Browse Topic: Drive cycles

Items (844)
This work presents a computationally inexpensive but effective method for an initial assessment of component sizing and power-split for fuel cell hybrid electric heavy-duty trucks. As a first step, the proposed method employs a prototypical longitudinal vehicle model to generate power demand at every instant of a representative drive cycle. Subsequently, six fuel cell and battery sizing combinations, each providing a peak continuous system power of 400 kW, are identified based on drive cycle power demands, commercially available fuel cell sizes, and Department of Energy (DOE) sizing targets. Ultimately, for each sizing combination, a proportional-integral (PI) controller with anti-windup is implemented to split power between the fuel cell and battery. In this study, the controller is tuned to reduce hydrogen consumption while meeting the instantaneous power demand and maintaining the battery state-of-charge (SOC) between 0.3 and 0.7. The results indicate that increasing the fuel cell
Mandviwala, AliYesilyurt, SerhatStefanopoulou, Anna
The adoption of hybrid electric vehicles (HEVs) is becoming more popular during the last few years due to government incentives and favourable legislation both for automotive companies and final users. This type of vehicle claims very low carbon dioxide emissions while eliminating the range anxiety associated with battery electric vehicles thanks to the on-board range extender being able to recharge the battery throughout the journey. Unfortunately, the low emissions values are more representative of the particular mathematical model implemented by the legislation than the measured real driving emissions. Specifically, the legislation does not take into account the CO2 embedded in production of the batteries or of the electrical energy stored in it. This work analyses these aspects by means of a numerical model of the BMW i3 94Ah vehicle. The results obtained are collected from simulations conducted over the Worldwide harmonized Light vehicles Test Cycle (WLTC) by using the commercial
Turner, JamesVorraro, Giovanni
An experimental study was conducted on a multi-cylinder engine equipped with both intake and exhaust continuously variable valve duration (CVVD). Due to CVVD and continuous variable valve timing (CVVT), valve closing and opening timings of both intake and exhaust sides became decoupled, so that four valve timings (opening and closing timings of intake as well as exhaust sides) can be optimized under each engine condition. Theses independent valve timings allowed reductions of fuel consumption as well as particle number (PN) and stoichiometry combustion under full-load condition without compromise of performance. In addition, to reduce raw gaseous emissions and shorten light-off time of catalyst under catalyst heating condition, various valve timings were tested in the engine test bench. As results, nitrogen oxides (NOx) – total hydrocarbon (THC) trade-off relation was relieved by optimal valve timings including negative valve overlap duration compared to the base engine. As the last
Jung, JinyoungHan, SangyeonPark, SangjaeKwon, Ki YoungSon, YousangKim, Back-SikKim, Youngnam
This study presents a control co-design method that utilizes a bi-level optimization framework for parallel electric-hydraulic hybrid powertrains, specifically targeting heavy-duty vehicles like class 8 semi-trailer trucks. The primary objective is to minimize battery energy consumption, particularly under high torque demand at low speed, thereby extending both battery lifespan and vehicle driving range. The proposed method formulates a bi-level optimization problem to ensure global optimality in hydraulic energy storage sizing and the development of a high-level energy management strategy. Two nested loops are used: the outer loop applies a Genetic Algorithm (GA) to optimize key design parameters such as accumulator volume and pre-charged pressure, while the inner loop leverages Dynamic Programming (DP) to optimize the energy control strategy in an open-loop format without predefined structural constraints. Both loops use a single objective function, i.e. battery energy consumption
Taaghi, AmirhosseinYoon, Yongsoon
The depletion of fossil fuels and the emergence of global warming propel public sectors to explore alternative energy such as renewable electricity and hydrogen to reduce greenhouse gas (GHG) emissions. Numerous studies have demonstrated substantial environmental benefits of electric light-duty vehicles. However, research focusing on heavy-duty vehicles is still relatively scarce, and the transition to zero emissions heavy-duty trucks is facing enormous technical and economic challenges. This work investigated GHG emissions during the manufacturing and assembly phase of heavy-duty vehicles (HDVs), including battery electric trucks (BETs) and gaseous hydrogen fuel cell electric trucks (FCETs) using SimaPro software package with wildly accepted Ecoinvent database based on UK grid mix scenarios. A comparative analysis of greenhouse gas (GHG) emissions during the production phase of 700 bar- and 350 bar-H2 FCETs and their battery electric counterparts (eqBETs) was conducted under two UK
Zhao, JianboLi, HuBabaie, MeisamLi, Kang
In recent years, simulation-based performance of the models is a highly effective way to finalize the model at design stage itself. But simulation-based models are complex owing to more parameters involved hence resulting in more computational time. With the increasing demand for electric vehicles, the development time for electric vehicle (EV) powertrain is reduced, thereby increasing pressure on original equipment manufacturers (OEMs) to develop products faster. Digital twin is a platform where replication of physical models is made possible with extremely limited data to predict the performance of the model hence providing the most accurate results in a short time. Electric vehicles are the best alternatives for reducing emissions. An Electric vehicle is run by an electric motor which in turn is powered by a battery. Interior permanent magnet synchronous motors (IPMSMs) are the conventional type of motors in electric vehicles because of their high-power density and efficiency. This
Shroff, RoopeshUpase, Balasaheb
