A Two-Level MPC for Energy Management Including Velocity Control of Hybrid Electric Vehicles

• Published in: IEEE transactions on vehicular technology Vol. 68; no. 6; pp. 5494 - 5505
• Main Authors: Uebel, Stephan, Murgovski, Nikolce, Baker, Bernard, Sjoberg, Jonas
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Automobile industry; Automobiles; Batteries; Cruise control; Dynamic programming; Electric vehicles; Energy conservation; Energy consumption; Energy management; Engines; Force; Frequency modulation; Hybrid electric vehicles; hybrid vehicles; Ice; Maximum principle; model predictive control; Optimal control; Pontryagin's maximum principle; Powertrain; Predictive control; sequential quadratic program; Travel time; Velocity control
• ISSN: 0018-9545; 1939-9359; 1939-9359
• DOI: 10.1109/TVT.2019.2910728

Todays cruise control systems try to keep a constant speed given by the driver without regarding the energy consumption. There is, however, possibilities to save energy by choosing the optimal velocity is neglected. Solving the underlying control problem for long distances with an algorithm that runs on a vehicle control unit is not straightforward, especially when it comes to hybrid electric vehicles (HEV). In order to overcome the computational burden, this paper presents a two-level model predictive control approach. It uses a sequential quadratic program (SQP) that comprises the total travel distance computing a target set for a lower layer that uses discrete state-space dynamic programming and Pontryagin's maximum principle to compute the optimal control for a short horizon. The computational efficiency is shown by a case study for an HEV passenger car with parallel powertrain, which reveals a cost advantage of up to 39% compared to a benchmark solution that exactly follows the velocity of the SQP while both approaches yield the same travel time.