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    <title>Transport Research International Documentation (TRID)</title>
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    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
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    <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>
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      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Probabilistic inference of gear and engine dynamics from GPS speed: a MOVES-compatible framework for drivetrain-aware CO2 emission modeling</title>
      <link>https://trid.trb.org/View/2697001</link>
      <description><![CDATA[Conventional emission frameworks such as the U.S. EPA’s MOVES estimate on-road emissions using operating-mode (OpMode) bins defined by vehicle speed, acceleration, and vehicle-specific power (VSP). However, real-world telematics datasets rarely include engine speed or gear information, even though drivetrain operation strongly governs fuel consumption and CO2 output. We propose a MOVES-compatible probabilistic framework that infers discrete gear states from GPS-based vehicle speed using a Markov-switching dynamic regression model. The inferred posteriors P(st=j) act as latent variables that (i) stabilize OpMode assignment through gear-consistent filtering, (ii) correct drivetrain-induced bias in emission factors, and (iii) propagate drivetrain uncertainty into emission estimates. Across six gasoline vehicles (Class I–II, 5–10 gears) equipped with portable emission measurement systems (PEMS), conditioning on inferred gear states reduced within-OpMode CO2 variance, lowered second-by-second MAE by 2–11%, and improved R2 by 5–15% relative to standard MOVES binning. Requiring only GPS speed and fewer than 50 parameters per vehicle, the method remains fully interoperable with MOVES while substantially enhancing its physical interpretability and uncertainty awareness. This study demonstrates a scalable pathway toward drivetrain-informed, uncertainty-aware microscale CO2 inventories. Code is available at: https://github.com/Chogaliu/probabilistic-inference-of-gear-and-engine-dynamics-from-GPS-speed.]]></description>
      <pubDate>Thu, 30 Apr 2026 16:39:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2697001</guid>
    </item>
    <item>
      <title>Hybrid Power Units (HPUs) for eVTOL Urban Air Mobility (UAM) Vehicles: A Conceptual Analysis and Future Research Directions</title>
      <link>https://trid.trb.org/View/2659191</link>
      <description><![CDATA[This article explores hybrid power systems (HPSs) as a promising solution to enhance payload (PL) capacity and flight endurance (FE), promoting the adoption of electric vertical takeoff and landing (eVTOL) technology for urban air mobility (UAM). First, a review of relevant aviation technologies, including fully electric and hybrid eVTOL vehicles, is conducted. Second, an algorithm is introduced, systematically utilizing key performance metrics such as maximum takeoff weight (MTOW), power-to-MTOW, fuel weight (FW), FE, and total energy (TE) generation. A new metric, usable weight (UW), is proposed to quantify the increase in FE or PL capacity. Third, five distinct hybrid power unit (HPU) case studies are analyzed based on the proposed algorithm, incorporating a supercar piston engine (PE), a turboshaft engine (TSE), a Wankel engine, a fixed-wing adapted engine, and a new proposed engine. Each HPU is virtually run with a predefined flight profile. This analysis estimates FE, TE, and total FW, highlighting that the proposed PE represents a tradeoff between power, weight, and fuel efficiency. Fourth, a comparative framework evaluates the TE output of five HPUs and a fuel cell (FC)–battery hybrid system from the literature, each with a maximum power output of 450 kW, for a 2000-kg MTOW eVTOL and a powertrain weight limit of 560 kg (including fuel). Fifth, seven commercial aircraft PEs were assessed for their suitability as prime movers in eVTOL UAM applications. The analysis identified only one candidate engine, with a power-to-weight ratio (PWR) of 1.4 kW/kg, capable of delivering FE of ≥ 1 h. Finally, virtual aviation engines with power outputs up to 450 kW (dual 225-kW configuration) and PWR spanning 1–3 kW/kg were investigated. Results demonstrate that engines with PWR  ≤ 1.1 kW/kg fail to satisfy the minimum power density requirements for sustained eVTOL FE (≥ 1 h). By establishing a systematic framework for HPU prime mover selection, this study advances the design of next-generation eVTOLs for UAM with medium-to-long endurance capabilities while simultaneously providing actionable insights to connect academic research with industrial development.]]></description>
      <pubDate>Wed, 29 Apr 2026 16:50:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659191</guid>
    </item>
    <item>
