Hybrid Electric Vehicle Simulation Research Articles

Electric vehicles survey and a multifunctional artificial neural network for predicting energy consumption in all-electric vehicles

This study contains a survey on the architecture of electric vehicles and an artificial neural network application for prediction of energy consumption in all-electric vehicles. In this study, the term “electric vehicles” (EVs) refers to various types of electrified vehicles. The technologies behind these electric vehicles were also discussed. The survey focuses on hybrid electric vehicles (HEVs), pure electric vehicles (PEVs), and plug-in hybrid electric vehicles (PHEVs). The study also presents the design simulation of a typical hybrid electric vehicle. A hybrid electric vehicle was designed using ADVISOR, and it was compared with another car known as the targeted car. The fuel consumption of the designed car was found to be lower than that of the targeted car. The study also introduced a multifunctional artificial neural network model for predicting electrical energy consumption in all-electric vehicles. The proposed model has nine input variables, which are virtual functions calculated from the nine selected parameters using a virtual function formula. The number of input variables was made to be equal to the number of output variables so that the artificial neural network could simulate a unique solution. The proposed model was compared with a multi-output inverse function model of an artificial neural network. The accuracy of the proposed model was 1.23–6.85 times higher than that of the inverse function model for the nine case studies considered in terms of mean square error.

Modelling and Simulation of Hybrid Electric Vehicle Based on MATLAB/ Simulink

Abstract: Due to the more vigorous regulations on carbon gas emissions and fuel economy, Fuel Cell Electric Vehicles (FCEV) are becoming more popular in the automobile industry. This paper presents a neural network based Maximum Power Point Tracking (MPPT) controller for 1.26 kW Proton Exchange Membrane Fuel Cell (PEMFC), supplying electric vehicle powertrain through a high voltage-gain DC-DC boost converter. The proposed neural network MPPT controller uses Radial Basis Function Network (RBFN) algorithm for tracking the Maximum Power Point (MPP) of the PEMFC. High switching frequency and high voltage gain DC-DC converters are essential for the propulsion of FCEV. In order to attain high voltage gain, a three-phase high voltage gain Interleaved Boost Converter (IBC) is also designed for FCEV system. The interleaving technique reduces the input current ripple and voltage stress on the power semiconductor devices. The performance analysis of the FCEV system with RBFN based MPPT controller is compared with the Fuzzy Logic Controller (FLC) in MATLAB/Simulink platform.

Powertrain modeling and performance simulation of a novel flywheel hybrid electric vehicle

To improve vehicle performance and energy utilization, a novel planetary gear set based flywheel hybrid electric powertrain (PGS-FHEP) is proposed. The PGS-FHEP involves an internal combustion engine, a planetary gear set that integrated a control motor and an energy storage flywheel, which combines the high efficiency of the mechanical flywheel energy storage system with the flexible and controllable characteristics of the electric motor. The powertrain is analyzed and modeled using lever analogy method, and a rule-based control strategy is designed and verified under different test cycles. The simulation results indicate that compared with the traditional manual transmission vehicle, the fuel economy of the vehicle equipped with PGS-FHEP can be improved by more than 50%, and the acceleration performance can be increased by 28.01%. Up to 60.61% of vehicle kinetic energy can be recovered by PGS-FHEP, among which 37.85% can be directly captured by the energy storage flywheel. In addition, the battery charging power is reduced, which is beneficial to prolong the battery life.

A multivariable output neural network approach for simulation of plug-in hybrid electric vehicle fuel consumption

