Hybrid Vehicle Model Research Articles

Investigation With Rule-Based Controller of Energy Consumption of Parallel Hybrid Vehicle Model in Different States of Charge

Investigation With Rule-Based Controller of Energy Consumption of Parallel Hybrid Vehicle Model in Different States of Charge

Present and Future Trends of Electric Vehicles in India

The electric vehicles market in India is making significant progress in terms of environmental sustainability. Various new car companies are revolutionizing the industry by introducing innovative models. The interest in electric vehicles is rapidly growing, and with the introduction of new pricing models, the market is expected to expand even further. Recognizing this shift, existing car manufacturers are striving to unveil new hybrid or electric vehicle models. Developed countries such as the UK and France have already announced plans to ban the sale of diesel and petrol cars by 2040. Experts predict that Europe will witness a complete transition to electric vehicles five years prior to the deadline. This transformation has led to increased commitments and competition among leading car manufacturers and businesses in the automotive industry. Norway, for instance, is experiencing remarkable progress in the electric vehicles market, with nearly 30% of new car sales in December 2017 being electric battery-powered vehicles. Energy experts anticipate that China and India will drive the demand for electric vehicles, as these countries prioritize the development of low-carbon emission transportation solutions. In response to the growing industry, many companies are planning to introduce electric vehicle charging points to support the increasing demand for electric vehicles.

Control and Optimization of Pre-Transmission Parallel Hybrid Vehicle with Fuzzy Logic Method and Comparison with Conventional Rule Based Control Strategy

In this study, a model of a pre-transmission parallel hybrid vehicle was created in MATLAB/Simulink environment. A torque control strategy has been developed for the hybrid vehicle on the created model. The hybrid vehicle's torque control is provided by fuzzy logic and conventional rule-based control strategies. The control strategies of the hybrid vehicle have been developed based on four input parameters: accelerator and brake pedal position, battery state of charge and the operating mode of the electric motor. The regenerative braking system of the pre-transmission parallel hybrid vehicle has been provided with a fuzzy logic control strategy in both vehicle structures. In the fuzzy logic torque split controller, a control algorithm has been created in which the safety, performance and fuel consumption values of the vehicle meet at the optimum point. Under different driving cycle conditions, the effects of control strategies on engine operating points have been examined and average fuel consumption values have been obtained. In the control of the parallel hybrid vehicle with fuzzy logic method, it has been obtained that it provides 12.87%, 6.62%, 2.27% and 6.23% fuel savings under FTP-75, NEDC, EUDC and ECE-15 driving cycle conditions, respectively. It has been observed that the vehicle cannot provide sufficient performance in US06 driving cycle conditions with the use of conventional rule-based control strategy.

FIRE HAZARD OF HYBRID AND ELECTRICAND VEHICLES

In the following time, we received information from many countries of the world about accidents related to fires and explosions of motor vehicles with electric and hybrid vehicles. The popularity of such news is caused by the fact that accidents and fires of cars with internal combustion engines have long become commonplace, which do not attract the attention of the general public. However, many numerous and conducted scientific studies say about the danger of electric and hybrid vehicle models with lithium-ion batteries that they are greatly exaggerated, i.e., damage to the traction battery (battery) can cause a self-destructive chain reaction, but the degree the danger of electrolyte is less than that of gasoline or diesel fuel and much less than that of hydrogen fuel in hybrid modifications. The article examines the causes of the fire hazard of electric and hybrid cars and their consequences. Scientific reasons and innovations regarding the prevention and immediate elimination of short circuits, fires and explosions in the closed space of lithium-ion batteries during the operation of electric hybrid cars are provided. Electric vehicles and hybrids tend to suffer from traction battery technical and other issues. Although a battery flash is dangerous and more difficult to extinguish than a gas flash, the cause of the fire is usually different and these results are found in a defect in the battery pack: "ruptured anode" and "complex separator". The Hyundai Kona and Chevrolet Bolt EV had the most electric vehicle recalls related to fires this year, totaling 152,000 vehicles, while the top two hybrid recalls totaled more than 32,000 vehicles. A lithium-ion type of car catches fire usually not immediately, but after some time. This determines the driver and passengers to immediately leave the interior of the vehicle. When extinguishing a fire, there is a risk of damage to passengers, eyewitnesses and others who are near it by the electric current of high-voltage systems. It is recommended to get out of the interior of the car engulfed in fire and smoke in a timely manner and call the rescuers of the Ministry of Emergency Situations by calling the emergency service. The main task of the firefighters who arrived in the city at the first stage of the event was to disconnect the high-voltage cable using an emergency disconnector or mechanically and to disconnect the high-voltage traction battery. Although the risk of flash in EVs and hybrids exists, it may be receiving a disproportionate amount of attention. It is important for car manufacturers to eliminate the risk of ignition in their vehicles as much as possible, but obviously no form of driving is completely safe. Keywords: hybrid and electricand car, fire, explosion, lithium-ion battery.

