Plug-in Hybrid Research Articles

Optimal selection and design of grid-connected hybrid renewable energy system in three selected communities of Rivers State

The pursuit of electrifying rural areas has been a multi-decade quest, emblematic of Nigeria's longstanding efforts to diversify its energy sources and reduce dependence on traditional fossil fuels. Accordingly, this study aims to evaluate the electrification status and perform a techno-economic analysis of a hybrid system to ensure electricity reliability, bill reduction and reduced grid demand for three representative rural communities in Rivers State using HOMER Pro Software. Subsequently, five hybrid system scenarios that encompassed different combinations of the grid, PV modules, wind and battery, were examined using the CRITIC-PROMETHEE II methodologies. Each community was assessed based on its own hourly electricity demand, using data obtained from the injection substation. The Aluu, Choba and Rumuekini communities experienced an average daily electricity supply of 4.6, 5.4 and 6.2 h respectively from the national grid with energy usage of 5436.4 MW for Aluu, 3375.4 MW for Choba and 1626.1 MW for Rumuekini, in 2022. The findings indicate that the grid/PV module was the optimal choice for the three communities. Moreover, the cost of energy (COE) for the proposed systems ranged from 0.0181 $/kWh to 0.0185 $/kWh, significantly lower than the grid's COE of 0.08625 $/kWh in the respective communities. This research further includes a sensitivity analysis of the optimal system, examining variations in PV cost, discount rate, inflation rate, annual scaled solar irradiance and electrical demand. The results provide a comprehensive framework to support decision-making, optimize system design and align renewable energy solutions with project goals and stakeholder priorities.

Propulsion Electrification Architecture Selection Process and Cost of Carbon Abatement Analysis for Heavy-Duty Off-Road Material Handler

<div>The heavy-duty off-road industry continues to expand efforts to reduce fuel consumption and CO<sub>2</sub>e (carbon dioxide equivalent) emissions. Many manufacturers are pursuing electrification to decrease fuel consumption and emissions. Future policies will likely require electrification for CO<sub>2</sub>e savings, as seen in light-duty on-road vehicles. Electrified architectures vary widely in the heavy-duty off-road space, with parallel hybrids in some applications and series hybrids in others. The diverse applications for different types of equipment mean different electrified configurations are required. Companies must also determine the value in pursuing electrified architectures; this work analyzes a range of electrified architectures, from micro hybrids to parallel hybrids to series hybrids to a BEV, looking at the total cost, total CO<sub>2</sub>e, and cost per CO<sub>2</sub>e (cost of carbon abatement, or cost of carbon reduction) using data for the year 2021. This study is focused on a heavy-duty off-road material handler, the Pettibone Cary-Lift 204i. This machine’s specialty application, including events like unloading large oil pipes from a railcar, requires a unique electrified architecture that suits its specific needs. However, the results from this study may be extrapolated to similar machinery to inform fuel savings options across the heavy-duty off-road industry. In this study, a unique electrified architecture is determined for the Cary-Lift. This architecture is informed by multiple rounds of a Pugh matrix decision analysis to select a shortened list of desirable electrified architectures. The shortened list is modeled and simulated to determine CO<sub>2</sub>e, cost, and cost per CO<sub>2</sub>e. A final architecture is determined as a plug-in series hybrid that reduces fuel consumption by 65%, targeting the large fuel and CO<sub>2</sub>e savings that are likely to be required for the future of the heavy-duty off-road industry.</div>

Fractional Order PID Based Five-Step Li-Ion Battery Charger in Plug-in Hybrid Electric Vehicles

Fractional Order PID Based Five-Step Li-Ion Battery Charger in Plug-in Hybrid Electric Vehicles

Comparative study of real-time A-ECMS and rule-based energy management strategies in long haul heavy-duty PHEVs

