Gerçek Dünya Verilerini Kullanarak Elektrikli Otobüs Operasyonlarının Trafik Bilinçli 1 Hz Enerji Modellemesi ve Rejeneratif Frenleme Analizi

Öz

Elektrikli toplu taşıma sistemlerinde enerji verimliliğini belirleyen unsurların anlaşılması, gerçek dünya trafik koşulları altında işletme maliyetlerinin azaltılması ve araç menzilinin uzatılması açısından kritik öneme sahiptir. Bu çalışma, kentsel bir güzergâhtan toplanan saniye bazlı (1 Hz) operasyonel verileri kullanarak Elektrikli Otobüs sistemleri için yüksek çözünürlüklü, trafik bilinçli bir enerji modelleme çerçevesi önermektedir. Trafik sıkışıklığı göstergeleri, araç dinamiği, rejeneratif frenleme davranışı ve makine öğrenmesi tabanlı sürüş davranışı analizinin entegre edilmesiyle, geleneksel ortalama temelli değerlendirmelerin ötesine geçen kapsamlı bir enerji tüketimi analizi sunulmaktadır. Elde edilen sonuçlar, güzergâh bazında ortalama 3,3 kWh/km enerji tüketiminin, özellikle sıkışıklık kaynaklı dur-kalk dinamikleri tarafından yönlendirilen önemli zamansal değişkenliği gizlediğini göstermektedir. Trafik sıkışıklığı, yalnızca hızın düşmesi yoluyla değil, aynı zamanda ivme değişkenliğini ve frenleme sıklığını artırarak enerji tüketimini %22’ye kadar yükseltmektedir. Optimum sürüş menzilinin korunabilmesi için toplam frenleme enerjisinin yaklaşık %78,32’sinin rejeneratif frenleme yoluyla geri kazanılması gerekmektedir; bu gereksinim ise büyük ölçüde trafik durumu ve sürüş davranışı tarafından belirlenmektedir. Sürücüler arasındaki toplam enerji tüketimi farkları görece sınırlı (%6–9) görünse de, altta yatan kontrol stratejilerinin anlamlı ölçüde farklılaştığı ve bunun filo ölçeğinde uzun vadeli kümülatif etkilere yol açtığı tespit edilmiştir. Aynı trafik koşulları altında otonom sürüş, daha düzgün boylamsal kontrol uygulayarak insan sürüşüne kıyasla toplam enerji tüketimini %11–14 oranında azaltmakta ve rejeneratif frenleme kullanımını %9–12 oranında artırmaktadır. Bulgular, E-Otobüs sistemlerinde enerji verimliliğinin trafik dinamikleri ile kontrol davranışının ortak etkisi altında şekillendiğini ortaya koymakta; yeni nesil elektrikli toplu taşıma sistemleri için davranış bilinçli sürüş stratejilerinin ve otonom kontrol yaklaşımlarının gerekliliğini vurgulamaktadır.

Anahtar Kelimeler

• Elektrikli otobüs
• trafiğe duyarlı enerji modellemesi
• rejeneratif frenleme
• 1 Hz çözünürlük
• makine öğrenimi
• otonom sürüş
• kentsel toplu taşıma.

Traffic Aware 1 Hz Energy Modeling and Regenerative Braking Analysis of E-Bus Operations Using Real-World Data

Abstract

Understanding the determinants of energy efficiency in electric public transport is critical for reducing operational costs and extending vehicle range under real-world traffic conditions. This study proposes a high resolution, traffic aware energy modeling framework for Electric Bus (E-Bus) systems using second by second (1 Hz) operational data collected from an urban route. By integrating traffic congestion metrics, vehicle dynamics, regenerative braking behavior, and machine learning based driving behavior analysis, the study provides a comprehensive assessment of energy consumption mechanisms beyond conventional average based evaluations. The results show that the average route level energy consumption of 3.3 kWh/km conceals substantial temporal variability primarily driven by congestion induced stop and go dynamics. Traffic congestion increases energy consumption by up to 22%, not merely by reducing speed but by amplifying acceleration variance and braking frequency. To maintain the optimal driving range, approximately 78.32% of total braking energy must be recuperated through regenerative braking, a requirement that is strongly conditioned by traffic state and driving behavior. Although driver to driver differences in total energy consumption appear moderate (6–9%), the underlying control strategies differ significantly, leading to cumulative long term impacts at fleet scale. Under identical traffic conditions, autonomous driving reduces total energy consumption by 11–14% and increases regenerative braking utilization by 9–12% compared to human driven operation by enforcing smoother longitudinal control. The findings demonstrate that energy efficiency in E-Bus systems is jointly governed by traffic dynamics and control behavior, highlighting the necessity of behavior aware driving strategies and autonomous control for next generation electric public transport systems.

Keywords

• E-Bus
• traffic aware energy modeling
• regenerative braking
• 1 Hz resolution
• machine learning
• autonomous driving
• urban public transport.

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