Plug-in Hybrid Research Articles

Physics-informed data-driven modeling approach for commuting-oriented hybrid powertrain optimization

The physics-informed data-driven approach is instructive in powertrain modeling with high-fidelity, which can be adopted for powertrain optimization to further improve vehicle performances. In this paper, a plug-in hybrid electric vehicle (PHEV) equipped with a novel powertrain is considered as the research object, and a series of vehicle bench experiments are carried out for operative data acquisition. With the collected data, the physics-informed data-driven models for engine fuel consumption and battery state of health (SOH) loss are proposed. The accuracy and superiority of the proposed model are demonstrated compared with other approaches. In order to realize the commuting-oriented powertrain optimization, the driving cycle representing the commuting scenario is reconstructed based on the collected traffic data in Beijing. The synthesized driving cycle generated by the proposed method can describe the practical application scenario in Beijing within the allowable error range. In addition, the non-dominated sorting genetic algorithm-II (NSGA-II) is adopted to further optimize the parameters for the hybrid powertrain according to vehicle performances. The multi-objective optimization is completed with the engine fuel consumption and battery state of health (SOH) loss as targets. The results indicate that the optimized configuration parameters can reduce fuel consumption by 4.85% and battery capacity loss by 3.7% compared with the original vehicle.

Research on economical shifting strategy for multi-gear and multi-mode parallel plug-in HEV based on DIRECT algorithm

In response to the discrepancy between the mechanical-electric torque distribution rules used in the shifting calculation of plug-in hybrid electric vehicles and the actual operating conditions, this study focuses on a specific vehicle model known as the multi-gear and multi-mode parallel plug-in hybrid electric vehicle (MGMMP-PHEV). Various control parameters including vehicle speed, pedal opening, battery state of charge (SOC), and operating mode are taken into account by considering the impact of coordinated operation between the dual power sources and battery SOC. In order to address this issue, an enhanced dual-parameter shifting calculation principle and method are proposed, and an optimized shifting strategy is developed with the objective of achieving the highest system comprehensive efficiency and ensuring battery charge-discharge balance. The shifting points under each mode are optimized offline using the DIviding RECTangles (DIRECT) algorithm, and a multi-parameter fuzzy controller is introduced to dynamically adjust the shifting speed. The improved shifting strategy, implemented in Matlab/Simulink, is compared with the initial shifting strategy in terms of its economic performance. The results demonstrate that the DIRECT-optimized and dynamically adjusted fuzzy shifting strategy effectively maintains the balance of battery charge and discharge. Specifically, under the WLTC working condition, it achieves a 5.59 % reduction in fuel consumption compared to the dual-parameter shifting strategy. Furthermore, this optimized strategy leads to a slight increase in shifting delay and a decrease in shifting frequency, thus verifying the effectiveness and superiority of the proposed controller.

An artificial bee colony algorithm for medical goods distribution and pharmacological waste collection by hybrid vehicles considering environmental criteria

PurposeThis paper aims to focus on a medical goods distribution problem and pharmacological waste collection by plug-in hybrid vehicles with some real-world restrictions. In this research, considering alternative energy sources and simultaneous pickup and delivery led to a decrease in greenhouse gas emissions and distribution costs, respectively.Design/methodology/approachHere, this problem has been modeled as mixed-integer linear programming with the traveling and energy consumption costs objective function. The GAMS was used for model-solving in small-size instances. Because the problem in this research is an NP-hard problem and solving real-size problems in a reasonable time is impossible, in this study, the artificial bee colony algorithm is used.FindingsThen, the algorithm results are compared with a simulated annealing algorithm that recently was proposed in the literature. Finally, the results obtained from the exact solution and metaheuristic algorithms are compared, analyzed and reported. The results showed that the artificial bee colony algorithm has a good performance.Originality/valueIn this paper, medical goods distribution with pharmacological waste collection is studied. The paper was focused on plug-in hybrid vehicles with simultaneous pickup and delivery. The problem was modeled with environmental criteria. The traveling and energy consumption costs are considered as an objective function.

