The Parameters Matching And Control Strategy Study Of EREV

With the growing issues of energy crisis, environmental pollution, global warmingand other so on, developing new energy vehicles have become an inevitable trend ofchange in the automotive industry. For electric vehicle battery energy density due to thelow driving range and short technical bottlenecks, aiming at extend range electricvehicle powertrain program parameters to match the integrated control, that improve theutilization of energy, thereby increasing the driving range; combined with auxiliarypower unit’s work status (referred to as APU) of extend range electric vehicle, thatdevelop appropriate control strategies and constraints in order to obtain optimaleconomic performance. The above studies for improving the technology andperformance levels of extend range vehicle are important theoretical significance.This thesis relies on Chongqing Natural Science Fund Project "electric vehiclepowertrain matching optimization and integrated control"(2011BA3019) as well asscientific and technological project of Chongqing "pure electric mini-car R&D anddemonstration and application"(2010AA6046), launched transmission systemparameter matching optimization and integrated control of pure electric vehicles. Themain works are as follows:Parameter matching and optimization of extend range vehicles transmission system.For vehicle dynamic performance constraints, analyzing the relationship betweenacceleration, maximum speed and climbing ability and powertrain parameters,especially the parameters of driving motor; setting an intermediate variable of gear ratedpoint speed to analyze the influence of the transmission gear median, combining withthe economy shift schedule and dynamic constraint, thereby to optimize the speed ratio;according to the driving range, the motor power rating, voltage levels and otherparameters, complete the parameters of matching battery power, mainly rated capacityand voltage platform; Finally, according to vehicle driving power demand and economicconstraints and other conditions vs NEDC, match parameters of APU, including enginepower, ISG motor parameters, tank size and so on.Based on the two stages of pure electric driving and extend driving range, dividethe operating modes of extend range vehicle and then determine the status of powerbattery pack, APU, driving motor under different operating modes; combining with thelast chapter on matching battery pack power output characteristics, the lower limit battery state of charge is determined when the APU start and stop; for the levyparameters of statistical NEDC cycle, combining with the fuzzy control algorithm, setup the constraints of engine’s work areas, namely fuel economy curve, then simulatewhen the SOC0is0.95to ensure the verification of fuzzy control feasibility strategy;under NEDC different road driving range, according to the genetic algorithms and fuzzycontrol policy, optimize the constraints of APU’s start and stop and the the upper andlower limit region of APU’s work area, then leaving the trends for the next chaptersimulation bedding.Based on Matlab/Simulink R2009a platform and the optional reverse simulationmethod, set up the simulation model of extend range vehicle, especially2speed AMTmodel, APU start and stop control model, APU work area optimization models; first,according to the economy shift schedule of2speed AMT, analyze the vehicle dynamicsimulation performance, such as acceleration, climbing and battery performance; second,based on the FC algorithm and GA algorithm, make some authentication of worthyimpact on the optimization of APU’s start and stop and the work area under the NEDCdifferent driving range.