Research On Parameter Optimization And Energy Management Strategy Of Hybrid Energy Storage System For Electric Vehicles

Due to the advantages of energy-saving,high efficiency,safety etc.,electric vehicles(EVs)are becoming one of the main transportation tools for the development of low-carbon intelligent cities,which are also the main developing trend and focus of new energy vehicles and academic research.Owing to lower power density,weaker environment adaptability,EVs powered by only battery still face several challenges,such as long charge-discharge time,short lifetime etc..Supercapacitors,as emerging energy storage systems,have much higher power density,shorter charge-discharge time,longer lifetime compared with batteries.Weighing with factors such as power and performance,it is thus compelling and very advantageous to pair a supercapacitor with a battery to form a hybridized energy storage system(HESS)with combined power density and energy density to meet various driving demands on both performance and travel range,for greener and more sustainable future energy storage products.From the perspective of energy management,an in-depth research on optimal parameter design,energy state estimation and energy management system is carried out.In the part of optimal capacity design,both the performance and cost of HESS are influenced by supercapacitor capacity,which can be well balanced through proper optimization.However,the problem has yet been studied in present literature.The capacity of supercapacitor is optimized,and the optimum method is described detailedly in this dissertation.An optimal control problem has been established where the cost function of takes into battery degradation and supercapacitor capacity,the driving cycle is the combination of two typical standard cycles.The cost is optimized by a direct method.Simulation results have revealed that the performance of the HESS can be ensured associated with the system cost is minimized.As for energy state estimation methods,the assumption that the process noise and the measurement noise are mutually uncorrelated white Gaussian random process is required for Kalman filters(kalman filter,extended kalman filter and unscented kalman filter)to be satisfied.A novel energy state estimation method has been proposed to overcome this problem in this dissertation,which is a combination of partical filter and unscented kalman filter.The posterior probability density function distribution of system state particle is generated accurately,and then a resample process for system state particle is implemented.Finally the observation is updated by using unscented kalman filter.Therefore,the energy state could be estimated accurately even though the distribution of the process noise and the measurement noise fails to meet Gaussian random process.The proposed method is validated based on the two assumption cases that the battery model parameters are accurate and inaccurate.The driving cycles could provide effective information for energy management of HESS.However,there has been little research found that the influence of driving mode on performance of energy management system to be analyzed.From the prior art,the driving cycle for energy management has been mainly based on speed and acceleration parameters.A driving mode identification energy management strategy has been proposed in this dissertation.The driving mode is defined,and a neural network model is developed,trained and validated using the statistical data of driving cycle characteristic parameter.Moreover,the length selection method of the sliding time window is also discussed.Owing to the difference of dynamic characteristics between battery and supercapacitor,with the wavelet-transform-based power management algorithm,the decomposed high frequent component of the driving cycle is distributed to the supercapacitor,and the remainder is distributed to the battery.Validation results show that the proposed strategy can increase system energy efffiency and battery lifetime.Recently,advanced technologies in global positioning systems and geographic information systems,have supplied more opportunities for prediction of future driving conditions,which will help more reasonable use of the system power demand by extending the planning horizon.However,the influence of random errors on performance of energy management of HESS is often neglected in present developed energy management strategy.A stochastic predicetive energy management strategy has been proposed in this dissertation and the random error concept is introduced in system energy management.The cost models of battery degradation and energy consumption are developed,on the basis of the models,the influence of random errors on performance of energy management of HESS is studied in detail.To optimize the cost of stochastic system,a stochastic predicetive probabilistic energy management strategy is developed.Validation results show that the proposed strategy can minimize the system cost effectively.