Backward-Facing Analysis for the Preliminary Estimation of the Vehicle Fuel Consumption

In this paper, a methodology for the estimation of fuel consumption using backward-facing analysis is presented. The method for the determination of fuel consumption was based on the evaluation of the total work required to drive the vehicle along a specific drive cycle. At the same time, the potential fuel economy was estimated using the energy that can be harvested from the wheel. The results obtained using this model were compared with complex MATLAB/Simulink models developed using the forward-facing strategy. The MATLAB Simulink model details conventional and hybrid vehicle models capable of estimating fuel consumption. The components of the longitudinal forces opposing the vehicle during driving were investigated and analyzed using the results of the analytical and simulation models. The procedure can be successfully applied to provide a quick estimate of the fuel consumption performance before detailing complex simulation models.

Research on the Intelligent Energy Governance of Parallel Hybrid Vehicle Based on Deep Learning

To realize the intelligent energy governance of hybrid vehicles, a Deep-Q-Network energy controller based on the construction of parallel hybrid vehicle model is proposed, which aiming at energy loss problem of parallel hybrid vehicles and combining deep learning with reinforcement learning, and it is simulated through the ADVISOR software platform and compared with the traditional fuzzy logic strategy. The experimental results indicate that the DQN-based control strategy proposed in this paper reduces both the energy consumption and exhaust emissions of parallel hybrid vehicles. Compared with the traditional fuzzy control strategy, fuel consumption is reduced by 0.43L while the fuel economy increases by 10.9%. and exhaust gas such as CO44, CO, NOxx the emission were reduced by 28.9%, 0.2%, and 7.4%, respectively. It shows the feasibility and effectiveness of the proposed methods.

Drive cycle simulation of light duty mild hybrid vehicles powered by hydrogen engine

In the ongoing efforts to reduce CO2 and pollutant emissions, hydrogen combustion engine can provide immediately available mature technology for carbon-free transportation. Hydrogen combustion does not produce on-site CO2 emissions, the principal pollutant is NOx (which can be minimized using appropriate combustion control and aftertreatment), and the available ICE technology can be readily modified to accommodate for hydrogen use. The paper provides a prediction of the performance of a hydrogen combustion engine in passenger vehicles, aiming at extending or updating the available research with the current powertrain trends, namely downsizing, turbocharging, and hybridization. Data gathered from a single-cylinder engine fueled by a lean hydrogen mixture are used as input into a mild hybrid vehicle model, which is used for quasi-static drive cycle simulations. The results show NOx emission around the EURO VI limit without the use of any aftertreatment and fuel consumption as low as 1.1 kgH2/100 km in WLTC.

An extreme gradient boosting-based thermal management strategy for proton exchange membrane fuel cell stacks

The working temperature is one of the critical factors for proton exchange membrane fuel cell (PEMFC) stacks, as it directly influences the performance and working life of PEMFCs. Due to the inherent nonlinearity of the PEMFC stack thermal management system model, current thermal management strategies (TMSs) generally face with the drawbacks of lower accuracy and robustness. In this research, a novel machine learning (ML) algorithm, i.e. the extreme gradient boosting (Xgboost) algorithm is applied to the TMS of a PEMFC stack to control the inlet and outlet temperatures of the PEMFC stack, where the proton exchange membrane (PEM) water content is also considered and kept at a reasonable level. In order to evaluate the effectiveness of the proposed strategy, a fuel cell hybrid vehicle (FCHV) model from Autonomie software is selected and the proposed TMS is tested and compared to other strategies under a mixed driving cycle for the PEMFC stack. Results show that the proposed Xgboost-based TMS presents the best control performance, which reduces the maximum deviation of the PEMFC stack temperature and the variation range of the PEM water content compared to other ML-based TMSs and the fuzzy logic-based TMS.

