E-CoDrive: A Co-Simulation Framework for Testing Energy-Critical Driving Scenarios
2026-07-06 • Software Engineering
Software Engineering
AI summaryⓘ
The authors created a tool called E-CoDrive to help test how electric self-driving cars use energy in city traffic. This tool combines models for energy use, traffic patterns, and driving simulations to see how different traffic situations affect battery life. Their tests showed that traffic conditions can greatly change how much energy these cars consume. This helps researchers check if electric self-driving cars can finish trips before their batteries run out.
Autonomous Electric VehiclesEnergy ConsumptionMicro-traffic SimulatorDriving SimulatorClosed-loop Co-simulationAutowareUrban TrafficScenario-based TestingBattery Depletion
Authors
Manfredi Napolitano, Alessandra Somma, Alessio Gambi, Andrea Stocco, Nicola Mazzocca
Abstract
Autonomous driving research has largely focused on safety while giving limited attention to non-functional aspects such as energy consumption and sustainability. As Autonomous Electric Vehicles (AEVs) become increasingly common in urban traffic, understanding how complex traffic dynamics influence their energy consumption is paramount to test whether AEVs can complete trips before battery depletion. To support energy-aware scenario-based testing of AEVs, we present E-CoDrive, a framework for reproducible closed-loop driving co-simulations that integrates an energy consumption model, a micro-traffic simulator, and a high-fidelity driving simulator to test AEV software stacks in urban scenarios. This tool paper describes the architecture of E-CoDrive and demonstrates its applicability by testing an Autoware-based AEV stack. Our evaluation shows that varying traffic conditions produce substantial differences in vehicle energy consumption. The artifact is publicly available at https://doi.org/10.6084/m9.figshare.32244783, and a screencast showing the tool is available at https://youtu.be/yX9fWHqCvgc.