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Advanced compression ignition combustion system which reduces simultaneously both nitride oxides (NOx) and particulate matter (PM) is a promising approach to meet future emission regulations. In order to achieve advanced compression ignition, flexible fuel injection is required for ultra-early and post-TDC injections, which conventional injector fails to accomplish due to wall-wetting effect. In this work, special injectors with the spray angle of 60 degree are applied on a 4 cylinder mass-production diesel engine without modification of the engine configuration. For application-oriented study, sweep experiments of injection timings and durations, fuel injection pressure and the boost pressure are carried out to investigate the relationships between the control parameters and the engine performance. Model based calibration and real application tests validate the maximum applicable operation range of maximum speed of 2200 RPM and IMEP of 8.0 bar.

In low-temperature environment, heat supply requires considerable energy, which significantly increases energy consumption and shortens the mileage of electric vehicle. In the fuel cell vehicles, waste heat generated by the fuel cell system can supply heat for vehicle. In this paper, a thermal management system is designed for a the fuel cell interurban bus. Thermal management strategy aiming at temperature regulation for the fuel cell stack and the passenger compartment and minimal energy consumption is proposed. System model is developed and simulated based on AMESim and Matlab/Simulink co-simulation. Simulation results show that the fuel cell system can provide about 78 % energy of maximum heat requirement in -20 °C ambient temperature environment.

Energy saving is becoming one of the most important issues for the next generation of commercial vehicles. The fuel consumption limits for commercial vehicles in China have stepped into the third stage, which is a great challenge for heavy duty commercial vehicles. Hybrid technology provides a promising method to solve this problem, of which the dual motor coaxial series parallel configuration is one of the best options. Compared with parallel configuration, the powertrain can not only operate in pure electric or parallel mode, but also can operate in series mode, which shows better flexibility. In this paper, regulations on test cycle, fuel consumption limits and calculation method of the third stage will be introduced in detail. Then, the quasi-static models of the coaxial series parallel powertrain with/without gearbox under C-WTVC (China worldwide transient vehicle cycle) are built. The control strategies are designed based on engine and motor performance.

This study investigates the performance of a diesel engine fueled with naphtha under different load by varying injection parameters and exhaust gas recirculation (EGR) rate. The experiments were conducted on a 1.9-liter common rail diesel engine with a compression ratio of 17.5. Naphtha with a research octane number of 60.5 was tested. Three multi-injection strategies were designed. Each injection strategy, aided with EGR, conducts a characteristic combustion mode. Multi-injection strategies and single-injection strategy were tested and compared at one operating point under different main injection timing and EGR conditions. Results indicate that the well-designed multi-injection strategy has advantages over the single injection strategy in lowering noise, emissions and improving combustion efficiency. Among the three strategies, the strategy with 15-degree pilot timing and 2mg/cycle pilot injection could achieve both low NOx and PM emissions without sacrificing much fuel efficiency.

The lithium-ion battery plays an important role in saving energy and lowering emissions. Many parameters like temperature have an influence on the characteristic of the battery and this phenomenon becomes more serious in an electric vehicle. In this paper, the application of a boost DC/DC converter to the battery system of high power for online AC impedance identification is proposed. The function of the converter is to inject a current excitation signal into the battery at work and the normal output current is drawn by a load. Through analyzing the average state space equations and deriving the small signal model of the converter, the gain function is deduced of the fluctuated current signal against the fluctuated duty cycle which controls the converter. The control algorithm is designed and the system model is verified using Matlab/Simulink with respect to the disturbance current signal generation, the gain function and its variation with frequency range.

Electric vehicle (EV) is a worldwide researching focus due to its environmental friendliness, but the inaccurate Remaining Driving Range (RDR) estimation hinders the EVs' popularity, and an accurate determination of the battery Residual Usable Energy (RUE) is the key factor to obtain a precise RDR value. A common RUE estimation method is based on State-of-Charge (SOC) estimation, in which the RUE is proportionally related to the current SOC. However, the battery voltage varies significantly under real-world conditions, and the traditional method results in certain estimation errors. An adaptive RUE prediction method (AEP) is introduced in this paper, in which the dynamic voltage is predicted based on the future discharge profile and a battery model, while the RUE is then calculated by the predicted voltage and current sequences.

In order to reduce driver's anxiety about range and energy, a direct and effective approach is to offer the remaining driving range based on the vehicle's states. Consequently, the estimation accuracy of the battery's remaining energy is very important. This paper introduces a experiment-based model for predicting the remaining energy, which considers many factors, such as current, temperature, difference between battery cells, and so on. This approach ensures the accuracy of the remaining driving range. Finally the method is validated through the environment space test. Validation results show that this method can offer exact remaining energy, which reduces the estimation error of the remaining range greatly.