Automotive Innovation最新文献

Efficient detection of performance limits is critical to autonomous driving. As autonomous driving is difficult to be realized under complicated scenarios, an improved genetic algorithm-based evolution test is proposed to accelerate the evaluation of performance limits. It conducts crossover operation at all positions and mutation several times to make the high-quality chromosome exist in candidate offspring easily. Then the normal offspring is selected statistically based on the scenario complexity, which is designed to measure the difficulty of realizing autonomous driving through the Analytic Hierarchy Process. The benefits of modified cross/mutation operators on the improvement of scenario complexity are analyzed theoretically. Finally, the effectiveness of improved genetic algorithm-based evolution test is validated after being applied to evaluate the collision avoidance performance of an automatic parallel parking system.

With the fast-developing deep learning models in the field of autonomous driving, the research on the uncertainty estimation of deep learning models has also prevailed. Herein, a pyramid Bayesian deep learning method is proposed for the model uncertainty evaluation of semantic segmentation. Semantic segmentation is one of the most important perception problems in understanding visual scene, which is critical for autonomous driving. This study to optimize Bayesian SegNet for uncertainty evaluation. This paper first simplifies the network structure of Bayesian SegNet by reducing the number of MC-Dropout layer and then introduces the pyramid pooling module to improve the performance of Bayesian SegNet. mIoU and mPAvPU are used as evaluation matrics to test the proposed method on the public Cityscapes dataset. The experimental results show that the proposed method improves the sampling effect of the Bayesian SegNet, shortens the sampling time, and improves the network performance.

Lithium-ion batteries (LIBs) are commonly used in electric vehicles (EVs) due to their good performance, long lifecycle, and environmentally friendly merits. Heating LIBs at low temperatures before operation is vitally important to protect the battery from serious capacity degradation and safety hazards. This paper reviews recent progress on heating methods that can be used onboard. The existing methods are divided into two categories, namely external heating methods and internal heating methods, mechanisms, advantages and limitations of each method are systematically reviewed. Then, the rates of temperature rise, energy consumptions, and maximum temperature gradient of different methods are quantitatively summarized to compare the heating performances of each method. In addition, features related to the onboard application of each method are qualitatively compared, which is essential for the rapid cold start of EVs in frigid weather. Finally, prospects of external and internal heating methods are given. This paper aims to provide researchers and engineers with guidelines about how to select a method based on their requirements and application environments.

Over the past decade, the electric vehicle industry of China has developed rapidly, reaching one of the highest technological levels in the world. Nevertheless, most electric buses currently serve urban areas, being unsuitable for all-climate operations. In response to the objective of massively adopting electric vehicles for transportation during all the events of the 2022 Beijing Winter Olympics, a dual-motor coaxial propulsion system for all-climate electric vehicles is proposed. The system aims to meet operating requirements such as high speed and adaptability to mountainous roads under severely cold environments. The system provides three operating modes, whose characteristics are analyzed under different conditions. In addition, dual-motor collaborative control strategy with collaborative gearshift and collaborative power distribution is proposed to eliminate power interruption during gearshift process and achieve intelligent power distribution, thus improving the gearshift quality and reducing energy consumption. Finally, gear position calibration for all-climate operation and proper gearshift is introduced. Experimental results demonstrate the advantages of the proposed dual-motor coaxial propulsion system regarding gearshift compared with the conventional single-motor automatic transmission.

A high-speed motor in a drive system causes several challenges to the reliability of the mechanical parts of electric vehicles and leads to issues with noise, vibration and harshness (NVH). Thus, a two-speed powershift transmission is considered an effective way to improve the dynamic, economic and comfort performance of electric vehicles. A newly designed dual-pump hydraulic control system for a two-speed powershift transmission with two wet clutches is presented, in which the mechanical oil pump is linearly affected by the vehicle speed and the electric oil pump is controllable. By integrating the dynamic model of the hydraulic system into one of the powertrains with a two-speed transmission, a co-simulation dynamic model is proposed. To satisfy the flow and pressure demand of the hydraulic system, a dual-pump control strategy is presented, in which the electric oil pump is controlled by the mechanical oil pump following the minimum energy consumption principle. The World Light Vehicle Test Procedure (WLTP) cycle simulation results show that the energy consumption of the proposed hydraulic system can be reduced by 58.2% compared to the previous single-pump system developed by the authors with a constant main-line pressure control strategy. On the basis, the best configuration of the two pumps can further reduce the energy consumption of the hydraulic system by 23.2% compared to that of two-oil pumps with preset displacement.

