HWFET Cycle Research Articles

Advanced energy management system with road gradient consideration for fuel cell hybrid electric vehicles

Hybrid electric fuel cell vehicles (FCHEVs) represent a promising pathway for sustainable transportation by integrating fuel cells, batteries, and supercapacitors to power their propulsion systems. However, ensuring the efficiency and longevity of FCHEV components remains a significant challenge due to stress on batteries and fuel cells, as well as fluctuations in the DC bus voltage. This paper presents an innovative approach to enhance the effectiveness and lifespan of FCHEV storage elements through an energy management system (EMS) based on a multi-objective genetic optimization algorithm that accounts for road gradient variations. The proposed methodology dynamically allocates energy among the fuel cell, battery, and supercapacitor, optimizing three key objectives simultaneously: minimizing fluctuations in fuel cell and battery power, reducing variations in DC bus voltage, and balancing performance, efficiency, and storage system longevity. Extensive simulations demonstrate that simultaneous optimization of these three objectives yields the best results. Under the NEDC cycle, the optimized EMS resulted in a 12 % increase in fuel cell stress but a significant 73 % decrease in battery stress, along with a 39 % reduction in DC bus voltage variations. For the UDDS cycle, there was a 37 % decrease in fuel cell stress, a 69 % decrease in battery stress, and a 29 % reduction in DC bus voltage variations. During the HWFET cycle, the optimized EMS achieved an 8 % decrease in fuel cell stress, a 49 % decrease in battery stress, and an 8 % reduction in DC bus voltage variations. By incorporating road gradient considerations, the EMS aims to maintain the battery state of charge and hydrogen consumption within acceptable ranges while enhancing FCHEV efficiency under various road gradient conditions. This study advances FCHEV technology, offering insights into the design and operation of more sustainable and efficient transportation solutions.

Q-learning based control for energy management of series-parallel hybrid vehicles with balanced fuel consumption and battery life

The present study investigates an energy management strategy based on reinforcement learning for series-parallel hybrid vehicles. Hybrid electric vehicles allow using more advanced power management policies because of their complexity of power management. Towards this feature, a Q-Learning algorithm is proposed to design an energy management strategy. Compared to previous studies, an online reward function is defined to optimize fuel consumption and battery life cycle. Moreover, in the provided method, prior knowledge of the cycle and exact modeling of the vehicle are not required. The introduced strategy is simulated for four driving cycles in MATLAB software linked with ADVISOR. The simulation results show that in the HWFET cycle, the fuel consumption decreases by 1.25 %, and battery life increases by 65% compared to the rule-based method implemented in ADVISOR. Also, the results for the other driving cycles confirm the self-improvement property. In addition, it has been depicted that in the case of change in the driving cycle, the method performance has been maintained and gained better performance than the rule-based controller.

Design and application of electromechanical flywheel hybrid device for electric vehicle

The electromechanical flywheel hybrid power device has the dual attributes of energy supply and power output, which can provide more design space for the optimization of vehicle energy utilization efficiency. It involves two key problems of device parameters design and energy management. Therefore, the optimization design of electromechanical flywheel device’s parameters and the energy management strategy are the main research objects in this paper. Firstly, based on the working principle of planetary gear mechanism, the dynamic characteristics of electromechanical flywheel device under different topological structure schemes are analyzed, and the design schemes of speed regulating motor connecting to the sun gear, flywheel connecting to the ring gear and output end connecting to the planet carrier are determined. Then, according to the dynamic characteristics of the vehicle, the parameters of flywheel, speed regulating motor and planetary gear mechanism were designed. Furthermore, considering the state of energy (SOE) of the electromechanical flywheel device, 15 working modes of the vehicle were designed, and the corresponding energy management strategies were proposed. Finally, a vehicle test platform was constructed to analyze the dynamic and economic performances of the electromechanical flywheel hybrid electric vehicle. Results show that, compared with the original vehicle driven by a single motor of the front axle, owing to the power intervention of the electromechanical flywheel hybrid device, the average acceleration from 0 km/h to 100 km/h is increased by 22.32% and the acceleration time is reduced by 18.25%, which effectively improves the vehicle dynamic performance. Moreover, the new hybrid system designed is conducive to the miniaturization design of the drive motor, increasing the load rate of the motor and improving the efficiency of powertrain system. With the advantage of short-term energy supply of electromechanical flywheel, the average discharge current of lithium battery can be reduced to improve its efficiency. Under J1015, NEDC and HWFET cycles, the average operating efficiencies of the powertrain system are increased by 8.2%, 5.6% and 4.3% respectively; the efficiency improvements of lithium battery also reach 6.7%, 4.2% and 4.1% respectively. The research of this paper is devoted to applying electromechanical flywheel to electric vehicles to help improve the performance of vehicles and promoting the development of hybrid electric vehicles in the future.

