Hybrid Electric Vehicle Technology Research Articles

Review of Mechanical, Electrochemical, Electrical, and Hybrid Energy Storage Systems Used for Electric Vehicles

The population rate in the world is increasing rapidly. Depending on the population, the need for transportation increases at the same rate. Traditional vehicles, which provide great convenience in transportation, bring with them some disadvantages. For example, the fossil fuel used in conventional vehicles creates greenhouse gases such as CO2 and N2O. This has a negative impact on global warming. To eliminate these negativities, interest in electric vehicle (EV) and hybrid electric vehicles (HEV) technology studies has increased recently. Some problems have arisen with these technological studies. The range problem in vehicles is the biggest of these problems. Therefore, various solutions are sought for energy storage problems in vehicles. In this article, studies on HEV and energy storage in EVs are examined. According to the data obtained because of this examination, the performance analysis of the Energy Storage Systems (ESS) was made. The performances of the electrochemical batteries used in HEVs and EVs were compared. In addition to these, flywheel energy storage system was also investigated in HEVs and EVs to recover the energy lost because of braking.

Energy management of multi-mode plug-in hybrid electric vehicle using multi-agent deep reinforcement learning

The recently emerging multi-mode plug-in hybrid electric vehicle (PHEV) technology is one of the pathways making contributions to decarbonization, and its energy management requires multiple-input and multiple-output (MIMO) control. At the present, the existing methods usually decouple the MIMO control into single-output (MISO) control and can only achieve its local optimal performance. To optimize the multi-mode vehicle globally, this paper studies a MIMO control method for energy management of the multi-mode PHEV based on multi-agent deep reinforcement learning (MADRL). By introducing a relevance ratio, a hand-shaking strategy is proposed to enable two learning agents to work collaboratively under the MADRL framework using the deep deterministic policy gradient (DDPG) algorithm. Unified settings for the DDPG agents are obtained through a sensitivity analysis of the influencing factors to the learning performance. The optimal working mode for the hand-shaking strategy is attained through a parametric study on the relevance ratio. The advantage of the proposed energy management method is demonstrated on a software-in-the-loop testing platform. The result of the study indicates that the learning rate of the DDPG agents is the greatest influencing factor for learning performance. Using the unified DDPG settings and a relevance ratio of 0.2, the proposed MADRL system can save up to 4% energy compared to the single-agent learning system and up to 23.54% energy compared to the conventional rule-based system.

Critical Performance Analysis of Four-Wheel Drive Hybrid Electric Vehicles Subjected to Dynamic Operating Conditions

Hybrid electric vehicle technology (HEVT) is emerging as a reliable alternative to reduce the constraints of battery-only driven pure electric vehicles (EVs). HVET utilizes an electric motor as well as an internal combustion engine for its operation. These components would work on battery power and fossil fuels, respectively, as a source of energy for vehicle mobility. The power is delivered either from battery or fuel or both sources based on user requirements, road conditions, etc. HEVT uses three major propelling systems, namely, front-wheel drive (FWD), rear-wheel drive (RWD), and four-wheel drive (4WD). In these propelling systems, the 4WD model provides torque to all four wheels at the same time. It uses all four wheels to propel thereby offering better driving capability, better traction, and a strong grip on the surface. The 4WD-based HEVs comprise four architectures, namely, series, parallel, series-parallel, and complex. The literature focuses primarily on any one type of architecture for analysis in the context of component optimization, fuel reduction, and energy management. However, a focus on dynamic analysis that gives a real performance insight was not conducted, which is the main motivation for this paper. The proposed work provides an extensive critical performance analysis of all four 4WD architectures subjected to various dynamic operating conditions (continuous, pulse, and step-up accelerations). Under these conditions, various performance parameters such as speed (of vehicle, engine, and motor), power (of engine and battery), battery electrical losses, charge patterns, and fuel consumption are measured and compared. Further, the 4WD architecture performance is validated with FWD and RWD architectures. From MATLAB/Simulink-based evaluation, 4WD HEV architectures have shown superior performance in most of the cases when compared to FWD type and RWD type HEVs. Moreover, 4WD parallel HEV architecture has shown superior performance compared to 4WD series, 4WD series-parallel, and 4WD complex architectures.