This study is laser focused on the simulation of fuel consumption and fuel economy label parameters of plug-in hybrid electric vehicles. While fuel economy is a key factor in the design of plug-in hybrid electric vehicles, a fuel economy label can educate customers about the economic advantage of purchasing a particular car. The fuel economy label of a PHEV consists of parameters like driving range, electrical energy consumption, fuel economy for city, highway, and combined use, battery recharge time, and fuel consumption rates. The study used an inverse function model of an artificial neural network to simulate and calculate the parameters of the fuel economy labels of PHEVs. Firstly, the selected parameters of the fuel economy label of plug-in hybrid electric vehicles were used to develop a single output model. The output variable of the single output model was then merged with dummy functions to form input variables for the inverse function model. The output variables simulated were engine size in litres; estimated driving range when the battery is fully charged in km, battery recharged time in hours, city fuel consumption (L/100 ​km), highway fuel consumption (L/100 ​km), combined fuel consumption (L/100 ​km), estimated driving range when the tank is full, carbon dioxide (CO2) emission in grams/km, electric motor power in kW, number of cylinders, and electrical charges consumed in kWh/100 ​km. Different cases of input variables were considered for the inverse function model. The accuracy of the model was 29.1 times greater than that of the conventional inverse artificial neural network model.

Simulation of Hybrid Electric Vehicle Using Solar and Wind With MATLAB

This paper proposes a Hybrid Electric Vehicle (HEV) system which resolves the major glitches of fuel and pollution. The renewable energy is vibrant for today’s world as in near coming the non-renewable sources that we are using are going to get bushed. The hybrid electric vehicle is a phase in saving these non-renewable sources of energy. The elementary principle of solar vehicle is to usage energy that is kept in a battery during and after charging it from a solar panel. Power generated by renewable energy sources has newly become one of the most hopeful resolutions for the electrification of islands and distant rural areas. But high reliance on weather conditions and the impulsive nature of these renewable energy sources are the main drawbacks. To overcome this feebleness, changed green energy sources and power electronic converters need to be combined with each other. The charged batteries are used to initiative the motor which attends here as an engine and interchanges the vehicle in reverse or forward direction. This idea, in future, may help to shield our fuels from getting quenched.

Hybrid Electric Vehicle Design and Simulation Performance Using Isolated DC/DC Converter Based MFO Algorithm

The accuracy and effectiveness of the vehicle’s motor, DC-DC converter, controller, and electrical system are crucial for Hybrid Electric Vehicles (HEV). The study is given with a DC-DC converter architecture and motor based on the MFO control algorithm for smooth driving and quick charging of hybrid electric vehicles. The front-end AC-DC converter’s output is translated by the suggested DC-DC converter architecture, which then provides a controlled supply to the EV propulsion battery. The suggested system consists of a drive train model created in MATLAB using the SIMULINK tool, a motor drive, a DC-DC converter, an inverter, and a drive train. A Simulink model with motor drive model was constructed and successfully simulated in order to operate and evaluate the performance of hybrid electric vehicles utilizing an MFO control method. The simulation findings are examined its performance of EV system and results clearly stated the hybrid car’s fuel efficiency advantages over the conventional vehicle model.

A Study of Vehicle Driving Condition Recognition Using Supervised Learning Methods

In vehicle operation, in order to maximize the fuel economy, the propulsion system control can easily adapt to pressure and temperature variations as these variations can be measured by sensors. However, it is challenging to detect driving cycles. With growing progress made in the artificial intelligence field, pattern recognition gains momentum in various applications. This study presents a study on driving cycle pattern recognition based on supervised learning. Training data 2-D visualization is achieved by the t-distributed stochastic neighbor embedding (t-SNE) algorithm. Ten out of 12 supervised learning algorithms predict driving conditions with an accuracy of 88% or higher, and the extra tree (ET) algorithm leads the recognition accuracy at 90.26%. To improve the recognition accuracy, two hierarchical frameworks are proposed by integrating multiple supervised learning methods using weighted average and vote methods. The two hierarchical frameworks boost the driving condition recognition accuracy from 90.26% to 90.43% (weighted average) and 91.76% (vote). The results are further validated in the holdout test. In addition, a plug-in hybrid electric vehicle simulation shows 3.88%–5.82% fuel economy improvement compared to the baseline method. Two hierarchical methods outperform the ET method by 2% fuel economy. In summary, supervised learning shows great potential to detect driving cycles for vehicle energy saving.

