基于冗余工序编码的高速列车节能驾驶智能算法

doi:10.13229/j.cnki.jdxbgxb.20220094

吉林大学学报(工学版) ›› 2023, Vol. 53 ›› Issue (12): 3404-3414.doi: 10.13229/j.cnki.jdxbgxb.20220094

• 交通运输工程·土木工程 • 上一篇

基于冗余工序编码的高速列车节能驾驶智能算法

应沛然¹(),曾小清¹(),沈拓^2,³,袁腾飞^4,⁵,宋海峰⁶,王奕曾⁷

^1.同济大学道路与交通工程教育部重点实验室，上海 201804
^2.上海理工大学光电与计算机工程学院，上海 200093
^3.上海市轨道交通结构耐久与系统安全重点实验室，上海 201804
^4.上海大学悉尼工商学院，上海 201800
^5.上海城市基础设施更新工程技术研究中心，上海 200032
^6.北京航空航天大学电子信息工程学院，北京 100191
^7.香港城市大学计算机科学系，香港 999077

收稿日期:2022-01-24 出版日期:2023-12-01 发布日期:2024-01-12
通讯作者: 曾小清 E-mail:prying@tongji.edu.cn;zengxq@tongji.edu.cn
作者简介:应沛然（1996-），男，博士研究生.研究方向：轨道交通控制与安全.E-mail：prying@tongji.edu.cn
基金资助:
国家自然科学基金项目(61903021);国家留学基金项目(202106260137);上海市科委项目(20dz1202903-0.1);上海市住建委项目(JS-KY18R022-7);北京市自然科学基金项目(L211021)

Redundant operation code-based intelligent algorithm for energy⁃efficient driving of high-speed train

Pei-ran YING¹(),Xiao-qing ZENG¹(),Tuo SHEN^2,³,Teng-fei YUAN^4,⁵,Hai-feng SONG⁶,Yi-zeng WANG⁷

^1.Key Laboratory of Road and Traffic Engineering，Ministry of Education，Tongji University，Shanghai 201804，China
^2.School of Optical-Electrical and Computer Engineering，University of Shanghai for Science and Technology，Shanghai 200093，China
^3.Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety，Shanghai 201804，China
^4.SHU-UTS SILC Business School，Shanghai University，Shanghai 201800，China
^5.Shanghai Engineering Research Center of Urban Infrastructure Renewal，Shanghai 200032，China
^6.School of Electronic Information Engineering，Beihang University，Beijing 100191，China
^7.Department of Computer Science，City University of Hong Kong，Hongkong 999077，China

Received:2022-01-24 Online:2023-12-01 Published:2024-01-12
Contact: Xiao-qing ZENG E-mail:prying@tongji.edu.cn;zengxq@tongji.edu.cn

摘要/Abstract

摘要：

针对任意类型坡道组合和限速环境下高速列车节能驾驶优化问题，依据极大值原理推导得到最优解必要性条件，提出了包括冗余工序及其切换点区间的冗余工序编码概念和生成规则，设计了冗余工序编码分别与粒子群算法和有限变异遗传算法相结合的PMP-PSO算法和PMP-LMGA算法，用以归并部分冗余工况并优化切换点位置。实验结果表明：在满足各项运行规则前提下，运用冗余工序编码可以显著加快复杂场景下的求解速度，稳定且有效降低总能耗，同时，应用多智能体并行计算可进一步提升求解效率。

关键词: 铁路运输, 冗余工序编码, 有限变异遗传算法, 粒子群算法

Abstract:

Two redundant operation code-based methods were proposed to solve the energy-efficient driving problem of a high-speed train with steep gradients and speed limits. Based on the necessary conditions for the optimal solution derived from the Pontryagin's maximum principle， the concept of redundant operation code， including redundant operation sequence and switching area， and the generation rules were proposed. Applying the new concept， PMP-LMGA and PMP-PSO algorithms were developed to merge redundant operations and find optimal switching points. The experimental results indicate that the redundant operation code can significantly speed up the calculation in complex scenarios. Total energy consumption can be effectively and stably reduced while meeting various operation rules. Multi-agent parallel computing can further improve solution efficiency.

Key words: railway transportation, redundant operation code, limited mutation genetic algorithm, particle swarm optimization

中图分类号:

U238

应沛然,曾小清,沈拓,袁腾飞,宋海峰,王奕曾. 基于冗余工序编码的高速列车节能驾驶智能算法[J]. 吉林大学学报(工学版), 2023, 53(12): 3404-3414.

Pei-ran YING,Xiao-qing ZENG,Tuo SHEN,Teng-fei YUAN,Hai-feng SONG,Yi-zeng WANG. Redundant operation code-based intelligent algorithm for energy⁃efficient driving of high-speed train[J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(12): 3404-3414.

图/表 14

图1

图2

表1

最优控制工况"

控制工况	速度	辅助协态变量	控制输出
全力牵引	$v ≤ V ˉ$	$ω > 1$	$u (x) = U ˉ (v)$
牵引巡航	$v = m i n v b q, V ˉ$	$ω = 1$	$u (x) = w 0 (v) + w j (x) ≥ 0$
惰行	$v ≤ V ˉ$	$ρ < ω < 1$	$u (x) = 0$
制动巡航	$v = m i n v b z, V ˉ$	$ω = ρ$	$u (x) = w 0 (v) + w j (x) ≤ 0$
全力制动	$v ≤ V ˉ$	$ω < ρ$	$u (x) = U ? (v)$

表1

图3

图4

图5

图6

图7

图8

表2

案例的基本参数"

参数		案例一	案例二	参数		案例一	案例二
适应度函数	$?$	15	3	冗余工序编码	$α$	1.50	2.00
	$ξ$	95	6		$β$	1.00	0.70
	$ψ$	260	35		$η$	1.00	0.70
	$θ$	425	80		$?$	0.70	0.20

表2

表3

算法的基本参数"

算法	案例	参数
E-GA¹	一&二	$r c = S t r 2,0.40,0.20, r o u n d 0.50 ? N r m = S t r 3,0.20,0.04, r o u n d 0.10 ? N$
PMP-GA¹	一	$r c = S t r 2,0.60,0.30, r o u n d 0.50 ? N r m = S t r 2,0.80,0.10, r o u n d 0.20 ? N$
PMP-GA¹	二	$r c = S t r 4,0.30,0.10, r o u n d 0.70 ? N r m = S t r 2,0.80,0.05, r o u n d 0.10 ? N$
PMP-LMGA¹	一&二	$r c = S t r 4,0.40,0.10, r o u n d 0.50 ? N r m = S t r 2,0.80,0.40, r o u n d 0.20 ? N φ = S t r 3,1.00,0.001, r o u n d 0.15 ? N$
PMP-PSO	一	$ω = S t r 1,0.60,0.60, r o u n d 0.50 ? N c 1 = S t r 2,0.60,0.30, r o u n d 0.50 ? N c 2 = S t r 4,0.10,0.80, ? r o u n d 0.40 ? N$
PMP-PSO	二	$ω = S t r 3,0.90,0.40, r o u n d 0.14 ? N c 1 = S t r 1,0.40,0.40, r o u n d 0.40 ? N c 2 = S t r 4,0.05,0.65, r o u n d 0.40 ? N$

表3

图9

图10

图11

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基于冗余工序编码的高速列车节能驾驶智能算法

Redundant operation code-based intelligent algorithm for energy⁃efficient driving of high-speed train

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