Plug-in Hybrid Research Articles

Multi-Objective Dynamic System Model for the Optimal Sizing and Real-World Simulation of Grid-Connected Hybrid Photovoltaic-Hydrogen (PV-H2) Energy Systems

Hybrid renewable-hydrogen energy systems offer a promising solution for meeting the globe’s energy transition and carbon neutrality goals. This paper presents a new multi-objective dynamic system model for the optimal sizing and simulation of hybrid PV-H2 energy systems within grid-connected buildings. The model integrates a Particle Swarm Optimisation (PSO) algorithm that enables minimising both the levelised cost of energy (LCOE) and the building carbon footprint with a dynamic model that considers the real-world behaviour of the system components. Previous studies have often overlooked the electrochemical dynamics of electrolysers and fuel cells under transient conditions from intermittent renewables and varying loads, leading to the oversizing of components. The proposed model improves sizing accuracy, avoiding unnecessary costs and space. The multi-objective model is compared to a single-objective PSO-based model that minimises the LCOE solely to assess its effectiveness. Both models were applied to a case study within Robert Gordon University in Aberdeen, UK. Results showed that minimising only the LCOE leads to a system with a 1000 kW PV, 932 kW electrolyser, 22.7 kg H2 storage tank, and 242 kW fuel cell, with an LCOE of 0.366 £/kWh and 40% grid dependency. The multi-objective model, which minimises both the LCOE and the building carbon footprint, results in a system with a 3187.8 kW PV, 1000 kW electrolyser, 106.1 kg H2 storage tank, and 250 kW fuel cell, reducing grid dependency to 33.33% with an LCOE of 0.5188 £/kWh.

Development of Intelligent MPPT to Resolve Voltage Dips in a Grid-Connected Hybrid Power Generation PV System in a Hot Climate

Development of Intelligent MPPT to Resolve Voltage Dips in a Grid-Connected Hybrid Power Generation PV System in a Hot Climate

Integration of Wind and Solar Hybrid Power Generation Systems into The Grid and Control of Power Quality with Fuzzy Logic

In this study, a grid-connected hybrid system composed of a solar and wind power generation system is modelled and analyzed in Matlab/Simulink. In addition, the quality of power generated by the hybrid power generation system is improved by using Fuzzy Logic (FL) and Proportional-Integral (PI) controllers. Measurements of the frequency, voltage, power and harmonics of the system and the grid are evaluated. In addition, the voltage and frequency fluctuation times prior to synchronization between the hybrid generation system and the grid are analyzed. The voltage rises that occur on the grid while the system is idling have been checked. After synchronization, the voltage, power, frequency and phase angle values measured at the load and at the grid in the hybrid system were observed individually. According to the measured values obtained as a result of the simulation study, the performance of the hybrid power generation system is analyzed.

Improving Power Quality and Fault Ride-Through (FRT) in Grid-Connected Hybrid Energy Systems: Design and Optimization of Dynamic Voltage Restorers

The integration of renewable energy sources (RESs) into grid-connected systems has become pivotal in addressing climate change and building a sustainable, carbon-neutral society. However, challenges like power quality issues, grid stability, and fault ride-through (FRT) requirements remain significant. This study explores strategies to enhance FRT capabilities and improve power quality in hybrid energy systems. It emphasizes the role of advanced devices such as Dynamic Voltage Restorers (DVRs), EV charging stations, and energy storage solutions. The effectiveness of these technologies is analyzed under various fault scenarios, highlighting improvements in transient response, voltage stability, and harmonic reduction. Additionally, hybrid energy systems, combining multiple RESs and backup sources, are proposed as viable solutions to address intermittency and system reliability. MATLAB/SIMULINK simulations validate the proposed approaches, showcasing their effectiveness in mitigating grid disturbances and maintaining operational efficiency. The findings underline the importance of innovative control strategies and hybrid configurations in ensuring a resilient and sustainable energy infrastructure.

