Next Article in Journal
RANS- and TFC-Based Simulation of Turbulent Combustion in a Small-Scale Venting Chamber
Next Article in Special Issue
Switched Optimal Control of a Heavy-Duty Hybrid Vehicle
Previous Article in Journal
The Ability of a Soil Temperature Gradient-Based Methodology to Detect Leaks from Pipelines in Buried District Heating Channels
Article

Development and Evaluation of Velocity Predictive Optimal Energy Management Strategies in Intelligent and Connected Hybrid Electric Vehicles

1
Department of Systems Engineering, Colorado State University, Fort Collins, CO 80523, USA
2
Department of Mechanical and Aerospace Engineering, Western Michigan University, Kalamazoo, MI 49008, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Mario Marchesoni
Energies 2021, 14(18), 5713; https://doi.org/10.3390/en14185713
Received: 20 July 2021 / Revised: 30 August 2021 / Accepted: 7 September 2021 / Published: 10 September 2021
In this study, a thorough and definitive evaluation of Predictive Optimal Energy Management Strategy (POEMS) applications in connected vehicles using 10 to 20 s predicted velocity is conducted for a Hybrid Electric Vehicle (HEV). The presented methodology includes synchronous datasets gathered in Fort Collins, Colorado using a test vehicle equipped with sensors to measure ego vehicle position and motion and that of surrounding objects as well as receive Infrastructure to Vehicle (I2V) information. These datasets are utilized to compare the effect of different signal categories on prediction fidelity for different prediction horizons within a POEMS framework. Multiple artificial intelligence (AI) and machine learning (ML) algorithms use the collected data to output future vehicle velocity prediction models. The effects of different combinations of signals and different models on prediction fidelity in various prediction windows are explored. All of these combinations are ultimately addressed where the rubber meets the road: fuel economy (FE) enabled from POEMS. FE optimization is performed using Model Predictive Control (MPC) with a Dynamic Programming (DP) optimizer. FE improvements from MPC control at various prediction time horizons are compared to that of full-cycle DP. All FE results are determined using high-fidelity simulations of an Autonomie 2010 Toyota Prius model. The full-cycle DP POEMS provides the theoretical upper limit on fuel economy (FE) improvement achievable with POEMS but is not currently practical for real-world implementation. Perfect prediction MPC (PP-MPC) represents the upper limit of FE improvement practically achievable with POEMS. Real-Prediction MPC (RP-MPC) can provide nearly equivalent FE improvement when used with high-fidelity predictions. Constant-Velocity MPC (CV-MPC) uses a constant speed prediction and serves as a “null” POEMS. Results showed that RP-MPC, enabled by high-fidelity ego future speed prediction, led to significant FE improvement over baseline nearly matching that of PP-MPC. View Full-Text
Keywords: HEV; V2X; fuel economy; Dynamic Programming; MPC; ANN; LSTM; systems engineering HEV; V2X; fuel economy; Dynamic Programming; MPC; ANN; LSTM; systems engineering
Show Figures

Figure 1

MDPI and ACS Style

Rabinowitz, A.; Araghi, F.M.; Gaikwad, T.; Asher, Z.D.; Bradley, T.H. Development and Evaluation of Velocity Predictive Optimal Energy Management Strategies in Intelligent and Connected Hybrid Electric Vehicles. Energies 2021, 14, 5713. https://doi.org/10.3390/en14185713

AMA Style

Rabinowitz A, Araghi FM, Gaikwad T, Asher ZD, Bradley TH. Development and Evaluation of Velocity Predictive Optimal Energy Management Strategies in Intelligent and Connected Hybrid Electric Vehicles. Energies. 2021; 14(18):5713. https://doi.org/10.3390/en14185713

Chicago/Turabian Style

Rabinowitz, Aaron, Farhang M. Araghi, Tushar Gaikwad, Zachary D. Asher, and Thomas H. Bradley. 2021. "Development and Evaluation of Velocity Predictive Optimal Energy Management Strategies in Intelligent and Connected Hybrid Electric Vehicles" Energies 14, no. 18: 5713. https://doi.org/10.3390/en14185713

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop