Search Results (152)

This study addresses the Plug-in Hybrid Electric Vehicle Routing Problem (PHEVRP), an extension of the classical VRP that incorporates energy mode switching and partial charging strategies. We propose a novel routing model that integrates three energy modes—fuel-only, electric-only, and hybrid—along with partial recharging decisions to enhance energy flexibility and reduce operational costs. To overcome the computational challenges of large-scale instances, a Dantzig–Wolfe decomposition algorithm is designed to efficiently reduce the solution space via column generation. Experimental results demonstrate that the hybrid-mode with partial charging strategy consistently outperforms full-charging and single-mode approaches, especially in clustered customer scenarios. To further evaluate algorithmic performance, an Ant Colony Optimization (ACO) heuristic is introduced for comparison. While the full model fails to solve instances with more than 30 customers, the DW algorithm achieves high-quality solutions with optimality gaps typically below 3%. Compared to ACO, DW consistently provides better solution quality and is faster in most cases, though its computation time may vary due to pricing complexity. Full article

This study investigates a variant of the shortest path problem (SPP) tailored for plug-in hybrid electric vehicles (PHEVs), incorporating two practical features: hybrid energy mode switching and partial charging. A novel modeling framework is proposed that enables PHEVs to dynamically switch between electricity and fuel along each edge and to recharge partially at charging stations. Unlike most prior studies that rely on more complex modeling approaches, this paper introduces a compact mixed-integer linear programming (MILP) model that remains directly solvable using commercial solvers such as Gurobi. To address large-scale networks, a customized labeling algorithm is developed for an efficient solution. Numerical results on benchmark networks show that the hybrid mode and partial charging can reduce total cost by up to 29.76% and significantly affect route choices. The proposed algorithm demonstrates strong scalability, solving instances with up to 33,000 nodes while maintaining near-optimal performance, with less than 5% deviation in smaller cases. Full article

This study investigates the operational cost competitiveness of battery electric vehicles (BEVs) in the United States, considering regional climates, energy prices, and driving patterns. By comparing BEVs with plug-in hybrid electric vehicles (PHEVs), hybrid electric vehicles (HEVs), and the alternative use of BEVs and conventional vehicles (Convs), the analysis incorporates thermal dynamometer tests, real-world vehicle miles traveled (VMT), and state-specific energy prices. Using detailed simulations, the study evaluates energy consumption across varying temperatures and driving distances. The findings reveal that, while BEVs remain cost-effective for short trips in moderate climates, PHEVs are more economical for long-range trips and cold environments, due to the excessive cost of using external direct current fast chargers (DCFCs) and reduced BEV efficiency at low temperatures. HEVs are identified as the most cost-efficient option in regions like New England, characterized by high residential electricity prices. These insights are critical for shaping vehicle electrification strategies, particularly under diverse regional and seasonal conditions, and for advancing policies on alternative energy and fuels. Full article

Appropriate energy management strategy can further improve the fuel economy of plug-in hybrid electric vehicles (PHEV). Rule-based control strategies are dominant in actual vehicles because of their fast calculation and easy implementation. However, incorrect parameter settings and suboptimal control strategies may lead to substantial performance variations, preventing optimal fuel efficiency and emissions reduction. In this paper, the dynamic programming algorithm is implemented to design the control strategy for a dual-motor PHEV. The MATLAB/Simulink environment is used to construct models of the key components and powertrain controller, and simulation platforms for both rule-based and optimization-based strategies are established. Through the calculation results of dynamic programming (DP) algorithm, the rule of working mode switching and torque distribution is analyzed to improve the performance of rule-based control strategy. WLTC driving cycle simulation results show that the improved rule control effectively improves the economy of PHEV, and its comprehensive consumption per 100 km decreases by 2.853%. Full article