Supervisory controller for minimizing fuel consumption and NOx emissions of plug-in hybrid electric vehicles operating in zero-emission zones

This paper introduces a supervisory controller designed for Plug-in Hybrid Electric Vehicles (PHEVs) to minimize total energy consumption and tailpipe NOx emissions while adhering to Zero Emission Zone (ZEZ) constraints. The case study exploits historical driving cycle data from an urban bus route in Zurich to analyze trip correlations, where a ZEZ restriction was added to assess the vehicle performance under such conditions. A PHEV bus model was built, integrating the powertrain and After-Treatment System (ATS), where an electric heater is included to mitigate NOx emissions. The supervisory controller is tasked with determining the optimal power split and the electric heater power to ensure adherence to feasible operating conditions along the route. Historical driving cycle data analysis demonstrates that the speed profiles along the selected route exhibit similarities. This observation is leveraged by a Dynamic Programming (DP) optimization, where an arbitrary bus trip is employed to generate a cost-to-go matrix. Then, the resulting cos-to-go matrix was indexed by the position in the route and is used on the real-time controller by applying the one-step look-ahead roll-out algorithm. Simulation results demonstrate the controller effectiveness in addressing ZEZ restrictions, presenting a trade-off between energy consumption and tailpipe NOx emissions. A benchmark was carried out, comparing the results obtained with the DP solution as the baseline, assuming perfect knowledge of driving cycle disturbances, revealing a 7% increase in tailpipe NOx emissions and a 1.3% increase in fuel consumption compared to the theoretical minimum and fulfilling the ZEZ restrictions in all the cases.

Should Charging Stations Provide Service for Plug-In Hybrid Electric Vehicles During Holidays?

The development of the new energy vehicle (NEV) market in China has promoted the sustainability of the automotive industry, but has also brought pressures to NEV charging infrastructure. This paper aims to determine the strategic role of charging stations, particularly on whether they should provide service for plug-in hybrid electric vehicles (PHEVs) in the highway service area during peak holidays. Firstly, the charging service resource allocation for a charging station that provides services for both electronic vehicles (EVs) and PHEVs is studied. Secondly, different queueing disciplines are compared. At last, a comparison between scenarios where charging services are limited to EVs and those where services extend to both EVs and PHEVs is conducted. A queueing system considering customer balking and reneging is developed. The impacts of parameters, such as the NEV arrival rate and patience degree of different NEV drivers, on the optimal allocation plan, profit, and comparison results are discussed. The main conclusions are as follows: (1) If the EV arrival rate is greater than the charging service rate, the charging station should not provide charging services for PHEVs. Providing service only for EVs derives more revenues and profits and results in a shorter waiting queue. Conversely, if the total arrival rate of NEVs (including EVs and PHEVs) is lower than the charging service rate, then the charging station should also serve PHEVs. (2) If providing service for PHEVs, a mixed queueing discipline should be applied when the total arrival rate approximates the service rate. When the total NEV arrival rate is significantly lower than the charging service rate, the separate queueing discipline should be adopted. (3) When applying a separate queueing discipline, if a certain type of NEV has a higher arrival rate and the drivers exhibit greater patience, then more charging resources should be allocated to this type of NEV. If the charging service is less busy, the more patient the drivers are, the less service resources should be allocated to them, whereas, during peak times, the more patient the drivers are, the more service resources should be allocated to them.

Vehicle or power grid? Subsidy policy performance evaluation of China's plug in hybrid electric vehicles promotion and power grid cleaning construction

China has implemented strong incentives to promote the market of plug-in hybrid electric vehicles (PHEVs). The increase of PHEV will lead to an increase in electricity consumption, and more power generation will also lead to more carbon dioxide emissions. Therefore, it is necessary to evaluate the environmental benefits of the subsidy policy. In this study, we calculated the CO2 emission reduction and the increase of grid power generation and the CO2 emission from the investment and construction of clean energy power generation facilities caused by the subsidy of PHEV from 2016 to 2020, and evaluated the effect of PHEV subsidy through the multi-period DID model. The results show that replacing ICEV with a PHEV will reduce CO2 emissions by 12.38 kg per 100 km. Under the existing grid structure and power generation conditions, the promotion of PHEV will have a positive impact on CO2 emissions, but the effect is short-lived. Without considering other costs, the investment in clean energy power generation facilities at this stage will achieve better CO2 emission reduction effect than the promotion of PHEV.

