Traditional Internal Combustion Engine Vehicles Research Articles

A REVIEW ON INTEGRATION OF ELECTRIC VEHICLES AND OPTIMAL POWER MANAGEMENT STRATEGY FOR SUSTAINABLE DEVELOPMENT

The integration of electric vehicles (EVs) into our transportation system is a pivotal step towards sustainable development. EVs are gaining traction as a cleaner and more energy-efficient alternative to traditional internal combustion engine vehicles. Their adoption holds the promise of reducing greenhouse gas emissions and decreasing our reliance on fossil fuels, contributing significantly to environmental sustainability .One of the critical aspects of effective EV integration is the development and implementation of optimal power management strategies. These strategies play a pivotal role in ensuring that the charging and discharging of EV batteries are not only efficient but also seamlessly integrated with the existing power grid infrastructure. An optimal power management system takes into consideration factors such as load balancing, peak demand management, and the integration of renewable energy sources, making it a key component of a sustainable and resilient energy ecosystem. By efficiently scheduling when and how EVs charge or discharge, these strategies can reduce the strain on the electrical grid during peak periods, lower electricity costs, and minimize the carbon footprint associated with charging. Moreover, they can contribute to grid stability and reliability, making the entire energy system more sustainable. This research explores the synergistic potential of EVs and smart grid technologies, aiming to create a harmonious relationship between transportation and energy systems. Key Words: Electric Vehicles, Sustainable Development, Optimal Power Management, greenhouse gas emissions, fossil fuels, clean environment, smart grid technology.

Signal pre-processing techniques for fault signature enhancement in a bearing health monitoring system used in the automotive industry

Traditional internal combustion engine vehicles have low transmission bearing failure rates in their lifespans. However, the prolonged lifespan of electric and autonomous vehicles can surpass the reliable life of bearing designs, which poses a risk of bearing failure and loss of propulsion. Compared to replacing bearings on a fixed schedule to ensure reliability, a bearing health monitoring system is a more cost-effective solution. Despite extensive research on bearing condition monitoring, implementing well-known methods such as vibration spectrum analysis in vehicles can be challenging due to vibrations from vehicle components and the road. This paper explores and compares the effect of various pre-processing techniques on the spectrum of a faulty bearing with various fault levels. To achieve this objective, faults with the width size of 0.1 mm (mild), 0.5 mm (moderate) and 2 mm (severe) were injected into the inner race of a ball bearing. A bench setup was then used to capture the vibrations of multiple vehicle components including the faulty ball bearing under various speed/ load conditions. Phase domain transform, envelope and Fourier transform were used as the core signal processing steps, and advanced signal processing methods for removing discrete frequencies from other components and enhancing the fault signature were explored. 4 health indicators were then developed from the vibration spectrum of the vibration signals and calculated for the captured data. Next, for each fault level, the area under Receiver operating characteristic (ROC) curve was calculated and used as a metric to compare the performance of our health monitoring system for classification of faulty and healthy bearings. For our best health indicator, the results show that applying minimum entropy deconvolution, and spectral kurtosis-based band pass filtering increases the ROC area from 0.40, 0.99, 1.0 to 0.86, 1.0 and 1.0 for the mild, moderate, and severe inner race faults, respectively. This implies that although applying only phase domain transform, envelope and Fourier transform might be enough for moderate and severe faults, advanced signal processing is needed to enhance the fault signature for early detection of mild faults.

Technology and limitation of electrification of transportation

In the modern world, electric vehicles (EVs) are becoming more and more prevalent, and many individuals are beginning to consider getting one for their mobility. In comparison to traditional internal combustion engine vehicles, EVs are more environmental-friendly, have higher energy conversion efficiency and produce lower noise. However, in terms of electrification of transportation, EVs also have some inherent limitations. This paper provides an overview of electrification of transportation. First, this paper will focus on the related technologies such as motor drive technology, power electronic technology and battery management. In addition, the limitations of the electrification of transportation, include range anxiety, long charging time, charging station accessibility and unsatisfactory performances in extreme temperatures, will also be presented. Despite these challenges, EVs still exhibit promises for further development and will play a leading role in transportation electrification. Considering the technological factors will help address the issues and push the future development of EV forward.

