A multiphase optimal control method for multi-train control and scheduling on railway lines

We consider a combined train control and scheduling problem involving multiple trains in a railway line with a predetermined departure/arrival sequence of the trains at stations and meeting points along the line. The problem is formulated as a multiphase optimal control problem while incorporating complex train running conditions (including undulating track, variable speed restrictions, running resistances, speed-dependent maximum tractive/braking forces) and practical train operation constraints on departure/arrival/running/dwell times. Two case studies are conducted. The first case illustrates the control and scheduling problem of two trains in a small artificial network with three nodes, where one train follows and overtakes the other. The second case optimizes the control and timetable of a single train in a subway line. The case studies demonstrate that the proposed framework can provide an effective approach in solving the combined train scheduling and control problem for reducing energy consumption in railway operations

Nonlinear programming methods based on closed-form expressions for optimal train control

This paper proposes a novel approach to solve the complex optimal train control problems that so far cannot be perfectly tackled by the existing methods, including the optimal control of a fleet of interacting trains, and the optimal train control involving scheduling. By dividing the track into subsections with constant speed limit and constant gradient, and assuming the train’s running resistance to be a quadratic function of speed, two different methods are proposed to solve the problems of interest. The first method assumes an operation sequence of maximum traction – speedholding – coasting – maximum braking on each subsection of the track. To maintain the mathematical tractability, the maximum tractive and maximum braking functions are restricted to be decreasing and piecewise-quadratic, based on which the terminal speed, travel distance and energy consumption of each operation can be calculated in a closed-form, given the initial speed and time duration of that operation. With these closed-form expressions, the optimal train control problem is formulated and solved as a nonlinear programming problem. To allow more flexible forms of maximum tractive and maximum braking forces, the second method applies a constant force on each subsection. Performance of these two methods is compared through a case study of the classic single-train control on a single journey. The proposed methods are further utilised to formulate more complex optimal train control problems, including scheduling a subway line while taking train control into account, and simultaneously optimising the control of a leader-follower train pair under fixed- and moving-block signalling systems

Evaluation and optimisation of traction system for hybrid railway vehicles

Over the past decade, energy and environmental sustainability in urban rail transport have become increasingly important. Hybrid transportation systems present a multifaceted challenge, encompassing aspects such as hydrogen production, refuelling station infrastructure, propulsion system topology, power source sizing, and control. The evaluation and optimisation of these aspects are critical for the adaptation and commercialisation of hybrid railway vehicles. While there has been significant progress in the development of hybrid railway vehicles, further improvements in propulsion system design are necessary. This thesis explores strategies to achieve this ambitious goal by substituting diesel trains with hybrid trains. However, limited research has assessed the operational performance of replacing diesel trains with hybrid trains on the same tracks. This thesis develops various optimisation techniques for evaluating and refining the hybrid traction system to address this gap. In this research's first phase, the author developed a novel Hybrid Train Simulator designed to analyse driving performance and energy flow among multiple power sources, such as internal combustion engines, electrification, fuel cells, and batteries. The simulator incorporates a novel Automatic Smart Switching Control technique, which scales power among multiple power sources based on the route gradient for hybrid trains. This smart switching approach enhances battery and fuel cell life and reduces maintenance costs by employing it as needed, thereby eliminating the forced charging and discharging of excessively high currents. Simulation results demonstrate a 6% reduction in energy consumption for hybrid trains equipped with smart switching compared to those without it. In the second phase of this research, the author presents a novel technique to solve the optimisation problem of hybrid railway vehicle traction systems by utilising evolutionary and numerical optimisation techniques. The optimisation method employs a nonlinear programming solver, interpreting the problem via a non-convex function combined with an efficient "Mayfly algorithm." The developed hybrid optimisation algorithm minimises traction energy while using limited power to prevent unnecessary load on power sources, ensuring their prolonged life. The algorithm takes into account linear and non-linear variables, such as velocity, acceleration, traction forces, distance, time, power, and energy, to address the hybrid railway vehicle optimisation problem, focusing on the energy-time trade-off. The optimised trajectories exhibit an average reduction of 16.85% in total energy consumption, illustrating the algorithm's effectiveness across diverse routes and conditions, with an average increase in journey times of only 0.40% and a 15.18% reduction in traction power. The algorithm achieves a well-balanced energy-time trade-off, prioritising energy efficiency without significantly impacting journey duration, a critical aspect of sustainable transportation systems. In the third phase of this thesis, the author introduced artificial neural network models to solve the optimisation problem for hybrid railway vehicles. Based on time and power-based architecture, two ANN models are presented, capable of predicting optimal hybrid train trajectories. These models tackle the challenge of analysing large datasets of hybrid railway vehicles. Both models demonstrate the potential for efficiently predicting hybrid train target parameters. The results indicate that both ANN models effectively predict a hybrid train's critical parameters and trajectory, with mean errors ranging from 0.19% to 0.21%. However, the cascade-forward neural network topology in the time-based architecture outperforms the feed-forward neural network topology in terms of mean squared error and maximum error in the power-based architecture. Specifically, the cascade-forward neural network topology within the time-based structure exhibits a slightly lower MSE and maximum error than its power-based counterpart. Moreover, the study reveals the average percentage difference between the benchmark and FFNN/CNFN trajectories, highlighting that the time-based architecture exhibits lower differences (0.18% and 0.85%) compared to the power-based architecture (0.46% and 0.92%)

