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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.

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.

Idling losses constitute a significant amount of the fuel consumption of internal combustion engines. Therefore, shutting down the engine during idling phases can improve its overall efficiency. For driver acceptance a fast restart of the engine must be guaranteed. A fast engine start can be performed using a powerful electric starter and an appropriate battery which are found in hybrid electric vehicles, for example. However, these devices involve additional cost and weight. An alternative method is to use a tank with pressurized air that can be injected directly into the cylinders to start the engine pneumatically. In this paper, pneumatic engine starts using camshaft driven charge valves are discussed. A general methodology for an air-optimal charge valve design is presented which can deal with various requirements. The proposed design methodology is based on a process model representing pneumatic engine operation. A design example for a two-cylinder engine is shown, and the resulting optimized pneumatic start is experimentally verified on a test bench engine. The engine’s idling speed of 1200 rpm can be reached within 350 ms for an initial pressure in the air tank of 10 bar. A detailed system analysis highlights the characteristics of the optimal design found. Les pertes à vide représentent une partie essentielle de la consommation des moteurs à combustion interne. La mise à l’arrêt du moteur pendant la marche à vide peut, par conséquent, en améliorer son efficacité générale. Pour être accepté par le conducteur, le redémarrage du moteur doit être rapide. On peut réaliser ce démarrage rapide du moteur, moyennant un démarreur électrique puissant conjointement avec un accumulateur approprié, solution retenue par exemple, pour les véhicules à système hybride électrique. Cependant, ces derniers augmentent le coût et le poids. Une alternative consiste dans le démarrage pneumatique du moteur en utilisant de l’air comprimé stocké dans un réservoir sous pression et injecté directement dans les cylindres. Cette étude présente le démarrage pneumatique du moteur en utilisant des valves de chargement commandées par arbre à cames. On présente une méthodologie visant à une consommation de l’air optimale en mesure de respecter des exigences différentes. La démarche proposée s’appuie sur le modèle d’un processus représentant l’opération pneumatique du moteur. La vérification expérimentale du démarrage pneumatique est réalisée et optimisée sur un moteur 2 cylindres sur banc d’essai. Avec une pression initiale de 10 bar dans le tank d’air, la vitesse de rotation à vide de 1 200 tr.min−1 peut être atteinte en 350 ms Une analyse détaillée confirme les caractéristiques du système optimisé.

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.

In Formula 1, race strategies are adapted according to evolving race conditions and competitors' actions. This paper proposes a reinforcement learning approach for multi-agent race strategy optimization. Agents learn to balance energy management, tire degradation, aerodynamic interaction, and pit-stop decisions. Building on a pre-trained single-agent policy, we introduce an interaction module that accounts for the behavior of competitors. The combination of the interaction module and a self-play training scheme generates competitive policies, and agents are ranked based on their relative performance. Results show that the agents adapt pit timing, tire selection, and energy allocation in response to opponents, achieving robust and consistent race performance. Because the framework relies only on information available during real races, it can support race strategists' decisions before and during races.

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.

Since 2014, the Fédération Internationale de l'Automobile has prescribed a parallel hybrid powertrain for the Formula 1 race cars. The complex low-level interactions between the thermal and the electrical part represent a non-trivial and challenging system to be controlled online. We present a novel controller architecture composed of a supervisory controller for the energy management, a feedforward cylinder deactivation controller, and a track region-dependent low-level nonlinear model predictive controller to optimize the engine actuators. Except for the nonlinear model predictive controller, the proposed controller subsystems are computationally inexpensive and are real time capable. The framework is tested and validated in a simulation environment for several realistic scenarios disturbed by driver actions or grip conditions on the track. In particular, we analyze how the control architecture deals with an unexpected gearshift trajectory during an acceleration phase. Further, we demonstrate how an increased maximum velocity trajectory impacts the online low-level controller. Our results show a suboptimality over an entire lap with respect to the benchmark solution of 49 ms and 64 ms, respectively, which we deem acceptable. Compared to the same control architecture with full knowledge of the disturbances, the suboptimality amounted to only 2 ms and 17 ms. For all case studies we show that the cylinder deactivation capability decreases the suboptimality by 7 to 8 ms.

This paper presents an optimization framework to model multi-agent racing dynamics. By incorporating physically accurate interaction models and accounting for the optimal responses of competing agents, our approach reveals strategic behaviors typical of motorsport. Aerodynamic wake effects, trajectory optimization, and energy management are captured and evaluated on a representative case study, based on a Formula 1 scenario. We describe the minimum lap time problem with two agents as either a Nash or a Stackelberg game, and by employing the Karush-Kuhn-Tucker conditions during the problem formulation, we recover the structure of a nonlinear program. In addition, we introduce an algorithm to refine local Stackelberg solutions, using the Nash costs as upper bounds. The resulting strategies are analyzed through case studies. We examine the impact of slipstreaming on trajectory selection in corners, straights, and high-speed sections, while also identifying optimal overtaking locations based on energy allocation strategies. Exploiting the structural similarities of the game formulations, we are able to compare symmetric and hierarchical strategies to analyze competitive racing dynamics. The proposed methodology closes the gap between theoretical game theory and practical applications, with relevance in multi-agent systems with coupled nonlinear dynamics.

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.