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It is well known that lithium-ion batteries pose a certain safety risk. The thermal runaway of a cell and the subsequent thermal propagation through the battery are considered particularly dangerous. Effective solutions for their mitigation are therefore of great interest. Previous studies have shown the significant influence
of a cell’s state of charge (SOC) on its behavior during thermal runaway. This relation may be exploitable in a battery pack to improve its safety. This study aims to assess the leverage of active SOC reduction in the imminent threat of thermal runaway. Implementing such a technique could become feasible with the emergence of reconfigurable battery systems. Four experiments were conducted, each with a module of three fresh 63 Ah high energy pouch cells in a spring-loaded bracing. The experiments studied different stationary SOC configurations, uniform (100% and 60%) and non-uniform (100%–60%–100% and 100%–20%–100%). The results indicate that thermal propagation is substantially delay (87 s) by discharging a cell in its path. The SOC reduction primarily decreases the maximum temperature of the respective cell. Further effects are a calmer thermal runaway and prolonged propagation time within the cell as well as to the next cell. In comparison, the SOC reduction has little impact on the cell’s own triggering time, as the triggering time is mainly determined by the thermal energy transferred from the preceding cell and hence by its SOC. Furthermore, the analysis of the experimental data (temperature, voltage, pressure, video) gives insights into the propagation of thermal runaway through the individual layers of a cell. With reference to the position of a cell relative to the origin of the thermal propagation, a decrease of its mass loss and an increase of its internal propagation time is observed. This effect is attributed to the decreasing module pressure due to progressive loss of material. The assessment shows that active SOC reduction techniques have great leverage for mitigating or even stopping thermal propagation in a battery pack.
Environment perception using camera, radar, and/or lidar sensors has significantly improved in the last few years because of deep learning-based methods. However, a large group of these methods fall into the category of supervised learning, which requires a considerable amount of annotated data. Due to uncertainties in multi-sensor data, automating the data labeling process is extremely challenging; hence,
it is performed manually to a large extent. Even though full automation of such a process is difficult, semiautomation can be a significant step to ease this process. However, the available work in this regard is still very limited; hence, in this paper, a novel semi-automatic annotation methodology is developed for labeling RGB camera images and 3D automotive radar point cloud data using a smart infrastructure-based sensor setup. This paper also describes a new method for 3D radar background subtraction to remove clutter and a new object category, GROUP, for radar-based object detection for closely located vulnerable road users. To validate the work, a dataset named INFRA-3DRC is created using this methodology, where 75% of the
labels are automatically generated. In addition, a radar cluster classifier and an image classifier are developed, trained, and tested on this dataset, achieving accuracy of 98.26% and 94.86%, respectively. The dataset and Python scripts are available at https://fraunhoferivi.github.io/INFRA-3DRC-Dataset/.
Collaborative perception in automated vehicles leverages the exchange of information between agents, aiming to elevate perception results. Previous camera-based collaborative 3D perception methods typically employ 3D bounding boxes or bird's eye views as representations of the environment. However, these approaches fall short in offering a comprehensive 3D environmental prediction. To bridge this gap, we introduce the first method for collaborative 3D semantic occupancy prediction. Particularly, it improves local 3D semantic occupancy predictions by hybrid fusion of (i) semantic and occupancy task features, and (ii) compressed orthogonal attention features shared between vehicles. Additionally, due to the lack of a collaborative perception dataset designed for semantic occupancy prediction, we augment a current collaborative perception dataset to include 3D collaborative semantic occupancy labels for a more robust evaluation. The experimental findings highlight that: (i) our collaborative semantic occupancy predictions excel above the results from single vehicles by over 30%, and (ii) models anchored on semantic occupancy outpace state-of-the-art collaborative 3D detection techniques in subsequent perception applications, showcasing enhanced accuracy and enriched semantic-awareness in road environments.
