A relationship between vehicle coastdown performance metrics and fuel efficiency

Abstract

In contemporary times, societal and legal institutions are earnestly committed to mitigating carbon dioxide (CO2) emissions into the atmosphere, thereby averting global warming. The automotive industry mirrors this commitment, necessitating the development of vehicles characterized by heightened energy efficiency. Such vehicles should boast an extended range while minimizing fuel consumption and CO2 emissions. A pivotal aspect in vehicle development involves the early determination of fuel economy (FE), contingent upon the projected characteristics of the vehicle and its intended market positioning with regard to fuel economy. Over time, diverse methodologies for calculating FE have emerged. These include hybrid regression models incorporating key input variables such as instantaneous vehicle speed and acceleration measurements, theoretical approximation methods derived from the physical properties of engine-vehicle systems, correlations among traffic-related parameters, power-demand models, as well as artificial neural networks and genetic algorithms. These approaches leverage various vehicle input data, encompassing engine speed, torque, fuel flow, intake manifold mean temperature, make of the car, engine style, weight of the car, vehicle type, transmission system type, and other relevant factors. Regrettably, the literature lacks a straightforward and expeditious means of estimating FE for a group of analogous vehicles, a deficiency addressed by the present work. The proposed empirical physical model facilitates FE estimations for a vehicle using a parsimonious set of input information: engine displacement, performance (recovery speed times), and road load data. These inputs are subjected to mathematical parameter adjustments within the empirical model. To validate the efficacy of this approach, the empirical model is applied in a Brazilian case study. The results attest to the success of the method, demonstrating a margin of error within 6% when compared to the official Brazilian government public data for the tested vehicle, particularly in the contexts of city and highway cycles.