Energy Management Improvement in the Charge-Sustaining Mode of a Plug-In Hybrid Electric Vehicle

Energy Management Improvement in the Charge-Sustaining Mode of a Plug-In Hybrid Electric Vehicle

Hierarchical Coordinated Optimization and Energy Management Control for Plug-In Hybrid Electric Heavy-Duty Truck Platoon in Coal Mine Transportation System

Hierarchical Coordinated Optimization and Energy Management Control for Plug-In Hybrid Electric Heavy-Duty Truck Platoon in Coal Mine Transportation System

Controlling power fluctuations in grid-connected hybrid PV system with energy management using hierarchical fuzzy logic

Controlling power fluctuations in grid-connected hybrid PV system with energy management using hierarchical fuzzy logic

Controlling power fluctuations in grid-connected hybrid PV system with energy management using hierarchical fuzzy logic

Controlling power fluctuations in grid-connected hybrid PV system with energy management using hierarchical fuzzy logic

Multi-objective nutcracker optimization algorithm based on fast non-dominated sorting and elite strategy for grid-connected hybrid microgrid system scheduling

Multi-objective nutcracker optimization algorithm based on fast non-dominated sorting and elite strategy for grid-connected hybrid microgrid system scheduling

Analysis of Energy Supply Scenarios Using Grid-Connected Hybrid Systems: Investigating Time-of-Use Demand Response Strategy for Improving Operational Performance

Analysis of Energy Supply Scenarios Using Grid-Connected Hybrid Systems: Investigating Time-of-Use Demand Response Strategy for Improving Operational Performance

A Review of Multi-Objective Optimization Methods of Grid-Connected Hybrid Renewable Energy Systems Combined with Electrical Vehicle Technique

Renewable Energy Sources (RESs) are regarded as highly promising and rapidly advancing forms of renewable energy. The commonly used RESs are solar energy and wind energy. However, obstacles are found in designing RESs. Using vehicle-to-grid (V2G) technology in combination with electric vehicles (EVs) and RESs in smart grid are of great significance for ensuring energy security, preventing air pollution, and promoting energy saving and emission reduction. Over the past decade, V2G technology has enabled EVs to become a potential energy storage capacity for alleviating the random fluctuation in renewable energy generation. Nevertheless, the primary drawbacks of these systems are inefficient energy conversion and substantial initial investment. The sizing of each piece of equipment in the hybrid renewable energy system (HRES) is challenging. Hence, it is imperative to employ a precise sizing technique to determine an ideal arrangement and meet the requisite load requirements. Hence, before the installation of RESs, it is crucial to consider the types and arrangements of photovoltaic (PV) panels and wind turbines (WTs), mathematical models of PV modules and WTs, storage battery options, environmental-economic-technical considerations, sizing methods based on techno-economic objectives, and the ultimate selection of the most optimal configuration. This work lists the general classification of optimization formulation framework, and multi-objective optimization methods of HRESs integrated with electrical vehicle technique are reviewed. This workprovided a thorough examination of the current advancements in the design optimization of HRESs using multi-objectiveoptimization (MOO). This study aims to select the most appropriate design before installing HRESs and provides afoundational platform for scholars interested in exploring the integration of RESs with EVs for further advancement.

Optimization Of Solar Panel Usage In Grid-Connected Hybrid Energy Systems Using Fuzzy Method

The hybrid grid-connected power generation system combines solar power, wind power, and the PLN grid to meet the electricity demands of facilities such as schools, laboratories, mosques, and kindergartens at MTs Parmiyatu Wassa'adah School. Due to insufficient wind speed below the turbine's operational threshold, wind turbines cannot contribute to electricity generation, making solar power the primary energy source. Solar power capacity is crucial for meeting the electricity needs of these facilities. This study applies the Fuzzy method to analyze the optimal utilization of solar panels in a grid-connected hybrid system for electricity demand. Simulation results indicate three levels of solar panel utilization, with the most optimal performance achieved when school electricity usage is low, and additional loads are minimized.

Equivalent Cost Minimization Strategy for Plug-In Hybrid Electric Bus with Consideration of an Inhomogeneous Energy Price and Battery Lifespan

The development of energy-saving vehicles is an important measure to deal with environmental pollution and the energy crisis. On this basis, more accurate and efficient energy management strategies can further tap into the energy-saving potential and energy sustainability of vehicles. The equivalent consumption minimization strategy (ECMS) has shown the ability to provide a real-time sub-optimal fuel efficiency performance. However, when taking the different market prices of fuel and electricity cost as well as battery longevity cost into account, this method is not very accurate for total operational economic evaluation. So, as an improved scheme, the instantaneous cost minimization strategy is proposed, where a comprehensive cost function, including the market price of the electricity and fuel as well as the cost of battery aging, is applied as the optimization objective. Simulation results show that the proposed control strategy for series-parallel hybrid electric buses can reduce costs by 41.25% when compared with the conventional engine-driven bus. The approach also impressively improves cost performance over the rule-based strategy and the ECMS. As such, the proposed instantaneous cost minimization strategy is a better choice for hybrid electric vehicle economic evaluation than the other main sub-optimal strategies.