Fault testing in the production line of automotive traction machines is essential to ensure the desired lifetime. Since repetitive partial discharges (PDs) caused by anomalies in the insulation system lead to premature breakdowns of electrical machines, a reliable PD detection is of great importance. This paper proposes deep learning (DL) methods to improve the discrimination of PD from background noise in comparison with the state-of-the-art amplitude based PD detection in the production line. First, a systematic data extraction and labeling procedure is introduced to obtain correctly labeled datasets from arbitrary PD measurements. In addition, datasets are enhanced with low signal-to-noise ratio PD pulses by applying a special data augmentation approach. 13 different convolutional, recurrent and fully connected neural networks are compared for various time-frequency representations of the input signals. Hyperparameters for input transform, network topology and solver are optimized for all 13 combinations to ensure a fair case study. As a result, the two-dimensional convolutional neural network with continuous wavelet transform achieves the best accuracy of around 99.76% on a test dataset of PD signals originating from previously not utilized test objects. All DL models considered in this comparison outperform the state-of-the-art threshold-based PD classification. Even for PD events with an amplitude close to the noise level, the detection rate is still around 95% for the best network. Furthermore, without applying the proposed data augmentation procedure, the DL models investigated are not able to distinguish small PD pulses from noise.
Sensor data fusion is essential for environmental perception within smart traffic applications. By using multiple sensors cooperatively, the accuracy and probability of the perception are increased, which is crucial for critical traffic scenarios or under bad weather conditions. In this paper, a modular real-time capable multi-sensor fusion framework is presented and tested to fuse data on the object list level from distributed automotive sensors (cameras, radar, and LiDAR). The modular multi-sensor fusion architecture receives an object list (untracked objects) from each sensor. The fusion framework combines classical data fusion algorithms, as it contains a coordinate transformation module, an object association module (Hungarian algorithm), an object tracking module (unscented Kalman filter), and a movement compensation module. Due to the modular design, the fusion framework is adaptable and does not rely on the number of sensors or their types. Moreover, the method continues to operate because of this adaptable design in case of an individual sensor failure. This is an essential feature for safety-critical applications. The architecture targets environmental perception in challenging time-critical applications. The developed fusion framework is tested using simulation and public domain experimental data. Using the developed framework, sensor fusion is obtained well below 10 milliseconds of computing time using an AMD Ryzen 7 5800H mobile processor and the Python programming language. Furthermore, the object-level multi-sensor approach enables the detection of changes in the extrinsic calibration of the sensors and potential sensor failures. A concept was developed to use the multi-sensor framework to identify sensor malfunctions. This feature will become extremely important in ensuring the functional safety of the sensors for autonomous driving.
Evaluating the Aging-Induced Voltage Slippery as Cause for Float Currents of Lithium-ion Cells
(2023)
This paper provides a comprehensive exploration of float current analysis in lithium-ion batteries, a promising new testing method to assess calendar aging. Float currents are defined as the steady-state trickle charge current after a transient part. In the literature, a correlation to capacity loss was reported. Assuming the float current compensates for the voltage decay over time and is linked to calendar aging, effects from voltage slippery must be considered. The dU/dQ analysis suggests solely a loss of active lithium. Therefore, we investigate the solid electrolyte interphase (SEI) growth as the general aging mechanism to explain the origin of float currents. Our results show that the voltage slippery theory holds true within the low to middle test voltage ranges. However, the theory’s explanatory power begins to diminish at higher voltage ranges, suggesting the existence of additional, yet unidentified, factors influencing the float current. A shuttle reaction or lithiation of the cathode by electrolyte decomposition are the most promising alternative aging mechanisms at high voltages. The paper proposes a unique voltage slippery model to check for correlations between aging mechanisms, the float current test and the check-up test. For a better understanding, test strategies are proposed to verify/falsify the aging mechanisms beyond SEI.
