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    <title>Transport Research International Documentation (TRID)</title>
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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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    <item>
      <title>Kentucky Model Minimum Uniform Crash Criteria (MMUCC) Sixth Edition Report Card</title>
      <link>https://trid.trb.org/View/2642341</link>
      <description><![CDATA[The National Highway Traffic Safety Administration published the Model Minimum Uniform Crash Criteria (6th Edition) (MMUCC) in 2024. This updated version of the MMUCC contains recommendations for improving and standardizing electronic crash data. Its guidelines address the collection and presentation of 105 data elements and 1,056 data attributes. To facilitate a possible redesign of Kentucky’s crash reporting process, Kentucky Transportation Center (KTC) researchers reviewed the updated criteria and evaluated the degree to which the state complies with its recommended methods of gathering, storing, and presenting crash data. At the level of data elements, Kentucky is 25 percent compliant; for data attributes the state is 60 percent compliant. Adopting or more fully implementing several MMUCC recommended best practices can improve Kentucky’s crash reporting system: (1) Develop a web- or wizard-based interface tailored to the state’s unique reporting needs; (2) Link the crash reporting system to other databases so the system can retrieve data from external sources and use them to autopopulate data fields; (3) Structure lists and menus so they are intuitive to navigate and consistent with user expectations; (4) Incorporate visual aids and organize crash reports so their flow aligns with how officers mentally process crash information; (5) Use design features and logic structures that minimize errors and provide clear instructions for correcting errors; and (6) Involve system users in the design, construction, and testing of the crash reporting system so they can provide feedback to improve its functionality. Taking these high-level best practices as a starting point, the report advances multiple strategies to enhance the user interface and functionality of the state’s crash reporting system.]]></description>
      <pubDate>Tue, 23 Dec 2025 13:31:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2642341</guid>
    </item>
    <item>
      <title>Minimale Betriebsbedingungen für Straßentunnel. Leitfaden</title>
      <link>https://trid.trb.org/View/2640180</link>
      <description><![CDATA[Im Forschungsprojekt RITUN wurden erste Ansätze zur Verbesserung der Resilienz von Straßentunneln entwickelt. Erkannt wurde, dass Maßnahmen, die der beschleunigten Wiederherstellung der (vollen) Funktionalität von Tunneln beziehungsweise der Reduktion der Wiederherstellungsdauer dienen, in der Praxis stark unterrepräsentiert sind. Mit der Festlegung von sogenannten minimalen Betriebsbedingungen sollen zulässige Abweichungen vom Regelbetrieb definiert werden, die es Tunnelbetreibern ermöglichen, bei einzelnen technischen Störungen oder Anlagenausfällen einen Straßentunnel unter bestimmten Bedingungen weiter zu betreiben und den Verkehrsfluss im besten Fall vollständig aufrecht zu erhalten. Im Hinblick auf eine möglichst einheitliche Herangehensweise in Deutschland wurde im Rahmen des Projekts 15.700/2022/ERB der Leitfaden zur Festlegung von minimalen Betriebsbedingungen für Straßentunnel erarbeitet.]]></description>
      <pubDate>Thu, 11 Dec 2025 10:28:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2640180</guid>
    </item>
    <item>
      <title>An adaptive energy management control method for Plug-in fuel cell electric buses operating on non-fixed bus routes</title>
      <link>https://trid.trb.org/View/2625538</link>
      <description><![CDATA[Plug-in fuel cell electric buses (PFCEBs) have the potential to effectively reduce energy usage through efficient energy management. However, when PFCEBs operate on non-fixed bus routes, noise factors such as driving cycle, stochastic vehicle mass, and driving distance make it challenging to achieve optimal fuel economy. To tackle this issue, this study introduces an adaptive energy management control method for PFCEBs operating on non-fixed bus routes, considering the aforementioned factors. Firstly, an adaptive energy management control method based on the algorithm of Pontryagin’s Minimum Principle (PMP) is proposed to be integrated into the energy management system, allowing for real-world adaptive control. Secondly, a Design for Six Sigma (DFSS) methodology is proposed to address the noise disturbance problem caused by the driving cycle, stochastic vehicle mass, and driving distance. The main objective of DFSS is to find the “flat” zone of the design space (constituted by co-state and normalized distance), whilst minimizing the mean hydrogen consumption and its standard deviation. Validation results from Monte Carlo Simulation (MCS) demonstrate the effectiveness and applicability of the DFSS methodology in the energy management design for PFCEBs operating on non-fixed bus routes. Furthermore, simulation results indicate that the proposed robust co-state design method can achieve fuel economy comparable to that of an off-line PMP control strategy. In comparison to the rule-based strategy, the fuel economy improves by an average of 18.01%, 20.06%, and 18.91% for bus route 1, 2, and 3, respectively.]]></description>
