Browse Topic: Vehicles, Equipment, and Performance

Items (59,311)
Electric vehicles (EVs) are particularly susceptible to high-frequency noise, with rubber eigenmodes significantly influencing these noise characteristics. Unlike internal combustion engine (ICE) vehicles, EVs experience pronounced variations in dynamic preload during torque rise, which are substantially higher. This dynamic preload variation can markedly impact the high-frequency behaviour of preloaded rubber bushings in their installed state. This study investigates the effects of preload and amplitude on the high-frequency dynamic performance of rubber bushings specifically designed for EV applications. These bushings are crucial for vibration isolation and noise reduction, with their role in noise, vibration, and harshness (NVH) management being more critical in EVs due to the absence of traditional engine noise. The experimental investigation examines how preload and excitation amplitude variations influence the dynamic stiffness, damping properties, and overall performance of
Hazra, SandipKhan, Arkadip Amitava
Abstract The technological advancements in the automotive industry have seen a significant leap with the introduction of automated driving system (ADS)-equipped Vehicles (AVs), with potential for enhanced safety, efficiency, and mobility. As the development of an AV transitions from the stages of conceptual design to deployment, assessing the maturity of the technology through a structured framework is crucial. This paper proposes the adaptation of the Technology Readiness Level (TRL) framework originally developed by NASA (and adopted widely in a variety of industries) to the AV industry to provide a consistent, understandable, and transparent method to describe an AV product’s stage of development. The TRL framework is mated to the existing safety case framework (SCF) developed in the Automated Vehicle – Test and Evaluation Process (AV-TEP) Mission, a collaboration between Science Foundation Arizona and Arizona State University. The claim that the AV is ready to transition from one
Swaminathan, SunderWishart, JeffreyZhao, JunfengRusso, BrendanRahimi, Shujauddin
Electric vehicles (EVs) are gaining popularity due to their zero tailpipe emissions, superior energy efficiency, and sustainable nature. EVs have various limitations, and crucial one is the occurrence of thermal runaway in the battery pack. During charging or discharging condition of battery pack may result in thermal runaway condition. This promotes the requirement of effective cooling arrangement in and around the battery pack to avoid localized peak temperature. In the present work, thermal management of a 26650 Lithium iron phosphate (LFP) cell using natural convection air cooling, composite biobased phase change material (CBPCM) and its combination with copper fins is numerically investigated using multi-scale multi dimension - Newman, Tiedenann, Gu and Kim (MSMD-NTGK) battery model in Ansys Fluent at an ambient temperature of 306 K. Natural convection air cooling was found effective at discharge rates of 1C to 3C, maintaining cell temperature below the safe limit of 318 K for 80
Srivastav, DurgeshPatil, Nagesh DevidasShukla, Pravesh Chandra
This study investigates the influence of magnetorheological (MR) dampers in semi-active suspension systems (SASSs) on ride comfort, vehicle stability, and overall performance. Semi-active suspension systems achieve greater flexibility and efficacy by combining MR dampers with the advantages of active and passive suspension systems. The study aims to measure the benefits of MR dampers in improving ride comfort, vehicle stability, and overall system performance. The dynamic system model meets all required performance criteria. This study demonstrates that the proposed artificial intelligence approach, including a fuzzy neural networks proportional-integral-derivative (FNN-PID) controller, significantly enhances key performance criteria when tested under various road profiles. The control performance requirements in engineering systems are evaluated in the frequency and time domains. A quarter-car model with two degrees of freedom (2 DOF) was simulated using MATLAB/Simulink to assess the
M.Faragallah, MohamedMetered, HassanAbdelghany, M.A.Essam, Mahmoud A.