This paper implements high-fidelity models to analyze the system-level interactions of high-power traction motor drives in modern battery electric vehicles. With the continuous rise in demand for more hybrid and battery electric vehicles on the road, the performance requirements are becoming more demanding and the time to market is significantly shorter. The stringent cost, efficiency, and power density targets and along with the reduced design/development time, necessitate rapid and high-fidelity models for achieving optimized designs that satisfy the demands. Pulse-width modulation (PWM) strategies such as space vector and discontinuous are used widely in traction applications. The resultant harmonics generated from the inverter lead to increased electromagnetic noise, vibration and harshness (e-NVH) factors such as torque ripple and radial force harmonics, as well as harmonic losses in the stator and rotor. These unintended side effects of PWM are significant and need to be included
Balamurali, AiswaryaMohammadi, HossainMistry, JigarNasirizarandi, Reza
In pursuit of reducing carbon emissions and to fulfill the customers’ needs for fuel-saving and environmentally friendly cars, car manufacturers have been increasingly offering different choices of electrified cars to their customers. Among those different powertrain solutions, with a balance of energy source between on-board electricity and fossil fuels, plug-in hybrid electric vehicles (PHEV) are becoming a choice for more and more end users, particularly in regional car markets such as China in recent years. Owing to the diversified vehicle operating conditions, new challenges are brought to the engine oil to protect the hardware from issues such as piston deposit, water/oil emulsification, oil thinning caused by fuel dilution, stop-start bearing wear and corrosion. This technical paper seeks to understand the impact of different operating modes of PHEV on engine oil performance. One key finding is that extreme conditions were needed to accumulate water content in the oil. When the
Zhang, RuifengAndrew, RhiannMartin, EtienneHu, Gang
To tackle the issue of lacking slope information in urban driving cycles used for vehicle performance evaluation, a construction method for urban ramp driving cycle (URDC) is formulated based on self-organizing map (SOM) neural network. The fundamental data regarding vehicles driving on typical roads with urban ramp characteristics and road slopes were collected using the method of average traffic flow, which were then pre-processed and divided into short-range segments; and twenty parameters that can represent the operation characteristics of vehicle driving on urban ramp were selected as the feature parameters of short-range segments. Dimension of the selected feature parameters was then reduced by means of principal component analysis. And a SOM neural network was applied in cluster analysis to classify the short-range segments. An URDC with velocity and slope information were constructed by combination of short-range segments with highly relevant coefficients according to the
Yin, XiaofengWu, ZhiminLiang, YimingWang, PengXie, Yu
Electrifying truck fleets has the potential to improve energy efficiency and reduce carbon emissions from the freight transportation sector. However, the range limitations and substantial capital costs with current battery technologies imposes constraints that challenge the overall cost feasibility of electrifying fleets for logistics companies. In this paper, we investigate the coupled routing and charge scheduling optimization of a delivery fleet serving a large urban area as one approach to discovering feasible pathways. To this end, we first build an improved energy consumption model for a Class 7-8 electric and diesel truck using a data-driven approach of generating energy consumption data from detailed powertrain simulations on numerous drive cycles. We then conduct several analyses on the impact of battery pack capacity, cost, and electricity prices on the amortized daily total cost of fleet electrification at different penetration levels, considering availability of fast
Wendimagegnehu, Yared TadesseAyalew, BeshahIvanco, AndrejHailemichael, Habtamu
The hybrid electric drive system has the potential to make a significant contribution to the energy sustainability of the automotive industry. This paper investigates the improved adaptive equivalent consumption minimization strategy (A-ECMS) for a multi-mode series-parallel hybrid electric vehicle. First, a basic ECMS algorithm for the series-parallel vehicle is established, which considers the instantaneous optimal torque matching in the electric, serial hybrid, and engine driving modes. Under the condition that the future traffic information scenario is known, it is desired to realize the global optimal planning based on the combination of dynamic programming (DP) and ECMS. The SOC, engine speed, and torque results calculated by the DP strategy are used as benchmarks to develop the improved SOC-AECMS and S-AECMS strategies, which better incorporate the advantages of the global optimization results. Finally, a hardware-in-the-loop simulation platform is set up to validate the real
Zhu, JingyuHan, MengweiLiu, ChongfanYang, ChenfanNishida, Keiya
Plug-in hybrid electric vehicles combine the benefits of both battery electric and internal combustion engine drivetrains. There are multiple possibilities for hybrid configurations, each with its own advantages and disadvantages. In this study, two newly developed traction electric machines were employed alongside a gasoline engine in various hybrid configurations. These configurations, ranging from P1 to P4 and their combinations, were evaluated in terms of vehicle performance, energy consumption, and emissions. The impact of battery capacity was also examined. With a larger battery providing higher discharge power, the electric acceleration time significantly decreases from around 8.6 seconds to approximately 5.2 seconds as the battery capacity increases from 20 kWh to 40 kWh in configurations featuring two traction electric machines. In hybrid mode, the reduction in acceleration time is less pronounced, with a decrease of around 0.7 seconds compared to the configuration with a 20
Nguyen, Duc-KhanhTokat, AlexandraKristoffersson, AnnikaOlsson, Jan-Ola
