ELEKTRİKLİ ARAÇ ENERJİ TÜKETİMİNİN SOSYO-TEKNİK BELİRLEYİCİLERİ: GERÇEK SÜRÜŞ VERİLERİ ÜZERİNE BİR ÇALIŞMA
Abstract
Bu çalışma, elektrikli araçların (EA) enerji tüketimini yalnızca mühendislik temelli teknik göstergeler üzerinden değil; sürücü davranışları, gündelik hareketlilik pratikleri, trafik koşulları ve mekânsal bağlamın karşılıklı etkileşimi çerçevesinde ele alan sosyo-teknik bir yaklaşımla incelemektedir. Araştırma, Edirne ili ve çevresinde dört aylık bir dönemde (Ağustos–Kasım 2025) gerçek sürüş koşullarında toplanan 108 geçerli sürüş kaydına dayanmaktadır. Veriler, OBD-II cihazı aracılığıyla elde edilmiş olup hız, rejeneratif frenleme ve enerji kullanımıyla ilişkili 22 sensör temelli değişkeni kapsamaktadır. Enerji tüketimi, 100 km başına düşen enerji kullanımı olarak tanımlanmış; ortalama hız, rejeneratif frenleme miktarı ve toplam mesafeyi içeren çoklu doğrusal regresyon modeli aracılığıyla analiz edilmiştir.
Analiz sonuçları, modelin düşük açıklama gücüne (R² = %6,1) sahip olduğunu göstermektedir. Bu bulgu, elektrikli araç enerji tüketiminin yalnızca teknik değişkenlerle açıklanamayacak kadar çok boyutlu ve bağlama duyarlı bir yapıya sahip olduğunu ortaya koymaktadır. Rejeneratif frenlemenin yönsel olarak tüketimi azaltıcı bir etkiye sahip olduğu, ancak bu etkinin trafik yoğunluğu, dur-kalk sıklığı ve sürüş örüntülerine bağlı olarak değiştiği belirlenmiştir. Ortalama hız ile enerji tüketimi arasındaki ilişkinin doğrusal olmadığı; düşük hızlarda yoğun dur-kalk trafiğinin, yüksek hızlarda ise aerodinamik direncin tüketimi artırdığı görülmüştür. Çalışma, elektrikli araç enerji tüketiminin teknik bir çıktı olmanın ötesinde, sosyal pratikler, kullanıcı alışkanlıkları ve kentsel bağlam tarafından şekillenen bir sosyo-teknik olgu olduğunu vurgulamakta; sürdürülebilir ulaşım politikaları, sürücü eğitimi ve kullanıcı odaklı enerji verimliliği açısından önemli çıkarımlar sunmaktadır.
Keywords
Elektrikli araçlar (EV) enerji verimliliği sürüş dinamikleri rejeneratif frenleme sosyo-teknik sistemler.
References
- Al-Wreikat, Y., Serrano, C., & Sodré, J. R. (2021). Driving behaviour and trip condition effects on the energy consumption of an electric vehicle under real-world driving. Applied Energy, 297, 117096, 1-8. https://doi.org/10.1016/j.apenergy.2021.117096
- Al-Wreikat, Y., Serrano, C., & Sodré, J. R. (2022). Effects of ambient temperature and trip characteristics on the energy consumption of an electric vehicle. Energy, 238, 122028, 1-9. https://doi.org/10.1016/j.energy.2021.122028
- Chen, Y. (2021). A review and outlook of energy consumption estimation models for electric vehicles. SAE International Journal of Sustainable Transportation, Energy, Environment, and Policy, 2(1), 79–96. https://doi.org/10.4271/13-02-01-0005
- Hardman, S. (2019). Understanding the impact of recurring and non-financial incentives on plug-in electric vehicle adoption: A review. Transportation Research Part D: Transport and Environment, 119, 1–14. https://doi.org/10.1016/j.tra.2018.11.002
- Lee, G. (2023). Study on energy consumption characteristics of passenger electric vehicle according to the regenerative braking stages during real-world driving conditions. Energy, 283, 128745, 1-11. https://doi.org/10.1016/j.energy.2023.128745
- Lee, R., & Brown, S. (2020). Evaluating the role of behavior and social class in electric vehicle adoption and charging demands. Cell Press, 1-32. https://dx.doi.org/10.2139/ssrn.3724667
- Python Software Foundation. Python language reference (Version 3.11). https://docs.python.org/3.11/
- Rainieri, G., Buizza, C., & Ghilardi, A. (2023). The psychological, human factors and socio-technical contribution: A systematic review towards range anxiety of battery electric vehicle drivers. Transportation Research Part F: Traffic Psychology and Behaviour, 99, 52–70. https://doi.org/10.1016/j.trf.2023.10.001
- Şahin, D.Ö., Çandırlı, M., Erşen, A., & Akar, M.H., (2025). Elektrikli araba veri kümesi üzerinde öğrenmesi ve derin öğrenme algoritmaları kullanarak şarj durumu tahmini, Mühendis ve Makine, Cilt:66, Sayı:719, 361-387. https://doi.org/10.46399/muhendismakina.1728570
- Xie, Y., Li, Y., & Zhao, Z. (2020). Microsimulation of electric vehicle energy consumption and driving range. Applied Energy, 267, 115081, 1-15.
