Hybrid Electric Vehicle Model Research Articles

The Technology Innovation of Hybrid Electric Vehicles: A Patent-Based Study

A hybrid electric vehicle (HEV) is a relatively practical technology that has emerged as electric vehicle technology has gradually matured. The analysis of the HEV patent lifecycle is crucial for understanding its impact on the development of this technology. This lifecycle tracks the progress of HEV technologies from their inception and patenting, through their market adoption, and to the expiration of their patent protection. In this study, we aimed to evaluate the technology lifecycle of the HEV industry using the growth S-curve method. The purpose of this study is to describe the technological lifecycle trajectory and current stage of the HEV industry, as well as the technical stages of each sub-technology, to facilitate better decision making. As part of this study, we used patent family data collected from the Derwent Innovation Index database from 1975 to 2022 and established an S-curve model for HEVs and their sub-technologies using logistic regression. In 2022, the technological maturity of HEVs reached 44%. The sub-technologies with the most substantial diffusion capabilities are energy management, propulsion systems, and cooling circuits. According to predictions, the saturation period for the patent family quantity related to HEVs is estimated to be around 53 years.

A centralized fleet management system for electrified transportation

Abstract Hybrid Electric Vehicles are a promising alternative to Conventional and Electric Vehicles, as they offer better fuel economy, lower emissions, and long drive range without requiring extensive charging infrastructure. Limited driving range and battery degradation in electric vehicles are the main challenges in electrified fleets working in large metropolitan. For these challenges, the hybrid electric vehicle fleet management system is an effective solution to optimize power consumption and mitigate electric vehicle and conventional vehicle challenges. This paper proposes a hybrid electric vehicle fleet management system for public transportation, to analyze and investigate the performance and energy consumption rate of conventional, electric, and hybrid electric vehicles on different routes using representative driving cycles. The hybrid electric vehicle fleet management system enhances operational efficiency for the fleet management system and reduces power consumption in transportation. The proposed work comprises a hybrid electric vehicle model that is based on object-oriented programming. The proposed work offers the ability to add the input of the initial state of charge, power split ratio, and route data for each bus, and the hybrid electric vehicle model computes the state of charge and fuel consumption rate along the road. Finally, The proposed work shows how a hybrid electric vehicle fleet management system has a significant potential to be a solution for public transportation in Egyptian urban areas at this time compared with conventional vehicle and electric vehicle fleet management systems.

Suitability of Hybrid Passenger Vehicles in Metropolitan Cities: A Study of Paris and Cairo Using Toyota Prius

Abstract Hybrid electric vehicles (HEVs) reduce metropolitan city air pollution and traffic congestion. This paper evaluates the performance of the Toyota Prius, a passenger HEV model, in Paris and Cairo, using driving cycle data incorporated in the validated digital tool (Advanced Vehicle Simulator - ADVISOR ). The paper compares exhaust gas emissions and fuel consumption of the selected HEV in those two cities. The results show a reduction in exhaust gas emissions and fuel consumption in Paris than in Cairo and a significant reduction in greenhouse gas emissions. The paper suggests that the HEV Toyota Prius is a suitable and sustainable solution for urban transportation in metropolitan cities.

Design of Two-Wheeler Hybrid Electric Vehicle using MATLAB-Simulink

Abstract: With the rising gap between the supply and demand of oil and the market monopoly of the countries with oil reserves, the world has turned its back on traditional fuels. Not to mention the everyday rising pollution. The work and advancement in electric vehicles are reaching their peak every day and new inventions are being made in this sector, just like the mega-market players and industries. A lot of research is concentrated on the four-wheelers nevertheless, two- wheelers are a major part of transportation in the world and as compared to the four-wheelers two-wheelers don’t find the same spotlight. Our project highlighted this problem and created the simulation model of a two-wheeler hybrid electric vehicle which is a series-parallel hybrid. This simulation is constructed using MATLAB-Simulink software. This is a simulation model of a two- wheeler hybrid electric vehicle which uses a DC source (Battery) as a power source accompanied by the IC Engine. This system can shift load between the battery and IC engine so that power output can be constant, and the system can run smoothly. A DCDC bidirectional chopper is used to convert power between battery and generator which increases the efficiency of the system. In a proposed system a two-wheeler is taken into consideration with an IC engine of rpm of 2000 (for reference) is taken into consideration for simulation.