Superhydrophobic Microchannel Heat Exchanger for Electric Vehicle Heat Pump Performance Enhancement

Battery-powered electric vehicles (EVs) have emerged as an environmentally friendly and efficient alternative to traditional internal combustion engine vehicles, while their single-charge driving distances under cold conditions are significantly limited due to the high energy consumption of their heating systems. Heat pumps can provide an effective heating solution for EVs, but their coefficient of performance (COP) is hampered by heat transfer deterioration due to frost accumulation. This study proposes a solution to this issue by introducing a microchannel heat exchanger (MHE) with superhydrophobic surface treatment (SHST) as a heat pump evaporator. A computational fluid dynamics MHE model and a dynamic heat pump model are developed and rigorously validated to examine the detrimental impact of frost accumulation on heat transfer, airflow resistance, and heat pump performance. When the frost layer thickness is 0.8 mm at a given air-side velocity of 1.0 m/s, the air-side heat transfer coefficient can be reduced by about 75%, and the air-side pressure drop sharply increases by 28.4 times. As frost thickness increases from 0 to 0.8 mm, the heating capacity drops from 3.97 to 1.82 kW, and the system COP declines from 3.17 to 2.30. Experimental results show that the frost thickness of the MHE with SHST reaches approximately 0.4 mm after 30 min, compared to that of 0.8 mm of the MHE without SHST, illustrating the defrosting capability of the superhydrophobic coating. The study concludes by comparing the performance of various heating methods in EVs to highlight the advantages of SHST technology. As compared to traditional heat pumps, the heating power consumption of the proposed system is reduced by 48.7% due to the defrosting effect of the SHST. Moreover, the single-charge driving distance is extended to 327.27 km, an improvement of 8.99% over the heat pump without SHST.

Longevity of Electric Vehicle Operations

The global transition towards sustainable transportation solutions has spurred the rapid growth of electric vehicles (EVs) as a promising alternative to traditional internal combustion engine vehicles. However, as the adoption of EVs continues to accelerate, the focus has shifted towards ensuring the longevity of electric vehicle operations worldwide. This abstract aims to provide an in-depth exploration of the multifaceted aspects surrounding the longevity of EV operations on a global scale. The longevity of electric vehicle operations encompasses various dimensions, including technological advancements, infrastructure development, policy support, and consumer behavior. Firstly, advancements in battery technology play a pivotal role in determining the lifespan of EVs. The abstract delves into the evolution of battery chemistries, energy densities, and thermal management systems, which collectively impact battery life and overall vehicle longevity. Additionally, insights into battery recycling and second-life applications are discussed as essential strategies to mitigate environmental impacts and enhance the sustainability of EV operations.

Hybrid vigor: Why hybrids with sustainable biofuels are better than pure electric vehicles

Life cycle assessment (LCA) was used to evaluate the greenhouse gas emissions (GHG) of traditional internal combustion engine vehicles (ICEV), hybrid vehicles (non-plug-in or plug-in), and battery electric vehicles (BEV), fueled with biofuels or recharged with electricity from Brazilian or European matrix, including recharging losses. The study shows that calculated GHG emissions for hybrid vehicles using biofuels are lower than observed for BEVs even in Brazil, where the carbon intensity of the electricity matrix is low compared to most countries. In addition, we show that the emissions of a non-hybrid traditional internal combustion vehicle using biomethane is lower than a BEV. It was also observed that combining Brazilian biofuels with hybrid vehicles results in a higher traveled distance for each kilogram of GHG emitted compared to a BEV.The carbon footprint reduction for metallic batteries in future scenarios was considered in the sensitivity analysis, which shows that biofuels still remain a better option. We hope these results can be useful for guiding public policies for transport decarbonization, considering hybrid vehicles fueled with biofuels as an economical and more effective alternative than battery electric vehicles to reach the sustainable goal of carbon net zero emissions by 2050.

Interpretable Machine Learning Approach to Predicting Electric Vehicle Buying Decisions

To address the problem of climate change emissions from the transport sector, many countries are promoting electric vehicles (EVs). To support such efforts, it is essential to know what influences the choice of an EV over a traditional internal combustion engine vehicle (ICEV). To study this, a discrete choice experiment was developed, and 2,015 valid responses were gathered from Canadian adults with a driver’s license. In place of a more traditional analysis, a machine learning approach, XGBoost, was applied. However, two key issues were addressed with respect to its application. First, a practical question related to how best to split the training and testing data was examined. A new technique based on the Coyote optimization algorithm (COA) is developed that automatically determines the split that leads to the greatest prediction accuracy. The policy-relevant results of the analysis found that an individual’s Climate Change-Stage of Change (CC-SoC) and the price ratio of EVs to ICEVs are the most important direct influences. The interaction effect of the first two (CC-SoC and price ratio) is also influential. However, this leads to the second key issue: interpretability. Although high prediction accuracy (87.1%) was achieved, the black-box nature of the approach limits its policy relevance. As such, this research applied a technique, Accumulated Local Effects (ALE), that can determine the strength and direction of influence of the variable. This research demonstrates how machine learning can be applied to a policy-relevant question and provide information that is useful to policy decision makers.