Optimizing engine mounting systems is a complex task that requires balancing the isolation of vehicle vibrations with controlling powertrain movement within a limited dynamic envelope. Six Degrees of Freedom (6DOF) optimization is widely used for mounting stiffness and location optimization. This study investigates the application of various optimization algorithms for 6DOF analysis in engine mount design, where the system’s stochastic behaviour and probabilistic characteristics present additional challenges. Selecting an appropriate optimization framework is essential for achieving accurate and efficient NVH results. Recent advancements in research have introduced several 6DOF optimization algorithms to determine the optimal stiffness and location of engine mounts. The study evaluates a range of optimization methods, including Simultaneous Hybrid Exploration that is Robust, Progressive and Adaptive (SHERPA), Quadratic Programming (QP), Genetic Algorithm (GA), Particle Swarm
Hazra, SandipKhan, Arkadip
The Autocycle is a style of vehicle that most often utilizes a reverse-tricycle design with two front wheels and a single rear wheel. Modern autocycles in the United States are often utilized in a recreational role. This work presents physical measurements of two modern autocycles for use in accident reconstruction and pursues a deeper understanding of the unique attributes and handling associated with these vehicles. Vehicles were used to measure physical properties and subjected to cornering tests presented herein, and the data is compared to that for a conventional automobile. Observations on tire scuff marks are made from cornering tests unique to these vehicles. Strengths and challenges with this type of vehicle design are presented for various use cases as compared to conventional automobiles. Data and knowledge from this study are published to aid accident reconstruction efforts.
Warner, WyattSwensen, GrantWarner, Mark
Electric vehicles (EVs) have experienced significant growth, and the battery safety of EVs has drawn increased attention. However, the mechanical responses of battery during crashes have rarely been studied. Hence, the objective of this study was to understand EV battery package mechanics during side-pole crashes at different impact locations and speeds beyond regulated side-pole test with one specific speed and one location. An EV finite element (FE) model with a battery package was used. Side-pole impact simulations were conducted at four impact locations, including the baseline impact location according to side-pole impact regulation, plus three positions by moving the rigid pole 400 mm toward the back of the EV and moving the pole 400 and 800 mm toward the front of the EV. In addition, the impact velocities at 32, 50, and 80 km/h were simulated. Based on simulations, the peak relative displacement, the maximum change in gap between batteries, the maximum change in gap between the
Chen, JianBian, KeweiMao, Haojie
In driving condition, the electric drive system of electric vehicles generates significant heat, which increases temperature of the motor, leading to reduced performance and energy loss. To manage the motor temperature and recover energy, a plate-fin heat exchanger (PFHE) is used to facilitate heat exchange between the electric drive system and the vehicle's thermal management system. In this study, Computational Fluid Dynamics (CFD) method was used to investigate the fin structure on thermal flow performance within the PFHE. The mathematical models of pressure drop and heat transfer of plate-fin heat exchanger are established in this paper, and an empirical formula for the friction factor was derived by using test data. The NTU method was applied to fit the formula of convective heat transfer coefficient, enabling the derivation of an empirical formula for the Colburn factor. A CFD simulation model was developed for a local heat exchange unit, considering the generic boundary
Yin, JintaiYin, ZhihongLu, XuanWang, MengmengLiu, Qian
With increasing attention to complex aerodynamic conditions such as crosswinds, gusts, road turbulence, and vehicle drafting, accurately reconstructing these unsteady and turbulent environments in automotive wind tunnels has become a significant challenge. Addressing this challenge is crucial for broadening experimental conditions and advancing research in unsteady aerodynamics. However, the integration of turbulence generation systems impacts low-frequency fluctuation phenomena, leading to pressure and velocity inaccuracy, and also affects the flow structure in the test section as well, especially in the jet shear layer. In this paper, the impact of an active turbulence generation system on turbulence characteristics and flow structures within jet shear layer in a wind tunnel is numerically investigated. By comparing the flow structure among the empty wind tunnel, and wind tunnel with static and dynamic active turbulence generation system, the mechanisms underlying these
Jia, QingQin, LanweiZhao, CivilWang, YikunXia, ChaoYang, ZhigangWei, Huanxia
