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.