Validation and Calibration of Energy Models with Real Vehicle Data from Chassis Dynamometer Experiments

TL;DR

This work tackles the need for fast, interpretable vehicle energy models that retain fidelity to physical energy behavior. It proposes a pipeline that derives semi-principled and simplified energy models from a high-fidelity Autonomie model and validates them against real chassis-dynamometer data for a Toyota RAV4. Calibrating the Autonomie baseline to the real vehicle yields a final root model that enables reliable fuel-rate estimates across cruising and transient drive cycles, with the semi-principled and simplified models closely tracking dynamometer measurements. The results support the practical use of reduced energy models for large-scale transportation applications, where speed and interpretability are essential alongside fidelity to real-world energy behavior.

Abstract

Accurate estimation of vehicle fuel consumption typically requires detailed modeling of complex internal powertrain dynamics, often resulting in computationally intensive simulations. However, many transportation applications-such as traffic flow modeling, optimization, and control-require simplified models that are fast, interpretable, and easy to implement, while still maintaining fidelity to physical energy behavior. This work builds upon a recently developed model reduction pipeline that derives physics-like energy models from high-fidelity Autonomie vehicle simulations. These reduced models preserve essential vehicle dynamics, enabling realistic fuel consumption estimation with minimal computational overhead. While the reduced models have demonstrated strong agreement with their Autonomie counterparts, previous validation efforts have been confined to simulation environments. This study extends the validation by comparing the reduced energy model's outputs against real-world vehicle data. Focusing on the MidSUV category, we tune the baseline Autonomie model to closely replicate the characteristics of a Toyota RAV4. We then assess the accuracy of the resulting reduced model in estimating fuel consumption under actual drive conditions. Our findings suggest that, when the reference Autonomie model is properly calibrated, the simplified model produced by the reduction pipeline can provide reliable, semi-principled fuel rate estimates suitable for large-scale transportation applications.

Figures (14)

• Figure 1: This image depicts a vehicle undergoing testing on a chassis dynamometer, with a human driver at the helm, executing the intended driving pattern Chassis_dyno.
• Figure 2: This diagram provides an overview of the energy modeling pipeline, beginning with Autonomie and culminating in the development of a simplified polynomial-fitted model.
• Figure 3: This figure represents the vehicle architecture in Autonomie, highlighting the specific components that require modification and detailing the necessary adaptations to extract the default vehicle parameters and maps essential for deriving the semi-principled model.
• Figure 4: This plot presents the speed profiles of the Standard Drive Cycles used in Light-Duty Vehicle modeling on the Virtual Chassis Dynamometer, which closely mirror those employed in the Real-Vehicle Chassis Dynamometer Experiment.
• Figure 5: This plot shows the trend of engine water temperature over time as the driver on the chassis dynamometer attempts to execute the target driving pattern. During the first cycle of this example driving pattern, the engine water temperature is still warming up, approaching the "hot mode" condition.