Plug-in hybrid electric vehicles (PHEVs) typically employ batteries with relatively small capacities due to constraints on chassis space and vehicle cost. Consequently, under conditions such as acceleration and hill climbing, these vehicles often experience high-current battery discharges, which can significantly compromise the battery’s lifespan. To address this issue, this paper focuses on a plug-in hybrid passenger vehicle, introducing supercapacitors to form a hybrid energy storage system (HESS) in conjunction with the PHEV battery, and it proposes a dual-layer energy management strategy for PHEVs. First, a PHEV model is developed, and a rule-based energy management strategy is designed. By conducting simulation comparisons of the CLTC under three control rules with different thresholds, the strategy yielding the lowest fuel consumption is selected, and its battery discharge characteristics are analyzed. Subsequently, the required power parameters of the supercapacitor are calculated, and, taking chassis space constraints into account, the number and specifications of the supercapacitors are determined. Subsequently, a dual-layer energy distribution strategy for PHEVs equipped with supercapacitors is proposed. In the upper layer, an equivalent fuel consumption minimization method is applied to optimize the torque distribution between the engine and the motor, while the lower layer employs a rule-based strategy for power allocation between the battery and the supercapacitor. A proportional feedback factor is introduced for the real-time adjustment of the engine and motor torque distribution, and simulations under the CLTC are conducted to evaluate the PHEV’s torque distribution and fuel consumption. The results indicate that the dual-layer energy management strategy reduces the duration of high-current battery discharge in the supercapacitor-equipped PHEV by 73.61%, decreases the peak current by 30.76%, and lowers the overall vehicle fuel consumption by 5%. Unlike other studies, this paper analyzes and calculates the specifications, dimensions, and quantity of supercapacitors based on the available chassis space of the PHEV passenger car. The results demonstrate that the designed supercapacitor array effectively mitigates the high-current discharge of the PHEV battery, and the proposed dual-layer energy management strategy is capable of reducing the overall fuel consumption of the vehicle. Full article

This paper introduces a novel strategy for an intelligent plug-in hybrid electric vehicle (PHEV) energy optimization strategy based on machine learning (ML) prediction of the upcoming journey, without recourse to navigation or other external data, which underpins many of the existing approaches. This study, based on extended real-world data (journeys history from 10 vehicles over 12 months), shows that trip patterns can be learnt quite effectively using classic ML classification algorithms. In particular, the RusBoosted ensemble classifier performed consistently well across the heterogeneous dataset (volume of data for training and variable imbalance in the datasets, reflecting the natural variability in the vehicle usage profiles), providing sufficiently accurate predictions for the proposed EMS strategy. Performance evaluation experiments were carried out using a model-in-the-loop (MIL) simulation set-up developed in this research. The results demonstrated that the proposed strategy has the potential to deliver significant reductions in engine running time (up to 76% on routine short journeys), with associated benefits in CO₂ consumption and tailpipe emissions, as well as enhanced engine reliability. The broader importance of this study is that it demonstrates the great potential of using predictive insights from computation-efficient and robust ML to learn vehicle usage patterns to optimize the control strategies without reliance on uncertain external inputs. Full article

The vehicle fleets in Latin America are increasingly incorporating hybrid electric vehicles due to the economic and non-economic incentives provided by governments aiming to reduce energy consumption and emissions in the transportation sector. However, the impacts of implementing hybrid vehicles remain uncertain, especially in Latin American, which poses a risk to the achievement of environmental objectives in developing countries. The aim of this study is to evaluate the benefits of incorporating hybrid vehicles to replace internal combustion vehicles, considering the improvement in the level of emission standards. This study uses data reported by Colombian vehicle importers during the homologation process in Colombia and the number of vehicles registered in the country between 2010 and 2022. The Gompertz model and logistic growth curves are used to project the total number of vehicles, taking into account the level of hybridization and including conventional natural gas and electric vehicles. In this way, tailpipe emissions and energy efficiency up to 2040 are also projected for different hybrid vehicle penetration scenarios. Results show that the scenario in which the share of hybrid vehicles remains stable (Scenario 1) shows a slight increase in energy consumption compared to the baseline scenario, about 1.72% in 2035 and 2.87% in 2040. The scenario where the share of MHEVs, HEVs, and PHEVs reaches approximately 50% of the vehicle fleet in 2040 (Scenario 2) shows a reduction in energy consumption of 24.64% in 2035 and 33.81% in 2040. Finally, the scenario that accelerates the growth of HEVs and PHEVs while keeping MHEVs at the same level of participation from 2025 (Scenario 3) does not differ from Scenario 2. Results show that the introduction of full hybrids and plug-in hybrid vehicles improve fleet fuel consumption and emissions. Additionally, when the adoption rates of these technologies are relatively low, the benefits may be questionable, but when the market share of hybrid vehicles is high, energy consumption and emissions are significantly reduced. Nevertheless, this study also shows that Mild Hybrid Electric Vehicles (MHEVs) do not provide a significant improvement in terms of fuel consumption and emissions. Full article