Detailed modelling and performance analysis of power flow topology in a hybrid electric vehicle having series-parallel architecture

Despite massive investment and carbon-neutral transition goals set around the world, gasoline-based internal combustion engine vehicles still form an absolute majority in the transportation sector. With the advent of technology, access to electricity, uncertainties in fuel prices and health awareness, people worldwide are moving towards a better, reliable, cost-effective and environmentally friendly mode of transportation, a hybrid electric vehicle. Such a vehicle, with its powerful electric motor and compact gasoline-based engine, offers better efficiency in terms of operating cost and reliability. Considering the advent of hybrid electric vehicles taking pace, studies related to its architecture types, power flow dynamics, control and modelling of its various components will form an essential part of the automobile industry and research. This paper proposes a power flow topology for a hybrid electric vehicle with series-parallel architecture. The developed vehicle model with such an architecture type consists of three main sub-systems: the electrical system, the control system and the mechanical system. The presented power flow topology being modelled and analysed in detail in the Simulink tool is being implemented via a mode logic controller, which forms part of the central control system. The developed hybrid electric vehicle model demonstrates various modes of operation, from starting to accelerating to de-accelerating and then finally coming to a complete rest. Each mode yields and explains the following: the vehicle reference speed; the engine and generator turn functions on/off; the dc bus and battery voltage; the motor, battery, and generator current; the motor, generator, and engine speed; engine torque; engine power; throttle demand; and the vehicle’s actual speed. The results thus obtained show that the waveforms associated with such topology, during its various modes of operation, are pretty stable and acceptable, thereby depicting and validating the operation of the developed hybrid electric vehicle model with proposed power flow topology in a precise and transparent manner.

Dynamic coordinated control strategy of a dual-motor hybrid electric vehicle based on clutch friction torque observer

The hybrid power system with dual motors and multiple clutches experiences significant torque fluctuation during mode switching process due to the different torque response characteristics of the motor and engine. To address this issue, this paper focuses on the estimation of clutch friction torque and the development of dynamic coordinated control strategies for the components. Firstly, based on the dynamic model of the novel dual-motor hybrid electric vehicle, a torque observer based on the Kalman filter algorithm is developed to predict the friction torque generated in the clutch sliding friction stage. Secondly, the control strategies are developed for the mode switching process from single-motor to dual-motor and from dual-motor to parallel drive on a co-simulation platform. Thirdly, a power level Hardware-In-the-Loop test platform is built, and the performance of the designed control strategies is verified by the HIL platform. The results show that for the mode switching process from dual-motor to parallel drive, compared with the control strategy using the engine target speed, the control strategy based on engine idle speed proposed in this paper reduces the clutch sliding friction work and the maximum longitudinal jerk of the vehicle by 42.5% and 25.4%, respectively.

Determination of Energy Savings via Fuel Consumption Estimation with Machine Learning Methods and Rule-Based Control Methods Developed for Experimental Data of Hybrid Electric Vehicles

In this study, a parallel hybrid electric vehicle produced within the scope of our project titled “Development of Fuel Efficiency Enhancing and Innovative Technologies for Internal Combustion Engine Vehicles” has been modeled. Firstly, a new rule-based control method is proposed to minimize fuel consumption and carbon emission values in driving cycles in the experimental model of the parallel hybrid electric vehicle produced within the scope of this project. The proposed control method ensures that the internal combustion engine (ICE) operates at the optimum point. In addition, the electric motor (EM) is activated more frequently at low speeds, and the electric motor can also work as a generator. Then, a new dataset was also created on a traffic-free racetrack with the proposed control method for fuel consumption estimation of a parallel hybrid electric vehicle using ECE-15 (Urban Driving Cycle), EUDC (Extra Urban Driving Cycle), and NEDC (New European Driving Cycle) driving cycles. The data set is dependent on 11 different input variables, which complicates the system. Afterward, the fuel estimation process is made with seven different machine learning methods (ML), and these methods are compared using the obtained data set. To avoid overfitting machine learning, two different test data sets were created. The Random Forest algorithm is the most suitable technique in terms of training and testing the fuel consumption model using correlation coefficient (R2), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) simulation appropriateness for both test datasets. Moreover, the random forest algorithm achieved an impressive accuracy of 97% and 90% for both test datasets, outperforming the other algorithms. Furthermore, the proposed method consumes 4.72 L of fuel per 100 km, while the gasoline-powered vehicle consumes 7 L of fuel per 100 km. The results show that the proposed method emits 4.69 kg less CO2 emissions. The effectiveness of the Random Forest Algorithm has been verified by both simulation results and real-world data.