      <title>Cost-benefit analysis methodology for new rail vehicle concepts with alternative powertrain systems</title>
      <link>https://trid.trb.org/View/2657086</link>
      <description><![CDATA[There is currently an increased interest in the reactivation of secondary railway lines in Germany. These lines are mostly in bad condition and are not electrified. As diesel-powered vehicles are to be phased out in the future, in order to reduce greenhouse gas emissions, emission-free vehicles are required.Market available vehicles are too large and not suited to the needs of low-frequency secondary lines. Due to the high cost of track electrification, vehicles with alternative drive systems are brought into focus. In addition, high infrastructure costs are incurred for track reactivation.The aim of this work is to develop a comprehensive evaluation approach for a cost-benefit analysis to analyse the economic viability of low frequented railway lines and lines to be reactivated. The advantages of small rail vehicles were discussed on the basis of two tracks to be reactivated and two lines currently in operation. Based on these operational scenarios, cost analyses and cost-benefit analyses were carried out using four different types of vehicles. A sensitivity analysis was used to analyse the impact of passenger utilization and reactivation costs on economic viability.The results show the significant influence of infrastructure-related costs associated with reactivating tracks, as well as track access charges and station fees for tracks in operation. Small vehicle concepts such as rail buses can contribute to cost-efficient operation on low-frequency secondary lines. This study aims to contribute to a better understanding of the factors influencing the economic viability of rail operations on lines with low passenger utilization rates.]]></description>
      <pubDate>Tue, 28 Apr 2026 11:20:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2657086</guid>
    </item>
    <item>
      <title>A Comparative Assessment of Fuel Cell and Battery Powertrains for
                    Formula Student Electric Applications</title>
      <link>https://trid.trb.org/View/2696794</link>
      <description><![CDATA[The organizers of the most prominent Formula Student competitions have recently                     initiated a preliminary feasibility study on the application of hydrogen-based                     propulsion technologies in future single-seater race vehicles. These include                     electric powertrains with electrochemically converted hydrogen in fuel                     cell–powered vehicles, competing within the electric championship league. Based                     on the initial set of regulations, this study presents a model-based comparison                     between battery-powered (BEVs) and fuel cell–powered electric vehicles (FCVs)                     for Formula Student. The analysis is conducted using energy, power, and                     efficiency metrics from four candidate models of propulsion systems, implemented                     in an open and publicly available MATLAB script: two BEVs with varying battery                     capacities, and two FCVs employing different hybridization strategies. The aim                     of this study is to pinpoint and quantify the advantages and disadvantages of                     each technology for the Formula Student use case, and to identify the optimal                     solution combining the different requirements of maximum acceleration and                     endurance race.]]></description>
      <pubDate>Mon, 27 Apr 2026 16:13:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2696794</guid>
    </item>
    <item>
      <title>Enhancing MPPT efficiency in wind conversion systems: comparison between PI, sliding mode control, and backstepping control</title>
      <link>https://trid.trb.org/View/2652090</link>
      <description><![CDATA[A maximum power point tracker (MPPT) integrating a speed control strategy becomes more essential for maximising wind energy capture from a wind turbine, especially under fluctuating wind conditions. This paper aims to study, compare, and evaluate three different control strategies: proportional-integral (PI), sliding mode control (SMC), and backstepping control (BSC) by testing them under step and real wind speed profiles using MATLAB/Simulink software. The effectiveness of tracking the reference values of generator speed (Ωg), tip speed ratio (λ), produced power, and power coefficient (Cp) for the three control strategies is compared in terms of response time, stability, and error indices (IAE and ISTE) against wind variations. After theoretical formulation and implementation of each control strategy, results demonstrate that the PI controller provides acceptable performance under nominal conditions, but struggles with the nonlinearities of WECS and strong disturbances. SMC effectively handles strong wind fluctuations, but suffers from chattering. While, BSC provides enhanced stability and adaptability even it requires a more complex design.]]></description>
