Heavy-load freight direct-current electric locomotive on-board platform and train operation curve calculation method

By combining the state variables of current, operating condition transition, and air braking state with dynamic programming algorithm, the train operation curve is optimized, which solves the problem of current acceleration, deceleration and operating condition transition constraints in DC electric locomotives, and realizes more efficient and safer train operation.

CN121291550AActive Publication Date: 2026-01-09CASCO SIGNAL LTD
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Patent Information

Application Number
CN202511559515.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-10-29
Publication Date
2026-01-09
Estimated Expiration
2045-10-29

AI Technical Summary

Technical Problem

Existing train operation curve planning methods cannot effectively meet the constraints of current acceleration, deceleration, and operating condition transitions in DC electric locomotives, making it difficult to optimize operating efficiency and safety.

Method used

A dynamic programming algorithm is adopted, which combines the current state, the operating condition transition state, and the air brake state as state variables. A combination of variable and fixed step sizes is used to optimize the action selection through the objective cost function. A greedy strategy and an accelerated search dictionary mechanism are employed to calculate the train operation curve.

Benefits of technology

It significantly improves the smoothness, safety, and algorithm efficiency of train operation, enabling more accurate prediction of train speed and energy consumption changes, reducing redundant calculations, and improving the system's real-time response capability and convergence speed.

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Abstract

The invention discloses a heavy-load freight direct-current electric locomotive on-board platform and a train operation curve calculation method. Step-by-step iterative optimization is carried out through a dynamic programming algorithm. Presetting operation steps and step lengths, and defining a selectable action set in each step length; and with the current state of the train as a starting point, evaluating each action in the action set in each iteration step, and calculating the next train state which may be transferred to. And evaluating all possible train state changes by adopting a target cost function, sorting according to the cost values from small to large, and preferentially selecting the action with the minimum cost value for subsequent calculation. And if all the action cost values of the current step exceed the limit, backtracking to the previous iteration step for reselection. And after iteration to the maximum step number, storing all train state changes, and finally obtaining an optimal train operation curve under the constraint of the target function. The method optimizes a control strategy through a dynamic programming algorithm, improves prediction precision, optimizes action selection, accelerates convergence, reduces redundancy, and improves operation stability, safety and algorithm efficiency.
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