Review, Modelling and Simulation of Two-Mode Hybrid Vehicle Architecture
Bibliographic record
Abstract
Hybrid vehicle technology is beginning to make a significant mark in the automotive industry, most notably by the Toyota Prius THS-II and its one-mode technology, but also by two-mode architectures recently introduced. GM-Allison, Renault, and the Timken Company have attempted to capitalize on the advantages over simpler series and parallel architectures that the series-parallel configuration confers on the Prius while also improving the design by allowing the powertrain configuration to physically shift and operate in two different modes depending on the driving load. This work provides an overview of the state-of-the-art in two-mode hybrid vehicle architectures, and demonstrates the performance of this technology in comparison to the market-leading Toyota Prius one-mode hybrid vehicle technology and conventional ICE technology. Simulations in the NREL ADVISOR® software compare the performances of the one- and two-mode architectures against a parallel-full design and the ICE baseline for four different drive cycles and a vehicle with varying weight that simulates a commercial vehicle application. A configuration that is a variation of those designed by GM-Allison was chosen as the representative of the two-mode architectures. The performance metric was fuel economy. The fuel economy was measured over the course of the drive cycles: (1) Urban Dynamometer Driving Schedule for Heavy Duty Vehicles (UDDSHDV); (2) New York City Truck (NYCT); (3) City-Suburban Heavy Vehicle Route (CSHVR); and (4) Highway Fuel Economy Test (HWFET). The vehicle model uses a module developed in-house for a Kenworth T400 truck with a payload that varies from empty to completely full. The results demonstrate that the two-mode architecture provides significantly improved performance to that of the conventional non-hybrid design and comparable performance to that of the parallel-full hybrid design. Furthermore, the one-mode design is shown to be sub-optimal for this vehicle type. Development and optimization of the control strategy, which is the direction of the current research, should allow for additional improvement in fuel economy; optimization of vehicular components could result in improvements in acceleration ability, gradeability, and top speed performance, which lags behind the performance capabilities of the conventional powertrain vehicle in these metrics. The study confirms that two-mode architecture presents unique advantages for constantly changing driving cycles and vehicle payloads and represents the future of hybrid vehicle technology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".