Fabio Widmer

Last Name

Widmer

First Name

Fabio

ORCID

0000-0003-0604-3856

Organisational unit

08840 - Onder, Christopher (Tit.-Prof.)

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Publications 1 - 10 of 11

Energy Management of Hydrogen Hybrid Electric Vehicles—Online-Capable Control
Machacek, David; Yasar, Nazim; Widmer, Fabio; et al. (2024)
Energies
The results shown in this paper extend our research group’s previous work, which presents the theoretically achievable hydrogen engine-out NO (Formula presented.) (H (Formula presented.) -NO (Formula presented.)) Pareto front of a hydrogen hybrid electric vehicle (H (Formula presented.) -HEV). While the Pareto front is calculated offline, which requires significant computing power and time, this work presents an online-capable algorithm to tackle the energy management of a H (Formula presented.) -HEV with explicit consideration of the H (Formula presented.) -NO (Formula presented.) trade-off. Through the inclusion of realistic predictive data on the upcoming driving mission, a model predictive control algorithm (MPC) is utilized to effectively tackle the conflicting goal of achieving low hydrogen consumption while simultaneously minimizing NO (Formula presented.). In a case study, it is shown that MPC is able to satisfy user-defined NO (Formula presented.) limits over the course of various driving missions. Moreover, a comparison with the optimal Pareto front highlights MPC’s ability to achieve close-to-optimal fuel performance for any desired cumulated NO (Formula presented.) target on four realistic routes for passenger cars.
Real-Time Graph Construction Algorithm for Probabilistic Predictions in Vehicular Applications
Ritter, Andreas; Widmer, Fabio; Niam, Jen Wei; et al. (2021)
IEEE Transactions on Vehicular Technology
Reducing the energy consumption of vehicles is one of the greatest challenges we are facing in the mobility sector. A major step in this direction has been taken with the introduction of hybrid electric vehicles. Their performance, however, depends strongly on the energy management strategy used, which exploits the additional degree of freedom of the propulsion system and is inevitably limited by the lack of knowledge about the exact future driving conditions. Various attempts are being made to offer predictions, one of which is to exploit recorded travel data. In this paper, we propose an incremental graph construction algorithm that encapsulates this data in a digital representation of the road network and captures the actual travel routes of the vehicle along with the sequences of the specified measurement signals. The algorithm processes each location estimate separately, together with any desired simultaneously recorded measurement signal such as the vehicle speed, and constructs a directed graph in whose vertices the measurement data is stored. The real-time capability of this algorithm allows an up-to-date representation of both the road network and the signals it contains at all times. Whenever the vehicle is driving on an already visited route, we can obtain distance-resolved predictions by traversing the graph in the direction of travel and querying the stored measurement data. We present two techniques to efficiently store and predict this data, i.e., by using frequentist prediction intervals and Gaussian process regression. Our algorithm runs in real time and without any manual initialization, pre-, or post-processing. Verifications both during real operation on a trolley bus in public transportation and by simulation on a publicly available dataset demonstrate that the algorithm is real-time capable, that it consistently captures and predicts the recorded signals, and that it works in practice.
Optimization of charging infrastructure and strategy for an electrified public transportation system
Moradi, Mohammad Hossein; Widmer, Fabio; Turin, Raymond C.; et al. (2024)
Energy
Government policies and incentives aimed at reducing the carbon footprint are increasingly focusing on the electrification of public transportation, particularly transit buses. However, electrification faces significant challenges, including optimizing the charging infrastructure, battery size and type, and charging strategies. Addressing these challenges is crucial for the effective deployment and operation of electric bus fleets. This study presents an innovative method for optimizing these aspects of electric bus systems under diverse route conditions. By leveraging general transit feed specification (GTFS) and GeoTIFF data, the developed approach ensures scalability and is grounded in real-world data. A detailed physical model, especially concerning battery degradation, adds a unique dimension to the study, providing more accurate and reliable results. The optimization method employed in this study is dynamic programming (DP), which allows for a comprehensive evaluation of various factors influencing the performance and efficiency of electric buses. The proposed approach has been validated through three distinct case studies. The findings of this study indicate that the optimized solution can lead to a substantial cost reduction of nearly 35% for operators compared to current state-of-the-art practices in Zurich, which underscores the potential of the proposed approach to contribute to more sustainable and cost-effective public transportation systems.
Highly Efficient Year-Round Energy and Comfort Optimization of HVAC Systems in Electric City Buses
Widmer, Fabio; Ritter, Andreas; Achermann, Mathias; et al. (2023)