Simulation of Energy Dissipation in BLDC Motor and Analysis of Speed-Acoustic Characteristics

The hybrid electric vehicle combines dual propulsion systems: one employs the traditional internal combustion engine, while the other utilizes a PMDC or BLDC motor. Generally, BLDC motors exhibit an efficiency of 85% to 90%. Conversely, the PMDC motor inherently achieves an efficiency of 88% under optimal running conditions. Moreover, improving the efficiency of both motors is impeded by their design constraints. Power losses in these motors primarily stem from factors such as heat generation in the rotor (I²R losses), bearing friction, vibrations, and noise. This paper aims to investigate energy loss in a 4 kW BLDC motor manufactured by Bosch, attributable to speed-induced acoustic noise effects. Harmonic analysis of the phase current reveals the presence of odd-order harmonics, specifically those below the 11th order, resulting in current ripples. These dominant harmonic frequencies interact, ultimately generating noise and consuming a certain amount of power. These predominant frequencies correlate with fundamental electrical parameters: current frequency (Hz), RMS current (A), and threshold (%r), all associated with the motor's input power source. The 'K' factor, a parameter dependent on these three electrical factors, is calculated to determine harmonic stability. Variations in stable speeds (ranging from 1280 to 7500 RPM) are used to record 'K' factor readings. Based on IEEE Standard 85-1980 guidelines, motors are classified as either acceptable (OK) or not acceptable (not OK) according to their harmonic stability 'K' factor. Readings for each speed step are captured at a specific predominant frequency, facilitating subsequent comparisons. To validate the experimental findings, MATLAB/Simulink and a Fluke scope meter are employed to conduct power analyses for each motor, thus assessing energy loss attributed to noise. The waveform of the phase current proves instrumental in quantifying energy consumption in the BLDC motor. Notably, higher-frequency noise waves with shorter wavelengths entail greater energy consumption. Evaluating energy loss significantly contributes to the analysis of BLDC motor speed acoustics, serving to inform the design of energy-efficient hybrid electric vehicle models powered by BLDC motors, which are intended for customer service.

An investigation into hybrid energy storage system control and power distribution for hybrid electric vehicles

This study aims to develop a hybrid energy storage system (HESS), targeting a commercialised Hybrid Electric Vehicle model (Hyundai Sonata), that consists of battery and supercapacitor cells, to evaluate its benefits on the battery's health and vehicle's performance. The different possible configurations of combining the two energy storage devices are discussed, while a semi-active configuration is considered for the purpose of simulation and testing, where the supercapacitor is connected to a bidirectional DC-DC converter, implemented with a newly developed control logic for the HESS. The analysis of the HESS is done by modelling the vehicle powertrain on the simulation software Ricardo IGNITE, while adapting the WLTC Class 2 drive cycle. The results are validated by comparing HESS model to the sole battery model, which showed the working status of the battery is stabilised by the addition of the supercapacitor in the HESS model during both the traction and regenerative modes. There is a significant reduction in the battery peak current, 15.26% and 20.54% for the charge and discharge current, respectively. Other improvements include a 0.43% increase on the average battery SOC, a 6.8% decrease in the battery's maximum temperature and 47.56% decrease in the average battery power. On the other hand, there was a 0.46% increase on vehicle's average L/100 km fuel consumption, due to losses in the supercapacitor system. Various other results are also compared in the discussion section, justifying the addition of the supercapacitor. The investigation finishes off by conducting a parametric study based on the number of supercapacitor modules in parallel, and comparing results such as current, power, fuel consumption and battery SOC. The results show that 2 supercapacitor modules provided the optimal results in terms of battery's current and power behaviour.

