In order to provide a basis for the establishment of the leakage fault diagnosis method of lithium-ion battery electrolyte, search the parameters closely related to battery leakage, electrolyte leakage fault simulation experiments were designed, DC impedance tests as well as electrochemical impedance spectra tests were performed on leaking batteries and normal batteries, and the kinetic characteristics of the battery were analyzed based on the second-order equivalent circuit model parameters and the relaxation time distribution method. A set of DC impedance and AC impedance parameters to characterize the leakage fault was proposed, which provided a basis for the diagnosis of the leakage fault of lithium-ion batteries.

To solve the resonance problem of the thermal management integrated module assembly after the installation of the motor, based on modal solution theory of elastic mechanics, a 3D model of the assembly was established, utilizing the motor acceleration test data as the excitation, and the response parameters under multimodal modes were obtained by Finite Element Analysis (FEA). The analysis discovers that there is a risk of resonance between module assembly and single pump at low frequencies, corresponding to the 5th, the 8th order models of module assembly and the 4th, the 10th order models of single pump respectively. The resonance region is concentrated around 1 310 Hz and 1 900 Hz at high frequencies. Based on the above analysis, the centroid position of the module assembly and the inner wall structure of the pump volute were optimized and verified experimentally, the research shows that the sound pressure level of the optimized assembly structure is reduced by 7.2 dB(A), 0.2 dB(A), 2.3 dB(A), 7.1 dB(A), 2.3 dB(A) in the X+, X-, Y+,Y- and Z+ directions respectively. Compared with the noise test data, the discrepancy is less than 5%, and the resonance is suppressed effectively.

In order to address the issue of high heating energy consumption in electric vehicles in winter, which affects their driving range, this paper studied the impact of surface wettability on the frosting performance of heat exchangers in electric vehicle heat pump systems. Through microscale quantitative characterization of the condensation and frosting processes on superhydrophobic surfaces, it was found that the coalescence induced droplet jumping phenomenon on superhydrophobic surfaces is an important factor for antifrosting. In order to achieve the effect of inhibiting frosting on full-size heat exchangers, superhydrophobic treatment was carried out on the full size heat exchanger using a hydrothermal method and a low surface energy treatment method. Antifrosting tests under heating condition were conducted, the results show that compared with the untreated original heat exchanger, the superhydrophobic heat exchanger had a frost formation time extension of over 70%, and a reduction of over 40% in the number of defrosting times per unit time.

In order to explore the energy loss and component efficiency of Electric Vehicle (EV) at low-temperature, so as to optimize the energy structure of EVs, this paper considered 2 common use scenarios and designed 2 test conditions, namely one-time complete driving test and intermittent multiple driving test. The energy flow test was carried out under the conditions of -10 ℃ and -20 ℃, and the energy flow analysis model was established. In this paper, the efficiency and energy consumption features of major components, such as power batteries at low temperatures, were quantitatively analyzed. It has been found that the high energy consumption of electric heaters and the limited battery recycling ability are the main factors leading to the reduction of the low-temperature driving range of EVs.

The argon power cycle hydrogen engine encounters limitations due to the occurrence of abnormal combustion phenomena resulting from the high specific heat ratio of the working mass. Consequently, the thermal efficiency enhancement potential of this concept remains underutilized. A combination of simulation and experimental analysis was employed to investigate the impact of port water injection on the thermodynamic parameters of the argon power cycle hydrogen engine as well as the influence of knock suppression. By employing a comprehensive approach that incorporates hydrogen direct injection, intake boost and simultaneous optimization of ignition, hydrogen injection and water injection strategies, of a notable improvement in the maximum indicated thermal efficiency, reaching 62.41% (gross indicated thermal efficiency 58.62%), was ultimately achieved.

To investigate the motion posture, submarining tendency and the effect of submarining on abdominal injury of child occupant in highly reclined seats in frontal collision, the dynamic simulation was conducted in the ECE R129 frontal collision trolley, with the validated child booster seat model and 6-year-old PIPER child biomechanical model in various sitting postures. The criterion for assessing the risk of child submarining was developed based on the anterior superior iliac spine of pelvis. The results indicate that when the seat back inclination exceeds 35° in the 50 km/h frontal collision, child occupant submarines, the abdominal compression and viscous index increase by 234% and 527% respectively and the maximum first principal strain of the liver increases by 45%, there is a risk of AIS 3 injury in the abdomen, measures to inhibit pelvis rotation of can diminish the inclination of submarining.

By summarizing the laws, regulations and standards formulated in China and foreign countries during the development of intelligent and connected vehicles, this paper sorted out the standardization construction system of intelligent and connected vehicles, which included 4 parts: admission, term definition, testing system and accident liability of intelligent and connected vehicles. Through the interpretation of standards, the understanding of the evolution of relevant laws and regulations in the process of the transformation of traditional vehicles to intelligent and connected vehicles was deepened, the technical requirements and social problems faced by the current intelligent and connected vehicle industry were clarified.

The existing passive safety related standard restricts the development of new intelligent vehicle technologies. To solve this problem, this research team developed China construction scheme of intelligent vehicles passive safety standards and the development roadmap according to the revision of the relevant passive safety standards of intelligent vehicles by the international standard organizations and the American standards institution, and then took one national mandatory standards as an example. This research can provide necessary support for the applicability developing and revising China’s passive safety standards.

In order to realize the accurate prediction of the trajectory of surrounding vehicles, a driving intention recognition and trajectory prediction model based on graph neural network and Gated Recurrent Unit (GRU) was designed by using deep learning method. The driving intention recognition model constructed the interaction relationship between vehicles into a space-time graph, used the graph neural network to learn its interaction rules and used Softmax function to calculate the probability of different driving intentions. The trajectory prediction model adopted an encoded-decoded GRU network and the encoder encoded the vehicle history trajectory information and fused the recognized driving intention information and then realized trajectory prediction through the decoder. Finally, the Next Generation Simulation (NGSIM) dataset was used to train and verify the model and the results show that the proposed model can better identify the driving intention of the vehicle, and the vehicle trajectory prediction model considering the driving intention can effectively improve the prediction accuracy.

Based on the multi-point preview model, a trajectory tracking model optimized by Grated Re-circulated Unit (GRU) neural network was designed to improve trajectory tracking accuracy of intelligent vehicle. Firstly, on the vehicle’s 2 degree of freedom model, 3 preview trajectory tracking models were established based on the preview theory. The simulation results show that the multi-point preview model has the best tracking effect. Then, the parameters such as multi-point preview lateral displacement deviation and steering wheel angle were used as the inputs of GRU neural network. After training, the optimized steering wheel angle was as output to control the driving direction of the vehicle. The verification results show that the trajectory tracking model optimized by GRU has better tracking effect under double shift path and S-curve path.