Sustainable Electric Vehicle charging scheme: User requirements in designing energy trading algorithm

Promoting Electrical Vehicles (EVs) as a cleaner alternative to traditional Internal Combustion Engine (ICE) vehicles is an important step towards transformation to a sustainable world. However, usage of grid electricity in charging EVs hinders achieving zero carbon emissions, hence, necessitates developing a renewable energy-based model for EV charging. On the other hand, encouraging renewable energy (RE) usage contributes to United Nations 2030 agenda for sustainable development. An energy trading algorithm which facilitates trading excess renewable energy generation of households to charge EVs is a key component in such renewable energy-based EV charging model. However, to design an algorithm beneficial for both stakeholders, i.e., EV users and households with solar power systems, user requirements of both stakeholders should be taken into consideration. This paper investigates the user requirements and specifications for an improved energy trading algorithm from both stakeholders’ point of view. Semi-structured interviews were conducted with 20 EV users and 16 household solar users in the United Kingdom (UK) to explore the above. According to the findings of the interviews, both participant categories highlight renewable energy utilisation as a key requirement, whereas EV users highlight non- financial user- experience related requirements such as improving convenience and selection of preferred charging speed and household solar panel users highlight trading with less time and effort as their requirements. Additionally, households also expect higher returns to cover capital costs of solar panel installations. These findings can be used by practitioners from the industry in developing effective energy trading solutions for end users.

Social-Aware Long-Distance Trip Planner for Electric Vehicles Using Genetic Algorithm

Abstract In the past decade, the number of battery electric vehicles (BEVs) on the road has been growing rapidly in response to global climate change and cyclic gasoline shortages. Due to the limited driving range of most commercial BEVs, individuals who use BEVs for long-distance travel tend to spend much more time on the road than owners of traditional internal combustion engine vehicles. To reduce travel time in long-distance trips, a social-aware trip planner is necessary to coordinate driving speed, vehicle charging, and social activities (e.g., dining, visit of places of interest). This paper formulates this travel time minimization problem into a mixed-integer programming model and utilizes genetic algorithm (GA) to solve for the optimal driving speed, vehicle charging, and the schedule of dining. The proposed planner is first tested numerically based on two real-world routes. Then, Monte Carlo simulations are performed to give a thorough analysis on the performance of the proposed planner. The simulation results show that the proposed method outperforms the baseline on both routes. Additionally, real-world tests are conducted to further validate the accuracy of the mixed-integer programming model.

Three Voltage Vector Duty Cycle Optimization Strategy of the Permanent Magnet Synchronous Motor Driving System for New Energy Electric Vehicles Based on Finite Set Model Predictive Control

Faced with the increasingly serious energy crisis and environmental pollution problems, traditional internal combustion engine vehicles are receiving more and more resistance, which has rapidly promoted the development of new energy electric vehicles. Permanent magnet synchronous motors are widely used in new energy electric vehicles and in other fields because of their simple structure, light weight, small size, and high power density. With the continuous advancement of production technology, the requirements of accuracy, rapidity, and stability in permanent magnet synchronous motor systems have gradually increased. Among many advanced control technologies, this paper proposes an optimized model predictive torque control strategy based on voltage vector expansion. This strategy involves the construction of a reference stator flux linkage vector based on the analytical relationship between electromagnetic torque, reference stator flux linkage amplitude, and rotor flux linkage and the transfer of the separate control of electromagnetic torque and flux linkage amplitude into flux linkage vector control. At the same time, the optimal duty cycle corresponding to the two adjacent extended voltage vectors and the zero vector is calculated according to geometric relationships so as to realize the three voltage vector duty cycle optimization control. Experimental results show the effectiveness and superiority of the proposed strategy.