      <pubDate>Mon, 27 Apr 2026 14:59:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2652090</guid>
    </item>
    <item>
      <title>Opportunities and Challenges for Polymeric Insulation Materials in Electric Vehicles Powertrains</title>
      <link>https://trid.trb.org/View/2694683</link>
      <description><![CDATA[The electrification of the automotive industry has intensified the focus on advanced insulation materials for electric vehicle (EV) powertrains, which include batteries, electric motors (EMs), and power electronics. These components operate under high voltages, elevated temperatures, and compact designs, presenting unique challenges for insulation systems. As polymeric insulation materials are pivotal in EV powertrains, offering benefits such as lightweight construction, design flexibility, and cost-effectiveness, this review examines the current state of polymeric insulation materials used in EV powertrains, highlighting recent advancements, persistent challenges, and future directions. Their inherent electrical insulation properties are essential for ensuring safety and efficiency in high-voltage systems. However, these materials face significant challenges, including exposure to elevated temperatures, mechanical stresses, and potential chemical degradation over time. Innovative materials such as polyimides (PI), polyether ether ketones (PEEK), and polyvinylidene fluoride (PVDF), modified with inorganic nanofillers like SiO₂, Al₂O₃, and BaTiO₃, have demonstrated enhanced dielectric strength, thermal stability, and mechanical resilience. Additionally, advancements in polymer chemistry and modification technologies present opportunities to enhance performance in desired properties. This review emphasizes the increasing stress on insulation systems driven by high-power-density designs, elevated electrical stresses, and operating temperatures, as well as the adoption of wide-bandgap (WBG) semiconductors. Key challenges include partial discharge (PD) activity, thermal management, and long-term environmental exposure. Future perspectives underscore the need for multifunctional materials that combine electrical, thermal, and mechanical performance while addressing polymeric insulation materials for EV powertrains, ensuring safety, performance, and environmental sustainability and scalability simultaneously.]]></description>
      <pubDate>Mon, 27 Apr 2026 14:58:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2694683</guid>
    </item>
    <item>
      <title>Short Review: Optimization Formulations for Enhancing Electric and Hybrid Electric Powertrain Performance</title>
      <link>https://trid.trb.org/View/2580013</link>
      <description><![CDATA[This paper presents a concise review of objective function formulations employed for optimizing the sizing of powertrain components in electric and hybrid electric powertrains, within the scope of the EU-funded Horizon Europe ESCALATE project. The objective is to analyze the techniques used to achieve improved performance and efficiency for powertrains. Efficient utilization of available energy sources, improved range, reduced GHG emissions, and overall system performance are crucial goals. Objective function formulations serve as essential tools for achieving these objectives. A broad spectrum of optimization techniques is employed in the objective function formulations for electric and hybrid electric vehicles (EVs/HEVs). These include multiple objective functions which simultaneously optimize conflicting/complementary goals, allowing for trade-offs and Pareto-optimal solutions. The review explores the key parameters considered during the optimization process, with a focus on the sizing of electric powertrain components. The goal is to identify the optimal combination of these variables to achieve an optimal powertrain design that maximizes energy efficiency while minimizing cost and environmental impact. Furthermore, the review delves into the challenges and prospects of objective function formulations in the context of electric and hybrid electric powertrains, including the potential to focus on sustainability of designs which has not previously been researched.]]></description>
      <pubDate>Mon, 27 Apr 2026 14:55:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2580013</guid>
    </item>
    <item>
      <title>Design Optimization of Electric Vehicle Drivetrains Using Surrogate Modeling Frameworks</title>
      <link>https://trid.trb.org/View/2658687</link>