An Energy Management Strategy for Power-split Hybrid Electric Vehicle using Model Predictive Control

To fulfil future demand for energy and to control pollution, a power-split hybrid electric vehicle is a promising solution combining attributes of a conventional vehicle and an electric vehicle. Since energy is available from two subsystems i.e, engine and battery, there is the freedom to manage it optimally. In this work, model predictive control strategy, that has the constraint handling which makes it a better choice over other strategies for efficient energy management of hybrid electric vehicles. A detailed mathematical model of the power split configured hybrid electric vehicle is developed that encompasses the engine, planetary gear, motor/generator, inverter, and battery. An interior-point optimizer based-nonlinear model predictive control strategy is applied to the developed model by incorporation of operational constraints and cost function. The objective is to curtail fuel consumption while the battery’s state of charge should be maintained within predefined limits. The complete developed model was simulated in MATLAB for motor, generator, engine speed, and battery SoC. Computed specific fuel consumption from the proposed MPC during the NEDC and the HWFET cycles are 4.356liters/100km and 2.474 litres/100 km, respectively. These findings are validated by the rule-based strategy of ADVISOR 2003 that provides 4.900 litres/100 km and 3.600 litres/100 km over the NEDC and the HWFET cycles, respectively. This indicates that the proposed MPC shows 11.11 % and 31.26 % improvement in specific fuel consumption in the NEDC and HWFET drive cycles respectively.

Particulate matter emissions from flex-fuel vehicles with direct fuel injection

Otto cycle vehicles with gasoline direct injection (GDI) can reduce fuel consumption by 5–20%. In compliance with current legislation in Brazil, this technology was introduced to the domestic market in the form of a first flex-fuel GDI vehicle in 2013. However, despite gains in efficiency, studies in the literature have shown that GDI engines tend to have higher particulate matter emissions than port fuel injection (PFI) engines. This paper provides measurements of the emissions of GDI flex-fuel vehicles fuelled with gasoline containing 22% anhydrous ethanol (E22), hydrous ethanol (E100) and a 50% mixture of both (E61). The blending fuels were tested in accordance with the recommendations made in the widely known FTP 75 and HWFET cycles. The particulate-matter emissions formed during the combustion process were closely linked to fuel-type properties. The results showed the particles that adhere to sampling filters have higher concentrations of fluorine and carbon and lower amounts of silicon and oxygen, as well as sizes of around 2 μm. The black carbon emissions tend to increase directly with the gasoline content in the fuel. The results also provided evidence that the HWFET test cycle showed a higher emission rate of total particulate matter than the FTP 75 test cycle, and that overall, there were higher emissions for E22 than E100.

A Predictive Energy Management Strategy for Multi-mode Plug-in Hybrid Electric Vehicle based on Long short-term Memory Neural Network

Abstract The plug-in hybrid electric vehicles (PHEV) provide a promising solution to increasingly severe tailpipe pollution and make possible the high-efficiency utilization of energy. To strengthen the overall fuel economy of a multi-mode PHEV, this paper proposes a real-time predictive energy management strategy (EMS) based on the Long Short-term Memory (LSTM) neural network. In order to forecast the short-term vehicle velocity with speed of previous time steps, a LSTM network is built and trained using speed profile of multiple representative driving cycles; a model predictive control architecture solved by dynamic programming (DP) in prediction horizon is also structured to realize successive online optimization of power allocation between the internal combustion engine and electric motors. The simulation of proposed strategy, convention adaptive equivalent consumption minimization strategy (A-ECMS) and the offline global optimization are carried out with UDDS and HWFET cycles to investigate the effectiveness and adaptivity of the proposed strategy, the result shows that the near-optimality of fuel economy is realized, and the fuel economy is elevated compared with the A-ECMS, therefor the potential for practical application of the proposed strategy is proved.