The improvement of heat dissipation performance of ventilated brake discs is vital to braking safety. Usually, the technical approaches shall be material optimization or structural improvement. In this paper, a simulation model of the heat transfer of brake discs is established using STAR-CCM+ software. Cast iron, aluminum metal matrix composite (Al-MMC), and carbon-ceramic composite materials (C-SiC) are compared. The results show that: Al-MMC has better thermal conductivity so that a more uniform temperature gradient distribution shall be formed; C-SiC has poorer heat capacity yet, according to previous studies, it has better thermal stability, which is the ability to ensure its friction factor under high-temperature condition; cast iron performs better with convective heat transfer rate, which enhances the heat transfer between the surface and surrounding flow field. Based on the results, this paper proposes four types of material combined brake discs using different friction
Wang, JiaruiJia, QingZhao, WentaoXia, ChaoYang, Zhigang
Model-Based Systems Engineering (MBSE) enables requirements, design, analysis, verification, and validation associated with the development of complex systems. Obtaining data for such systems is dependent on multiple stakeholders and has issues related to communication, data loss, accuracy, and traceability which results in time delays. This paper presents the development of a new process for requirement verification by connecting System Architecture Model (SAM) with multi-fidelity, multi-disciplinary analytical models. Stakeholders can explore design alternatives at a conceptual stage, validate performance, refine system models, and take better informed decisions. The use-case of connecting system requirements to engineering analysis is implemented through ANSYS ModelCenter which integrates MBSE tool CAMEO with simulation tools Motor-CAD and Twin Builder. This automated workflow translates requirements to engineering simulations, captures output and performs validations. System
Upase, BalasahebShroff, Roopesh
Abstract Real-world driving data is an invaluable asset for several types of transportation research, including emissions estimation, vehicle control development, and public infrastructure planning. Traditional methods of real-world driving data collection use expensive GPS-based data logging equipment which provide advanced capabilities but may increase complexity, cost, and setup time. This paper focuses on using the Google Maps application available for smartphones due to the potential to scale-up real-world driving data logging. Samples of the potential data processing and information that can be gathered by such a logging methodology is presented. Specifically, two months of Google Maps driving data logged by a rural Michigan resident on their smartphone may provide insights on their driving range, duration, and geographic area of coverage (AOC) to guide them on future vehicle purchase decisions. Aggregating such statistics from crowd-sourcing real-world driving data via Google
Manoj, AshwinYin, SallyAhmed, OmarVaishnav, ParthStefanopoulou, AnnaTomkins, Sabina
As one of the most important design choices in the powertrain design cycle, motor selection is conventionally performed according to given automotive requirements. Motor-related powertrain design parameters like gear ratio, power output ratio between different axles, are excluded from the motor design process. In this paper, three comparative studies are performed to investigate the impact of these motor-related powertrain design parameters on the motor performance and the weight/cost/efficiency of the entire EV powertrain. In the first study, three PM motor designs—characterized by high, medium, and low rated speeds—will be assessed for a two-axle EV using various gear ratio configurations. The same motor design will be used for both axles. In the second study, five motor designs with varying power and ratings (PM, non-PM) but identical rated speeds will be evaluated for a two-axle EV, permitting different power ratings for the front and rear axles. The design trade-offs between motor
Movahed, EhsanGodbehere, JonathanJia, Yijiang
In the domain of new energy vehicles, the role of the bidirectional DC/DC converter holds great significance. Based on the two-phase interleaved parallel BOOST topology, this paper adopts the approach of combining the double-loop PI controller with the feedforward control algorithm respectively from the aspects of following the target voltage and response speed, and conducts research on the performance of the DC/DC converter in BOOST mode in terms of output voltage overshoot, steady-state error, and system adjustment time. The test results fully validate the feasibility and effectiveness of the design scheme. The test results indicate that the double-loop PI control + feedforward control method accelerates the circuit response speed, reduces the steady-state error, and significantly reduces the input/output current ripple, fully verifying the feasibility and effectiveness of the control method. Furthermore, regarding the overvoltage issue that occurs after a large accelerator pedal in
Jing, JunchaoLiu, YiqiangZuo, BotaoHuang, WeishanDai, Zhengxing
Roller bearings are used in many rotating power transmission systems in the automotive industry. During the assembly process of the power transmission system, some types of roller bearings (e.g., tapered roller bearings) require a compressive preload force. Those bearings' rolling resistance and lifespan strongly depend on the preload set during the installation process. Therefore, accurate preload setting can improve bearing efficiency, increase bearing lifespan, and reduce maintenance costs over the life of the vehicle. A new method for bearing preload measurement has shown potential for high accuracy and fast cycle time using the frequency response characteristics of the power transmission system. One open problem is the design of the production controller, which relies on a detailed sensitivity study of the system frequency response to changes in the bearing and system design parameters. Recently, an analytical model was developed for multi-row tapered roller bearings that includes
