Göhner, Ulrich
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Many cities in Europe and around the world are concerned with reducing their CO2-emissions. One step on this agenda is the introduction of electric buses to replace combustion engines. The electrification of urban buses requires an accurate prediction of the energy demand. In this pa per, an energy model and the underlying calibration process is presented. This approach leverages substantial tracking data from 10 electric buses operated in Göttingen, Germany. It was shown that, with the use of additional information from the directly measured tracking data, like auxiliary power, charging power and vehicle weight, it is possible to precisely calibrate models based on physical equations with regard to generally poorly identifiable parameters like rolling friction coefficient or efficiency of the electric machine. With a multilayered approach for simulating the energy demand, it is possible to validate the results on the mechanical layer and on the electrical layer separately. This enables a far better parametrization and elimination of uncertainties from the different parameters. Furthermore, we compare the results to other publications for sections with 1 km, 100 km and 230 km, respectively. The relative errors between the simulated and measured electrical power consumption are below 0.3%, 3% and 6.5%, respectively. Hence, the yielded model is appropriate for electric urban bus network planning applications. And the found parameters should be a good starting point for other energy prediction models. To further enable comparability with other approaches the dataset used for calibration is made publicly available.
Many cities in Europe and around the world are concerned with reducing their CO2-emissions. One step on this agenda is the introduction of electric buses to replace combustion engines. The electrification of urban buses requires an accurate prediction of the energy demand. In this pa per, an energy model and the underlying calibration process is presented. This approach leverages substantial tracking data from 10 electric buses operated in Göttingen, Germany. It was shown that, with the use of additional information from the directly measured tracking data, like auxiliary power, charging power and vehicle weight, it is possible to precisely calibrate models based on physical equations with regard to generally poorly identifiable parameters like rolling friction coefficient or efficiency of the electric machine. With a multilayered approach for simulating the energy demand, it is possible to validate the results on the mechanical layer and on the electrical layer separately. This enables a far better parametrization and elimination of uncertainties from the different parameters. Furthermore, we compare the results to other publications for sections with 1 km, 100 km and 230 km, respectively. The relative errors between the simulated and measured electrical power consumption are below 0.3%, 3% and 6.5%, respectively. Hence, the yielded model is appropriate for electric urban bus network planning applications. And the found parameters should be a good starting point for other energy prediction models. To further enable comparability with other approaches the dataset used for calibration is made publicly available.
Impact of Inductive Charging Infrastructure at Intersections on Battery Electric Bus Operations
(2023)
Battery electric buses are the go-to solution for clean public transport at the moment. But they come with operational challenges. One technology that could potentially help in some of those is inductive in-motion charging, as it reduces additional dwell times and can help minimize battery size. In addition, the infrastructure can also be used by other road users. This paper presents a framework for evaluating the impact of intersection-based inductive charging infrastructure on battery electric bus operations using a traffic simulation and a physics-based energy demand model. The results are split into two categories: first, investigations of the charging lane lengths on a single intersection with increasing traffic volume, and second, implementation of charging infrastructure along a real bus line to better understand the implications of the charged energy in relation to the energy demand. The findings from the analysis reveal that the higher the traffic volume, the longer the charging lanes need to be to make use of the resulting delay times. The analysis indicates that, in our scenario, the bus can charge around 100% of the required energy with a charging lane length of around 80 meters and a charging power of 250kW. This work can inform decision-making for the deployment of charging infrastructure in urban environments and contribute to the development of sustainable urban transportation systems.
Battery electric buses (BEBs) are gaining prominence in public transportation systems. In this paper, we investi-gate the impact of road grade, passenger load, and recuperation power limits on the energy consumption of BEBs using a physics-based model with tuned parameters. The model was employed to conduct a sensitivity analysis taking into account different altitude data sources, passenger load assumptions, and maximum recuperation power limits. The results highlight the importance of considering the route topology and its interaction with dynamic passenger loading for energy consumption predictions. Further-more, the results indicate that various altitude data sources are feasible to estimate the road grade for this purpose. Apart from that, the sensitivity for recuperation power limitations is shown and put into context. Within a broader framework, the findings suggest that physics-based energy consumption models with optimised parameters can serve as a powerful tool for enhanced operations and planning of BEBs.
As the adoption of battery electric buses (BEBs) in public transportation systems grows, the need for precise energy consumption forecasting becomes increasingly important. Accurate predictions are essential for optimizing routes, charging schedules, and ensuring adequate operational range. This paper introduces an innovative forecasting methodology that combines a propulsion and auxiliary energy model with a novel concept, the environment generator. This approach addresses the primary challenge in electric bus energy forecasting: estimating future environmental conditions, such as weather, passenger load, and traffic patterns, which significantly impact energy demand. The environment generator plays a crucial role by providing the energy models with realistic input data. This study validates various models with different levels of model complexity against real-world operational data from a case study of over one year with 16 electric buses in Göttingen, Germany. Our analysis thoroughly examines influencing factors on energy consumption, like altitude, temperature, passenger load, and driving patterns. In order to comprehensively understand energy demands under varying operational conditions, the methodology integrates data-driven models and physical simulations into a modular and highly accurate energy predictor. The results demonstrate the effectiveness of our approach in providing more accurate energy consumption forecasts, which is essential for efficient electric bus fleet management. This research contributes to the growing body of knowledge in electric vehicle energy prediction and offers practical insights for transit authorities and operators in optimizing electric bus operations.