Toward 2030 CO2 targets – high efficiency engine with gasoline compression ignition technology on a hybrid electric vehicle simulation

The pressure to reduce CO2 and pollutants of internal combustion engine (ICE) powered vehicles is higher than ever. The existing gasoline and diesel engine vehicles face a critical challenge to adjust to these stringent demands. By combining a gasoline like clean fuel with a high efficiency thermodynamic cycle (compression ignition), it is possible to create a powertrain that is clean, both globally and locally, and so breaking the historical trade-off between decreasing CO2 versus pollutants. However, very low vehicle out CO2 cannot be achieved if ICEs are not combined with a hybrid electric powertrain. In this paper, the potential of the gasoline like fuel in compression ignition (GCI) vehicle in combination with hybridization is assessed. Three different hybrid topologies have been theoretically assessed in a first step. Afterwards, the most promising has been deeply analyzed by means of longitudinal vehicle simulation. Here, the corresponding hybrid control strategy is optimized. The battery size has been adjusted as well targeting the optimum capacity when using a HEV instead of a PHEV. The combination of highly efficient GCI engine in combination with such an optimized hybrid topology represents an extreme value regarding CO2 reduction for passenger cars. Such vehicles could contribute largely to the emission targets set for 2030 by the European Commission. In summary, this paper presents engineering solution to comply with future greenhouse gas regulations through advanced engine technology and HEV technology.

Modelling of hybrid electric vehicle drive train using simulink

Simulation of a series-parallel hybrid electric vehicle is presented in this paper with practical considerations like vehicle weight, air drag and road incline. This incorporates the factors which necessitate the development of hybrid electric vehicles, their classification based on topology (series hybrid, parallel hybrid or series-parallel hybrid), comparison with internal combustion engines and fully electric vehicles. A detailed Simulink model was also used to accurately understand the working of a two-mode hybrid electric vehicle in a practical scenario. Also, strengths and weaknesses of various types of electrical machines used for traction drive in hybrid vehicles were studied.

Battery Sizing for Mild P2 HEVs Considering the Battery Pack Thermal Limitations

Small capacity and passively cooled battery packs are widely used in mild hybrid electric vehicles (MHEV). In this regard, continuous usage of electric traction could cause thermal runaway of the battery, reducing its life and increasing the risk of fire incidence. Hence, thermal limitations on the battery could be implemented in a supervisory controller to avoid such risks. A vast literature on the topic shows that the problem of battery thermal runaway is solved by applying active cooling or by implementing penalty factors on electric energy utilization for large capacity battery packs. However, they do not address the problem in the case of passive cooled, small capacity battery packs. In this paper, an experimentally validated electro-thermal model of the battery pack is integrated with the hybrid electric vehicle simulator. A supervisory controller using the equivalent consumption minimization strategy with, and without, consideration of thermal limitations are discussed. The results of a simulation of an MHEV with a 0.9 kWh battery pack showed that the thermal limitations of the battery pack caused a 2–3% fuel consumption increase compared to the case without such limitations; however, the limitations led to battery temperatures as high as 180 °C. The same simulation showed that the adoption of a 1.8 kWh battery pack led to a fuel consumption reduction of 8–13% without thermal implications.

Development of a Hybrid Electric Vehicle Simulation Tool with a Rule-Based Topology

The performance of hybrid electric vehicles (HEVs) greatly depends on the various sub-system components and their architecture, and designers need comprehensive reviews of HEVs before vehicle investigation and manufacturing. Simulations facilitate development of virtual prototypes that make it possible to rapidly see the effects of design modifications, avoiding the need to manufacture multiple expensive physical prototypes. To achieve the required levels of emissions and hardware costs, designers must use control strategies and tools such as computational modeling and optimization. However, most hybrid simulation tools do not share their principles and control logic algorithms in the open literature. With this motivation, the author developed a hybrid simulation tool with a rule-based topology. The major advantage of this tool is enhanced flexibility to choose different control and energy management strategies, enabling the user to explore a wide range of hybrid topologies. The tool provides the user with the ability to modify any sub-system according to one’s own requirements. In addition, the author introduces a simple logic control for a rule-base strategy as an example to show the flexibility of the tool in allowing the adaptation of any logic algorithm by the user. The results match the experimental data quite well. Details regarding modeling principle and control logic are provided for the user’s benefit.

