Automotive Innovation

Reliability Growth Test Planning and Verification of Commercial Vehicles

Liu Jicheng, Huang Liyan, Zhou Renmin, Markus Volmer

2019, 2(4): 328-337. doi:10.1007/s42154-019-00082-0

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Reliability and durability are two important technical indicators in automobile research and development. A research-and-design and testing organization can increase inherent quality attributes by adopting a systematic approach based on statistical tools and clearly defined processes. The process affects the design phase, validation through testing, and quality assurance in production. On the basis of reliability growth theory and the Duane model, this study established an estimation method for the definition of the target mileage and specific test cycles in reliability growth testing. A construction method for defining test conditions was proposed that adopts the theory of the design of experiments. The simulation was conducted under a variety of typical test conditions including differing operation times, loads, and logistics modes to predict customer use and detect failures. Failure cases were then analyzed in detail. At the same time, a reliability growth prediction model was established on the basis of the initial test data and used for test process tracking and risk control.

Experimental Study on the Combustion and Energy Flows of Vehicle Engine Under NEDC of Cold Start

Zhichao Zhao, Zaiqiang Meng, Lu Li, Shuqian Wang, Jianqin Fu, Jingping Liu

2019, 2(4): 314-327. doi:10.1007/s42154-019-00078-w

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To enhance the fuel economy of a vehicle powered by a gasoline engine under road conditions, an energy flow test of a vehicle was performed experimentally under the New European Driving Cycle of cold start. The energy distributions and related influencing factors were analyzed using the test data. Results show that the effective power and thermal efficiency are mainly affected by the engine load except in the early stage of the New European Driving Cycle. Because of the retarded CA50 and longer CA10-90, the effective thermal efficiency is lower in the early phase of driving conditions. Initially, the heat transfer loss mainly comprises the loss of the heating, ventilation, and air conditioning system. The radiator then plays the major role, with its percentage affected by the engine load and decreasing under the extra-urban driving cycle. The exhaust gas loss is decided by the temperature and flow rate of the exhaust gas, while its percentage is mainly affected by the temperature of the exhaust gas. In the early phase of driving conditions, the retarded spark advance angle leads to a higher temperature of the exhaust gas and a greater exhaust gas loss. The pumping loss and its percentage are mainly determined by the engine speed under the urban driving cycle, and both decrease under the extra-urban driving cycle except at maximum vehicle speed.

Ion Current Features of HCCI Combustion in a GDI Engine

Guangyu Dong, Liguang Li, Denghao Zhu, Jun Deng

2019, 2(4): 305-313. doi:10.1007/s42154-019-00074-0

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Homogeneous charge compression ignition (HCCI) gasoline engines have the merit of good fuel economy and very low NOx emissions. The ion current signal in a gasoline direct injection-HCCI engine is the main focus of this paper. Experiments showed that the ion signal is significant correlated with the combustion status. Fuel spray and air–fuel mixture motions significant decrease the signal-to-noise ratio of the ion current signal. However, the current waveforms still vary regularly as the boundary conditions change, and their phases have significant linear relationships with the combustion phases. By combining the analysis with cylinder pressure data, the current can be used to effectively detect the combustion phase when the air fuel mixture is not lean. When the mixture gets leaner, the signal amplitude diminishes dramatically, and the linear correlation decreases substantially. The ratio of two-stage fuel injection has a strong effect on signal amplitude and combustion stability, and the linear relationship between the signal and combustion characteristics becomes insignificant as the pre-injection fuel amount decreases. A reaction kinetics analysis of the mechanism for the ion current signal in the HCCI engine explains the experimental observations.

High-Efficiency and Clean Combustion Natural Gas Engines for Vehicles

Fubai Li, Zhi Wang, Yunfei Wang, Boyuan Wang

2019, 2(4): 284-304. doi:10.1007/s42154-019-00075-z

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Natural gas engines have become increasingly important in transportation applications, especially in the commercial vehicle sector. With increasing demand for high efficiency and low emissions, new technologies must be explored to overcome the performance limitations of natural gas engines such as limits on lean or dilute combustion, unstable combustion, low burning velocity, and high emissions of CH4 and NOx. This paper reviews the progress of research on natural gas engines over recent decades, concentrating on ignition and combustion systems, mixture preparation, the development of different combustion modes, and after-treatment strategies. First, the features, advantages, and disadvantages of natural gas engines are introduced, following which the development of advanced ignition systems, organization of highly turbulent flows, and the preparation of high-reactivity mixtures in spark ignition engines are discussed with a focus on pre-chamber jet ignition, combustion chamber design, and H2-enriched natural gas combustion. Third, the progress in natural gas dual-fuel engines is highlighted, including the exploration of new combustion modes, the development of novel pilot fuels, and the optimization of combustion control strategies. The fourth section discusses after-treatment systems for natural gas engines operating in different combustion modes. Finally, conclusions and future trends in the development of high-efficiency and clean combustion in natural gas engines are summarized.