The U.S. Department of Energy’s Vehicle Technologies Office (DOE-VTO) is driving advancements in highway transportation by targeting energy efficiency, environmental sustainability, and cost reductions. This study investigates the fuel economy potential and cost implications of advanced powertrain technologies using comprehensive system simulations. Leveraging tools such as Autonomie and TechScape, developed by Argonne National Laboratory, this study evaluates multiple timeframes (2023–2050) and powertrain types, including conventional internal combustion engines, hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), and battery electric vehicles (BEVs). Simulations conducted across standard regulatory driving cycles provide detailed insights into fuel economy improvements, cost trajectories, and total cost of ownership. The findings highlight key innovations in battery energy density, lightweighting, and powertrain optimization, demonstrating the growing viability of BEVs and their projected economic competitiveness with conventional vehicles by 2050. This work delivers actionable insights for policymakers and industry stakeholders, underscoring the transformative potential of vehicle electrification in achieving sustainable transportation goals. Full article

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. Full article

Battery energy storage systems (BESSs) and plug-in hybrid electric vehicles (PHEVs) are essential for microgrid operations to be financially viable. PHEVs can serve as mobile storage devices, storing excess energy during times of low demand and delivering it during times of high demand. By offering reliable on-site energy storage, BESSs improve cost efficiency by allowing the microgrid to store cheap, off-peak electricity and release it when prices increase. To minimize generation costs and alleviate grid stress during periods of high demand, load-shifting policies shift inelastic loads to off-peak hours when energy prices are lower. When combined, these tactics support dependable, affordable, and effective microgrid management. A recently developed RIME algorithm is used as the optimization tool to reduce the total operating cost (TOC) of an MG system for three cases and three situations. The cases emphasize a modified load demand style influenced by the optimal load-shifting method (OLSM) and order characteristics load-shifting policy (OCLSP), whereas the situations refer to the inclusion of ESS in the MG system. The TOC decreased from $2624 without ESS to $2611 and $2331 with PHEVs and BESSs, respectively. These costs were further reduced to $1192, $1162, and $1147, respectively, when OLSM was implemented to restructure the base load demand. Additionally, a balance between a minimal TOC and carbon emission was obtained when an OLSM-based load demand model was used with BESSs. The RIME algorithm outperformed many recently developed algorithms and is consistent and robust, yielding better quality solutions. Full article

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. Full article

Single-gear-ratio plug-in hybrid vehicles (SRPHEVs) are favored by major manufacturers due to their excellent energy-saving potential, simple structure, ease of maintenance and control, great cost-saving potential, and the benefits of vehicle lightweighting. Implementing an energy management strategy (EMS) is the key to realizing the energy-saving potential of PHEVs. In this paper, based on a newly developed coaxial configuration, P1-P3 SRPHEV, with the purpose of reducing PHEV fuel consumption, the advantages of various methods were synthesized. An improved intelligent optimization algorithm, the Particle Swarm Optimization (PSO) algorithm, was used to find the optimal rule-based strategy parameters. The PSO algorithm could be easily adjusted to the parameters and obtains the desired results quickly. Different long-distance speed profiles tested under real-world driving cycle (RDC) conditions were used to validate the fuel savings. And an energy flow analysis was conducted to further investigate the reasons for the algorithm optimization. The results show that the optimization plans of the PSO algorithm in different cycle conditions can improve the equivalent fuel consumption of vehicles in different long-distance conditions. Considering the optimization effect of the equivalent fuel consumption and actual fuel consumption, the best case of the equivalent fuel consumption is improved by 2.98%, and the actual fuel consumption is improved by 2.37%. Through the energy flow analysis, it is found that the energy-saving effect of the optimization plan lies in the following principle: lowering the parallel mode switching threshold to increase the parallel mode usage time and to reduce the fuel–mechanical–electrical transmission path loss, resulting in increasing the energy utilization of the whole vehicle. Full article