      <pubDate>Tue, 02 Dec 2025 09:58:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2625538</guid>
    </item>
    <item>
      <title>Genetic Programming-Based Energy Management Strategy for Fuel Cell Vehicles Considering Temperature and Aging Factors</title>
      <link>https://trid.trb.org/View/2603947</link>
      <description><![CDATA[Combining fuel cells and lithium-ion batteries (LiBs) in a hybrid vehicle presents a promising automotive energy supply technology. The durability of power components, fuel consumption, and safety all depend on effective energy management strategies (EMSs). This article is the first to use the context-aware typed multipopulation genetic programming (CAT-MPGP) algorithm to extract interpretable rules for the offline near-optimal solution of Pontryagin’s minimum principle (PMP), which are applied to the real-time EMS considering system aging factors. First, this article integrates the thermal model of LiBs into the system model to balance system lifespan and energy consumption. The PMP is employed to find the offline near-optimal power allocation results. Second, Bagging method is used to construct the driving condition recognizer, and the interpretable data relationship between driving information and power distribution is mined by MPGP algorithm, and embedded in real-time EMS. Finally, simulations using a real driving route and two standard periodic datasets are conducted as the test sets to compare the results of different EMSs. Hardware-in-the-loop (HIL) test is adopted to verify the feasibility of the controller in practical application. The proposed method achieves a well-performing real-time EMS, which reaches 94.6% of the offline reference results.]]></description>
      <pubDate>Mon, 24 Nov 2025 10:24:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2603947</guid>
    </item>
    <item>
      <title>Über die Herkunft des Rückrechnungsverbots bei einer gemessenen Blutalkoholkonzentration von unter 0,15 Promille</title>
      <link>https://trid.trb.org/View/2596429</link>
      <description><![CDATA[Zur Bestimmung der Mindest-Blutalkoholkonzentration (BAK) zum Vorfallzeitpunkt darf bei einer gemessenen BAK von kleiner 0,15 Promille nicht zurückgerechnet werden. Diese Regel wird in der Gutachtenpraxis der forensischen Alkohologie beachtet und findet sich in zahlreichen Lehrbüchern wieder. Ziel der Arbeit war, den experimentellen Ursprung dieser Regel mithilfe einer Literaturrecherche zu ergründen. Falls möglich, sollte versucht werden, aus den publizierten Rohdaten vertretbare Verfahren zu formulieren, um Rückrechnungen der Mindest-BAK zu realisieren, die keinesfalls einen Nachteil für den Probanden bedingen würden. Die Literaturrecherche erfolgte netzbasiert unter Verwendung von PubMed sowie gängiger Suchmaschinen. Darüber hinaus wurden in der Bestandsbibliothek des Frankfurter Instituts für Rechtsmedizin Zeitschriften sowie Lehr- und Handbücher systematisch durchsucht. Die Lehrbücher behandeln die Thematik heterogen, ein großer Anteil äußert sich gar nicht dazu. Insgesamt existieren offenbar nur wenige experimentelle Grundlagen, diese gehen zurück bis in das Jahr 1933. Mehrere Recherchewege führten auf eine Dissertation aus dem Jahr 1967: Scheer begründet die 0,15-Promille-Grenze damit, dass bei einer BAK von kleiner 0,10 Promille eine veränderte Abbaukinetik vorliegt. Aufgrund der zur damaligen Zeit bestehenden methodischen Schwächen und daraus resultierenden Unsicherheiten schlägt er eine Art Sicherheitszuschlag von 0,05 Promille und dementsprechend vor, dass unterhalb einer gemessenen BAK von 0,15 Promille nicht zurückgerechnet werden soll. Es erscheint angesichts des aktuellen Stands der Technik angemessen, die Adäquanz des Rückrechnungsgrenzwerts von 0,15 Promille neu zu betrachten und eine Reduktion dieser Schwelle zu evaluieren. Da sich aus der Literatur keine verwendbaren Rohwerte extrahieren ließen, sind neue Trinkversuche mit dem Fokus der Ethanol-Abbaukinetik unterhalb von 0,15 Promille notwendig. (A) ABSTRACT IN ENGLISH: Retrograde extrapolation is inadmissible in estimating the minimum blood alcohol concentration (BAC) at the time of a prior incident in cases with a measured BAC of less than 0.15 per mille at the time of sampling. This rule is obeyed in practice by experts in forensic alcohology and can be found in numerous text books. The goal of this study was to elucidate the experimental basis for this rule through a literature review. Another