Cameras mounted on intelligent roadside infrastructure units and vehicles can detect humans on the road using state-of-the-art perception algorithms, but these algorithms are presently not trained to distinguish between human and adult. However, this is a crucial requirement from a safety perspective because a child may not follow all the traffic rules, particularly while crossing the road. Moreover, a child may stop or may start playing on the road. In such situations, the separation of a child from an adult is necessary. The work in this paper targets to solve this problem by applying a transfer-learning-based neural network approach to classify child and adult separately in camera images. The described work is comprised of image data collection, data annotation, transfer learning-based model development, and evaluation. For the work, Mask-RCNN (region-based convolutional neural network) with different backbone architectures and two different baselines are investigated and the perception precision of the architectures after transfer-learning is compared. The results reveal that the best performing trained model is able to detect and classify children and adults separately in different road scenarios with segmentation mask AP (average precision) of 85% and bounding box AP of 92%.
An AC-battery or multilevel inverter used to increase safety and flexibility is realizable by switching the cells and modules on and off in a defined way and thus can replace the bidirectional converter. Assessing possible additional aging due to switching, the results of a previous study for a high-power optimized cell showed no influence on the current rates or the switching. In this paper, a highly energy-optimized LG 18650-cell is investigated to discuss the influence of switching during the charge and discharge process, respectively, as well as combining both processes together with clear performance differences when applying higher charge and discharge currents. Moreover, the influence of switching is discussed for the two frequencies (50 Hz and 10 kHz) and different duty cycles. The aging is analyzed by capacity loss and resistance increase, by dV/dQ analysis, and by electrochemical impedance spectroscopy. We found no clear negative influence of switching but a positive effect if the cells are switched during charge. The best performance is found for switching during charge as well as during discharge. The cell aging during switching is clearly determined by the average and not the maximum current applied. This work shows no negative effects of multilevel inverter applications on the tested cells.
Due to their high efficiency and advanced battery management capability, cascaded multilevel inverters are an exciting option for battery electric powertrains. A promising, new and highly efficient cascaded multilevel inverter is the Parallel Enhanced Commutation Integrated Nested Multilevel Inverter. The inverter, with four semiconductor switches per submodule, can reconfigure individual battery cells in series and parallel and generate positive and negative phase voltages in regular four-quadrant operation. Therefore, emerging degrees of freedom in battery management and inverter operation must be managed and mapped into a specific Switching State for every switch. As controlling the high number of switches is safety-relevant, this publication profoundly explains the inverter’s functionality. We introduce a Switching Function that accepts easy-to-understand functional states as input, simplifying research on higher-level control algorithms and advanced single-cell battery-management capabilities. As the Switching Function guarantees safe operation and the correct contribution of every cell to the overall functionality of the inverter, it enables researchers to confidently use and thereby accelerate research on the promising new topology. The method we describe is fast, simple, deterministic and designed to convert setpoint specifications into an executable Switching Pattern. We prove that our Switching Function is operable on an FPGA with a twenty-kilohertz setpoint update operating a 17-level inverter.
Long-Term Self-Discharge Measurements and Modelling for Various Cell Types and Cell Potentials
(2023)
Self-discharge of lithium-ion cells leads to voltage decay over time. In this work, the self-discharge was measured at 30 °C for three cell types at various voltage levels for about 150 days in a constant voltage mode determining the current at a high precision (float current). All cells exhibit a transient part leading to a steady-state, which is no longer influenced by reversible effects. To study the effect of the check-ups on the steady-state float current, the cells, interrupted and not interrupted by check-ups, were compared. The results indicate that both the transient processes and steady-state currents are highly reproducible. In the first period of the float current, the polarization dominates the measured current, followed by the anode overhang effect dominating the process for a period of 5–30 days. After the decline of both processes, a mostly constant steady-state in the order of µA is observed. The check-up interruption generally shows no apparent effect on the reached steady-state and results only in an extended settling time. A model to simulate the transient process and steady-state of float currents was developed. The model shows a high accuracy in reproducing the results and identifying the time needed to reach the steady-state.