Development of Optimization Based Control Strategy for P2 Hybrid Electric Vehicle including Transient Characteristics of Engine

Models based on steady-state maps estimate fuel consumption to be 2–8% lower than real experimental measured values. This is due to the fact that during transient phases, the engine consumes more fuel than in steady phases. Some literature has addressed the conventional vehicle engine model that improves fuel consumption estimation’s accuracy during the transient state. However, the characteristics of the engine in the scope of hybrid electric vehicles (HEVs) with an integrated control strategy is yet to be covered. The controller is designed to minimize engine operation in the transient phase to enhance energy savings. In this paper, the correlation between fuel enrichment in transient and steady-state fuel estimation is established as transient correction factor (TCF). Its explanatory variable was the engine torque change rate. This paper describes the influence of engine transient characteristics on the fuel consumption of a mild HEV. The work attempts to improve the fuel economy of the HEV by introducing a penalty factor in the controller to optimize the use of the engine in transient regimes. A backward vehicle model was developed for a production vehicle with a conventional powertrain and validated experimentally using data available online. The corresponding hybrid vehicle model was developed by integrating the electric motor and battery components with the conventional vehicle model. A P2 off-axis configuration was chosen to this end as the HEV architecture. A conventional equivalent consumption minimization strategy (ECMS) was used to split the torque request between the engine and the electric motor. This control strategy was modified with TCF to penalize the engine torque change rate. The results of the simulation show that due to less transient operation of the engine, the fuel consumption was reduced from 923 g to 918 g under the US06 driving cycle. The fuel economy of the model has been simulated for UDDS and HW drive cycles too, and fuel consumption improved by 4.4 g and 3.2 g, respectively. It has been verified that by increasing the battery capacity twice (14s24p), the limitations imposed by the battery capacity can be minimized and the fuel usage can be reduced by 9 g in the UDDS cycle.

The Development of CO2 Instantaneous Emission Model of Full Hybrid Vehicle with the Use of Machine Learning Techniques

Road transport contributes to almost a quarter of carbon dioxide emissions in the EU. To analyze the exhaust emissions generated by vehicle flows, it is necessary to use specialized emission models, because it is infeasible to equip all vehicles on the road in the tested road sections with the Portable Emission Measurement System (PEMS). However, the currently used emission models may be inadequate to the investigated vehicle structure or may not be accurate due to the used macroscale. This state of affairs is especially related to full hybrid vehicles, since there are none of the microscale emission models that give estimated emissions values exclusively for this kind of drive system. Several automakers over the past decade have invested in hybrid vehicles with great opportunities to reduce costs through better design, learning, and economies of scale. In this work, the authors propose a methodology for creating a CO2 emission model, which takes relatively little computational time, and the models created give viable results for full hybrid vehicles. The creation of an emission model is based on the review of the accuracy results of methods, such as linear, robust regression, fine, medium, coarse tree, linear, cubic support vector machine (SVM), bagged trees, Gaussian process regression (GPR), and neural network (NNET). Particularly in the work, the best fit for the road input data for the CO2 emission model creation was the GPR method. PEMS data was used, as well as model training data and model validation. The model resulting from this methodology can be used for the analysis of emissions from simulation tests, or they can be used for input parameters for speed, acceleration, and road gradient.

Application of Automobile Modelling Optimization Design Based on Virtual Reality Technology

Automobile architecture is a crucial phase in the process of design. The model assessment process in the hybrid vehicle modeling assessment becomes more in-depth, and designers exchange ideas. The critical aspect of automotive modeling is that various models have varying views on vehicle styling. The evaluation process is long, complicated, and efficient in automotive simulation. The evaluation process takes time. Therefore, in the paper, an Automobile Modelling Optimization Design Based on Virtual Reality Technology (AMOD-VRT) is proposed to improve automobile modeling and design research to be more interactive. Computational Design Method is used to create and design the entire vehicle as well as its surrounding environment. An Integrated Virtual Analysis can enclose the models mentioned above and create a virtual environment in which the user can interact to design a model virtually by themselves. The paper provides complete virtual design modeling of an automobile, allowing the user to work more efficiently based on the results. By applying virtual reality technology in the automobile design phase, interactive and network-based remote research on automobile modeling can make the automobile design progression more appropriate, easier to communicate with designers and decrease automobile design’s development cost and cycle. The simulation analysis is performed based on safety, performance, and efficiency, proving the reliability of the proposed framework. Various parameters obtain the simulation results on automobile design as the automobile vehicle design performance ratio is 87.16%, increasing the visual effects in AMOD ratio is 88.77%, improving visual effects with optimized design ratio is 84.5%, interaction and collaboration of automobile modeling ratio are 82.26% and reduce vehicles accident using virtual reality ratio is 93.80%. The computer-based virtual reality technology in the automotive industry can effectively accelerate new product development in the future.