Thermal management of an internal combustion engine focused on vehicle performance maximization: A numerical assessment

In extreme ambient conditions, engine temperature and air charge temperature (ACT) can be so high that they compromise the vehicular performance. To preserve the structural engine reliability, it is necessary to reduce the load through an increase in the engine speed to maintain the power output, which minimizes fuel conversion efficiency degradation. However, high engine speeds also lead to enhanced friction losses and combustion frequency, which reduces the engine thermal efficiency. Therefore, this work seeks to numerically study the best thermal management strategy to minimize performance losses arising from an engine power derate strategy, while also optimizing the design of a cooling system to withstand extreme engine stress conditions, characteristics of the Davis Dam tests. Different radiator lengths and fan power were numerically simulated to conclude about the most influential parameter on fuel consumption in the FTP-75 + HWFET cycle. The results showed positive effects from the engine power derate strategy, and the engine speed control was able to mitigate up to 23% derate by cooling temperature. There was a 0.8% increase in fuel consumption for every 2.5% aerodynamic drag coefficient increase, which reinforces the need to perform robust thermal management procedures instead of oversizing a vehicular cooling system for high-load operation.

Acceleration, Drive Cycle Efficiency, and Cost Tradeoffs for Scaled Electric Vehicle Drive System

This article investigates and quantifies, for varying drive system ratings (0.5–2.0 times the rating of a small and large reference system), the tradeoff relations between the electric vehicle acceleration performance and energy consumption during a wide range of drive cycles, using detailed load-dependent loss models. Additionally, the results are related to estimated drive system cost by transparently determined scalable electric motor and inverter cost models. When reducing the system rating to half, the cost is 83% of the small reference system and 76% of the large. The acceleration time (0–100 km/h) decreases nonlinearly with increasing system rating. Interestingly, the drive cycle energy consumption generally decreases with decreasing drive system rating, and most cycles show a minimum consumption with a downscaled drive system. For the small system, the strongest impact was noted for the HWFET cycle where the energy consumption is reduced 2% when downscaling the drive system by 0.5 relative to the reference system. For the large system, NYCC shows the largest reduction in energy consumption: 4% when scaled by 1.6 relative to the reference system.

Investigation of integrated uninterrupted dual input transmission and hybrid energy storage system for electric vehicles

Transportation sector is one of the major sources of pollutants as it contributes more than 80% of CO, while almost all HC and 90% of NOx and PM. Although battery electric vehicles are well-known for reducing environmental pollution, the driving range and battery lifespan put a significant barrier to its large-scale commercialization. In this study, an integrated system, which includes an uninterrupted dual input transmission and hybrid energy storage system, is proposed to improve energy efficiency and extend battery lifespan. Given the limitations of dynamic programming in practice, a real-time optimal control strategy is designed to evaluate the power loss and battery capacity degradation of the proposed integrated system based on detailed mathematical models of individual powertrain components. To achieve a desirable trade-off between battery degradation, energy consumption, and acquisition cost, a mixed-integer multi-objective genetic algorithm is implemented to optimize the parameters of the hybrid energy storage system, while Pareto principal is adopted to find the proper solution according to different purposes. The simulation results reveal that the proposed integrated system shows the potential of saving 15.85%–20.82% of the energy consumption in typical driving cycles and more than 22.61%–31.11% Life-cycle cost compared with the single-ratio transmission-based battery electric vehicles. The selected Pareto front can further enhance Life-cycle cost from 26.53% to 28.13% in the HWFET cycle. It can be concluded that the integrated uninterrupted dual input transmission and hybrid energy storage system not only can improve motor efficiency and reduce energy consumption, it also can extend the battery lifespan to decrease Life-cycle cost compared to conventional single-ratio battery-only EV.