Features of designing scenarios for the development of hybrid technologies in the Russian and foreign automotive industry

The relevance of the development of hybrid electric vehicle technology in the automotive industry lies in its diversity. Various degrees of hybridization allow you to get bonuses for both conventional cars with an internal combustion engine and pure electric vehicles, while controlling the level of electrification of your cars, from soft hybrids close to cars with only an internal combustion engine to plug-in hybrids more similar to pure electric vehicles. Depending on the policy of the automaker and the requirements of the market, it is possible to use various designs, including those based on existing developments. The article analyzes the modern automotive market in Russia and the share of cars with hybrid technology in it. A comparison of the development of hybrid technology in modern automotive construction of Russian and foreign automakers is made. The specificity of designing scenarios for the development of hybrid technologies in the Russian and foreign automotive industries is reflected in the very process of using hybrid technology in the models of foreign automakers. Thus, the use of mild hybrid technology is mainly used by automakers with the aim of a smooth transition to more electrified versions, the design and implementation of mild hybrid technology is used by automakers to improve the environmental parameters of their cars while maintaining dynamic performance or to increase the latter. Full hybridization technology can also be used as a transitional for full electrification of models, however, it is mainly used in electric vehicles aimed at a hybrid device. Sequential hybridization technology has not been widely used in passenger cars.

Analisis Teknologi Manajemen Energi Pada Kendaraan Listrik Hibrida Berbasis Tinjauan Pustaka

The application of hybrid electric vehicle technology has grown rapidly in recent years. This article aims to describe and discuss energy management strategies in hybrid electric vehicles. The research method is qualitative with a systematic literature review based on database searches on IEEE, Garuda SINTA, ArXiv, Preprints. The results obtained 13 articles from the IEEE database by describing the results of the energy management strategy of each article. The conclusion is that the technology used for energy management strategies includes algorithm settings, namely reinforcement learning and Q-learning combined with several control systems, namely predictive control models, Equivalent Consumption Minimization Strategy, and Dynamic Programming.

An Agent-Based Model of Heterogeneous Driver Behaviour and Its Impact on Energy Consumption and Costs in Urban Space

By 2020, over 100 countries had expanded electric and plug-in hybrid electric vehicle (EV/PHEV) technologies, with global sales surpassing 7 million units. Governments are adopting cleaner vehicle technologies due to the proven environmental and health implications of internal combustion engine vehicles (ICEVs), as evidenced by the recent COP26 meeting. This article proposes an agent-based model of vehicle activity as a tool for quantifying energy consumption by simulating a fleet of EV/PHEVs within an urban street network at various spatio-temporal resolutions. Driver behaviour plays a significant role in energy consumption; thus, simulating various levels of individual behaviour and enhancing heterogeneity should provide more accurate results of potential energy demand in cities. The study found that (1) energy consumption is lowest when speed limit adherence increases (low variance in behaviour) and is highest when acceleration/deceleration patterns vary (high variance in behaviour); (2) vehicles that travel for shorter distances while abiding by speed limit rules are more energy efficient compared to those that speed and travel for longer; and (3) on average, for tested vehicles, EV/PHEVs were £233.13 cheaper to run than ICEVs across all experiment conditions. The difference in the average fuel costs (electricity and petrol) shrinks at the vehicle level as driver behaviour is less varied (more homogeneous). This research should allow policymakers to quantify the demand for energy and subsequent fuel costs in cities.

Investigation by modelling of a plug-in hybrid electric commercial vehicle with diesel engine on WLTC