Measurements of Hydrogen emissions from vehicle exhaust have been often substituted for prediction models, partly due to the lack of Hydrogen analyzers targeted for combustion gases. A previous study using a Hydrogen mass spectrometer revealed that the ratio of Hydrocarbons entering a Three-Way Catalyst (TWC) and Hydrogen leaving the catalyst was inconstant throughout a standardized driving cycle. Although Hydrogen by itself is not currently a target of emission regulations, its omission during catalyzer optimization may disrupt the intended performance of the integrated aftertreatment system. The highest emissions of unwanted gases are commonly seen during vehicle cold start. Thus, this study focuses on intermittent operation of an engine, such as that of full hybrid vehicles. In particular, this study measures how the gases trapped in the aftertreatment system continue to react over the TWC as it cools down after the engine stops. Hydrocarbons, NOx, NH3 and H2 are measured before and
Lamas, Jorge EduardoLacdan, Ma CamilleHara, KenjiOtsuki, Yoshinori
This study presents a comparative analysis of Samsung lithium-ion batteries, which are the INR21700 30T high-power (HP) cell and INR21700 50E high-energy (HE) cell, examining their design differences and performance characteristics. Based on teardown data reported in literature, the HP cell features higher porosity, thicker current collectors, and thinner electrode coatings compared to the HE cell, while the HE cell incorporates approximately 6% silicon oxide in its graphite anode for increased energy density. Cell-level characterization test results demonstrated superior rate capability of the HP cell, maintaining 93.8% of its capacity at 2C discharge, while the HE cell retained 93.4% at 1.6C. The HP cell also exhibited better cycle life stability due to its silicon-free design. Pseudo-two-dimensional (P2D) models were constructed using both teardown experimental parameters and adjusted parameters. Simulation results revealed significant discrepancies using teardown parameters
Yao, QiKollmeyer, PhillipChen, JunranPanchal, SatyamGross, OliverEmadi, Ali
Energy efficient configuration schemes are critical to the fuel economy and power of hybrid vehicles. Single planetary gear (PG) configurations are highly integrated, simple and reliable, but have limited fuel saving potential. To overcome these problems, a new multi-gear power split (PS) powertrain has been proposed because of their high efficiency and excellent overall performance. Only one PG and one synchronizer are required. In order to systematically explore all possible designs of multi-gear-PS hybrid designs, this paper proposes a topological tree graph method: 1) inspired by the “D” matrix automatic modeling method, a new configuration tree matrix is proposed, which is used to complete the isomorphism determination, mode feature classification, and dynamics modeling; a design synthesis method for the multi-gear PS configuration is investigated; 2) A new near-optimal energy management strategy, the improved Rapid-DP (IR-DP), is proposed for the fast computation of the near
Zou, YungeZhang, YuxinYang, Yalian
This paper aims to model and simulate a design specification for a fuel cell electric powertrain tailored for Extreme H motorsport applications. A comprehensive numerical model of the powertrain was constructed using GT-SUITE v2024, integrating the 2025 Extreme H regulations, which include specifications for the fuel cell stack, electric motors, hydrogen storage, and battery systems. A detailed drive cycle representing the real-world driving patterns of Extreme E vehicles was developed, utilizing kinematic parameters derived from literature and real-world data. The performance of the Extreme H powertrain was benchmarked against the Toyota Mirai fuel cell vehicle to validate the simulation accuracy under the same racing conditions. The proposed design delivers a maximum power output of 400 kW, with 75 kW supplied by the fuel cell and 325 kW by the battery, ensuring optimal performance within the constraints set by the Extreme H 2025 regulations. Additionally, the design maintains an
Moreno Medina, JavierSamuel, Stephen
Electric vehicles (EVs) represent a promising solution to reduce environmental issues and decrease dependency on fossil fuels. The main drawback associated with the direct torque control (DTC) scheme is that it is incapable of improving the efficiency and response time of the EVs. To overcome this problem, integrating deep learning (DL) techniques into DTC offers a valuable solution to enhance the performance of the drive system of EVs. This article introduces three control methods to improve the output for DTC-based BLDC motor drives: a traditional proportional–integral for speed controller (speed PI), a neural network fitting (NNF)-based speed controller (speed NNF), and a custom neural (CN) network-based speed controller (speed CN). The NNF and CN are DL techniques designed to overcome the limitations of conventional PI controllers, such as retaining the percentage overshoot, settling times, and improving the system’s efficiency. The CN controller reduced the torque ripple by 15
Patel, SandeshYadav, ShekharTiwari, Nitesh
This paper presents a method for predicting the operating parameters of an FPLG based on the demanded power. First, a 1D FPLG model was developed in AMESim, based on established structural principles and a characterization of stable operation. The model was validated at specific operating points using an experimental prototype. Due to the limited number of available operating points in the prototype, the model boundaries were explored, and the influence of input variables was analyzed. Ultimately, injected mass, spark timing, and injection timing were selected as the primary control parameters. Further analysis examined how variations in these parameters affect the system’s steady-state operation, and the relationship between input parameters, output efficiency, and power was established. Based on this relationship, two rules—optimal efficiency and stable operation—were proposed. These rules were integrated with a three-layer coupled machine learning model to form an FPLG-specific
Zhao, WenboZhang, Zhenyu
The degradation of vehicle performance resulting from powertrain degradation throughout the lifecycle of alternative energy vehicles (AEVs) has consistently been a focal issue among scholars and consumers. The purpose of this paper is to utilize a one-dimensional vehicle simulation model to analyze the changes in power performance and economy of fuel cell vehicles as the Proton Exchange Membrane Fuel Cell (PEMFC) stack degrades. In this study, a simulation model was developed based on the design parameters and vehicle architecture of a 45kW fuel cell vehicle. The 1D model was validated for accuracy using experimental data. The results indicate that as the stack performance degrades, the attenuation rate of the fuel cell engine is further amplified, with a degradation of up to 13.6% in the system's peak output power at the End of Life (EOL) state after 5000 hours. Furthermore, the level of economic performance degradation of the complete vehicle in the EOL state is dependent on the
