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Zergatik bi plangilek auto berari erantzun desberdina ematen

Auto bera, ibilbide bera, egunean bera. Batak hiru geldialdi eta sei ordu esaten ditu, besteak bi geldialdi eta bost. Inork ez du gezurrik esaten — benetan ziurgabetasunari buruzko gauzetan desadostasunak dituzte.

Eguneratuta 2 min irakurri 40 aipamenak Emaitzen indarra 3/5

Autobide baten gaineko zubitik irteera bidearen norabidean ikuspegia
Dietmar Rabich · CC BY-SA 4.0 · Wikimedia Commons

Abiadura desberdinak asumitzen dituzte

Aerodinamika drag-a abiaduraren karratuarekin igotzen da, eta gainditzeko behar den potentzia kuboarekin. Beraz, 110 km/h eta 130 km/h arteko aldea ez da kontsumoan %20ko desberdintasun bat; askoz handiagoa da. Abiadura muga gaindituko duzula asumitzen duen plangile bat eta azpitik ibiliko zarela asumitzen duen bat bi bidaia desberdin modelatzen ari dira.

Tresna gehienek hau ezartzen uzten dute eta jende gehienak inoiz ez du egiten. Bi plangile desadostatzen direnean eta gauza bat bakarrik egiaztatu dezakezunean, egiaztatu abiadura asumitua.

Kargatzeko kurba desberdin modelatzen dute, edo ez dute batere egiten

Lithio-ion pakete batek potentzia handia onartzen du karga egoera baxuan eta zorrotz murrizten da betetzen den heinean [3]. Ondorio praktikoa da kargaren azken %20ak lehen berrogeita %50ak bezainbeste denbora har dezakeela, beraz, estrategia optimoa normalean geldialdi gehiago izatea da, bakoitza laburragoa, gutxiago eta luzeagoak izan beharrean.

Hau behar bezala modelatzen duen plangile batek iruditzen den ibilbide bat sortuko du — lau geldialdi bi baino — eta hala ere lehenago iritsiko zara. Kargatzea tasa finkotzat jotzen duen plangile batek plan garbiago bat sortzen du, baina okerra da.

Eguna desberdinak dituzte

Hotzak erabilgarri den irismena murrizten du eta aldi berean paketeak karga onartzeko abiadura moteldu egiten du [4]. Bi efektu hauek norabide berean bultzatzen dute, eta eguraldi leuneko kontsumoan oinarritutako plan bat akats txiki batek ez duen moduan porrot egin dezake.

Hau da plangileen arteko desberdintasunak ondorio handiena duen lerroa, eta horregatik gaitasun taulak eguraldi modelatzea lehen mailako ezaugarri gisa tratatzen du, luxuzko zerbait bezala baino.

Zerretan desadostatzen dira

Iristeko bufferra — plangile batek geldialdi batera iristean utzi beharreko karga kopurua — ez da kantitate fisikoa. Politika bat da. %10era iristeko ezarrita dagoen tresna batek eta %20ra iristeko ezarrita dagoen batek irteera materialki desberdinak sortuko dituzte sarrera identikoetatik, eta inork ez da oker.

Irismena kezka ondo dokumentatutako eragina da gidariek nola jokatzen duten, autoak zer egin dezakeen baino desberdina [5]. Hori ezartzen uzten duen plangile batek zure nerbioak prezioan jartzen uzten dizu, eta hori erabaki horren leku egokia da.

Hiru-bidetako britainiar autobidea, bi norabidetako trafikoarekin, zeru gris baten azpian
Klaus with K · CC BY-SA 3.0 · Wikimedia Commons

Zer egin horren inguruan

  • Ezarri abiadura asumitua benetan gidatzen duzunarekin, ez zuk asmoa duzunarekin.
  • Egun hotz batean, planifikatu pessimista den tresnarekin eta optimista dena kasu onena bezala tratatu.
  • Eman gehiago, geldialdi laburragoak dituen plangile bati konfiantza, gutxiago, luzeagoak dituen bati baino.
  • Bidaia erdian berriz planifikatu, hasieran ez. Sarrera bakoitza orduan desbideratu da.
Orri honen atzean dagoen froga 40 argitalpenen konposizioa ikerketa motaren arabera erakusten duen barra pilatua. 40other (40)
40 argitalpen, 2012–2026. Hau behin-behineko oinarri bat da. Gauzak elkarrekin gertatzen direla ezartzen du; ez du zehazten zein den bestea eragiten duena. Iturria: orri honen aipamen zerrenda, behean.

Erreferentziak

Hemen azpiko aipamen guztiak PubMed-en edo DOI bidezko jatorrizko peer-reviewed erregistroari lotzen diote. Hemen ez dago mediku aholkurik ordezkatzeko.