Energy Management Strategy Based on Deep Reinforcement Learning and Speed Prediction for Power‐Split Hybrid Electric Vehicle with Multidimensional Continuous Control

An efficient energy management strategy (EMS) is significant to improve the economy of hybrid electric vehicles (HEVs). Herein, a power‐split HEV model is built and validated against test results, and then the EMS is proposed for this model based on vehicle speed prediction and deep reinforcement learning (DRL) algorithms. The rule‐based local controller and global optimal empirical knowledge are introduced to enhance the convergence speed. It is shown in the results that the twin delayed deep deterministic policy gradient algorithm (TD3) achieves more satisfactory performance on converge speed and energy efficiency. The networks of the DRL algorithm with continuous control update more robustly during iterations, in contrast to the discrete ones. Although the power‐split HEV with lower control dimension can reduce the learning burden for DRL EMS; however, the multidimensional control space shows greater optimization potential. As a result, the equivalent fuel consumption of TD3‐based EMS with multidimensional continuous control differences from the global optimal algorithm only by 4.92%. Herein, it is demonstrated in the results that long short‐term memory recurrent neural network (LSTM RNN) performs better for vehicle speed prediction than classical RNN and BP neural network, and the predictive vehicle speed feature helps improve fuel economy by 0.55%.

Parameter Optimization and Control Strategy of Hybrid Electric Vehicle Transmission System based on Improved GA Algorithm

Most of the traditional hybrid electric vehicles (HEVs) choose to optimize the transmission ratio parameters, and the parameter changes of the whole vehicle and other components are only calculated as fixed values. It is difficult to give consideration to the optimization of the economy and power of hybrid vehicles. Therefore, the research proposes to build the transmission ratio, the required power of the vehicle’s working mode, and other models through the dynamic analysis. The parameters of the whole vehicle are optimized on the basis of parameter matching. At the same time, this paper chooses to adopt a hybrid optimization algorithm, combining particle swarm optimization (PSO) and genetic algorithm (GA). The weighted average method and constraint method are used to design the fitness function. The simulation experiment is carried out by Cruise software and MATLAB. Compare the iterative fitness of the PSO-GA algorithm with the traditional PSO and GA algorithm. It can be concluded that PSO-GA converges at the 12th iteration, with an average optimal fitness of 0.5239, which is higher than the traditional algorithm. At the same time, the parameter optimization of PSO-GA and the simulated annealing algorithm is compared. It is found that in the same task, the gasoline consumption after SA algorithm optimization is 0.561 L, while the fuel consumption under PSO-GA algorithm optimization is 0.475 L. The method proposed in this study has improved the power and economy of the HEV model and is effective.

Design and Modeling of Hybrid Electric Vehicle Powered by Solar and Fuel Cell Energy with Quadratic Buck/Boost Converter

Considering technological advancements and international standards mandating reduced greenhouse gas emissions, automobile manufacturers have shifted their focus toward innovative technologies related to fuel-cell electric vehicles. Despite having an all-electric powertrain, fuel cell electric vehicles (FCEVs) utilize a fuel cell stack as their energy source, which runs on hydrogen and results in the emission of only water and heat. Due to the absence of tailpipe pollutants, fuel-cell electric vehicles are considered zero-emission vehicles. Among fuel cell types, low-temperature and low-pressure fuel cells, such as Proton Exchange Membrane Fuel Cells (PEMFCs), are well-suited for vehicular applications as they exhibit high power density, operate at lower temperatures (60-80°C), and are less prone to corrosion than other types of fuel cells. The main objective of this paper is to investigate solar-assisted electric fuel cell vehicles that efficiently integrate the fuel cell system with an electrolyzer and solar power to fulfill the fluctuating power demands of the electric motor and auxiliary systems. A novel EV configuration with a fuel cell, electrolyzer and onboard PV cell is proposed. An onboard PV cell can assist the fuel cell when the irradiation is enough to generate the power. During the idle conditions of vehicles, PV-generated power can be converted into chemical energy using an electrolyzer and generated hydrogen can be stored in a hydrogen tank. To match the voltage required by the motor and sources a quadratic bidirectional buck-boost converter is employed. The proposed configuration is examined by considering variable irradiance and variable speed values. To obtain the maximum power output from the photovoltaic (PV) panel, a Maximum Power Point Tracking (MPPT) algorithm is employed to regulate the PV system. To enhance the efficiency and cost-effectiveness of PV systems, an enhanced version of the incremental conductance algorithm is utilized as the MPPT control strategy. Outer voltage and inner current control are adopted to regulate the DC output voltage of the QBBC converter. An indirect vector-controlled induction motor is used as vehicle drive. Simulations are performed to investigate the proposed EV configuration in MATLAB/SIMULINK.

