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Your search keyword '"TÜRKER, Erkan"' showing total 2 results
Start Over Author "TÜRKER, Erkan" Topic fuel economy
2 results on '"TÜRKER, Erkan"'

1. Development of range extended electric vehicle and estimation of energy management characteristics with neural networks

Author

Türker, Erkan, Öztürk, Ferruh, and Bursa Uludağ Üniversitesi/Fen Bilimleri Enstitüsü/Otomotiv Mühendisliği Anabilim Dalı.

Subjects
Sinir ağları, Emisyon seviyeleri, Range extended vehicle, Fuel economy, Menzil artırıcı sistem, 1 boyutlu analiz, Energy management, Neural networks, Surrogate model
Abstract

Bu çalışmada, menzil artırıcı donanımlar ile elektrikli bir aracın desteklenmesi ve aracın performans özelliklerinin tahmin edilmesidir. Menzil artırıcı çalışma sistematiğini oluşturabilmek için, içten yanmalı motor, jeneratör ve batarya şarj durumunun başlıca parametreler olarak girdi sağlayacağı düşünülerek simülasyon modelleri oluşturularak analizler yapılmış ve sinirsel ağlar kullanılarak içten yanmalı motorun çalışma stratejisi belirlenmesi için tahminlere dayalı bir yaklaşım uygulanmıştır. İçten yanmalı motorun gürültü ve titreşim seviyesinin ölçülmesi sonucunda motorun çalışacağı sınır şartlar belirlenmiştir. Menzil artırıcı güç ünitesinin hafif ticari araç üzerine entegrasyonu, tasarımsal ve fiziksel olarak gerçekleştirilerek, son durumda deneysel yakıt tüketimi testleri gerçekleştirildi. Hafif ticari aracın ve içten yanmalı motorun teknik özelliklerinin simülasyon modele aktarılması ile değişken şartlara bağlı olarak yakıt tüketimi değerleri belirlendi. Tezin amacı, hafif ticari bir elektrikli araç için ek güç ünitesi uygulamasını ve araç entegrasyonunun sağlanması ile sinir ağı tabanlı bir model kullanarak enerji yönetiminin tahmininin menzil artırıcı motor için yakıt tüketimini azaltmak üzere farklı yükleme koşullarına göre çalışma stratejisini uyarlayarak elde edilmesidir. In this study, the energy management strategy based on fuel economy is presented to achieve a further range enlargement of the range extender light commercial vehicle. Estimation of energy management strategy is carried out using neural networks based surrogate model for range extended vehicle based on fuel economy under various conditions. The surrogate based optimization plays an essential role in the optimization processes, especially when the optimization model is established based on complex problems with uncertainties in data sets due to various conditions. Neural networks have advantages in creating surrogate based models in case of complex problems with uncertainties in data sets to evaluate the process and estimate the outputs. This study discusses additional power unit application and vehicle integration for a light commercial electric vehicle. It provides preliminary design work and techniques for identifying NVH problems in particular. SIMULINK and neural network based surrogate models are established, and the changeable parameters of the vehicle, such as mass, battery/fuel-tank capacity, internal combustion engine power and electric motor power units are simulated at different dynamic and static conditions to determine energy management strategy for range extended vehicle based on fuel economy under various conditions. It is seen that APU parameters and energy management strategy significantly affect the fuel consumption of REX. The results show that the estimation and optimization of energy management using a neural network based surrogate model can be achieved by adapting the operating strategy according to different loading conditions to reduce fuel consumption for REX.

Published
2023

2. Estimation of Energy Management Strategy Using Neural-Network-Based Surrogate Model for Range Extended Vehicle.

Author

Türker, Erkan, Bulut, Emre, Kahraman, Arda, Çakıcı, Mehmet, and Öztürk, Ferruh

Subjects
ENERGY management, PLUG-in hybrid electric vehicles, HYBRID electric vehicles, ENERGY consumption, INTERNAL combustion engines, ELECTRIC power, COMMERCIAL vehicles
Abstract

In this paper, an energy-management strategy based on fuel economy is presented to achieve a further range increase for range-extended light commercial vehicles. Estimation of the energy-management strategy was carried out using a neural-network-based surrogate model for an range-extended vehicle. Surrogate-based optimization plays an important role in optimization problems, which are based on complex structures with uncertainties in data sets due to various conditions. Neural networks have advantages in creating surrogate-based models in cases of complex problems with uncertainties in data sets to evaluate the process and estimate the outputs. This study discusses additional power-unit applications and vehicle integration for a light commercial electric vehicle. It provides preliminary design work and techniques for identifying NVH problems in particular. SIMULINK and neural-network-based surrogate models are established, and the changeable parameters of the vehicle, such as mass, battery/fuel-tank capacity, internal combustion engine power and electric motor power units are simulated in different dynamic and static conditions to determine an energy-management strategy for a range-extended vehicle based on fuel economy under various conditions. It was seen that APU parameters and an energy-management strategy significantly affected the fuel consumption of REX. A neural-network-based surrogate-model approach gave high-precision results in predicting the operating strategy according to different loading conditions to reduce fuel consumption. In some cases, it can be required to determine the fuel consumption results in various conditions with the variables, which may be out-of-boundary conditions. It was seen that the proposed neural-network-model also offers higher prediction ability in cases of unexpected results in data sets of various conditions compared to regression analysis. The results show that estimation and optimization of energy management using a neural-network-based surrogate model can be achieved by adapting the operating strategy according to different loading conditions to reduce fuel consumption. This study presents an approach for future new vehicle projects by transforming a prototype light commercial electric vehicle to REX. The proposed approach was developed to design the most efficient range-extended vehicle by changing all variables without costly computations and time-consuming analysis. It is possible to generate variable data sets and to have reference knowledge for future vehicle projects. [ABSTRACT FROM AUTHOR]

Published
2022
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