In the context of global carbon peak and carbon neutrality goals, researching the driving forces and influencing factors behind the growth in sales of new energy vehicles (NEVs) is particularly urgent and crucial. Although the academic community has extensively explored various factors affecting NEV sales, technological innovation, as the core engine driving industry progress, is yet to receive sufficient and in-depth exploration regarding its potential profound impact on sales. Therefore, this study thoroughly analyzes the mechanism by which technological innovation influences sales, supplementing the existing literature, exploring sustainable industry development, and guiding the optimization of business strategies and precise government policies. This research employs the quality of NEV technology patents as a proxy variable for technological innovation, and conducts an empirical analysis using China’s NEV sales data from 2015 to 2022. The results of the study show the following: (1) the quality of technological innovation significantly contributes to the growth of new energy vehicle sales. This conclusion still holds when the administrative protection of intellectual property rights and the government’s public innovation environment indicators are used as instrumental variables of technological innovation quality. (2) Technological innovations in different links of the new energy vehicle industry chain have a positive impact on sales, especially those in the midstream battery, motor, and downstream charging services. This shows that in the critical period of new energy vehicle development, not only is the breakthrough of key technologies crucial, but also, non-critical technologies can promote sales growth by improving user experience and product performance. (3) Compared with plug-in hybrid electric vehicles (PHEVs), the overall technological innovation of new energy vehicles (NEVs) has a more significant effect on the sales of battery electric vehicles (BEVs). (4) In addition, the study finds that there is no significant difference between high-quality and low-quality technological innovations on the promotion of new energy vehicle sales, which indicates that the market values the overall effect of technological innovations more than the mere level of quality. The study of the impact of technological innovation on sales in different branches of the new energy industry chain can help to clarify the direction of technological innovation, optimize resource allocation, promote the synergistic development of the industry chain, enhance market competitiveness, and provide a scientific basis for policy formulation. Full article

This study establishes a life cycle assessment model to quantitively evaluate and predict material resource consumption, fossil energy consumption and environmental emissions of plug-in hybrid electric vehicles (PHEVs) by employing the GaBi software. This study distinguishes the environmental impact of different vehicle working conditions, power battery degradation scenarios, and mileage scenarios on the operation and use stages of PHEVs, BEVs, and HEVs. The findings indicate that under urban, highway, and aggressive driving conditions, PHEVs’ life cycle material resource and fossil fuel consumption exceed that of BEVs but are less than HEVs. Battery degradation leads to increased material resource consumption, energy use, and environmental emissions for both PHEVs and BEVs. When the power battery degrades to 85%, the material resource and fossil energy consumption during the operation and use phase increases by 51.43%, 72.68% for BEVs and 29.37%, 36.21% for PHEVs compared with no degradation, respectively, indicating that the environmental impact of BEVs are more sensitive than those of PHEVs to the impact of power battery degradation. Among different mileage scenarios, PHEVs demonstrate the lowest sensitivity to increased mileage regarding life cycle material resource consumption, with the smallest increase. Future projections for 2025 and 2035 suggest life cycle GWP of HEV, PHEV and BEV in 2035 is 1.21 × 10⁴, 1.12 × 10⁴ and 1.01 × 10⁴ kg CO₂-eq, respectively, which shows reductions of 48.7%, 30.9% and 36.1% compared with those in 2025. The outcomes of this study are intended to bolster data support for the manufacturing and development of PHEV, BEV and HEV under different scenarios and offer insights into the growth and technological progression of the automotive sector. Full article

The key to improving the fuel economy of plug-in hybrid electric vehicles (PHEVs) lies in the energy management strategy (EMS). Existing EMS often neglects engine operating conditions, leading to frequent start–stop events, which affect fuel economy and engine lifespan. This paper proposes an Integrated Engine Start–Stop Dynamic Programming (IESS-DP) energy management strategy, aiming to optimize energy consumption. An enhanced rule-based strategy is designed for the engine’s operating conditions, significantly reducing fuel consumption during idling through engine start–stop control. Furthermore, the IESS-DP energy management strategy is designed. This strategy comprehensively considers engine start–stop control states and introduces weighting coefficients to balance fuel consumption and engine start–stop costs. Precise control of energy flow is achieved through a global optimization framework to improve fuel economy. Simulation results show that under the World Light Vehicle Test Cycle (WLTC), the IESS-DP EMS achieves a fuel consumption of 3.36 L/100 km. This represents a reduction of 6.15% compared to the traditional DP strategy and 5.35% compared to the deep reinforcement learning-based EMS combined with engine start–stop (DDRL/SS) strategy. Additionally, the number of engine start–stop events is reduced by 43% compared to the DP strategy and 16% compared to the DDRL/SS strategy. Full article