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A significant disadvantage of battery electric vehicles compared to vehicles with internal combustion engines is their sharply decreased driving range at low temperatures. Two factors are primarily responsible for this decreased range. On the one hand, the energy demand of cabin heating needs to be supplied by the vehicle’s battery since less waste heat is available from the powertrain, which could be used to cover heating demands. On the other hand, a limited capability to recuperate at low temperatures serves to protect the battery from accelerated aging, which ultimately leads to less energy regeneration. This paper analyzes the impact of both factors separately on a battery electric vehicle’s driving range. Additionally, this paper provides technical requirements for the implementation of an electrothermal recuperation system. Such a system has the potential to reduce the impact of both abovementioned factors on driving range by enabling the direct usage of regeneratable energy for heating when battery charging is limited under cold conditions. The presented analysis is based on BMW i3 and Tesla Model 3 datasets, which combined cover more than 125 trips in and around Munich at different ambient conditions. The results show that the range can decrease by up to 31.9% due to heating and by up to 21.7% due to limited recuperation, which gives a combined maximum range decrease of approximately 50% under cold conditions. Additionally, it was found that a heater with a short reaction time in the lower millisecond range and a power capability of 20 kW would be sufficient for an electrothermal recuperation system to enable the utilization of unused regenerative braking potentials directly for heating.
Matthias Steinstraeter; Tobias Heinrich; Markus Lienkamp. Effect of Low Temperature on Electric Vehicle Range. World Electric Vehicle Journal 2021, 12, 115 .
AMA StyleMatthias Steinstraeter, Tobias Heinrich, Markus Lienkamp. Effect of Low Temperature on Electric Vehicle Range. World Electric Vehicle Journal. 2021; 12 (3):115.
Chicago/Turabian StyleMatthias Steinstraeter; Tobias Heinrich; Markus Lienkamp. 2021. "Effect of Low Temperature on Electric Vehicle Range." World Electric Vehicle Journal 12, no. 3: 115.
Although battery electric vehicles (BEVs) are locally emission-free and assist automakers in reducing their carbon footprint, two major disadvantages are their shorter range and higher production costs compared to combustion engines. These drawbacks are primarily due to the battery, which is generally the heaviest and most expensive component of a BEV. Lightweight measures (strategies to decrease vehicle mass, e.g., by changing materials or downsizing components) lower energy consumption and reduce the amount of battery energy required (and in turn battery costs). Careful selection of lightweight measures can result in their costs being balanced out by a commensurate reduction in battery costs. This leads to a higher efficiency vehicle, but without affecting its production and development costs. In this paper, we estimate the lightweight potential of BEVs, i.e., the cost limit below which a lightweight measure is fully compensated by the cost savings it generates. We implement a parametric energy consumption and mass model and apply it to a set of BEVs. Subsequently, we apply the model to quantify the lightweight potential range (in €/kg) of BEVs. The findings of this paper can be used as a reference for the development of cheaper, lighter, and more energy-efficient BEVs.
Lorenzo Nicoletti; Andrea Romano; Adrian König; Peter Köhler; Maximilian Heinrich; Markus Lienkamp. An Estimation of the Lightweight Potential of Battery Electric Vehicles. Energies 2021, 14, 4655 .
AMA StyleLorenzo Nicoletti, Andrea Romano, Adrian König, Peter Köhler, Maximilian Heinrich, Markus Lienkamp. An Estimation of the Lightweight Potential of Battery Electric Vehicles. Energies. 2021; 14 (15):4655.
Chicago/Turabian StyleLorenzo Nicoletti; Andrea Romano; Adrian König; Peter Köhler; Maximilian Heinrich; Markus Lienkamp. 2021. "An Estimation of the Lightweight Potential of Battery Electric Vehicles." Energies 14, no. 15: 4655.
Roof-mounted photovoltaic systems play a critical role in the global transition to renewable energy generation. An analysis of roof photovoltaic potential is an important tool for supporting decision-making and for accelerating new installations. State of the art uses 3D data to conduct potential analyses with high spatial resolution, limiting the study area to places with available 3D data. Recent advances in deep learning allow the required roof information from aerial images to be extracted. Furthermore, most publications consider the technical photovoltaic potential, and only a few publications determine the photovoltaic economic potential. Therefore, this paper extends state of the art by proposing and applying a methodology for scalable economic photovoltaic potential analysis using aerial images and deep learning. Two convolutional neural networks are trained for semantic segmentation of roof segments and superstructures and achieve an Intersection over Union values of 0.84 and 0.64, respectively. We calculated the internal rate of return of each roof segment for 71 buildings in a small study area. A comparison of this paper’s methodology with a 3D-based analysis discusses its benefits and disadvantages. The proposed methodology uses only publicly available data and is potentially scalable to the global level. However, this poses a variety of research challenges and opportunities, which are summarized with a focus on the application of deep learning, economic photovoltaic potential analysis, and energy system analysis.