tentative goal was to see if, on the basis of the published data, tenable procedures could be formulated that would allow retrograde extrapolation for the estimation of the minimum BAC without any detriment to the subject. An online literature search was conducted using PubMed and other established search engines. In addition, the in-house library (i.e. journals, text books, reference manuals) at the Institute for Legal Medicine in Frankfurt was systematically searched for pertinent information. The topic was found to be heterogeneously covered in text books, with many textbooks not covering the topic at all. Overall, the threshold rule appears to be based on only few experiments, dating as far back as 1933. Various search paths led to a dissertation on the topic, published by Scheer in 1967: Herein, Scheer justified the 0.15 per mille threshold rule for retrograde extrapolation with the altered alcohol elimination kinetics that were observed for BACs of less than 0.10 per mille. Due to the methodological limitations of the time and the resulting uncertainties, Scheer suggested adding a „safety margin" of 0.05 per mille to this value and that, hence, retrograde extrapolation should be considered invalid for measured BACs of less than 0.15 per mille. In Iight of the current advances in technology, it seems appropriate to reappraise the adequacy of the 0.15 per mille BAC threshold value for retrograde extrapolation and to evaluate whether the number should be lowered. Since the literature did not yield usable data in this respect, new drinking experiments focusing on the kinetics of alcohol elimination at concentrations below 0.15 per mille are called for. (A)]]></description>
      <pubDate>Mon, 15 Sep 2025 10:27:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2596429</guid>
    </item>
    <item>
      <title>Real-time coordinated control strategy for the hybrid electric propulsion system of a flying car with model adaptation and game theory</title>
      <link>https://trid.trb.org/View/2570790</link>
      <description><![CDATA[Coordination among the multiple power components is essential for the hybrid electric propulsion systems since the decision made by one impacts the state and decisions of others due to the coupling mechanical and electrical dynamics. Traditional model-based approaches with weight-sum objectives may result in misleading optimization or even unstable situations because of the inherent poor scalability and synergy. To overcome this issue, this paper proposes a novel game theory-based control strategy with model adaptation mechanism for the hybrid electric flying car to enhance the control performance, system stability, and robustness. Firstly, a control-oriented model of the system is derived with the utilization of the recursive least square parameter estimation method. The non-cooperative game framework is then established with the turboshaft engine subsystem and electric supply subsystem treated as two independent players. The performance of Nash equilibrium solutions with closed-loop and open-loop information structures are investigated where the problem is iteratively solved by exploiting Pontryagin’s Minimum Principle and dynamic programming, respectively. The simulation results demonstrate that the game theory-based controllers can outperform MPC with the weight-sum objectives in terms of control efficiency and robustness improvement and the game theory-based controller with open-loop information structure shows excellent computation efficiency. Moreover, the result of the hardware-in-the-loop experiment demonstrates the real-time performance of the proposed controller.]]></description>
      <pubDate>Fri, 18 Jul 2025 09:05:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2570790</guid>
    </item>
    <item>
      <title>Study on energy management strategy of fuel cell patrol vehicle hybrid power system</title>
      <link>https://trid.trb.org/View/2550982</link>
      <description><![CDATA[The slow dynamic response of fuel cells has limited the development of fuel cell vehicles to a certain extent, and reasonable energy management strategies are one of the critical issues that need to be resolved to promote the industrialization of fuel cell vehicle. Based on the complex operating conditions of fuel cell patrol vehicles, this paper establishes a model of the vehicle and the component, exploring the impact of energy management strategies on vehicle performance. The simulation results indicate that Fuzzy logic control (FLC) causes significant fluctuations in fuel cell output power under both light and heavy load conditions. Pontryagin minimum principle (PMP) and Adaptive Pontryagin minimum principle (APMP) maintain a constant lower output power near a fixed value and change slightly according to demand. Comprehensively analyzing the two operating conditions, PMP and APMP can stably maintain State of charge (SOC) around the reference value and converge toward the reference value, and the SOC of the FLC strategy is generally lower than the reference value. Under light and heavy load conditions, the average efficiency of the APMP fuel cell system reaches 50.44% and 49.58%, respectively, which is similar to the PMP strategy and is respectively improved by 0.67% and 0.32% compared to the FLC strategy.]]></description>
