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The purpose of this study is to investigate the possibility of acetone-butanol-ethanol (ABE) blended with diesel without further component recovery which has high costs blocking the industrial-scale production of bio-butanol. The combustion characteristics of ABE and diesel blends were studied in a constant volume chamber. In this study, 50% and 80% vol. ABE (without water) were mixed with diesel and the vol. % of acetone, butanol and ethanol were kept at 30%, 60% and 10% respectively. The in-cylinder pressure was recorded using a pressure transducer and the time-resolved natural luminosity was captured by high speed imaging. Combustion visualization using laser diagnostics would provide crucial fundamental information of the fuel's combustion characteristics. With the different percentage of the ABE blended in the diesel, the soot oxidation, the ignition delay and the soot lift-off length were studied in this work.

The traditional vehicle design methods of hybrid electric vehicles are based on the rule-based control strategy, which often adopt the trial and error methods and the model-based numerical optimization methods. But these methods require a large number of repeated tests and a longer-term development cycle. In this paper, approximately the global optimization algorithm was used in control parameters designing through rational design of the penalty weights of objective function. But the optimized parameters apply only to vehicles that operating in the special drive cycle to get better fuel economy. Therefore, a drive cycle recognition algorithm was proposed to identify types of drive cycles in real-time, then an off-line genetic algorithm was adopted to acquire the optimization of control parameters under the various drive cycles, through drive cycle recognition results to choose the best control parameters.

Coordinated control of mode transition is an important part of the multi-mode hybrid vehicles' control strategy, combined with a vehicle torque distribution strategy to realize an optimal working condition of the power sources, as well as achieve smooth mode switching. This paper builds hybrid electric vehicle driveline dynamics model and depth analyzes drive mode transition process, coordinated control methods were provided to solve three types of mode switching, neural network algorithm was provided to estimate the engine torque. The results show that coordinated control can reduce torque fluctuations and decrease jerk during the transition of different modes to improve the vehicle drivability.

In order to achieve the best shifting performance, the traditional hybrid vehicles shift schedule design based on multi-parameter shift schedule, these shift methods can improve fuel economy and acceleration performance to a certain extent. but it is difficult to obtain the optimal performance because it is a compromise between power and economy shift schedule. This paper provides adaptive shift strategy based on driving style recognition to select the optimal shift schedule, thereby improving the dynamic performance of the vehicle as well as reduced fuel consumption.