High-efficiency 2D nanosheet exfoliation by a solid suspension-improving method

Two-dimensional (2D) materials with mono or few layers have wide application prospects, including electronic, optoelectronic, and interface functional coatings in addition to energy conversion and storage applications. However, the exfoliation of such materials is still challenging due to their low yield, high cost, and poor ecological safety in preparation. Herein, a safe and efficient solid suspension-improving method was proposed to exfoliate hexagonal boron nitride nanosheets (hBNNSs) in a large yield. The method entails adding a permeation barrier layer in the solvothermal kettle, thus prolonging the contact time between the solvent and hexagonal boron nitride (hBN) nanosheetand improving the stripping efficiency without the need for mechanical agitation. In addition, the proposed method selectively utilizes a matching solvent that can reduce the stripping energy of the material and employs a high-temperature steam shearing process. Compared with other methods, the exfoliating yield of hBNNSs is up to 42.3% at 150°C for 12 h, and the strategy is applicable to other 2D materials. In application, the ionic conductivity of a PEO/hBNNSs composite electrolytes reached 2.18×10−4 S cm−1 at 60°C. Overall, a versatile and effective method for stripping 2D materials in addition to a new safe energy management strategy were provided.

An Energy Management Strategy and Parameter Optimization of Fuel Cell Electric Vehicles

This study involved a detailed analysis of an energy distribution strategy and the parameters of key components of fuel cell electric vehicles (FCEVs). In order to better utilize the advantages of multiple energy sources, the wavelet-fuzzy energy management method was used to adjust the demand power allocation among multiple energy sources, and particle swarm optimization (PSO) was used to solve highly nonlinear optimization problems under multi-dimensional and multi-condition constraints. The multi-objective optimization problem of predefined driving cycle powertrain parameters about fuel economy and system durability was studied. The parameters of the key components of the system were optimized, including the size parameters of the air com-pressor and the number of batteries and ultra-capacitors. Furthermore, the driving state under specific working conditions was analyzed, and a nonlinear model with system durability and fuel economy as the optimization objectives were established, which greatly reduced the costs, reduced the fuel consumption rate and extended the battery life. The simulation results showed that for a UDDS cycle, the FCS’s maximal net output power of 83 kW was optimal for the fuel economy and system durability of a fuel cell city bus.

Home Energy Management Strategy-Based Meta-Heuristic Optimization for Electrical Energy Cost Minimization Considering TOU Tariffs

It is well documented that both solar photovoltaic (PV) systems and electric vehicles (EVs) positively impact the global environment. However, the integration of high PV resources into distribution networks creates new challenges because of the uncertainty of PV power generation. Additionally, high power consumption during many EV charging operations at a certain time of the day can be stressful for the distribution network. Stresses on the distribution network influence higher electricity tariffs, which negatively impact consumers. Therefore, a home energy management system is one of the solutions to control electricity consumption to reduce electrical energy costs. In this paper, a meta-heuristic-based optimization of a home energy management strategy is presented with the goal of electrical energy cost minimization for the consumer under the time-of-use (TOU) tariffs. The proposed strategy manages the operations of the plug-in electric vehicle (PEV) and the energy storage system (ESS) charging and discharging in a home. The meta-heuristic optimization, namely a genetic algorithm (GA), was applied to the home energy management strategy for minimizing the daily electrical energy cost for the consumer through optimal scheduling of ESS and PEV operations. To confirm the effectiveness of the proposed methodology, the load profile of a household in Udonthani, Thailand, and the TOU tariffs of the provincial electricity authority (PEA) of Thailand were applied in the simulation. The simulation results show that the proposed strategy with GA optimization provides the minimum daily or net electrical energy cost for the consumer. The daily electrical energy cost for the consumer is equal to 0.3847 USD when the methodology without GA optimization is used, whereas the electrical energy cost is equal to 0.3577 USD when the proposed methodology with GA optimization is used. Therefore, the proposed optimal home energy management strategy with GA optimization can decrease the daily electrical energy cost for the consumer up to 7.0185% compared to the electrical energy cost obtained from the methodology without GA optimization.

Q-Learning-based fuzzy energy management for fuel cell/supercapacitor HEV

A novel fuzzy energy management strategy (EMS) based on improved Q-learning controller and genetic algorithm (GA) is proposed for the real-time power split between fuel cell and supercapacitor of hybrid electric vehicle (HEV). Different from driving pattern recognition–based method, Q-Learning controller takes actions by observing the driving states and compensates to fuzzy controller, that is, no need to know the driving pattern in advance. Aimed to prolong the fuel cell lifetime and decrease its energy consumption, the initial values of Q-table are optimized by GA. Moreover, to enhance the environment adaptation capability, the learning strategy of Q-learning controller is improved. Two adaptive energy management strategies have been compared, and simulation results show that current fluctuation can be reduced by 6.9% and 41.5%, and H2 consumption can be saved by 0.35% and 6.08%, respectively. Meanwhile, state of charge (SOC) of supercapacitor is sustained within the desired safe range.

SSNN-Based Energy Management Strategy in Grid Connected System for Load Scheduling and Load Sharing

The proposed research work focused on energy management strategy (EMS) in a grid connected system working in islanding mode with the connected renewable energy resources and battery storage system. The energy management strategy developed provides a balancing operation at its output by utilizing perfect load sharing strategy. The EMS technique using smart superficial neural network (SSNN) is simulated, and numerical analyses are presented to validate the effectiveness of the centralized energy management strategy in a grid connected islanded system. A SSNN prediction model is unified to forecast the associated household load demand, PV generation system under various time horizons (including the disaster condition), EV availability, and status on EV section and distance. SSNN is one the most reliable forecasting methods in many of the applications. The developed system is also accounted for degradation battery model and its associated cost. The incorporation of energy management strategy (EMS) reduces the amount of energy drawn from the grid connected system when compared with the other optimized systems.