Automotive Innovation最新文献

Real driving scenarios, due to occlusions and disturbances, provide disordered and noisy measurements, which makes the task of multi-object tracking quite challenging. Conventional approach is to find deterministic data association; however, it has unstable performance in high clutter density. This paper proposes a novel probabilistic tracklet-enhanced multiple object tracker (PTMOT), which integrates Poisson multi-Bernoulli mixture (PMBM) filter with confidence of tracklets. The proposed method is able to realize efficient and robust probabilistic association for 3D multi-object tracking (MOT) and improve the PMBM filter’s continuity by smoothing single target hypothesis with global hypothesis. It consists of two key parts. First, the PMBM tracker based on sets of tracklets is implemented to realize probabilistic fusion of disordered measurements. Second, the confidence of tracklets is smoothed through a smoothing-while-filtering approach. Extensive MOT tests on nuScenes tracking dataset demonstrate that the proposed method achieves superior performance in different modalities.

Autonomous vehicles are prone to instability when the motors of the four-wheel independent-driving electric vehicles fail at high driving speed on low-adhesion roads. To improve the vehicle tracking performance in the expected path and ensure vehicle stability when the motor fails, this paper designs an integrated path-following and passive fault-tolerant controller. The path-following controller is designed to improve the vehicle path-following performance based on model predictive control (MPC), while the passive fault-tolerant controller is used to ensure vehicle stability when the motor fails. First, a vehicle dynamic model is established and simplified, and an MPC controller based on a state-space equation is designed. Then, taking the motor fault as a fault factor, a first-order sliding mode fault-tolerant controller is developed. The first-order sliding mode fault-tolerant controller takes the vehicle’s yaw rate and sideslip angle into account. Furthermore, to address the chattering problem of the traditional first-order sliding mode fault-tolerant controller, a second-order sliding mode fault-tolerant controller with a disturbance observer is developed. Finally, the developed controller is tested using the Simulink/Carsim platform and applied to a Raspberry Pi 4B for controller hardware-in-the-loop experiment. Simulation and experiment results show the practicability and effectiveness of the proposed integrated control strategy.

With the continuous improvement of automated driving technology, how to evaluate the performance of an automated driving system is attracting more and more attention. Meanwhile, with the creation of scenario-based test methods, the traditional evaluation index based on a single test can no longer meet the requirements of high-level safety verification for automated driving system, and the performance evaluation of such a system in logical scenarios will be the mainstream. Based on the scenario-based test method and Turing test theory, a performance evaluation method for an automated driving system in the whole parameter space of a logical scenario is proposed. The logical scenario parameter space is partitioned according to the risk degree of concrete scenario, and the evaluation process in different zones are determined. Subsequently, the anthropomorphic index in the safe zone and the collision-avoidance index in the danger zone are defined by comparing test results of human driving and ideal vehicle motion. Taking front vehicle low-speed and cut-out scenarios as examples, two automated driving algorithms are tested in the virtual environment, and the test results are evaluated both by the proposed method and by human observation. The results show that the results of the proposed method are consistent with the subjective feelings of humans; additionally, it can be applied to scenario-based tests and the verification process of an automated driving system.

This paper presents a comprehensive evaluation of system interactions in a battery electric vehicle caused by temperature sensitivity of permanent magnet synchronous machines (PMSM). An analytical model of a PMSM considering iron losses and thermal impact is implemented on a field programmable gate array suitable for hardware-in-the-loop testing. By the presented virtual prototyping approach, different machine characteristics defined by the design are used to parameterize the analytical model. The investigated temperature effect is understood as an interacting influence between machine characteristics and control, which are investigated in terms of torque generation, voltage utilization and efficiency under closed-loop condition in a vehicle environment. In particular, using a surface permanent magnet rotor and an interior permanent magnet rotor, the performance of both machine designs is analyzed by varying temperature-adjusted feedforward control strategies on the basis of a driving cycle from a racetrack. The comparison shows that the machine design with surface-mounted magnets is associated with higher temperature sensitivity. In this case, the temperature consideration in the feedforward control provides a (14,%) loss reduction in closed-loop vehicle test operation. It can be summarized that the electromagnetic torque is less sensitive to a temperature variation with increasing reluctance. The presented development approach demonstrates the impact of interactions in electric powertrains without the need of real prototypes.