Li, YouDu, JingGuo, DonglaiWang, KaiWang, Yupeng
The optimization of gear shifting is a critical process in heavy-duty trucks for adjusting engine operating points, enabling a multi-objective balance between power, fuel efficiency, and comfort. However, this process is challenged by the nonlinear characteristics of engine fuel consumption, power interruptions during AMT (Automated Manual Transmission) shifts, and uncertainties in driving conditions. This study proposes a rolling optimization shift strategy for heavy trucks equipped with AMT, based on a multi-scale prediction of internal combustion engine fuel consumption on the road. Firstly, a predictive model for the energy efficiency and dynamics of heavy-duty trucks with AMT was developed, accounting for the engine’s engine’s operating condition points and power interruptions during shifting. Secondly, a future power demand, vehicle speed, and fuel consumption prediction algorithm was designed, iterating based on accelerator pedal position forecasts and dynamic modeling. Finally
Liu, XingyiZhou, QuanyuZhang, LeiboLv, DongxuanSun, XiaopengGao, JinhaoSong, KangXie, Hui
As a clean energy, low carbon and pollution-free, hydrogen is the preferred alternative fuel for traditional internal combustion engines. However, how to use hydrogen internal combustion engine to achieve satisfactory performance under vehicle conditions is still a challenge.In this paper, a vehicle simulation model is established based on a modified 25-ton hydrogen internal combustion engine truck, and the model is designed as a hybrid model by selecting a suitable motor. The two models are used to simulate the CHTC (China Heavy-duty Commercial Vehicle Test Cycle) cycle conditions. According to the simulation results, compared with the original vehicle's power performance and economy, the results show that the power performance is increased by 100%, and the economy is increased by 20%. Hybrid technology can effectively improve the performance of the vehicle.
Bai, Xueyan
Energy management strategy (EMS) based on vehicle speed prediction has been widely used in fuel cell vehicles (FCVs). Actually, not only the actual power demand but also other factors affect the optimal power allocation between fuel cell system (FCS) and battery. However, this relationship is difficult to express in formulas especially under urban conditions because the power demand fluctuates greatly under the above conditions. To address the issue, a novel EMS for FCV based on short-term power demand and FCS output power is proposed. In the offline part, the short-term SOC change rate is used to characterize short-term power allocation. Besides, the average of short-term power demand and the FCS output power are selected as input factors. The feedforward neural network is used to learn the relationship of the above three state variables based on historical driving cycles. In the online part, a long short-term memory (LSTM) network is used to predict the short-term speed based on the
Wu, HuiduoMin, HaitaoZhao, HonghuiSun, Weiyi
This paper proposes a theoretical drive cycle for the competition, considering the battery pack project under design. The vehicle has a non-reversible, double-stage gear train, created without a dynamic investigation. To evaluate the effect on performance, several ratios were analyzed. Dynamic model uses Eksergian’s Equation of Motion to evaluate car equivalent mass (generalized inertia), and external forces acting on the vehicle. The circuit is divided into key locations where the driver is likely to accelerate or brake, based on a predicted behavior. MATLAB ODE Solver executed the numerical integration, evaluating time forward coordinates, creating the drive cycle. Linear gear train results provided data as boundary conditions for a second round of simulations performed with epicyclic gear trains. Model is updated to include their nonlinearity by differential algebraic equation employment with Lagrange multipliers. All data undergoes evaluation to ascertain the mechanical and
Rodrigues, Patrícia Mainardi TortorelliSilveira, Henrique Leandro
In recent years, Lithium Iron Phosphate (LFP) has become a popular choice for Li-ion battery (LIB) chemistry in Electric Vehicles (EVs) and energy storage systems (ESS) due to its safety, long lifecycle, absence of cobalt and nickel, and reliance on common raw materials, which mitigates supply chain challenges. State-of-charge (SoC) is a crucial parameter for optimal and safe battery operation. With advancements in battery technology, there is an increasing need to develop and refine existing estimation techniques for accurately determining critical battery parameters like SoC. LFP batteries' flat voltage characteristics over a wide SoC range challenge traditional SoC estimation algorithms, leading to less accurate estimations. To address these challenges, this study proposes EKF and PF-based SoC estimation algorithms for LFP batteries. A second-order RC Equivalent Circuit Model (ECM) was used as the dynamic battery model, with model parameters varying as a function of SoC and
Ns, Farhan Ahamed HameedJha, KaushalShankar Ram, C S
The transition from Internal Combustion Engine (ICE) Vehicles to Electric Vehicles (EVs) has catalyzed significant advancements in battery technology, prioritizing safer and more reliable energy storage solutions. Although Lithium Iron Phosphate (LFP) batteries are recognized for their safety, they rely on critical and market-volatile elements such as copper, lithium, and graphite. To address these challenges, sodium-ion batteries (SIBs) have emerged as sustainable alternatives that are particularly suited for low-speed EVs. Ensuring the seamless integration of SIBs into EV battery packs necessitates preparedness for the rapid evolution of SIB technology. Model-based approaches, including Equivalent Circuit Models (ECMs), are crucial for developing advanced Battery Management Systems (BMSs) and State of Charge (SoC) estimation algorithms that enable precise battery control. This study comprehensively evaluates various order Resistance-Capacitance (RC) ECM configurations to accurately
Ns, Farhan Ahamed HameedGupta, ShubhamJha, Kaushal
