Pan, X;
Chen, B;
Evangelou, S;
(2020)
Optimal Vehicle Following Strategy for Joint Velocity and Energy Management Control of Series Hybrid Electric Vehicles.
In:
IFAC-PapersOnLine.
(pp. pp. 14161-14166).
Elsevier
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Abstract
Recent advances in information and communication technologies present opportunities to optimally control the driving speed and powertrain energy management of vehicles under dynamic traffic circumstances. This paper addresses the energy-efficient car following problem of a series hybrid electric vehicle (HEV) by an enhanced adaptive cruise control (EACC) method. EACC is based on a nonlinear model predictive control framework, in which the behaviour of the lead vehicle is forecast by a neural network predictor trained by common test cycles. With the real-time predicted reference speed, EACC simultaneously optimizes the velocity and energy source power split of the ego HEV, while keeping the inter-vehicular distance within the desired range. The performance of EACC is benchmarked against a practical adaptive cruise control (ACC) that performs drafting and an impractical optimal control (OC) solved throughout the entire journey. Numerical examples show that the EACC can effectively close the gap between ACC and OC in terms of optimality with a remarkable fuel saving over ACC, while the computational load of EACC is comparable to ACC, which is much more efficient than the OC. Further design insight of the methodology is also provided by an investigation into the influence of the prediction horizon.
| Type: | Proceedings paper |
|---|---|
| Title: | Optimal Vehicle Following Strategy for Joint Velocity and Energy Management Control of Series Hybrid Electric Vehicles |
| Event: | 21st IFAC World Congress |
| Location: | Berlin, Germany |
| Dates: | 12 July 2020 - 17 July 2020 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.ifacol.2020.12.1031 |
| Publisher version: | https://doi.org/10.1016/j.ifacol.2020.12.1031 |
| Language: | English |
| Additional information: | Copyright © 2020 The Authors. This is an open access article under the CC BY-NC-ND license.(https://creativecommons.org/licenses/by-nc-nd/4.0/) |
| Keywords: | Hybrid Electric Vehicle, Optimal Control, Energy Management, Velocity Control, Model Predictive Control |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10096690 |
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