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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.

The traditional energy management algorithm is mainly based on a single driving cycle, it is obvious that many factors might be often neglected by designer, such as different driving cycles would suit for different control strategies. But they tend to make decisions on the balance of torque distribution and battery power that based on a single driving cycle. Therefore, it is very difficult to achieve the optimal control in each case. In this paper we introduce a new design concept of Multi-operating mode energy management, a mathematical model of the energy management applied to a hybrid vehicle system is presented. Results of simulations using the model with the Multi-operating mode energy management were compared with results of simulations using a model with the single mode energy management, allowing the energy efficiency evaluation of the proposed energy management system.

When vehicle traveling on the bumpy road or vehicle acceleration and deceleration, which will cause the body vibration of vehicle, at the same time, a large part of energy would be absorbed by the shock absorber transforms the mechanical energy into heat energy dissipated. In order to recycle the energy of vibration and keep the stability of running car, this paper provides the shock absorber of energy recovery that recycling the energy dissipated from the traditional absorber. The shock absorber includes rod and rodless chamber cavity, the two parts contain oil outlet and oil inlet, which connected to a bridge type loop of hydraulic to make pulsating oil pressure towards one direction, when the shock absorber vibration causes pulsating oil pressure, it drives hydraulic pump operation. Because the output shaft of the hydraulic pump fixedly attached to the input shaft of generator, so the generator produces electricity for recycling energy[1].

Electric power steering system is developed by applying the electric machine as a steering assistant power based on mechanical steering system. It can change the steering force transmission characteristics easily, reduce the driver load, and improve the vehicle maneuverability and handling performance.[1] But as the users increasing demand in handling and stability of vehicle, we need take adaptive control stability in steering system, especially when yaw velocity or lateral acceleration exceeding the threshold valve, taking the adaptive control stability. This paper presents a new concept to prevent over-steering in the turning. Installed yaw velocity sensor in the vehicle, detecting the yaw velocity signal and delivering to the CPU, who should calculates the value of steady turning to control the power steering motor, and to ensure under-steering in the turning. We build a simulation model in the AMEsim software, gaining the yaw velocity value at the neutral steering.