Commercial vehicles have different missions every day: urban or extra-urban itineraries, uphill and downhill roads, and different loads during their trips. Based on these requirements, the vehicle configuration has to enable an immediate traction switching, depending on the type of itinerary and conditions, in this way making an important innovation towards environmentally friendly mobility. Hybrid Electric Vehicle (HEV) is considered as a valid solution to not only reduce dependence on costly oil fuels but also to cut the harmful emissions. In the last decade, the HEV technology has proven efficient outcomes with a better performance and lower emissions, compared to conventional light-duty vehicles. The paper presents a commercial hybrid electric vehicle with detailed diesel engine modelling. A computational analysis has been performed starting from the experimental activity at test bench carried out on the F1C 3.0 l diesel engine. Through ADvanced VehIcle SimulatOR (ADVISOR) software, simulations of driving cycles have been performed, to evaluate consumption and emissions. In particular, the performances of the vehicle have been tested referring to the Worldwide harmonized Light-duty vehicles Test Cycles (WLTC), which is a harmonized cycle valid for the homologation of the vehicle around the world. The goal is to analyze and compare parallel hybrid solutions respect to the conventional commercial diesel vehicle, and to quantify more specifically the amount of fuel saved. Moreover, two different control strategies of the hybrid vehicle have been tested: Charge Depleting (CD) and Charge Sustaining (CS) modes. Results from the comparative analysis confirm that hybrid solution represents a significant starting point for the vehicle's electrification. Simulation based on the analysis of a vehicle represents an efficient approach in designing the power traction, and the results of this methodology could offer a base to investigate systematically various design alternatives and to obtain in short time a reliable solution.

Development of EMS for Hybrid Electric Two-Wheeler

Rising the emission level from conventional vehicles (CV) is a major concern of today’s world. Electric vehicle (EV) and hybrid electric vehicles (HEV) technology are two ways to deal with it. EV technology is an emerging technology but they have some challenges in adopting the EV technology. So, this paper deals with modelling and simulation of a hybrid electric vehicle such as series hybrid electric vehicle and parallel hybrid electric vehicle with different energy management strategies. The heart of HEV development is energy management strategy (EMS), on which vehicle’s performance and fuel consumption can be optimized. For series HEV charge depleting charge sustaining (CDCS) EMS has been used and for parallel HEV rule-based (RB) EMS has been used. Supervisory control algorithms play important role to allow smooth power flow between the engine and electric source. There are different EMSs such as rule-based, optimization-based (OB), and learning-based (LB). These EMSs are used to split the power flow. In this paper, fuzzy logic controller (FLC) as an energy management strategy is proposed for a parallel hybrid electric vehicle, further this strategy is compared with RBEMS. The simulation results are compared and analyzed which shows there is improvement in range and reduction fuel consumption. In this study, MATLAB/Simulink and QSS toolbox are used for the simulation.

Simulative design and performance analysis of hybrid optimization technique for PEM fuel cell stack based EV application

The fuel cell power generation systems are using in most of the present hybrid Electric Vehicle (EV) technology. The output characteristics of fuel cell arenonlinear. Also, the fuel operating point fluctuate continuously based on its operating temperature condition. In order to track and stabilize the operating point fuel cell, an Artificial Neuro Fuzzy Inference System-Genetic Algorithm Optimization (ANFIS-GAO) method is used. The merits of this proposed Maximum Power Point Tracking Technique (MPPT) are high extraction power capacity, high reliability, less oscillations, and fast tracking speed. The fuel cell is having a drawback of high output current and its less output voltage generation capability. To overcome this issue, in this work, an interleaved dc-dc converter circuit is used to enhance the fuel cell energy. The advantages of the proposed dc-dc converter are low voltage stress across the switches, less input current ripples, faster response, reduced electromagnetic emission, and high reliability. The proposed hybrid power point tracking technique performance is evaluated against with the Variable Step-Perturb & Observe (VS-P&O) algorithm. The MATLAB/Simulink environment is used for the performance analysis of proposed system.

A review on hybrid source energy management strategies for electric vehicle

Electric and hybrid electric vehicle technology demonstrates a performance to replace the internal combustion engines (ICE) in the current scenario. It attracts attention with improved fuel or energy efficiency with lower emissions. The overall system performance depends on powertrains, types of energy sources, electro-electronic interfaces, and energy management strategies (EMS). Significant issues of battery-powered electric vehicles (EV) are effects on the range, battery life, EV performance, battery maintenance, and replacement cost. Hybrid power source system (HPSS) solves EV challenges to a large extent. A hybrid combination of battery and supercapacitor (SC) to power the EV enhances the overall performance and life of the vehicle. Learning and integrated-based EMSs are gaining attention, with their ability in accurate and fast response in power handling among various sources. This paper analyses various DC-DC converter topologies of HPSS and compares multiple EMS with recent developments. In the context of challenges involved in EVs and research gaps that are discussed in the paper, EMSs need to be enhanced. The EMSs must consider the inputs for varying driving behaviors, road traffic, load, and environmental conditions to assure the flexibility of EV among different users across the globe. This is achieved by the management of SC power available to support the vehicle during sudden power requirements and enabling it to recuperate braking energy to improve the energy efficiency throughout the trip. Lastly, recommending precise research directions to achieve the development and improvement of the EMS and power electronic interfaces.