Sebastian Krapf; Nils Kemmerzell; Syed Khawaja Haseeb Uddin; Manuel Hack Vázquez; Fabian Netzler; Markus Lienkamp. Towards Scalable Economic Photovoltaic Potential Analysis Using Aerial Images and Deep Learning. Energies 2021, 14, 3800 .
AMA StyleSebastian Krapf, Nils Kemmerzell, Syed Khawaja Haseeb Uddin, Manuel Hack Vázquez, Fabian Netzler, Markus Lienkamp. Towards Scalable Economic Photovoltaic Potential Analysis Using Aerial Images and Deep Learning. Energies. 2021; 14 (13):3800.
Chicago/Turabian StyleSebastian Krapf; Nils Kemmerzell; Syed Khawaja Haseeb Uddin; Manuel Hack Vázquez; Fabian Netzler; Markus Lienkamp. 2021. "Towards Scalable Economic Photovoltaic Potential Analysis Using Aerial Images and Deep Learning." Energies 14, no. 13: 3800.
Automotive traffic scenes are complex due to the variety of possible scenarios, objects, and weather conditions that need to be handled. In contrast to more constrained environments, such as automated underground trains, automotive perception systems cannot be tailored to a narrow field of specific tasks but must handle an ever-changing environment with unforeseen events. As currently no single sensor is able to reliably perceive all relevant activity in the surroundings, sensor data fusion is applied to perceive as much information as possible. Data fusion of different sensors and sensor modalities on a low abstraction level enables the compensation of sensor weaknesses and misdetections among the sensors before the information-rich sensor data are compressed and thereby information is lost after a sensor-individual object detection. This paper develops a low-level sensor fusion network for 3D object detection, which fuses lidar, camera, and radar data. The fusion network is trained and evaluated on the nuScenes data set. On the test set, fusion of radar data increases the resulting AP (Average Precision) detection score by about 5.1% in comparison to the baseline lidar network. The radar sensor fusion proves especially beneficial in inclement conditions such as rain and night scenes. Fusing additional camera data contributes positively only in conjunction with the radar fusion, which shows that interdependencies of the sensors are important for the detection result. Additionally, the paper proposes a novel loss to handle the discontinuity of a simple yaw representation for object detection. Our updated loss increases the detection and orientation estimation performance for all sensor input configurations. The code for this research has been made available on GitHub.
Felix Nobis; Ehsan Shafiei; Phillip Karle; Johannes Betz; Markus Lienkamp. Radar Voxel Fusion for 3D Object Detection. Applied Sciences 2021, 11, 5598 .
AMA StyleFelix Nobis, Ehsan Shafiei, Phillip Karle, Johannes Betz, Markus Lienkamp. Radar Voxel Fusion for 3D Object Detection. Applied Sciences. 2021; 11 (12):5598.
Chicago/Turabian StyleFelix Nobis; Ehsan Shafiei; Phillip Karle; Johannes Betz; Markus Lienkamp. 2021. "Radar Voxel Fusion for 3D Object Detection." Applied Sciences 11, no. 12: 5598.
With the rising demand for electric vehicles with a fast-charging ability, high currents are applied to lithium-ion batteries to develop accurate battery models and intelligent fast-charging strategies. In order to achieve reliable results for automotive applications, single cell test conditions should be as close as possible to the conditions present in the battery pack of the battery electric vehicle. As cells are irreversibly connected in a battery pack, electrical contact resistance (ECR) is usually in the magnitude of <1 mΩ, and thus far lower than in reversible contacts during lab-testing. An interesting question arises as to whether this ECR has any unintended influence on the battery cell during testing. In this article, various experiments with high charge rates of up to 5 C are performed in order to assess the impact of the ECR of the measurement setup on the cells’ behavior. Two different commercial contact probes with different ECRs are tested on a 18650 lithium-ion battery, and compared to a laser-welded cell as a benchmark. ECRs of the lab-testing setups are measured with a micro ohmmeter, and the temperature evolution of all cells studied is measured at both cell tabs and the cell mantle during cycling. The results show that high peak temperature differences due to parasitic joule heat at the lithium-ion battery tabs occur when applying full charge cycles from 0.5 C to 5 C. Repetitive cycling with a multistage fast-charging strategy indicates a correlation of ECR with peak temperatures and aging spread. As a consequence, high ECRs could negatively affect drawn conclusions of cycle life tests. They should therefore be taken into account and kept low in any examination of fast-charging strategies.
