The Research On Supercapacitor Modelling,State-of-Charge Estimation And Control Strategy For Hybrid Electric Vehicles

The development of vehicles with electrical power is becoming one of the most important technological revolutions for the global energy conservation and emission reduction.The hybrid electric vehicle(HEV)technology significantly improves the fuel economy and emission level using both engine and electric motor as power sources without the infrastructure construction,expensive and abundant energy storage devices(ESDs),and pollution problem of discarded secondary battery.Integrating renewable energy sources with ICE,HEV can achieve a better fuel economy and lower tailpipe emissions for peak power demand provided by ESDs.As one of the core components of the hybrid power system,the performance of ESDs directly influence the achievement of multi-objective optimization while not having a reduced vehicle dynamic performance.At present,electrochemical battery,such as NiMH battery and lithium-ion battery are the most commonly used energy storage for HEV.However,in terms of the current energy storage technology,it is almost impossible for these electrochemical batteries to provide high power conversion efficiency and long cycle life while providing peak power demand frequently.In contrast,supercapacitors(SCs)with higher power density,better conversion efficiency,excellent temperature characteristics,and longer cycle life have great potential.Given the above-mentioned facts and challenges,this paper is based on the parallel HEVs as the research object,and using SCs as ESDs.Performance testing of SCs:important issues and uncertainties,mathematic modeling,model parameter identification,SOC estimation and HEV energy management strategy are systematically researched for the purpose to achieve fuel economy improvement.The research work carried out includes:1.Performance testing of SCs:important issues and uncertainties.The SCs characteristic test platform has been set up for the advanced vehicle applications.Systematic performance testing of SCs method has been developed for the construction of a reliable SCs test database so designers or engineers can make reasonable decisions regarding the selection of the energy storage components.The effect of different test procedures on the characteristic parameters of both commercial and prototype SCs including EDLCs,pseudo-SCs and hybrid SCs have been studied and compared.2.Mathematic modelling for SCs.Fractional-order model is turned out to be more precise based on the analysis of the dynamic response characteristics of SCs.The fractional-order equivalent circuit model of SCs has been proposed with the theory of fractional calculus.Compared with the integer-order model,the fractional-order model has the advantages of higher precision,less parameters and lower complexity.The introduction of fractional calculus leads to the higher precision of the mathematical model,which accurately reflects the electrochemical characteristics of carbon-based,metal oxide-based and conducting-polymer-based SCs.3.Parameter identification for SCs mathematical model.Hybrid evolutionary optimization algorithm(HEOA)has been proposed based on the parallel fusion concept.The HEOA designed effectively combines the global search capability of the seeker optimization algorithm,and the optimization accuracy and convergence speed of the Nelder-mead simplex method.Therefore,the multi-parameter optimization capability of nonlinear problem is improved significantly.The HEOA has the advantages of global optimality,parallel efficiency and robustness.It has the potential to accurately identify the parameters of SCs model against uncertain factors.4.SOC estimation of SCs for vehicle applications.Based on the SCs fractional-order model,H_?state observer has been put forward for SOC estimation.Compared with Kalman filtering method based on optimized self-regression data processing,H_?state observer does not need to obtain the observation information of process noise and measurement noise in advance.The SOC state estimation process shows good reliability and robustness for external disturbance and measurement noise.5.SCs sizing based on desired power and energy performance.Typical driving cycle on-road test has been carried out.Micro-trips are extracted from driving cycle database based on the kinematic characteristic parameters definition.SCs sizing is designed based on the power and energy demand from the instantaneous profiles of micro-trips in the distribution statistics.Synthetic analysis of the dynamic indexes and practical demand for the design objective,the optimization design of the ESD sizing is concluded with four specific performances.6.The energy management strategy for parallel HEV with SCs.Real-time optimization energy management strategy based on the Pontryagin’s Minimum Principle is developed for parallel HEVs.Equivalent fuel consumption minimization strategy(ECMS)can instantaneously identify the optimal power distribution between ICE and motor powered by SCs to minimize the overall fuel consumption.In the proposed method,the comprehensive principal component score(CPCS)is created to cluster the micro-trips into more homogeneous groups of observations.The equivalent factor map is developed as a function of CPCS,which well matches the driving cycle characteristics with high resolution.A-ECMS can achieve a better optimization result with the Advisor simulation.7.Rapid control prototyping and dynamometer test.Base on the SCs modelling,SOC estimation,sizing design and energy management strategy,rapid control prototyping using MicroAutobox hardware platform has been set up.Furthermore,mild HEV equipped with SCs as energy devices is developed.The dynamometer test with UDDS and NEDC test procedures is conducted at ITS-UCDavis to verify the effectiveness of A-ECMS strategy,and the assessment results show a significant improvement for the fuel economy.