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The operation management of electric Taxi fleets requires cooperative optimization of Charging and Dispatching. The challenge is to make real-time decisions about which is the optimal charging station or passenger for each vehicle in the fleet. With the rapid advancement of Vehicle Internet of Things (VIOT) technologies, the aforementioned challenge can be readily addressed by leveraging big data analytics and machine learning algorithms, thereby contributing to smarter transportation systems. This study focuses on optimizing real-time decision-making for charging and dispatching in large-scale electric taxi fleets to improve their long-term benefits. To achieve this goal, a spatiotemporal decision framework using Bi-level optimization is proposed. Initially, a deep reinforcement learning-based model is built to estimate the value of charging and order dispatching under uncertainty.

Tire forces and moments play an important role in vehicle dynamics and safety. X-by-wire chassis components including active suspension, electronic powered steering, by-wire braking, etc can take the tire forces as inputs to improve vehicle’s dynamic performance. In order to measure the accurate dynamic wheel load, most of the researches focused on the kinematic parameters such as body longitudinal and lateral acceleration, load transfer and etc. In this paper, the authors focus on the suspension system, avoiding the dependence on accurate mass and aerodynamics model of the whole vehicle. The geometry of the suspension is equated by the spatial parallel mechanism model (RSSR model), which improves the calculation speed while ensuring the accuracy. A suspension force observer is created, which contains parameters including spring damper compression length, push rod force, knuckle accelerations, etc., combing the kinematic and dynamic characteristic of the vehicle.

With the continuous popularization of electric vehicles (EVs), ensuring the best performance of EVs has become a significant concern, and lithium-ion power batteries are considered as the essential storage and conversion equipment for EVs. Therefore, it is of great significance to quickly evaluate the state of power batteries. This paper investigates a fast state estimation method of power batteries oriented to after-sales and maintenance. Based on the battery equivalent circuit model and heuristics optimization algorithm, the battery model parameters, including the internal ohmic and polarization resistance, can be identified using only 30 minutes of charging or discharging process data without full charge or discharge. At the same time, the proposed method can directly estimate the state of charge (SOC) and maximum available capacity of the battery without knowing initial SOC information.

The formation is a crucial step in the production process of lithium-ion batteries (LIBs), during which the solid electrolyte interphase (SEI) is formed on the surface of the anode particles to passivate the electrode. It determines the performance of the battery, including its capacity and lifetime. A meticulously designed formation protocol is essential to regulate and optimize the stability of the SEI, ultimately achieving the optimal performance of the battery. Current research on formation protocols in lithium-ion batteries primarily focuses on temperature, current, and voltage windows. However, there has been limited investigation into the influence of different initial pressures on the formation process, and the evolution of cell pressure during formation remains unclear. In this study, a pressure-assisted formation device for lithium-ion pouch cells is developed, equipped with pressure sensors.

Ultra-lean combustion of GDI engine could achieve higher thermal efficiency and lower NOx emissions, but it also faces challenges such as ignition difficulties and low-speed flame propagation. In this paper, the sparked-spray is proposed as a novel ignition method, which employs the spark to ignite the fuel spray by the cooperative timing control of in-cylinder fuel injection and spark ignition and form a jet flame. Then the jet flame fronts propagate in the ultra-lean premixed mixture in the cylinder. This combustion mode is named Sparked-Spray Induced Combustion (SSIC) in this paper. Based on a 3-cylinder 1.0L GDI engine, a 3D simulation model is established in the CONVERGE to study the effects of ignition strategy, compression ratio, and injection timing on SSIC with a global equivalence ratio of 0.50. The results show it is easier to form the jet flame when sparking at the spray front because the fuel has better atomization and lower turbulent kinetic energy at the spray front.

The problem of keeping the stable starting performance of diesel engine under high altitude and low temperature conditions has been done a lot of research in the field of diesel engine, but there is a lack of research on extreme conditions such as above 2000 meters above sea level and below 0°C. Aiming at solving the cold start problem of diesel engine in extreme environment, a set of chamber system of cold start environment diesel engine was constructed to simulate environment of 3000m altitude and -20°C. A series of experimental research was conducted on cold start efficiency optimization strategy of a certain type of diesel engine at 3000m altitude and -20°C. In parallel, a diesel engine model was constructed through Chemkin to explore the influence of the three parameters of compression ratio, stroke length, and fuel injection advance angle on the first cold start cycle of diesel engine at 4000m altitude and -20°C.