This paper has the goal to propose an approach to enable reliable planning of BEBs in a traffic simulation which acts as the virtual environment. It was shown that the defaults for the vehicle trajectories from the traffic simulation SUMO, introduce significant errors. With some fine tuning and optimization of the vehicle behavior, reasonable results can be obtained from the traffic simulation. This can be used for planning of new bus networks considering many aspects, like energy demand, impact of traffic on delays and the resulting charging times at terminal stations.
A solution to the electrical urban transit routing problem with heterogeneous characteristics
(2023)
The already highly complex Urban Transit Routing Problem (UTRP) that serves to find efficient travelling routes for Public Transport (PT) systems is extended into the Heterogeneous Electric - Urban Transit Routing Problem (HE-UTRP). This extension focuses on step-by-step transformation of public bus transportation systems to electric mobility. The heterogeneity characteristics refers to the fleet and charging infrastructure. This article presents a framework that allows the generation, analysis and optimisation of PT Route Networks (RNs) for the HE-UTRP. In addition to the analysis of different charging technologies and Charging Locations (CLs), the approach enables a transformation process towards electrification of PT systems by presenting substitution scenarios as well as the resulting cost structure. The framework, based on a Sequence-based Selection Hyper-heuristic - with Great Deluge (SS-GD), is tested against varying objective functions and UTRP, HE-UTRP and Electric Transit Route Network Design Problem (E-TRNDP) instances.
This paper proposes a framework for optimizing an electric urban bus network, based on data of an existing diesel bus fleet. Therefore, the required input data is described as well as the required steps to analyze and preprocess the data from the busses. With this aggregated data a mathematical model was formulated to optimize the cost of a future electric bus network. This is accomplished by choosing optimal batterie sizes and charging station locations as well as charging powers. • With extensive real-world data logging, we could validate assumptions about energy demand, waiting times and different traffic situations during the day • To use the data for the model implementation of a robust data pipeline including Interpreting, preprocessing and aggregating the data, was necessary • Based on the data of the diesel busses we simulated the electric buses energy demand • The Integer Linear Programming model finds cost optimal solutions for charging infrastructure and batterie sizes • The algorithm is very adjustable to specific needs of public transport operators and can generate optimal solutions in a short amount of time
In manufacturing industry, product failure is costly, as it results in financial and time losses. Understanding the causes of product failure is critical for reducing the occurrence of failure and optimising the manufacturing process. As a result, a number of studies utilising data-driven approaches such as machine learning have been conducted to reduce the occurrence of this failure and to improve the manufacturing process. While these data-driven approaches enable pattern recognition, they lack the advantages associated with knowledge-driven approaches, such as knowledge representation and deductive reasoning. Similarly, knowledge-driven approaches lack the pattern-learning capabilities inherent in data-driven approaches such as machine learning. Therefore, in this paper, leveraging the advantages of both data-driven and knowledge-driven approaches, we present a strategy with a prototype implementation to reduce manufacturing product failure. The proposed strategy combines a data-driven technique, Bayesian structural learning, with a knowledge-based technique, knowledge graphs.
The electrification of urban bus fleets is a challenging task, especially for smaller public transport operators. The main challenge lies in the uncertainty about many technical aspects, like range of vehicles under different circumstances or charging times, that are new for the operators. The purpose of this research is to introduce an approach to solve this problem by incorporating all available data from an existing bus fleet and finding an optimal solution with discrete mathematical optimization. Extensive data logging in the project enabled us to leverage tracking data from the whole bus network including trajectories, powertrain data, and operational data. This enabled us to validate assumptions about the energy demand, waiting times, and different traffic situations during the day. To get better insights into the requirements of an urban bus fleet, we simulated the potential electric buses in detail and extracted other necessary data like actual dwell times. Based on the simulation results and processed data, we implemented a linear programming model to search for a cost-optimal configuration of vehicles and charging infrastructure. We tested the framework with a scenario in which we analyzed the solutions with different numbers of diesel buses in the fleet. The application of our algorithm shows that it can produce optimal results in a short amount of time, for a medium-sized city in Germany. We also demonstrate that the flexible and constraint-based formulation of this approach allows it to be incorporated in the planning process of most public transport operators.
