<rss version="2.0" xmlns:atom="https://www.w3.org/2005/Atom">
  <channel>
    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
    <description></description>
    <language>en-us</language>
    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
    </image>
    <item>
      <title>Recovery of engine waste heat in low temperature environment of plug-in hybrid electric vehicle</title>
      <link>https://trid.trb.org/View/2563920</link>
      <description><![CDATA[The performance and life of electric vehicle power batteries will be reduced at low temperatures, and the lower temperature in the electric vehicle will also affect the comfort of drivers and passengers. Taking into account the winter temperatures and the unique drive structure of the plug-in hybrid electric vehicle, a specially designed driving mode for low-temperature environment is implemented. Based on this drive mode, a plug-in hybrid electric vehicle (PHEV) integrated thermal management structure is proposed to heat the battery and the passenger compartment, thereby improving energy efficiency. A mathematical model is used to establish the entire vehicle thermal management system, which is then experimentally validated. Under the NEDC (New European Driving Cycle) at ambient temperatures of −5°C, −10°C, −15°C, and −20°C, the calculation results of engine waste heat utilization and PTC (Positive Temperature Coefficient) heating are compared and analyzed. The results show that the average heating rate of the thermal management system proposed in this study is 23% faster than that of PTC heating at low temperature. The SOC decreases to 63.43% when engine waste heat utilization is adopted. When PTC heating is used, the SOC decreases to 49.18%. However, the advantage of the faster rate of engine waste heat compared to PTC heating becomes less pronounced as the ambient temperature decreases.]]></description>
      <pubDate>Fri, 21 Nov 2025 08:44:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2563920</guid>
    </item>
    <item>
      <title>Constrained Optimal Fuel Consumption of HEV: A Constrained Reinforcement Learning Approach</title>
      <link>https://trid.trb.org/View/2511507</link>
      <description><![CDATA[Hybrid electric vehicles (HEVs) are becoming increasingly popular because they can better combine the working characteristics of internal combustion engines and electric motors. However, the minimum fuel consumption of an HEV for a battery electrical balance case under a specific assembly condition and a particular speed curve still needs to be clarified in academia and industry. Regarding this problem, this work provides the mathematical expression of the constrained optimal fuel consumption (COFC) problem from the perspective of constrained reinforcement learning (CRL) for the first time globally. Also, two mainstream approaches of CRL, Lagrangian-based approaches and the constrained variational policy optimization (CVPO) approach, are utilized for the first time to obtain the vehicle’s minimum fuel consumption under the battery electricity balance condition. The authors conduct case studies on the well-known Prius TOYOTA hybrid system (THS) under the New European Driving Cycle (NEDC) condition. The authors give vital steps to implement CRL approaches and compare the performance between the Lagrangian-based and CVPO approaches. The authors' case study found that Lagrangian-based and CVPO approaches can obtain the lowest fuel consumption while satisfying the battery state of charge (SOC) balance constraint. The CVPO approach converges stably, but the Lagrangian-based approach can obtain the lowest fuel consumption at 3.95 L/100 km, though with more significant oscillations. This result verifies the effectiveness of the authors' proposed CRL approaches to the COFC problem.]]></description>
      <pubDate>Tue, 22 Apr 2025 15:51:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2511507</guid>
    </item>
    <item>
      <title>Development of turbocharged engine bench test adapted to multiple driving cycles for lubricants fuel economy</title>
      <link>https://trid.trb.org/View/2366329</link>
      <description><![CDATA[Aimed to more truly and stably measure the fuel economy of a vehicle under different driving cycles, simultaneously reduce prime cost and short experimental period. An engine fuel economy test method and bench which could adapt to multiple cycle conditions was developed. The fuel economy test of a certain SUV under NEDC/WLTC was carried out, and the transient operating parameters of the engine were collected. The engine fuel economy test bench was established and accurately controlled according to transient parameters. The FEI of five different formulations of engine oils was tested. The results indicate that the actual bench parameters are in good agreement with the target transient parameters. Multiple FC measurements of baseline oil and multiple FEI measurements of high reference oil show that the engine bench has good repeatability. The data fluctuation degree of bench test is obviously smaller than that of vehicle test. The correlation coefficients of comprehensive FEI between bench test and vehicle test are 95.25% (NEDC) and 96.39% (WLTC). It means that the bench test can completely replace the vehicle test for fuel economy analysis. The FEI of No.3 oil which has the best fuel improvement are 1.888% (NEDC) and 1.248% (WLTC) respectively. Compared with the vehicle test, the engine bench testing can save 75% of the time. The fuel economy bench and test methods are also suitable for the verification of fuel economy improvement of other engine optimization measures under different driving cycles. The development period of engine technologies can be shortened, and the test cost can be reduced.]]></description>