Road transport is a significant contributor to Green House Gas (GHG) emissions in the European Union (EU) and is responsible for approximately 25% of total GHG emissions in the EU13. The European Commission has proposed stricter CO2 emissions targets for heavy-duty vehicles that require manufacturers to achieve a reduction of 90% in average fleet emissions from new vehicles by 2040 compared to a 2019 baseline. To meet these ambitious targets, there is an urgent need to explore alternative pathways away from conventional fossil fuel-based powertrain systems like electrified powertrains and carbon–neutral fuels. One promising option is the plug-in hybrid electric powertrain concept, which combines the advantages of both power sources, enabling improved fuel efficiency and reduced CO2 emissions. However, the potential of such a plug-in hybrid powertrain needs to be evaluated for heavy-duty trucks.This study focuses on the development of control strategies for the Energy Management System (EMS) of the above powertrain concept. First, a control strategy for the non-predictive EMS’s start/stop functionality and optimization of the torque split between the combustion engine and electric machine is developed and calibrated for efficient engine operation depending on the battery state of charge. In addition, a predictive EMS’s control strategy is developed that uses horizon information of the driving route for optimal utilization of electrical energy within the prediction horizon, thereby further enhancing fuel consumption reduction. The predictive EMS uses Adaptive Equivalent Consumption Minimization Strategy (A-ECMS) with Pontryagin’s Minimization Principle (PMP) for online equivalence factor adaptation, providing local optimal solutions in real-time. The study concludes with the energy savings achieved through the implementation of these strategies using real-world driving cycles in the heavy-duty transport sector.

Co-Optimization of Velocity Planning and Energy Management for Intelligent Plug-In Hybrid Electric Vehicles Based on Adaptive Dynamic Programming

Co-Optimization of Velocity Planning and Energy Management for Intelligent Plug-In Hybrid Electric Vehicles Based on Adaptive Dynamic Programming

Cost/Performance Analysis of Battery Pack placed in Spare Tire Area for Extending the Range of Hybrid, and Electric Vehicles

Recently, electric vehicles have been an alternative form of transportation promising a bright future as sales figures are increasing rapidly beyond expectations. In the near fu-ture, electric vehicles are expected to dominate the market with numerous advantages over vehicles with internal combustion engines. The hybrid electric vehicles are often con-sidered a transition between the two due to added advantages of both models. Out of hy-brid models, plugin hybrid vehicles have more resemblance to the fully electric vehicles because of longer electric range and lower cost per mile. This paper investigates the use of the spare tire location to convert an existing battery pack or to install a new battery pack for a hybrid vehicle. Cost and range calculations are performed based on two most com-monly sold vehicles that use 16”, 17” and 18” size tires. Resulting battery packs are demonstrated through various battery dimensions and chemistries. Results are provided with cost and environmental benefits of utilizing an electric power for driving.

Recent Trends and Future Prospects in Electric Vehicle Technologies: A Comprehensive Review

In addressing sustainability challenges within the transportation sector, the integration of electric vehicles (EVs) with renewable energy sources, notably solar and wind power, presents a promising solution. This comprehensive review delves into recent advancements and trends in EV technologies, covering crucial areas such as battery innovations, charging infrastructure, vehicle design, and market dynamics. Various EV technologies, including Plug-in Hybrid Electric Vehicles (PHEVs), battery-based EVs, Solar-powered EVs, and solar-wind hybrid EVs, are meticulously examined, with a particular focus on the feasibility and efficacy of solar-wind hybrid solutions. The review extends to battery technologies, emphasizing advancements in lithium-ion batteries and emerging chemistries like solid-state and lithium-sulfur batteries, which address barriers to EV adoption through enhanced energy density, charging efficiency, and cost-effectiveness. Additionally, the review scrutinizes the expansion of charging infrastructure, encompassing fast-charging stations, wireless charging technologies, and initiatives integrating smart grids, all aimed at providing convenient and efficient charging solutions to alleviate range anxiety and bolster EV attractiveness. Economic considerations, including initial investments, operational savings, and government incentives, are thoroughly analyzed alongside environmental benefits such as reduced greenhouse gas emissions and air pollution. Furthermore, a critical examination of the regulatory and policy framework supporting EVs sheds light on future policy directions, tax incentives, and regulatory measures. Real-world case studies showcasing successful implementations of solar-wind hybrid EV projects underscore their effectiveness and multifaceted impacts across diverse geographical areas. In conclusion, this review underscores the recent trends in EV technologies, emphasizing the feasibility and benefits of solar-wind hybrid EVs in achieving minimal emissions and incentivizing sustainable transportation practices.

