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Modeling techniques matter a lot in many fields of engine engineering. Models are requested not only for control design but also for dynamic prediction. However, problems might be encountered during modeling process either because of the system complexity or the unaffordable modeling cost. As a result, a new modeling technique based on disturbance estimation is proposed in this paper. By employing the proposed modeling technique, models are set up in real time with the online information from input and output. The uncertainties of system dynamics are handled as internal disturbance of the system, while the perturbation from outside are taken as the external disturbance, and the combination of the two can be estimated online by a kind of active observer called extended state observer (ESO).

The fuel economy of plug-in hybrid electric city bus (PHEV) is deeply affected by driving cycle and travel distance. To improve the adaption of energy management strategy, the equivalent coefficient of fuel is the key parameter that needs to be pre-optimized based on the predicted driving cycle. An iterative learning method was proposed and implemented in order to get the best equivalent coefficient based on the predicted driving cycle and battery capacity. In the iterative learning method, the energy model and kinematics model of the bus were built. The ECMS (Equivalent Consumption Minimization Strategy) method was applied to obtain the best fuel economy with the given equivalent coefficient. The driving paths and running time of city buses were relatively fixed comparing with other vehicles, and their driving cycle can be predicted by route content. The proposed optimized strategy was applied on the factory sets of plug-in hybrid electric city bus.

The spark ignition-controlled auto-ignition (SI-CAI) hybrid combustion is promising in achieving smooth transition between SI and CAI combustion but, it is limited by the combustion cyclic-variation at late combustion phasing to avoid too high pressure rise rate (PRR). In this paper, to stabilize the combustion and reduce PRR, the in-cylinder fuel-stratification strategy is investigated in a gasoline engine, equipped with port fuel injection combined with single pulse direct injection (PFI-DI). Experimental results confirm the benefits of employing PFI-DI in comparison with PFI and single-pulse DI strategy. The influence of DI timing (Start of injection, SOI) on the combustion process is found to be quite complicated, in terms of combustion phasing, combustion stability, PRR and thermal efficiency. It makes the optimal-SOI calibration time-intensive, since complex trade-off between PRR and thermal efficiency is needed.

For spark-ignition gasoline engines, the air-fuel ratio during transient operation is an important index concerning the emission performance and the torque generation quality, and the challenge is from the dynamics of air intake path, the fuel injection path and the sensor delay. This paper presents a simple model of intake dynamics-based air-charge estimation for the individual cylinder air charge. Then, with the model, a feed-forward based air-fuel ratio control law is developed in which the intake dynamics and the sensor delay are taken into account by introducing the cylinder air-charge prediction. The proposed control strategy is validated by the experiment results conducted on a commercial vehicle-used six-cylinder gasoline engine control test-bench.

We consider the modeling and control design of the regenerative braking system for heavy duty hybrid electric vehicles (HEVs) which have an isolated air-over-hydraulic (AOH) brake system and a generator. A nonlinear model is set up to characterize the behavior of the brake system. Then, the brake control is formulated as a torque tracking problem according to the driver's operations. The AOH brake system is appointed to track a constant brake torque; meanwhile, the generator is designed to track the torque error between the desired braking torque and the torque output of the AOH brake system. Finally, numerical experiments are carried out to verify the proposed model and control algorithms.