The automotive industry faces significant obstacles in its efforts to improve fuel economy and reduce carbon dioxide emissions. Current conventional automotive powertrain systems are approaching their technical limits and will not be able to meet future carbon dioxide emission targets as defined by the tank-to-wheel benchmark test. As automakers transition to low-carbon transportation solutions through electrification, there are significant challenges in managing energy and improving overall vehicle efficiency, particularly in real-world driving scenarios. While electrification offers a promising path to low-carbon transportation, it also presents significant challenges in terms of energy management and vehicle efficiency, particularly in real-world scenarios. Battery electric vehicles have a favorable tank-to-wheel balance but are constrained by limited range due to the low battery energy density inherent in their technology. This limitation has led to the development of hybrid
Kraljevic, IvicaSpicher, Ulrich
Vehicle electrification has gained prominence in various industries and offers sustainability opportunities, especially in the context of heavy-duty vehicles such as school buses. Despite the prevalence of conventional diesel school buses (CDSB), the adoption of electric school bus (ESB) and other eco-friendly alternatives is increasing. In the United States alone, there has been a notable increase in the adoption of ESBs, indicating substantial growth. The electrification of school buses not only promises energy savings, but also offers health benefits to children, reduced greenhouse gas emissions, and environmentally friendly transportation practices, aligned with broader eco-friendly initiatives. This paper investigates the potential for energy savings and reduction in environmental footprint through electrification of school buses in the Columbus, OH area. Analyzing current bus routes and road terrain data allows one to estimate energy demand and environmental impact, accounting
Moon, JoonHanif, AtharAhmed, Qadeer
For a three-wheeler, this research studies the aging effects on an LFP battery across a realistic three-wheeler commercial vehicle cycle simulated in GT-SUITE. The study evaluates how thermal management affects battery aging with different battery cooling methods and triggering temperatures for cooling activation. The three-wheeler analysis cycle includes a real-world drive cycle, followed by battery recharging, and then a rest period. This sequence repeats until the battery ages to 80% of its original capacity (end of life). Battery life is determined using various methods of battery cooling and the temperatures that trigger the activation of cooling mechanisms. Different heat transfer coefficients (HTCs) are derived or assumed based on the cooling method used.
Chandna, AshishChopra, Ujjwal
In an electric vehicle, nevertheless, the primary component is the electric motor (e-motor). Understanding the thermal performance of the e-motor is paramount in ensuring the overall efficient functioning of the electric vehicle. Usually, the high-power e-motors are oil-cooled due to relatively high thermal loads. The e-motor thermal response is monitored under extreme conditions like warm-up cycle allowing the vehicle to move in a circular track multiple-times. In this condition, the vehicle undergoes heavy lateral and longitudinal accelerations, the e-motor speed varies and the consequent thermal losses from the rotor and stator components also vary accordingly. Importantly, the cooling oil sloshes rigorously that affects the heat removal capacity of the oil. The advanced capabilities of Computational Fluid Dynamics (CFD) allow to virtually simulate the warm-up cycle and capture the extremely transient thermal response of the e-motor in the given conditions. In the current effort, a
Pasunurthi, Shyam SundarSrinivasan, ChiranthChaudhari, NiravMaiti, Dipak
Nowadays, Hybrid Electric Vehicles (HEVs) and Electric Vehicles (EVs) are becoming popular globally due to increasing pollution levels in the environment and expensive conventional non-renewable fuels. Li-ion battery EV’s have gained attention because of their higher specific energy density, better power density and thermal stability as compared to other cell chemistries. Performance of the Li-ion battery is affected by temperatures of the cells. For Li-ion cells, optimum operating temperature range should be between 15-35 °C [1]. Initially, small battery packs which are cooled by air were used but nowadays, large battery packs with high power output capacities being used in EV’s for higher vehicle performance. Air based cooling system is not sufficient for such batteries, hence, liquid coolant based cooling systems are being introduced in EV’s. Computational Fluid Dynamics (CFD) simulation can be used to get better insight of cell temperature inside battery. But it is complex, time
Kumar, VivekSHENDRE, Mohit
The aim of paper is to present the workflow of battery sizing for electric L7e-CU type vehicle. The intention is to use it as last-mile delivery multi-purpose vehicle. Based on legislation limits and pursuing the real-world driving cycle, major vehicle characteristics as total vehicle mass including payload and wheel size are determined. Vehicle total energy consumption is calculated knowing vehicle power in time. Accordingly, to selected gearbox ratio the electric motor nominal power-speed curve is defined as well as the nominal torque-speed curve. Applying vehicle acceleration dynamics involving limits considering resistive forces, acting on the vehicle, e.g. slope, friction, air drag, and total inertia, referred to the electric motor through the gearbox the electric motor over-load-ability characteristics are calculated. Next, the motor design is defined and optimized. Defining required vehicle range at given driving cycle and knowing the vehicle and all powertrain characteristics
Rupnik, UrbanVukotić, MarioManko, RomanAlić, AlenČorović, SelmaMiljavec, Damijan
Electrification or hybridization of commercial vehicles offers a promising avenue for mitigating emissions in urban environments. This concept is particularly applicable to waste collection vehicles, which move in urban contexts along repeatedly chosen driving cycles. Municipal waste collection and transport are functional tasks which have a significant impact on the urban environment in terms of energy consumption and CO2 emissions. In this work, the evaluation of a full-electric powertrain was carried out for a small size waste collection vehicle operating in the historic center of the city of Perugia (Italy). First, the vehicle model was developed and validated against literature data using a full-electric powertrain. The model allows to evaluate energy consumption and system efficiency considering the real driving path and the mass variation due to the waste collected during the route. Real driving data (position, slope, collection stops) were obtained through an experimental
