Search Results (17)

Today, reducing vehicle energy consumption is a crucial topic. For electric vehicles, reducing energy consumption is essential to address some of the most critical issues associated with this type of vehicle, such as the limited range of electric powertrains and the long battery recharging times. To lower the environmental impact during the vehicle’s use phase and reduce energy consumption, vehicle mass reduction (lightweighting) is an effective strategy. The objective of this work is to analyze the vehicle parameters that influence lightweighting outcomes on a real driving cycle, representative of the home-to-work travel in northern Italy. In particular, a previous work carried out on standard driving cycles is repeated in order to observe whether it is possible to draw the same conclusions regarding the variability in the lightweighting outcome. This study was conducted using two opposite vehicle models, a compact car and an N1 vehicle, simulated through a well-established vehicle simulation tool for energy consumption estimation. To conduct this analysis, several simulations with variable vehicle mass, and with different vehicle parameters, such as aerodynamics and rolling resistance, were performed to estimate energy consumption across a real-world driving cycle, acquired via GPS on board the vehicle during a home-to-work journey in northern Italy. This study reveals that even for the real driving cycle, as for the WLTC and US06 standards, the parameters that most influence the outcome of the lightening are the rolling resistance, the characteristics of the battery pack, the aerodynamic coefficients, and the efficiency of the transmission. Finally, the standard cycle that best fits with the real one considered in this study is the Artemis Urban Cycle. Full article

Electric vehicles (EVs) encounter substantial obstacles in effectively managing energy, particularly when faced with varied driving circumstances and surrounding factors. This study aims to evaluate the performance of three different control systems in a fully operational hybrid energy storage system (HESS) installed in the Nissan Leaf. The objective is to improve the performance of EVs by focusing on optimising energy management in response to different global environmental and driving circumstances. This study utilises an analytical strategy by developing a distinct energy management system model using MATLAB/Simulink. This model is specifically designed for optimising the integration and control of batteries and supercapacitors (SCs) in a fully active HESS. This model mimics the performance of the controllers under three different driving cycles—Artemis rural, Artemis motorway, and US06. The findings demonstrate notable progress in managing the battery state of charge (SOC) and the system’s responsiveness, especially when employing the radial basis function (RBF) controller. This study emphasises the capacity of HESSs to enhance the effectiveness and durability of EVs, therefore promoting wider acceptance and progress in electric transportation technology. Full article

This study presents a reinforcement-learning-based approach for energy management in hybrid electric vehicles (HEVs). Traditional energy management methods often fall short in simultaneously optimizing fuel economy, passenger comfort, and engine efficiency under diverse driving conditions. To address this, we employed a Q-learning-based algorithm to optimize the activation and torque variation of the internal combustion engine (ICE). In addition, the algorithm underwent a rigorous parameter optimization process, ensuring its robustness and efficiency in varying driving scenarios. Following this, we proposed a comparative analysis of the algorithm’s performance against a traditional offline control strategy, namely dynamic programming. The results in the testing phase performed over ARTEMIS driving cycles demonstrate that our approach not only maintains effective charge-sustaining operations but achieves an average 5% increase in fuel economy compared to the benchmark algorithm. Moreover, our method effectively manages ICE activations, maintaining them at less than two per minute. Full article

Replicative DNA polymerases are blocked by nearly all types of DNA damage. The resulting DNA replication stress threatens genome stability. DNA replication stress is also caused by depletion of nucleotide pools, DNA polymerase inhibitors, and DNA sequences or structures that are difficult to replicate. Replication stress triggers complex cellular responses that include cell cycle arrest, replication fork collapse to one-ended DNA double-strand breaks, induction of DNA repair, and programmed cell death after excessive damage. Replication stress caused by specific structures (e.g., G-rich sequences that form G-quadruplexes) is localized but occurs during the S phase of every cell division. This review focuses on cellular responses to widespread stress such as that caused by random DNA damage, DNA polymerase inhibition/nucleotide pool depletion, and R-loops. Another form of global replication stress is seen in cancer cells and is termed oncogenic stress, reflecting dysregulated replication origin firing and/or replication fork progression. Replication stress responses are often dysregulated in cancer cells, and this too contributes to ongoing genome instability that can drive cancer progression. Nucleases play critical roles in replication stress responses, including MUS81, EEPD1, Metnase, CtIP, MRE11, EXO1, DNA2-BLM, SLX1-SLX4, XPF-ERCC1-SLX4, Artemis, XPG, FEN1, and TATDN2. Several of these nucleases cleave branched DNA structures at stressed replication forks to promote repair and restart of these forks. We recently defined roles for EEPD1 in restarting stressed replication forks after oxidative DNA damage, and for TATDN2 in mitigating replication stress caused by R-loop accumulation in BRCA1-defective cells. We also discuss how insights into biological responses to genome-wide replication stress can inform novel cancer treatment strategies that exploit synthetic lethal relationships among replication stress response factors. Full article