A Comparison between New Energy Vehicles Owner Reviews And Internal Combustion Engine Vehicle Owner Reviews in the Chinese Market

The issue of energy has received much attention from all aspects across the world, including the automotive industry. Both policies and technology development have pushed automotive manufacturers to turn to new energy vehicles (NEVs) from traditional internal-combustion engine vehicles (ICEVs). To understand how consumers in the market perceive these two different types of vehicles, the study sets out to investigate car owners’ reviews of both types of vehicles. A total of 30000 reviews from 20 models (10 from each type) including reports of the most satisfying parts and the most unsatisfying parts of the vehicles are analyzed. The results show that the two types of car owners demonstrate differences when discussing satisfaction in dimensions of exterior design, engine power, and space. The results also show that they demonstrate differences when discussing unsatisfaction in dimensions of interior design, space, range and battery, new car pickup time, and engine power, of which the latter three are specific to vehicle type. Finally, the study also finds a stronger unsatisfied sentiment in ICEV owners’ reviews than in NEV owners’ reviews.

Power Electronics Converters for Electric Vehicle Auxiliaries: State of the Art and Future Trends

Electric vehicles (EVs) are expected to take over the transportation and mobility market over traditional internal combustion engine (ICE) vehicles soon. The internal power demands of EVs are expected to increase. The reason for this is to achieve a longer driving range for the EV and to provide the required power for the low-voltage (LV) network auxiliary loads. To illustrate, there are extra added sensors, cameras, and small actuating motors, especially for future autonomous vehicles. Therefore, a new electrical/electronic (E/E) architecture is required to convert the high-voltage (HV) traction battery voltage (e.g., 320–800 V DC) to the standard LV levels with high current ratings of 5 kW and more. This HV-LV DC-DC converter is known in the literature as an auxiliary power module (APM). The standard LV rails in an EV are the 12 V/24 V rail to supply for an instant the EV’s lighting and electronic control units (ECUs), while the 48 V rail is required for propulsive loads, such as air compressors and electric power steering systems. Furthermore, in a few applications, this converter is responsible for voltage upwards to support the start of a hybrid vehicle or emergency backup power handling, which requires bidirectional capability. Therefore, in this paper, possible APM topologies for EV applications are presented. In line with this, the main standards and safety requirements of the APMs are presented. Detailed quantitative and qualitative comparisons between topologies and their associated control schemes are discussed. In addition, the placement of the APM in the EV cooling cycle has been investigated and demonstrated. Finally, the industrial trends and future research targets for the APM in automotive applications are outlined.

Mathematical Analysis of the Impact of French Standardization Strategy and Electric Vehicle Development on the Breakthrough Development of EVs in China

The problems of operating range and costs are the two most critical bottlenecks restricting the extensive application of electric vehicles (EVs) in China and some other countries. There are also some prominent problems in China’s EVs, which lead to poor competitiveness of EVs compared with traditional internal combustion engine vehicles. This paper analyzes the key mathematical factors restricting the development and popularization of EVs in China from the aspects of strategic policy, sales situation, and self‐problems. For the best‐selling family cars in China, select the representative models with the same body structure, and make statistics on various parameters most concerned by consumers, establish a database through data statistics and analysis software. Through summarizing France’s standardization development strategy and relevant issues reflected by the development data of its EVs, this paper puts forward the hypothesis leading the development of EVs through standardization to enhance their competitiveness, gives the specific suggestions, and briefly analyzes the feasibility from the aspects of product situation. The research content of this paper provides a certain basis and ideas for the future research work.

Adjusting the service frequency of hybrid vehicles based on the use of diagnostics tools

Currently, traditional internal combustion engine vehicles are be-ing replaced by hybrid vehicles, which are more advanced and energy effi-cient than the former. According to the current technical regulation on the safety of wheeled vehicles, a hybrid is a vehicle that combines two types of power units in its design: an internal combustion engine and an electric mo-tor. The main advantage of these vehicles is the combination of efficiency and environmental friendliness. The trend towards an increase in the pro-duction of cars with hybrid power plants is due to a fundamental increase in the requirements of international standards for environmental safety and ef-ficiency of cars. In the coming years, the number of brands of cars with hy-brid power plants offered to the consumer by European, Chinese, Japanese, Korean, as well as domestic automakers would significantly increase in Rus-sia. The lack of scientifically based approaches to the organization of maintenance and repair of vehicles with hybrid power plants leads to a de-crease in the efficiency of the technical operation of this type of vehicle. Hence, there is a need to organize the maintenance of vehicles with hybrid power plants based on the optimization of maintenance modes. The choice of topic is justified by the possibility of improving the efficiency of opera-tion of vehicles with hybrid power plants.

