Andreas Ritter

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Ritter

First Name

Andreas

ORCID

0000-0002-7073-7118

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Publications 1 - 10 of 12

Engine On/Off Control for the Energy Management of a Serial Hybrid Electric Bus via Convex Optimization
Elbert, Philipp; Nüesch, Tobias; Ritter, Andreas; et al. (2014)
IEEE Transactions on Vehicular Technology
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.
Method for predicting future driving conditions for a vehicle
Naef, Alex; Gisler, Hans-Jörg; Widmer, Martin; et al. (2022)
In a method for predicting future driving conditions for a vehicle (1), sensor data (2) are gathered while the vehicle (1) is traveling on a route. A position of the vehicle (1) is also determined. The gathered data are associated with the determined vehicle position. A map (9) is created depending on the associated data. When the route is traveled again, the map is updated in real time depending on associated data from the repeated traveling. Finally, a prediction of future driving conditions is obtained based on the determined vehicle position and the map (9).
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.
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.
Energy saving potential of a battery-assisted fleet of trolley buses
Ritter, Andreas; Elbert, Philipp; Onder, Christopher H. (2016)
IFAC-PapersOnLine ~ 8th IFAC Symposium on Advances in Automotive Control, AAC 2016. Proceedings
This article investigates the opportunities of integrating battery-assisted trolley buses into a given trolley bus network in public transportation. In this new generation of vehicles, the diesel-powered auxiliary unit is replaced with a high-performance traction battery. On the one hand, the new vehicles can be operated without the overhead wire, while on the other hand the battery capacity improves the overall system efficiency. The energy saving potential is identified via simulation of a realistic trolley bus line including the optimization of the energy management strategy. The problem is formulated as a convex optimal control problem. The results show that up to 20% of energy can be saved, compared to the case with conventional trolley buses only.
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.
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.
The Faster the Better? Optimal Warm-Up Strategies for a Micro Combined Heat and Power Plant
Zobel, Tammo; Ritter, Andreas; Onder, Christopher H. (2023)
Energies
The warm-up process is a critical operation phase for micro Combined Heat and Power (mCHP) plants, directly impacting their efficiency, reliability, and lifetime. As small decentralized power generation units are increasingly expected to be operated on demand, start-ups will occur more frequently and thus the importance of the warm-up process will further increase. In this study, we address this problem by presenting a mathematical optimization framework that finds optimal actuator trajectories that significantly reduce the warm-up time and improve the thermal efficiency of an mCHP plant. The proposed optimization framework is highly flexible and adaptable to various objective functions, such as maximizing efficiency or minimizing the deviation from desired temperature references. The underlying mathematical model has been experimentally validated on a physical mCHP test rig. Selected case studies further demonstrate the effectiveness and flexibility of the framework and show that with the optimized actuator trajectories, the mCHP plant can reach its steady-state operating temperature in 40% less time. The results also indicate that the shortest warm-up time does not necessarily lead to the highest thermal efficiency. Accordingly, the methodology proposed in this paper provides a powerful tool to study higher-level operational strategies of mCHP plants and thus to maximize their overall performance, which directly translates into an improved operational cost-effectiveness, particularly in demand-driven energy landscapes.
Optimal Control of Battery-Assisted Trolley Buses
Ritter, Andreas (2021)

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Andreas Ritter

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