Improving caravan design by modelling of crosswind

It is a well-known fact that towing a caravan over long distances can be a very expensive exercise especially with the rise in cost of fuel. Caravans by design are generally not seen to exhibit any standout aerodynamic features and as such can increase the fuel consumption of the tow vehicle by more than double. The effects of wind on the aerodynamics of the caravan are also of importance. Of particular interest, the effect that cross wind flow has on caravans is somewhat of an under stated issue. This project aims to analyze the effect of crosswind flow, propose some caravan modifications and evaluate any advantages to the tow vehicle regarding fuel economy. The project aims to use Computational Fluid Dynamics to evaluate the caravan under a variety of operating conditions. By conducting a parametric study into various design features on the caravan it is possible to evaluate these proposal with CFD to obtain data that can show the potential increases in efficiency and economy over the original baseline design. The results show that there are significant forces at play when analyzing crosswind flow on the caravan. The results also show that by carrying out modifications to key areas such as the gap between the car and caravan and also its general shape, there is potential for significant gains to be made in reducing the drag forces at play and subsequently enhancing the fuel economy of the tow vehicle. Results confirm that these forces can be reduced by up to 18%

DEVELOPMENT OF A CONTROL ALGORITHM FOR A PARALLEL HYBRID POWERTRAIN

The current legislation calls for fast electrification of vehicle powertrains, since it is necessary to fulfil the CO2 requirements for the vehicle fleets. The hybrid electric vehicles (HEV) with parallel powertrain topologies – together with pure battery electric vehicles (BEV) – are the most common ways of electrification. However, the HEV powertrain – opposed to the BEV or conventional powertrain – poses an interesting challenge associated with the control system design to achieve the ideal power split between an internal combustion engine (ICE) and electrical machines (EM) during the whole vehicle operation.The presented paper sums up the specific functions and requirements on a control system, together with the description of general control strategy options for a HEV powertrain. The proposed control strategy then combines heuristic rules with a suboptimal numerical control method, calculating the optimal power split ratio based on the efficiencies of ICE and EMs. This control strategy is built into a modular algorithm in Matlab/Simulink for two different parallel HEV powertrain topologies: P2 and P0P4. It is subsequently coupled with a vehicle models created in GT-Suite environment and tested on a WLTC homologation driving cycles. The following simulation tests show the fuel consumption reduction potential for chosen HEV topologies working in hybrid modes, in comparison to a base operation with conventional mode only. Yet, the heuristic rules can be further optimized to obtain even better overall results.Současná legislativa tlačí výrobce vozidel k okamžité elektrifikaci pohonu, protože je to v tuto chvíli jediná možnost, jak dostát požadavkům na flotilové emise CO2. Nejběžnější formou elektrifikace pohonu jsou v dnešní době vozidla s paralelním hybridním pohonem anebo bateriové elektromobily. Nicméně hybridní pohon, na rozdíl právě od konvenčního nebo čistě elektrického pohonu, představuje zajímavé výzvy spojené s návrhem řídicího algoritmu, který musí v každém okamžiku zajišťovat optimální rozdělení výkonu mezi spalovací motor a elektromotor.Tento článek v úvodu krátce shrnuje specifické funkce a požadavky na takový řídicí algoritmus, společně s obecným přehledem možných řídicích strategií hybridních vozidel. Následně je navržena řídicí strategie kombinující heuristická pravidla se suboptimální numerickou metodou, která vypočítává parametr optimálního dělení výkonu na základě účinností spalovacího motoru a elektromotoru. Na základě navrhnuté strategie je v programu Matlab/Simulink vytvořen modulární řídicí algoritmus pro dvě paralelní hybridní topologie: P2 a P0P4, který je následně propojen s modely vozidel vytvořenými v simulačním prostředí GT-Suite a testován v homologačním cyklu WLTC. Nakonec je prezentováno několik testů řídicího algoritmu, které demonstrují úsporu paliva vybraných topologií hybridního vozidla pracujících v hybridních režimech, ve srovnání s provozem pouze v konvenčním režimu pohonu. Avšak heuristická pravidla mohou být dále optimalizována, s cílem dosáhnout ještě příznivějších celkových výsledků