      <pubDate>Mon, 09 Jun 2025 14:49:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2550982</guid>
    </item>
    <item>
      <title>Investigation on the Influence of Clutches on the EVT-Based HEV Powertrain by Efficient DP-PMP</title>
      <link>https://trid.trb.org/View/2511979</link>
      <description><![CDATA[This article studies the influence of additional clutches on the fuel-saving performance of a hybrid electric vehicle (HEV) with a double-rotor electrical variable transmission (EVT). One clutch (C1) is introduced on the engine shaft to fix it to the ground, which enables two extra pure electric driving modes. The other (C2) is placed between the two rotors of the EVT to connect them together, resulting in the parallel hybrid and engine directly driving mode. To compare with the no-clutch EVT powertrain, a computationally efficient optimization framework, which combines dynamic programming (DP) and Pontryagin’s minimum principle (PMP), is employed to search the optimal fuel economy and mode controls under 15 various driving cycles. A bi-level formulation, containing a lower level that solves the underlying static optimization problems of components operation in combined driving modes and an upper level minimizes the Hamiltonian values, is incorporated into the optimization framework to further decrease the computation burden. The comparative analysis on three EVT powertrains, one without any clutches, one with two clutches, and one with only C1, demonstrates that C1 could gain at most 5.66% of fuel saving under urban cycles, while the contribution of C2 is trivial to whichever driving conditions.]]></description>
      <pubDate>Fri, 23 May 2025 15:34:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2511979</guid>
    </item>
    <item>
      <title>Electric vehicle eco-driving strategy at signalized intersections based on optimal energy consumption</title>
      <link>https://trid.trb.org/View/2420877</link>
      <description><![CDATA[Electric vehicles (EVs), which are a great substitute for gasoline-powered vehicles, have the potential to achieve the goal of reducing energy consumption and emissions. However, the energy consumption of an EV is highly dependent on road contexts and driving behavior, especially at urban intersections. This paper proposes a novel ecological (eco) driving strategy (EDS) for EVs based on optimal energy consumption at an urban signalized intersection under moderate and dense traffic conditions. Firstly, the authors develop an energy consumption model for EVs considering several crucial factors such as road grade, curvature, rolling resistance, friction in bearing, aerodynamics resistance, motor ohmic loss, and regenerative braking. For better energy recovery at varying traffic speeds, the authors employ a sigmoid function to calculate the regenerative braking efficiency rather than a simple constant or linear function considered by many other studies. Secondly, the authors formulate an eco-driving optimal control problem subject to state constraints that minimize the energy consumption of EVs by finding a closed-form solution for acceleration/deceleration of vehicles over a time and distance horizon using Pontryagin’s minimum principle (PMP). Finally, the authors evaluate the efficacy of the proposed EDS using microscopic traffic simulations considering real traffic flow behavior at an urban signalized intersection and compare its performance to the (human-based) traditional driving strategy (TDS). The results demonstrate significant performance improvement in energy efficiency and waiting time for various traffic demands while ensuring driving safety and riding comfort. The authors' proposed strategy has a low computing cost and can be used as an advanced driver-assistance system (ADAS) in real-time.]]></description>
      <pubDate>Mon, 30 Sep 2024 18:17:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2420877</guid>
    </item>
    <item>
      <title>Event-Driven Energy-Efficient Driving Control in Urban Traffic for Connected Electric Vehicles</title>
      <link>https://trid.trb.org/View/2402128</link>
