Investigation of Vehicle Speed Prediction from Neural Network Fit of Real World Driving Data for Improved Engine On/Off Control of the EcoCAR3 Hybrid Camaro

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

doi:10.4271/2017-01-1262

Cited by 18 publications

(13 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Results from existing research demonstrate that a 15 or 30 second prediction window is an ideal tradeoff between prediction accuracy and FE improvement potential [48]. Because ADAS technology provides near term prediction data such as identification of a red light or a slowing vehicle, a 15 second prediction window was used.…”

Section: Planning Subsystem Modelmentioning

confidence: 99%

“…Therefore, presenting intermediate drive cycle results concerning velocity prediction accuracy does not provide meaningful insight. For a discussion on velocity prediction error analysis refer to other work [48,57]. An overall conceptual diagram of the planning subsystem model is shown in Figure 12.…”

Section: Planning Subsystem Modelmentioning

confidence: 99%

“…From the limited research that does exist, initial results show that incorporation of traffic information improves prediction quality [45,46] and that an artificial neural network may provide the most robust predictions [47]. Additionally, recent research suggests that using 15-30 second prediction windows may yield the best FE results when sensors and signals are limited [48]. None of these studies use ADAS technologies to enable prediction.…”

mentioning

confidence: 99%

“…In this study, we seek to enable prediction for use in an Optimal EMS using a database of similar drive cycles [47,48,49,50] but with the addition of commercially available ADAS and travel time technology. This novel incorporation of ADAS and travel time technology is then used to generate predictions for use in an Optimal EMS.…”

mentioning

confidence: 99%

See 3 more Smart Citations

Enabling Prediction for Optimal Fuel Economy Vehicle Control

Asher

Tunnell

Baker

et al. 2018

SAE Technical Paper Series

View full text Add to dashboard Cite

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.

show abstract

Section: Planning Subsystem Modelmentioning

confidence: 99%

Section: Planning Subsystem Modelmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Enabling Prediction for Optimal Fuel Economy Vehicle Control

Asher

Tunnell

Baker

et al. 2018

SAE Technical Paper Series

View full text Add to dashboard Cite

show abstract

“…However, an Optimal EMS requires prediction of the entire drive cycle to be globally optimal [16,17]. Despite this seemingly difficult requirement, our initial research shows that FE improvements using limited prediction with an Optimal EMS are possible [25,26].…”

mentioning

confidence: 99%

Towards Improving Vehicle Fuel Economy with ADAS

Tunnell

Asher

Pasricha

et al. 2018

SAE Technical Paper Series

View full text Add to dashboard Cite

Modern vehicles have incorporated numerous safety-focused advanced driver-assistance systems (ADAS) in the last decade including smart cruise control and object avoidance. In this article, we aim to go beyond using ADAS for safety and propose to use ADAS technology to enable predictive optimal energy management and improve vehicle fuel economy (FE). We combine ADAS sensor data with a previously developed prediction model, dynamic programming (DP) optimal energy management control, and a validated model of a 2010 Toyota Prius to explore FE. First, a unique ADAS detection scope is defined based on optimal vehicle control prediction aspects demonstrated to be relevant from the literature. Next, during real-world city and highway drive cycles in Denver, Colorado, a camera is used to record video footage of the vehicle environment and define ADAS detection ground truth. Then, various ADAS algorithms are combined, modified, and compared to the ground truth results. Lastly, the impact of four vehicle control strategies on FE is evaluated: (1) the existing vehicle control, (2) actual ADAS detection for prediction and optimal energy management (we consider two variants ADAS1 and ADAS2 for this strategy), (3) ground truth ADAS detection for prediction and optimal energy management, and (4) 100% accurate prediction and optimal energy management. Results show that the defined ADAS scope and algorithms provide close correlation with ADAS ground truth and can enable FE improvements as part of a prediction-based optimal energy management strategy (EMS). Our proposed approach can leverage existing ADAS technology in modern vehicles to realize prediction-based optimal energy management, thus obtaining FE improvements with minor modifications.

show abstract