The aim of this project is to design and implement a drive system and a driving strategy for a lightweight, battery-driven rail vehicle partaking in the Delsbo Electric student competition. The goal of the competition is to create a vehicle which consumes as little energy as possible.  A simulation model of the vehicle is developed in Simulink, based on existing hybrid car models. Different drive cycles are written in MATLAB and tested in the vehicle simulation, which calculates energy consumption, power and torque usage and other important data. This data is used to select an optimal driving strategy and dimension the drive system components.  The final drive system design consists of a permanent-magnet synchronous motor powered by lead acid batteries and controlled by a microcontroller and motor driver through a user interface consisting of a control board with buttons and switches.  The chosen driving strategy combines slow acceleration and constant speed in slopes with the pulse and glide strategy on flat parts of the track. The simulation shows a total energy consumption of 0.67 Wh/person and km, which is in the same order of magnitude as results from previous years, which is promising for the competition. However, the actual energy consumption can not be known until the vehicle has been built and tested. There is a lot of uncertainty around its parameters at this stage, which affects the reliability of the simulations. / Syftet med det här projektet är att designa och implementera ett drivsystem och en körstrategi för ett lättviktigt, batteridrivet rälsfordon. Fordonet ska användas i studenttävlingen Delsbo Electric. Målet med tävlingen är att bygga ett fordon som förbrukar så lite energi som möjligt.  För att göra detta utvecklas en simuleringsmodell av fordonet i Simulink, baserat på redan existerande modeller av hybridbilar. Olika körprogram skrivs i MATLAB och testkörs i modellen, som beräknar energiåtgång, använd effekt och vridmoment och annan viktig data. Dessa värden används sedan för att optimera körstrategin och dimensionera drivsystemets komponenter.  Det färdigdesignade drivsystemet består av en permanentmagnetiserad synkronmotor som matas från blyackumulatorer och styrs av en mikrokontroller och en driver via en kontrollpanel med knappar och switchar. Den valda körstrategin kombinerar låg acceleration och konstant hastighet i backarna med pulse-and-glide-strategin på de platta delarna av banan. Enligt simuleringarna ger den en total energiåtgång på 0.67 Wh/person-km, vilket är i samma storleksordning som tävlingsresultat från tidigare år. Detta bådar gott inför tävlingen, men det går inte att veta hur stor den faktiska energiförbrukningen kommer bli förrän fordonet är byggt och testat. Än så länge är många av dess parametrar osäkra, vilket påverkar tillförlitligheten hos simuleringarna. / Kandidatexjobb i elektroteknik 2022, KTH, Stockholm

In order to ensure safe travel, railway vehicles must be stable under every condition along the track. Thus, a vehicle can be certified for operation only when it can fulfil certain criteria related to the ride stability. The stability of the vehicle is highly dependent on the wheel-rail friction coefficient: higher friction results in worse ride. So, to ensure a good evaluation of the stability, the friction should be high enough during tests. The same applies to the risk of wheel flange climbing. At the present time, the wheel-rail friction can not be measured directly but there are different procedures utilized to ensure that the conditions are suitable for testing the stability of the vehicle. In this study an algorithm is proposed to estimate the wheel-rail friction coefficient by using quantities which can be measured in reality. The algorithm is tried out in computer simulations. The algorithm has two parts: in Part 1 the friction coefficient is proposed to be equal to the ratio of the total creep force divided by the normal force; in Part 2 the total creep and spin creep are estimated to observe their correlation to the estimated friction. The contact angle in Part 1 is estimated by a contact point function. In the simulations, different conditions are tried. There are four horizontal radii: tangent track, R1300m, R1000m, and R400m. Three friction coefficients are used: 0.5, 0.4 and 0.3. In addition to this, track irregularities are included. A single vehicle is simulated in two modes: capable and incapable of passive radial steering. The track irregularities caused high values of the proposed estimated friction coefficient. The values in some instances were close or equal to the input friction coefficient of the simulation. Thus, if the highest values of the estimated friction were taken over a certain distance or time, the friction of the simulation could be approximated. In most cases, the total creep was following the trend of the estimated friction. The total creep and spin creep were used as a quality factor to determine how close the estimated friction was to the simulation’s friction. In this study when the total creep was greater than 0.006 and the spin creep was less than 1.0 m-1, the estimated friction was close to the input friction. The closeness was dependent on the simulation’s friction. Higher input friction resulted in larger deviation compared to lower friction. A sensitivity analysis has been performed by deliberately introducing errors in the position of the contact point and the angle of attack. The analysis shows that the errors are not critical when the contact point is close to the tread circle. When the contact point is close to the flange, a good measurement of the wheel profile and the contact point position required to obtain accurate results. On the other hand, the errors affect the friction estimate for high spin and low total creepage. These results are discarded by the algorithm, the influence of the errors is minimized.

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Transit operators are increasingly interested in improving efficiency, reliability, and performance of commuter trains while reducing their operating costs. In this context, the application of optimal control theory to the problem of train control can help towards achieving some of these objectives. However, the traction and braking systems of commuter trains often exhibit significant time delays, making the control of commuter trains highly challenging. Previous literature on optimal train control ignores delays in actuation due to the inherent difficulty present in the optimal control, and in general, the control, of input-delay systems. In this thesis, optimal control of a commuter train is presented under two cases: (i) equal, and (ii) unequal time delays in the train traction and braking commands. The solution approach uses the economic model predictive control framework, which involves formulating and solving numerical optimization problems to achieve minimum mixed energy-time optimal control in discretized spatial and time domains. The optimization problems are re-solved repeatedly along the track for the remainder of the trip, using the latest sensor measurements. This would essentially establish a feedback mechanism in the control to improve robustness to modelling errors. A key feature of the proposed methods is that they are model-based controllers, they explicitly incorporate model information, including time delays, in controller synthesis hence avoiding performance degradation and potential instability. To address the issue of input-delays, the well-established predictor approach is used to compensate for input-delays. The case of equal traction-braking delays is treated in discretized spatial domain, which uses an already developed convex approximation to the optimization problem. The use of the convex approximation allows for robust and rapid computation of the optimal control solution. The non-equal traction-braking delays scenario is formulated in time domain, leading to a nonconvex optimization problem. An alternative formulation for minimum-time optimal control problems is presented for delay-free systems that simplifies the solution of minimum-time optimal control problems compared to conventional minimum-time optimal control formulations. This formulation along with the predictor approach is used to help solve the train optimal control problem in the case of non-equal traction-braking delays. The non-equal traction-braking delay controller is compared with the equal traction-braking delay controller by insertion of an artificial delay to make the shorter delays equal to the longer delay. Results of numerical simulations demonstrate the validity and effectiveness of the proposed controllers. / Thesis / Master of Applied Science (MASc)