Nikolaos Wassiliadis; Manuel Ank; Leo Wildfeuer; Michael K. Kick; Markus Lienkamp. Experimental investigation of the influence of electrical contact resistance on lithium-ion battery testing for fast-charge applications. Applied Energy 2021, 295, 117064 .
AMA StyleNikolaos Wassiliadis, Manuel Ank, Leo Wildfeuer, Michael K. Kick, Markus Lienkamp. Experimental investigation of the influence of electrical contact resistance on lithium-ion battery testing for fast-charge applications. Applied Energy. 2021; 295 ():117064.
Chicago/Turabian StyleNikolaos Wassiliadis; Manuel Ank; Leo Wildfeuer; Michael K. Kick; Markus Lienkamp. 2021. "Experimental investigation of the influence of electrical contact resistance on lithium-ion battery testing for fast-charge applications." Applied Energy 295, no. : 117064.
The megatrends of individualization and sharing will dramatically change our consumer behavior. The needs of a product’s users will be central input for its development. Current development processes are not suitable for this product development; thus, we propose a combination of a genetic algorithm and a fuzzy system for user-centered development. We execute our new methodological approach on the example of autonomous vehicle concepts to demonstrate its implementation and functionality. The genetic algorithm minimizes the required number of vehicle concepts to satisfy the mobility needs of a user group, and the fuzzy system transfers user needs into vehicle-related properties, which are currently input for vehicle concept development. To present this method, we use a typical family and their potential mobility behavior. Our method optimizes their minimal number of vehicle concepts to satisfy all mobility needs and derives the properties of the vehicle concepts. By integrating our method into the entire vehicle concept development process, autonomous vehicles can be designed user-centered in the context of the megatrends of individualization and sharing. In summary, our method enables us to derive an optimized number of products for qualitatively described, heterogeneous user needs and determine their product-related properties.
Ferdinand Schockenhoff; Maximilian Zähringer; Matthias Brönner; Markus Lienkamp. Combining a Genetic Algorithm and a Fuzzy System to Optimize User Centricity in Autonomous Vehicle Concept Development. Systems 2021, 9, 25 .
AMA StyleFerdinand Schockenhoff, Maximilian Zähringer, Matthias Brönner, Markus Lienkamp. Combining a Genetic Algorithm and a Fuzzy System to Optimize User Centricity in Autonomous Vehicle Concept Development. Systems. 2021; 9 (2):25.
Chicago/Turabian StyleFerdinand Schockenhoff; Maximilian Zähringer; Matthias Brönner; Markus Lienkamp. 2021. "Combining a Genetic Algorithm and a Fuzzy System to Optimize User Centricity in Autonomous Vehicle Concept Development." Systems 9, no. 2: 25.
State-of-the-art 3D object detection for autonomous driving is achieved by processing lidar sensor data with deep-learning methods. However, the detection quality of the state of the art is still far from enabling safe driving in all conditions. Additional sensor modalities need to be used to increase the confidence and robustness of the overall detection result. Researchers have recently explored radar data as an additional input source for universal 3D object detection. This paper proposes artificial neural network architectures to segment sparse radar point cloud data. Segmentation is an intermediate step towards radar object detection as a complementary concept to lidar object detection. Conceptually, we adapt Kernel Point Convolution (KPConv) layers for radar data. Additionally, we introduce a long short-term memory (LSTM) variant based on KPConv layers to make use of the information content in the time dimension of radar data. This is motivated by classical radar processing, where tracking of features over time is imperative to generate confident object proposals. We benchmark several variants of the network on the public nuScenes data set against a state-of-the-art pointnet-based approach. The performance of the networks is limited by the quality of the publicly available data. The radar data and radar-label quality is of great importance to the training and evaluation of machine learning models. Therefore, the advantages and disadvantages of the available data set, regarding its radar data, are discussed in detail. The need for a radar-focused data set for object detection is expressed. We assume that higher segmentation scores should be achievable with better-quality data for all models compared, and differences between the models should manifest more clearly. To facilitate research with additional radar data, the modular code for this research will be made available to the public.