      <description><![CDATA[The traffic light in urban areas dominates the traffic flow, resulting in variation of energy consumption of the vehicles involved. To mitigate the impact of traffic bias on the energy efficiency of electric vehicles (EVs), this article proposes an event-driven energy-efficient driving control (EEDC) strategy based on a receding horizon two-stage control framework, which harnesses the Internet of Vehicles to incorporate the traffic light and preceding vehicle for adaption of different driving scenarios. At the core of the first stage are the vehicle driving event classification rules, which classified the urban traffic scenarios into four events. This article contributes to empirical solutions on the design of the traffic scenario classifier considering conflict goals, including driving efficiency and safety. In the second stage, the speed trajectory in each driving event is optimized using Pontryagin’s minimum principle to reduce vehicle energy consumption. A real-time solution for the energy-efficient driving problem is derived with the consideration of vehicle dynamics, control input, and speed limit constraints. Finally, extensive simulations and road tests are conducted to evaluate the effectiveness of the EEDC. The results show that the EEDC is excellent in energy efficiency improvement over two benchmark strategies in different traffic scenarios while satisfying the constraints in inter-vehicle driving safety and travel time. Moreover, the road tests demonstrate that the EEDC is capable of energy saving in real-world driving.]]></description>
      <pubDate>Fri, 23 Aug 2024 15:26:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2402128</guid>
    </item>
    <item>
      <title>A Safety Requirements’ Adaptive NMPC Strategy for Electric Vehicle Stability Control With Computationally Efficient Optimization</title>
      <link>https://trid.trb.org/View/2394983</link>
      <description><![CDATA[Handling and stability are vital for vehicle safety, especially under extreme conditions, such as low friction surfaces, violent steering, and urgent acceleration/deceleration. Electric vehicles (EVs) are a promising way to improve the stability due to their rapid and accurate responses. However, vehicle states are influenced by highly coupled and nonlinear dynamics. The safety requirements are different under various conditions. To solve the above problems, a nonlinear model predictive control (NMPC)-based strategy is proposed for stability control. First, a 3-D stability space, which considers yaw, lateral, and longitudinal motions, is proposed to analyze the degree of vehicle stability under different conditions. Then, an adaptive control strategy is proposed to meet various safety requirements. Moreover, to improve the control performance under extreme conditions, a nonlinear vehicle dynamic model is adopted to predict the future states. To enable vehicular applications with low-cost hardware, a Pontryagins minimum principle (PMP)-based solving method is proposed for computationally efficient online optimization. Finally, hardware-in-loop experiments are conducted to check the effectiveness and superiority of the proposed method. The experimental results show that the proposed strategy has a better performance improving vehicle handling and stability under extreme conditions.]]></description>
      <pubDate>Thu, 22 Aug 2024 15:09:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2394983</guid>
    </item>
    <item>
      <title>Adaptive Optimization Control Strategy for Electric Vehicle Battery Thermal Management System Based on Pontryagin’s Minimal Principle</title>
      <link>https://trid.trb.org/View/2394973</link>
      <description><![CDATA[Excessive temperature affects battery aging and stability, causing a significant impact on the economy and safety of electric vehicles (EVs). The battery thermal management (BTM) system is the key to keeping the battery temperature suitable, but it also consumes considerable energy. To minimize energy consumption for maintaining the battery temperature in the optimal range, a novel adaptive Pontryagin’s minimum principle (PMP) optimization strategy based on velocity prediction is proposed in this article, which is achieved by online updating of the costate in the Hamiltonian function. A multimode velocity prediction model based on driving pattern recognition (DPR) is proposed to enhance the accuracy of the prediction. Moreover, the built self-learning Markov pattern recognizer distinguishes real-time driving segments into one of three predefined driving patterns, and the corresponding pattern velocity predictor is selected. The accuracy and effectiveness of the velocity predictor and driving pattern recognizer are verified. A comparison with the results is obtained by adopting typical controllers to indicate the feasibility and effectiveness of the proposed strategy. In addition, the BTM system energy consumption is evaluated in multiple test cycles and the results show that the energy consumption based on the proposed methods is reduced by 18.9%–24.9% which leads to considerable energy saving.]]></description>
      <pubDate>Fri, 16 Aug 2024 09:53:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2394973</guid>
    </item>
    <item>
      <title>Learning-Based Model Predictive Control for the Energy Management of Hybrid Electric Vehicles Including Driving Mode Decisions</title>
      <link>https://trid.trb.org/View/2408273</link>
