This work addresses the ecological-adaptive cruise control (EACC) problem for connected electric vehicles by a computationally efficient robust control strategy. The problem is formulated in the space-domain with a realistic description of the nonlinear electric powertrain model and motion dynamics to yield a convex optimal control problem (OCP). The OCP is solved by a novel robust model predictive control (RMPC) method handling various disturbances due to modeling mismatch and inaccurate leading vehicle information. The RMPC problem is solved by semidefinite programming relaxation (SDPR) and single linear matrix inequality (sLMI) techniques for further enhanced computational efficiency. The performance of the proposed real-time robust EACC (REACC) method is evaluated using an experimentally collected driving cycle. Its robustness is verified by comparison with a nominal MPC which is shown to result in speed-limit constraint violations. The energy economy of the proposed method outperforms a state-of-the-art time-domain RMPC scheme, as a more precisely fit convex powertrain model can be integrated into the space-domain scheme. The additional comparison with a traditional constant distance following strategy (CDFS) further verifies the effectiveness of the proposed REACC. Finally, it is verified that the REACC can be potentially implemented in real-time owing to the sLMI and resulting convex algorithm.
A Real-Time Robust Ecological-Adaptive Cruise Control Strategy for Battery Electric Vehicles
IEEE Transactions on Transportation Electrification ; 10 , 3 ; 7389-7404
2024-09-01
13772962 byte
Article (Journal)
Electronic Resource
English