Generating characteristic maps of battery cell parameters from sparse input data

Describing characteristic maps based on sparse input data (data points) is a problem that occurs in a variety of different disciplines. For the conventional interpolation of input data, various standard interpolation methods are available in most applications. Depending on the data’s complexity to be modeled and the available input data, these methods often produce results that do not meet the required accuracy. This paper presents an interpolation method to generate characteristic maps in which data points are added iteratively evident. The data can be arranged arbitrarily in an n-dimensional space. A comparison with standard interpolation methods for map generation is presented. The presented method shows a better fit with selected reference maps compared to applied standard interpolation methods. As an application example, the method is shown in the context of a Hardware-in-the-Loop application. Electrical loads resulting from two different dynamic load scenarios from a plug-in hybrid electric vehicle simulation were applied simultaneously to the simulated and existing battery module to compare the simulated and measured thermal behavior that directly depends on the generated characteristic maps’ accuracy. The results show that the battery module’s simulated temperature deviates by a maximum of approximately 0.3 K from the measured temperature.

Modelling and Simulation of Series Parallel Hybrid Electric Vehicle

The present paper intends to discuss the architectural structure and the different modes of operation of a series parallel hybrid electric vehicle during a drive cycle. Here an attempt has been made to simulate a standard series parallel HEV using the MATLAB and SIMULINK software to estimate the consumption of fuel for the different drive cycles. The results obtained from the model in terms of the fuel consumption have also been compared with that of a conventional internal combustion engine based vehicle. The study thus reinforces the need and viability of the use of hybrid electric vehicles as an intermediate transition and a pre-requisite to the full electric vehicle for the developing countries like India.

GPS data integration to reduce driving emissions of MHEVs in real operating conditions

Increasingly emission reduction requests and environmental pollution cut require improved optimization of current transportations. Control strategies and latest vehicles technologies developed in the automotive industry must be optimized, taking into account more information is possible from cars sensors to save energy and reduce emissions. The paper presents a possible strategy for reducing emissions with integration of GPS data in a Mild Hybrid Electric Vehicle simulator. In this study, real GPS data are evaluated to improve the system and introduce real environmental conditions in the system. The data obtained are analysed selectively in the paper. This paper is aimed to investigate the impact of real conditions on fuel consumption. Through theoretical analysis, the conclusion will be drawn that GPS technology is attractive to reduction of emissions.

A Publicly Available Simulation of Battery Electric, Hybrid Electric, and Gas-Powered Vehicles

Volatility in energy markets has made the purchase of battery electric vehicles (BEV) or hybrid vehicles (HEVs) attractive versus internal combustion engine vehicles (ICEVs). However, the total cost of ownership (TCO) and true environmental effects, are difficult to assess. This study provides a publicly available, user-driven simulation that estimates the consumer and environmental costs for various vehicle purchase options, supporting policymaker, producer, and consumer information requirements. It appears to be the first to provide a publicly available, user interactive simulation that compares two purchase options simultaneously. It is likely that the first paper to simulate the effects of solar recharging of electric vehicles (EV) on both cost-benefit for the consumer and environmental benefit (e.g., carbon dioxide, oxides of nitrogen, non-methane organic gasses, particulate matter, and formaldehyde) simultaneously, demonstrating how, as an example, solar-based charging of BEVs and HEVs reduces carbon emissions over grid-based charging. Two specific scenarios are explicated, and the results of show early break-even for both BEV and Plug-in HEV (PHEV) options over ICEV (13 months, and 12 months, respectively) with CO2 emissions about ½ that of the gasoline option (including production emissions.) The results of these simulations are congruent with previous research that identified quick break-even for HEVs versus ICEV.