Gruzwalski, DavidMynderse, James
As the agricultural industry seeks to enhance sustainability and reduce operational costs, the introduction of mild hybrid technology in tractors presents a promising solution. This paper focuses on downsizing internal combustion (IC) engine, coupled with integration of electric motor, to reduce fuel consumption and meet stringent emission regulations while maintaining power requirement for agricultural applications in India. The hybridization aims to deliver instant power boosts during peak loads and capitalizes on energy recovery during part loads and braking. Furthermore, the idle avoidance feature minimizes fuel consumption during periods of inactivity thus improving fuel efficiency. The hybridization also aims to hybridize auxiliary systems for flexible power management, enabling operation of either engine, auxiliaries, or both as needed. A newly developed hybrid supervisory control prototype efficiently manages electric power and mechanical power, enabling intelligent management
Prasad, Lakshmi P.PS, SatyanarayanaPaygude, TejasGangsar, PurushottamThakre, MangeshChoudhary, NageshGitapathi, Ajinkya
The rise of electric and hybrid vehicles with separate axle or wheel drives enables precise torque distribution between the front and rear wheels. The smooth control of electric motors allows continuous operation on high-resistance roads, optimizing torque distribution and improving efficiency. In hybrid vehicles, synergistic control of both internal combustion engines and electric motors can minimize energy consumption. Using the internal combustion engine for steady driving and electric power for acceleration enhances dynamic performance. Keeping the internal combustion engine at a constant speed is key to improving energy efficiency and vehicle responsiveness. The proposed method aids in selecting optimal power levels for both engines during the design phase. As acceleration time decreases, the ratio of electric motor power to internal combustion engine power increases. The torque distribution system, relying on sensors for axle loads, vehicle speed, and engine power, can reduce
Podrigalo, MikhailSergyjovych, Oleksandr PolianskyiKaidalov, RuslanDubinin, YevhenAbramov, DmytriiMolodan, AndriiAndrey, KorobkoKholodov, MykhailoOmelchenko, VasylKrasnokutskyi, Maksym
Electric vehicles rely on accurate estimation of battery states to operate safely and efficiently. Traditionally, the state estimation is pack level and based on empirical models developed to capture the dynamics of a representative battery pack and hence falls short in accounting for cell-to-cell variations. These variations become more pronounced as the cells age within a battery pack under non-homogeneous mechanical, thermal, manufacturing, and electrical conditions. It is challenging to adapt the traditional physics-based model to changing battery dynamics in real-time. To improve the state estimation at the cell level, a data-driven approach utilizing streamed data from vehicles enabled by connectivity has been shown in this paper. While traditional data-driven approaches result in large models and require large quantities of data for training, the proposed method relies on combining the underlying physics of the electrochemical model with novel data-driven modeling techniques
Gupta, ShobhitHegde, BharatkumarHaskara, IbrahimShieh, Su-YangChang, Insu
The motor controller, as one of the important controllers in the electric drive system, may cause unexpected acceleration or deceleration of the vehicle by the driver due to systematic failure and random hardware failure. Conducting research on the functional safety of drive motors for new energy vehicles is of great significance for reducing the systematic failure and random hardware failure of the electric drive. This paper has carried out designs including the allowable motor torque design for safety monitoring, the motor torque prediction design for safety monitoring, the rationality judgment design of the motor torque for safety monitoring, the rationality judgment design of the motor direction for safety monitoring, the functional safety motor degradation design, and the active discharge state monitoring of the motor, so that the system can transition to a safe state when an error occurs. Among them, the motor torque prediction design for safety monitoring includes predicting the
Jing, JunchaoZuo, BotaoLiu, YiqiangHuang, WeishanDai, Zhengxing
This paper presents a methodology to optimally select between routes proposed by mapping software. The objective of the optimization is to make the best trade-off between travel time and energy consumption when deciding between different routes. The method uses an Intelligent driver model to convert the data from the mapping software into a vehicle speed & torque profile, then uses a reduced order energy model to find the vehicle energy consumption for each route. Weightings are applied to the difference in energy and travel time for each route compared to the primary route. The vehicle used in this investigation is the Stellantis Pacifica PHEV. Results support energy savings of up to 20% compared to the primary route, which depends on the routes and initial battery State of Charge (SOC).