With the increasing adoption of electric vehicles (EVs), optimizing lithium-ion battery capacity is critical for overall powertrain performance. Recent studies have optimized battery capacity in isolation without considering interactions with other powertrain components. Furthermore, even when the battery is considered within the full powertrain, most works have only modeled the electrical behavior without examining thermal or ageing dynamics. However, this fails to capture systemic impacts on overall performance. This study takes a holistic approach to investigate the effects of battery capacity optimization on convergence of the full EV powertrain. A battery multiphysics model was developed in MATLAB/Simulink, incorporating experimental data on electrical, thermal, and ageing dynamics and interactions with other components. The model was evaluated using real-world WLTP and Artemis driving cycles to simulate realistic conditions lacking in prior works. The findings reveal significant impacts of battery optimization on total powertrain performance unaccounted for in previous isolated studies. By adopting a system-level perspective and realistic driving cycles, this work provides enhanced understanding of interdependent trade-offs to inform integrated EV design. Full article

The article presents the analysis of operational parameters and thermal analysis of the wheel hub motor during operation in the car drive. The authors proposed an analysis of the operation of the wheel hub motor mounted in a hybrid car, during Artemis driving cycles and while driving on the road with different slopes. The simulations were carried out in the Ansys Motor-CAD program. The calculations are based on coupled models of the electromagnetic circuit and thermal models of the motor. The conducted research is a proposal of an approach to the design of electric vehicle propulsion motors, which allows us to consider problems related to predicting at the motor design stage what are its possibilities and what risks during operation in a real drive. The analysis also includes the impact of the applied motor control strategy and the variation of the supply voltage. These are aspects that are extremely important in wheel hub motors, as they are weight-optimized motors with a limited volume and a relatively high power and torque density. Full article

Four outer rotor surface-mounted permanent magnet synchronous machines (SMPSM), supplied by a seven-phase drive system, are proposed in this study, considering different q (number of stator slot per phase per pole ratio) to achieve a satisfying value of electromagnetic torque and Back-Electromotive Force (Back-EMF) with lower torque pulsation. Accordingly, the proposed configurations are investigated, and results are comparatively reported. Thus based on the results, the best-performing configuration, the candidate model, which presents the lowest torque pulsation with a desirable value of T_avg and Back-EMF is selected. In order to demonstrate the advantages of this candidate model, an optimization analysis is performed using 2D Finite Element Analysis (FEA). The resultant values of the variables are applied, designing three optimized models. Performance results of the optimized models demonstrate that T_Cog reduced noticeably and T_Ripple declined below 5%. The Artemis Drive-Cycles analysis results are also included for the best-optimized model, considering E-Motorcycle requirements and properties for urban, rural, and motorway driving conditions. Accordingly, in terms of In-Wheel application of the optimized machine, high torque/power density along with high values of PF and efficient performance are provided for E-Motorcycle application. Full article

The parameter extraction of parameters for Li-ion batteries is regarded as a critical topic for assessing the performance of battery energy storage systems (BESSs). The supply–demand algorithm (SDA) is used in this work to identify a storage system’s unknown parameters. The parameter-extracting procedure is represented as a nonlinear optimization task in which the state of charge (SOC) is approximated using nonlinear features related to the battery current and the initial SOC condition. Furthermore, the open-circuit voltage is approximated using the resulting SOC, which is performed in a nonlinear formula, as well. When used in the dynamic nonlinear BESS model, the SDA was used to verify the fitness values and standard deviation error. Furthermore, the results that were acquired using SDA are compared to recently developed approaches, which are the gradient-based, tuna swarm, jellyfish, heap-based, and forensic-based optimizers. Simulated studies were paired with experiments for the 40 Ah Kokam Li-ion battery and the ARTEMIS driving-cycle pattern. The numerical outcomes showed that the proposed SDA is an approach which is excellent at identifying the parameters. Furthermore, when compared to the other current optimization techniques, for both the Kokam Li-ion batteries and the ARTEMIS drive-cycle pattern, the suggested SDA exhibited substantial precision. Full article

