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Kolmanovsky, Ilya

Publications and source records attributed to Kolmanovsky, Ilya.

6 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Multi-range vehicle speed prediction using vehicle connectivity for enhanced energy efficiency of vehicles

An integrated speed prediction framework based on historical traffic data mining and real-time V2I communications for CAVs. The present framework provides multi-horizon speed predictions with different fidelity over short and long horizons. The present multi-horizon speed prediction is integrated with an economic model predictive control (MPC) strategy for the battery thermal management (BTM) of connected and automated electric vehicles (EVs) as a case study. The simulation results over real-world urban driving cycles confirm the enhanced prediction performance of the present data mining strategy over long prediction horizons. Despite the uncertainty in long-range CAV speed predictions, the vehicle level simulation results show that 14% and 19% energy savings can be accumulated sequentially through eco-driving and BTM optimization (eco-cooling), respectively, when compared with normal-driving and conventional BTM strategy.

Amini, Mohammad Reza↗
DOE OSTI2021

A Multirange Vehicle Speed Prediction With Application to Model Predictive Control-Based Integrated Power and Thermal Management of Connected Hybrid Electric Vehicles

Abstract Connectivity and automated driving technologies have opened up new research directions in the energy management of vehicles which exploit look-ahead preview and enhance the situational awareness. Despite this advancement, the vehicle speed preview that can be obtained from vehicle-to-vehicle/infrastructure (V2V/I) communications is often limited to a relatively short time-horizon. The vehicular energy systems, specifically those of the electrified vehicles, consist of multiple interacting power and thermal subsystems that respond over different time-scales. Consequently, their optimal energy management can greatly benefit from long-term speed prediction beyond that available through V2V/I communications. Accurately extending the look-ahead preview, on the other hand, is fundamentally challenging due to the dynamic nature of the traffic environment. To address this challenge, we propose a data-driven multirange vehicle speed prediction strategy for arterial corridors with signalized intersections, providing the vehicle speed preview for three different ranges, i.e., short-, medium-, and long-range. The short-range preview is obtained by V2V/I communications. The medium-range preview is realized using a neural network (NN), while the long-range preview is predicted based on a Bayesian network (BN). The predictions are updated in real-time based on the current state of traffic and incorporated into a multihorizon model predictive control (MH-MPC) for integrated power and thermal management (iPTM) of connected vehicles. The results of design and evaluation of the performance of the proposed data-informed MH-MPC for iPTM of connected hybrid electric vehicles (HEVs) using traffic data for real-world city driving are reported.

Automation & Control Systems↗
DOE OSTI2020

Long-Term Vehicle Speed Prediction via Historical Traffic Data Analysis for Improved Energy Efficiency of Connected Electric Vehicles

Connected and automated vehicles (CAVs) are expected to provide enhanced safety, mobility, and energy efficiency. While abundant evidence has been accumulated showing substantial energy saving potentials of CAVs through eco-driving, traffic condition prediction has remained to be the main challenge in capitalizing the gains. The coupled power and thermal subsystems of CAVs necessitate the use of different speed preview windows for effective and integrated power and thermal management. Real-time vehicle-to-infrastructure (V2I) communications can provide an accurate speed prediction over a short prediction horizon (e.g., 30 s to 60 s), but not for a long range (e.g., over 180 s). Therefore, advanced approaches are required to develop detailed speed prediction for robust optimization-based energy management of CAVs. This paper presents an integrated speed prediction framework based on historical traffic data classification and real-time V2I communications for efficient energy management of electrified CAVs. The proposed framework provides multi-range speed predictions with different fidelity over short and long horizons. The proposed multi-range speed prediction is integrated with an economic model predictive control (MPC) strategy for the battery thermal management (BTM) of connected and automated electric vehicles (EVs). The simulation results over real-world urban driving cycles confirm the enhanced prediction performance of the proposed data classification strategy over a long prediction horizon. Despite the uncertainty in long-range CAVs’ speed predictions, the vehicle-level simulation results show that 14% and 19% energy savings can be accumulated sequentially through eco-driving and BTM optimization (eco-cooling), respectively, when compared with normal driving (i.e., human driver) and conventional BTM strategy.

Engineering↗