      <pubDate>Tue, 28 May 2024 10:43:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2366329</guid>
    </item>
    <item>
      <title>Impact of Driving Cycles on the Range Performance of Battery Electric Vehicle</title>
      <link>https://trid.trb.org/View/2367788</link>
      <description><![CDATA[When compared to traditional cars with internal combustion engines (ICEs), electric vehicles (EVs) are seen as a more environmentally friendly option. However, the widespread acceptance of EVs in India faces several obstacles, including the high cost of the technology, inadequate charging infrastructure, and limited driving range. Additionally, potential customers are concerned about the actual range of EVs, which often falls short of the certified range. The certified range is determined based on a standardized driving cycle so selecting the appropriate driving cycle for range estimation is of utmost importance. In India, the modified Indian drive cycle (MIDC) has been implemented, which is comparable to the New European Driving Cycle (NEDC). Modified Indian Driving Cycle (MIDC) consists of four Urban Driving Cycles (Part I) and one Extra Urban Driving Cycle (Part II), however range measured with Part-I of the modified Indian driving cycle is considered as the approved/certified value of the electric vehicle's range.The objective of this research is to analyze the influence of standard driving cycles, namely NEDC, MIDC (Part I), and WLTC (World harmonized Light-duty vehicles Test Cycle), on the range of electric vehicles (EVs). A 1D- Vehicle simulation model has been developed to investigate the impact of these driving cycles on the range of EVs. The vehicle model is evaluated against published energy consumption values, which show a reasonable level of accuracy with an error range of 1.8% to 7.3% between simulation and experimental results for auxiliary loads of 150W to 250W on MIDC Part I cycle. The simulation findings have confirmed that the choice of driving cycle significantly affects the range of EVs. It has been observed that MIDC (Part I) is not suitable for Indian driving conditions. Therefore, it is recommended that India adopt the WLTC or an equivalent driving cycle to accurately determine the range of EVs. This will help bridge the gap between the certified range and the actual range of EVs.]]></description>
      <pubDate>Mon, 29 Apr 2024 13:58:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2367788</guid>
    </item>
    <item>
      <title>Research on composite braking control strategy of battery electric vehicle based on road surface recognition</title>
      <link>https://trid.trb.org/View/2348230</link>
      <description><![CDATA[Concerning the issue of low energy recovery in composite braking of battery electric vehicles, a composite braking control strategy based on road recognition is proposed. The structure of the composite braking system of electric vehicles is analyzed, and the road surface identifier is designed by fuzzy algorithm to track the peak adhesion coefficient of the road surface to obtain the maximum brake braking force. In addition, fuzzy inference is used to modify the proportional coefficient of the motor braking force to the total braking force so as to maximize the energy recovery, and CRUISE and MATLAB are used for joint simulation analysis. The results show that the proposed control strategy not only ensures the safety and stability of the vehicle braking, but also has larger motor braking torque, smaller braking distance and shorter braking time. Under New European Driving Cycle(NEDC), Federal Test Procedure (FTP) and Extra Urban Driving Cycle (EUDC), the battery state of charge (SOC) values are increased by 3.66%, 1.89%, and 0.85% respectively, and the energy recovery is increased by 169.7, 633.2, and 37.1 kJ respectively.]]></description>
      <pubDate>Mon, 11 Mar 2024 09:11:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2348230</guid>
    </item>
    <item>
      <title>Torque allocation strategy based on economy and stability for electric vehicle considering controllability after motors failure</title>
      <link>https://trid.trb.org/View/2259859</link>
      <description><![CDATA[To reduce the energy consumption and improve the stability of distributed drive electric vehicles, a torque allocation strategy based on an economy and stability optimisation function (ESOF) and a fuzzy proportional-integral-derivative rule control (FPRC) strategy are proposed while considering motor efficiency, braking energy recovery and motor failure. First, the vehicle dynamics and motor equivalent models are established. Subsequently, a torque prediction model and fuzzy controller for the vehicle are designed to calculate the total desired torque and yaw moment, respectively. A torque optimisation function is established to minimise power losses in the electric motor and maximise braking energy recovery, and it is solved using an improved genetic algorithm. While satisfying vehicle driving constraints, the ESOF-based controller can effectively