LS-DYNA on Demand License
(2019)
This paper drafts a new interaction-aware approach to predict road user occupancy through vehicle-specific probabilities of presence. Instead of presenting another behavior prediction approach which tries to predict the most likely vehicle positions, the aim is to determine the likelihood of all feasible vehicle positions, called probability of presence. Thus the occupied area derived from this probability of presence blocks less space compared to existing approaches in the field of occupancy prediction. For this purpose, based on a physical model the future presence areas of a vehicle are fully calculated in a discrete consideration. Each movement possibility is evaluated taking into account the statistically typical driver behavior and interactions with static and dynamic objects. This gives rise to a qualified prediction of the future behavior of vehicles which blocks less space compared to all feasible future vehicle positions, like demonstrated in the numerical example.
This paper presents a new approach to determining the occupancy area of a pedestrian for autonomous driving. To do this, a probabilistic prediction of pedestrian behavior is calculated, which results in the probability of presences. The occupancy prediction can be determined on the basis of this probability as a function of an accepted risk. To predict the behavior, the first step involves using a physical model to determine the possible presence locations. The subsequent assessment of the movement options based on the statistically representative pedestrian behavior, the relevant static objects and the interaction of dynamic objects allows the probabilities of presences to be determined in arbitrary situations. The effectiveness of the prediction is illustrated by using a numerical example which indicates the reduction of occupancy area size by using a suitable prediction method.
Assumptions of Lateral Acceleration Behavior Limits for Prediction Tasks in Autonomous Vehicles
(2019)
This paper presents an analysis of the euroFot data set to determine limits for the typical lateral acceleration behavior of drivers. Since recent studies indicate that lateral accelerations close to the physically possible limit are rarely used by drivers, predictions tasks for autonomous driving could consider a smaller, so-called natural lateral acceleration interval (NLAI) instead of all physically possible lateral accelerations. This NLAI should be as small as possible while still fulfilling all safety aspects. Therefore, valid assumptions are required on which the interval can be derived. Since a valid assumption which leads to minimal NLAI is yet unknown, four different assumptions concerning the lateral acceleration behavior are derived and evaluated in this paper. Thereby, detailed examinations regarding the relative frequencies of violations are presented. Finally, two assumptions are recommended for introducing an NLAI, depending on prediction time and safety requirements. Additionally, the advantages of utilizing an NLAI instead of all physically possible lateral accelerations are highlighted by comparing the results of an occupancy prediction approach.
The prediction of the future behavior of drivers is a challenging research topic. Therefore, this paper presents a new approach for occupancy prediction of the surrounding vehicles based on a static overapproximation of the driver behavior in longitudinal direction and a situation specific overapproximation of the driver behavior in lateral direction. Compared to existing probabilistic motion prediction approaches no prior knowledge of the situation is necessary. Therefore, the presented approach is not limited to specific situations and can be used to predict the occupancy in unstructured environments. The evaluation of the approach with real world data from the common road benchmark dataset shows the reduction of the occupancy area size up to 70% compared to a baseline method. Nevertheless, the prediction is accurate up to a prediction time of 2 seconds whereby the safety of the autonomous vehicle is ensured. The presented approach successfully handles the trade-off between occupancy area size and prediction safety while being applicable to all situations.
Many publications work on optimization of driving styles in motor vehicles. Most conclude that they can improve energy efficiency through training. In recent years the tools to address those problems evolved towards machine learning. To get appropriate data for learning algorithms we developed a method to judge a driving style with respect to energy efficiency. This approach leveraged handpicked criteria like acceleration extracted from GPS. Like related works, this method does not scale, since it requires substantial preprocessing. The goal of this evaluation was to reduce the resistance energy of a driven trip, while maintaining a natural traffic flow. This was accomplished by mimicking a low-pass filter on the speed profile. On top excessive speeding gets punished. It was possible to use our data with over 1 million kilometers for training a Recurrent Neural Network. In respect to the RNN the training data was used, to let it map the obtained function. The provided data was adjusted in different stages, until it was only the raw GPS data. The RNN learned to handle most GPS errors, only in initial phases the results are mixed. A RNN Network is well suited to handle GPS data and learn higher level features on its own. The result is a NN which judges the driving style using only raw GPS data.
Eine der größten Barrieren der heutigen Elektrofahrzeuge ist ihre limitierte Reichweite. Obwohl die Mehrheit der Autofahrer nicht mehr als 40 Kilometer pro Tag fährt ([MiD2008]), ist das Kaufverhalten für Elektrofahrzeuge mit viel kleinerer Reichweite als solcher mit Verbrennungsmotoren sehr zurückhaltend. Mit dieser Arbeit adressieren wir die zusätzlichen Informationsbedürfnisse der Fahrer von E-Fahrzeugen durch die Entwicklung einer Applikation für Standard-Smartphones, welche sowohl eine detaillierte und genaue grafische Repräsentation der Reichweite des Elektrofahrzeugs ermöglicht, als auch einen fortgeschrittenen Routing-Algorithmus implementiert, der den spezifischen Energieverbrauch des Elektrofahrzeugs berücksichtigt.