  1. Effect of Ambient Temperature on Electric Vehicles’ Energy Consumption and Range: Model Definition and Sensitivity Analysis Based on Nissan Leaf Data Iora P, Tribioli L · World Electric Vehicle Journal · 2019 · Journal article DOI
  2. Electric Vehicle Energy Consumption Modelling and Prediction Based on Road Information Wang J, Besselink I, Nijmeijer H · World Electric Vehicle Journal · 2015 · Journal article DOI
  3. Machine learning-based uncertainty quantification for energy consumption and driving range estimation in electric cargo vehicles Gandhi M, Chaudhari A · Energy Informatics · 2026 · Journal article DOI
  4. Estimation of Energy Consumption in Battery-electric Motorcycles Using a Virtual Vehicle Model and the Development of a Customized Measurement System Göntér Á, Sipos T · Periodica Polytechnica Transportation Engineering · 2026 · Journal article DOI
  5. Dynamic Electric Vehicle Route Planning via Traffic Flow Prediction and Charging Service Integration Zhang Y, Shen X, Wang Y · Processes · 2026 · Journal article DOI
  6. Optimizing Electric Delivery Vehicle Route Planning: A Hybrid Approach Integrating Clustering and Ant Colony Algorithm for Sustainable Transportation Heng S, Sharma A, Xiao J · Sustainability · 2026 · Journal article DOI
  7. Modeling Electric Vehicle Adoption in Thailand: The Impact of Ecosystem and Policy Support via Perceived Value and Charging Anxiety Suvittawat A, Suvittawat N · World Electric Vehicle Journal · 2026 · Journal article DOI
  8. Revisiting Electric Mobility: How Individual Perceived Value Shapes Battery Electric Vehicle Adoption—Insights into Technophilia, Range Anxiety, and Battery Cost in China Jia H, Zhao H, Uchiyama Y · World Electric Vehicle Journal · 2026 · Journal article DOI
  9. MACHINE LEARNING-DRIVEN OPTIMIZATION OF ELECTRIC VEHICLE CHARGING WITH DRIVER SATISFACTION MODELING CHERUVU AYESHA, Mrs.B.JYOTHSHA, Mr.P. VISWANATHA REDDY · ETDT · 2026 · Journal article DOI
  10. Electric Vehicle Charging Station Location Planning Based on Range Anxiety in Last Mile Logistics in Yogyakarta Haryanto Z, Afraah S · Jurnal Teknologi · 2025 · Journal article DOI
  11. Systematic Planning of Electric Vehicle Battery Swapping and Charging Station Location and Driver Routing with Bi-Level Optimization Chen B, Chen J, Feng H · World Electric Vehicle Journal · 2025 · Journal article DOI
  12. Influencer-Mediated Range Anxiety Mitigation: Examining Social Media Marketing Pathways to Electric Vehicle Adoption in Vietnam's Digital Economy NGUYEN T · Journal of Economics, Finance And Management Studies · 2025 · Journal article DOI
  13. An Electric Vehicle Optimal Charging Path Planning based on an Improved A* Algorithm Chen X · Frontiers in Computing and Intelligent Systems · 2025 · Journal article DOI
  14. Optimisation of Electric Vehicle Charging Stations Planning Based on Macro and Micro Perspectives WANG Q, DENG K, YAN J, et al. · Promet - Traffic&Transportation · 2025 · Journal article DOI
  15. Personalised electric vehicle charging stop planning through online estimators Shafipour E, Stein S, Ahipasaoglu S · Autonomous Agents and Multi-Agent Systems · 2024 · Journal article DOI
  16. Integrated model construction for state of charge estimation in electric vehicle lithium batteries Liu Y, Dun W · Energy Informatics · 2024 · Journal article DOI
  17. Optimization model of battery electric vehicle charging facility layout towards embedded system and data mining algorithm Zhang P, Liu J, Luo N, et al. · International Journal of Emerging Electric Power Systems · 2024 · Journal article DOI
  18. Electric Vehicle Distribution Route Optimisation and Charging Strategy Considering Dynamic Loads Wu Q, Tian M · Polish Journal of Environmental Studies · 2024 · Journal article DOI
  19. Multi-Objective Electric Vehicle Route and Charging Planning with Contraction Hierarchies Cuchý M, Vokřínek J, Jakob M · Proceedings of the International Conference on Automated Planning and Scheduling · 2024 · Journal article DOI
  20. Electric Vehicle Health Monitoring with Electric Vehicle Range Prediction and Route Planning Jayaram J, Chetan J, Nayak B · Journal of Informatics and Web Engineering · 2024 · Journal article DOI