Thermal Management Technologies Used for High Heat Flux Automobiles and Aircraft: A Review

In recent years, global automotive industries are going through a significant revolution from traditional internal combustion engine vehicles (ICEVs) to electric vehicles (EVs) for CO2 emission reduction. Very similarly, the aviation industry is developing towards more electric aircraft (MEA) in response to the reduction in global CO2 emission. To promote this technology revolution and performance advancement, plenty of electronic devices with high heat flux are implemented on board automobiles and aircraft. To cope with the thermal challenges of electronics, in addition to developing wide bandgap (WBG) semiconductors with satisfactory electric and thermal performance, providing proper thermal management solutions may be a much more cost-effective way at present. This paper provides an overview of the thermal management technologies for electronics used in automobiles and aircraft. Meanwhile, the active methods include forced air cooling, indirect contact cold plate cooling, direct contact baseplate cooling, jet impingement, spray cooling, and so on. The passive methods include the use of various heat pipes and PCMs. The features, thermal performance, and development tendency of these active and passive thermal management technologies are reviewed in detail. Moreover, the environmental influences introduced by vibrations, shock, acceleration, and so on, on the thermal performance and reliability of the TMS are specially emphasized and discussed in detail, which are usually neglected in normal operating conditions. Eventually, the possible future directions are discussed, aiming to serve as a reference guide for engineers and promote the advancement of the next-generation electronics TMS in automobile and aircraft applications.

Fault Injection Method and Ground-truth Development to Enable a Low-cost Bearing Fault Monitoring System in the Automotive Industry

Bearing condition monitoring is a widely studied field, but applications to the automotive industry have received little attention as the bearing failure rates are typically low in traditional internal combustion engine vehicles with 200 – 300k mile lifespans. The rapid advancement of electric and autonomous vehicles enables vehicles with million-mile lifespans. This implies that the reliable life of existing bearing designs is exceeded throughout the vehicle life, which can potentially lead to vehicle failure. To enable the development of a bearing fault detection and prognostics system, healthy and faulty bearing data must be collected, and the ground-truth states of the health of bearings need to be determined for algorithm refinement and validation. This work explores the fault injecting options, and ground-truthing together with their limitations. Two methods based on precision machining and seeded spalling are developed and used to inject inner race faults in a ball bearing. A non-invasive ground-truthing method is proposed to quantify the state of health of the fault injected bearings in which bench test data is collected under various speed and load conditions. The vibration signals from the bench tests are used to calculate the root-square of the area under the acceleration Power Spectral Density curve (known as GRMS) for each speed and load condition. To remove the dependency of the results on load and speed conditions, a speed-load-GRMS plot is generated, and a plane is fitted to the data for each fault level. Next, the volume under the plot is calculated, yielding a single cumulative GRMS value for each fault level. This value is used as the ground-truth health of bearing for each fault level. For the bearing with the faults injected using precision machining fault injection, the obtained ground-truth values are 1.56, 3.68, and 4.36 times larger than the same figure for the healthy bearing for the faults with the widths of 0.1 mm, 0.5 mm, and 2 mm, respectively. The observed correlation between the fault sizes and the calculated ground-truth values validates the proposed method which can provide a good separation among different health states of a bearing.

Life cycle carbon footprint of electric vehicles in different countries: A review

Facing with upward pressure on carbon emissions from the transportation sector, governments actively promote the automation and electrification of the transportation industry, and intelligent electric vehicles (EVs) are ushering in their golden age. However, the debate over whether EVs are lower carbon than traditional internal combustion engine vehicles (ICEVs) has never stopped. To objectively evaluate the role of EVs in mitigating climate change and carbon emissions, this study provides an extensive review of the literature on the life cycle carbon footprint of various EVs, and compares the carbon emissions of EVs and ICEVs. Considering that the carbon emissions of EVs vary significantly geographically due to differences in the power mix, ambient temperature, and driving conditions, this review further compares the carbon emission reduction effects of deploying EVs in different countries. The results show that the life cycle carbon footprint of EVs is lower than that of ICEVs, despite the higher carbon emissions from battery production. According to the power generation situation of each country, countries dominated by renewable energy power generation are more suitable for adopting EVs, while in countries with a predominantly coal-fired power generation, the popularization of EVs should be accompanied by a focus on decarbonization of the electricity sector and infrastructure improvements. Overall, improving the production technology of EVs and increasing the proportion of clean energy generation will be helpful to achieve the decarbonization goal in the transportation sector.