Experimental Analysis of Calculation of Fuel Consumption Rate by On-Road Mileage in a 2.0 L Gasoline-Fueled Passenger Vehicle

The five-driving test mode is vehicle driving cycles made by the Environment Protection Association (EPA) in the United States of America (U.S.A.) to fully reflect actual driving environments. Recently, fuel consumption value calculated from the adjusted fuel consumption formula has been more effective in reducing the difference from that experienced in real-world driving conditions, than the official fuel efficiency equation used in the past that only considered the driving environment included in FTP and HWFET cycles. There are many factors that bring about divergence between official fuel consumption and that experienced by drivers, such as driving pattern behavior, accumulated mileage, driving environment, and traffic conditions. In this study, we focused on the factor of causing change of fuel efficiency value, calculated according to how many environmental conditions that appear on the real-road are considered, in producing the fuel consumption formula, and that of the vehicle’s accumulated mileage in a 2.0 L gasoline-fueled vehicle. So, the goals of this research are divided into four major areas to investigate divergence in fuel efficiency obtained from different equations, and what factors and how much CO2 and CO emissions that are closely correlated to fuel efficiency change, depending on the cumulative mileage of the vehicle. First, the fuel consumption value calculated from the non-adjusted formula, was compared with that calculated from the corrected fuel consumption formula. Also, how much CO2 concentration levels change as measured during each of the three driving cycles was analyzed as the vehicle ages. In addition, since the US06 driving cycle is divided into city mode and highway mode, how much CO2 and CO production levels change as the engine ages during acceleration periods in each mode was investigated. Finally, the empirical formula was constructed using fuel economy values obtained when the test vehicle reached 6500 km, 15,000 km, and 30,000 km cumulative mileage, to predict how much fuel consumption of city and highway would worsen, when mileage of the vehicle is increased further. When cumulative mileage values set in this study were reached, experiments were performed by placing the vehicle on a chassis dynamometer, in compliance with the carbon balance method. A key result of this study is that fuel economy is affected by various fuel consumption formula, as well as by aging of the engine. In particular, with aging aspects, the effect of an aging engine on fuel efficiency is insignificant, depending on the load and driving situation.

Fuel economy optimization of power split hybrid vehicles: A rapid dynamic programming approach

Fuel economy of hybrid vehicles is affected by their powertrain configurations, powertrain parameters, and energy management strategies. It is most beneficial to optimizing all the three factors simultaneously. However, when the design search space is large, an exhaustive, optimal control strategy, such as dynamic programming (DP), is too computationally expensive. Hence, a faster optimization method with higher computational efficiency and acceptable accuracy is required. Based on the DP approach, an approximate optimization method, called rapid dynamic programming (Rapid-DP), is developed and discussed in this paper. This method effectively reduces the decision-making time (the time can be reduced by a factor of 700, compared to the DP approach) for optimal control. The optimization processes and results are described and then compared with the original DP and PEARS + methods under two different driving cycles: FTP72 and HWFET. In conjunction with particle swarm optimization (PSO), the rapid-DP is leveraged, for the first time, to optimize key powertrain parameters for power split hybrid electric vehicles. Based on two power-split hybrids: Toyota Prius and Prius++, the joint optimization approach is exploited to examine vehicular fuel savings attributed to synergistic parameters optimization and operating-mode increase. The multi-mode configuration with optimal component parameters is demonstrated to be most fuel-efficient, with 6.56% and 3.15% fuel reductions under FTP72 and HWFET cycles, respectively, with respect to the original Prius 2010.

Electro-thermal analysis of Lithium Iron Phosphate battery for electric vehicles

Lithium ion batteries offer an attractive solution for powering electric vehicles due to their relatively high specific energy and specific power, however, the temperature of the batteries greatly affects their performance as well as cycle life. In this work, an empirical equation characterizing the battery's electrical behavior is coupled with a lumped thermal model to analyze the electrical and thermal behavior of the 18650 Lithium Iron Phosphate cell. Under constant current discharging mode, the cell temperature increases with increasing charge/discharge rates. The dynamic behavior of the battery is also analyzed under a Simplified Federal Urban Driving Schedule and it is found that heat generated from the battery during this cycle is negligible. Simulation results are validated with experimental data. The validated single cell model is then extended to study the dynamic behavior of an electric vehicle battery pack. The modeling results predict that more heat is generated on an aggressive US06 driving cycle as compared to UDDS and HWFET cycle. An extensive thermal management system is needed for the electric vehicle battery pack especially during aggressive driving conditions to ensure that the cells are maintained within the desirable operating limits and temperature uniformity is achieved between the cells.