Optimization of Energy Management Strategy for Series Hybrid Electric Vehicle Equipped with Dual-Mode Combustion Engine Under NVH Constraints

Energy management strategies (EMSs) are a core technology in hybrid electric vehicles (HEVs) and have a significant impact on their fuel economy. Optimal solutions for EMSs in the literature usually focus on improving fuel efficiency by operating the engine within a high efficiency range, without considering the drivability, which is affected by noise–vibration–harshness (NVH) constraints at low vehicle speeds. In this paper, a dual-mode combustion engine was implemented in a plug-in series hybrid electric vehiclethat could operate efficiently either at low loads in homogeneous charge compression ignition (HCCI) mode or at high loads in spark ignition (SI) mode. An equivalent consumption minimization strategy (ECMS) combined with a dual-loop particle swarm optimization (PSO) algorithm was designed to solve the optimal control problem. A MATLAB/Simulink simulation was performed using a well-calibrated model of the target HEV to validate the proposed method, and the results showed that it can achieve a reduction in fuel consumption of around 1.3% to 9.9%, depending on the driving cycle. In addition, the operating power of the battery can be significantly reduced, which benefits the health of the battery. Furthermore, the proposed ECMS-PSO is computationally efficient, which guarantees fast offline optimization and enables real-time applications.

Light-duty plug-in electric vehicle adoption: County-level emissions benefits using consumption-based emissions intensities

The electrification of light-duty vehicles (LDVs) is essential for decarbonizing the transportation sector in the United States. Both federal and state governments have begun promoting and incentivizing the adoption of plug-in electric vehicle (PEV) (battery electric vehicles (BEV) and plug-in hybrid electric vehicles (PHEV)) to reduce LDV greenhouse gas (GHG) emissions greatly. However, there remains a critical need for a robust methodology to accurately quantify the distributed emissions impacts of PEV adoption at a granular regional level. Additionally, the role of electricity traded across electrical grids in regional GHG mitigation efforts often goes unrecognized. This study addresses these gaps by developing a top-down approach for assessing county-level emissions benefits arising from PEV adoption while accounting for upstream emissions due to electricity flow across regions. Our findings underscore the significant influence of regional variations in future PEV adoption rates and vehicle usage patterns on emissions reduction potential. Nevertheless, these benefits can be tempered by local emission intensities associated with electricity generation. Importantly, our study reaffirms the necessity of considering electricity flow dynamics across grids in estimating local GHG mitigation outcomes.

Quadruple Deep Q-Network-Based Energy Management Strategy for Plug-In Hybrid Electric Vehicles

This study proposes the use of a Quadruple Deep Q-Network (QDQN) for optimizing the energy management strategy of Plug-in Hybrid Electric Vehicles (PHEVs). The aim of this research is to improve energy utilization efficiency by employing reinforcement learning techniques, with a focus on reducing energy consumption while maintaining vehicle performance. The methods include training a QDQN model to learn optimal energy management policies based on vehicle operating conditions and comparing the results with those obtained from traditional dynamic programming (DP), Double Deep Q-Network (DDQN), and Deep Q-Network (DQN) approaches. The findings demonstrate that the QDQN-based strategy significantly improves energy utilization, achieving a maximum efficiency increase of 11% compared with DP. Additionally, this study highlights that alternating updates between two Q-networks in DDQN helps avoid local optima, further enhancing performance, especially when greedy strategies tend to fall into suboptimal choices. The conclusions suggest that QDQN is an effective and robust approach for optimizing energy management in PHEVs, offering superior energy efficiency over traditional reinforcement learning methods. This approach provides a promising direction for real-time energy optimization in hybrid and electric vehicles.

Testing Exhaust Emissions of Plug-In Hybrid Vehicles in Poland

Testing Exhaust Emissions of Plug-In Hybrid Vehicles in Poland