hybrid grid, the weight factor effect is measured.

A Study on the Characteristics of the Concentrated Solar Photovoltaic/thermal Integrated System

This paper mainly introduces a comprehensive solar energy utilization system with low-CPC and polycrystalline silicon solar photovoltaic components. Outdoor experiments have been made to test the electrical efficiency, heat efficiency and their changes of the system with fixed temperature of outlet water and different sunshine conditions. Besides, this paper also compares the effects of outlet water at various temperatures on the efficiency of the whole system as well as the temperatures and flow rate responses with different PID parameters. Through analysis of these experimental data, the most desirable temperature of outlet water and PID parameters have been obtained in CPC-PV/T Hybrid Thermal-electric System, thus providing references for relevant experimental research.