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Reinforcement learning is a promising approach to solve the energy management for hybrid electric vehicles. In this paper, based on the DQN (Deep Q-Network) reinforcement learning algorithm which is widely used at present, double DQN, dueling DQN and learning from demonstration are integrated; states, actions, rewards and the experience pool based on the characteristics of series-parallel multi-speed hybrid powertrain are designed; the hybrid energy management strategy based on D4QN (Double Dueling Deep Q-Network with Demonstrations) algorithm is established. Based on the training results of D4QN algorithm, multi-parameter analysis under state and action space, HCU (Hybrid control unit) application and MIL (Model in-loop) test research are conducted.

The fuel-saving potential of a series hybrid electric vehicle (SHEV) was investigated in this work based on the future goals and technical roadmaps proposed by China's automobile and internal combustion engine (ICE) industry. The genetic algorithm optimization method and dynamic programming energy management strategy are used to optimize the key component parameters of a typical SHEV SUV to improve the fuel economy of the vehicle. Results showed that the fuel consumption of the vehicle would be 3.24 L / 100km in 2035, which is 37.21% less than 5.16 L / 100km in 2020, following the industries’ roadmaps. The results also indicated that the improvement of the ICE’s thermal efficiency is the main reason for the decrease of the vehicle’s fuel consumption. In addition, the improvement of working points and the reduction of energy losses of the key components also contribute to the improvement of the fuel economy.

High altitudes have a significant effect on the real driving emissions (RDE) of vehicles due to lower pressure and insufficient oxygen concentration. In addition, type approval tests for light-duty vehicles are usually conducted at altitudes below 1000 m. In order to investigate the influence of high altitude on vehicles fuel economy and emissions, RDE tests procedure had been introduced in the China VI emission regulations. In this study, the effect of altitude on fuel economy and real road emissions of three light-duty gasoline vehicles was investigated. The results indicated that for vehicles fuel economy, fuel consumption (L/100 km) for the tested vehicles decreased while the mean exhaust temperature increased with an increase in altitudes. Compared to near sea level, the fuel consumption (L/100 km) of the tested vehicle was reduced by up to 23.28%.

The catalyzed diesel particulate filter with Pt and Pd noble metals as the main loaded active components are widely used in the field of automobile engines, but the high cost makes it face huge challenges. Rare earth element doping can improve the soot oxidation performance of the catalyzed diesel particulate filter and provide a new way to reduce its cost. In this paper, thermogravimetric tests and chemical reaction kinetic calculations were used to explore the effect of Pt-Pd catalysts doped Ce, and La rare earth elements on the oxidation properties of soot. The results shown that, among Pt-Pd-5%Ce, Pt-Pd-5%La, and Pt-Pd-5%Ce-5%La catalysts, Pt-Pd-5%La catalyst has the highest soot conversion, the highest low-temperature oxidation speed, and the activation energy is the smallest. Compared with soot, this catalyst reduced T10 and T20 by 82% and 26%, respectively, meaning the catalytic activity of Pt-Pd-5%La catalyst was the best.

As a prerequisite for energy management of hybrid vehicles, the results of speed prediction can optimize the performance of vehicles and improve fuel efficiency. Energy management strategies are usually developed based on standard driving cycles, which are too generalized to show the variability of driving conditions in different time and locations. Therefore, this paper constructs a representative driving cycle based on driving data of the corresponding time and location, used as historical information for prediction. We propose a method to construct the driving cycle based on Markov chain theory before constructing the prediction model. In this paper, multiple prediction methods are compared with traditional parametric methods. The difference in prediction accuracy between multiple prediction methods under the single time scale and multiple time scale were compared, which further verified the advantages of the speed prediction method based on Markov chain theory.

The driving conditions of commercial logistics vehicles have the characteristics of combined urban and suburban roads with relatively fixed mileage and cargo load alteration, which affect the vehicular fuel economy. To this end, an adaptive equivalent consumption minimization strategy (A-ECMS) with vehicle speed and weight recognition is proposed to improve the fuel economy for a range-extender electric van for logistics in this work. The driving conditions are divided into nine representative groups with different vehicle speed and weight statuses, and the driving patterns are recognized with the use of the bagged trees algorithm through vehicle simulations. In order to generate the reference SOC near the optimal values, the optimal SOC trajectories under the typical driving cycles with different loads are solved by the shooting method and the optimal slopes for these nine patterns are obtained.

Numerical analysis of thermal efficiency improvement up to 45% of an 1.8-liter turbocharged direct-injection (DI) gasoline engine was conducted in this study in response to the need of improving vehicle fuel economy. 1D thermodynamics simulations and 3D computational fluid dynamics (CFD) modeling were carried out to investigate the technical approaches for improving engine thermal efficiency. Effects of various technologies on the improvement in the engine performance were evaluated, and then the technical routes to achieve 41% and 45% brake thermal efficiency were summarized, respectively. It is concluded that 41% thermal efficiency can be reached under stoichiometric combustion conditions, while it is expected lean burn technology is needed for the target of 45% thermal efficiency. The effects of high tumble intake flow on accelerating burning speed and of high compression ratio on intensifying knocking were analyzed.