Modeling and thermal management of proton exchange membrane fuel cell for fuel cell/battery hybrid automotive vehicle

The proton exchange membrane fuel cell (PEMFC) stack is a key component in the fuel cell/battery hybrid vehicle. Thermal management and optimized control of the PEMFC under real driving cycle remains a challenging issue. This paper presents a new hybrid vehicle model, including simulations of diver behavior, vehicle dynamic, vehicle control unit, energy control unit, PEMFC stack, cooling system, battery, DC/DC converter, and motor. The stack model had been validated against experimental results. The aim is to model and analyze the characteristics of the 30 kW PEMFC stack regulated by its cooling system under actual driving conditions. Under actual driving cycles (0–65 kW/h), 33%–50% of the total energy becomes stack heat; the heat dissipation requirements of the PEMFC stack are high and increase at high speed and acceleration. A PID control is proposed; the cooling water flow rate is adjusted; the control succeeded in stabilizing the stack temperature at 350 K at actual driving conditions. Constant and relative lower inlet cooling water temperature (340 K) improves the regulation ability of the PID control. The hybrid vehicle model can provide a theoretical basis for the thermal management of the PEMFC stack in complex vehicle driving conditions.

Multi-System Coupling DMi Hybrid Vehicle Modeling and Its Performance Analysis Based on Simulation

Key subsystems, such as driving resistance, component performance, and energy management strategy, determine the power performance and energy consumption of hybrid electric vehicles. Qin Plus performs excellently in fuel consumption due to its low driving resistance, high thermal efficiency of the engine, and multi-factor multi-mode energy management strategy. This article aims to explain the outstanding energy consumption of DMi vehicles by analyzing the driving resistance, component parameters of Qin Plus and introducing the drive modes selection and vehicle energy management strategy through multi-system modeling and simulation. The ultra-low fuel consumption of 3.8 L is obtained under the NEDC driving cycle and evaluated by the corresponding experiment.

Efficiency Analysis of Various Batteries with Real-time Data on a Hybrid Electric Vehicle

Battery selection remains an up-to-date engineering problem for hybrid and electric vehicle manufacturers. Type of battery and its capacity will depend on the trip and vehicle parameters. An electric vehicle produced with the ideal bat-tery type will undoubtedly be preferred by customers. Data collected from black boxes of trolleybuses operated by Malatya Metropolitan Municipality were used in this study. The real road and driver characteristics were included in the study with the experimentally obtained data. These data are the accelerator pedal data obtained from vehicles driven by different drivers in regular and congested traf-fic hours. In this study, four different battery chemistries were run separately on a hybrid vehicle model and analyzed. Chosen battery chemistries are the most commonly used by manufacturers. These are Lead Acid, Nickel Cadmium, Nickel Metal Hydride and Lithium Iron Phosphate batteries. The results of the study are presented in detail comparatively. Among the battery chemistries, Lithium iron phosphate is observed to be the most ideal battery type for hybrid electric vehi-cles.

Energy Management Strategy of a Hybrid Power System Based on V2X Vehicle Speed Prediction.

Energy consumption in vehicle driving is greatly influenced by traffic scenarios, and the intelligent traffic system (ITS) has a key role in solving the real-time optimal control of hybrid vehicles. To this end, a new energy management control strategy based on vehicle-to-everything (V2X) communication for vehicle speed prediction was proposed to dynamically adjust the engine and motor power output according to the traffic conditions. This study is based on intelligent network connectivity technology to obtain forward traffic state data and use a deep learning algorithm to model vehicle speed prediction using the traffic state data. The energy economy function was modeled using the MATLAB/Sinumlink platform and validated with a plug-in hybrid vehicle model simulation. The results indicate that the proposed strategy improves the vehicle energy economy by 13.02% and reduces CO2 emissions by 16.04% under real vehicle driving conditions, compared with the conventional logic threshold-based control strategy.

Proposing effective strategies for meeting an environmental regulation with attainable technology improvement targets

AbstractThe Japanese government will adopt the CAFE standard after 2020. By using a “modified” slacks‐based measure (SBM) model, this study analyzed the technical efficiency of 113 gasoline vehicle models (GVs) and 54 hybrid vehicle models (HVs) sold by Japanese manufacturers in 2016. We also estimated attainable fuel efficiency of specific vehicle models that can be further improved referring to the nearest point on the best practice frontier. The improved CAFE values and standards of the nine automobile manufacturers were calculated. The technology gap from the vehicle technology frontier was more noticeable among gasoline vehicles than among hybrids. Moreover, most automobile manufacturers can achieve the CAFE standard through an effective achievement strategy based on best practice technologies, whereas the others will not achieve the CAFE standard even given a rapid technology innovation beyond the best practice frontiers for GVs and HVs.

