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Fabio Widmer

Publications and source records attributed to Fabio Widmer.

4 recordsLinked to original sources

Optimal Electric Bus Depot Charging: Cost Savings, Grid Limits, and Robustness Trade-Offs

Depot charging of electric bus fleets must minimize electricity costs, respect grid limits, and remain feasible despite uncertain trip energy demand. While cost-optimal charging is well studied, its value under different electricity prices and grid connection capacities, as well as the economic cost of robustness, remain poorly quantified. We address these gaps with a convex robust formulation in which bounded demand uncertainty is enforced through worst-case state-of-energy constraints. The formulation is evaluated against charge-on-arrival using realistic service schedules for four Swiss depots containing 7-35 buses. In the depots studied, smaller depots achieve the greatest relative benefit from optimization, with total electricity cost reductions exceeding 50%, because optimization mitigates charging peaks that strongly affect their costs. For all depot sizes, the savings from optimization increase with electricity price volatility. Optimization can also lower the grid capacity required for feasible operation by over 40%, although tight limits reduce peak shaving potential. Protection against energy-demand deviations of 10% increases total electricity cost by less than 0.1%. The resulting charging power profiles exhibit interpretable price-threshold and peak-shaping behavior, providing practical guidance for real-world implementations.

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Optimization of the Energy-Comfort Trade-Off of HVAC Systems in Electric City Buses Based on a Steady-State Model

The electrification of public transport vehicles offers the potential to relieve city centers of pollutant and noise emissions. Furthermore, electric buses have lower life-cycle greenhouse gas (GHG) emissions than diesel buses, particularly when operated with sustainably produced electricity. However, the heating, ventilation, and air-conditioning (HVAC) system can consume a significant amount of energy, thus limiting the achievable driving range. In this paper, we address the HVAC system in an electric city bus by analyzing the trade-off between the energy consumption and the thermal comfort of the passengers. We do this by developing a dynamic thermal model for the bus, which we simplify by considering it to be in steady state. We introduce a method that is able to quickly optimize the steady-state HVAC system inputs for a large number of samples representative of a year-round operation. A comparison between the results from the steady-state optimization approach and a dynamic simulation reveals small deviations in both the HVAC system power demand and achieved thermal comfort. Thus, the approximation of the system performance with a steady-state model is justified. We present two case studies to demonstrate the practical relevance of the approach. First, we show how the method can be used to compare different HVAC system designs based on a year-round performance evaluation. Second, we show how the method can be used to extract setpoints for online controllers that achieve close-to-optimal performance without any predictive information. In conclusion, this study shows that a steady-state analysis of the HVAC systems of an electric city bus is a valuable approach to evaluate and optimize its performance.

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Highly Efficient Year-Round Energy and Comfort Optimization of HVAC Systems in Electric City Buses

In this paper, we present a novel approach to perform highly efficient numerical simulations of the heating, ventilation, and air-conditioning (HVAC) system of an electric city bus. The models for this simulation are based on the assumption of a steady-state operation. We show two approaches to obtain the minimum energy requirement for a certain thermal comfort criterion under specific ambient conditions. Due to the computationally efficient approach developed, we can evaluate the model on a large dataset of 7500 scenarios in various ambient conditions to estimate the year-round performance of the system subject to different comfort requirements. Compared to a heating strategy based on positive temperature coefficient (PTC) elements, we can thus show that a heat pump (HP) can reduce the annual mean power consumption by up to 60%. Ceiling-mounted radiant heating elements complementing a PTC heating system can reduce the annual mean power consumption by up to 10%, while they cannot improve the energy efficiency when used in conjunction with a HP. Finally, a broad sensitivity study reveals the fact that improving the HP's heating-mode coefficient of performance (COP) manifests the largest leverage in terms of mean annual power consumption. Moreover, the annual energy expenditure for cooling are around eight times smaller than those for heating. The case study considered thus reveals that the advantages of improving the COP of the cooling mode are significantly lower.

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ZTBus: A Large Dataset of Time-Resolved City Bus Driving Missions

This paper presents the Zurich Transit Bus (ZTBus) dataset, which consists of data recorded during driving missions of electric city buses in Zurich, Switzerland. The data was collected over several years on two trolley buses as part of multiple research projects. It involves more than a thousand missions across all seasons, each mission usually covering a full day of operation. The ZTBus dataset contains detailed information on the vehicle's power demand, propulsion system, odometry, global position, ambient temperature, door openings, number of passengers, dispatch patterns within the public transportation network, etc. All signals are synchronized in time and include an absolute timestamp in tabular form. The dataset can be used as a foundation for a variety of studies and analyses. For example, the data can serve as a basis for simulations to estimate the performance of different public transit vehicle types, or to evaluate and optimize control strategies of hybrid electric vehicles. Furthermore, numerous influencing factors on vehicle operation, such as traffic, passenger volume, etc., can be analyzed in detail.

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