An apprenticeship-reinforcement learning scheme based on expert demonstrations for energy management strategy of hybrid electric vehicles

Deep reinforcement learning (DRL) is a potential solution to develop efficient energy management strategies (EMS) for hybrid electric vehicles (HEV) that can adapt to the changing topology of electrified powertrains and the uncertainty of various driving scenarios. However, traditional DRL has many disadvantages, such as low efficiency and poor stability. This study proposes an apprenticeship-reinforcement learning (A-RL) framework based on expert demonstration (ED) model embedding to improve DRL. First, the demonstration data, calculated by dynamic programming (DP), were collected, and domain adaptive meta-learning (DAML) was used to train the ED model with the adaptive capability of working conditions. Then combined apprenticeship learning (AL) with DRL, and the ED model was used to guide the DRL to output action. The method was validated on three HEV models, and the results show that the training convergence rate increases significantly under the framework. The average increase that the apprenticeship-deep deterministic policy gradient (A-DDPG) based method applied to three HEVs achieved was 34.9 %. Apprenticeship-twin delayed twin delayed deep deterministic policy gradient (A-TD3) achieved 23 % acceleration in the power-split HEV. Because A-DDPG's EMS is more forward-looking and can mimic ED to some extent, the frequency of engine operation in the high-efficiency range has increased. Therefore, A-DDPG can improve the fuel economy of the series hybrid electric bus (HEB) by 0.2–2.7 %, and improvements averaged to about 9.6 % in the series–parallel HEV while maintaining the final SOC. This study aims to improve the sampling efficiency and optimal performance of EMS-based DRL and provide a basis for the design and development of vehicle energy saving and emission reduction.

Fault mode detection of a hybrid electric vehicle by using support vector machine

Hybrid electric vehicle (HEV) is one of the ideal transportation tools to face the challenge of ‘Carbon peak carbon neutralization’. The high complexity of the latest HEVs result to the difficulties in vehicle fault diagnostics, which is considered to be the main factors of vehicle durability. In this study, a multi-fault mode detection method of a P2 diesel HEV was investigated by using support vector machine(SVM). The HEV physical model was built and validated by using the experimental data under China typical urban driving cycle (CTUDC). The SVM algorithm was coded in Matlab/Simulink environment. The training data vectors for normal mode and fault mode were acquired from the HEV model. Through the analysis of failure mode, the OVO-SVM method was proved as the best accuracy, the success rate of diagnosis reaches 98%. Case studies of single fault mode and multi-fault mode fault detection were conducted. Offline and online level test were performed to show the effectiveness of the detection algorithm. The study may support the development of intelligent diagnostic methods for the different types of HEVs.

Modeling, Simulation and Control Strategy Optimization of Fuel Cell Hybrid Electric Vehicle

This work represents the development of a Fuel Cell Hybrid Electric Vehicle (FCHEV) model, its validation, and the comparison of different control strategies based on the Toyota Mirai (1st generation) vehicle and its subsystems. The main investigated parameters are hydrogen consumption, and the variation of the state of charge, current, and voltage of the battery. The FCHEV model, which is made up of multiple subsystems, is developed and simulated in MATLAB® Simulink environment using a rule-based control strategy derived from the real system. The results of the model were validated using the experimental data obtained from the open-source Argonne National Laboratory (ANL) database. In the second part, the equivalent consumption minimization strategy is implemented into the controller logic to optimize the existing control strategy and investigate the difference in hydrogen consumption. It was found that the ECMS control strategy outperforms the rule-based one in all drive cycles by 0.4–15.6%. On the other hand, when compared to the real controller, ECMS performs worse for certain considered driving cycles and outperforms others.

Cooperative optimization of speed planning and energy management for hybrid electric vehicles based on Nash equilibrium

The longitudinal automatic driving of hybrid electric vehicles (HEVs) is a complex multi-objective problem, and the coupling relationship between speed planning and energy management is difficult to reveal using the commonly used hierarchical control algorithm. Thus, in this paper, a collaborative control strategy is proposed for cruise speed optimization and energy management of HEVs based on Nash equilibrium. The strategy in this paper can optimize the control of vehicle-level and hybrid-system-level at the same time. First, this paper builds the key component model of HEV under the Simulink environment, which lays the foundation for vehicle control. Then, a multi-objective problem-solving framework based on game theory is proposed aiming at the economy and safety of HEV cruising. Afterward, a weight factor adjustment method is proposed based on fuzzy logic to judge the brake/drive logic. Finally, the utility functions are used for coordinated control based on Nash equilibrium. The simulation results show that the control method proposed in this paper has improved in various aspects as compared with hierarchical model predictive control, including vehicle following, safety, and comfort. In addition, this strategy can significantly reduce energy consumption costs while meeting the cruise speed requirements of vehicles. In the whole driving cycle, the strategy proposed in this paper has a high termination of SOC and a fuel consumption reduction of 15.33%. It can better deal with the relationship between speed planning and torque distribution than the traditional hierarchical control method.