Which Policies and Factors Drive Electric Vehicle Use in Nepal?

Electric vehicles (EVs) offer a viable technological solution for mitigating greenhouse gas emissions in the transportation industry, addressing pressing societal concerns regarding climate change, air pollution, and sustainable energy consumption. To effectively promote widespread adoption of EVs, it is crucial to understand consumer preferences and evaluate market dynamics. In Nepal, where proven fossil fuel reserves are absent, the government is actively working towards accelerating EV adoption, leveraging the nation’s significant hydroelectric power generation potential to fulfill EVs’ charging demands. To gain insight into consumer preferences and evaluate market dynamics regarding EVs in Nepal, this study employs a comprehensive approach. Stated preference data are collected through a meticulously designed survey, and sophisticated analytical techniques, namely, the mixed logit model and latent class model, are applied for estimation purposes. The results of this study show that potential EV consumers with small family sizes, lower monthly travel distances, heightened environmental awareness, and substantial knowledge about electric vehicles are more inclined to embrace EV technology. Notably, the study highlights that a reduction in the purchase price exerts the most significant influence on increasing consumers’ likelihood of adopting battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs). Market simulation results suggest that a policy mix scenario, encompassing a combination of supportive measures, proves more effective in promoting EV adoption compared to relying on single policy initiatives. Furthermore, through latent class estimation, the study identifies three distinct classes of consumers within Nepal, each exhibiting significant variations in preferences. Recognizing and addressing these variations within policy frameworks is crucial for the successful promotion and widespread acceptance of EVs in Nepal.

Energy management algorithm based on average power demand prediction for plug-in hybrid electric trucks

This paper examines potential fuel consumption savings through the implementation of a predictive equivalent consumption minimisation strategy, as a control algorithm in the energy management system of hybrid heavy-duty vehicles. A plug-in hybrid electric truck simulation model was developed as a demonstrator and was validated with experimental data. The baseline vehicle model was based on the same algorithm as VECTO, which is further extended in the current study by novel factors that contain the average predicted power demand. The potential benefits of the proposed algorithm are examined for different heavy-duty categories over regulatory mission profiles in both charge-sustain and charge-depletion modes. The results show that the fuel consumption can be reduced by up to 5.9 % and 4.4% in charge sustaining and charge depleting modes, respectively, depending on the mission profile. If such an algorithm is adopted by commercial vehicles, this could lead to substantial CO2 and fuel consumption savings on the road.

A COMPREHENSIVE ANALYSIS OF ISOLATED AND NON-ISOLATED CONVERTER TOPOLOGIES AND FAST CHARGING TECHNOLOGIES FOR HYBRID ELECTRIC VEHICLES

Electric vehicles (EVs) are a promising solution for future transportation systems due to their significant role in reducing carbon emissions, making them a central focus of research and development efforts. One crucial aspect of EVs is rapid charging technology, which aims to deliver refuelling experiences comparable to traditional gasoline cars. This article delves into the current state of electric vehicle charging infrastructure, with a particular emphasis on the importance of rapid charging technology to meet the present and future refuelling demands of EVs. It presents a comparative analysis of different DC-DC converter topologies for both battery electric and plug-in hybrid vehicles, considering factors such as performance, output power, current and voltage ripples, conduction loss, recovery loss, switching frequency loss, reliability, durability, and cost. Additionally, the article explores the architecture, advantages, and disadvantages of AC-DC and DC-DC converter topologies for rapid charging stations. Moreover, the study addresses critical issues and challenges associated with direct current rapid charging in electric vehicles. Finally, the article offers valuable technical insights and contributions to the ongoing development of electric vehicle systems. In essence, this article highlights the environmental benefits of electric vehicles and their growing popularity, emphasizing the need for advanced rapid charging technology while providing a comprehensive analysis of related technologies and challenges. Key Words: Power output, Current fluctuations, Voltage fluctuations, Voltage fluctuations, Conductive losses, Recovery inefficiencies, Switching frequency-related inefficiencies, Dependability, Longevity, Economic considerations, AC-DC converter configurations, DCDC converter designs.