Smart charging process development based on ant colony optimization machine learning for controlling lead-acid battery charging capacity

The Indonesian government has targeted 2.1 million two-wheeled electric vehicles and 2,200 four-wheeled electric vehicles (EV) by 2025. This is hampered by limited electricity supply and EV charging, which takes long time. Multi device interleaved DC-DC bidirectional converter has been applied and assessed as the most suitable method for battery EV and plug-in hybrid EV because it produces high power >10 kW. For power below 10 kW, it is recommended to use a sinusoidal, Z-Source, and boost amplifier type converter. The smart charging (SC) system will be applied to electric vehicles, which only require a minimum charging power of around 169 W for four lead acid batteries. This paper focuses on an SC system that is capable of charging the battery quickly while still paying attention to the state of health (SoH) of the battery. The SC developed uses a DC-DC boost converter to increase the voltage produced by the switch mode power supply (SMPS). Estimated charging time is less than 30 minutes and still pay attention to the battery SoH. SC will also use pulse width modulation (PWM) as a duty power cycle regulator. This research applies a multi-layer perceptron (MLP) classifier to a neural network (NN). The results of the research show that smart charging can charge up to 600 W with an estimated charging time of around 11 minutes. The charging condition is above 60 % and the power duty cycle setting is 100 %. The power estimation results processed using the ant colony optimization (ACO) based neural network method show a root mean square deviation value of 0.010013430 for charging four lead acid batteries. These results are useful to help solve the problem of capacity requirements and battery charging speed for EVs, with good SoH

Coordinated Charging Scheduling Approach for Plug-In Hybrid Electric Vehicles Considering Multi-Objective Weighting Control in a Large-Scale Future Smart Grid

The growing popularity of plug-in hybrid electric vehicles (PHEVs) is due to their environmental advantages. But uncoordinated charging of a large number of PHEVs can lead to a significant surge in peak loads and higher charging costs for PHEV owners. To end this, this paper introduces an innovative approach to address the issue by proposing a multi-objective weighting control for coordinated charging of PHEVs in a future smart grid, which aims to find an economically optimal solution while also considering load stabilization with large-scale PHEV penetration. Technical constraints related to the owner’s demand and power limitations are considered. In the proposed approach, the charging behavior of PHEV owners is modeled by a normal distribution. It is observed that owners typically start charging their vehicles when they arrive home and stop charging when they go to their workplace. The charging cost is then calculated based on the tiered electricity price and charging power. By adjusting the cost weighting factor and the load stability weighting factor in the multi-objective function, the grid allows for flexible weight selection between the two objectives. This approach effectively encourages owners to actively participate in coordinated charging scheduling, which sets it apart from existing works. The algorithm offers better robustness and adaptability for large-scale PHEV penetration, making it highly relevant for the future smart grid. Finally, numerical simulations are presented to demonstrate the desirable performance of theory and simulation.

An Agile Approach for Adopting Sustainable Energy Solutions with Advanced Computational Techniques

In the face of the burgeoning electricity demands and the imperative for sustainable development amidst rapid industrialization, this study introduces a dynamic and adaptable framework suitable for policymakers and renewable energy experts working on integrating and optimizing renewable energy solutions. While using a case study representative model for Sub-Saharan Africa (SSA) to demonstrate the challenges and opportunities present in introducing optimization methods to bridge power supply deficits and the scalability of the model to other regions, this study presents an agile multi-criteria decision tool that pivots on four key development phases, advancing established methodologies and pioneering refined computational techniques, to select optimal configurations from a set of Policy Decision-Making Metrics (PDM-DPS). Central to this investigation lies a rigorous comparative analysis of variants of three advanced algorithmic approaches: Swarm-Based Multi-objective Particle Swarm Optimization (MOPSO), Decomposition-Based Multi-objective Evolutionary Algorithm (MOEA/D), and Evolutionary-Based Strength Pareto Evolutionary Algorithm (SPEA2). These are applied to a grid-connected hybrid system, evaluated through a comprehensive 8760-hour simulation over a 20-year planning horizon. The evaluation is further enhanced by a set of refined Algorithm Performance Evaluation Metrics (AL-PEM) tailored to the specific constraints. The findings not only underscore the robustness and consistency of the SPEA2 variant over 15 runs of 200 generations each, which ranks first on the AL-PEM scale, but the findings also validate the strategic merit of combining multiple technologies and empowering policymakers with a versatile toolkit for informed decision-making.