Felix Nobis; Felix Fent; Johannes Betz; Markus Lienkamp. Kernel Point Convolution LSTM Networks for Radar Point Cloud Segmentation. Applied Sciences 2021, 11, 2599 .
AMA StyleFelix Nobis, Felix Fent, Johannes Betz, Markus Lienkamp. Kernel Point Convolution LSTM Networks for Radar Point Cloud Segmentation. Applied Sciences. 2021; 11 (6):2599.
Chicago/Turabian StyleFelix Nobis; Felix Fent; Johannes Betz; Markus Lienkamp. 2021. "Kernel Point Convolution LSTM Networks for Radar Point Cloud Segmentation." Applied Sciences 11, no. 6: 2599.
The modelling and simulation process in the automotive domain is transforming. Increasing system complexity and variant diversity, especially in new electric powertrain systems, lead to complex, modular simulations that depend on virtual vehicle development, testing and approval. Consequently, the emerging key requirements for automotive validation involve a precise reliability quantification across a large application domain. Validation is unable to meet these requirements because its results provide little information, uncertainties are neglected, the model reliability cannot be easily extrapolated and the resulting application domain is small. In order to address these insufficiencies, this paper develops a statistical validation framework for dynamic systems with changing parameter configurations, thus enabling a flexible validation of complex total vehicle simulations including powertrain modelling. It uses non-deterministic models to consider input uncertainties, applies uncertainty learning to predict inherent model uncertainties and enables precise reliability quantification of arbitrary system parameter configurations to form a large application domain. The paper explains the framework with real-world data from a prototype electric vehicle on a dynamometer, validates it with additional tests and compares it to conventional validation methods. It is published as an open-source document. With the validation information from the framework and the knowledge deduced from the real-world problem, the paper solves its key requirements and offers recommendations on how to efficiently revise models with the framework’s validation results.
Benedikt Danquah; Stefan Riedmaier; Yasin Meral; Markus Lienkamp. Statistical Validation Framework for Automotive Vehicle Simulations Using Uncertainty Learning. Applied Sciences 2021, 11, 1983 .
AMA StyleBenedikt Danquah, Stefan Riedmaier, Yasin Meral, Markus Lienkamp. Statistical Validation Framework for Automotive Vehicle Simulations Using Uncertainty Learning. Applied Sciences. 2021; 11 (5):1983.
Chicago/Turabian StyleBenedikt Danquah; Stefan Riedmaier; Yasin Meral; Markus Lienkamp. 2021. "Statistical Validation Framework for Automotive Vehicle Simulations Using Uncertainty Learning." Applied Sciences 11, no. 5: 1983.
Autonomous electric buses (AEB) have widely been envisioned in future public transportation systems due to their large potential to improve service quality while reducing operational costs. The requirements and specifications for AEBs, however, remain uncertain and strongly depend on the use case. To enable the identification of the optimal vehicle specifications, this paper presents a holistic design optimization framework that explores the impacts of implementing different AEB concepts in a given set of routes/network. To develop the design optimization framework, first, a multi-objective, multi-criteria objective function is formulated by identifying the attributes of bus journeys that represent overall value to the stakeholders. Simulation models are then developed and implemented to evaluate the overall performance of the vehicle concepts. A genetic algorithm is used to find the concepts with the optimal trade-off between the overall value to the stakeholders and the total cost of ownership. A case study is presented of a single bus line in Singapore. The results show an improvement in the waiting time with the use of a smaller sized AEB with a capacity of 20 passengers. However, the costs and emissions increase due to the requirement of a larger fleet and the increase in daily distance traveled compared to a 94-passenger capacity AEB.
Aditya Pathak; Silvan Scheuermann; Aybike Ongel; Markus Lienkamp. Conceptual Design Optimization of Autonomous Electric Buses in Public Transportation. World Electric Vehicle Journal 2021, 12, 30 .
AMA StyleAditya Pathak, Silvan Scheuermann, Aybike Ongel, Markus Lienkamp. Conceptual Design Optimization of Autonomous Electric Buses in Public Transportation. World Electric Vehicle Journal. 2021; 12 (1):30.
Chicago/Turabian StyleAditya Pathak; Silvan Scheuermann; Aybike Ongel; Markus Lienkamp. 2021. "Conceptual Design Optimization of Autonomous Electric Buses in Public Transportation." World Electric Vehicle Journal 12, no. 1: 30.
