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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%.

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.

Increasingly strict emission regulations and unfavorable economic climate bring severe challenges to the energy conservation of marine low-speed engine. Besides traditional methods, the energy and exergy analysis could acknowledge the losses of fuel from a global perspective to further improve the engine efficiency. Therefore, the energy and exergy analysis is conducted for a marine low-speed engine based on the experimental data. Energy analysis shows the exhaust gas occupies the largest proportion of all fuel energy waste, and it rises with the increment of engine load. The heat transfer consumes the second largest proportion, while it is negatively correlated to engine load. The energy analysis indicates that the most effective way to improve the engine efficiency is to reduce the energy wasted by exhaust gas and heat transfer. However, the latter exergy analysis demonstrates that there are other effective approaches to improve the engine efficiency.

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.

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.

The energy management strategies (EMS) for hybrid electric vehicles (HEV) have a great impact on the fuel economy (FE). The Pontryagin's minimum principle (PMP) has been proved to be a viable control strategy for HEV. The optimal costate of the PMP control can be determined by the given information of the driving conditions. Since the full knowledge of future driving conditions is not available, this paper proposed a dynamic optimization method for PMP costate without the prediction of the driving cycle. It is known that the lower fuel consumption the method yields, the more efficiently the engine works. The selection of costate is designed to make the engine work in the high efficiency range. Compared with the rule-based control, the proposed method by the principle of Hamiltonian, can make engine working points have more opportunities locating in the middle of high efficiency range, instead of on the boundary of high efficiency range.

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.

It is well known that engine downsizing is still the main energy-saving technology for spark-ignition direct-injection (SIDI) engine. However, with the continuous increase of the boosting ratio, the gasoline engine is often accompanied by the occurrence of knocking, which has the drawback to run the engine at retarded combustion phasing. Besides, in order to protect the turbine blades from being sintered by high exhaust temperature, the strategies of fuel enrichment are often taken to reduce the combustion temperature, which ultimately leads to a high level of particulate number emission. Therefore, to address the issues discussed above, the port water injection (PWI) techniques on a 1.2-L turbocharged, three-cylinder, SIDI engine were investigated. Measurements indicate that the optimization of spark timing has a significant impact on its performance.

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.

Multiple injection strategies are commonly used in conventional Diesel engines due to the flexibility for optimizing heat-release timing with a consequent improvement in fuel economy and engine-out emissions. This is also desirable in low-temperature combustion (LTC) engines since it offers the potential to reduce unburned hydrocarbon and CO emissions. To better utilize these benefits and find optimal calibrations of split injection strategies, it is imperative that the fundamental processes of multiple injection combustion are understood and computational fluid dynamics models accurately describe the flow dynamics and combustion characteristics between different injection events. To this end, this work is dedicated to the identification of suitable methodologies to predict the multiple injection combustion process.

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.

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.

It has been revealed by researches that lubricant properties have a great effect on the low-speed pre-ignition (LSPI) frequency in downsizing turbocharged direct-injection engines which are developed for better fuel economy. Droplets of lubricant or lubricant-gasoline mixture are considered to be the potential pre-ignition sources. Those droplets fly into the combustion chamber and ignite the gasoline-air mixture. To study lubricant droplets fundamentally, a novel set of droplet auto-ignition system is designed based on a Dibble Burner for this experiment. Influences of metallic additive contents, viscosities, lubricant diluted with gasoline and waste lubricant on the ignition delay of droplets are investigated by testing 12 groups of lubricants or lubricant-gasoline mixture. The equivalent diameter of each droplet generated by micro-syringes is around 2.1 mm. The co-flow temperature varies from 1123 K to 1223 K, and the experiments are carried out at atmospheric pressure.