【作者】 张弛；

【导师】 曾国宏；

【作者基本信息】 北京交通大学 ， 电气工程， 2024， 博士

【摘要】 随着锂电池技术的不断发展,以锂电池和柴油发电机组为动力源的混合动力列车成为了近年来的研究热点。与传统的内燃机车相比,装载锂电池的油电混合动力列车具有油耗小、效率高、噪音低以及污染排放少等优势。因此,大力发展油电混合动力列车不仅可以降低非电气化铁路的运营成本,而且可以保护环境、减少碳排放。对于油电混合动力列车,设计方面的混合动力系统配置参数,控制方面的列车驾驶策略以及能量管理,均是影响列车全寿命周期成本的关键。目前,国内外对于混合动力系统配置参数、列车驾驶策略以及能量管理的研究多是独立进行,忽略了它们之间的内在联系。为此,本文将从整体的角度出发进行研究,考虑它们之间的耦合关系,从而最大限度地提升混合动力列车的性能。首先,针对混合动力列车的不同离线优化控制问题展开研究,对比分析了列车单独驾驶策略优化、单独能量管理优化、驾驶策略和能量管理解耦式的顺序优化以及驾驶策略和能量管理的联合优化。鉴于现阶段联合优化复杂且难以求解的问题,提出了一种驾驶策略和能量管理的双环式联合优化架构。在双环式联合优化的外环,构建了基于遗传算法的节能驾驶,减少了列车运行油耗并保证了准点率。在双环式联合优化的内环,推演了能量管理的凸规划模型,降低了列车油耗并保证了混动系统的安全输出。借助双环架构,实现了驾驶策略外环和能量管理内环的信息交互,使得驾驶策略迭代优化的过程中考虑了能量管理的影响。通过列车实际的运行线路,将双环式联合优化与单独速度优化、单独能量管理优化以及解耦式的顺序优化进行对比,验证了联合优化在节油方面的优势,为后续的研究指明了方向。其次,针对混合动力列车的离线优化控制方法展开深入研究,鉴于现阶段优化方法在求解驾驶策略和能量管理联合优化问题时,无法保证计算速度的同时兼顾优化性的难题,提出了一种基于二阶锥规划的快速联合优化方法。该方法通过线性化、凸松弛以及等效替代等数学变换,将非线性非凸的联合优化问题转化为二阶锥规划问题。借助二阶锥规划的特性,利用成熟的求解理论,快速准确地获取了列车运行油耗最小的速度曲线和电池荷电状态曲线。通过列车实际的运行线路,将该方法与双环式联合优化以及求解非线性最优解的动态规划方法进行对比。结果表明,所提出的二阶锥规划方法不仅保证了列车最小的油耗,还将优化计算时间从“小时级”缩短到了“秒级”,极大的提升了求解速度,为后续列车在线优化控制提供了方法支撑。然后,针对混合动力系统配置参数的优化设计进行研究,鉴于现阶段优化配置未考虑驾驶策略影响的问题,提出了一种协同驾驶策略和能量管理的混合动力系统经济性配置方法。该方法实现了一体化的设计思想,借助第二章的双环架构以及第三章基于二阶锥规划的快速联合优化方法,确定了全寿命周期成本最小的电池类型以及容量配置方案。在优化设计的外环,构建了基于粒子群算法的经济性配置模型,从而减少全寿命周期总成本。在优化设计的内环,将列车驾驶策略和能量管理的联合优化问题推演为二阶锥规划,进而提高列车燃油效率。以一条实际的列车全线路工况为基础,求解了三种电池体系下的最优容量参数,并对三种电池体系进行纵向对比。结果表明钛酸锂电池凭借较高的充放电倍率特性以及较长的寿命,更加适用于轨道交通的混合动力系统。最后,针对混合动力列车在线优化控制进行研究,鉴于现阶段列车在线优化控制无法兼顾实时性、经济性以及鲁棒性的问题,提出了一种多预测尺度分层在线优化控制。该在线优化控制包括两个不同的预测尺度。在上层长预测尺度的在线优化控制中,建立了列车最小时分控制和列车准点节油控制的数学优化模型,并提出了求解方法,为下层短预测尺度的在线优化控制提供两种运行模式下的列车速度轨迹以及电池荷电状态轨迹。在下层短预测尺度的在线优化控制中,建立了列车速度轨迹跟踪控制以及混合动力系统能量管理的数学优化模型,并提出了相应的求解方法,实现了列车对上层运行速度的跟踪以及混合动力系统以节油为导向的精细化功率分配。最终,针对实际的运行线路,对比了不同控制方法,验证了所提的在线控制方法的经济性、实时性以及有效性。此外,还对线路临时限速情况进行模拟,验证了所提方法的安全性和鲁棒性。更多还原