Optimal Management of a Grid-Connected Hybrid Energy System Using FLC-ANN Hybrid Technique

Optimal Management of a Grid-Connected Hybrid Energy System Using FLC-ANN Hybrid Technique

Machine Learning and Optimization in Energy Management Systems for Plug-In Hybrid Electric Vehicles: A Comprehensive Review

Machine Learning and Optimization in Energy Management Systems for Plug-In Hybrid Electric Vehicles: A Comprehensive Review

Meta-heuristic-based power quality improvement in UPQC-based grid-connected hybrid renewable energy system

Meta-heuristic-based power quality improvement in UPQC-based grid-connected hybrid renewable energy system

A REVIEW ON HYBRID ELECTRIC VEHICLES USING VARIOUS ENERGY STORAGE: A MODERN APPLICATION OF RENEWABLE ENERGY SOURCES

Climate change, diminishing reserves of fossil fuels, and energy security demand alternatives to our current course of energy usage and consumption. Energy storage technology has become an enabling technology for renewable energy applications and enhancing power quality in the transmission and distribution power systems. There are various types of energy storage technology are present nowadays, including compressed air energy storage(CAES), flywheel energy storage(FES), pumped hydro energy storage(PHES), battery energy storage(BES), flow battery energy storage(FBES), super conducting magnetic energy storage(SMES), super capacitor energy storage(SCES), hydrogen energy storage, synthetic fuels, and thermal energy storage(TES) and today’s one of the most talked technology using these Energy storage system is Electric Vehicles. Electric vehicles are superior to internal combustion engines (ICE) in efficiency, endurance, durability, acceleration capability and simplicity. Also, they can recover some energy during regenerative braking and they are also friendly with the environment. There are now four types of electric cars: battery electric vehicles (BEVs), plug-in hybrid electric vehicles (PHEVs), conventional hybrid electric vehicles (HEVs), and fuel-cell electric vehicles (FCEVs). And there are three charging methods for electric vehicles, namely conductive, inductive, and battery exchange. This paper will show a detail study of different energy storage systems’ characteristics with their suitability as a storage unit of Electric vehicle.

Enhancing Grid-Connected Hybrid Renewable Energy System with Super Lift Luo Converter for Improved Power Management and Reduced THD

Enhancing Grid-Connected Hybrid Renewable Energy System with Super Lift Luo Converter for Improved Power Management and Reduced THD

Adaptive real-time ECMS with equivalent factor optimization for plug-in hybrid electric buses

—The plug-in hybrid electric bus (PHEB) is one of the vehicles that can address global environmental and energy problems. Due to the complex characteristics of the urban cycle condition, a fixed parameter energy management strategy may not be suitable for fuel economy. Thus, an adaptive real-time control strategy seems to be of great significance for PHEBs. The paper designs an advanced equivalent consumption minimization strategy (ECMS) with segments divided from the driving cycle and optimized equivalent factor (EF). The classification of different segments is based on the time that the vehicle stops. The EF is optimized under two different driving conditions by using the grey wolf optimization (GWO) algorithm. A new model, as the difference between the actual state of charge (SOC) and the reference SOC, is presented and minimized by optimizing the EF. Segmented EF optimization adds a correction factor, which can adjust EF according to the kinematic characteristics of each segment. The results show that the fuel consumption decreased by 18.72 % compared with the conventional ECMS.

Financial subsidy, government audit and new transportation technology: Evidence from the new energy vehicle pilot city program in China

As the global transportation sector grapples with challenges related to the energy crisis and climate change, New Energy Vehicles (NEVs) have emerged as a promising solution. In order to promote this new transportation technology, China has implemented strategic policies, such as the two-round NEV demonstration and pilot city program. Using a novel firm-month level panel data and the Difference-in-Difference (DID) strategy, this paper assesses the impact of the NEV pilot city program on NEV production from 2009 to 2016. Our findings reveal a significant increase in the production of Battery Electric Vehicles (BEVs) by 37.0% and Plug-in Hybrid Electric Vehicles (PHEVs) by 6.7%, primarily driven by financial subsidies in the private sector. The impact of the financial subsidy in both private and public sectors was further enhanced by government audit. Heterogeneous analysis shows the effect of the program was more pronounced in eastern regions, medium and small cities, and key cities for air pollution control. Based on the empirical findings, we discuss policy options for fostering new transportation technology in developing countries.