Numerical and experimental investigation of fuel consumption and CO2 emission performance for a parallel hybrid vehicle

In this study, fuel consumption map was created by obtaining a large number of data from gasoline engine passenger car through high-speed data acquisition with ISO-15765–4-CAN Protocol. Subsequently, CO2 map was also generated theoretically. To obtain precise results, ANN was used while creating the maps. Later, conventional vehicle model was created with Matlab/Simulink. Afterwards, hybrid vehicle model was developed by implementing electrical system in compliance with single-shaft pre-transmission parallel configuration with one electric motor. Hybrid powertrain control algorithm was created by considering efficiencies of mechanical and electrical power sources and sinks. Charge-neutral simulations were run with conventional and hybrid models over WLTC and a local route which is created by collected in-city speed data. The hybrid and conventional vehicles were enabled to be compared with the developed method. Detailed comparison of ICE operations both between two vehicle models and two cycles was presented. Moreover, obtained results, including overall electrical system efficiencies, were compared over WLTC and the local route. Significant improvements both in consumption and CO2 values were concluded. It is also concluded that using the hybrid system in local route with higher proportion of in-city driving conditions caused higher improvements despite a slight decrease in overall electrical system efficiency.

Determination of Lithium Ion Battery Characteristics for Hybrid Vehicle Models

In this study, the characteristic properties of a lithium ion battery cell which is in the dimension standard of “18650’’, creates the battery pack of a hybrid vehicle are determined. The test setup that is able to measure instantaneous current, open circuit voltage and circuit voltage of the bat-tery cell during charging and discharging is prepared. The battery cell is periodically subjected to charge and discharge processes on the test set-up and current, battery voltage and open circuit voltage values are ob-tained. Based on the results of the test, internal resistance and efficiency values are calculated depending on status of the battery charge ratio by using ‘‘Equivalent circuit’’ and ‘‘Rint method’’. With the test results ob-tained, efficiency values were calculated depending on the battery charge rate during the charge and discharge process of the battery. Internal re-sistance and efficiency graphs of the battery cell are created based on the status of the battery charge ratio after the result of the process performed during the charging and discharging. It is determined that if the battery cell charge level is between 15% to %95, the average internal resistance value is 51,452 mOhm during discharhing and 57,48 mOhm during charg-ing. It is also determined that if the battery cell charge level is between 15% to %95, the average efficiency is 98,58 % during discharhing (1A) and 98,496 % during charging (1A).

Fuel consumption and emission comparison of conventional and hydrogen feed vehicles

Current environmental concerns on nitrogen oxides (NOx) and particulate matter (PM) emissions caused by diesel engines have led researchers to be interested in investigating vehicles with alternative power sources. Because of this reason, vehicle models with SI engine were adopted in the conducted study. Firstly, as an initial step, 1-D SI engine models were created with use of AVL Boost software. A four-cylinder engine model was created for conventional vehicle model, while a two-cylinder downsized engine was adopted as a subsystem of hybrid vehicle model. The models were based on experimental data obtained from a laboratory test setup with a single-cylinder engine. Subsequently, detailed engine maps on emissions and fuel consumption were generated with the developed ANN model. The fuel consumption and emission data, which were gathered from NEDC and WLTC simulations, were compared for conventional ICE, PEM FC and PEM FC + ICE powered vehicles with the help of the vehicle model which was developed by using Matlab Simulink software. Based on the results, it was concluded that there might be sufficient improvement in fuel consumption and significant improvement in emissions with the use of PEM FC that a hybrid driving system (PEM FC + ICE) can be utilized, and that emissions can be at 0 with the sole use of PEM FC.

Development of a Fuzzy Logic Controller for Real-time Energy Optimization of a Hybrid vehicle

The ability to combine positive features of an internal combustion engine with those of an electric motor has been fundamental to the advancement of the high-performance energy-optimized hybrid vehicles. However, due to a lack of reliable and realistic hybrid vehicle models, much of the hybrid vehicle controller research has been limited to computer simulations. To overcome this shortcoming, this paper utilizes a highly reliable vehicle model (Autonomie) for simulation. A state-of-the-art fuzzy logic controller was developed that considers the battery state of charge, wheel torque demand and vehicle speed as the input variables. An ARM Cortex M3 microcontroller-based control hardware prototype was developed and the processor in loop simulation was performed to verify the feasibility of the developed controller in an embedded real-time application. The results of this study indicate that the developed fuzzy logic controller significantly improved the performance (up to 48%) of the hybrid vehicle in a real-time application compared to Autonomie's built-in controller. The processor in loop test results provide evidence of the effectiveness of the developed control algorithm in the embedded real-time form