Robare, AndrewPoovalappil, AmanUdipi, AnirudhBhure, MayurBahramgiri, MojtabaRobinette, DarrellNaber, JeffreyChen, Bo
Most of the plug-in electric vehicles (EVs) available today are retrofitted versions of the corresponding co-existing higher-volume internal combustion (IC) engine-based models. In order to make the former category of vehicles more attractive in terms of driving range, a Li-ion battery pack of substantive energy capacity (in kWh) is needed. The latter requirement is likely to add to the weight of an EV in relation to its conventional counterpart. This potential weight increase can to an extent be checked by aggressively scouring for opportunities for weight reduction of the BIW (Body-In-White) of the original platform. The current work suggests a practical and efficient CAE (Computer-Aided Engineering)-driven approach for weight optimization of the BIW of a vehicle without affecting its styling, modal frequencies and front crashworthiness performance. It is assumed that there would be no major changes to manufacturing resources associated with the current design although limited
Deb, AnindyaZhu, Feng
Toyota Motor Corporation pursuing an omnidirectional strategy that includes battery electric vehicle (BEV), plug-in hybrid electric vehicle (PHEV), and fuel cell electric vehicle (FCEV) to accelerate electrification. One of the technical challenges with our xEV batteries which feature good degradation resistance and long battery life, is that regenerative braking cannot be fully effective due to the decrease in regenerative power in some situations, such as low battery temperature. For the electrified vehicles with an internal combustion engine such as PHEVs, the solution has been running the engine to increase deceleration through engine braking during coasting. PHEVs are expected to extend their cruising range and enhance EV driving experience as "Practical BEVs". While increasing battery capacity and enhancing convenience, the restrictions on EV driving opportunity due to low battery temperature may negatively affect PHEV’s appealing. As an alternative, introducing a battery heater
Hoshino, Yu
Thanks to greatly increased energy density of battery, the average driving range of an electric vehicle has been advanced quite a lot. However, drastic reduction of driving range in cold ambient conditions still greatly restricts the wide application of electric vehicles. This paper presents a methodology of establishing multi-discipline coupled full vehicle model in AMESim to investigate the energy consumption of a pure electric vehicle in cold ambient conditions. Different strategies of battery heating through Positive Temperature Coefficient (PTC) part and/or combination of Motor Waste Heat Recovery (MWHR) were also investigated to study whether there is an improvement of driving range. Firstly, basic framework of the full vehicle model established in AMESim was introduced. Next, modeling details of individual sub-systems were illustrated respectively. Then, full vehicle energy consumption test was carried out in -7°C ambient condition to check the simulation accuracy. Finally, a
Zhou, ShuaiLiu, HuaijuYU, HuiliYan, XuYan, Junjie
The paper provides a detailed analysis of the transmission system design under the single motor drive scheme, with a focus on the 2024 Formula SAE (FSAE). The selection of the motor type is determined based on race rules and battery box output power limits. In terms of transmission ratio design, this study takes into account the car's power, balancing acceleration ability and maximum speed to determine an optimal transmission ratio through theoretical calculations and empirical values. Furthermore, it explores how to optimize overall drive system performance by considering technical parameters, power requirements, economic considerations of each system assembly, and validates these findings through software simulations. Notably, significant improvements in reliability are achieved with the newly designed transmission system and wheel rim system while also proposing lightweighting methods for key components. We have carried out extensive verification in both simulation and real vehicle
Wang, LiuxinLi, ChengfengZhu, XiranLiu, Minmin
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
This paper presents the development of a new vehicle simulation software, the Power- and Usage-Based Simulator Tool (referred to as the Power-Based Model), designed to predict fuel consumption and evaluate advanced powertrain technologies for off-road mobile machinery. The Power-Based Model integrates current research on fuel consumption simulation in the off-road vehicle sector and serves as a platform for development of advanced powertrain technologies such as battery-electric and fuel cell powertrains. The tool predicts the battery capacity and hydrogen storage required for the transition to these advanced powertrains, allowing users to accurately calculate component sizes and reductions in fuel consumption. The Power-Based Model was developed with a strong focus on the unique operational characteristics of off-road machinery, ensuring that it realistically reflects real-world energy consumption and the competitive advantages of various fuel-saving technologies. This paper describes