  21. Electric Vehicle Charging Route Planning for Shortest Travel Time Based on Improved Ant Colony Optimization Tan A, Wang C, Wang Y, et al. · Sensors · 2024 · Journal article DOI
  22. Optimal number of charging station and pricing strategy for the electric vehicle with component commonality considering consumer range anxiety Yu W, Zhang L, Lu R, et al. · PLOS ONE · 2023 · Journal article DOI
  23. Electric Vehicle Charging Sessions Generator Based on Clustered Driver Behaviors Van Kriekinge G, De Cauwer C, Sapountzoglou N, et al. · World Electric Vehicle Journal · 2023 · Journal article DOI
  24. Charging after Lockdown: The Aftermath of COVID-19 Policies on Electric Vehicle Charging Behaviour in The Netherlands van der Koogh M, Wolbertus R, Heller R · World Electric Vehicle Journal · 2023 · Journal article DOI
  25. Evaluation of Electric Vehicle Charging Usage and Driver Activity Mahlberg J, Desai J, Bullock D · World Electric Vehicle Journal · 2023 · Journal article DOI
  26. Energy Cost Analysis and Operational Range Prediction Based on Medium- and Heavy-Duty Electric Vehicle Real-World Deployments across the United States Qiu Y, Dobbelaere C, Song S · World Electric Vehicle Journal · 2023 · Journal article DOI
  27. Research and Evaluation of Electric Vehicle Charging Station Layout Planning Based on Greedy Algorithm Yu C, Chen M, Lu H, et al. · Highlights in Science, Engineering and Technology · 2023 · Journal article DOI
  28. Simulation model for rendering and analyzing the prediction of electric vehicle energy consumption in Matlab/Simulink Janković F, Mujović S · ETF Journal of Electrical Engineering · 2023 · Journal article DOI
  29. Electric Vehicle Charging Station Location Model considering Charging Choice Behavior and Range Anxiety Liu H, Li Y, Zhang C, et al. · Sustainability · 2022 · Journal article DOI
  30. Electric Vehicle Range Estimation Using Regression Techniques Ahmed M, Mao Z, Zheng Y, et al. · World Electric Vehicle Journal · 2022 · Journal article DOI
  31. Rapid Evaluation Method for Accuracy of Range Estimation of Pure Electric Vehicle Range Estimation Based on CLTC-P Dai T, Zhou B, Zhang Y, et al. · E3S Web of Conferences · 2021 · Journal article DOI
  32. An Optimal Control Algorithm with Reduced DC-Bus Current Fluctuation for Multiple Charging Modes of Electric Vehicle Charging Station Chen T, Fu P, Chen X, et al. · World Electric Vehicle Journal · 2021 · Journal article DOI
  33. Research on Establishment of Vehicle Energy Distribution Model and Energy Consumption Optimization Based on Electric Hybrid System Liang P, He H, Cui H, et al. · World Electric Vehicle Journal · 2021 · Journal article DOI
  34. Optimization Approach for Long-Term Planning of Charging Infrastructure for Fixed-Route Transportation Systems Blat Belmonte B, Rinderknecht S · World Electric Vehicle Journal · 2021 · Journal article DOI
  35. Development of Hybrid Vehicle Energy Consumption Model for Transportation Applications—Part II: Traction Force-Speed Based Energy Consumption Modeling Pitanuwat S, Aoki H, IIzuka S, et al. · World Electric Vehicle Journal · 2019 · Journal article DOI
  36. Economic Microgrid Planning Algorithm with Electric Vehicle Charging Demands Yoon S, Kang S · Energies · 2017 · Journal article DOI
  37. Online Prediction of Battery Electric Vehicle Energy Consumption Wang J, Besselink I, Nijmeijer H · World Electric Vehicle Journal · 2016 · Journal article DOI
  38. Model-Based Remaining Driving Range Prediction in Electric Vehicles by using Particle Filtering and Markov Chains Oliva J, Weihrauch C, Bertram T · World Electric Vehicle Journal · 2013 · Journal article DOI
  39. Measuring Range Anxiety: the Substitution-Emergency-Detour (SED) Method Lin Z · World Electric Vehicle Journal · 2012 · Journal article DOI
  40. Energy Consumption Prediction of a Vehicle along a User-Specified Real-World Trip Karbowski D, Pagerit S, Calkins A · World Electric Vehicle Journal · 2012 · Journal article DOI

Orri hau automatikoki itzuli da ingelesez. Aipamenak eta zenbakiak aldatu gabe daude. Irakurri ingelesezko jatorrizkoa ezer arraro irakurtzen bada. Irakurri ingelesezko jatorrizkoa