      <description><![CDATA[In early phases of electric vehicle development, powertrain design requires a system-level approach with sufficiently accurate component models. This paper presents optimization frameworks for electric motor sizing and transmission gear ratio selection, focusing on electric motor modeling. Specifically, we express motor losses and operational limits as functions of scaling factors, which proportionally adjust a reference design in axial and radial directions. Thereby we apply surrogate modeling techniques in three ways on a computationally expensive high-fidelity motor design tool. The first framework integrates Bayesian optimization with the high-fidelity tool and drive cycle simulation in the loop. The second and third frameworks use scalable motor models in a static optimization problem, employing convex and Gaussian radial basis function surrogate models, respectively. We demonstrate these methods in a case study for an electric crossover SUV, optimizing motor size and gear ratio while meeting performance requirements. Validation shows that the drift in energy consumption below 0.6 %. The resulting motor designs and gear ratios differ minimally across frameworks, with only a 0.3 % energy consumption improvement favoring the radial basis function model. This suggests that all three frameworks provide effective optimization strategies with little deviations in the design and the energy efficiency between the frameworks.]]></description>
      <pubDate>Thu, 23 Apr 2026 13:54:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658687</guid>
    </item>
    <item>
      <title>Advancing Electric Drive Unit Design with Smoothed Particle Hydrodynamics</title>
      <link>https://trid.trb.org/View/2692156</link>
      <description><![CDATA[Thermal and lubrication management is critical for the performance characteristics of Electric Drive Units (EDUs) in electrified powertrains. Accurate assessment of lubrication flow, particularly in terms of wetting behavior and churning losses, is essential for optimizing EDU performance across various driving conditions. This study presents a comprehensive numerical investigation of lubrication flow behavior within an EDU using an advanced Smoothed Particle Hydrodynamics (SPH) method. The mesh-free SPH approach provides significant advantages in modeling intricate oil dynamics, such as oil splashing, and the behavior of oil in contact with rotating components. The primary focus of this study is to investigate the phenomena of oil splashing, wetting behavior characterized by the Wetting Fraction(WF), and churning losses within the gearbox environment. Key flow characteristics such as oil distribution, particle trajectories, torque resistance due to fluid drag, and oil volume fraction are analyzed under varying operational parameters. The EDU design is then refined through multiple design iterations using the SPH method to enhance splashing characteristics and improve WF for critical components. This work demonstrates the effectiveness of the SPH method as a robust virtual prototyping tool for next-generation EDU lubrication system design.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692156</guid>
    </item>
    <item>
      <title>AI-Powered Requirements Engineering &amp; Design Optimization of Electric Powertrains</title>
      <link>https://trid.trb.org/View/2692120</link>
      <description><![CDATA[The development of electric vehicle powertrains is driven by diverse and often conflicting requirements. In early development phases, these requirements are often vague, incomplete, continuously refined and subject to change as development progresses. Moreover, powertrain designs must be competitive regarding multiple key performance indicators (KPIs) such as performance, cost, energy efficiency, and package integration. This challenges engineers to concurrently develop the powertrain design alongside the requirements on which the design is based on. Managing this combination of uncertain requirements and multi-KPI design optimization represents a complex challenge in automotive engineering. The present work introduces a requirements engineering approach based on OPED (Optimization of Electric Drives). OPED digitalizes the transition from requirements to technical solutions by integrating parametric system models with an AI-based evolutionary optimization algorithm. This enables systematic exploration of trade-offs, robust handling of uncertainties, and the effective specification of requirements. The outcome is a Pareto front of optimal and feasible powertrain solutions, providing engineers and decision makers with a quantitative basis for requirement definition and product design in the development process. A case study demonstrates the approach by determining the optimal requirement regarding the maximum speed of an electric passenger car. OPED evaluates the influence of the maximum speed requirement on cost, energy efficiency, and ensures a suitable package integration. A Pareto front is generated that contains optimal powertrain solutions alongside the respective maximum speed requirement. Results show that OPED effectively combines requirements engineering and system design optimization, thereby supporting agile and robust powertrain development.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692120</guid>