Das Robotaxi gilt aktuell als der heilige Gral der Ingenieurskunst. Technologisch faszinierend soll dieser Artikel allerdings auch die Schattenseiten und Hindernisse beleuchten und schlägt einen alternativen Weg über geteilte Fahrten mit Privatfahrzeugen vor.
Markus Lienkamp. Das Robotaxi – eine kritische Einschätzung. Mobilität der Zukunft 2021, 417 -422.
AMA StyleMarkus Lienkamp. Das Robotaxi – eine kritische Einschätzung. Mobilität der Zukunft. 2021; ():417-422.
Chicago/Turabian StyleMarkus Lienkamp. 2021. "Das Robotaxi – eine kritische Einschätzung." Mobilität der Zukunft , no. : 417-422.
The launch of both battery electric vehicles (BEVs) and autonomous vehicles (AVs) on the global market has triggered ongoing radical changes in the automotive sector. On the one hand, the new characteristics of the BEV powertrain compared to the combustion type have resulted in new central parameters, such as vehicle range, which then become an important selling point. On the other hand, electric components are as yet not optimized and the sensors needed for autonomous driving are still expensive, which introduces changes to the vehicle cost structure. This transformation is not limited to the vehicle itself but also extends to its mobility and the necessary infrastructure. The former is shaped by new user behaviors and scenarios. The latter is impacted by the BEV powertrain, which requires a charging and energy supply infrastructure. To enable manufacturers and researchers to develop and optimize BEVs and AVs, it is necessary to first identify the relevant parameters and costs. To this end, we have conducted an extensive literature review. The result is a complete overview of the relevant parameters and costs, divided into the categories of vehicle, infrastructure, mobility, and energy.
Adrian König; Lorenzo Nicoletti; Daniel Schröder; Sebastian Wolff; Adam Waclaw; Markus Lienkamp. An Overview of Parameter and Cost for Battery Electric Vehicles. World Electric Vehicle Journal 2021, 12, 21 .
AMA StyleAdrian König, Lorenzo Nicoletti, Daniel Schröder, Sebastian Wolff, Adam Waclaw, Markus Lienkamp. An Overview of Parameter and Cost for Battery Electric Vehicles. World Electric Vehicle Journal. 2021; 12 (1):21.
Chicago/Turabian StyleAdrian König; Lorenzo Nicoletti; Daniel Schröder; Sebastian Wolff; Adam Waclaw; Markus Lienkamp. 2021. "An Overview of Parameter and Cost for Battery Electric Vehicles." World Electric Vehicle Journal 12, no. 1: 21.
Connected and autonomous vehicles (CAVs) could reduce emissions, increase road safety, and enhance ride comfort. Multiple CAVs can form a CAV platoon with a close inter-vehicle distance, which can further improve energy efficiency, save space, and reduce travel time. To date, there have been few detailed studies of self-driving algorithms for CAV platoons in urban areas. In this paper, we therefore propose a self-driving architecture combining the sensing, planning, and control for CAV platoons in an end-to-end fashion. Our multi-task model can switch between two tasks to drive either the leading or following vehicle in the platoon. The architecture is based on an end-to-end deep learning approach and predicts the control commands, i.e., steering and throttle/brake, with a single neural network. The inputs for this network are images from a front-facing camera, enhanced by information transmitted via vehicle-to-vehicle (V2V) communication. The model is trained with data captured in a simulated urban environment with dynamic traffic. We compare our approach with different concepts used in the state-of-the-art end-to-end self-driving research, such as the implementation of recurrent neural networks or transfer learning. Experiments in the simulation were conducted to test the model in different urban environments. A CAV platoon consisting of two vehicles, each controlled by an instance of the network, completed on average 67% of the predefined point-to-point routes in the training environment and 40% in a never-seen-before environment. Using V2V communication, our approach eliminates casual confusion for the following vehicle, which is a known limitation of end-to-end self-driving.
Sebastian Huch; Aybike Ongel; Johannes Betz; Markus Lienkamp. Multi-Task End-to-End Self-Driving Architecture for CAV Platoons. Sensors 2021, 21, 1039 .
AMA StyleSebastian Huch, Aybike Ongel, Johannes Betz, Markus Lienkamp. Multi-Task End-to-End Self-Driving Architecture for CAV Platoons. Sensors. 2021; 21 (4):1039.
Chicago/Turabian StyleSebastian Huch; Aybike Ongel; Johannes Betz; Markus Lienkamp. 2021. "Multi-Task End-to-End Self-Driving Architecture for CAV Platoons." Sensors 21, no. 4: 1039.