Apparent Aging during Accelerated Cycling Aging Test of Cylindrical Silicon Containing Li-Ion Cells
(2023)
Accelerated cyclic aging tests are very important for research and industry to quickly characterize lithium-ion cells. However, the accentuation of stress factors and the elimination of rest periods lead to an apparent capacity fade, that can be subsequently recovered during a resting phase. This effect is attributed to the inhomogeneous lithium distribution in the anode and is observable with differential voltage analysis (DVA). We tested cylindrical 18,650 cells with Li(NixCoyAlz)O2-graphite/silicon chemistry during two cycling and resting phases. The capacity, the pulse resistance, the DVA, and the capacity difference analysis are evaluated for cells cycled at different average SOC and current rates. An apparent capacity loss of up to 12% was reported after 200 FCE for cells cycled under the presence of pressure gradients, while only 1% were at low-pressure gradients. The subsequent recovery was up to 80% of the apparent capacity loss in some cases. The impact of silicon cannot be estimated as it shows no features in the dV/dQ curves. We observe a recovery of apparent resistance increase, which is not reported for cells with pure graphite anodes. Finally, we demonstrate the strong impact of apparent aging for the lifetime prediction based on standard accelerated cyclic aging tests.
Three bipolar plates (BPP) comprised of a composite of polypropylene or polyvinylidene fluoride polymer and varying average graphite particle size were studied for application in a vanadium redox flow battery (VRFB). The BPPs were electrochemically aged via 3000 cyclic voltammetry curves in 1.8 M VOSO4 + 2.0 M H2SO4 electrolyte. After every 500th cycle the aging progression was determined by performing cyclic voltammetry on the bipolar plates in 0.1 M H2SO4 solution where the double layer capacitance, the quinone/hydroquinone and the vanadium species redox activity were quantitatively evaluated. Prior to the aging, the composite plates were extensively characterized using various physical methods. The performed studies reveal that the wettability, surface roughness and accessible porosity of the bipolar plates significantly influence their electrochemical stability. Cycling tests in vanadium redox flow single cells at a constant current density of 60 mA cm−2 revealed a close correlation of the cell efficiencies to the electrochemical stability of the bipolar plates. Thus, the proposed electrochemical characterization method can be an effective foresight to predict the applicability of a bipolar plate in a vanadium redox flow battery.
Sensor data fusion is essential for environmental perception within smart traffic applications. By using multiple sensors cooperatively, the accuracy and probability of the perception are increased, which is crucial for critical traffic scenarios or under bad weather conditions. In this paper, a modular real-time capable multi-sensor fusion framework is presented and tested to fuse data on the object list level from distributed automotive sensors (cameras, radar, and LiDAR). The modular multi-sensor fusion architecture receives an object list (untracked objects) from each sensor. The fusion framework combines classical data fusion algorithms, as it contains a coordinate transformation module, an object association module (Hungarian algorithm), an object tracking module (unscented Kalman filter), and a movement compensation module. Due to the modular design, the fusion framework is adaptable and does not rely on the number of sensors or their types. Moreover, the method continues to operate because of this adaptable design in case of an individual sensor failure. This is an essential feature for safety-critical applications. The architecture targets environmental perception in challenging time-critical applications. The developed fusion framework is tested using simulation and public domain experimental data. Using the developed framework, sensor fusion is obtained well below 10 milliseconds of computing time using an AMD Ryzen 7 5800H mobile processor and the Python programming language. Furthermore, the object-level multi-sensor approach enables the detection of changes in the extrinsic calibration of the sensors and potential sensor failures. A concept was developed to use the multi-sensor framework to identify sensor malfunctions. This feature will become extremely important in ensuring the functional safety of the sensors for autonomous driving.