IFAC-PapersOnLine ~ 22nd IFAC World Congress
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.
Long-term stochastic model predictive control for the energy management of hybrid electric vehicles using Pontryagin’s minimum principle and scenario-based optimization
Ritter, Andreas; Widmer, Fabio; Duhr, Pol; et al. (2022)
Applied Energy
This paper presents a new approach to efficiently integrate long prediction horizons subject to uncertainty into a stochastic model predictive control (MPC) framework for the energy management of hybrid electric vehicles. By exploiting Pontryagin’s minimum principle, we show that the energy supply required to obtain a certain change in the state of charge (SOC) of the battery can be approximated using a quadratic equation. The parameters of these mappings depend on the power request imposed by the driving mission and thus allow to compress the time-resolved power profile into only three scalar variables. Having a driving mission divided into several segments of arbitrary length, the corresponding sequence of quadratic approximations allows to reformulate the original energy management problem as a quadratic program, which offers an efficient way to include a large number of future scenarios. The resulting scenario-based stochastic MPC approach prevents SOC boundary violations with a certain probability, which can be controlled by the number of scenarios considered. To validate the quadratic approximation, we study two numerical examples using two different vehicles, a series hybrid electric passenger car and a battery-assisted trolley bus. Finally, a case study based on the operation of the latter is provided, which demonstrates the expected behavior and the real-time capability of the stochastic MPC approach. While the SOC is maintained in predefined boundaries with high probability, the required energy supply is only increased by 1.41% compared to the non-causal optimum.
Battery health target tracking for HEVs: Closed-loop control approach, simulation framework, and reference trajectory optimization
Widmer, Fabio; Ritter, Andreas; Ritzmann, Johannes; et al. (2023)
eTransportation
In this paper, we address the trade-off between primary energy consumption and battery wear for hybrid electric vehicles in an optimal manner, for which we provide three contributions: First, we suggest a control structure to track a battery lifetime target in a closed control loop by incorporating periodic measurements of the state of health. This feedback enables the energy management system to reliably meet the target lifetime in the presence of disturbances and model mismatch. We validate the control scheme in a case study featuring a battery-assisted trolley bus. In this case study, we show that without the proposed measurement feedback and in the presence of disturbances and model mismatch, the sub-optimal use of the battery can either result in an increase in energy consumption of up to 9% over the vehicle's lifetime or in a prematurely required battery replacement. Second, to speed up the necessary calculations, we devise an algorithm that is able to perform simulations of a complete vehicle lifetime in less than a minute. A comparison to a standard simulation approach shows that our approach is able to accurately calculate both energy consumption and battery degradation with an error of less than 1% on average, while the execution time is reduced by a factor of about 70000. Third, we numerically optimize the battery health trajectory over the vehicle lifetime. We show that, while a quadratic health trajectory leads to improved energy efficiency, for the specific vehicle and cell technology considered in our case study, a linear trajectory results in only a small energy penalty of 0.05% over the vehicle lifetime.
ZTBus: A Large Dataset of Time-Resolved City Bus Driving Missions
Widmer, Fabio; Ritter, Andreas; Onder, Christopher H. (2023)
Scientific Data
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.
Optimization-based online estimation of vehicle mass and road grade: Theoretical analysis and experimental validation
Ritter, Andreas; Widmer, Fabio; Vetterli, Basil; et al. (2021)
Mechatronics
The gross vehicle mass (GVM) and the road grade are two factors that both have a substantial influence on the performance of a vehicle’s powertrain. In this paper, we propose a novel model-based estimation method for the GVM and the road grade that exploits entire sequences of powertrain measurements at once and is formulated as a nonlinear program (NLP). The estimator is based on a simple model for the vehicle’s longitudinal dynamics with only few intuitive vehicle parameters. By assuming the GVM to remain constant during certain sections of the trip and by describing the road grade profile in the distance domain, we achieve a separation of scales, which enhances disturbance rejection and significantly lowers the number of optimization variables. The resulting estimator is thoroughly analyzed both analytically and numerically. We show that a closed-form solution exists for the grade profile as a function of the GVM. Furthermore, if the GVM can be assumed to be constant during the journey considered, the estimation problem can be translated to a scalar NLP for finding the GVM. Although a rigorous proof is missing, our experiments show that in practice, the objective function is quasi-convex on a reasonable interval of GVM values and that thus a unique solution exists. Furthermore, robustness and sensitivity studies are conducted, where various perturbations are considered in a controlled environment, including uncorrelated and correlated noise, sensor offset, and model mismatches. Compared to two well-known recursive filters described in literature, our estimator shows significantly higher robustness with respect to all perturbations. Finally, we validate the estimator and the two recursive filters on real data from an electric city bus. The proposed estimator outperforms the recursive algorithms and achieves an average relative GVM estimation error of 3.4%. On a standard personal computer, the NLP for a driving phase of around one hour is solved in roughly 7.5 s, while the scalar NLP representing a driving phase of around 75 s is solved in roughly 12 ms. Both results indicate the real-time applicability of our algorithm.