coordinate the operation of each motor in the high-efficiency range under driving and braking conditions. After one motor fault is detected, the ESOF-based controller is replaced with an FPRC-based controller to distribute the vehicle demand torque. A co-simulation platform integrating MATLAB/Simulink and CarSim is developed to verify the effectiveness of the proposed ESOF-based controller in the New European Driving Cycle (NEDC) and Federal Test Procedure 75 (FTP75) driving cycles. The effectiveness of the FPRC-based controller in step steering condition is verified using the co-simulation platform. The simulation results indicate that the vehicle economy and driving range of the ESOF-based controller improved compared with the results afforded by the typical torque distribution strategy based on the front–rear axle dynamic load ratio. The average efficiencies of the motors in the NEDC and FTP75 driving cycles increased by 2.94% and 2.4%, respectively. More importantly, the FPRC-based controller can more significantly improve the steering stability of a vehicle with motor failure compared with the ESOF-based controller.]]></description>
      <pubDate>Mon, 16 Oct 2023 09:06:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2259859</guid>
    </item>
    <item>
      <title>A Clustering-Based Optimization Method for the Driving Cycle Construction: A Case Study in Fuzhou and Putian, China</title>
      <link>https://trid.trb.org/View/2040178</link>
      <description><![CDATA[Driving cycle is a crucial topic for the auto industry. It is developed to provide a quantitative measure on the fuel consumption and emission of a vehicle. In recent years, massive amount of driving data has been collected but has not yet been commonly used for the evaluation of driving cycle. The authors believe the collection of such data and the advancement in analytics models may provide a fresh perspective for the construction of driving cycle. Therefore, they propose a novel clustering-based optimization method for the construction of driving cycles. They employ the principal component analysis and spectral clustering algorithms to eliminate redundant features and analyze data structure. They further develop an adaptive optimization algorithm to select the appropriate kinematic segments to form a representative driving cycle. To demonstrate the effectiveness of their method, they compare their performance against the baselines including the New European Driving Cycle (NEDC), Federal Test Procedure (FTP), and Markov chain-based methods. The model performance is evaluated with real driving data from two cities in Fujian, China. The authors' proposed method is shown to be superior to all baselines. In addition, based on their optimized driving cycle, they can also estimate the fuel consumption to evaluate its energy economy. To sum up, this study offers a novel methodology to establish the driving cycle based on real and localized traffic data, where the constructed driving cycle can further be used for the development of energy economy and emission control.]]></description>
      <pubDate>Fri, 30 Dec 2022 16:58:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2040178</guid>
    </item>
    <item>
      <title>Relationships Between CO₂ Emitted from New Passenger Cars in European Union and Their Engine and Vehicle Characteristics</title>
      <link>https://trid.trb.org/View/1818930</link>
      <description><![CDATA[The exhaust carbon dioxide (CO₂) emissions on the New European Driving Cycle and its urban and extra-urban part of the new passenger cars of European Union are correlated with four engine and vehicle characteristics: vehicle weight, engine displacement, engine max. power and engine max. specific power. Weight has the best correlations in the case of Diesel passenger cars and displacement in the case of gasoline ones. In the case of models with two variables, the model weight+specific power is the best in the case of Diesel passenger cars, while the model displacement+specific power is the best in the case of gasoline ones.]]></description>
      <pubDate>Mon, 29 Aug 2022 09:27:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/1818930</guid>
    </item>
    <item>
      <title>DPF Loading Analysis by a New Experimental Modus Operandi</title>
      <link>https://trid.trb.org/View/1819279</link>
      <description><![CDATA[The loading of a DPF entails the need of trap regeneration by particulate combustion, whose efficiency and frequency are somehow affected by the way soot is deposited along the channels. The aim of this work is therefore the development of a new experimental methodology able to provide fundamental information about the soot loading process inside the DPF, in order to take advantage of this insight for DPF design and optimization purposes. Small lab-scale 300 cpsi DPF samples were loaded downstream of the DOC in an ad hoc designed reactor capable of hosting 5 samples, by diverting part of the entire flow produced by an automotive diesel engine at 2500 rpm × 8 BMEP, selected as representative of the most critical operating conditions for soot production during the New European Driving Cycle (NEDC).         Soot layer thickness was then estimated by means of FESEM observations after sample sectioning at progressive locations, obtained through a procedure specifically defined in order to avoid affecting the soot distribution inside the filter and to enable estimation of the actual soot thickness along the channel length.]]></description>
