To more accurately define and decompose charging time metrics while effectively improving charging efficiency, the paper focuses on the core scenario of BEV charging and conducts an in-depth investigation into the key influencing factors during DC fast charging from 10% to 80% State Of Charge (SOC) under ambient temperature conditions. From the perspective of strategy research, this paper systematically analyzes the impact of charging time generated by current compensation and battery over-temperature control factors. The results show that the charging time from 10% to 80% SOC at ambient temperature is reduced by 8.9 min, and the charging capacity within 20 min is increased by 15.4 A·h.

To shorten the charging time and optimize the charging strategy for new energy vehicles, the paper transforms the charging strategy optimization into a constrained path-finding problem using machine learning algorithms. The ant colony algorithm is employed to achieve rapid convergence, enabling the global co-optimization of the charging strategy and thermal management system capability. A segmented charging control strategy is proposed by comprehensively accounting for the effects of cell temperature rise and the vehicle thermal management system. Real-vehicle test results demonstrate that the optimized fast-charging time is reduced from 30 min to 27.5 min without battery overheating, verifying the effectiveness of the proposed strategy and providing a reference for advancing fast-charging technology in electric vehicles.

In response to the challenges of cost, lightweight design and safety posed to vehicle-side charging connection systems by high-power charging for electric vehicles, the paper proposes an on-board liquid-cooled high-voltage charging connection system scheme. It explores the development of vehicle-side liquid-cooled charging harnesses and components, reusing the vehicle's existing cooling system to dissipate heat. This approach enables the use of small-gauge conductors to achieve a continuous current-carrying capacity exceeding 800 A, meeting megawatt-level charging power demands. The proposed solution not only delivers ultra-high currentcarrying capacity but also fulfills the requirements for low cost and lightweight design in vehicle applications. Coupled with a coordinated charging-cooling control strategy, it significantly reduces electro-thermal safety risks in the vehicle-side charging connection system.

To investigate the differences and influencing factors between charging efficiency and battery heating efficiency under varying ambient temperatures, the paper proposes a testing methodology for evaluating charging efficiency and heating efficiency of Battery Electric Vehicle (BEV) under low-temperature conditions. Research is conducted on power battery charging efficiency and heating efficiency across different temperature operating conditions. The study quantitatively analyzes the attenuation patterns of charging efficiency and energy losses during the heating process, thereby providing a reference for optimizing the low temperature charging performance of BEV.

To mitigate the impact of centralized Electric Vehicle (EV) charging on the power grid, this study proposes an optimal EV scheduling strategy driven by dynamic electricity pricing, aiming to reduce users'comprehensive charging costs and achieve peak shaving and valley filling of the grid. First, a travel chain model based on naive Bayes is constructed to generate detailed information on future trips of users in the target area using historical travel data. Then, user trip information is combined with an established energy consumption model and a charging decision model to obtain the spatiotemporal distribution of regional charging load. Later, a dynamic electricity pricing model is established to describe the relationship between electricity prices and grid load. Subsequently, with the objective of minimizing charging costs and battery aging costs, a genetic algorithm is adopted to solve and update the EV charging and discharging strategies in real time throughout the day. Simulation results show that the travel chain data generated Its optimization strategy can effectively reduce user costs, achivev grid peak shaving and valley filling and provide a theoretical basis and control method for efficient coordination between electric vehicles and the power grid.

To address issues such as the current imbalance between supply and demand of charging facilities, the increasing pressure on the grid due to unregulated charging and insufficient collaboration among multiple stakeholders, the paper clarifies the path of multi-value co-creation and the matching rules between supply and demand in the integrated operation of "vehicle-pile-grid-source" within the EV charging service network. It systematically elaborates on the current development status of this integrated operation. Firstly, the matching rules between EV charging supply and demand are analyzed, and the current research status of the value co-creation model is expounded from the perspective of "source-vehicle". Secondly, from the perspective of"vehicle-pile", the impact of the co-construction model of charging piles and charging scheduling strategies on grid stability is analyzed. Meanwhile, technologies related to network structure layout optimization under the interactive response of "pile-grid" are analyzed, and the strategies for efficiency improvement and quality enhancement under the collaborative operation of "grid-vehicle" and the current status of economic optimization and scheduling of the grid are further elaborated. Finally, the challenges faced by the integrated operation of "vehicle-charging pile-grid-source" in the EV charging service network are summarized, and future developments are prospected.

To address the problems of strong subjectivity in subjective evaluation and disconnection between objective data and subjective perception in the drivability assessment of commercial vehicles equiped with Automated Mechanical Transmissions (AMTs), a comprehensive drivability evaluation method based on the AVL-Drive test system is proposed. By designing test schemes for typical driving conditions, the AVL-Drive system is used to accurately collect multi-dimensional objective data such as vehicle speed, acceleration, and engine speed. Combined with a standardized subjective evaluation scale, a quantitative correlation model between subjective perception and objective indicators is established. The test results show that this method can realize the objective characterization of core drivability sub-items including starting smoothness and shift quality, effectively identify the drivability shortcomings of AMT commercial vehicles, provide a clear technical path for rearch and development optimization.

To investigate the influence of different traffic flow conditions on vehicle driving characteristics and urban road testing, a statistical analysis is conducted on dynamic parameters such as vehicle speed, gear position, throttle opening, accelerator pedal speed, brake master cylinder pressure, braking frequency, and steering wheel angle, based on real vehicle CAN bus data collected during peak and off-peak hours. The results show that during peak hours, vehicles operate for extended periods at low speeds and in low gears, with significantly higher frequencies of start-stop transitions, accelerating and braking operations, and more frequent activations of driver assistance systems. In contrast, during off-peak hours, vehicles predominantly operate at medium to high speeds and in medium to high gears, with relatively higher power loads and smoother operational stability. The high-frequency, high-load conditions during peak hours are more suitable for verifying the reliability and durability of the vehicle powertrain, braking system, and driver assistance functions in congested environments, while off-peak hours are more conducive to evaluating the overall performance of vehicles under medium-to high-speed conditions.

Aiming at the main problem of the imbalance between supply and demand of shared cars in terms of time and space, by collecting the usage data of shared car users in Beijing, the high-frequency usage stations and peak hours are analyzed and obtained. Design a questionnaire based on the three dimensions of socio-economic attributes, time of car use and space of car use, and go to the high-frequency stations during the high-frequency hours to carry out field surveys on the users. The results show that most of the car sharing users in Beijing are male office workers who own private cars; the time of using the car is concentrated in the weekdays during 7: 00~9: 00, during 17: 00~19: 00, and in bad weather; the location of using the car is mostly near the transport facilities and business districts, which is a clear feature of commuting; and the 5 qualitative factors, including age and occupation, and six quantitative factors, including frequency of use, affect the satisfaction of the users significantly. Strengthening vehicle scheduling during morning and evening peak hours and inclement weather, setting up more one-way return stations in inner-ring business districts, and lowering the cost of time-share models are the key solutions to the problem.