Experimental Investigation of State and Parameter Estimation within Reconfigurable Battery Systems
(2023)
The battery system is one of the most-important, but also -critical components in the electric power-train. The battery’s system states and parameters are commonly tracked by the battery monitoring system. However, in reality, the accuracy of the state and parameter estimation may suffer from insufficient excitation of the system. Since the current states and parameters serve as the basis for many battery management system functions, this might lead to incorrect operation and severe damage. Reconfigurable battery systems allow enhancing the system’s excitation by applying a switching operation. In this contribution, the state and parameter estimation of a reconfigurable battery module were simulated and tested experimentally. Thereby, a low-exciting and a high-exciting drive cycle were compared. Furthermore, the switching patterns were applied to enhance the excitation and, hence, improve the estimation of an extended Kalman filter. The cells were switched via a pulse-width modulation signal, and the influence of frequency and duty cycle variation on the estimation accuracy were investigated. Compared to the low-excitation input, a significant improvement in the estimation of up to 46% for the state of charge and 78% for the internal resistance were achieved. Hereby, low frequencies and duty cycles proved to be particularly advantageous. Switching, however, has only a limited influence on an already highly excited system and may lead to additional aging due to higher heat generation.
This paper presents the joint work from the “HORIS” project, with a focus on pedestrian detection at bus-stops by radar sensors mounted in the infrastructure to support future autonomous driving and protecting pedestrians in critical situations. Two sensor systems are investigated and evaluated. The first based on single radar sensor phase-sensitive raw data analysis and the second based on sensor data fusion of cluster data with two radar sensors using neural networks to predict the position of pedestrians.
The smart infrastructure units can play a vital role to develop smart cities of the future and in assisting automated vehicles on the road by providing extended perception and timely warnings to avoid accidents. This paper focuses on the development of such an infrastructure unit, that is specifically designed for a pedestrian crossing junction. It can control traffic lights at the junction by real-time environment perception through its sensors and can optimize the flow of vehicles and passing vulnerable road users (VRUs). Moreover, it can assist on-road vehicles by providing real-time information and critical warnings via a v2x module. This paper further describes different use-cases of the work, all major hardware components involved in the development of smart infrastructure unit, referred to as an edge, different sensor fusion approaches using the camera, radar, and lidar mounted on the edge for environment perception, various modes of communication including v2x, system design
Pareto optimization of wavelet filter design for partial discharge detection in electrical machines
(2022)
A reliable detection of partial discharges (PDs) in the insulation system of electrical machines is essential to prevent premature breakdowns. Wavelet filters (WFs) are often recommended for denoising this PD measurement data. However, filtering performance strongly depends on numerous settings, which impedes an optimum filter choice. We propose an approach to systematically select the best filter for denoising PD signals. First, representative PD pulses are determined from measurement data by an averaging and a singular value decomposition method. Next, realistic benchmark signals are generated for filter assessment. Further, all possible WF combinations from a set of different mother wavelet selection methods, decomposition trees, and thresholding approaches are evaluated. On the basis of two contradicting objectives, namely mean square error of noise intervals and pulse amplitude distortion, Pareto optimization is applied to choose optimal WF settings. The corresponding filters dominate several common WF proposals from previous literature.
To ensure a reliable and safe operation of battery systems in various applications, the system’s internal states must be observed with high accuracy. Hereby, the Kalman filter is a frequently used and well-known tool to estimate the states and model parameters of a lithium-ion cell. A strong requirement is the selection of a suitable model and a reasonable initialization, otherwise the algorithm’s estimation might be insufficient. Especially the process noise parametrization poses a difficult task, since it is an abstract parameter and often optimized by an arbitrary trial-and-error principle. In this work, a traceable procedure based on the genetic algorithm is introduced to determine the process noise offline considering the estimation error and filter consistency. Hereby, the parameters found are independent of the researcher’s experience. Results are validated with a simulative and experimental study, using an NCA/graphite lithium-ion cell. After the transient phase, the estimation error of the state-of-charge is lower than 0.6% and for internal resistance smaller than 4mΩ while the corresponding estimated covariances fit the error well.