Short-Term Optimal Energy Management of Power-Split Hybrid Electric Vehicles Under Velocity Tracking Control

In this paper, a powertrain control strategy for the real-time energy optimization of a hybrid electric vehicle (HEV) with planetary gear system is presented. Based on the deduced actual powertrain dynamics, the aim is to develop a short-term energy economy optimization strategy to fit the frequent transient operations of the HEV. The effort is paid to design a controller that is not the high-level control for long-term optimization with constraint of the battery state of charge (SoC). Taking the real-time driver demand as constraint, a hierarchical control scheme that guarantees the asymptotic tracking for the references of the powertrain status in terms of vehicle speed and engine speed is proposed by using the framework of Lyapunov stability theory. Under this precondition, an optimal torque distribution solution that minimizes the instantaneous energy consumption defined as the total cost on both of the fuel and electricity is found. The performance of the control system is validated by closed-loop simulations with a full-scale prototype HEV simulator. The results demonstrate the potential of the proposed strategy in terms of energy economy and in tracking the references of the powertrain status.

Battery SOC Prediction for HEV based on Extreme Learning Machine

The extreme learning machine (ELM) is proposed based on the prediction of the battery’s state of charge (SOC) of the hybrid electric vehicles (HEV). The HEV simulation system is developed under the environment of advanced vehicle simulator (ADVISOR). Considering the influence of working condition to SOC, the Urban Dynamometer Driving Schedule (UDDS) is adopted as the working condition. At the same time, the energy feedback is also taken into consideration when HEV under regenerative braking mode is working. The working voltages, currents and surface temperature of battery are employed to predict the real-time value of SOC, and the results indicate that the prediction model possesses higher predicted accuracy.

A Multi-Agent System for Smart Energy Management Devoted to Vehicle Applications: Realistic Dynamic Hybrid Electric System Using Hydrogen as a Fuel

Real-time simulation test beds for new zero-emission hybrid electric vehicles are considered as an attractive challenge for future transport applications that are fully recommended in the laboratory environment. In contrast, new zero-emission hybrid electric vehicles have a more complicated charging procedure. For this reason, an efficient simulation tools development for hydrogen consumption control becomes critical. In this vein, a New Zero Emission Hybrid Electric Vehicle Simulation (NZE-HEVSim) tool for the dynamic Fuel Cell Hybrid-Electric System is proposed to smartly control multisource activities. The designed system consists of a proton-exchange membrane fuel cell used to provide the required energy demand and a Supercapacitor system for energy recovery assistance in load peak or in fast transient. To regulate the supplied power, an efficient Real-Time Embedded Intelligent Energy Management (RT-EM-IEM) is implemented and tested through various constraints. The proposed intelligent energy management system aims to act quickly against sudden circumstances related to hydrogen depletion in the basis required fuel consumption prediction using multi-agent system (MAS). The proposed MAS strategy aims to define the proper operating agent according to energy demand and supply. The obtained results prove that the designed system meets the objectives set for RT-EM-IEM by referring to an experimental velocity database.

Real‐time energy management for commute HEVs using modified A‐ECMS with traffic information recognition

To further improve fuel consumption performance of hybrid electric vehicles (HEVs) running on commute route in the face of time-varying traffic information, this paper investigates a real-time energy management strategy based on the adaptive equivalent consumption minimization strategy (A-ECMS) framework with traffic information recognition. The proposed management strategy integrates the global near optimization and the real-time performance. The simple traffic recognition is constructed by utilising k-means clustering algorithm to deal with the historical traffic data to form four clusters. The adaptive equivalence factor of the A-ECMS is designed as a three-dimensional mapping on each cluster and the system states by employing stochastic dynamic programming (SDP) policy iteration to solve offline the stochastic optimal control problem formulated by each cluster statistical characteristic. In real-time energy management controller online, the instantaneous power split is performed by the ECMS with a proper equivalent factor, which is obtained from mappings according to the cluster recognised by the current traffic situation and the state-of-charge (SOC). The effectiveness of the designed control strategy is verified by the simulation test conducted on GT-suite HEV simulator over real driving cycles.

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.