3D Vehicle Detection Based on LiDAR and Camera Fusion

Yingfeng Cai, Tiantian Zhang, Hai Wang, Yicheng Li, Qingchao Liu, Xiaobo Chen

2019, 2(4): 276-283. doi:10.1007/s42154-019-00083-z

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Nowadays, the deep learning for object detection has become more popular and is widely adopted in many fields. This paper focuses on the research of LiDAR and camera sensor fusion technology for vehicle detection to ensure extremely high detection accuracy. The proposed network architecture takes full advantage of the deep information of both the LiDAR point cloud and RGB image in object detection. First, the LiDAR point cloud and RGB image are fed into the system. Then a high-resolution feature map is used to generate a reliable 3D object proposal for both the LiDAR point cloud and RGB image. Finally, 3D box regression is performed to predict the extent and orientation of vehicles in 3D space. Experiments on the challenging KITTI benchmark show that the proposed approach obtains ideal detection results and the detection time of each frame is about 0.12 s. This approach could establish a basis for further research in autonomous vehicles.

A Comparative Study of Charging Voltage Curve Analysis and State of Health Estimation of Lithium-ion Batteries in Electric Vehicle

Xuebing Han, Xuning Feng, Minggao Ouyang, Languang Lu, Jianqiu Li, Yuejiu Zheng, Zhe Li

2019, 2(4): 263-275. doi:10.1007/s42154-019-00080-2

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Lithium-ion (Li-ion) cells degrade after repeated cycling and the cell capacity fades while its resistance increases. Degradation of Li-ion cells is caused by a variety of physical and chemical mechanisms and it is strongly influenced by factors including the electrode materials used, the working conditions and the battery temperature. At present, charging voltage curve analysis methods are widely used in studies of battery characteristics and the constant current charging voltage curves can be used to analyze battery aging mechanisms and estimate a battery’s state of health (SOH) via methods such as incremental capacity (IC) analysis. In this paper, a method to fit and analyze the charging voltage curve based on a neural network is proposed and is compared to the existing point counting method and the polynomial curve fitting method. The neuron parameters of the trained neural network model are used to analyze the battery capacity relative to the phase change reactions that occur inside the batteries. This method is suitable for different types of batteries and could be used in battery management systems for online battery modeling, analysis and diagnosis.

Comprehensive Analysis and Optimization of Dynamic Vibration-Absorbing Structures for Electric Vehicles Driven by In-Wheel Motors

Yechen Qin, Zhenfeng Wang, Kang Yuan, Yubiao Zhang

2019, 2(4): 254-262. doi:10.1007/s42154-019-00079-9

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Distributed-drive electric vehicles (EVs) replace internal combustion engine with multiple motors, and the novel configuration results in new dynamic-related issues. This paper studies the coupling effects between the parameters and responses of dynamic vibration-absorbing structures (DVAS) for EVs driven by in-wheel motors (IWM). Firstly, a DVAS-based quarter suspension model is developed for distributed-drive EVs, from which nine parameters and five responses are selected for the coupling effect analysis. A two-stage global sensitivity analysis is then utilized to investigate the effect of each parameter on the responses. The control of the system is then converted into a multiobjective optimization problem with the defined system parameters being the optimization variables, and three dynamic limitations regarding both motor and suspension subsystems are taken as the constraints. A particle swarm optimization approach is then used to either improve ride comfort or mitigate IWM vibration, and two optimized parameter sets for these two objects are provided at last. Simulation results provide in-depth conclusions for the coupling effects between parameters and responses, as well as a guideline on how to design system parameters for contradictory objectives. It can be concluded that either passenger comfort or motor lifespan can be reduced up to 36% and 15% by properly changing the IWM suspension system parameters.

Driving Space for Autonomous Vehicles

Diange Yang, Xinyu Jiao, Kun Jiang, Zhong Cao

2019, 2(4): 241-253. doi:10.1007/s42154-019-00081-1

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Driving space for autonomous vehicles (AVs) is a simplified representation of real driving environments that helps facilitate driving decision processes. Existing literatures present numerous methods for constructing driving spaces, which is a fundamental step in AV development. This study reviews the existing researches to gain a more systematic understanding of driving space and focuses on two questions: how to reconstruct the driving environment, and how to make driving decisions within the constructed driving space. Furthermore, the advantages and disadvantages of different types of driving space are analyzed. The study provides further understanding of the relationship between perception and decision-making and gives insight into direction of future research on driving space of AVs.