OPTIMASI DIMENSI HINGGA MENGGUNAKAN PENGALI LAGRANGE UNTUK KONTROL EFISIENSI ENERGI PADA KERETA API TAKSAKA

Kereta api merupakan salah satu moda transportasi yang cukup digemari oleh penduduk Indonesia dalam kaitannya mobilisasi. Penelitian dengan topik kereta api sudah dilakukan sejak tahun 1800 di Benua Eropa. Sejarah perkeretaapian di Indonesia sendiri dilatarbelakangi oleh Belanda yang pernah berkuasa selama lebih kurang 350 tahun. Kereta api sebagai moda transportasi memberikan kemudahan dalam mobilisasi antar kota dan memberikan kemudahan dalam langsir logistik. Ketepatan waktu, keterjangkauan harga tiket, dan efektivitas moda transportasi kereta api menjadikannya unggul daripada moda transportasi lainnya. Kereta api sebagai benda bergerak tentu memiliki dinamika yang cepat berubah setiap waktunya, oleh karenanya penelitian tugas akhir ini mengambil objek kereta api. Penelitian ini berfokus untuk menemukan nilai efisiensi energi dengan studi kasus KA Taksaka dengan memodifikasi mode mengemudinya sehingga ditemukan mode mengemudi optimal. Penelitian ini melibatkan metode pengali Lagrange untuk menganalisis dinamika yang terjadi pada kereta api dan melibatkan simulasi numerik untuk perhitungannya. Melalui pemodelan matematika dan analisis fungsi – fungsi kompleks yang menyangkut kereta api, di akhir penelitian dapat mencapai nilai – nilai efisiensi energi sebagai bentuk pertimbangan untuk mengoptimalkan dinamika kereta api. Hubungan antara strategi mengemudi yang optimal dan tercapainya efisiensi energi menjadi fokus dalam penelitian tugas akhir ini, di mana fase – fase yang diterapkan saat mengemudi sangat mempengaruhi dinamika energi yang bekerja pada sistem perkeretaapian

Control of Energy Storage

Energy storage can provide numerous beneficial services and cost savings within the electricity grid, especially when facing future challenges like renewable and electric vehicle (EV) integration. Public bodies, private companies and individuals are deploying storage facilities for several purposes, including arbitrage, grid support, renewable generation, and demand-side management. Storage deployment can therefore yield benefits like reduced frequency fluctuation, better asset utilisation and more predictable power profiles. Such uses of energy storage can reduce the cost of energy, reduce the strain on the grid, reduce the environmental impact of energy use, and prepare the network for future challenges. This Special Issue of Energies explore the latest developments in the control of energy storage in support of the wider energy network, and focus on the control of storage rather than the storage technology itself

Power loss minimization in electric cars by wheel force allocation

The need for lowering the emission levels has never been greater than now. In the vehicle industry, electrification seems to be an irreversible way ahead but user-related challenges such as limited range delay electricity as the primary energy source for personal transportation. Other control-related challenges are also introduced as electric cars are over-actuated, i.e. several actuators can be used for the same purpose. Over-actuation introduces the possibility to choose more freely which actuator to use when. Can this freedom of choice be used to improve energy efficiency of electric cars by e.g. minimizing power losses? In this thesis, two wheel force distribution algorithms have been developed with a method called control allocation. The algorithms minimize power losses in the electric drivetrain, transmission and tires. They were tested in a simulated city cycle in a Volvo V60 configuration with four electric motors, each connected to a wheel through a single speed transmission and coupling respectively. It was found that by using developed algorithms, up to 3.9% energy could be saved. In a next step, the transmission ratio on the front motors and rear motors were optimized in combination with one of the algorithms. By using a larger transmission ratio in the front than in the rear, the energy consumption reduced even further. With these development steps, up to 7.9% energy could be saved compared to the original vehicle