In order to electrify the transport sector, scores of charging stations are needed to incentivize people to buy electric vehicles. In urban areas with a high charging demand and little space, decision-makers are in need of planning tools that enable them to efficiently allocate financial and organizational resources to the promotion of electromobility. As with many other city planning tasks, simulations foster successful decision-making. This article presents a novel agent-based simulation framework for urban electromobility aimed at the analysis of charging station utilization and user behavior. The approach presented here employs a novel co-evolutionary learning model for adaptive charging behavior. The simulation framework is tested and verified by means of a case study conducted in the city of Munich. The case study shows that the presented approach realistically reproduces charging behavior and spatio-temporal charger utilization.
Lennart Adenaw; Markus Lienkamp. Multi-Criteria, Co-Evolutionary Charging Behavior: An Agent-Based Simulation of Urban Electromobility. World Electric Vehicle Journal 2021, 12, 18 .
AMA StyleLennart Adenaw, Markus Lienkamp. Multi-Criteria, Co-Evolutionary Charging Behavior: An Agent-Based Simulation of Urban Electromobility. World Electric Vehicle Journal. 2021; 12 (1):18.
Chicago/Turabian StyleLennart Adenaw; Markus Lienkamp. 2021. "Multi-Criteria, Co-Evolutionary Charging Behavior: An Agent-Based Simulation of Urban Electromobility." World Electric Vehicle Journal 12, no. 1: 18.
The advancement of electric mobility as a measure to comply with international climate targets and sustain renewable resources in the future has led to an electrification of the mobility sector in recent years. This trend has not been spared in the logistics and commercial vehicle sector. Emerging electric powertrain concepts for long-haul vehicles have since been developed and adapted to different use cases and axle concepts. In this paper, the authors show the influence of the powertrain topology and the associated design of the electric machine on the efficiency and energy consumption of commercial vehicles. For this, existing series or prototype long-haul axle topologies are analyzed regarding their efficiency and operating points within four driving cycles. Additionally, a sensitivity analysis on the influence of the total gearbox ratio tests the assumed designs. We find that single-machine topologies offer efficiency advantages over multiple-machine topologies. However, this study highlights a joint consideration of application-specific machine design and topology to realize the full technological potential.
Sebastian Wolff; Svenja Kalt; Manuel Bstieler; Markus Lienkamp. Influence of Powertrain Topology and Electric Machine Design on Efficiency of Battery Electric Trucks–A Simulative Case-Study. Energies 2021, 14, 328 .
AMA StyleSebastian Wolff, Svenja Kalt, Manuel Bstieler, Markus Lienkamp. Influence of Powertrain Topology and Electric Machine Design on Efficiency of Battery Electric Trucks–A Simulative Case-Study. Energies. 2021; 14 (2):328.
Chicago/Turabian StyleSebastian Wolff; Svenja Kalt; Manuel Bstieler; Markus Lienkamp. 2021. "Influence of Powertrain Topology and Electric Machine Design on Efficiency of Battery Electric Trucks–A Simulative Case-Study." Energies 14, no. 2: 328.
Electrification and automatization may change the environmental impact of vehicles. Current eco-driving approaches for electric vehicles fit the electric power of the motor by quadratic functions and are limited to powertrains with one motor and single-speed transmission or use computationally expensive algorithms. This paper proposes an online nonlinear algorithm, which handles the non-convex power demand of electric motors. Therefore, this algorithm allows the simultaneous optimization of speed profile and powertrain operation for electric vehicles with multiple motors and multiple gears. We compare different powertrain topologies in a free-flow scenario and a car-following scenario. Dynamic Programming validates the proposed algorithm. Optimal speed profiles alter for different powertrain topologies. Powertrains with multiple gears and motors require less energy during eco-driving. Furthermore, the powertrain-dependent correlations between jerk restriction and energy consumption are shown.
Alexander Koch; Tim Bürchner; Thomas Herrmann; Markus Lienkamp. Eco-Driving for Different Electric Powertrain Topologies Considering Motor Efficiency. World Electric Vehicle Journal 2021, 12, 6 .
AMA StyleAlexander Koch, Tim Bürchner, Thomas Herrmann, Markus Lienkamp. Eco-Driving for Different Electric Powertrain Topologies Considering Motor Efficiency. World Electric Vehicle Journal. 2021; 12 (1):6.