The characterization of vehicle exhaust emissions of volatile organic compounds (VOCs) is essential to estimate their impact on the formation of secondary organic aerosol (SOA) and, more generally, air quality. This paper revises and updates non-methane volatile organic compounds (NMVOCs) tailpipe emissions of three Euro 5 vehicles during Artemis cold urban (CU) and motorway (MW) cycles. Positive matrix factorization (PMF) analysis is carried out for the first time on proton transfer reaction time-of-flight mass spectrometer (PTR-ToF-MS) datasets of vehicular emission. Statistical analysis helped to associate the emitted VOCs to specific driving conditions, such as the start of the vehicles, the activation of the catalysts, or to specific engine combustion regimes. Merged PTR-ToF-MS and automated thermal desorption gas chromatography mass spectrometer (ATD-GC-MS) datasets provided an exhaustive description of the NMVOC emission factors (EFs) of the vehicles, thus helping to identify and quantify up to 147 individual compounds. In general, emissions during the CU cycle exceed those during the MW cycle. The gasoline direct injection (GDI) vehicle exhibits the highest EF during both CU and MW cycles (252 and 15 mg/km), followed by the port-fuel injection (PFI) vehicle (24 and 0.4 mg/km), and finally the diesel vehicle (15 and 3 mg/km). For all vehicles, emissions are dominated by unburnt fuel and incomplete combustion products. Diesel emissions are mostly represented by oxygenated compounds (65%) and aliphatic hydrocarbons (23%) up to C₂₂, while GDI and PFI exhaust emissions are composed of monoaromatics (68%) and alkanes (15%). Intermediate volatility organic compounds (IVOCs) range from 2.7 to 13% of the emissions, comprising essentially linear alkanes for the diesel vehicle, while naphthalene accounts up to 42% of the IVOC fraction for the gasoline vehicles. This work demonstrates that PMF analysis of PTR-ToF-MS datasets and GC-MS analysis of vehicular emissions provide a revised and deep characterization of vehicular emissions to enrich current emission inventories. Full article

Hybrid energy storage systems (HESSs) including batteries and supercapacitors (SCs) are a trendy research topic in the electric vehicle (EV) context with the expectation of optimizing the vehicle performance and battery lifespan. Active and semi-active HESSs need to be managed by energy management strategies (EMSs), which should be realized on real-time onboard platforms. A widely used approach is the filter-based EMS thanks to its simplicity and effectiveness. However, one question that always arises with these algorithms is how to determine the appropriate constant cut-off frequency. To tackle this challenge, this paper proposed three adaptive schemes for the filtering strategies based on the SC “ability” and evaluated their performance during the vehicle operation via an intensive comparative study. Offline simulation and experimental validation using signal hardware-in-the-loop (HIL) emulation showed that the proposed adaptive filtering EMS can reduce the battery rms current considerably. Specifically, the SC-energy-based, SOC-based, and voltage-based algorithms minimized the battery rms by up to 69%, 66%, and 64%, respectively, when compared to a pure battery EV in a fluctuating driving condition such as the urban Artemis cycle. Full article

Vehicle exhaust emission tests use exhaust sampling systems that dilute the exhaust gas with ambient air. The dilution factor DF is calculated assuming that the combustion is complete, and that the engine is operated at a stoichiometric air-fuel ratio (AFR). These assumptions are not always met. This is especially true for diesel engines. This article discusses the tests to find out what the average lambda (λ) over the ARTEMIS, WLTC and NEDC driving cycles is and how this affects the result of the emission measurements. Measurements were carried out on a chassis dynamometer equipped with a standard emission measurement system used during the homologation. The λ was calculated using the Brettschneider equation. The dilution ratio DR was also determined by measuring the CO₂ concentration in the raw exhaust gas. The CO₂-tracer method used for this was modified. The median of the λ for a CI vehicle was 1.23–3.31, which makes the relative percentage difference between the DF and DR (Δ_DF) in the range of 28–167%. For a SI vehicle homologated under the WLTP procedure, the median of the λ for the WLTC and ARTEMIS cycles was close to one and Δ_DF for most cycles does not exceed 10%. In order to reduce the influence of the error of DF determination on the result of the emission measurement, it is recommended to use exhaust gas sampling systems that allow to determine the actual dilution ratio or to use the lowest possible dilution. The PAS-CVS system seems to be the most promising. Full article

A driving cycle is a record intended to reflect the regular use of a given type of vehicle, presented as a speed profile recorded over a certain period of time. It is used for the assessment of engine pollutant emissions, fuel consumption analysis and environmental certification procedures. Different driving cycles are used, depending on the region of the world. In addition, drive cycles are used by car manufacturers to optimize vehicle drivelines. The basis of the work presented in the manuscript was a developed computer tool using tests on the Toyota Camry LE 2018 chassis dynamometer, the results of the optimization process of neural network structures and the properties of fuels and biofuels. As a result of the work of the computer tool, the consumption of petrol 95, ethanol, methanol, DME, CNG, LPG and CO₂ emissions for the vehicle in question were analyzed in the following driving tests: Environmental Protection Agency (EPA US06 and EPA USSC03); Supplemental Federal Test Procedure (SFTP); Highway Fuel Economy Driving Schedule (HWFET); Federal Test Procedure (FTP-75–EPA); New European Driving Cycle (NEDC); Random Cycle Low (×05); Random Cycle High (×95); Mobile Air Conditioning Test Procedure (MAC TP); Common Artemis Driving Cycles (CADC–Artemis); Worldwide Harmonized Light-Duty Vehicle Test Procedure (WLTP). Full article

