In order to accurately evaluate the dark current of pure electric bus and avoid vehicle failure to start due to insufficient battery power, 2 models of pure electric buses were selected, the power supply mode and vehicle dark current of their on-board electrical equipment were analyzed systematically. At the same time, on the basis of summarizing the conventional dark current measurement methods, the long-scale measurement method was proposed, using the electric power tester and measurement module to build a detection scheme to record the dark current and complete the real vehicle test. The results of theoretical analysis and experimental data comparison show that the method based on long scale measurement can accurately evaluate the dark current value of pure electric bus and control it in a reasonable range, then guide the calculation of vehicle outage time.

A hierarchical control strategy based on Radial Basis Function Neural Network (RBFNN) and Stochastic Dynamic Programming (SDP) was proposed for Plug-in Hybrid Electric Vehicle (PHEV) queue. Firstly, the powertrain structure and mathematical model of PHEV were analyzed in detail, then a hierarchical control framework was constructed. The upper layer adopted RBFNN to train driving data derived from Model Predictive Control (MPC) to generate the speed tracking controller. According to the information of the speed and power demand transmitted from the upper layer, a Markov chain model was established for the lower layer controller, the Markov chain model can realize the optimal energy distribution between PHEV traction battery and the engine based on the SDP theory. The simulation results show that compared with CD/CS strategy and rule-based strategy, the energy consumption of PHEVs in the queue is significantly reduced while ensuring safe driving under high-speed conditions based on the proposed strategy.

A solution was proposed to address the issue of low generalization ability of diagnostic models for electric vehicle power battery faults caused by sparse data, which utilized a data augmentation method based on Generative Adversarial Networks (GAN). According to the augmented data, a fault diagnosis scheme was designed using the Random Forest (RF) model combined with the Bayesian Optimization (BO) method to form a GAN-RF-BO battery fault diagnosis framework. The proposed fault diagnosis approach was compared with the common Multilayer Perceptron (MLP), Support Vector Machine (SVM) and Gradient Boosting Decision Tree (GBDT) model on a real fault dataset. The results show that the accuracy of the proposed method is improved by 19.66%, 19.71% and 16.31% compared to the MLP, SVM, and GBDT models respectively. The GAN-RF-BO framework can better utilize sparse data to troubleshoot problems with power 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.

A battery fault online diagnosis method based on Kalman filtering and feature indexing was proposed for the faults such as thermal runaway and internal short circuit in the battery pack. Firstly, data noise reduction was performed based on historical data and Kalman filtering method to effectively remove voltage anomalies and a feature indexing method was proposed to extract and amplify the voltage characteristics between battery pack cells. Finally, a fault value calculation method based on cosine similarity was proposed in order to reduce the false alarm due to battery pack inconsistency and to automatically detect and locate the faulty battery online. Verification in cloud-based vehicle data shows that the proposed battery fault diagnosis algorithm based on Kalman filtering and feature indexing can effectively detect faulty batteries and provide early warning.

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 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.

In order to reveal the nail penetration safety characteristics of power lithium-ion batteries for electric vehicles, an experimental platform was built for 18650/21700 power lithium-ion batteries. With the help of the classical internal short circuit model based on the electronic flow direction of the internal and external circuits of the batteries, the effects of associated influencing factors including different State of Charge (SOC), nail speed, nail diameter and battery size on the safety characteristics of battery nail penetration were explored. The results show that the higher the state of charge, the faster the nail speed and the larger the nail diameter and battery size are, the higher the risk of thermal runaway of battery nail penetration is. When the SOC of 18650/21700 power lithium-ion battery is in the range of 40% and 20% respectively, the battery has good nail penetration safety.

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.

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.