Optimization of the energy-comfort trade-off of HVAC systems in electric city buses based on a steady-state model
Widmer, Fabio; van Dooren, Stijn; Onder, Christopher H. (2025)
Control Engineering Practice
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.
Holistic Traction and Thermal Energy Management of Electric City Buses
Widmer, Fabio (2024)
While the electrification of city buses offers the potential to reduce lifecycle greenhouse gas (GHG) emissions as well as relieve city centers of pollutant and noise emissions, their widespread adoption faces two significant challenges. On the one hand, the heating, ventilation, and air-conditioning (HVAC) systems of such vehicles consume large amounts of energy, thus requiring large batteries to achieve a certain driving range. On the other hand, the batteries are still the heaviest drivetrain component, accounting for a large part of the total cost of ownership and the life-cycle environmental footprint of electric city buses. Hence, battery size should be minimized, leading to higher average load and thus accelerated degradation of the battery cells. In this thesis, we propose holistic approaches to address these issues. We present five main contributions: First, we introduce the publicly available Zürich Transit Bus (ZTBus) dataset, which consists of data recorded during driving missions of electric city buses in Zürich. The dataset consists of more than 1400 missions across all seasons, each of which containing detailed time-resolved information on the vehicle’s power demand, odometry, global position, number of passengers, etc. It serves as the empirical basis for the case studies presented in this thesis but may also prove valuable as a foundation for a variety of studies and analyses beyond the scope of this work. Second, we investigate the joint optimization of the power split of a battery-assisted trolley bus along with its hot water system subject to a minimum battery lifetime requirement. As part of this investigation, we conduct a case study and show that, when using an optimized rather than a heuristic heating strategy, energy consumption can be reduced by up to 7% on some driving missions without sacrificing battery life time. If, in addition, the design of the thermal system is co-optimized, some further, yet smaller improvements are possible. Third, we build upon these optimization results by developing an online controller for this hot water system. Applying Pontryagin’s minimum principle (PMP), we develop a simple controller that is able to approximate the optimal heating strategy, but does not require any predictive data. We implement this controller on a real prototype bus, thus allowing performance validation under real-life operating conditions. We demonstrate that real-life bus operation using this controller leads to a reduction in battery degradation of up to 10%. Fourth, we introduce a novel way to address the trade-off between energy consumption and battery wear typically observed in hybrid electric vehicles (HEVs). The suggested approach involves tracking a battery lifetime target in a closed control loop by incorporating periodic measurements of the state of health (SOH). This approach enables the energy management system to reliably meet the target battery lifetime in the presence of disturbances and modeling errors. Furthermore, in order to facilitate interactive controller design, we devise an algorithm that is able to carry out simulations of a complete vehicle lifetime in less than a minute, which is about 70 000 times faster than a standard approach with only negligible concessions in terms of simulation accuracy. Thanks to this significant speed improvement, we can numerically optimize the battery health trajectory over the vehicle lifetime. Hence, we are able to show that, a linear trajectory results in only a small energy penalty of 0.05% over the vehicle lifetime in our case. Fifth, 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 cabin, which we simplify by considering it to be in a steady state. We introduce a method that is able to quickly optimize the steady-state HVAC system inputs. We validate the steady-state approximation by comparing its results to dynamic simulations. We then present two case studies to demonstrate the practical relevance of the approach. In the first one, we show how the method can be used to compare different system designs based on an annual performance evaluation. In the second one, we show how the method can be used to extract setpoints for online controllers that achieve close-to-optimal performance without any predictive information.

Publications 1 - 10 of 11

Fabio Widmer

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