Chicago/Turabian StyleAlexander Koch; Tim Bürchner; Thomas Herrmann; Markus Lienkamp. 2021. "Eco-Driving for Different Electric Powertrain Topologies Considering Motor Efficiency." World Electric Vehicle Journal 12, no. 1: 6.
With the evolution of self-driving cars, autonomous racing series like Roborace and the Indy Autonomous Challenge are rapidly attracting growing attention. Researchers participating in these competitions hope to subsequently transfer their developed functionality to passenger vehicles, in order to improve self-driving technology for reasons of safety, and due to environmental and social benefits. The race track has the advantage of being a safe environment where challenging situations for the algorithms are permanently created. To achieve minimum lap times on the race track, it is important to gather and process information about external influences including, e.g., the position of other cars and the friction potential between the road and the tires. Furthermore, the predicted behavior of the ego-car's propulsion system is crucial for leveraging the available energy as efficiently as possible. In this paper, we therefore present an optimization-based velocity planner, mathematically formulated as a multi-parametric Sequential Quadratic Problem (mpSQP). This planner can handle a spatially and temporally varying friction coefficient, and transfer a race Energy Strategy (ES) to the road. It further handles the velocity-profile-generation task for performance and emergency trajectories in real time on the vehicle's Electronic Control Unit (ECU).
Thomas Herrmann; Alexander Wischnewski; Leonhard Hermansdorfer; Johannes Betz; Markus Lienkamp. Real-Time Adaptive Velocity Optimization for Autonomous Electric Cars at the Limits of Handling. IEEE Transactions on Intelligent Vehicles 2020, PP, 1 -1.
AMA StyleThomas Herrmann, Alexander Wischnewski, Leonhard Hermansdorfer, Johannes Betz, Markus Lienkamp. Real-Time Adaptive Velocity Optimization for Autonomous Electric Cars at the Limits of Handling. IEEE Transactions on Intelligent Vehicles. 2020; PP (99):1-1.
Chicago/Turabian StyleThomas Herrmann; Alexander Wischnewski; Leonhard Hermansdorfer; Johannes Betz; Markus Lienkamp. 2020. "Real-Time Adaptive Velocity Optimization for Autonomous Electric Cars at the Limits of Handling." IEEE Transactions on Intelligent Vehicles PP, no. 99: 1-1.
State of the art powertrain optimization compares the energy consumption of different powertrain configurations based on simulations with fixed driving cycles. However, this approach might not be applicable to future vehicles, since speed advisory systems and automated driving functions offer the potential to adapt the speed profile to minimize energy consumption. This study aims to investigate the potential of powertrain optimization with respect to energy consumption under optimal energy-efficient driving for electric buses. The optimal powertrain configurations of the buses under energy-efficient driving and their respective energy consumptions are obtained using powertrain-specific optimized driving cycles and compared with those of human-driven unconnected buses and buses with non-powertrain-specific optimal speed profiles. Based on the results, new trends in the powertrain design of vehicles under energy-efficient driving are derived. The optimized driving cycles are calculated using a dynamic programming approach. The evaluations were based on the fact that the buses under energy-efficient driving operate in dedicated lanes with vehicle-to-infrastructure (V2I) communication while the unconnected buses operate in mixed traffic. The results indicate that deviating from the optimal powertrain configuration does not have a significant effect on energy consumption for optimized speed profiles; however, the energy savings from an optimized powertrain configuration can be significant when ride comfort is considered. The connected buses under energy-efficient driving operating in dedicated lanes may reduce energy consumption by up to 27% compared to human-driven unconnected buses.
Alexander Koch; Olaf Teichert; Svenja Kalt; Aybike Ongel; Markus Lienkamp. Powertrain Optimization for Electric Buses under Optimal Energy-Efficient Driving. Energies 2020, 13, 6451 .
AMA StyleAlexander Koch, Olaf Teichert, Svenja Kalt, Aybike Ongel, Markus Lienkamp. Powertrain Optimization for Electric Buses under Optimal Energy-Efficient Driving. Energies. 2020; 13 (23):6451.
Chicago/Turabian StyleAlexander Koch; Olaf Teichert; Svenja Kalt; Aybike Ongel; Markus Lienkamp. 2020. "Powertrain Optimization for Electric Buses under Optimal Energy-Efficient Driving." Energies 13, no. 23: 6451.