Zembi, JacopoBistoni, LorenzoCinti, GiovanniCastellani, BeatriceBattistoni, Michele
Light commercial vehicles are an indispensable element for the transport of people and the delivery of goods, especially on extra-urban and long-distance routes. With a view to sustainable mobility, it is necessary to think about hybridizing these vehicles to reduce the fuel consumption as well as greenhouse gas emissions and particulate matter. These types of vehicles are generally powered by diesel and travel many kilometers a day. On the other hand, the use of light commercial vehicles in battery electric vehicle (BEV) configuration has already been started but is not receiving widespread recognition. In this panorama, starting from a study already developed for the hybridization of a plug-in light commercial vehicle in Worldwide harmonized Light vehicles Test Cycle (WLTC) condition, the simulation analysis has been extended to the plug-in hybrid vehicle (PHEV) operating in real driving emission conditions (RDE). In particular, using Advisor software, a vehicle has been simulated in
Mancaruso, EzioMeccariello, GiovanniRossetti, Salvatore
Electric and hybrid powertrains are steadily gaining popularity, showcasing their efficacy in reducing greenhouse gas emissions and pollution, particularly in urban environments. This also applies to medium and heavy-duty vocational trucks. Truck manufacturers have been expanding their electrified portfolio and some of them have already announced their plans to phase out fossil fuels. Vocational trucks are essential for the industry of commercial vehicles, represent an extremely heterogeneous class, and are often upfitted by third-party companies. In general, vocational trucks are designed for specific jobs. Typically, they are driven on short routes, but they may work for longer hours in comparison to freight transportation vehicles. Most importantly, among the broad category of vocational trucks, some vehicles greatly exploit power take-offs to drive auxiliary systems, like refuse trucks, utility trucks, cement trucks, and sweeper trucks. The benefits resulting from the kinetic
Beltrami, DanieleVillani, ManfrediIora, PaoloRizzoni, GiorgioUberti, Stefano
Growing environmental concerns drive the increasing need for a more climate-friendly mobility and pose a challenge for the development of future powertrains. Hydrogen engines represent a suitable alternative for the heavy-duty segment. However, typical operation includes dynamic conditions and the requirement for high loads that produce the highest NOx emissions. These emissions must be reduced below the legal limits through selective catalytic reduction (SCR). The application of such a control system is time-intensive and requires extensive domain knowledge. We propose that almost human-like control strategies can be achieved for this virtual application with less time and expert knowledge by using Deep Reinforcement Learning. A proximal policy optimization (PPO) -based agent is trained to control the injection of Diesel exhaust fluid (DEF) and compared with the performance of a manually tuned controller. The performance is evaluated based on the restrictive emission limits of a
Itzen, DirkAngerbauer, MartinHagenbucher, TimoGrill, MichaelKulzer, Andre
Sustainable mobility is a pressing challenge for modern society. Electrification of transportation is a key step towards decarbonization, and hydrogen Fuel Cell Hybrid Electric Vehicles (FCHEVs) offer a promising alternative to Battery Electric Vehicles (BEVs), especially for long-range applications: they combine a battery system with a fuel cell, which provides onboard electric power through the conversion of hydrogen. Paramount importance is then given to the design and sizing of the hybrid powertrain for achieving a compromise between high performance, efficiency, and low cost. This work presents a Hardware-in-the-Loop (HIL) platform developed for designing and testing the powertrain layout of an FCHEV. The platform comprises two systems: a simulation model reproducing the dynamics of a microcar and a hardware system for the fuel cell hybrid electric powertrain. The former simulates the vehicle's behavior, while the latter is composed of a 2kW real fuel cell stack and a 100Ah Li-ion
Bartolucci, LorenzoCennamo, EdoardoCordiner, StefanoDonnini, MarcoGrattarola, FedericoMulone, Vincenzo
The need to reduce vehicle-related emissions in the great cities has led to a progressive electrification of urban mobility. For this reason, during the last decades, the powertrain adopted for urban buses has been gradually converted from conventional Internal Combustion Engine (ICE), diesel, or Compressed Natural Gas (CNG), to hybrid or pure electric. However, the complete electrification of Heavy-Duty Vehicles (HDVs) in the next years looks to be still challenging therefore, a more viable solution to decarbonize urban transport is the hybrid powertrain. In this context, the paper aims to assess, through numerical simulations, the benefits of a series hybrid-electric powertrain designed for an urban bus, in terms of energy consumption, and pollutants emissions. Particularly a Diesel engine, fueled with pure hydrogen, is considered as a range extender. The work is specifically focused on the design of the Energy Management Strategy (EMS) of the series-hybrid powertrain, by comparing
Nacci, GianlucaCervone, DavideFrasci, EmmanueleLAKSHMANAN, Vinith KumarSciarretta, AntonioArsie, Ivan
Accurate estimation of vehicle energy consumption plays an important role in developing advanced energy-saving connected automated vehicle technologies such as Eco Approach and Departure, PHEV mode blending, and Eco-route planning. The present study developed a reduced-order energy model with second-order response surfaces and torque estimation to estimate the energy consumption while just relying on the drive cycle information. The model is developed for fully electric Chevrolet Bolt using chassis dynamometer data. The dyno test data encompasses the various EPA test cycles, real-world, and aggressive maneuvers to capture most powertrain operating conditions. The developed model predicts energy consumption using vehicle speed and road-grade inputs for a drive cycle. The accuracy of the model is validated by comparing the prediction results against track and road test data. The developed model was able to accurately predict the energy consumption for track drive cycles within the error