Real-world particle and NOx emissions from hybrid electric vehicles under cold weather conditions

Hybrid electric vehicle (HEV) technology is critical to reduce the impact of the internal combustion engines on air pollution and greenhouse gases. HEVs have an advantage in market penetration due to their lower cost and higher driving range compared to battery electric vehicles (BEVs). On the other hand, HEVs use an internal combustion engine and still emit air pollutants. It is hypothesized that HEV performance is impacted by the weather conditions as a result of many factors. It was beyond the scope of this work to systematically evaluate all factors so instead we measured emissions from two vehicles driving city and highway routes in Minneapolis, Minnesota in the winter (−5 °C) and looked for major differences in emissions relative to each vehicle and relative to results that would be obtained from a chassis dynamometer in a controlled laboratory setting at a higher temperature approximately 20 °C). The study then looked to associate differences in emissions with the prevailing conditions to gain new insights. Emissions of interest included the total particle number (TPN), solid particle number (SPN), particulate matter mass (PM), and NOx. One key difference in vehicle engine technology was PFI (port fuel injection) versus GDI (gasoline direct injection). We found the frequency at which the Prius hybrid engine reignited was much higher than the Sonata for city and highway driving, although for both vehicles the catalyst temperature remained high and appeared to be unaffected by the reignitions, despite the cold weather. For most conditions, the Prius emitted more NOx but fewer particles than the Sonata. In some cases, NOx and particle emissions exceeded the most comparable laboratory-based emissions standards.

Generation and Analysis of Hybrid-Electric Vehicle Transmission Shift Schedules with a Torque Split Algorithm

The increased concern over global climate change and lack of long-term sustainability of fossil fuels in the projected future has prompted further research into advanced alternative fuel vehicles to reduce vehicle emissions and fuel consumption. One of the primary advanced vehicle research areas involves electrification and hybridization of vehicles. As hybrid-electric vehicle technology has advanced, so has the need for more innovative control schemes for hybrid vehicles, including the development and optimization of hybrid powertrain transmission shift schedules. The hybrid shift schedule works in tandem with a cost function-based torque split algorithm that dynamically determines the optimal torque command for the electric motor and engine. The focus of this work is to develop and analyze the benefits and limitations of two different shift schedules for a position-3 (P3) parallel hybrid-electric vehicle. a traditional two-parameter shift schedule that operates as a function of vehicle accelerator position and vehicle speed (state of charge (SOC) independent shift schedule), and a three-parameter shift schedule that also adapts to fluctuations in the state of charge of the high voltage batteries (SOC dependent shift schedule). The shift schedules were generated using an exhaustive search coupled with a fitness function to evaluate all possible vehicle operating points. The generated shift schedules were then tested in the software-in-the-loop (SIL) environment and the vehicle-in-the-loop (VIL) environment and compared to each other, as well as to the stock 8L45 8-speed transmission shift schedule. The results show that both generated shift schedules improved upon the stock transmission shift schedule used in the hybrid powertrain comparing component efficiency, vehicle efficiency, engine fuel economy, and vehicle fuel economy.

Emissions reduction from passenger cars with RCCI plug-in hybrid electric vehicle technology

