Two natural ways of modelling Formula 1 race outcomes are a probabilistic approach, based on the exponential distribution, and econometric modelling of the ranks. Both approaches lead to exactly ...soluble race-winning probabilities. Equating race-winning probabilities leads to a set of equivalent parametrisations. This time-rank duality is attractive theoretically and leads to quicker ways of dis-entangling driver and car level effects.
•F1 races are modelled probabilistically and by regression modelling of the ranks.•Equating race-winning probabilities leads to a set of equivalent parameterisations.•This time-rank duality helps pinpoint driver and car level effects.•This is much less data intensive compared to previous approaches.•Results imply Max Verstappen and Fernando Alonso out-perform their respective cars.
Formula 1 racing, known for its speed and innovation, faces an increasingly urgent need to address environmental concerns. This study investigates the environmental impacts of various fuel types used ...in Formula 1, aiming to bridge a critical gap in the automotive industry's perception of sustainability. With sustainability at the forefront, we conducted a comprehensive Life Cycle Assessment (LCA) of different fuel types, including Gasoline, E5, E10, and E85, specifically analyzing their effects during the Belgian Grand Prix race. Through a thorough literature review and careful scoping, we sought to provide valuable insights into the environmental performance of these fuels in the context of high-performance racing. Our findings reveal a stark contrast between marketed sustainability claims and actual environmental impacts. Contrary to expectations, our analysis demonstrates that E85, often advertised as a sustainable alternative, exhibits the highest emissions across various environmental indicators. Specifically, E85 showed the highest impacts on water consumption, fine particulate matter, ozone formation, and human health. These results carry profound implications for the automotive industry, highlighting the importance of informed decision-making in fuel selection within the racing industry. By prompting further discussions on sustainable practices in motorsports, our study urges stakeholders to reevaluate fuel choices and embrace environmentally responsible alternatives.
Recently, the Formula 1 propulsion system has evolved from being a conventional combustion engine toward a highly integrated hybrid electric powertrain. Since 2014, the vehicles have been equipped ...with an electric motor for extra boosting and regenerative braking, and an electrified turbocharger to improve the engine's torque response and to recover waste heat from the exhaust gas. The powertrain is controlled with a dedicated energy management system, which significantly influences the vehicle's acceleration performance as well as its fuel and electric energy consumption. Therefore, the strategy must be carefully optimized. In this paper, we propose a computationally efficient method to evaluate the theoretic, optimal energy management strategy leading to the best possible lap time. Since the driving path cannot be influenced by the energy management strategy, but is rather determined by the driver's steering's input, we separate the optimization of velocity profile and energy management from the problem of finding the optimal driving path. By carefully introducing convex approximations and relaxations, we formulate the problem as a convex optimal control problem that can be solved efficiently using dedicated numerical solvers. The proposed method allows parameter studies to be conducted within a reasonable time frame of a few minutes, while the optimization results serve as a benchmark for any real-time energy management strategy ultimately to be used during a real race.
•A dramatic case of decision making failure in Formula 1 demonstrates the need for broader analysis from a practice-based perspective.•An inductive study of the Ferrari case in Abu Dhabi in 2010 ...reveals more complex and nuanced reasons for failure than those reported in the media that led to key strategists being ousted.•Three topics stand out for strategic IS research: (1) The situated nature and affordances of decision making; (2) The distributed nature of cognition in decision making; and (3) The performativity of the decision support system.
Decision support systems (DSS) are sophisticated tools that increasingly take advantage of big data and are used to design and implement individual- and organization-level strategic decisions. Yet, when organizations excessively rely on their potential the outcome may be decision-making failure, particularly when such tools are applied under high pressure and turbulent conditions. Partial understanding and unidimensional interpretation can prevent learning from failure. Building on a practice perspective, we study an iconic case of strategic failure in Formula 1 racing. Our approach, which integrates the decision maker as well as the organizational and material context, identifies three interrelated sources of strategic failure that are worth investigation for decision-makers using DSS and big data: (1) the situated nature and affordances of decision-making; (2) the distributed nature of cognition in decision-making; and (3) the performativity of the DSS. We outline specific research questions and their implications for firm performance and competitive advantage. Finally, we advance an agenda that can help close timely gaps in strategic IS research.