    </item>
    <item>
      <title>A New Approach for SI Combustion Modeling to Address the Upcoming Fuel Diversity Scenario</title>
      <link>https://trid.trb.org/View/2692017</link>
      <description><![CDATA[Climate change concerns demand a drastic reduction in CO2 emissions, tending to what is called carbon neutrality. Even if political guidelines promote electrification, considering the transportation sector, not all applications have the same requirements and boundary conditions, and hence, their optimal solution is not necessarily the same. In this context, in parallel with pure electric powertrains, the internal combustion engine (ICE) still has a relevant role to play, mainly in hybrid powertrains, working together with an electrical motor. In this hybridization context, the spark-ignition (SI) engine uses to be the most adopted solution because of its lower cost and complexity. Consequently, it can be concluded that the SI engine will still play a significant role in the near future. However, when ICEs are considered, the search for carbon neutrality requires the use of fuels other than fossil fuels. At this point, many alternatives arise, from biofuels to synthetic e-fuels, or even H2. This extreme variety of fuels introduces complexity from the combustion modeling point of view.This study proposes a methodology for developing a 0D combustion predictive model that characterizes the engine flame front effective area (FFEA) of the engine based on a reference fuel (gasoline) and extrapolates it to other engine conditions and/or to other fuels (e.g., H2), predicting the in-cylinder pressure evolution. To allow the prediction for any other fuel, the only requirement is to know the laminar flame speed of that fuel at different operating conditions. The results show that, for different engine conditions, the proposed methodology could predict the in-cylinder pressure with an average error of ~2.1% for the reference fuel and an average error of ~2.6% for Hydrogen (H2). These findings indicate that the proposed methodology has good performance and could be used for analyses requiring a limited number of experiments and a short computational time.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692017</guid>
    </item>
    <item>
      <title>Backlash Evasion Strategy for Enhancing Vehicle Cornering Performance</title>
      <link>https://trid.trb.org/View/2692016</link>
      <description><![CDATA[This study presents a torque distribution control strategy for EVs with e4WD powertrain to overcome the trade-off between ensuring vehicle acceleration and deceleration responsiveness and mitigating backlash shock in the driving system. The deterioration of the drivability which occurs from the intrinsic hardware characteristics of the drivetrain is prevented by designing a response-priority drive mode in which neither front or rear motor torque is allowed to change its sign. Instead, in such drive mode, the front motor torque is only allowed to perform regenerative braking while the rear motor torque is only allowed to produce positive acceleration torque. In order to avoid sacrificing the maximum acceleration by applying such strategy, the mode transition function is implemented as well. In addition, in order to prevent backlash impact due to drivetrain compliance, variable offset torque based on drivetrain compliance model is evaluated in real time and applied to each motor command generation strategy. The enhancement of vehicle drivetrain responsiveness directly leads to improved track driving performance, particularly for the neutral-balance phase during harsh cornering. The effectiveness of the suggested driveline torque distribution method is verified using an actual vehicle driven on the race track, and the vehicle responsiveness followed by track driving performance indices are numerically assessed for comparison.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692016</guid>
    </item>
    <item>
      <title>Methodology for Generating Off-Road Tracks for Powertrain Simulation of Off-Road Passenger Vehicles</title>
      <link>https://trid.trb.org/View/2692014</link>