Hybrid Electric Vehicles (HEVs) can be considered as a potential technology to promote the change from conventional mobility to e-mobility. However, the real benefits in terms of CO2 emissions depend on a great extent on their mode of use, vehicle design and electricity source. On the other hand, in the last few years, advanced combustion modes as Reactivity Controlled Compression Ignition (RCCI) showed great advantages in terms of NOx and soot emissions reduction. This paper has the purpose of assessing, through numerical simulations fed with experimental results, the potential of different hybrid vehicles when used together with a low temperature combustion mode. In particular, the dual-fuel Mild (MHEV), Full (FHEV) and Plug-in (PHEV) hybrid electric vehicles are tested and compared to the original equipment manufacturer (OEM) and the conventional dual-fuel powertrain, both no-Hybrid vehicles. The powertrains are optimized to meet the current European homologation legislation Worldwide Harmonized Light Vehicle Test Procedure (WLTP). After that, a deep analysis is performed in terms of performance and emissions. Lastly, a life-cycle analysis (LCA) is performed to evaluate the real potential of the different technologies. The results show that the PHEV has the highest benefits in terms of fuel consumption and engine-out emissions. With this technology, it is possible to achieve the 50 g/km CO2 target for the PHEVs with a medium battery size (15 kWh), while NOx and soot levels are under the Euro 6 limits. In addition, the RCCI technology shows great benefits to achieve the Euro6 soot level for the other hybrid platforms. The LCA shows that the PHEVs can achieve 12% reduction of the total CO2 with respect to the FHEVs, and 30% with respect to the no-hybrid diesel platform.

A survey on smart grid technologies and applications

The Smart Grid is an advanced digital two-way power flow power system capable of self-healing, adaptive, resilient and sustainable with foresight for prediction under different uncertainties. In this paper, a survey on various Smart Grid enabling technologies, Smart Grid metering and communication, cloud computing in Smart Grid and Smart Grid applications are explored in detail. Opportunities and future of Smart Grid is also described in this paper. For Smart grid enabling technologies Smart meters, smart sensors, vehicle to grid, plug in hybrid electric vehicle technology, sensor and actuator networks are explored. Advanced metering infrastructure, intelligent electronic devices, phasor measurement units, wide area measurement systems, local area network, home access network, neighborhood area network, wide area networks and cloud computing are explored for Smart Grid metering and communication. Home and building automation, smart substation, feeder automation is explored for smart grid applications. Associations of initial studies for the next step in smart grid applications will provide an economic benefit for the authorities in the long term, and will help to establish standards to be compatible with every application so that all smart grid applications can be coordinated under the control of the same authorities. Therefore, this study is expected to be an important guiding source for researchers and engineers studying the smart grid. It also helps transmission and distribution system operators to follow the right path as they are transforming their classical grids to smart grids.

Performance Comparison for Series and Parallel Modes of a Hybrid Electric Vehicle

Recently, the studies in the automotive sector has concentrated on hybrid electric vehicle. The better results in fuel economy and vehicle performance values have been achieved with the hybrid electric vehicle technology. In this study, the fuel consumption and vehicle performance values are compared to the series and parallel hybrid electric modes of a conventional street sweeper by using AVL Cruise. Nowadays, there are many driving cycles used in the analysis of fuel consumption and vehicle performans. However the current driving cycles are not appropriate for the vehicles designed for a particular function (carrying, lifting, sweeping etc.) such as street sweepers in terms of velocity and functionality. For this reason, a driving cycle was constructed by taking into consideration the daily tasks of the street sweepers with the help of AVL Cruise. The series and parallel hybrid electric configurations of the street sweeper were analyzed separately. Consequently, the demanded power (kW) from the internal combustion engine and its fuel consumption (L/h) were investiageted under the specific operating conditions in the driving cycle. The simulations show that the series hybrid mode has better fuel economy and vehicle performance.

A Series Hybrid Electric Vehicle Simulation and Analysis of Fuel Consumption Alteration in Different Driving Cycles

With decrease in amount of carbon fuels and aims of creating a cleaner environment, researches on hybrid electric vehicle technology have increased in years. The purpose of this study is to observe the change in fuel consumption of a simulated hybrid electric vehicle in different driving cycles. For this research, a series hybrid electric vehicle is modelled on MATLAB/Simulink. Subsequently, for simulating the tour of the vehicle, multiple driving cycles are modelled on the software and fed to vehicle model. For instance FTP-75, NEDC and UDDS are benefited and also a route from Turkey, the road from Istanbul to Ankara is modelled and simulated. Behavior of the vehicle on this route is also observed. After analyzing vehicle behavior in all these cycles, fuel consumption in each case is calculated and compared. As the result of this research, the most affordable cycle for this vehicle model is selected and possible ways of decreasing fuel consumption in other driving cycles is discussed.