The dynamic development of transport in recent decades reflects the level of economic development in the world. The transport sector today is one of the main barriers to the achievement of the European Union’s climate protection objectives. More and more restrictive legal regulations define permissible emission limits for the amounts of toxic substances emitted into the atmosphere. Numerical CO₂ modeling tools are one way to replace costly on-road testing. Driving cycles, which are an approximation of the vehicle’s on-road operating conditions, are the basis of any vehicle approval procedure. The paper presents a computer tool that uses neural networks to simulate driving tests. Data obtained from tests on the Mercedes E350 chassis dynamometer were used for the construction of the neural model. All the collected operational parameters of the vehicle, which are the input data for the built model, were used to create simulation control runs for driving tests: Environmental Protection Agency, Supplemental Federal Test Procedure, Highway Fuel Economy Driving Schedule, Federal Test Procedure, New European Driving Cycle, Random Cycle Low, Random Cycle High, Mobile Air Conditioning Test Procedure, Common Artemis Driving Cycles, Worldwide Harmonized Light-Duty Vehicle Test Procedure. Using the developed computer simulation tool, the impact on CO₂ emissions was analyzed in the context of driving tests of four types of fuels: Diesel, Fatty Acid Methyl Esters, rapeseed oil, butanol (butyl alcohol). As a result of the processing of this same computer tool, mass consumption of fuels and CO₂ emissions were analyzed in driving tests for the given analyzed vehicle. Full article

Introduced by the Civil Code, the rules of road safety are introducing continuously increasingly strict requirements on motor vehicles. These requirements relate to various aspects of the technical condition of vehicles, both those that determine traffic safety and those that affect the vehicle’s environmental impact. The law requires regular diagnosis of the technical condition of vehicles in service. Diagnostic tests conducted in the form of road tests or the tests performed in workshop conditions allow to determine the symptoms of dysfunctions of the tested vehicle, not always clearly defining the causes and location of damage. The purpose of the work is the design the simulation of a station for of vehicle dynamics tests up to 3.5 t using simulation programs OpenModelica and SciLab. A simulation of the work of the stand for testing the dynamics of vehicles in the form of a chassis dynamometer was achieved. The program enables the simulation of tests: NEDC (New European Drive Cycle), WLTP (Worldwide Harmonized Light Vehicle Test Procedure), CADCM150 (joint Artemis driving cycle—Motorway at vMax = 150 kph), CADCU (Common Artemis Driving Cycle—Urban), FTP75 EPA (Federal Test Procedure, Environmental Protection Agency). The simulator (for any assumed type of vehicle) can be used in two modes: 1. Introduction of the presumed cause—Generates the expected results in the functioning of the vehicle. This function can be used to create a cause–effect relational database. 2. Analysis of data from the actual diagnostic system suggesting the causes of the observed (measured) errors in the functioning of the system. The simulator can be used both to design and implement the technological development of intelligent diagnostic systems, and to support the creation of application software for a workshop diagnostic system. Introducing the simulator into practice will also enable the improvement of road safety management. Full article

The energy storage system (ESS) is the main issue in traction applications, such as battery electric vehicles (BEVs). To alleviate the shortage of power density in BEVs, a hybrid energy storage system (HESS) can be used as an alternative ESS. HESS has the dynamic features of the battery and a supercapacitor (SC), and it requires an intelligent energy management system (EMS) to operate it effectively. In this study, a real-time EMS is proposed, which is comprised of a fuzzy logic controller-based low-pass filter and an adaptive proportional integrator-based charge controller. The proposed EMS intelligently distributes the required power from the battery and SC during acceleration. It allocates the braking energy to the SC on the basis of the state of charge. A simulation study was conducted for three standard drive cycles (New York City cycle, Artemis urban cycle, and New York composite cycle) using MATLAB Simulink. Comparative analysis of conventional and proposed EMSs was carried out. The results reveal that the proposed EMS reduced the stress, temperature, and power losses of the battery. The steady-state charging performance of the SC was 98%, 95%, and 96% for the mentioned drive cycles. Full article