Hierarchical energy management of plug-in hybrid electric trucks based on state-of-charge optimization

Hierarchical energy management of plug-in hybrid electric trucks based on state-of-charge optimization

Utility Factor Curves for Plug-in Hybrid Electric Vehicles: Beyond the Standard Assumptions

The utility factor (UF) of a plug-in hybrid electric vehicle (PHEV) refers to the ratio of miles traveled in electric mode to the total miles traveled. Standard UF curves provide a prediction of the expected achievable UF by a PHEV given its all-electric range (AER), but such predictions entail assumptions about both the driving patterns (distance traveled and energy intensity) and charging behavior. Studies have attempted to compare the real-world UF achieved by PHEVs to their standard values, but deviations can stem from deviations in assumptions about: (i) achievable electric range, (ii) travel distance and (iii) charging frequency. In this paper, we derive analytical models for modified utility factor curves as a function of both AER and charging behavior. We show that average charging frequency is insufficient to exactly predict UF but can still estimate bounds. Our generalized model can also provide insights into the efficacy of PHEVs in reducing carbon emissions.

Solar-wind-power Hybrid Power Generation System

In comparison to traditional power generation techniques, renewable energy is reliable as well as efficient, clean, and environmentally friendly. Switching to renewable energy sources is the only way to alleviate the resource shortages that the world is now experiencing, particularly the lack of fuel resources. More and more people are turning to renewable energy sources like solar and wind power. The project's goal is to utilize the programming language MATLAB/Simulink to design a hybrid power producing system that is connected to the grid and uses both solar and wind energy. The geography, solar irradiance, daylight hours, temperature, wind speed, and wind direction were all considered during the model's creation. The grid-connected hybrid model includes photovoltaic cells, a maximum power point tracker (P&O), a boost converter, an inverter, a wind turbine, and a permanent magnet synchronous generator (PMSG). In addition, the hybrid system is what powers the grid. The output is measured under a range of irradiance and temperature conditions. To examine the hybrid model, simulations are done in MATLAB.

Reducing Greenhouse Emissions from Light-Duty Vehicles: Supply-Chain and Cost-Effectiveness Analyses Suggest a Near-Term Role for Hybrids

<div>Policy makers generally favor all-electric vehicles over hybrid-electric vehicles because of greater unit effectiveness in reducing carbon emissions. Since both systems use lithium-ion batteries, global demand for batteries is projected to grow 10-fold by 2030. If any step in the global battery supply-chain experiences bottlenecks, shortages can occur. We use a novel cost-effectiveness metric, carbon reduction per unit of battery capacity consumed, to rank emissions reductions accomplished by, alternatively, eight plug-in hybrid-electric vehicles, 75 hybrid-electric vehicles, and 230 mild hybrid-electric vehicles, which have the same total battery capacity as one all-electric vehicle. Our main finding, although counterintuitive, is that, with limited battery supplies, larger reductions in carbon emissions can be accomplished by hybrids than by all-electric vehicles. Insofar as carbon emission reduction is the policy goal, policies need to be reconsidered to ensure that they are not tilted too far in favor of all-electric vehicles relative to hybrids.</div>

Carbon dioxide life cycle assessment for Brazilian passenger cars fleet towards 2050

Brazil is the second largest producer of ethanol in the world, losing the first position to the United State of America. Nonetheless, several studies have shown that the sustainability of the ethanol produced from the sugarcane is better than their competitor's wheat, corn, and beet in terms of carbon dioxide emissions, energy rate (input/output), productivity, and energy content. Thus, the different incentives from the government since 1975 to the industry allow it to produce technology capable of using any mix of ethanol hydrous and gasoline in the engines, this also brought several developments to sugarcane productivity. So then, there is a new worldwide transition coming to the world with the focus of reducing carbon dioxide emissions. Therefore, this paperwork assesses the main problems of the sustainability transition from current Brazilian Flex-Fuel Vehicles to electric vehicles, in terms of Law 13.755, ROTA2030 and the National Plan of Energy - 2050. The results show that the best option for Brazil as a country, considering socioeconomic and environmental impacts, is to introduce as a priority the Hybrid Electric Vehicles with a flex-fuel engine and then the Plug-in Hybrid Electric Vehicles with a flex-fuel engine. The last priority for the transition of midsize and full-size vehicles should be the Battery Electric Vehicles.

