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

Aaron Rabinowitz, Farhang Motallebi Araghi, Tushar Gaikwad, Zachary D. Asher and Thomas H. Bradley
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Aaron Rabinowitz: Department of Systems Engineering, Colorado State University, Fort Collins, CO 80523, USA
Farhang Motallebi Araghi: Department of Mechanical and Aerospace Engineering, Western Michigan University, Kalamazoo, MI 49008, USA
Tushar Gaikwad: Department of Mechanical and Aerospace Engineering, Western Michigan University, Kalamazoo, MI 49008, USA
Zachary D. Asher: Department of Mechanical and Aerospace Engineering, Western Michigan University, Kalamazoo, MI 49008, USA
Thomas H. Bradley: Department of Systems Engineering, Colorado State University, Fort Collins, CO 80523, USA

Energies, 2021, vol. 14, issue 18, 1-22

Abstract: 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.

Keywords: HEV; V2X; fuel economy; Dynamic Programming; MPC; ANN; LSTM; systems engineering (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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