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Miks kaks planeerijat annavad samale autole erineva vastuse

Sama auto, sama marsruut, sama päev. Üks ütleb kolm peatuspaika ja kuus tundi, teine ütleb kaks peatuspaika ja viis. Ükski ei valeta — nad ei ole ühel meelel asjade osas, mis on tõeliselt ebakindlad.

Updated 2 min read 40 citations Evidence strength 3/5

Vaade maanteelt üle silla väljumisteele
Dietmar Rabich · CC BY-SA 4.0 · Wikimedia Commons

Nad eeldavad erinevaid kiirus

Aerodünaamiline takistus tõuseb kiirusest ruudus ja selle ületamiseks vajalik võimsus kuubis. Seetõttu ei ole vahe 110 km/h ja 130 km/h mitte kakskümmend protsenti tarbimises; see on palju suurem. Planeerija, kes eeldab, et te jääte piirkiiruseni, ja see, kes eeldab, et te jääte sellest alla, modelleerivad kahte erinevat reisi.

Enamik tööriistu võimaldab teil seda seadistada ja enamik inimesi ei tee seda kunagi. Kui kaks planeerijat on erimeelsusel ja te saate kontrollida ainult ühte asja, kontrollige eeldatavat kiirus.

Nad modelleerivad laadimiskõverat erinevalt või mitte üldse

Liitium-ioonaku aktsepteerib madala laadimistaseme juures suurt võimsust ja täitmisel järk-järgult väheneb [3]. Praktikas tähendab see, et laadimise viimased kakskümmend protsenti võivad võtta sama kaua kui esimesed viiskümmend, seega on optimaalne strateegia tavaliselt rohkem peatusi, igaühe puhul lühem, mitte vähem ja pikem.

Planeerija, kes modelleerib seda õigesti, koostab marsruudi, mis näeb välja ebaefektiivne — neli peatuspaika kahe asemel — ja jõuab siiski kiiremini kohale. Planeerija, kes käsitleb laadimist kui fikseeritud hinda, koostab korrastatud plaani, mis on vale.

Nad ei ole ühel meelel ilma üle

Külm vähendab kasutatavat ulatust ja samal ajal aeglustab see akupaki laadimiskiirus [4]. Mõlemad mõjud suunavad sama suunas ja plaan, mis põhineb mõõduka ilma tarbimisel, võib ebaõnnestuda viisil, mida väike veamarginaal ei kata.

See on rida, kus planeerijate vahe on kõige olulisem, ja see on põhjus, miks võimekuse tabel käsitleb ilma modelleerimist esmaklassilise omadusena, mitte lihtsalt mugavusena.

Nad ei ole ühel meelel selle üle, kui hirmul te peaksite olema

Saabumise varu — kui palju laadimist planeerija nõuab, et teil oleks jäänud, kui jõuate peatuspaika — ei ole füüsiline kogus. See on poliitika. Tööriist, mis on seadistatud saabuma 10% ja üks, mis on seadistatud saabuma 20%, koostavad identsete sisendite põhjal oluliselt erinevaid marsruute, ja kumbki ei ole vale.

Ulatusärevus on hästi dokumenteeritud mõju sellele, kuidas juhid tegelikult käituvad, erinev sellest, mida auto suudab teha [5]. Planeerija, kes lubab teil seda seadistada, laseb teil hinnata oma närve, mis on õige koht selle otsuse jaoks.

Kolme sõidureaga Briti maantee, millel liiklus mõlemas suunas halli taeva all
Klaus with K · CC BY-SA 3.0 · Wikimedia Commons

Mida sellega teha

  • Seadke kiirusetendus sellele, mida te tegelikult sõidate, mitte sellele, mida te kavatsete.
  • Külmal päeval planeerige pessimistliku tööriistaga ja käsitlege optimistlikku kui parimat juhtumit.
  • Usaldage planeerijat, kes annab teile rohkem, lühemaid peatusi, mitte seda, kes annab vähem, pikemaid.
  • Planeerige uuesti reisi keskel, mitte alguses. Iga sisend on siis juba muutunud.
Selle lehe tõendid Kuhjatud diagramm, mis näitab sellel lehel viidatud 40 publikatsiooni koostisosade jaotust uuringu tüübi järgi. 40other (40)
40 publikatsiooni, 2012–2026. See on peamiselt vaatluslik alus. See võib tõestada, et asjad esinevad koos; see ei saa kindlaks teha, kumb põhjustab teist. Allikas: selle lehe enda viidatud nimekiri allpool.

References

Every citation below links to the original peer-reviewed record on PubMed or via DOI. Nothing here is a substitute for medical advice.

  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
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  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
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  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
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This page was translated automatically from English. The citations and numbers are unchanged. Read the English original if anything reads oddly. Read the English original