Adaptive energy management strategy of plug-in hybrid electric bus

In this study, the online application of a new energy management strategy (EMS) is proposed for a power-split plug-in hybrid electric bus (PHEB) to improve its system's fuel economy and reduce power battery degradation. The new EMS employs Pontryagin's minimum principle (PMP) and the power battery state of charge (SOC) feedback method to adjust the covariates online. A model-free adaptive control (MFAC) EMS was designed and constructed according to the compact form dynamic linearization model to further obtain accurate SOC feedback; the time–domain cumulative as well as covariate upper and lower limit constraints were used to obtain a real-time MFAC-Ⅲ strategy for online applications. The average reduction in battery capacity loss on all ten routes was 8.19 %, resulting from the correlation between the MFAC-III EMS and MFAC EMS; evaluations conducted on both strategies indicated that the MFAC-III outperforms the MFAC strategy in hardware-in-the-loop.

Optimal power split control strategy for plug-in biofuel-electric hybrid vehicle using improvised adaptive ECMS control algorithm

Optimal power split control strategy for plug-in biofuel-electric hybrid vehicle using improvised adaptive ECMS control algorithm

Microgrid operational energy management with plug-in hybrid electric vehicles charging demand

Microgrid operational energy management with plug-in hybrid electric vehicles charging demand

Electric vehicle (EV) and driving towards sustainability: Comparison between EV, HEV, PHEV, and ICE vehicles to achieve net zero emissions by 2050 from EV

This study explores the environmental and economic implications of Internal Combustion Engine (ICE) vehicles, Hybrid Electric Vehicles (HEVs), Plug-in Hybrid Electric Vehicles (PHEVs), and Electric Vehicles (EVs). A case study in Indonesia is selected, focusing to achieve net zero emissions by 2050 and improve air quality. The research compares key factors such as emission costs, fueling time costs, maintenance costs, and vehicle selection for a comprehensive understanding of each vehicle type's viability in the automotive landscape. The analysis reveals that EVs exhibit the lowest CO and CO2 emissions of about 20 %, while HEVs and PHEVs demonstrate significant reductions compared to ICE vehicles. However, EVs produce higher NOx and N2O emissions of more than 70 %, indicating a dependence on fossil fuels for electricity generation. Air quality-related emissions, including SOx and PM10, are 90 % and 85 % higher in EVs, emphasizing the need for enhanced emission control technologies and the adoption of renewable energy sources. Despite their higher selling price and emission costs, EVs possess the lowest maintenance costs among the evaluated vehicles at only 0.00419 USD/km. Ultimately, HEVs present the most balanced combination of selling price, emission cost, and maintenance cost, making them an appealing option for the market. This study provides valuable insights for policymakers, automotive manufacturers, and consumers in transitioning towards more sustainable transportation solutions.

Neural network energy management strategy with optimal input features for plug-in hybrid electric vehicles

The neural network energy management strategy can be implemented online and effectively save the energy for the plug-in hybrid electric vehicles (PHEVs). However, the selection of input features and application to untrained driving cycles are two issues faced by the strategy. This paper proposes an energy management strategy with the optimal input features for the PHEV. The global optimal datasets are first obtained based on the dynamic programming (DP) algorithm. Then the random forest classification models are trained with different combinations of input features to select the input feature combination with the highest classification accuracy. A neural network with the selected input features is finally trained using the optimal datasets for online control. With the optimal input features, the strategy can be adopted to both trained driving cycles and untrained driving cycles. Results demonstrate that the proposed strategy can save 4.44% to 7.75% energy compared to the charge depleting and charge sustaining strategy on trained cycles and 3.80% to 7.70% on untrained cycles respectively. The efficiency of the proposed strategy is only less than 2.41% worse than the DP algorithm.

