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OAFuser: Online Adaptive Extended Object Tracking and Fusion using automotive Radar Detections
(2020)
This paper presents the Online Adaptive Fuser: OAFuser, a novel method for online adaptive estimation of motion and measurement uncertainties for efficient tracking and fusion by applying a system of several estimators for ongoing noise along with the conventional state and state covariance estimation. In our system, process and measurement noises are estimated with steady-state filters to obtain combined measurement noise and process noise estimators for all sensors in order to obtain state estimation with a linear Minimum Mean Square Error (MMSE) estimator and accelerating the system’s performance. The proposed adaptive tracking and fusion system was tested based on high fidelity simulation data and several real-world scenarios for automotive radar, where ground truth data is available for evaluation. We demonstrate the proposed method’s accuracy and efficiency in a challenging, highly dynamic scenario where our system is benchmarked with Multiple Model filter in terms of error statistics and run time performance.
Our current mobility paradigm increasingly faces economic, ecological, and social limits in urban areas. The aim of this paper is to analyse if a fleet of shared autonomous electric vehicles (AEVs) can meet these challenges while satisfying the current requirements of privately-owned internal combustion engine vehicles (ICEVs). Therefore, analytical models have been developed to simulate and investigate the impacts of mobility behaviour in Berlin and Stuttgart (Germany). The collected data were used to calculate the fleet size, the energy consumption, the emission of particulate matter, nitrogen oxides, and the carbon footprint of different shared AEVs in comparison with privately owned ICEVs. The approach shows that the system of a shared AEV fleet could lower externalities (accident avoidance, traffic jams, free spaces, parking costs and lifetime losses) in cities and generate cost benefits for customers.
This master thesis shows a holistic approach for the optimization of the energy management task for a plug-in hybrid electric vehicle. The ‘Equivalent Consumption Minimization Strategy’ (‘ECMS’) as a local optimal approach is implemented into an embedded controller and applied to a system simulation model in ‘GT-SUITE’, which integrates a hybrid drivetrain and the associated control structure with a thermal management model. Two modifications and one extension to the basic ‘equivalent consumption’ cost function are proposed for the favor of an unambiguous interpretation of the penalty factor term, an enhanced applicability of the ‘ECMS’ close to the battery state of charge limit and an effective applicability of the ‘EMCS’ to the thermal management task. All proposed modifications and extensions prove their applicability in the virtual test environment and recommend themselves for the utilization in further application areas, like the integration of exhaust aftertreatment system, the holistic evaluation of a fuel cell drivetrain or the holistic evaluation of a hybrid ship propulsion system.
Application of Induction Thermography for Detection of Near Surface Defects in Steel Products
(2020)
This paper describes the modelling, simulation and energy management of a fuel cell hybrid heavy-duty truck. For this purpose, a longitudinal dynamic model of a 26t truck was set up and the load requirement for the drive train was determined based on a driving cycle. To meet this load requirement as efficiently and dynamically as possible three different energy management strategies were implemented, tested and the impact on the overall system was analysed. In addition, the behaviour of the hybrid system with the various energy management strategies with different battery capacity is shown and analysed.
This paper provides an analysis of the trend in autonomous driving traffic and the development of infrastructural support, whereas the requirements on the infrastructural support will be analyzed. Then selected traffic scenes will be implemented in an autonomous driving simulator tool in order to figure out the required parameters to assist the autonomous vehicle from the infrastructure.
untitled document
(2020)
Application and machine learning methods for dynamic load point controls of electric vehicles (xEVs)
(2020)
From the customer's perspective, the appeal of electric vehicles depends on the simplicity and ease of their use, such as flexible access to electric power from the grid to recharge the batteries of their vehicles. Therefore, the expansion of charging infrastructure will be an important part of electric mobility. The related charging infrastructure is a big challenge for the load capacity of the grid connection without additional intelligent charge management: if the control of the charging process is not implemented, it is necessary to ensure the total of the maximum output of all xEVs at the grid connection point, which requires huge costs. This paper proposes to build a prediction module for forecasting dynamic charging load using machine learning (ML) techniques. The module will be integrated into a real charge management concept with optimization procedures for controlling the dynamic load point. The value of load forecasting through practical load data of a car park were taken to illustrate the proposed methods. The prediction performance of different ML methods under the same data condition (e.g., holiday data) are compared and evaluated.
Recently the production of electric cars is increasing worldwide. The main target is to lower the greenhouse gas emissions. Even if an electrified vehicle is locally emission-free the manufacturing of lithium ion batteries are producing significant amounts of CO2. In order to decrease the air pollution governments are considering recycling programs to extend battery life and usage of important raw materials. A new approach to recover LiNixMnyCozO2 (NMC) particles while saving the chemical and morphological properties using water was presented by Tim Sieber et al. [1]. With the presented study, we are focusing on the analysis of the effects on the Global Warming Potential (GWP) for the water based recycling process based on a reuse of NMC material in new batteries.
It is possible to reduce the ecological damage of the manufacturing process of Li-Ion battery cells even with little amounts of recovered cathode material that is used for the production of new battery cells. Based on the suggestion that 95% of the NMC cathode material can be recovered by the hydrometallurgical recovery and the reuse of 10% within the production of new batteries a reduction of the GWP by 7% ,can be identified for the cathode materials. For other impact categories such as Acidification Potential (AP), Eutrophication Potential (EP), and Photochemical Ozone Creation Potential (POCP), savings of 10%, 11%, and 8 % can be achieved respectively.
The studied water based recycling process can be quoted as environment-friendly and leads to a reduction of all impact categories by a re-use of 10% recovered NMC material. Based on this knowledge an additional recycling on substance level is recommended.