Low heat rejection (LHR) combustion has been recognized as a potential technology for further fuel economy improvement. This paper aims to simulate how the piston top’s thermal barrier coating affects the engine’s thermal efficiency and emissions. Accordingly, a Thin-wall heat transfer model in AVL Fire software was employed. The effects of increasing the piston top surface temperature, comparing different thermal barrier coating material, were simulated at the engine’s rated power operating point, so as the piston top’s surface roughness. In comparison to a standard diesel engine, the indicated thermal efficiency (ITE) could increase by 0.4% when the surface temperature of the piston top changed from 575K to 775K.

Activation loss, mass transfer loss and ohmic loss are the three main voltage losses of the polymer electrolyte membrane fuel cell. While the former two types are relevant to concentration of oxygen in catalyst layer and the later one is associated with the water content in membrane. Distributions of water content and oxygen in a single cell are inconsistent which cause that current densities in each segment of the single cell are different. For the dry inlet gas, the water in the segments near the gas inlet channel will be carried to the segments near the gas outlet channel, which causes high ohmic loss of the segments near the gas inlet channel. In this work, a transfer non-isothermal quasi-three-dimensional model is developed to investigate inconsistency of current densities.

In this paper, in order to avoid the frequent switching of engine operating points and improve the fuel economy during driving, this paper proposes a control strategy for the 4-wheel drive (4WD) hybrid vehicle based on wavelet transform. First of all, the system configuration and the original control strategy of the 4WD hybrid vehicle were introduced and analyzed, which summarized the shortcomings of this control strategy. Then, based on the analyze of the original control strategy, the wavelet transform was used to overcome its weaknesses. By taking advantage over the superiority of the wavelet transform method in multi signal disposition, the demand power of vehicle was decomposed into the stable drive power and the instantaneous response power, which were distributed to engine and electric motor respectively. This process was carried out under different driving modes.

Proton exchange membrane fuel cells (PEMFC), which convert the chemical energy into electrical energy directly through electrochemical reactions, are widely considered as one of the best power sources for new energy vehicles (NEV). Some of the major advantages of a PEMFC include high power density, high energy conversion efficiency, minimum pollution, low noise, fast startup and low operating temperature. The Membrane Electrode Assembly (MEA) is one of the core components of fuel cells, which composes catalyst layers (CL) coated proton exchange membrane (PEM) and gas diffusion layers (GDL). The performance of MEA is closely related to mass transportation and the rate of electrochemical reaction. The MEA plays a key role not only in the performance of the PEMFCs, but also for the reducing the cost of the fuel cells, as well as accelerating the commercial applications. Commercialized large-size MEA directly plays a major role in determining fuel cell stack and vehicle performance.

Due to the advantages of low weight, low emissions and good fuel economy, downsized turbocharged gasoline direct injection (GDI) engines are widely-applied nowadays. However, Low-Speed Pre-Ignition (LSPI) phenomenon observed in these engines restricts their improvement of performance. Some researchers have shown that auto-ignition of lubricant in the combustion chamber has a great effect on the LSPI frequency. To study the auto-ignition characteristics of lubricant, an innovative single droplet auto-ignition test system for lubricant and its mixture is designed and developed, with better accuracy and effectiveness. The experiments are carried out by hanging lubricant droplets on the thermocouple node under active thermo-atmosphere provided by a small “Dibble burner”. The auto-ignition process of lubricant droplets is recorded by a high-speed camera.

In recent years, particulate matters (PM) emissions from gasoline direct injection (GDI) engines have been gradually paid attention to, and the hydrous ethanol has a high oxygen content and a fast burning rate, which can effectively improve the combustion environment. In addition, Exhaust gas recirculation (EGR) can effectively reduce engine NOx emissions, and combining EGR technology with GDI engines is becoming a new research direction. In this study, the effects of hydrous ethanol gasoline blends on the combustion and emission characteristics of GDI engines are analyzed through bench test. The results show that the increase of the proportion of hydrous ethanol can accelerate the burning rate, shorten the combustion duration by 7°crank angle (CA), advance the peak moment of in-cylinder pressure and rate of heat release (RoHR) and improve the combustion efficiency. The hydrous ethanol gasoline blends can effectively improve the gaseous and PM emissions of the GDI engine.

To meet the request of China National 6b emission regulations which will be officially implemented in China, firstly including the RDE emission test limits, the transient emissions on real road condition are paid more attention. A non-plug-in hybrid light-duty gasoline vehicles (HEV) sold in the Chinese market was selected to study real road emissions employed fast response NOx analyzer from Cambustion Ltd. with a sampling frequency of 100Hz, which can measure the missing NO peaks by standard RDE gas analyzer now. Emissions from PEMS were also recorded and compared with the results from fast response NOx analyzer. The concentration of NOx emissions before and after the Three Way Catalyst (TWC) of the hybrid vehicle were also sampled and analyzed, and the working efficiency of the TWC in real road driving process was investigated.