Power Management Optimization of HES Connected to the Grid-Based IEMS

This paper presents a study of a grid-connected hybrid energy system (HES) consisting of photovoltaic/wind/fuel cell/battery/supercapacitor (SC). The purpose of using SC with the battery is to create a hybrid energy storage system (HESS) with high energy density. To prevent overcharging of the latter, a discharge load was employed (in our case, a resistance). The objectives of this research are to operate the photovoltaic generator and the wind power generator at their maximum power points by applying the perturbation and observation (P&O) technique and the tip speed ratio (TSR) technique, respectively. On the other hand, the energy management of the proposed system aims to achieve an energy balance between the grid and the HES despite climate changes and changes in energy demand to which the system is exposed, and to lengthen the life cycle of the HESS by keeping the state of charge (SOC) within a limited range. This management is accomplished by using a classical energy management strategy. To achieve optimal power management of the proposed system, a new intelligent energy management strategy (IEMS) based on the FLC-FOPI is proposed. A comparative analysis was also conducted between the classical strategy and the proposed strategy. As for the grid-side converter, direct power control based on SOSMC technique (DPC-SOSMC) has been proposed to regulate the flow of active and reactive power according to grid demand based on HRES. To demonstrate the effectiveness of the proposed controllers, simulation results evaluating the proposed system under variable conditions are presented using the Matlab-Simulink platform.

Neuroevolved bi-directional LSTM applied to zero emission zones management in urban transport

Plugin hybrid electric buses (PHEB) are nowadays an essential technology for the decarbonization of cities, proposing a more flexible solution than fully electric buses. There are a few studies for the design of operational strategies for PHEBs (i.e., to choose the propulsion method to use along the route) to minimize tailpipe emissions and/or maximize the electric range of the bus. However, all of them offer static strategies that cannot dynamically adapt to unforeseen traffic events, which can significantly impact on the performance of the bus. In this work, we propose modeling the problem of finding optimal PHEB operational strategies as a classification problem, allowing the generation of pseudo-optimal strategies in very short computational times. The method designed, called ML-EPBO, takes decisions based on local information of the bus, as well as from the route, so it can adapt to any events causing unexpected traffic conditions. Neuroevolution is used in this work to fine-tune ML-EPBO for optimal performance. Results show how ML-EPBO instantly offer solutions highly competitive with the best existing techniques in the literature, which do not offer the possibility of dynamically adapting to unexpected traffic events because they make use of global knowledge of the problem and have high computational complexity. Indeed, the average accuracy value obtained by the model is over 99.2%, and the strategies proposed are predicted over 70% of the cases with 100% of accuracy, even outperforming the state of the art results by up to 4.7% in the level of emissions or 1.82% in the electric range.

Plug-in Hybrid Power Buses: Control Method for Energy Distribution

Plug-in Hybrid Power Buses: Control Method for Energy Distribution

Performance improvement through nonlinear control design and power management of a grid-connected wind-battery hybrid energy storage system

This paper presents a study on the application of nonlinear control and optimal power management techniques in a grid-connected wind energy conversion system with battery storage. The objective is to efficiently supply power to a load while considering performance constraints. The energy system under investigation comprises a wind turbine, battery, AC-grid, and load. Power is delivered to the load through various converters and a DC-link. However, the intermittent nature of wind energy and frequent load fluctuations can negatively impact the battery's lifetime and charging performance. To address these challenges, a nonlinear controller and a novel energy management system are designed. The energy management system generates multiple energy flow scenarios to achieve equilibrium in energy distribution between the load and energy sources. This equilibrium aims to minimize overall system costs, ensure DC-grid stability, and enhance power quality. Two distinct operation modes are identified: i) MPPT mode, which utilizes an optimizer to maximize power extraction, and ii) adaptive power point tracking (APPT) mode, which considers demand-driven production and the state of charge (SOC) of the storage system. The convergence of the closed-loop control is formally analyzed, and simulation results confirm the improved performance of the proposed multi-criteria design for energy management.