      <pubDate>Thu, 21 Jul 2022 13:39:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/1819279</guid>
    </item>
    <item>
      <title>Off-cycle, Real-World Emissions of Modern Light Duty Diesel Vehicles</title>
      <link>https://trid.trb.org/View/1819276</link>
      <description><![CDATA[This paper investigates the emissions performance of modern European light-duty passenger vehicles with turbodiesel engines during real-world driving, notably during two extreme but not uncommon operating regimes: congested urban traffic and high-speed and performance driving. Four cars and one van were tested on a chassis dynamometer and/or on the road with a portable, on-board emissions monitoring system capable of online measurements of particulate and gaseous emissions. On all cars, operation at speeds and acceleration rates in excess to those within the applicable certification NEDC cycle resulted in higher concentrations of nitrogen oxide (NO) and particulate matter (PM). High-speed driving in excess of 120 km/h resulted in a marked increase in NO and PM concentrations, with further increases past 130-140 km/h. In urban driving, highest PM concentrations occurred at the onset of and during accelerations from low rpm. Aggressive, performance driving resulted in substantial increase in NO and PM emissions per kg of fuel compared to normal driving. No marked increases outside of NEDC regimes were, however, observed on the van. The results support the arguments against increase in 130 km/h freeway speed limits and for augmenting the EU certification tests for light diesel vehicles with supplemental cycles or tests.]]></description>
      <pubDate>Thu, 21 Jul 2022 13:39:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/1819276</guid>
    </item>
    <item>
      <title>Does European Type Approval Procedure Encourage the Diffusion of Hybrid and Other Low Emission Vehicles?</title>
      <link>https://trid.trb.org/View/1820938</link>
      <description><![CDATA[European Type approval procedure defines a synthetic driving cycle (the NEDC) over which one vehicle per type has to be tested. Euro 1, 2, 3, 4 and 5 differ (beside vehicle preconditioning and warm-up procedures introduced since Euro 3) only because limits for the different pollutants have been progressively lowered. This paper analyses through a number of experimental tests on spark-ignition cars, a hybrid and a conventional vehicle, the driving conditions responsible for most of the emissions and assesses how such conditions are reproduced by the type approval test. The engine conditions mostly responsible for emissions are: warm-up phase, full loads and transients. Only the warm-up is well covered by the NEDC for vehicles with more than 35 kW/ton power-weight ratio. Tests performed with the Honda Hybrid (the low environmental impact vehicle) and with the Alfa Romeo 147 1.6 (the conventional vehicle) showed how on the NEDC most emissions are produced in the warm up phase and are of the same order of magnitude for both cars while on more realistic driving cycles (ARTEMIS cycles have been used here) the capacity of the Hybrid to mitigate the transients effects results in a much lower emission rate. Full load conditions are not kept under control by the Hybrid as by the conventional vehicle when the O₂ exhaust sensor is disabled and the air-fuel ratio is no more stoichiometric. Even though this paper has not tested all possible vehicles types, it shows that European type approval procedure has some weaknesses in accounting for the main causes of vehicle emissions. Any new procedure addressing better transients and full load conditions would help the diffusion of low emission vehicles like hybrids more than progressively lowering the allowed emission thresholds on current type approval procedure.]]></description>
      <pubDate>Wed, 29 Jun 2022 13:26:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/1820938</guid>
    </item>
    <item>
      <title>Investigation about Predictive Accuracy of Empirical Engine Models using Design of Experiments</title>
      <link>https://trid.trb.org/View/1823751</link>
      <description><![CDATA[This study focuses on improvement of the predictive accuracy of empirical engine models using the Model Base Calibration (MBC) method. This research discusses the effects of the number of measurement points on the accuracy of models for different Design of Experiments (DoE) by using a direct-injection 4-cylinder diesel engine. The results show that the predictive accuracy of the models converges on fixed values when the number of measurement points is increased in Latin Hypercube Sampling (LHS) and D-Optimal Design. This is because the probability density distribution of the measurement data has little variation as the number of measurement points increases. Comparing LHS and D-Optimal indicates that D-Optimal displays a higher level of accuracy, it is able to extend the boundary model because of its greater number of measurement points at the boundaries of the boundary model. In addition, it is possible to predict the fuel consumption when empirical engine models are used in simulations of the New European Driving Cycle (NEDC) under hot conditions and cold start conditions.]]></description>
      <pubDate>Fri, 17 Jun 2022 09:20:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/1823751</guid>