Goyal, VasuDudekula, Ahammad BashaStutenberg, KevinRobinette, DarrellOvist, GrantNaber, Jeffery
Test cycle simulation is an essential part of the vehicle-in-the-loop test, and the deep reinforcement learning algorithm model is able to accurately control the drastic change of speed during the simulated vehicle driving process. In order to conduct a simulated cycle test of the vehicle, a vehicle model including driver, battery, motor, transmission system, and vehicle dynamics is established in MATLAB/Simulink. Additionally, a bench load simulation system based on the speed-tracking algorithm of the forward model is established. Taking the driver model action as input and the vehicle gas/brake pedal opening as the action space, the deep deterministic policy gradient (DDPG) algorithm is used to update the entire model. This process yields the dynamic response of the output end of the bench model, ultimately producing the optimal intelligent driver model to simulate the vehicle’s completion of the World Light Vehicle Test Cycle (WLTC) on the bench. The results indicate that the
Gong, XiaohaoLi, XuHu, XiongLi, Wenli
On the path to decarbonizing road transport, electric commercial vehicles will play a significant role. The first applications were directed to the smaller trucks for distribution traffic with relatively moderate driving and range requirements. Meanwhile, the first generation of a complete portfolio of truck sizes has been developed and is available on the market. In these early applications, many compromises were made to overcome component availability, but today, the supply chain has evolved to address the specific needs of electric trucks. With that, optimization toward higher performance and lower costs is moving to the next level. For long-haul trucks, efficiency is a driving factor for the total cost of ownership (TCO) due to the importance of the energy costs [1]. Besides the propulsion system, other related systems must be optimized for higher efficiency. This includes thermal management since the thermal management components consume energy and have a direct impact on the
Gajowski, DanielWenzel, WolfgangHütter, Matthias
Homologation is an important process in vehicle development and aerodynamics a main data contributor. The process is heavily interconnected: Production planning defines the available assemblies. Construction defines their parts and features. Sales defines the assemblies offered in different markets, where Legislation defines the rules applicable to homologation. Control engineers define the behavior of active, aerodynamically relevant components. Wind tunnels are the main test tool for the homologation, accompanied by surface-area measurement systems. Mechanics support these test operations. The prototype management provides test vehicles, while parts come from various production and prototyping sources and are stored and commissioned by logistics. Several phases of this complex process share the same context: Production timelines for assemblies and parts for each chassis-engine package define which drag coefficients or drag coefficient contributions shall be determined. Absolute and
Jacob, Jan D.
Society is moving towards climate neutrality where hydrogen fuelled combustion engines (H2 ICE) could be considered a main technology. These engines run on hydrogen (H2) so carbon-based emission are only present at a very low level from the lube oil. The most important pollutants NO and NO2 are caused by the exhaust aftertreatment system as well as CO2 coming from the ambient air. For standard measurement technologies these low levels of CO2 are hard to detect due to the high-water content. Normal levels of CO2 are between 400-500 ppm which is very close or even below the detection limit of commonly used non-dispersive-infrared-detectors (NDIR). As well the high-water content is very challenging for NOx measuring devices, like chemiluminescence detectors (CLD), where it results in higher noise and therefore a worse detection limit. Even for Fourier-transformed-infrared-spectroscopy-analysers (FT-IR) it is challenging to deal with water content over 15% without increased noise. The goal
Jakubec, PhilippRoiser, Sebastian
In response to global climate change, there is a widespread push to reduce carbon emissions in the transportation sector. For the difficult to decarbonize heavy-duty (HD) vehicle sector, hybridization and lower carbon-intensity fuels can offer a low-cost, near-term solution for CO2 reduction. The use of natural gas can provide such an alternative for HD vehicles while the increasing availability of renewable natural gas affords the opportunity for much deeper reductions in net-CO2 emissions. With this in consideration, the US National Renewable Energy Laboratory launched the Natural Gas Vehicle Research and Development Project to stimulate advancements in technology and availability of natural gas vehicles. As part of this program, Southwest Research Institute developed a hybrid-electric medium-HD vehicle (class 6) to demonstrate a substantial CO2 reduction over the baseline diesel vehicle and ultra-low NOx emissions. The development included the conversion of a 5.2 L diesel engine to
Wallace, JulianMitchell, RobertRao, SandeshJones, KevinKramer, DustinWang, YanyuChambon, PaulSjovall, ScottWilliams, D. Ryan
For battery-electric vehicles (BEVs), the climate control and the driving range are crucial criteria in the ongoing electrification of automobiles in Europe towards the targeted carbon neutrality of the automotive industry. The thermal management system makes an important contribution to the energy efficiency and the cabin comfort of the vehicle. In addition to the system architecture, the refrigerant is crucial to achieve high cooling and heating performance while maintaining high efficiency and thus low energy consumption. Due to the high efficiency requirements for the vehicle, future system architectures will largely be heat pump systems. The alternative refrigerant R-474A based on the molecule R-1132(E) achieved top performance for both parameters in various system and vehicle tests. An own-built energy efficiency tool was used to determine the possible energy reduction of R-474A in comparison to refrigerant alternatives that can be translated in a CO2 reduction of the thermal
