The probability of electric vehicle rollover accident can be effectively reduced by shortening the prediction time interval and improving the prediction accuracy. Based on a multilayer neural network, an improved time-to-rollover method is presented in this paper. Firstly, the force model of vehicle rollover is established and analyzed where the structure and mass of a battery box have an important influence on the occurrence of rollover. Then, the rollover indexes considering hyperparameters are divided into five categories, and the multi-layer neural network is used to simplify the algorithm structure of the time to rollover, and quickly calculate the operating state parameters with a variation step size in real time. Finally, the influence of the hyperparameters on the prediction results of neural network is studied, and higher efficiency is obtained by comparing with traditional methods.
As an essential sector in the world's future automotive industry, new energy vehicles will introduce significant differences into China's energy, environmental, economic, science and technology, and societal development. In order to better support development decision making and ensure healthy and sustainable development of the industry, this research group has thoroughly studied the influences that the new energy vehicles industry will bring to China. We affirm that this industry has great strategic significance for energy security, emission reduction, environmental conservation, and industry transformation and upgrading for China. After providing a study of supportive policy and of the product technology level of the new energy vehicles industry (both domestic and overseas), and a summary and evaluation of the pros and cons of China's automotive industry, we offer proposals for strategic positioning, route selection, and cultivation priority for the future development of China's new energy vehicles industry. Keywords: new energy vehicles; great significance; China-world comparison; strategic routes; key projects; policy proposal 2.1. Reducing oil import dependency and ensuring national energy security In 2014, vehicle's oil consumption in China was about 2.5×10 8 t
Electrochemical impedance spectroscopy (EIS) was used to study the micro-overcharge cycle damage mechanism of Lithium-ion batteries (LIBs). Micro-overcharge cycle experiments of LIBs were carried out, and the capacity fading of LIBs under different charging cut-off voltages were analyzed. It was found that the capacity fading rate of LIBs increased with the rising of overcharge cut-off voltages and the increasing of cycle numbers. The EIS results show that the main damage pattern of LIBs during micro-overcharge cycle is the active lithium loss when the cut-off voltage is between 4.3 V and 4.4 V. Lithium loss accounts for more than 80% damage proportion when LIBs cycling for more than 20 cycles.
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