Real-time energy-efficient optimal control of high-speed electric train

Zhou, Hao; Wan, Yiming; Ye, Hao; Jiang, Minghu; Wang, Jia

doi:10.1016/j.conengprac.2021.104825

Cited by 8 publications

(2 citation statements)

References 18 publications

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“…{ e (x) = e eq g min ≤ g (x) ≤ g max (35) where f represents the objective function, the decision variables x consist of traction force, braking force, and kinetic energy. e represents the equality constraints, including Equations ( 18), ( 29), (30), and (31). g represents the inequality constraints, including Equations ( 28), (32), and (33).…”

Section: Optimization Modelmentioning

confidence: 99%

“…Different with heuristic algorithms and machine learning methods, maximum principle method performs well in obtaining the optimal solution stably. However, the computational efficiency has always been a challenge to classic maximum principle methods [11,31] and several researches are trying to improve it. In particular, fluctuating speed limits in subways pose a potential computing burden.…”

Section: Introductionmentioning

confidence: 99%

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Real‐time multi‐objective speed planning ATO considering assist driving for subway

Wang,

Zhang,

Wang

et al. 2024

IET Intelligent Trans Sys

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Speed curve planning is one of the most important functions of automatic train operation (ATO). To improve the real‐time optimization capability and driver‐friendliness of the existing ATO, an extended ATO framework considering both automatic driving and assisted driving is designed. A multi‐objective optimization model based on quadratic programming is established considering energy‐saving, punctuality, and comfort. However, due to the influence of the weight of multi‐objectives, this method cannot directly obtain the speed curve satisfying the trip time constraint. Further, based on the analysis about the weight of multi‐objects, a time‐constrained quadratic programming algorithm is proposed. With the proposed method, the speed curve can be calculated in real‐time both before operations and during operations. For the former, time‐varying train mass and trip time are considered to guarantee an optimal solution. For the latter, deviations, delays, and maloperations on the way are corrected. Simulation experiments verify the solvability and real‐time performance of the proposed method. In particular, compared with the dynamic programming and (mixed‐integer linear programming) MILP method, the proposed method is more energy‐efficient and easier to be followed by the driver. In addition, a prototype is developed for commercial tests on a Beijing subway line. The relevant performance is verified in commercial tests.

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