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.

A widening gap has been found between the type-approval fuel consumption vs. real-world fuel consumption, which can bias the policy-making concerning energy saving and greenhouse gas reduction. An independent assessment of the gap’s contributions is significant to narrowing it. This study confirmed an average gap of 42% based on 0.95 billion second-by-second vehicle operating (speed, acceleration, etc.) and fuel consumption data collected from 395 light-duty vehicles through on-board diagnostics (OBD) devices. The impact of fuel consumption rate (FCR)-related (e.g., vehicle mass, tire pressure, temperature, etc.), engine load (or driving cycle)-related (e.g., congested, aggressive driving, etc.), and road grade discrepancies between the type-approval (or in-laboratory) test procedure vs. real-world driving conditions were independently assessed by setting the other impact factors as control variables. Results indicated that the FCR-related, engine load-related, and road grade discrepancies contributed 20.7%, 17.0%, and 3.2% to this gap, respectively (only1.0% of the gap has not been explained).