Kim, NamdooSeo, JiguVijayagopal, RamBurnham, Andrewmakarczyk, DavidFreyermuth, Vincent
The transportation sector is responsible for a significant portion of greenhouse gas emissions. Within the sector, truck freight is responsible for a third of the associated emissions. Alternative powertrains are seen as a viable approach to significantly reduce these emissions. Prior to making a large-scale transition, it is important to consider the following questions: will the power grid support a transition to alternative powertrains?; will the transition truly reduce carbon emissions?; and will the transition impose an unnecessary economic burden on companies within the industry? The answer to these questions, however, can vary by geography, maturity/capacity of the energy distribution network or predicted vehicle load. We focus on the latter two questions, investigating the variation in estimated total cost of ownership and carbon emissions across the United States at the zip code level for both heavy-duty battery electric vehicles and heavy-duty fuel cell electric vehicles. As
Goulet, NathanSun, RuixiaoFan, JunchuanSujan, VivekMiller, Brandon
Fuel economy and the ability to maintain the state of charge (SOC) of the battery are two key metrics for the energy management of a full-power fuel cell hybrid vehicle fitted with a small-capacity battery pack. To achieve stable maintenance of SOC and near-optimal fuel consumption, this paper proposes an adaptive equivalent consumption minimization strategy (PA-ECMS) based on power prediction. The strategy realizes demand power prediction through a hybrid deep learning model, and periodically updates the optimal equivalent factor (EF) based on the predicted power to achieve SOC convergence and ensure fuel economy. Simulation results show that the hybrid deep learning network model has high prediction accuracy with a root mean square error (RMSE) of only 0.733 m/s. Compared with the traditional ECMS based on SOC feedback, the PA-ECMS effectively maintains the battery SOC in a more reasonable range, reduces the situation of the fuel cell directly charging the power cell in the high
Gao, XinyuJu, FeiChen, GangZong, YuhuaWang, Liangmo
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 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
Path tracking control, which is one of the most important foundations of autonomous driving, could help the vehicle to precisely and smoothly follow the preset path by actively adjusting the front wheel steering angle. Although there are a number of advanced control methods with simple structure and reliable robustness that could assist vehicles achieving path tracking, these controllers have many parameters to be calibrated, and there is a lack of guidance documents to help non-professional test site engineers quickly master calibration methods. Therefore, this paper proposes a parameter virtual calibration method based on the deep reinforcement learning, which provides an effective solution for parameter calibration of vehicle path tracking controller. Firstly, the vehicle trajectory tracking model is established through the kinematic relationship between the vehicle and the target path, combined with the Taylor series expansion linearization method. Next, a vehicle path tracking
Zhao, JianGuo, ChenghaoZhao, HuiChaoZhao, YongqiangYu, ZhenZhu, BingChen, Zhicheng
A new hybrid power system was investigated by installing a motor on the axle of a conventional semi-trailer. The purpose is to reduce the fluctuation of longitudinal acceleration and improve driving comfort by filling the transmission output torque hole through the motor during the gear shift process. Models for the longitudinal motion of a commercial vehicle, the permanent magnet synchronous motor, and the motor power distribution method are established, and the system model is built using MATLAB/Simulink. The model-in-the-loop simulation control interface is created in ModelBase, and model-in-the-loop simulation under the full-throttle (WOT) and braking operating conditions is performed based on ModelBase. Due to the high-frequency jitter problem in the actual control of the motor, the torque output obtained from different control algorithms is investigated. Finally, the sliding mode control algorithm with perturbation observation is used to ensure the fast response and smoothness of
Zhang, HongyuWei, ZhengjunZhen, RanShangguan, Wen-Bin