In recent years, camera- and lidar-based 3D object detection has achieved great progress. However, the related researches mainly focus on normal illumination conditions; the performance of their 3D detection algorithms will decrease under low lighting scenarios such as in the night. This work attempts to improve the fusion strategies on 3D vehicle detection accuracy in multiple lighting conditions. First, distance and uncertainty information is incorporated to guide the “painting” of semantic information onto point cloud during the data preprocessing. Moreover, a multitask framework is designed, which incorporates uncertainty learning to improve detection accuracy under low-illumination scenarios. In the validation on KITTI and Dark-KITTI benchmark, the proposed method increases the vehicle detection accuracy on the KITTI benchmark by 1.35% and the generality of the model is validated on the proposed Dark-KITTI dataset, with a gain of 0.64% for vehicle detection.

With the continuous development of automotive intelligent networking and autonomous driving technologies, the number of in-vehicle electronic systems and applications is increasing rapidly. This change increases the amount of data to be transmitted in the vehicle and puts forward further requirements of higher speed and safety for in-vehicle communication. Traditional vehicle bus technologies are no longer sufficient to meet today’s high-speed transmission requirements, in which copper cables are used extensively, resulting in serious electromagnetic interference (EMI). Vehicle optical fiber communication technology, besides greatly improving the data transmission rate, has the advantages of anti-EMI, reducing cable space and vehicle mass. This paper first presents the motivation of applying vehicle optical fiber communication technology and reviews the development history of vehicle optical fiber communication technology. Then, the paper researches the development trend of automotive electrical and electronic architecture (EEA), from distributed EEA to domain centralized EEA and zone-oriented EEA. Based on the discussion of the development trend of automotive EEA, an EEA based on vehicle optical fiber communication technology is proposed. Finally, the key points and future directions of vehicle optical fiber communication technology research are highlighted, including vehicle multi-mode optical fiber technology, vehicle optical fiber network protocol, and topology.

Reliable and timely detection of an internal short circuit (ISC) in lithium-ion batteries is important to ensure safe and efficient operation. This paper investigates ISC detection of parallel-connected battery cells by considering cell non-uniformity and sensor limitation (i.e., no independent current sensors for individual cells in a parallel string). To characterize ISC-related signatures in battery string responses, an electro-thermal model of parallel-connected battery cells is first established that explicitly captures ISC. By analyzing the data generated from the electro-thermal model, the distribution of surface temperature among individual cells within the battery string is identified as an indicator for ISC detection under the constraints of sensor limitations. A convolutional neural network (CNN) is then designed to estimate the ISC resistance by using the cell surface temperature and the total capacity of the string as inputs. Based on the estimated ISC resistance from CNN, the strings are classified as faulty or non-faulty to guide the examination or replacement of the battery. The algorithm is evaluated in the presence of signal noises in terms of accuracy, false alarm rate, and missed detection rate, verifying the effectiveness and robustness of the proposed approach.

Power battery technology is essential to ensuring the overall performance and safety of electric vehicles. Non-invasive characteristic curve analysis (CCA) for lithium-ion batteries is of particular importance. CCA can provide characteristic data for further applications such as state estimation and thermal runaway warning without disassembling the batteries. This paper summarizes the characteristic curves consisting of incremental curve analysis, differential voltage analysis, and differential thermal voltammetry from the perspectives of exploring the aging mechanism of batteries and constructing the data-driven model. The process of quantitative analysis of battery aging mechanism is presented and the steps of constructing data-driven models are induced. Moreover, the recent progress and application of the main features and methodologies are discussed. Finally, the applicability of battery CCA is discussed by converting non-quantifiable battery information into transportable data covering macrostate and micro-reaction information. Combined with the cloud-based battery management platform, the above-mentioned battery characteristic curves could be used as a valuable dataset to upgrade the next-generation battery management system design.

Energy security planning is fundamental to safeguarding the traffic operation in large-scale events. To guarantee the promotion of green, zero-carbon, and environmental-friendly hydrogen fuel cell vehicles (HFCVs) in large-scale events, a five-stage planning method is proposed considering the demand and supply potential of hydrogen energy. Specifically, to meet the requirements of the large-scale events’ demand, a new calculation approach is proposed to calculate the hydrogen amount and the distribution of hydrogen stations. In addition, energy supply is guaranteed from four aspects, namely hydrogen production, hydrogen storage, hydrogen delivery, and hydrogen refueling. The emergency plan is established based on the overall support plan, which can realize multi-dimensional energy security. Furthermore, the planning method is demonstrative as it powers the Beijing 2022 Winter Olympics as the first “green” Olympic, providing both theoretical and practical evidence for the energy security planning of large-scale events. This study provides suggestions about ensuring the energy demand after the race, broadening the application scenarios, and accelerating the application of HFCVs.