For internal combustion engine systems, lean and diluted combustion is an important technology applied for fuel efficiency improvement. Because of the thermodynamic boundary conditions and the presence of in-cylinder flow, the development of a well-sustained flame kernel for lean combustion is a challenging task. Reliable spark discharge with the addition of enhanced delivered energy is thus needed at certain time durations to achieve successful combustion initiation of the lean air-fuel mixture. For a conventional transistor coil ignition system, only limited amount of energy is stored in the ignition coil. Therefore, both the energy of the spark discharge and the duration of the spark discharge are bounded. To break through the energy limit of the conventional transistor coil ignition system, in this work, an adaptive spark ignition system is introduced. The system has the ability to reconstruct the conductive ion channels whenever it is interrupted during the spark discharge.

The aftertreatment system is indispensable for the removal of the noxious pollutants emitted by diesel engines, whose efficiency depends largely on the exhaust gas temperature. Therefore, this study proposes a thermal management strategy including post injection, intake throttling and late post injection to improve the efficiency of the aftertreatment system for a heavy-duty diesel engine. In the experiments, the effects of main injection, post injection, injection pressure and throttle opening on the exhaust gas temperature at diesel oxidation catalyst (DOC) inlet were studied, with the influence of late post injection on the exhaust gas temperature at DOC outlet also investigated. The results showed that the reasonable control of throttle opening and post injection (such as the adjustment of injection timing and injection quantity) can significantly improve the average temperature at DOC inlet from 237.8°C to 333.6°C in the WHTC, with an increase of 40.3%.

The CO2 reduction request for automotive industry promotes the efforts on the engine thermal efficiency improvement. The goal of this research is to improve the thermal efficiency on an extremely downsized 3-cylinder 1.0 L turbocharged gasoline direct injection engine. Effects of compression ratio, exhaust gas recirculation (EGR), valve timing and viscosity of oil on fuel economy were studied. The results show that increasing compression ratio, from 9.6 to 12, can improve fuel economy at relative low load (below 12 bar BMEP), but has a negative effect at high load due to increased knock intensity. EGR can significantly reduce the pumping loss at low load, optimize combustion phase and reduce exhaust gas temperature. Therefore, the fuel consumption is reduced at all test points. The average brake thermal efficiency (BTE) benefit percentage is 3.47% with 9.6 compression ratio and 5.33 % with 12 compression ratio.

In this study, the real-world NOx and particle emissions of buses burning pure diesel fuel (D100), biodiesel fuel with 20% blend ratio (B20) and liquefied natural gas (LNG) were measured with portable emission measurement system (PEMS). The measurement conducted at 6 constant speed, which ranged from 10km/h to 60 km/h at 10km/h intervals, and a period of free driving condition. The relationship between vehicle specific power (VSP) and NOx/particle emissions of each bus were analyzed. The results show that the change rules of NOx, PN and PM emission factors with the increase of VSP were basically the same for the same bus, but for the bus using different fuel, the change rules may change. In VSP bin 0, the vehicles were mostly in idle condition and the emission factors of NOx, PN and PM of three buses were all in a relatively high level. In low VSP interval, which ranged from bin 0 to bin 4, the emissions of three buses first decreased and then increased with the growth of VSP.

Fuel cell hybrid electric vehicles (FCHEVs) composed of fuel cells and batteries can improve the dynamic response and durability of vehicle propulsion. In addition, braking energy can be recovered by batteries. The energy management strategy (EMS) for distributing the requested power through different types of energy sources plays an important role in FCHEVs. Reasonable power split not only improves vehicle performance but also enhances fuel economy. In this paper, considering the power tracking control strategy which is widely adopted in Advanced Vehicle Simulator (ADVISOR), a constrained nonlinear programming parameter optimization model is established for minimizing fuel consumption. The principal parameters of power tracking control strategy are set as the optimized variables, with the dynamic performance index of FCHEVs being defined as the constraint condition. Then, the genetic algorithm (GA) is applied in the control strategy design for solving the optimization problem.

Intelligent and connected vehicle (ICV) and new energy vehicle (NEV) will be two important directions of the automotive technology in the future, and the coordinated development of these two directions reflects relevantly the higher requirements put forward by nowadays society and people. Through the use of intelligent and connected technology (ICT), NEVs can exchange various traffic information data with the outside world (e.g. other running vehicles, road infrastructure, internet, etc.) in real time, which is so-called Vehicle to Everything (V2X). Based on the further analysis of the mutual traffic information, the vehicles can identify the current driving conditions and predict the future driving conditions effectively, which can realize the real time optimization of the energy management strategies (EMSs) of vehicles’ powertrain system, so as to meet the driving requirements of vehicles under different driving conditions.