Electric vehicle routing problem with machine learning for energy prediction

Basso, Rafael; Kulcsár, Balázs; Sánchez-Díaz, Iván

doi:10.1016/j.trb.2020.12.007

Cited by 107 publications

(46 citation statements)

References 45 publications

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“…Aguilar-Dominguez et al [40] used a machine-learning method to evaluate the availability of five vehicles participating in the vehicle-to-home services. Basso et al [41] applied the probabilistic Bayesian machine-learning model to solve the route selection problem and found the best route for EVs by predicting the energy consumption within the limited driving distance. Ma et al [42] adopted big data and textmining technologies to analyze online behavior of Chinese consumers and to identify the factors affecting consumers' preferences.…”

Section: Application Of Machine-learning Methods In the Field Of Evsmentioning

confidence: 99%

A Study on Selection Strategies for Battery Electric Vehicles Based on Sentiments, Analysis, and the MCDM Model

Ren

Sun

Yuan

2021

Mathematical Problems in Engineering

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Under the goal of carbon peak and carbon neutrality, developing battery electric vehicles (BEVs) is an important way to reduce carbon emissions in the transportation sector. To popularize BEVs as soon as possible, it is necessary to study selection strategies for BEVs from the perspective of consumers. Therefore, the Latent Dirichlet Allocation (LDA) model based on fine-grained sentiment analysis is combined with the multi-criteria decision-making (MCDM) model to assess ten types of BEV alternatives. Fine-grained sentiment analysis is applied to find the vehicle attributes that consumers care about the most based on the word-of-mouth data. The LDA model is suggested to divide topics and construct the indicator system. The MCDM model is used to rank vehicles and put forward the corresponding optimization path to increase consumer purchases of BEVs in China. The results show that (a) via the LDA model based on fine-grained sentiment analysis, attributes that consumers care most about are divided into five topics: dynamics, technology, safety, comfort, and cost; (b) based on the DEMATEL technique, the dimensions in the order of importance are as follows: safety, technology, dynamics, comfort, and cost; (c) the price is the most important criteria that affect customers’ satisfaction by the DANP model; and (d) based on the VIKOR model, the selection strategies present that Aion S is highlighted as the best choice, and the optimization path is discussed to promote the performance of BEVs to increase customers’ satisfaction. The findings can provide a reference for improving the sustainable development of the automobile industry in China. The proposed framework serves as the basis for further discussion of BEVs.

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Section: Application Of Machine-learning Methods In the Field Of Evsmentioning

confidence: 99%

A Study on Selection Strategies for Battery Electric Vehicles Based on Sentiments, Analysis, and the MCDM Model

Ren

Sun

Yuan

2021

Mathematical Problems in Engineering

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“…Problemin çözümü için literatürdekilere benzer deterministik bir model önerilmiştir. Sonuçta güzergâhların belirlendiği bir rota çizilmiştir [20]. Ceselli ve arkadaşları elektrikli araçların rotalarının oluşturulmasının dikkate alındığı problem için bir optimizasyon algoritması sunmuşlardır.…”

Section: Etik Standartların Beyanı (Declaration Of Ethical Standards)unclassified

Hastanelere Aşı Dağıtımı İçin Uygun Rotaların Belirlenmesi: Ankara İli Örneği

et al. 2023

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Çin’in Wuhan kentinde başlayıp tüm dünyaya yayılan COVID-19 salgını ile birlikte geleneksel uygulamalarda değişiklikler yaşanmaya başlamış ve bu gelişmelerle paralel olarak yeni problemler ortaya çıkmaya başlamıştır. COVID-19 salgınıyla ortaya çıkan bu problemler, araştırmacıların ilgisini çeken konular arasında yer almaktadır. Hem ülkemizin hem de dünyanın bu salgınla başa çıkabilmesi için tüm nüfusun aşılanarak toplum bağışıklığının sağlanması önem kazanmaktadır. Aşılanacak kişi sayısındaki artışla beraber, hastanelerin aşı kapasitelerinin belirlenmesi, aşı uygulaması yapılacak hastanelerin seçilmesi, hastanelere aşı dağıtım rotalarının belirlenmesi gibi yeni problemler karşımıza çıkmaktadır. Araç rotalama problemi ilk olarak 1950’li yıllarda literatürde yerini almaya başlamış ve günümüzde farklı varsayımlar altında yeni problem için uyarlanabilen, gelişmeye açık bir problem alanı olarak görülmektedir. Bu çalışmada, aşıların bir dağıtım merkezinden uygulama yapılacağı hastanelere en kısa sürede, minimum maliyet altında taşınmasının sağlanması amacıyla bir matematiksel model önerisinde bulunulmuştur. Bu problem için, literatürde yer alan araç rotalama problemi modelleri kullanılarak yeni bir matematiksel model önerisi yapılmıştır. Geliştirilen modelin etkinliğini analiz etmek için Ankara ili için bir uygulama yapılmış ve oluşturulan problem GAMS 24.1.3 programında kodlanarak çözülmüştür. Problemin çözümünde elde edilen sonuçlar değerlendirilerek, hastaneleri ziyaret edecek araçların minimum maliyetli rotaları belirlenmiştir.

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“…In [21], a probabilistic model of energy consumption using machine learning was proposed. The model makes it possible to estimate the energy consumption for road connections.…”

Section: Energy Consumption Models and Neural Networkmentioning

confidence: 99%

Prediction of Electric Buses Energy Consumption from Trip Parameters Using Deep Learning

Pamuła

Pamula²

2022

Energies

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The energy demand of electric buses (EBs) is a very important parameter that should be considered by transport companies when introducing electric buses into the urban bus fleet. This article proposes a novel deep-learning-based model for predicting energy consumption of an electric bus traveling in an urban area. The model addresses two important issues: accuracy and cost of prediction. The aim of the research was to develop the deep-learning-based prediction model, which requires only the data readily available to bus fleet operators, such as location of the bus stops (coordinates, altitude), route traveled, schedule, travel time between stops, and to find the most suitable type and configuration of neural network to evaluate the model. The developed prediction model was assessed with different types of deep neural networks using real data collected for several bus lines in a medium-sized city in Poland. Conducted research has shown that the deep learning network with autoencoders (DLNA) neural network allows for the most accurate energy consumption estimation of 93%. The proposed model can be used by public transport companies to plan driving schedules and energy management when introducing electric buses.

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Electric vehicle routing problem with machine learning for energy prediction

Cited by 107 publications

References 45 publications

A Study on Selection Strategies for Battery Electric Vehicles Based on Sentiments, Analysis, and the MCDM Model

A Study on Selection Strategies for Battery Electric Vehicles Based on Sentiments, Analysis, and the MCDM Model

Hastanelere Aşı Dağıtımı İçin Uygun Rotaların Belirlenmesi: Ankara İli Örneği

Prediction of Electric Buses Energy Consumption from Trip Parameters Using Deep Learning

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