Prediction Error Applied to Hybrid Electric Vehicle Optimal Fuel Economy

Asher, Zachary D.; Baker, David A.; Bradley, Thomas H.

doi:10.1109/tcst.2017.2747502

Cited by 27 publications

(24 citation statements)

References 22 publications

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“…A 2010 Toyota Prius is selected as the vehicle model due to its commercial prevalence and that it has the highest FE in its class. The model used to represent the Baseline EMS is consistent with previous research in that it is a modification of the publically available 2004 Toyota Prius in the Autonomie modeling software to represent a 2010 Toyota Prius [51]. The Autonomie modeling software has demonstrated strong correlation with real world testing and is generally accepted as the standard among industry and research professionals.…”

Section: Baseline Energy Management Strategy Simulationsupporting

confidence: 63%

“…The ADAS ground truth detection did not include the typical detection objectives that are required for safety focused ADAS implementation. Based on previous research identifying aspects of real-world driving that are most important for prediction [51], it was determined that the ADAS detection objective should only include identification of the state of the traffic light, identifying vehicle speed changes from the vehicle directly in front, identification of stop sign location, and identification of turn lanes. An analysis of automated ADAS detection algorithms and a comparison to ADAS ground truth is available in a separate article [54].…”

Section: Optimal Energy Management Strategy Simulationsmentioning

confidence: 99%

“…cycle speeds for the entire drive cycle before the trip has begun. These traffic levels for the entire drive cycle have demonstrated accuracy, as shown in Figure 8, but traffic information may not be as important for an Optimal EMS [51].…”

Section: Figurementioning

confidence: 99%

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Enabling Prediction for Optimal Fuel Economy Vehicle Control

Asher

Tunnell

Baker

et al. 2018

SAE Technical Paper Series

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V ehicle control using prediction based optimal energy management has been demonstrated to achieve better fuel economy resulting in economic, environmental, and societal benefits. However, research focusing on prediction derivation for use in optimal energy management is limited despite the existence of hundreds of optimal energy management research papers published in the last decade. In this work, multiple data sources are used as inputs to derive a prediction for use in optimal energy management. Data sources include previous drive cycle information, current vehicle state, the global positioning system, travel time data, and an advanced driver assistance system (ADAS) that can identify vehicles, signs, and traffic lights. To derive the prediction, the data inputs are used in a nonlinear autoregressive artificial neural network with external inputs (NARX). Two real world drive cycles were developed for analysis in the Denver, Colorado region: a city-focused drive cycle that passes through downtown as well as a highway-focused drive cycle that transitions across multiple interstates. A validated model of a 2010 Toyota Prius in Autonomie is used to determine the vehicle control fuel economy improvements that are possible from the NARX prediction. The optimal energy management control strategy is determined using dynamic programming due to its ease of use and that the solution produced is the globally optimal solution. The control strategies compared include the existing 2010 Toyota Prius control strategy as a baseline, the neural network prediction optimal energy management control strategy, and a 100% accurate prediction optimal energy management control strategy. Results show that inclusion of various sensors and signals enables a significant amount of the fuel economy improvement with respect to 100% accurate prediction. The conclusion is that prediction based optimal energy management enabled fuel economy improvements can be realized with currently available sensors and signals.

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Section: Baseline Energy Management Strategy Simulationsupporting

confidence: 63%

Section: Optimal Energy Management Strategy Simulationsmentioning

confidence: 99%

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Enabling Prediction for Optimal Fuel Economy Vehicle Control

Asher

Tunnell

Baker

et al. 2018

SAE Technical Paper Series

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“…Expand one-order Markov Model to High-order Markov Model [23], [67], [69] Integrating Route-based information into Prediction framework [10], [11], [33]- [35], [56]- [61], [71], [74], [75], [77]- [79], [87], [97], [98], [112], [113], [116] Self-learning/Adaptive Mechanism Adaptive/Self-learning Markov approaches [51], [52], [64], [72], [73] Moving window Approaches [93] Variable prediction horizon…”

Section: Reduce Future Uncertaintymentioning

confidence: 99%

“…Secondly, impacts of various mis-predictions bringing toPEMSs are different. In order to figure out influences caused by different mis-predictions, in[116], driving-derived prediction errors (predicted velocity, traffic conditions, additional starts/stops, route change etc.) and vehicle parameter predictionerrors (vehicle mass, drag coefficients, rolling resistance etc.…”

mentioning

confidence: 99%

A survey on driving prediction techniques for predictive energy management of plug-in hybrid electric vehicles

Zhou

Ravey

Péra

2019

Journal of Power Sources

197

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Driving prediction techniques (DPTs) are used to forecast the distributions of various future driving conditions (FDC), like velocity, acceleration, driver behaviors etc. and the quality of prediction results has great impacts on the performance of corresponding predictive energy management strategies (PEMSs), e.g., fuel economy (FE), lifetime of battery etc. This survey presents a comprehensive study on existing DPTs. Firstly, a review on prediction objectives and major types of prediction algorithms are presented. Then a comparative study on various prediction approaches is carried out and suitable application scenarios for each approach are provided according to their characteristics. Moreover, prediction accuracy-affecting factors are analyzed and corresponding approaches for dealing with mispredictions are discussed in detail. Finally, the bottlenecks of current researches and future developing trends of DPTs are given. In general, this paper not only gives a comprehensive analysis and review of existing DPTs but also indicates suitable application scenarios for each prediction algorithm and summarizes potential approaches for handling the prediction inaccuracies, which will help prospective designers to select proper DPTs according to different applications and contribute to the further performance enhancements of PEMSs for hybrid electric vehicles (HEVs) and plug-in hybrid electric vehicles (PHEVs).

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