arXiv subjects

Stuart Hillmansen

Publications and source records attributed to Stuart Hillmansen.

6 recordsLinked to original sources

arXiv2026

Real-Time Location-Aware Demand-Shaping for Power-Constrained AC Railway Corridors

Power-constrained 25kV AC railway sections, particularly under degraded feeding, are protected today by blunt, section-wide power limits that penalise every train irrespective of whether it contributes to the binding condition. This paper presents a real-time, location-aware controller that restores the electrical feasibility of a feeding section with minimal impact on the timetable: it curtails only the trains that bind, where and when they bind, evaluating feasibility and per-train available power online with a solver-free estimate as an in-loop surrogate for the full power flow. Because the estimate is accurate on average but slightly optimistic at the binding instants, the controller screens with a small voltage margin, and a full multi-conductor power-flow solver confirms the restored feasibility. The resulting selective-curtailment policy is delivered through a cloud-to-edge connected driver advisory system. On a representative GB 25kV corridor under outage feeding, solver-selected to be infeasible uncontrolled yet restorable, the controller is compared against the uncontrolled case, the incumbent static limit, and an offline genetic-algorithm optimum, with every feasibility figure solver-validated. The static limit restores feasibility at a large journey-time cost by throttling the whole section; the location-aware controller restores the same feasibility at one thirtieth of that cost by advising a single train, and matches the offline optimum's solution in about a second and a half against the optimiser's minute. Aggregate peak demand is unmoved, because the active constraint is local far-field voltage rather than gross demand. All claims are relative to the baselines on a representative corridor; a specific-route deployment study is future work.

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arXiv2026

Estimating Available Traction Power in Multi-Train AC Railway Networks from a Distance-Dependent Power Envelope

Decarbonisation is raising the electrical load on mainline alternating-current railway feeders that were not designed for sustained, simultaneous high-power demand. When several trains accelerate together on a shared feeder, the contact-line voltage can fall far enough to trigger rolling-stock current limitation or feeder protection, eroding capacity and reliability. Preventing this in real time requires a quantity conventional operation does not expose: a localised, continuously updated estimate of the traction power available to each train given the live network state. A railway power-flow model, with trains represented under a voltage-dependent automatic current-limitation characteristic, shows that the minimum network voltage is governed by the product of power and distance rather than by power alone, yielding a distance-dependent single-train power envelope. This envelope does not add up when several trains share a feeder, so a conservative pairwise screen is generalised to a solver-free multi-train estimate: a calibrated shared-path voltage model returning the minimum section voltage and the per-train available power for any number of trains. Calibration uses two short offline solver runs, one fixing the self-impedance and one the inter-train coupling through a separation-dependent factor. Its current-limitation behaviour follows EN 50388-1, and on matched multi-train cases the estimate tracks the full power flow to within about nine per cent on average across two-, three-, and four-train cases, improving as more trains share the feeder, while its online cost scales with the number of trains rather than the network size.

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arXiv2026

Algorithmic Energy Management in Constrained Railway Traction Networks: A Systematic Review

Decarbonising heavy-duty railway networks requires maximising the capacity of existing electrical infrastructure. Integrating heavy freight alongside fast passenger services exposes the hard physical limits of conventional AC traction networks, causing severe localised power quality degradation, phase unbalance, and low-voltage behaviour that triggers protective substation tripping. Because hardware upgrades are highly capital-intensive, software-based Energy Management Strategies (EMS) offer a potentially viable alternative. This systematic review synthesises the literature on algorithmic energy management for grid-constrained multi-train AC railway networks, classifying the reviewed studies along three axes: algorithm family, operational scope, and constraint coupling. Three findings emerge consistently. First, single-train trajectory optimisation, however mathematically refined, cannot represent the coupled electrical interactions that increasingly define network capacity on mixed-traffic networks. Second, while multi-train Train-Track-Power (TTP) simulations capture these interactions, the algorithm families used to solve them face well-documented trade-offs between computational tractability and constraint flexibility; predictive and distributed methods, including hierarchical model predictive control and decomposition-based schemes, narrow this trade-off substantially, but electrical fidelity and network-scale real-time operation have been demonstrated largely in separate studies rather than together. Third, the literature increasingly identifies a gap between mathematically optimal speed profiles and operationally executable ones, particularly on networks operated by human drivers rather than Automatic Train Operation systems. The review delineates where current methods succeed, where they fail, and which directions the literature has identified as open.

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arXiv2021

Convex Optimization for Fuel Cell Hybrid Trains: Speed, Energy Management System, and Battery Thermals

We optimize the operation of a fuel cell hybrid train using convex optimization. The main objective is to minimize hydrogen fuel consumption for a target journey time while considering battery thermal constraints. The state trajectories: train speed, energy management system, and battery temperature, are all optimized concurrently within a single optimization problem. A novel thermal model is proposed in order to include battery temperature yet maintain formulation convexity. Simulations show fuel savings and better thermal management when temperature is optimized concurrently with the other states rather than sequentially -- separately afterwards. The fuel reduction is caused by reduced cooling effort which is motivated by the formulation's awareness of active cooling energy consumption. The benefit is more pronounced for warmer ambient temperatures that require more cooling.

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arXiv2021

Mathematical Modeling for Holistic Convex Optimization of Hybrid Trains

We look into modeling fuel cell hybrid trains for the purpose of optimizing their operation using convex optimization. Models and constraints necessary to form a physically feasible yet convex problem are reviewed. This effort is described as holistic due to the broad consideration of train speed, energy management system, and battery thermals. The minimized objective is hydrogen fuel consumption for a given target journey time. A novel battery thermal model is proposed to aid with battery thermal management and thus preserve battery lifetime. All models are derived in the space-domain which along constraint relaxations guarantee a convex optimization problem. First-principle knowledge and real-world data justify the suitableness of the proposed models for the intended optimization problem.

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arXiv2021

Convex Optimization of Speed and Energy Management System for Fuel Cell Hybrid Trains

We look into minimizing the hydrogen fuel consumption of hydrogen hybrid trains by optimizing their operation. The powertrain considered is a fuel cell charge-sustaining hybrid. Convex optimization is utilized to compute optimal speed and energy management trajectories. The barrier method is used to solve the optimization problems quickly on the order of tens of seconds for the entire journey. Simulations show a considerable reduction in fuel consumption when both trajectories -- speed and energy management -- are optimized concurrently within a single optimization problem in comparison to being optimized separately in a sequential manner -- optimizing energy management after optimizing speed. It is concluded that the concurrent method greatly benefits from its holistic powertrain knowledge while optimizing all trajectories together within a single optimization problem.

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