Modeling and analysis of dynamic characteristics of rubber isolators for electric vehicles under high-frequency excitation

The rubber isolator is a key component connecting the vehicle body and the powertrain, which plays a significant role in reducing the vibration transmission from the engine to the vehicle body. With the development of electric vehicles, the excitation frequency generated by the powertrain can exceed 2 kHz. The conventional dynamic models are usually used to simulate the low-frequency dynamic characteristics of rubber isolators, in the range of 0–200 Hz, which is the main excitation frequency range of the traditional internal combustion engine vehicle. However, they cannot simulate the high-frequency dynamic characteristics accurately since the inertial force caused by the mass of rubber isolators increases greatly under high frequencies. And the high-frequency dynamic characteristics of rubber isolators is of great significance for isolator fatigue life prediction, powertrain system vibration isolation design and noise control caused by the high-frequency resonance of the isolator. In this paper, a general model with mass elements is proposed to describe the effect of the inertial force on high-frequency dynamic stiffness. Based on the proposed model and considering the different expressions of the viscoelastic properties of rubber isolators, three improved dynamic models of rubber isolators are established, and used to analyze the high-frequency dynamic characteristics of rubber isolators. It has been demonstrated that the inertial force plays a non-negligible role in the dynamic characteristics of rubber isolators under high-frequency excitation. The proposed model can be used to analyze the effect of the inertial force on the dynamic characteristics at high frequencies accurately. The high-frequency dynamic characteristic modeling method of rubber isolators presented in this paper is helpful to the vibration and noise analysis of electric vehicle powertrain systems under high-frequency excitation.

A review of the life cycle carbon footprint of electric vehicle batteries

Facing increasingly severe climate change, countries and regions around the world are actively promoting the electrification of the transportation sector and encouraging the use of electric vehicles (EVs) to replace traditional internal combustion engine vehicles (ICEVs). However, the consumption of energy, resources, and power during battery production and use results in EVs not being as low-carbon as we expect. Therefore, the carbon emissions of batteries should be fully considered when promoting EVs. In this context, we systematically reviewed the life cycle carbon footprint of batteries. Specifically, the carbon emissions of batteries in the production, use, secondary utilization, and recycling phases are summarized, and the main influencing factors of carbon emissions in different stages are analyzed. Furthermore, the relevant suggestions for reducing the life cycle carbon footprint of batteries are proposed, which provides guidance for the large-scale deployment of EVs, reducing transportation carbon emissions, achieving carbon neutrality, and sustainable development of energy, environment, and economy.

Addressing the range anxiety of battery electric vehicles with charging en route

Battery electric vehicles (BEVs) have emerged as a promising alternative to traditional internal combustion engine (ICE) vehicles due to benefits in improved fuel economy, lower operating cost, and reduced emission. BEVs use electric motors rather than fossil fuels for propulsion and typically store electric energy in lithium-ion cells. With rising concerns over fossil fuel depletion and the impact of ICE vehicles on the climate, electric mobility is widely considered as the future of sustainable transportation. BEVs promise to drastically reduce greenhouse gas emissions as a result of the transportation sector. However, mass adoption of BEVs faces major barriers due to consumer worries over several important battery-related issues, such as limited range, long charging time, lack of charging stations, and high initial cost. Existing solutions to overcome these barriers, such as building more charging stations, increasing battery capacity, and stationary vehicle-to-vehicle (V2V) charging, often suffer from prohibitive investment costs, incompatibility to existing BEVs, or long travel delays. In this paper, we propose Peer-to-Peer Car Charging (P2C2), a scalable approach for charging BEVs that alleviates the need for elaborate charging infrastructure. The central idea is to enable BEVs to share charge among each other while in motion through coordination with a cloud-based control system. To re-vitalize a BEV fleet, which is continuously in motion, we introduce Mobile Charging Stations (MoCS), which are high-battery-capacity vehicles used to replenish the overall charge in a vehicle network. Unlike existing V2V charging solutions, the charge sharing in P2C2 takes place while the BEVs are in-motion, which aims at minimizing travel time loss. To reduce BEV-to-BEV contact time without increasing manufacturing costs, we propose to use multiple batteries of varying sizes and charge transfer rates. The faster but smaller batteries are used for charge transfer between vehicles, while the slower but larger ones are used for prolonged charge storage. We have designed the overall P2C2 framework and formalized the decision-making process of the cloud-based control system. We have evaluated the effectiveness of P2C2 using a well-characterized simulation platform and observed dramatic improvement in BEV mobility. Additionally, through statistical analysis, we show that a significant reduction in carbon emission is also possible if MoCS can be powered by renewable energy sources.