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Selection of gearshift point plays an important role in the field of automatic transmission technology, which directly affects the vehicle dynamic and economic performance, etc. In order to designing optimal gearshift strategies for conventional passenger vehicles equipped with stepped automatic transmission, in this paper, the vehicle power demand was defined under different environment, different driving intention and different vehicle operating conditions. Dynamic programming (DP) method is used to solve the optimal static gearshift decision sequence based on the simplified model of powertrain system. The drivability is respected by imposing an inequality constraint on the power reserve limit and the fuel economy is the objective function. Considering the change of vehicle additional load and road slope, the gearshift strategy based on power reserve is proposed.

Hydraulic retarders are extensively used in heavy-duty vehicles because of their advantages, such as their large braking torque and long continuous operating hours. They can reduce the vehicle velocity by converting the kinetic energy of a traveling vehicle to the thermal energy of the working fluid. The water medium retarder is a new type of hydraulic retarder with the characteristics of high power density and simple structure. It uses the engine's coolant as the working medium, and the heat is directly taken away by the vehicle cooling system. Therefore, the heavy-duty vehicle can achieve long-term continuous braking during the downhill process. One of the main functions of water medium retarder is driving downhill at a constant speed which determines whether the vehicle drives stably and safely. Therefore, studying the constant-speed control strategy during downhill driving is particularly important.

The new DI diesel engine combustion system named Double-Layer Diverging Combustion System (DLDCS) results in a better Brake Specific Fuel Consumption (BSFC) and lower exhaust emissions. The previous results of numerical simulation and bench test of a single cylinder DI diesel engine showed that more homogeneous fuel distribution, better BSFC and lower emission level were obtained by employing this combustion system. In this research, further numerical simulation are employed to seek the best injection advance angle and investigate the influence of different volume fraction and type lines of upper layer with AVL Fire.

In a traditional shift control strategy, the gear range is selected based on the throttle opening and the vehicle speed. The disadvantage of two-parameter based system is that the shift map is lack of adaptability in certain special conditions. The driving environment and the true intentions of the drivers are not fully taken into account by the shift control system. Therefore, improving the feasibility of the shift control strategy for the true intentions of the driver and driving environment is of great significance. Under braking conditions, Automatic transmission shift map with two parameters is unable to use engine braking effectively, which affects the drivability and safety of vehicles greatly. This paper presents a newly developed shift control strategy under braking conditions. First of all, the necessity of engine braking was analyzed.

In the electro-pneumatic automated mechanical transmission (AMT) system, the manufacturing, assembly, wear, replacement and other issues often lead to gear position change and some differences in various gears of transmission, which reduces the success rate and even results in abnormally working. To solve these problems, based on the intelligent control theories of fuzzy PI control, this research developed an intelligent self-learning system for the AMT gear position. This system contains the position initialization module after assembling (offline module) and the position correction module in use (online module). The system can automatically recognize gear position deviation and actively correct it, which improves the robustness of shift actuator. The precise control of shift actuator is the key of this self-learning system.