This study is the first of a two-part paper. The overall study presents a new methodology to improve the accuracy of hybrid vehicles’ energy-consumption model over conventional transportation modeling methods. The first paper attempts to improve an equation for vehicles’ driving-power estimation to be more realistic and specific for a particular vehicle model or fleet. The second paper adopts the driving-power equation to estimate the requested driving power. Then, the data are utilized to construct the hybrid-vehicle energy-consumption model, namely, the traction-force–speed-based energy-consumption model (TFS model). The main concept of the first paper is to utilize the power-split hybrid powertrain’s accessible on-board diagnostics (OBD) dataset, and its dynamic model to estimate the total propulsion power. Then, propulsion power was applied as the main parameter for driving-power equation development and vehicle-specific coefficient calibration. For coefficient calibration, this study implemented the stepwise multiple regression method to select and calibrate an optimal set of coefficients. Results showed that conventional driving-power equations Vehicle-Specific Power (VSP) LDV 1999 and VSP Prius3Spec provide low prediction fidelity, especially under high-speed (>80 km/h) and heavy-load driving (≥50 kW). In contrast, D r v P w P r i u s 3 , proposed in this study, effectively improved prediction to become more accurate and reliable through all driving conditions and speed ranges. It dramatically helped to reduce prediction discrepancy over the conventional equations at heavy-load driving, from an R-square of 0.79 and 0.78 to 0.96. D r v P w P r i u s 3 also the prediction error at high-speed driving from the maximal error of approximately −20 to −5 kW. This study also discovered that aerodynamics and rolling resistance were the primary factors that caused the prediction error of conventional VSP equations. In addition, results in this study showed that both of the approaches used to establish the P P T d r v and D r v P w P r i u s 3 equations were valid for a power-split hybrid vehicle’s driving-power estimation. For the coefficient-calibration part, the stepwise and multiple regression method is low-cost and simple, allowing to calibrate an appropriate set of optimal coefficients for a specific vehicle model or fleet.
In the transportation sector, the fuel consumption model is a fundamental tool for vehicles’ energy consumption and emission analysis. Over the past decades, vehicle-specific power (VSP) has been enormously adopted in a number of studies to estimate vehicles’ instantaneous driving power. Then, the relationship between the driving power and fuel consumption is established as a fuel consumption model based on statistical approaches. This study proposes a new methodology to improve the conventional energy consumption modeling methods for hybrid vehicles. The content is organized into a two-paper series. Part I captures the driving power equation development and the coefficient calibration for a specific vehicle model or fleet. Part II focuses on hybrid vehicles’ energy consumption modeling, and utilizes the equation obtained in Part I to estimate the driving power. Also, this paper has discovered that driving power is not the only primary factor that influences hybrid vehicles’ energy consumption. This study introduces a new approach by applying the fundamental of hybrid powertrain operation to reduce the errors and drawbacks of the conventional modeling methods. This study employs a new driving power estimation equation calibrated for the third generation Toyota Prius from Part I. Then, the Traction Force-Speed Based Fuel Consumption Model (TFS model) is proposed. The combination of these two processes provides a significant improvement in fuel consumption prediction error compared to the conventional VSP prediction method. The absolute maximum error was reduced from 57% to 23%, and more than 90% of the predictions fell inside the 95% confidential interval. These validation results were conducted based on real-world driving data. Furthermore, the results show that the proposed model captures the efficiency variation of the hybrid powertrain well due to the multi-operation mode transition throughout the variation of the driving conditions. This study also provides a supporting analysis indicating that the driving mode transition in hybrid vehicles significantly affects the energy consumption. Thus, it is necessary to consider these unique characteristics to the modeling process.
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