      <description><![CDATA[This paper presents an online-capable controller for the energy management system of a parallel hybrid electric vehicle based on model predictive control. Its task is to minimize the vehicle's fuel consumption along a predicted driving mission by calculating the distribution of the driver's power request between the electrical and the combustive part of the powertrain, and by choosing the driving mode, which depends on the vehicle's clutch state. The inclusion of the clutch state in a model predictive control structure is not trivial because the underlying optimization problem becomes a mixed-integer program as a consequence. Using Pontryagin's Minimum Principle and a simplified vehicle model, it is possible to prove that a drive cycle-dependent critical power request P[subscript crit], which uniquely separates the optimal driving mode. Based on this result, a learning algorithm is proposed to determine P[subscript crit] during the operation of the vehicle. The learning algorithm is incorporated into a multi-level controller structure and the working principle of the resulting multi-level learning-based model predictive controller is analyzed in detail using three realistic driving missions. A comparison to the solution obtained by Dynamic Programming reveals that the proposed controller achieves close-to-optimal performance.]]></description>
      <pubDate>Mon, 29 Jul 2024 15:11:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2408273</guid>
    </item>
    <item>
      <title>Computationally Efficient Energy Management for a Parallel Hybrid Electric Vehicle Using Adaptive Dynamic Programming</title>
      <link>https://trid.trb.org/View/2389824</link>
      <description><![CDATA[Hybrid electric vehicles (HEVs) rely on energy management strategies (EMSs) to achieve optimal fuel economy. However, both model- and learning-based EMSs have their respective limitations which negatively affect their performances in online applications. This article presents a computationally efficient adaptive dynamic programming (ADP) approach that can not only rapidly calculate optimal control actions but also iteratively update the approximated value function (AVF) according to the actual fuel and electricity consumption with limited computation resources. Exploiting the AVF, the engine on/off switch and torque split problems are solved by one-step lookahead approximation and Pontryagin's minimum principle (PMP), respectively. To raise the training speed and reduce the memory space, the tabular value function (VF) is approximated by carefully selected piecewise polynomials via the parametric approximation. The advantages of the proposed EMS are threefold and verified by processor-in-the-loop (PIL) Monte Carlo simulations. First, the fuel efficiency of the proposed EMS is higher than that of an adaptive PMP and close to the theoretical optimum. Second, the new method can adapt to the changed driving conditions after a small number of learning iterations and thus has higher fuel efficiency than a non-adaptive dynamic programming (DP) controller. Third, the computation efficiencies of the proposed AVF and a tabular VF are compared. The concise data structure of the AVF enables faster convergence and saves at least 70% of onboard memory space without obviously increasing the average CPU utilization.]]></description>
      <pubDate>Tue, 23 Jul 2024 17:43:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2389824</guid>
    </item>
    <item>
      <title>A Predictive PMP Strategy for Plug-In Hybrid Electric Buses Considering Motor Thermal Characteristics and Loss Distribution</title>
      <link>https://trid.trb.org/View/2364908</link>
      <description><![CDATA[Energy management strategy (EMS) is the key for improving the fuel economy of the plug-in hybrid electric bus (PHEB). Some of the current studies mainly concentrate on reducing fuel consumption but do not fully consider the influence of the thermal dynamics of the vehicle motor. In order to solve the shortage of this research, a predictive EMS considering electromagnetic thermal control is proposed in this article. First, integrated with the actual working conditions and environment, the distribution of the loss of the main part of the motor under two extreme working conditions, namely, low-speed overload and high speed, is studied. Then, the motor thermal model is built to study the maximum temperature variation rule in the actual operating condition. Finally, the motor thermal model is combined with the model predictive control (MPC) strategy based on Pontryagin’s minimum principle (PMP) strategy to ensure fuel economy and avoid motor overheating. The research results have guiding significance for the improvement and design of subsequent EMSs considering the influencing factors of motor temperature rise.]]></description>
      <pubDate>Fri, 14 Jun 2024 10:39:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2364908</guid>
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