Understanding the sensitivity of vehicle energy consumption to average speed variations is critical for accurately assessing the environmental impacts of urban transportation systems. While the energy consumption patterns of conventional vehicles (CVs) have been extensively studied, the response characteristics of electric vehicles (EVs) and their fundamental differences from CVs remain insufficiently explored. This knowledge gap may lead to misguided policy interventions—for instance, implementing congestion mitigation strategies that may paradoxically increase EV energy demand. To address this research gap, we developed an energy consumption model based on vehicle-specific power (VSP) distribution analysis, calibrated with over 25 million second-by-second driving records from Beijing. The proposed comparative framework systematically evaluates the sensitivity of EV and CV energy consumption across different speed regimes. The results indicated that EV energy use exhibits a distinctive parabolic trend, with high energy use at both low and high speeds, and a notable increase beyond approximately 70 km/h. A case study indicates that, during the pandemic lockdown, which led to a significant increase in average speed, CV energy use generally decreased, whereas EV energy consumption increased. This discrepancy is primarily attributed to differences in energy consumption rates rather than variations in driving behavior, as reflected in VSP distributions.

The large-scale integration of autonomous vehicles (AVs) has led to fundamental changes in the energy consumption evolution mechanism of new hybrid traffic flows, rendering conventional mapping between traffic flow parameters and energy invalid. This study develops a “flow–speed–density–energy” model for hybrid traffic composed of autonomous and human-driven vehicles (HVs). In this study, energy (kJ/km/h) is mathematically expressed as the product of the energy factor (kJ/km/veh) and traffic flow (veh/h). By constructing flow and energy factor curves with varying penetration rates and platoon intensities, the study derives four–factor diagrams for hybrid traffic flows. The analysis yields several key findings. First, for internal combustion engine vehicles (ICEVs), energy in HV platoons decreases with increasing speed, whereas in AV platoons it first decreases and then rises as flow intensifies under high-speed conditions. Second, energy is inversely correlated with density, reflecting the negative speed–density relationship. Under uncongested conditions, energy increases with flow, while in congested regimes, sharp reductions in the energy factor leads to overall energy decline. Finally, the primary difference between electric vehicles (EVs) and ICEVs lies in the low-speed range, where the energy of ICEVs decreases rapidly with increasing speed, whereas the energy of EVs increases due to the slower decline of the energy factor and faster flow growth. This study addresses the challenges in the operation and energy consumption of hybrid traffic flow at the macroscopic traffic level, providing foundational support for energy management.

In car-following scenarios, trailing vehicles tend to fluctuate around the motion of the lead vehicle and gradually converge toward their preferred trajectories. With the large-scale integration of autonomous vehicles (AVs) into traffic streams, differences in desired trajectories among vehicles are amplified. This amplification intensifies fluctuation propagation and complicates platoon-level energy consumption mechanisms. This study addresses these dynamics through the development of a mixed-platoon energy consumption estimation model that accounts for gradually evolving traffic flows (MP-ECEMG), incorporating real-world data from light-duty human-driven vehicles (HVs) and AVs. Results indicate that the model leads to a broader range of energy consumption variation than conventional models due to complex vehicle interactions, while the transitions of energy consumption between vehicles are smoother than those predicted for theoretical AVs. For the three powertrains examined, potential energy savings at a 0.5 AV penetration rate (PR) are 11.4%, 18.1%, and 41.8% for internal combustion engine vehicles (ICEVs), recovery electric vehicles (EVs), and non-recovery EVs, respectively. In contrast, actual savings based on AV testing data in Apolloscope are 2.9%, 4.7%, and 11.5%. Regional energy performance is comparable between Apolloscope and Waymo, with both slightly outperforming A2D2. This study provides key insights into the effects of real-world AV technology on energy consumption.