Energy mix-driven dynamic life cycle assessment on greenhouse gas emissions of passenger cars in China

Amidst increasingly stringent global emission regulations, China's energy mix is undergoing profound transformation. To more precisely evaluate vehicle emissions, this study aimed to explore the dynamic responses on life cycle greenhouse gas (GHG) emissions of passenger cars in China, considering the change of energy mix. An energy mix-driven life cycle assessment model was established and inventory data were obtained. The electricity generation mix data in China were predicted, and the results in life cycle GHG emissions of internal combustion engine vehicle (ICEV), battery electric vehicle (BEV) and plug-in hybrid electric vehicle (PHEV) from 2020 to 2050 were analyzed. Uncertainty, sensitivity and breakeven distance analyses were performed to explore the impact of various parameters. The results showed that the average emission factor of electricity generation mix is projected to decrease by 87.11% in 2050 with the cleanliness of energy mix. The life cycle GHG emissions of ICEV, BEV and PHEV produced in 2020 were 37.16, 28.04 and 29.84 tCO2eq respectively. Based on the linear models, it is expected that the emissions from these vehicles produced in 2050 would be reduced by 11.48%, 61.31%, and 53.39% respectively compared to their levels in 2020. Policies and measures for emission reduction were proposed based on the sensitive parameters and calculation results.

Vehicle choice modeling for emerging zero-emission light-duty vehicle markets in California

To predict the market dynamics of various zero-emission vehicle (ZEV) technologies, this study introduces a dynamic discrete vehicle choice model (VCM) that investigates the probabilities associated with 14 decision factors, applying these to the purchase of ZEVs from 2020 to 2040. Market share and penetration results are presented under eight scenarios, that vary by vehicle costs infrastructure development and incentive strategies. The findings suggest that in the early years, incentives alone may not generate significant market penetration of ZEVs before the infrastructure meets the basic convenience for daily use, especially for fuel cell vehicles (FCVs). However, in later years, incentives play a more important role in the market penetration of ZEVs under well-defined infrastructure networks. By 2040, battery electric vehicles (BEVs) are projected to dominate the market in California. Plug-in hybrid electric vehicles (PHEVs) and FCVs may experience a decline in market share due to improved charging convenience, which benefits the market penetration of BEVs. However, fuel cell plug-in hybrid electric vehicles (FC-PHEVs) could still be beneficial if accessible models are available, considering the limited availability of hydrogen refueling stations. The goal set by the California Air Resources Board (CARB) is achievable, but it requires a sustained combination of measures; no single effort can achieve it. These measures include technological improvements to reduce the cost of ZEVs, a wider range of models available for consumers to choose from based on their desired performance, the establishment of infrastructure (battery chargers and hydrogen dispensers), and attractive incentives aimed at promoting ZEV adoption. The proposed methodology can be adapted for other regions in the United States and globally by carefully examining the inputs for each decision factor at the desired scale.

Navigating the Present and Future Dynamics of Electric Vehicle Fast Charging and its Impact on Grid

As electric vehicle (EV) adoption surges worldwide, the need for fast charging infrastructure becomes paramount. Fast charging technology alleviates range anxiety and bolsters EV practicality. This comprehensive analysis delves into current challenges and future prospects surrounding EV fast charging and its grid impact. Examining the state of fast charging technology, it scrutinizes charging standards, connector types, and power levels across global regions. Emphasis is placed on key market players and the imperative for interoperability and standardization to seamlessly integrate EVs into the power grid. Voltage fluctuations, harmonics, and power factor issues stemming from rapid, high-power charging pose technical hurdles. Solutions like advanced power electronics, grid management tactics, and enhanced communication between EVs and the grid are explored to uphold stability and power quality. Consideration is given to energy sources fueling fast chargers, including renewable integration and environmental repercussions. Economic facets, encompassing infrastructure deployment costs and grid upgrade analyses, are examined amidst escalating electricity demand and potential grid expansion needs. A forward-looking perspective addresses future challenges and opportunities. This includes advancements in battery tech, smart grid solutions, and bidirectional power flow potential between EVs and the grid. Index Terms: Battery electric vehicles (BEVs), Grid, Fast Charging, plug-in hybrid electric vehicles (PHEVs).