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Study On Energy Management Strategy For Parallel Hybrid Electric Vehicles

Posted on:2009-02-23Degree:DoctorType:Dissertation
Country:ChinaCandidate:H ShuFull Text:PDF
GTID:1102360272475330Subject:Mechanical design and theory
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The fuel economy and emission of hybrid electric vehicles (HEVs) are determined by the energy management strategy when the structure and characteristic of components are determined. The research on energy management strategy for HEVs plays important theoretical and application roles, and it needs to be researched thoroughly in the aspects about decreasing the fuel consumption to its best at driving mode and managing the state of charge (SOC) of battery reasonably. The study contents of this thesis are as follows1) The mathematic models of components of multi-power sources system for HEVs were set up. The driver's velocity control model with fuzzy logic was established, and the clutch's effect on the velocity control system is take into account in the process of vehicle's start up and gear shifting. The calculating model for driver's demanded driving power was built for the applications in the simulation and real time control. The dynamic equations of hybrid powertrain were also built. Based upon the above models, the fuel economy and performance simulation program with forward–facing approach is developed for parallel HEVs.2) Based upon the equivalent model of parallel HEVs proposed by Kim, the models of comprehensive charging/discharging efficiency for hybrid powertrain were built at the charging/discharging modes. The optimal instantaneous energy management strategy was proposed, with the goal of maximum of comprehensive discharging efficiency at the discharging mode, and the effect coefficient of regenerative braking energy recovery was introduced to reflect the effect of regenerative braking on the energy management strategy. The research shows that the discharging torque limits rises with the decline of battery SOC. Three kinds of optimal instantaneous energy management strategies were proposed at the charging mode, according to the different charging demands and efficiency at the different battery SOC, and the charging torque limits were get at the different battery SOC.3) An optimal energy management strategy of regenerative braking was proposed, with the goal of maximum of the regenerative braking power of battery at the charging mode, and reducing the lock probability of wheels and shorting the braking distance under the condition for satisfying the braking regulation of ECE. The calculation methods about the optimal braking torque of generator, front and rear brake torques were get under the conditions of the different battery SOC, generator speed and demanded braking power.4) Combined with the optimal instantaneous energy management strategy and battery SOC management strategy, an energy management strategy was proposed to reduce the vehicle's fuel consumption at driving mode. For discharging mode, the optimal instantaneous energy management strategy was also applied to the torque distribution between engine and motor, but for different initial SOC of battery at the start point of driving cycle, it was demanded that discharging and charging control strategy should make the battery SOC maintain at the different reasonable levels: If the initial SOC is medium or high at its optimal range, then the charge-sustain strategy is implement. If the initial SOC is about low or medium, or lower to the minimum limit of its optimal range, then the battery SOC should be kept at the lower and medium of its optimal range at the end of driving cycle. If the initial SOC is higher than the maximum limit of its optimal range, it is requested that SOC at the end of the driving cycle should drop to the level below their initial value, but not lower than the upper limit of its optimal range, and the higher the initial SOC of battery is, the more decrease of SOC at the end of the cycle. The aim of the management strategy is to improve the ability of discharge/charge control strategy to adapt to the actual driving conditions of vehicle, and reduce fuel consumption of HEVs.For charging mode, the optimal instantaneous energy management strategy is also applied to the torque distribution between engine and generator. The charging torque limits are redesigned according to the statistics characteristics of velocity and load of vehicle's driving mode. According to the distribution density of demanded torque and velocity distribution at the vehicle's driving mode, the velocity limits for entering charging mode were acquired. A method for deciding the entering condition of charging mode was proposed: that is, the reasonable charging velocity limits is chosen with the rules that the fuel consumption is minimum and the value of battery SOC at the end of cycle is satisfied by the demand of battery SOC management strategy, so that the charging control strategy at an certain scope is get. Changing the scopes of battery SOC and velocity limits, repeating the above process, finally the charging control strategies are got at the different scopes of battery SOC. The charging entering limits are determined by the battery SOC, velocity and / or charging torque limits.The results of fuel economy simulation of a mild HEV at NEDC cycle shows that the decrease of fuel consumption is remarkable compared to the conventional vehicle. 5) The main control program of real-time simulation based on rapid control prototyping technology of dSPACE real-time simulation system was designed. The control tests of the mild hybrid powertrain were carried out. The experiments of engine starting by ISG show that the stability of motor speed with fuzzy logic control is better than that of PI control. The control experiment of the hybrid powertrain from start up, accelerating with continuous shifting, cursing and braking deceleration was also carried out. The test results verified the validity of the simulation model of the mild HEVs.
Keywords/Search Tags:Hybrid Electric Vehicle, Energy Management Strategy, State of Charge of Battery, Fuel Consumption, Optimization and Simulation
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