The optimization of the energy management of modern hybrid-electric or fully electric race cars for minimum lap time requires a description of the vehicle dynamics performance envelope, that is, of ...the tires' grip limit in corners, braking zones and during acceleration. In this paper, we present a computationally efficient performance envelope model in the form of convex constraints on the achievable longitudinal and lateral acceleration, on the assumption that the path on the track is given. The proposed acceleration limits are modeled velocity-dependent to take into account the effect of aerodynamic downforce present in many circuit race cars. The formulation as linear equality, inequality and second-order cone constraints allows to embed the model in a convex energy management optimization framework. To showcase the approach, we identify the model with data obtained from a state-of-the-art hybrid-electric Formula 1 car and present results for the Silverstone and Spa-Francorchamps circuits. The optimal energy management strategies can be evaluated with a computational time of less than 1 s. The optimal velocity profile subject to the performance envelope constraints is close to the measured one. The good agreement between the optimal solution and the measurement data shows that the proposed model captures the vehicle dynamics accurately enough for the purposes of energy management optimization.
Since 2014, the Formula 1 car has been a hybrid electric vehicle with a turbocharged gasoline engine and electric motor/generator units connected to the axle, for kinetic energy recovery and ...boosting, and to the shaft of the turbocharger, mainly to recover waste heat from the exhaust gases. This system offers a new degree of freedom, namely, the power split, which is the ratio of power delivered by the electric traction motor in comparison to the overall propulsive power. In the straights, where the driver usually requests 100% acceleration, regulations allow the implementation of a thrust controller, since only by limiting the acceleration power, the maximum allowed fuel consumption of 100 kg gasoline per race can be achieved. The decisions of the corresponding energy management controller strongly influence the achievable lap time, and thus need to be carefully optimized. Furthermore, there exist several operational constraints imposed by the regulations that need to be tracked. This paper proposes a real-time implementable energy management strategy minimizing the lap time, by deriving the optimal control policy analytically. Optimality of the proposed controller is verified by comparing the results obtained with a benchmark simulator against the global optimal solution, while implementability and compatibility with the regulations are demonstrated using a high-fidelity nonlinear simulator.
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.
The hybrid-electric powertrain currently used in Formula 1 race cars draws its energy from the car's fuel tank and battery. The usable battery size is limited, and refueling during a race is ...forbidden by the regulations of the Formula 1 race series. From a strategic point of view, lap-by-lap targets for the fuel and battery consumption must be chosen and imposed on the energy management controller of the car. This task is non-trivial due to the influence of the on-board fuel mass on the achievable lap time, as well as the cross-couplings between the electric and the combustion part of the powertrain. A systematic approach is thus required to compute the energy allocation strategy that minimizes the total race time. In this paper, we devise an optimization framework in the form of a non-linear program, yielding the optimal battery and fuel consumption targets for each lap of the race. The approach is based on maps that capture the achievable lap time as a function of car mass and allocated battery and fuel energy. These maps are generated beforehand with a model-based single-lap optimization framework and fitted using artificial neural network techniques. To showcase the approach, we present three case studies: First, we compare the optimal strategy to a heuristic method. The improvement of 2s over the entire race is substantial, given that the difference only lies in the energy allocation, but not in the overall consumption. It underlines the importance of optimizing the energy allocation. Second, we leverage the framework to compute the optimal fuel load at the beginning of the race. Finally, we apply the developed non-linear program in a shrinking-horizon fashion. Our simulation results show that the resulting model predictive controller correctly reacts to disturbances that frequently occur during a race.
El trabajo pretende identificar grupos de turistas de un evento deportivo, el GP Europa de Fórmula 1, con relación a su motivación para el consumo deportivo y características básicas que definen ...dichos grupos para conocer los perfiles de turista de un gran evento deportivo. Para caracterizar a los sujetos se utilizó el análisis clúster con una muestra de 148 asistentes al evento, turistas nacionales e internacionales. Los resultados obtenidos muestran la existencia de cuatro grupos de sujetos bien definidos: los sociales (formado por los que obtienen fuertes connotaciones sociales asistiendo al evento, el 17,6% de la muestra), los ostentosos (son el 17,5% de la muestra, que sin una motivación especial para asistir destacan por sus ingresos netos mensuales elevados), los intrépidos (compuesto por el 37,8% de la muestra, movidos por la adrenalina y la emoción de la experiencia) y los experimentados (se trata del 20% restante, grandes conocedores del evento deportivo). A partir de ello se puede concluir que los turistas de un evento deportivo tienen características diferenciales y comportamientos de consumo distintos que deben tenerse en cuenta por los tomadores de decisiones del sector turístico.
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