Research On Energy Control Strategy And Fault Diagnosis For Parallel Hybrid Electric Vehicle Based On Intelligent Information Processing

The challenge for the traditional gas-engine vehicle has been faced due to the shortage of the energy and the environment pollution. Thus, it is necessary to develop the new energy-saving and environment-friendly vehicles in future, and the Parallel Hybrid Electric Vehicle (PHEV) has become the preferred model to realize the industrialization of the new energy auto. Energy control strategy is the key technique to determine the power performance, economical performance and emission performance. And the vibration and fault diagnosis is the key to the drive comfort. The researches on these two aspects have a very important theoretical meaning and application value to the development of new energy vehicles.The main contents of PHEV energy control strategy studies include:the engine and drive motor performance optimization and control, the battery packs state of charge (SOC) estimation, as well as the energy distribution between the various constituent units. The present studies, aiming to the optimization of the engine performance, are to determine the optimum economical routine or optimum power performance routine using polynomial fit technique according to the universal characteristics of the engine. However, it is noted that only one routine can be determined for the optimization of the engine performance based on this method. Due to the complexity of emission performance, there are quite a few studies on the optimization of both fuel economy and the less emissions of engine. Furthermore, the present studies on PHEV energy control strategy mainly include:control through some parameters such as velocity and torque, control along with fuel economy or emissions, as well as control through some variables such as the battery packs SOC. However, there are quite a few studies on PHEV energy optimization and control based on intelligent information processing. Due to the limitation of theory, it lacks of practical application. The accuracy of estimation of SOC based on battery voltage, current and resistance needs to be further improved because of more factors and more complex working conditions of vehicles.Because of Parallel hybrid electric vehicle (PHEV) taking on dynamic coupling and regenerative braking energy distribution, although the transmission of PHEV is the same as the traditional internal combustion engine vehicles, there are more complex working conditions and more difference in vibration and noise characteristics. It is very important to study the vibration and noise characteristics of PHEV transmission and to monitor the transmission operating using vibration fault diagnosis technology so as to enhance vehicle dynamic performance and improve comfort. There have been a large number of researches on mechanical fault diagnosis at home and abroad, whereas it lacks of in-depth research on PHEV vibration analysis and fault diagnosis.As analysis previously, based on the intelligent information processing theory and methods, there are two aspects completed in this paper:neural network and fuzzy logic are applied to PHEV energy management, wavelet transform and rough set theory are applied to the vibration fault diagnosis of PHEV transmission and its key components. The main research contents and innovations are as follows:1. The researches on the engine numerical modeling method. Based on CJY6470PHEV engine experiments, the engine model of universal characteristics is established using the neural network method. And the structure and parameters of the neural network model are optimal designed. Integrating neural network and grid interpolation, the data mining is developed for the engine model of universal characteristic. And using numerical method such as polynomial and spline functions, the optimum operating characteristics and its numerical relation with the electronic throttle of CJY6470PHEV engine are obtained. It is concluded that the engine characteristics optimal control can be achieved by adjusting the throttle accordingly.2. The researches on the design method and control strategy of the PHEV energy management system. Through the design of CJY6470PHEV energy management system, the optimal relationship between the throttle angle and the vehicle shifts is obtained, and the full hybrid control strategy of the energy distribution of the CJY6470PHEV is determined, also the mathematical model of the various constituent units is established. According to the analysis of the relationship among the vehicle power and torque, the engine and the drive motor's power and torque, and the battery packs SOC, a new method based on the dynamic energy equilibrium of electric motor drive system and engine drive system is originality proposed to estimate the battery packs SOC. The results show that the HEV energy management system has the capability of making the engine run at the optimal operating conditions, and guarantee the energy reasonable distribution of the electronic motor and engine according to the formulated strategy under the permission of battery SOC.3. Based on the basic theory of fuzzy logic and analysis of the human-machine interaction characteristics of HEV and its energy distribution strategy, the fuzzy logic energy management system of CJY6470PHEV is developed using the T-S (Takagi-Sugeno:T-S) fuzzy control model include the energy regenerative system and the energy management system in the process of normal driving. The T-S fuzzy model adapts the variable of the system state or the function of the input variables as a suffix of the if-then fuzzy rules, which can describe both the fuzzy controller and the dynamic model of controlled object, and to determine a linear function of the input and output, so as to simplify the model of control system. The results show that this system is appropriate to control the energy distribution between the engine and the motor, and enable motor working by a high efficiency in accordance with the manner determined.4. Vibration fault diagnosis method based on rough set theory is applied to PHEV transmission roller bearing. According to the analysis of the information loss caused by attribute reduction and the complementary redundant information, a new diagnosis method of complementary information and rough set theory-based is proposed. In this method, malfunction diagnosis is based on the multiple complementary reductions. So that in spite of the lack of some collecting information, malfunction diagnosis can be completed using other information. Consequently, this method is more suitable for engineering practice.5. Based on the basic theory of wavelet transform, the limitation of using wavelet transform to the roller bearing fault diagnosis is pointed out, namely it needs to calculate fault characteristic frequency accurately. And also the insensitivity of fault diagnosing using the common parameters is analyzed. A new diagnosis method is proposed for the energy feature extraction based on wavelet transform and for the rule acquisition based on set theory. The results showed that the fault diagnosis, based on wavelet transform and rough set theory, can be more appropriate in the engineering practice and have a higher accuracy.