In order to scrutinize the timing variables impacting the combustion performance and emissions of the Port Fuel Injection hydrogen engine (PFI-H2ICE), a model of a four-cylinder hydrogen engine is meticulously built utilizing the 1D software GT-POWER. The effect of excess air coefficients and timing strategies (including the intake valve opening timing (IVO), the start of injection timing (SOI), and ignition timing) is analyzed in this study. The main conclusions are as follows: The hydrogen engine remold from the Isuzu JE4N28 nature gas engine manifests a lean combustion threshold ranging between 2.0 and 2.5. Notably, advancing intake valve opening timing by 20°CA has proven beneficial to the brake thermal efficiency (BTE) of the hydrogen engine while reducing the NOx emissions by a substantial margin, and advancing intake valve opening timing bears the virtue of strengthen the positive influence of the start of injection timing upon the engine's combustion performance.

A DMS500 engine exhaust particle size spectrometer was employed to characterize the effects of injection strategies on particulate emissions from a turbocharged gasoline direct injection (GDI) engine. The effects of operating parameters (injection pressure, secondary injection ratio and secondary injection end time) on particle diameter distribution and particle number density of emission were investigated. The experimental result indicates that the split injection can suppress the knocking tendency at higher engine loads. The combustion is improved, and the fuel consumption is significantly reduced, avoiding the increase in fuel pump energy consumption caused by the 50 MPa fuel injection system, but the delayed injection increases particulate matter emissions.

The passive pre-chamber (PC) is valued for its jet ignition (JI) and is suitable for wide use in the field of gasoline direct injection (GDI) for small passenger cars, which can improve the performance of lean combustion. However, the intake, exhaust, and ignition combustion stability of the engine at low speed is a shortcoming that has not been overcome. Changing the structural design to increase the fluidity of the main chamber (MC) and pre-chamber (PC) may reduce jet ignition performance, affecting engine dynamics. This investigation is based on non-uniformly nozzles distributed passive pre-chamber, which is adjusted according to the working medium exchange between PC and MC. The advantages and disadvantages of the ignition mode of PC and SI in the target engine speed range are compared through optical experiments on a small single-cylinder GDI engine.

As a novel ignition technology, pre-chamber ignition can enhance ignition energy, promote flame propagation, and augment turbulence. However, this technology undoubtedly faces challenges, particularly in the context of emission regulations. Of this study, the transient characteristics of combustion and emissions in a hybrid electric vehicle (HEV) gasoline engine with active pre-chamber ignition (PCI) under the first combustion cycle of quick start are focused. The results demonstrate that the PCI engine is available on the first cycle for lean combustion, such as lambda 1.6 to 2.0, and exhibit particle number (PN) below 7×107 N/mL at the first cycle. These particles are predominantly composed of nucleation mode (NM, <50 nm) particles, with minimal accumulation mode (AM, >50 nm) particles.

In recent years, the burgeoning applications of hydrogen fuel cells have ignited a growing trend in their integration within the transportation sector, with a particular focus on their potential use in multi-rotor drones. The heightened mass-based energy density of fuel cells positions them as promising alternatives to current lithium battery-powered drones, especially as the demand for extended flight durations increases. This article undertakes a comprehensive exploration, comparing the performance of lithium batteries against air-cooled fuel cells, specifically within the context of multi-rotor drones with a 3.5kW power requirement. The study reveals that, for the specified power demand, air-cooled fuel cells outperform lithium batteries, establishing them as a more efficient solution.

With the extension of intelligent vehicles from individual intelligence to group intelligence, intelligent vehicle platoons on intercity highways are important for saving transportation costs, improving transportation efficiency and road utilization, ensuring traffic safety, and utilizing local traffic intelligence [1]. However, there are several problems associated with vehicle platoons including complicated vehicle driving conditions in or between platoon columns, a high degree of mutual influence, dynamic optimization of the platoon, and difficulty in the cooperative control of lane change. Aiming at the dual-column intelligent vehicle platoon control (where “dual-column” refers to the vehicle platoon driving mode formed by multiple vehicles traveling in parallel on two adjacent lanes), a multi-agent model as well as a cooperative control method for lane change based on null space behavior (NSB) for unmanned platoon vehicles are established in this paper.

With the development of brake-by-wire technology, electro-hydraulic composite braking technology came into being. This technology distributes the total braking force demand into motor regenerative braking force and hydraulic braking force, and can achieve a high energy recovery rate. The existing composite braking control belongs to single-channel control, i.e., the four wheel braking pressures are always the same, so the hydraulic braking force distribution relationship of the front and rear wheels does not change. For single-axle-driven electric vehicles, the additional regenerative braking force on the driven wheels will destroy the original braking force distribution relationship, resulting in reduced braking efficiency of the driven wheels, which are much easier to lock under poor road adhesion conditions.