Modelling and fuzzy logic based control scheme for a series hybrid electric vehicle

Ever stricter emission regulations, declining petroleum resources, increasing pollution, and global warming triggered an interest in e-mobility. Although fully electrified transportation is targeted, hybrid electric vehicles have become attractive during this transition period due to reasons such as battery challenges, range anxiety, grid capacity, and charging infrastructure. Hybrid electrical vehicles require challenging energy management systems due to the increasing number of components and energy conversions. This paper aims to introduce a simple yet effective control scheme to control the battery state-of-charge (SOC) and regenerative braking of a hybrid electric vehicle. For this purpose, a fuzzy logic controller is developed, three inputs as the SOC, driver demand, and vehicle velocity are defined. Instead of torque or power requirement, which are commonly used as controller inputs in the literature, a more straightforward method is adopted by using the accelerator and brake pedal positions. The controller manages the engine power and regenerative braking intensity. A series hybrid electric vehicle model is created in the MATLAB/Simulink environment to validate the performance of the proposed controller. The proposed controller aims to keep the SOC between 30-40% after charge depleting mode, and ensures prevention of regenerative braking at high SOC values to prevent overcharging. Simulations have been performed according to NEDC and WLTC, show that the proposed controller is able to realize design objectives.

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.

Optimal Energy Management of Series Hybrid Electric Vehicles With Engine Start–Stop System

This paper develops energy management (EM) control for series hybrid electric vehicles (HEVs) that include an engine start-stop system (SSS). The objective of the control is to optimally split the energy between the sources of the powertrain and achieve fuel consumption minimization. In contrast to existing works, a fuel penalty is used to characterize more realistically SSS engine restarts, to enable more realistic design and testing of control algorithms. The paper first derives two important analytic results: a) analytic EM optimal solutions of fundamental and commonly used series HEV frameworks, and b) proof of optimality of charge sustaining operation in series HEVs. It then proposes a novel heuristic control strategy, the hysteresis power threshold strategy (HPTS), by amalgamating simple and effective control rules extracted from the suite of derived analytic EM optimal solutions. The decision parameters of the control strategy are small in number and freely tunable. The overall control performance can be fully optimized for different HEV parameters and driving cycles by a systematic tuning process, while also targeting charge sustaining operation. The performance of HPTS is evaluated and benchmarked against existing methodologies, including dynamic programming (DP) and a recently proposed state-of-the-art heuristic strategy. The results show the effectiveness and robustness of the HPTS and also indicate its potential to be used as the benchmark strategy for high fidelity HEV models, where DP is no longer applicable due to computational complexity.

Evaluation of Real-World Fuel Consumption of Hybrid-Electric Passenger Car Based on Speed-Specific Vehicle Power Distributions

Fuel consumption differs between the hybrid electric vehicle (HEV) and the conventional vehicle (CV). However, traditional fuel consumption models developed for CVs are commonly applied to HEVs, which leads to uncertainties in the quantitative evaluation of energy consumption for passenger cars in traffic road networks. Considering the internal combustion engine (ICE) operating modes of hybrid vehicles among varying vehicle specific power (VSP) demand, we present a method to incorporate the HEV ICE speed to develop speed-specific VSP distributions for real-world driving conditions. Using vehicle trajectory and fuel consumption data in real traffic conditions, the results of this study show that the application of methods developed for CVs leads to a significant underestimation of fuel consumption for HEVs when the average speed is in the high-speed range (over 50 km/h) and a significant overestimation of fuel consumption when the average speed is in the low-speed range (below 30 km/h). The average relative error of the measured fuel consumption factor in each speed bin is 7.1% compared with real-world observations, which is an unacceptably large error. This paper proposes a method to develop the speed-specific VSP distribution, considering whether the internal combustion engine (ICE) of HEVs is on or off. This approach reduces the average relative error of the obtained fuel consumption compared with real-world observations to 2.2%, and the measuring accuracy at different average speeds is significantly improved. This method enhances the functionality and applicability of the VSP theory-based traffic energy model for HEVs.