    </item>
    <item>
      <title>The Effect of a Particle Oxidation Catalyst (POC®) on Particle Emissions of a GDI Car during Transient Engine Operation</title>
      <link>https://trid.trb.org/View/1828153</link>
      <description><![CDATA[Particle emissions have been generally associated to diesel engines. However, spark-ignition direct injection (SI-DI) engines have been observed to produce notable amounts of particulate matter as well. The upcoming Euro 6 legislation for passenger cars (effective in 2014, stricter limit in 2017) will further limit the particulate emissions from SI engines by introducing a particle number emission (PN) limit, and it is not probable that the SI-DI engines are able to meet this limit without resorting to additional aftertreatment systems. In this study, the solid particle emissions of a SI-DI passenger car with and without an installed Particle Oxidation Catalyst (POC®) were studied over the New European Driving Cycle (NEDC) on a chassis dynamometer and over real transient acceleration situations on road. It was observed that a considerable portion of particle number emissions occurred during the transient acceleration phases of the cycle. The application of the POC resulted in a reduction of those emission peaks and, as a conclusion, the car was able to meet the 2017 Euro 6 particle number emission limit with the POC. The on-road measurement confirms the results obtained on the chassis dynamometer in that the majority of particle number emissions associated with SI-DI engines arise from transient acceleration situations. The POC efficiency was verified also on road by significantly reducing the particle number emission peaks caused during accelerations.]]></description>
      <pubDate>Tue, 24 May 2022 10:09:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/1828153</guid>
    </item>
    <item>
      <title>Estimation of the Engine-Out NO2/NOx Ratio in a EURO VI Diesel Engine</title>
      <link>https://trid.trb.org/View/1827874</link>
      <description><![CDATA[The present work has the aim of developing a semi-empirical correlation to estimate the NO₂/NOᵪ ratio as a function of significant engine operating variables in a modern EURO VI diesel engine. The experimental data used in the present study were acquired at the dynamic test bench of ICEAL-PT (Internal Combustion Engine Advanced Laboratory at the Politecnico di Torino), in the frame of a research activity on the optimization of a General Motors Euro VI prototype 1.6-liter diesel engine equipped with a single-stage variable geometry turbine and a solenoid Common Rail system. The experimental tests were conducted over the whole engine map. A preliminary analysis was carried out to evaluate the uncertainty of the experimental acquired data and the NO₂/NOᵪ ratio. The main engine variables which were expected to be related to the NO₂ formation were then identified, and a second-order polynomial model was introduced to model the NO₂/NOᵪ ratio as a function of the abovementioned related engine variables. All the input variable combinations were considered in the investigation, in order to identify the most important for the evaluation of the NO₂/NOᵪ ratio. Finally, a simplified exponential correlation was established to estimate the NO₂/NOᵪ ratio, that could be used in several applications, including feed-forward model-based combustion control. The model was tested and validated on a NEDC cycle (New European Driving Cycle).]]></description>
      <pubDate>Mon, 25 Apr 2022 17:01:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/1827874</guid>
    </item>
    <item>
      <title>Universal Diesel Engine Simulator (UniDES) 2nd Report: Prediction of Engine Performance in Transient Driving Cycle Using One Dimensional Engine Model</title>
      <link>https://trid.trb.org/View/1828169</link>
      <description><![CDATA[The aim of this research is to develop the diesel combustion simulation (UniDES: Universal Diesel Engine Simulator) that incorporates multiple-injection strategies and in-cylinder composition changes due to exhaust gas recirculation (EGR), and that is capable of high speed calculation. The model is based on a zero-dimensional (0D) cycle simulation, and represents a multiple-injection strategy using a multi-zone model and inhomogeneity using a probability density function (PDF) model. Therefore, the 0D cycle simulation also enables both high accuracy and high speed. This research considers application to actual development. To expand the applicability of the simulation, a model that accurately estimates nozzle sac pressure with various injection quantities and common rail pressures, a model that accounts for the effects of adjacent spray interaction, and a model that considers the NOᵪ reduction phenomenon under high load conditions were added. In addition, engine, vehicle, and driver models using the commercial GT-Power code, and an electronic control unit (ECU) model were combined to predict transient phenomena. These models were used to make transient predictions for the New European Driving Cycle (NEDC). The results were sufficiently accurate for practical use. Further, as detailed analysis of transient phenomena is also possible, UniDES can help to make the development easy.]]></description>
      <pubDate>Wed, 20 Apr 2022 16:15:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/1828169</guid>
    </item>
  </channel>
</rss>