Optimal control of battery electric vehicle thermal management systems is essential for maximizi ng the driving range in extreme weather conditions. Vehicles equipped with advanced heating, ventilation and air-conditioning (HVAC) systems based on heat pumps with secondary coolant loops are more challenging to control due to actuator redundancy and increased thermal inertia. This paper presents the dynamic programming (DP)-based offline control trajectory optimization of heat pump-based HVAC aimed at maximizing thermal comfort and energy efficiency. Besides deriving benchmark results, the goal of trajectory optimization is to gain insights for practical hierarchical control strategy modifications to further improve real-time controllers’ performance. DP optimizes cabin inlet air temperature and flow rate to set the trade-off between thermal comfort and energy efficiency while considering the nonlinear dynamics and operating limits of HVAC system in addition to typically considered cabin
Cvok, IvanDeur, Josko
Experimental studies of wind tunnel blockage for road vehicles have usually been conducted in model wind tunnels. Models have been made in a range of scales and tested in a working section of fixed size. More recently CFD studies of blockage have been undertaken, which allow a fixed vehicle size and the blockage is varied by changing the cross section of the flow domain. This has some inherent advantages. A very recent database of CFD derived drag and lift coefficients for different road vehicle shapes and simple bodies tested in a closed wall tunnel with a wide range of blockage ratios has become available and provides some additional insight into the blockage phenomenon. In this paper a process is developed to derive the parameters influencing wind tunnel blockage corrections from CFD data. These are shown to be reasonably effective for correcting the measured drag and lift coefficients at blockage ratios up to 10%.
Howell, JeffButcher, DanielGleason, Mark
This paper presents a highly integrated 4-in-1 power electronics solution for 800V electric vehicle applications, combining on-board charging (OBC), DC boost charging, traction drive, and high-voltage/low-voltage (HV/LV) power conversion in a single housing. Integration is achieved through the use of motor windings for charging and a custom-designed three-port transformer that magnetically couples HV and LV batteries while ensuring galvanic isolation. The system also employs a three-phase open-ended winding machine (OEWM) to support both single-(1P) and three-phase (3P) AC charging. A dual-bank DC/DC architecture allows for seamless integration of a redundant auxiliary power module (APM), enhancing functional safety and autonomy. In AC charging mode, the three-level (3L) T-type inverter operates as a Vienna rectifier for 3P charging and as a totem-pole power factor correction (PFC) circuit for 1P charging, with the motor windings utilized as PFC inductors. In DC boost charging mode
Wang, YichengTaha, WesamAnand, Aniket
To address the challenges of complex operational simulation for Electric Vehicles (EVs) caused by spatial-temporal variations and driver behavior heterogeneity, this study introduces a dynamic operation simulation model that integrates both data-driven and physics-based principles, referred to as the Electric Vehicle-Dynamic Operation Simulation (EV-DOS) model. The physics-based component encompasses critical aspects such as the powertrain energy transfer module, heat transfer module, charge/discharge module, and battery state estimation module. The data-driven component derives key features and labels from second-by-second real-world vehicle driving status data and incorporates a Long Short-Term Memory (LSTM) network to develop a State-of-Health (SOH) prediction model for the EV power pack. This model framework combines the interpretability of physical modeling with the rapid simulation capabilities of data-driven techniques under dynamic operating conditions. Finally, this study
Jing, HaoHU, JianyaoOuyang, JianhengOu, Shiqi(Shawn)
This study addresses the challenges of electrifying heavy-duty vehicle fleets, particularly school buses, by focusing on the development of dedicated depot charging infrastructure and grid resilience. A key challenge is managing recharging limitations while considering grid resilience in the electrification of school bus fleets. Using real operational data, the study introduces a two-phase approach to optimize both charging infrastructure and scheduling. In the first phase, the optimal number of chargers is determined to ensure sustainable fleet operations. In the second phase, charging schedules are refined to reduce peak power demand and improve grid resilience. Experimental results demonstrate that approximately half the fleet size is required in chargers, with distributed charging and peak shaving strategies reducing peak power demand by 20% to nearly 45%. These findings offer practical insights for fleet managers, grid operators, and policymakers on enhancing grid resilience and
Moon, JoonHanif, AtharAhmed, Qadeer
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