Over recent years, the number of battery electric vehicles (BEVs) has drastically increased due to new European Union (EU) regulations. These regulations force vehicle manufacturers to adjust their product range in order to fulfill the imposed carbon dioxide limits. Such an adjustment enforces the usage of battery electric vehicles. However, research into the optimal BEV architectures and topologies is still in progress. Therefore, the aim of this paper is an analysis of all the current electric vehicle topologies. From this analysis, the authors identify different basic battery shapes. Subsequently, these shapes are used to describe the impact of the battery on the passenger compartment. As an initial result of this analysis, the authors create a new denomination method, via which it is possible to cluster the battery topologies. In a second step, the collected data is clustered using the novel denomination method. Finally, this paper presents the benchmark topologies for the analyzed segments.
Lorenzo Nicoletti; Franziska Ostermann; Maximilian Heinrich; Alois Stauber; Xue Lin; Markus Lienkamp. Topology analysis of electric vehicles, with a focus on the traction battery. Forschung im Ingenieurwesen 2020, 85, 457 -467.
AMA StyleLorenzo Nicoletti, Franziska Ostermann, Maximilian Heinrich, Alois Stauber, Xue Lin, Markus Lienkamp. Topology analysis of electric vehicles, with a focus on the traction battery. Forschung im Ingenieurwesen. 2020; 85 (2):457-467.
Chicago/Turabian StyleLorenzo Nicoletti; Franziska Ostermann; Maximilian Heinrich; Alois Stauber; Xue Lin; Markus Lienkamp. 2020. "Topology analysis of electric vehicles, with a focus on the traction battery." Forschung im Ingenieurwesen 85, no. 2: 457-467.
Defining a vehicle concept during the early development phase is a challenging task, since only a limited number of design parameters are known. For battery electric vehicles (BEVs), vehicle weight is a design parameter, which needs to be estimated by using an iterative approach, thus causing weight fluctuations during the early development phase. These weight fluctuations, in turn, require other vehicle components to be redesigned and can lead to a change in their size (secondary volume change) and weight (secondary weight change). Furthermore, a change in component size can impact the available installation space and can lead to collision between components. In this paper, we focus on a component that has a high influence on the available installation space: the wheels. We model the essential components of the wheels and further quantify their secondary volume and weight changes caused by a vehicle weight fluctuation. Subsequently, we model the influence of the secondary volume changes on the available installation space at the front axle. The hereby presented approach enables an estimation of the impact of weight fluctuations on the wheels and on the available installation space, which enables a reduction in time‑consuming iterations during the development process.
Lorenzo Nicoletti; Andrea Romano; Adrian König; Ferdinand Schockenhoff; Markus Lienkamp. Parametric Modeling of Mass and Volume Effects for Battery Electric Vehicles, with Focus on the Wheel Components. World Electric Vehicle Journal 2020, 11, 63 .
AMA StyleLorenzo Nicoletti, Andrea Romano, Adrian König, Ferdinand Schockenhoff, Markus Lienkamp. Parametric Modeling of Mass and Volume Effects for Battery Electric Vehicles, with Focus on the Wheel Components. World Electric Vehicle Journal. 2020; 11 (4):63.
Chicago/Turabian StyleLorenzo Nicoletti; Andrea Romano; Adrian König; Ferdinand Schockenhoff; Markus Lienkamp. 2020. "Parametric Modeling of Mass and Volume Effects for Battery Electric Vehicles, with Focus on the Wheel Components." World Electric Vehicle Journal 11, no. 4: 63.
We wish to make the following corrections to the published paper
Alexander Heilmeier; Michael Graf; Johannes Betz; Markus Lienkamp. Erratum: Heilmeier, A., et al. Application of Monte Carlo Methods to Consider Probabilistic Effects in a Race Simulation for Circuit Motorsport. Appl. Sci. 2020, 10, 4229. Applied Sciences 2020, 10, 5745 .
AMA StyleAlexander Heilmeier, Michael Graf, Johannes Betz, Markus Lienkamp. Erratum: Heilmeier, A., et al. Application of Monte Carlo Methods to Consider Probabilistic Effects in a Race Simulation for Circuit Motorsport. Appl. Sci. 2020, 10, 4229. Applied Sciences. 2020; 10 (17):5745.
Chicago/Turabian StyleAlexander Heilmeier; Michael Graf; Johannes Betz; Markus Lienkamp. 2020. "Erratum: Heilmeier, A., et al. Application of Monte Carlo Methods to Consider Probabilistic Effects in a Race Simulation for Circuit Motorsport. Appl. Sci. 2020, 10, 4229." Applied Sciences 10, no. 17: 5745.