【Abstract】 With the development of lithium battery technology,the hybrid train,which consists of the lithium battery and the diesel generator as a power source,has become a research hotspot in recent years.Compared with traditional internal combustion locomotives,hybrid trains loaded with lithium batteries have the advantages of low fuel consumption,high efficiency,low noise,and low pollution emissions.Therefore,the development of hybrid trains can not only reduce the operating costs of non-electrified railroads but also protect the environment and reduce carbon emissions.For hybrid trains,hybrid system sizing parameters,train driving strategy,and energy management are critical in influencing the whole life cycle cost of the train.Currently,domestic and international research on hybrid powertrain sizing parameters,train driving strategies,and energy management are mostly conducted independently,ignoring the intrinsic connection between them.To this end,this paper will examine the overall perspective and consider the coupling relationship between them to maximize the performance of the hybrid train.Firstly,different offline optimal control problems for hybrid trains are investigated.Comparative analyses of single-drive strategy optimization,single energy management optimization,decoupled sequential optimization,and joint optimization are presented.Because of the complexity and difficulty of solving the current joint optimization problem,a bi-loop joint optimization framework for driving strategy and energy management is proposed.In the outer loop of bi-loop joint optimization,a genetic algorithm-based energy-efficient driving strategy is constructed,which reduces the fuel consumption of train operations and ensures punctuality.In the inner loop of the bi-loop joint optimization,a convex programming model for energy management is derived,which reduces train fuel consumption and ensures the safe output of the hybrid powertrain.With the jointly optimized dual-loop architecture,the information interaction between the driving strategy and energy management is achieved,enabling the iterative optimization of the driving strategy to consider the impact of energy management.Based on real railroads,bi-loop joint optimization is compared with single-speed optimization,single-energy management optimization,and decoupled sequential optimization.The simulation results verify the advantages of the bi-loop joint optimization in terms of fuel savings and clarify the direction for the subsequent research problems.Secondly,an in-depth study is carried out on the offline optimal control method for hybrid trains.In view of the difficulty that the current optimization methods cannot guarantee the computational speed while taking optimality into account,a fast joint optimization method based on second-order cone programming is proposed.The method transforms a nonlinear,nonconvex joint optimization problem into a second-order cone programming problem through mathematical transformations such as linearization,convex relaxation,and equivalent substitution.With the help of the properties of second-order cone programming,the speed profile and the battery state of charge profile that minimize the fuel consumption of train operation are obtained quickly and accurately.The method is compared with the bi-loop optimization method and the dynamic programming method through the actual running line of the train.The results show that the proposed second-order cone programming method ensures the minimum fuel consumption of the train.Moreover,the optimization calculation time is reduced from "hourly" to "secondly," which greatly improves the solution speed.The proposed method provides support for subsequent online optimal control of trains.Then,a study is carried out for the optimal design of the sizing parameters of the hybrid powertrain.Because the optimized size at this stage does not consider the influence of driving strategies,a hybrid powertrain economy design method that combines energy-efficient driving and fuel-saving energy management is proposed.The methodology implements an integrated design idea.With the help of the bi-loop architecture in Chapter 2 and the fast joint optimization based on second-order cone programming in Chapter 3,the method identified the battery type and capacity configuration scheme with the lowest whole-life-cycle cost.In the outer loop of the optimal design,an economic configuration model based on particle swarm optimization is constructed to reduce the total life cycle cost.In the inner loop of the optimal design,the joint optimization problems of train driving strategy and energy management are derived as second-order cone programming.The optimal capacity parameters under the three battery types are solved based on an actual railroad line and compared with them.The results show that the lithium titanate battery is more suitable for hybrid powertrains in rail transportation because of its higher charge/discharge power characteristics and longer lifetime.Finally,the online optimization control of hybrid trains is investigated.Since the current online optimization control of trains cannot take into account the problems of real-time,economy,and robustness,a multi-prediction scale hierarchical online optimization control is proposed.This online optimization control includes two different prediction periods.In the upper layer with longer prediction scales,mathematical optimization models for trains’ minimum operating time control and on-time fuel-saving control are developed,and solution methods are proposed.The upper online optimization control provides the lower online optimization control with the train speed trajectory and the battery state of charge trajectory in two modes of operation.In the lower layer with short prediction scales,mathematical optimization models for train speed trajectory tracking control and energy management are established,and the corresponding solution methods are proposed.The lower-level online optimization control achieves the tracking of the train to the upper-level running speed and a refined power split of the hybrid powertrain with the objective of fuel saving.Different control methods are compared for the actual operating routes,and the economy,real-time performance,and effectiveness of the proposed online control method are verified.In addition,the temporary speed limit situation of the line is also simulated to validate the safety and robustness of the proposed method.更多还原