Dynamic Performance of Automotive Fuel Cell Systems with Low Platinum Loadings

A hierarchical algorithm is formulated to control and analyze the dynamic performance of an automotive polymer electrolyte fuel cell system with low platinum loading. Drive cycle simulations on combined urban (UDDS) and highway (HWFET) schedules indicate that the system performance and stack polarization curves deviate significantly from the steady-state results especially while the stack is initially cold. The dynamic response of the air management subsystem during deceleration can be improved by operating the motor as a motor-generator but the cathode stoichiometry remains high at low loads. A differential pressure sensor and control valves are used to regulate the flow of hydrogen on dynamic loads and to control the buildup of inert impurities in the anode gas channels. With a membrane humidifier, the relative humidity of the cathode air at the stack inlet depends on the power demand and the stack temperature. Anode gas recycle and in-cell water transport across the membrane are sufficient to humidify the feed hydrogen. The heat rejection system can maintain the stack at 75oC on the UDDS and HWFET cycles, but the stack temperature must be allowed to rise under some high power demand driving conditions.

Performance simulation and predictive model for a multi-mode parallel hybrid electric scooter drive

This paper presents a parallel hybrid electric scooter model developed on MATLAB-Simulink. The hybrid electric scooter model was developed to further understand and analyze as well as to predict its performance before proper construction of the prototype begins. The model consists of four main models that formed the complete hybrid scooter model: battery model, engine model, DC motor model and the dynamics model. The multi-mode controller predicts the required parameters to operate the scooter in an optimize condition. Experimental data were gathered and thus compared to the simulated data to check the model's feasibility and accuracy on four distinct driving cycles: modified urban dynamometer driving schedule (UDDS), New York city cycle (NYCC), European driving cycle (ECE-15) and modified highway fuel economy driving schedule (HWFET). Basic studies showed that the model developed had a maximum of 4% error. Maximum error achieved between the theoretical and the experimental readings is about 3.15%, which is for the modified HWFET cycle. Other cycles have acceptable errors; UDDS cycle with 1.91%, NYCC cycle with a low 0.68% and ECE-15 cycle with 1.15%. Operating costs between the internal combustion engine mode, motor mode and hybrid mode were also estimated, where final results showed an 18% reduction of the overall fuel consumption using the multi-mode controller between hybrid scooters and conventional scooters. The fuel consumptions were also predicted and compared the estimated results with one commercially available hybrid scooter, Piaggio Vespa MP3 hybrid scooter. Primary results showed a 4.4% increase in distance travelled per kilometer. Copyright © 2009 John Wiley & Sons, Ltd.

Power and thermal characterization of a lithium-ion battery pack for hybrid-electric vehicles

A 1D electrochemical, lumped thermal model is used to explore pulse power limitations and thermal behavior of a 6 Ah, 72 cell, 276 V nominal Li-ion hybrid-electric vehicle (HEV) battery pack. Depleted/saturated active material Li surface concentrations in the negative/positive electrodes consistently cause end of high-rate (∼25 C) pulse discharge at the 2.7 V cell −1 minimum limit, indicating solid-state diffusion is the limiting mechanism. The 3.9 V cell −1 maximum limit, meant to protect the negative electrode from lithium deposition side reaction during charge, is overly conservative for high-rate (∼15 C) pulse charges initiated from states-of-charge (SOCs) less than 100%. Two-second maximum pulse charge rate from the 50% SOC initial condition can be increased by as much as 50% without risk of lithium deposition. Controlled to minimum/maximum voltage limits, the pack meets partnership for next generation vehicles (PNGV) power assist mode pulse power goals (at operating temperatures >16 °C), but falls short of the available energy goal. In a vehicle simulation, the pack generates heat at a 320 W rate on a US06 driving cycle at 25 °C, with more heat generated at lower temperatures. Less aggressive FUDS and HWFET cycles generate 6–12 times less heat. Contact resistance ohmic heating dominates all other mechanisms, followed by electrolyte phase ohmic heating. Reaction and electronic phase ohmic heats are negligible. A convective heat transfer coefficient of h = 10.1 W m −2 K −1 maintains cell temperature at or below the 52 °C PNGV operating limit under aggressive US06 driving.