Argon Power Cycle (APC) is an innovative future potential power system for high efficiency and zero emissions, which employs an Ar-O2 mixture rather than air as the working substance. However, APC hydrogen engines face the challenge of knock suppression. Compared to hydrogen, methane has a better anti-knock capacity and thus is an excellent potential fuel for APC engines. In previous studies, the methane is injected into the intake port. Nevertheless, for lean combustion, the stratified in-cylinder mixture formed by methane direct injection has superior combustion performances. Therefore, based on a methane direct injection engine at compression ratio = 9.6 and 1000 r/min, this study experimentally investigates the effects of replacing air by an Ar-O2 mixture (79%Ar+21%O2) on thermal efficiencies, loads, and other combustion characteristics under different excess oxygen ratios. Meanwhile, the influences of varying the methane injection timing are studied.

With the increase of motor speed and the deterioration of operating environment, it is more difficult to predict the transient temperature field (TTF). Meanwhile, it is difficult to obtain the temperature test dataset of key nodes under various complete road conditions, so the cost of bench test or real vehicle test is high. Therefore, it is of great significance to establish a high fidelity, lightweight temperature prediction model which can be applied to real vehicle thermal management for ensuring the safe and stable operation of motor. In this paper, a physical model simulating electromagnetic-heat-flow multi-physical coupling of permanent magnet synchronous motor (PMSM) in electric drive gearbox (EDG) is established, and the correctness of the model is verified by the actual EDG bench test.

Alternating current (AC) heating is an efficient and homogeneous manner to warm Lithium-ion batteries (LIBs) up. The integrated design of AC heating combined with the motor drive circuit has been studied by many scholars. However, the problems of excessive heating frequency (>1kHz) and zeros torque output of the motor during the heating process have not been solved. High-frequency AC excitation may be detrimental to the battery because the effect of high-frequency AC excitation on the state of health of the battery is unknown. In addition, although the zero-torque output can be realized by controlling the q-axis current to zero, the torque ripple is still difficult to eliminate in a real-world application. To further solve the above problems, the motor’s neutral conductor is pulled out and connected to a large capacitor to increase the current amplitude of the AC heating at low frequencies.

With the growing prosperity of the long-distance freight and urban logistics industry, the demand for cargo trucks is gradually increasing. The connecting bracket is the critical connecting part of the truck chassis, which bears the load transmitted by the road excitation and reduces the damage to the frame caused by the load. However, the occurrence of rough road conditions is inevitable in heavy-duty transportation. In this paper, road durability tests and fatigue life analysis are carried out on the original structure to ensure the safety of the vehicle. Based on the known boundary and load constraints, a lightweight and high-performance structure is obtained through size optimization, as the original structure cannot meet the performance requirements. Firstly, the road test was conducted on the truck where the original bracket structure is located.

The intersection is recognized as the most dangerous area because of the restricted road structures and indeterminate traffic regulations. Therefore, according to the Vehicle-to-everything (V2X) communication, Intelligent Transportation Systems (ITS), and Digital Twin data, we present a potential energy field method to establish the general characteristics of intersection traffic safety, evaluate the safety situation of intersection and assist intersection traffic participants in passing through the intersection safer and more efficient. The resulting potential energy field method is established by the contour line of traffic participants' potential energy, which is constructed as a superposition of disparate energies, such as boundary potential energy, body potential energy, and velocity potential energy. The intersection traffic safety is evaluated by the potential energy field characteristic of simultaneous intersection traffic participants.

To overcome the shortcoming that vehicles with multiple steering modes need to switch steering modes at parking or very low speeds, a dynamic switch method of steering modes based on MOEA/D (Multi-objective Evolutionary Algorithm Based on Decomposition) was proposed for 4WID-4WIS (Four Wheel Independent Drive-Four Wheel Independent Steering) electric vehicle, considering the smoothness of dynamic switch, the lateral stability of the vehicle and the energy economy of tires. First of all, the vehicle model of 4WID-4WIS was established, and steering modes were introduced and analyzed. Secondly, the conditions for the dynamic switch of steering modes were designed with the goal of stability and safety. According to different constraints, the control strategy was formulated to obtain the target angle of the active wheels. Then aiming at the smoothness of the dynamic switch, the active wheel angle trajectory was constructed based on the B-spline theory.