- https://doi.org/10.1016/j.apenergy.2020.115081 Yılmaz, M., Çinar, E., & Yazıcı, A. (2024). Elektrikli araçlarda otonom batarya yönetim sistemi literatür incelemesi, EMO Bilimsel Dergisi, Cilt:14, Sayı:2, 7-22.
SOCIO-TECHNICAL DETERMINANTS OF ELECTRIC VEHICLE ENERGY CONSUMPTION: A STUDY BASED ON REAL-WORLD DRIVING DATA
Abstract
This study examines the energy consumption of electric vehicles (EVs) through a socio-technical perspective that goes beyond engineering-based technical indicators by considering the interaction between driver behavior, everyday mobility practices, traffic conditions, and spatial context. The research is based on 108 valid driving records collected under real-world driving conditions over a four-month period (August–November 2025) in Edirne, Türkiye, and its surrounding areas. The data were obtained using an OBD-II device and include 22 sensor-based variables related to speed, regenerative braking, and energy use. Energy consumption was defined as energy use per 100 km and analyzed using a multiple linear regression model incorporating average speed, the amount of regenerative braking, and total driving distance.
The analysis results indicate that the model has low explanatory power (R² = 6.1%), suggesting that electric vehicle energy consumption is a highly multidimensional and context-sensitive phenomenon that cannot be sufficiently explained by technical variables alone. Regenerative braking was found to have a directionally consumption-reducing effect; however, the magnitude of this effect varies depending on traffic density, stop-and-go frequency, and driving patterns. The relationship between average speed and energy consumption was observed to be non-linear, with intensive stop-and-go traffic increasing consumption at low speeds, while aerodynamic resistance leads to higher consumption at high speeds. Overall, the study emphasizes that electric vehicle energy consumption extends beyond a purely technical output and represents a socio-technical phenomenon shaped by social practices, user habits, and urban context, offering important implications for sustainable transportation policies, driver education, and user-oriented energy efficiency.
Keywords
Electric vehicles (EV) energy efficiency driving dynamics regenerative braking socio-technical systems
References
- Al-Wreikat, Y., Serrano, C., & Sodré, J. R. (2021). Driving behaviour and trip condition effects on the energy consumption of an electric vehicle under real-world driving. Applied Energy, 297, 117096, 1-8. https://doi.org/10.1016/j.apenergy.2021.117096
- Al-Wreikat, Y., Serrano, C., & Sodré, J. R. (2022). Effects of ambient temperature and trip characteristics on the energy consumption of an electric vehicle. Energy, 238, 122028, 1-9. https://doi.org/10.1016/j.energy.2021.122028
- Chen, Y. (2021). A review and outlook of energy consumption estimation models for electric vehicles. SAE International Journal of Sustainable Transportation, Energy, Environment, and Policy, 2(1), 79–96. https://doi.org/10.4271/13-02-01-0005
- Hardman, S. (2019). Understanding the impact of recurring and non-financial incentives on plug-in electric vehicle adoption: A review. Transportation Research Part D: Transport and Environment, 119, 1–14. https://doi.org/10.1016/j.tra.2018.11.002
- Lee, G. (2023). Study on energy consumption characteristics of passenger electric vehicle according to the regenerative braking stages during real-world driving conditions. Energy, 283, 128745, 1-11. https://doi.org/10.1016/j.energy.2023.128745
- Lee, R., & Brown, S. (2020). Evaluating the role of behavior and social class in electric vehicle adoption and charging demands. Cell Press, 1-32. https://dx.doi.org/10.2139/ssrn.3724667
- Python Software Foundation. Python language reference (Version 3.11). https://docs.python.org/3.11/
- Rainieri, G., Buizza, C., & Ghilardi, A. (2023). The psychological, human factors and socio-technical contribution: A systematic review towards range anxiety of battery electric vehicle drivers. Transportation Research Part F: Traffic Psychology and Behaviour, 99, 52–70. https://doi.org/10.1016/j.trf.2023.10.001
- Şahin, D.Ö., Çandırlı, M., Erşen, A., & Akar, M.H., (2025). Elektrikli araba veri kümesi üzerinde öğrenmesi ve derin öğrenme algoritmaları kullanarak şarj durumu tahmini, Mühendis ve Makine, Cilt:66, Sayı:719, 361-387. https://doi.org/10.46399/muhendismakina.1728570
- Xie, Y., Li, Y., & Zhao, Z. (2020). Microsimulation of electric vehicle energy consumption and driving range. Applied Energy, 267, 115081, 1-15.
- https://doi.org/10.1016/j.apenergy.2020.115081 Yılmaz, M., Çinar, E., & Yazıcı, A. (2024). Elektrikli araçlarda otonom batarya yönetim sistemi literatür incelemesi, EMO Bilimsel Dergisi, Cilt:14, Sayı:2, 7-22.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Management Information Systems |
| Journal Section | Research Article |
| Authors | |
| Submission Date | December 22, 2025 |
| Acceptance Date | December 29, 2025 |
| Publication Date | December 30, 2025 |
| IZ | https://izlik.org/JA43TE83EP |
| Published in Issue | Year 2025 Volume: 5 Issue: 1 |