Macrì, ChristianDe León, ÁlvaroFlohr, Felix
In recent years, the urgent need to fully exploit the fuel economy potential of Electrified Vehicles (xEVs) through the optimal design of their Energy Management System (EMS) has led to an increasing interest in Machine Learning (ML) techniques. Among them, Reinforcement Learning (RL) seems to be one of the most promising approaches thanks to its peculiar structure in which an agent learns the optimal control strategy by interacting directly with an environment, making decisions, and receiving feedback in the form of rewards. Therefore, in this study, a new Soft Actor-Critic (SAC) agent, which exploits a stochastic policy, was implemented on a digital twin of a state-of-the-art diesel Plug-in Hybrid Electric Vehicle (PHEV) available on the European market. The SAC agent was trained to enhance the fuel economy of the PHEV while guaranteeing its battery charge sustainability. The proposed control strategy's potential was first assessed on the Worldwide harmonized Light-duty vehicles Test
Rolando, LucianoCampanelli, NicolaTresca, LuigiPulvirenti, LucaMillo, Federico
Driving schedule of every vehicle involves transient operation in the form of changing engine speed and load conditions, which are relatively unchanged during steady-state conditions. As well, the results from transient conditions are more likely to reflect the reality. So, the current research article is focused on analyzing the biofuel-like lemon peel oil (LPO) behavior under real-world transient conditions with fuel injection parameter MAP developed from steady-state experiments. At first, engine parameters and response MAPs are developed by using a response surface methodology (RSM)-based multi-objective optimization technique. Then, the vehicle model has been developed by incorporating real-world transient operating conditions. Finally, the developed injection parameters and response MAPs are embedded in the vehicle model to analyze the biofuel behavior under transient operating conditions. The results obtained for diesel-fueled light commercial vehicle (LCV) have shown better
Saiteja, PajarlaAshok, B.
Battery-electric vehicles (BEVs) require new chassis components, which are realized as mechatronic systems mainly and support more and more by-wire functionality. Besides better controllability, it eases the implementation of integrated control strategies to combine different domains of vehicle dynamics. Especially powertrain layouts based on electric in-wheel machines (IWMs) require such an integrated approach to unfold their full potential. The present study describes an integrated, longitudinal vehicle dynamics control strategy for a battery electric sport utility vehicle (SUV) with an electric rear axle based on in-wheel propulsion. Especially the influence of electronic brake force distribution (EBD) and torque blending control on the overall performance are discussed and demonstrated through experiments and driving cycles on public road and benchmarked to results of previous studies derived from [1]. It is shown that the approach improves energy efficiency and energy recovery
Heydrich, MariusMitsching, ThomasGramstat, SebastianLenz, MatthiasIvanov, Valentin
The National Highway Traffic Safety Administration (NHTSA) plays a crucial role in guiding the formulation of Corporate Average Fuel Economy (CAFE) standards, and at the forefront of this regulatory process stands Argonne National Laboratory (Argonne). Argonne, a U.S. Department of Energy (DOE) research institution, has developed Autonomie—an advanced and comprehensive full-vehicle simulation tool that has solidified its status as an industry standard for evaluating vehicle performance, energy consumption, and the effectiveness of various technologies. Under the purview of an Inter-Agency Agreement (IAA), the DOE Argonne Site Office (ASO) and Argonne have assumed the responsibility of conducting full-vehicle simulations to support NHTSA's CAFE rulemaking initiatives. This paper introduces an innovative approach that hinges on a large-scale simulation process, encompassing standard regulatory driving cycles tailored to various vehicle classes and spanning diverse timeframes. What sets
Islam, Ehsan SabriKim, NamdooVijayagopal, RamMoawad, AymanRousseau, AymericSeitz, Matthew
Performances of battery electric vehicles (BEV) are affected by the thermal imbalance in the battery packs under driving cycles. BEV thermal management system (VTMS) should be managed efficiently for optimal energy consumption and cabin comfort. Temperature changes in the brick, module, and pack under the repeated transient cycles must be understood for model-based development. The authors conducted chassis dynamometer experiments on a fully electric small crossover sports utility vehicle (SUV) to address this challenge. A BEV is tested using a hub-type, 4-wheel motor chassis dynamometer with an air blower under the Worldwide Harmonized Light Vehicles Test Cycle (WLTC) and Federal Test Procedures (FTP) with various ambient temperatures. The mid-size BEV with dual-motor featured 80 thermocouples mounted on the 74-kWh battery pack, including the cells, upper tray, side cover, and pack cover. The authors analyzed battery pack temperature distribution behavior by changing the battery’s
Nandagopal, KamaleshwarSok, RatnakKishida, KentaroOtake, TomohiroKusaka, Jin
With the increasing demand for Battery Electric Vehicles (BEVs) capable of extended mileage, optimizing their efficiency has become paramount for manufacturers. However, the challenge lies in balancing the need for climate control within the cabin and precise thermal regulation of the battery, which can significantly reduce a vehicle's driving range, often leading to energy consumption exceeding 50% under severe weather conditions. To address these critical concerns, this study embarks on a comprehensive exploration of the impact of weather conditions on energy consumption and range for the 2019 Nissan Leaf Plus. The primary objective of this research is to enhance the understanding of thermal management for BEVs by introducing a sophisticated thermal management system model, along with detailed thermal models for both the battery and the cabin. These models are seamlessly integrated into a 2019 Nissan Leaf Plus BEV model developed in Autonomie, allowing for a holistic assessment of
Al Haddad, RabihMansour, CharbelKim, NamdooSeo, JiguNemer, Maroun
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