A New Regenerative Anti-Idling System for Service Vehicles: Load Identification, Optimal Power Management
Bibliographic record
Abstract
Service vehicles, such as refrigerator trucks and tour buses, are equipped with auxiliary devices, including refrigeration systems and cabin air conditioning systems, which consume significant amount of energy. The engine of these vehicles should idle to supply power for auxiliary devices when they stop for a long time, e.g. for loading and unloading goods. This study proposes a new anti-idling system for service vehicles that powers auxiliary devices by a battery pack and an engine-driven generator (or alternator). In addition to idle elimination which is the main objective of all current anti-idling systems, the proposed system called Regenerative Auxiliary Power System (RAPS) attempts to reduce fuel consumption by enabling regenerative braking and utilizing an optimal power management system. The objectives of this study are to identify drive and service loads of a service vehicle for component sizing of the RAPS and to develop an optimal power management system for more fuel saving. \nIn order to determine the size of required components (a battery pack and a generator) for the RAPS, drive and service loads of a given service vehicle should be identified. The drive load is the amount of power that is required for moving the vehicle, and the service load is the power consumption of the auxiliary devices. To identify drive and service loads, all the parameters in power balance equation of the engine should be either measured or estimated. As two inputs with unknown variations in this equation, vehicle mass and torque of auxiliary devices are required to be estimated. This study proposes a model-based algorithm that utilizes available signals in the CAN bus of the vehicle as well as a signal from a GPS receiver (road grade information) for simultaneous estimation of the vehicle mass and torque of auxiliary devices. \nThe power management system of the RAPS should determine the split ratio of auxiliary power demand between the generator and battery in order to minimize fuel consumption. It should also guarantee that the battery has enough energy for powering auxiliary devices at all the engine-OFF stops. To meet these objectives, a two-level control system is proposed in this study. In the high-level control system, a fast dynamic programming (DP) technique which utilizes extracted features of the predicted drive and service loads obtains an SOC trajectory. In the low-level control system, a refined Adaptive Equivalent Fuel Consumption Minimization (A-ECMS) technique is employed to track the SOC trajectory obtained by the high-level control scheme. \nMany numerical simulations are carried out to test the functionality of the proposed identification algorithm and power management system. Moreover, the numerical simulations are validated by Hardware-In-The-Loop (HIL) simulations. The results show the idling is completely eliminated and a significant amount of fuel is saved by implementing the RAPS on a service vehicle. Therefore, the cost of energy can be noticeably reduced and consequently the cost of RAPS is recouped in a short period of time.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".