      <description><![CDATA[For off-road driving, particularly on steep grades and over barriers, the engine torque is a key design criterion of off-road vehicles. In conventional powertrains with combustion engines, mechanical all-wheel-drive systems combined with differential locks are used to distribute the torque demand between the front and the rear axle based on wheel-specific traction. With the growing market share of electric powertrains, off-road applications are becoming increasingly relevant for electric passenger cars. In comparison to conventional powertrains, electric all-wheel-drive configurations do not have a mechanical torque transfer between the two axles. If one axle experiences low traction, the second axle can rely on its own torque capability only. Transfer of unused torque of the slipping axle to the other one is not possible. The challenge, therefore, is to specify the right torque requirements for each axle for off-road driving while avoiding over-dimensioning and high powertrain costs. The torque requirements must be defined in the very early stages of development, when real-world measurements are not available. As a result, these definitions must be based on simulation.This paper presents a simulation approach to address this engineering challenge. A key aspect is the modeling of the representative off-road track as input for the simulation. A method of track generation was developed by using real vehicle measurement data from off-road tracks, combined with GPS and road information. The virtual track modelling process was designed to match the overall torque behavior observed in both simulation and measurement to confirm a validated and trustful simulation approach. The validation of the approach will be shown.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692014</guid>
    </item>
    <item>
      <title>Simultaneous Reduction of NOx and Fuel Consumption for Off-Road Powertrains</title>
      <link>https://trid.trb.org/View/2692007</link>
      <description><![CDATA[Simultaneously reducing criteria pollutants and fuel consumption is important for clean air and improving vehicle total cost of ownership. The goal of this effort was focused on a 90% NOx reduction and 10% fuel savings for an off-road 407 kW diesel engine. The baseline was a production Fiat Powertrain 13L engine and aftertreatment system meeting 0.4 g/kW-hr NOx. The baseline system was quantified over the NRTC, RMC, new low load cycle and five field cycles. A next generation engine was built incorporating several fuel-efficient design features, including a higher compression ratio, increased fuel-rail pressure, low-friction piston rings, and a high-efficiency variable-geometry turbocharger. Cylinder deactivation and EGR pump technologies were added to this engine as well. The combination was optimized prior to adding advanced aftertreatment systems, showing the trade-off of engine out NOx and exhaust temperature. Two next-generation catalyst technologies were employed into a LO-SCR plus main SCR system, both with and without an electric heater upstream of the LO-SCR. These catalysts were hydrothermally aged to simulate significant field use. Dual SCR dosing with newly developed controls played a critical role in achieving the proper split between the upstream LO-SCR and the downstream main SCR. Adding a next generation mixer for the downstream SCR proved essential in obtaining the final results. The optimal configuration required adding an electric heater to elevate the exhaust temperature at the LO-SCR for early cycle NOx reduction. The final results showed a 94.8% NOx reduction and 15.7% fuel savings on the composite NRTC.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692007</guid>
    </item>
    <item>
      <title>Thermal Management of Electric Microtruck Motors</title>
      <link>https://trid.trb.org/View/2691817</link>
      <description><![CDATA[This study investigates the gradeability performance of an L7e-class electric                     micro truck from both vehicle dynamics and thermal perspectives. A 1D simulation                     model (Amesim) was developed and validated with multiple test results. Using                     inputs such as motor characteristics, drivetrain configuration, and vehicle                     mass, the model analyzed vehicle performance on a 20% gradient, calculating the                     required torque, achievable motor speed, and corresponding vehicle speed.                     Furthermore, gradeability limits were evaluated, and the effects of gear ratio                     and airflow rate around the air-cooled motor on both gradeability and thermal                     behavior were examined. The findings provide practical insights for improving                     the powertrain and cooling system design of lightweight electric vehicles. The                     results showed that selecting an appropriate gear ratio can enable the motor to                     operate more efficiently under demanding driving conditions. A 20% increase in                     the gear ratio was found to delay motor heating by up to 10%. However, its                     effect was observed to be negative under driving conditions such as WLTP, which                     require variable RPM demand.]]></description>
      <pubDate>Tue, 14 Apr 2026 14:56:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691817</guid>
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