Development Trends of Transmissions for Hybrid Electric Vehicles Using an Optimized Energy Management Strategy

Energy conservation and emissions reduction have become increasingly significant for automobiles due to the severity of the current energy situation. Hybrid electric vehicle (HEV) technology is one of the most promising solutions. This study investigated the total efficiency of a HEV powertrain. To improve the total efficiency, the engine should be regulated to work at its highest efficiency and drive the wheels directly as much as possible. To accomplish this, we developed an energy management strategy based on the direct drive area (DDA) of the engine’s efficiency map. Several typical HEV models were built to compare the fuel consumption using DDA and rule-based strategies. Furthermore, the function of the HEV transmission system with DDA was considered. The transmission in a HEV should regulate the engine to work at its highest efficiency as much as possible, which is rather different than the regulation in an internal combustion engine vehicle. The functional change may lead to transmission systems with fewer gears but optimal gear ratios. If this trend is realized, the manufacturing cost of HEVs could be largely reduced.

Free-Piston Engine (FPE) Technology with Different Applications

The free-piston engine (FPE) is a linear engine in which the requirement for a crankshaft system is eliminated and the piston assembly has a free and linear motion[1]. First proposed around 1930, FPEs were in use in the period 1930-1960 as air compressors and gas generators and provided some advantages over presenttime conventional combustion engines and gas turbine systems[2]. They are known to have a greater thermal efficiency (40-50%) than an equivalent and more conventional reciprocating engine (30-40%)[3]. A driving force behind the interest in free-piston engine generators is the automotive industry’s increasing interest in hybrid-electric vehicle technology. Much work has been undertaken by number of research groups worldwide, including the authors’ group, to explore the operation characteristics of FPEs[4,5].After initial investigations and development of freepiston related products during the early to mid-20th century, recent advances in control and real time actuation systems have enabled the technology to become a viable alternative to reciprocating technologies, and as such, research is now being carried out by number of groups worldwide [3,6-9]. Modern applications of the FPE concept have been proposed for the generation of electric and hydraulic power, typically in hybrid electric vehicles[10-15]. Known FPE applications include electric generators, hydraulic pumps and air compressors[2], which are summarised in the Table 1 below.

Driving shaft lifetime enhancement by hybrid electrification

As a classical definition, the main aim of hybrid electric vehicle technology is to decrease the fuel consumption and emissions with the assistance of its power management algorithm. However, hybrid electric vehicles could also be optimized for fatigue minimization of the driving shaft to enhance its lifetime. To the best of our knowledge, there are no studies on hybrid electric vehicles regarding this concept. In this study, we model a conventional vehicle, convert it into hybrid electric vehicle in simulation environment, and optimize the power management algorithm by considering its driving shaft lifetime enhancement. The optimization is done by redesigning one of the previous equivalent cost minimization strategy studies, which includes a new state of charge sustaining approach. In this work, we reformulate the solution considering the assumptive torque–cycle life curve of the driving shaft instead of fuel consumption or emissions. Longitudinal vehicle model is prepared for simulations and the performance of the new strategy is compared with the conventional vehicle under the real driving cycle data. The results demonstrate a significant enhancement potential of 26% in driving shaft’s lifetime. Finally, we show the additional electric motor’s optimum torque tracking performance under a real driving cycle using the experimental testbed.

A Real-Time Energy Management Strategy Based on Energy Prediction for Parallel Hybrid Electric Vehicles

Hybrid electric vehicle (HEV) technology is an effective way to resolve the problems of energy consumption and air pollution. Energy management strategies are critical to the performance of HEVs. In this paper, a novel energy management strategy of equivalent consumption minimization strategy (ECMS)-type is proposed for parallel HEVs based on energy prediction (ECMS-EP). The energy prediction is estimated based on the predicted velocity that is calculated by a chaining-neural-network method over different temporal horizons. A novel adaptive rule has been developed by eliminating the need to reset the initial equivalent factor (EF) based on the energy prediction to adjust the EF of ECMS-EP in real time. The control objective is to improve the fuel economy and sustain the state of charge (SoC). Then, via MATLAB/Simulink, simulations are conducted in three different prediction horizon lengths to verify the performance of the proposed ECMS-EP with adaptive rules. The simulation results show that the proposed ECMS-EP is able to achieve more stable SoC trajectories and better fuel economy with a fuel consumption reduction of 2.7%–7% compared with the traditional adaptive-ECMS.