The lithium-ion batteries have drawn much attention as the major energy storage system. However, the battery state estimation still suffers from inaccuracy under dynamic operational conditions, with the unstable environmental noise influencing the robustness of estimation. This paper presents a 5th-order cubature Kalman filter with improvements on adaptivity for real-time state-of-charge estimation. The second-order equivalent circuit model is developed for describing the characteristics of battery, and parameter identification is carried out according to particle swarm optimization. The developed method is validated in stable and dynamic conditions, and simulation results show a satisfactory consistency with the experimental results. The maximum estimation error under static conditions is less than 3% and the maximum error under dynamic conditions is 5%. Numerical analysis indicates that the method has better convergence and robustness than the traditional method under the disturbances of initial error, which demonstrates the potential for EV applications in harsh environments. The proposed method shows application potential for both online estimations and cloud-computing system, indicating its diverse application prospect in electric vehicles.

This paper proposes a hierarchical sizing method and a power distribution strategy of a hybrid energy storage system for plug-in hybrid electric vehicles (PHEVs), aiming to reduce both the energy consumption and battery degradation cost. As the optimal size matching is significant to multi-energy systems like PHEV with both battery and supercapacitor (SC), this hybrid system is adopted herein. First, the hierarchical optimization is conducted, when the optimal power of the internal combustion engine is calculated based on dynamic programming, and a wavelet transformer is introduced to distribute the power between the battery and the SC. Then, the fuel economy and battery degradation are evaluated to return feedback value to each sizing point within the hybrid energy storage system sizing space, obtaining the optimal sizes for the battery and the SC by comparing all the values in the whole sizing space. Finally, an all-hardware test platform is established with a fully active power conversion topology, on which the real-time control capability of the wavelet transformer method and the size matching between the battery and the SC are verified in both short and long time spans.

Universal challenge lies in torque feedback accuracy for steer-by-wire systems, especially on uneven and low-friction road. Therefore, this paper proposes a fusion method based on Kalman filter that combines a dynamics-reconstruction method and disturbance observer-based method. The dynamics- reconstruction method is designed according to the vehicle dynamics and used as the prediction model of the Kalman filter. While the disturbance observer-based method is performed as an observer model of the Kalman filter. The performance of all three methods is comprehensively evaluated in a hardware-in-the-loop system. Experimental results show that the proposed fusion method outperforms dynamics reconstruction method and disturbance observer-based method. Specifically, compared with the dynamics-reconstruction method, the root mean square error is reduced by 36.58% at the maximum on the flat road. Compared with the disturbance observer-based method, the root mean square error is reduced by 39.11% at the maximum on different-friction and uneven road.

The development of fuel cell vehicles (FCVs) has a major impact on improving air quality and reducing other fossil-fuel-related problems. DC-DC boost converters with wide input voltage ranges and high gains are essential to fuel cells and DC buses in the powertrains of FCVs, helping to improve the low voltage of fuel cells and “soft” output characteristics. To build DC-DC converters with the desired performance, their topologies have been widely investigated and optimized. Aiming to obtain the optimal design of wide input range and high-gain DC-DC boost converter topologies for FCVs, a review of the research status of DC-DC boost converters based on an impedance network is presented. Additionally, an evaluation system for DC-DC topologies for FCVs is constructed, providing a reference for designing wide input range and high-gain boost converters. The evaluation system uses eight indexes to comprehensively evaluate the performance of DC-DC boost converters for FCVs. On this basis, issues about DC-DC converters for FCVs are discussed, and future research directions are proposed. The main future research directions of DC-DC converter for FCVs include utilizing a DC-DC converter to realize online monitoring of the water content in FCs and designing buck-boost DC-DC converters suitable for high-power commercial FCVs.

To meet the challenges in software testing for automated vehicles, such as increasing system complexity and an infinite number of operating scenarios, new simulation methods must be developed. Closed-loop simulations for automated driving (AD) require highly complex simulation models for multiple controlled vehicles with their perception systems as well as their surrounding context. For the realization of such models, different simulation domains must be coupled with co-simulation. However, widely supported model integration standards such as functional mock-up interface (FMI) lack native support for distributed platforms, which is a key feature for AD due to the computational intensity and platform exclusivity of certain models. The newer FMI companion standard distributed co-simulation protocol (DCP) introduces platform coupling but must still be used in conjunction with AD co-simulations. As part of an assessment framework for AD, this paper presents a DCP compliant implementation of an interoperable interface between a 3D environment and vehicle simulator and a co-simulation platform. A universal Python wrapper is implemented and connected to the simulator to allow its control as a DCP slave. A C-code-based interface enables the co-simulation platform to act as a DCP master and to realize cross-platform data exchange and time synchronization of the environment simulation with other integrated models. A model-in-the-loop use case is performed with the traffic simulator CARLA running on a Linux machine connected to the co-simulation master xMOD on a Windows computer via DCP. Several virtual vehicles are successfully controlled by cooperative adaptive cruise controllers executed outside of CARLA. The standard compliance of the implementation is verified by exemplary connection to prototypic DCP solutions from 3rd party vendors. This exemplary application demonstrates the benefits of DCP compliant tool coupling for AD simulation with increased tool interoperability, reuse potential, and performance.