Ultracapacitor Modeling And Sate-of-charge Estimation For Electric Vehicles

In order to cope with the global challenges like fossil fuel depletion and environment protection,electrified vehicles(EVs)have been widely accepted as an enabling option for future ground mobility.In comparison to conventional combustion engine vehicles,EVs have the advantage of high efficiency,environment-friendly operation and excellent control flexibility.The energy storage system(ESS)is a key ingredient of an EV,and significantly affects its driving performance and cost effectiveness.The exploration of a vehicular ESS poses a formidable challenge,because of high power/energy demands and unpredictable driving environments.Li-ion batteries represent a main choice for this use,but suffer the drawbacks of low power density and poor recyclability.Recently,ultracapacitors(UCs),also referred to as supercapacitors(SCs),have gained increasing attention in energy storage community,thanks to their high power density,high efficiency,fast charge,wide operational range and excellent recyclability.These advantages make UCs a good augmentation to high-energy ESSs(e.g.,fuel cells,lithium-ion batteries).This combination represents a hybrid energy storage system(HESS)that can fully leverage the synergistic benefits of each constituent device.To ensure efficient,reliable and safe operations of UC systems,numerous challenges including modeling and characterization,and state estimation should be effectually surmounted.In order to meet the above mentioned challenges,the main works done in the dissertation include:1.A special test rig for UC characteristic investigation was purposely established in our laboratory.A test procedure was put forward to collect test data.A plethora of tests have been conducted on this test rig including capacity calibration,experimental impedance investigation under different temperatures and State-of-Charge(SOC),and dynamic cycling under standard driving environments and disparate temperatures,resulting in a rich UC database.2.The impedance characteristics of a commercial UC are experimentally investigated under different temperatures and SOC values.The results show that the impedance is highly sensitive to the temperature and SOC;and the temperature effect is more significant.In particular,the coupling effect between the temperature and SOC is illustrated,as well as the high-efficiency SOC window,which is highlighted.3.For SC modeling,we systematically examine three commonly used equivalent circuit models for UCs in terms of model accuracy,complexity and robustness in the context of EV applications.The genetic algorithm(GA)is employed to extract the optimal model parameters based on the Hybrid Pulse Power Characterization(HPPC)test.The performance of these models is evaluated and compared by measuring the model complexity,accuracy,and robustness against ?unseen? data collected in the Dynamic Stress Test(DST)and a self-designed pulse test(SDP).The validation results show that the dynamic model has the best overall performance for EV applications.4.Online parameter identification of UC models is researched.We proposed the extended Kalman Filter to recursively estimate the model parameters using the Dynamic Stress Test(DST)dataset,in which the dynamic model was used to represent the UC dynamics.The effectiveness and robustness of the proposed method was validated using another driving cycle database.6.We present a novel robust H infinity observer to realize the SOC estimation of a UC in real time.In comparison to the state-of-the-art Kalman filter-based methods(KF),the developed robust scheme can ensure high estimation accuracy even without prior knowledge of the process and measurement noise statistical properties.More significantly,the H infinity observer proves to be more robust/tolerant to modeling uncertainties arising from the change of thermal conditions and/or cell health status.7.A novel fractional-order model is proposed to emulate the UC dynamics.Relative to integer-order models,the fractional-order model has the merits of better model accuracy and fewer parameters.The novel fractional-order model consists of a series resistor,a constant-phase-element(CPE),and a Warburg-like element.The model parameters are optimally extracted using genetic algorithm(GA),based on the time-domain data acquired through the Federal Urban Driving Schedule(FUDS)test.By means of this fractional-order model,a fractional Kalman filter is synthesized to recursively estimate the UC SOC.Validation results prove that the proposed fractional-order modeling and state estimation scheme is accurate and outperforms current practice based on integral-order techniques.8.We present an optimal HESS sizing method using a multi-objective optimization algorithm,with the overarching goal of reducing the ESS cost and weight while prolonging battery life.To this end,a battery state-of-health(SOH)model is incorporated to quantitatively investigate the impact of component sizing on battery life.The wavelet-transform-based power management algorithm is adopted to realize the power coordination between the batteries and ultracapacitors.The results provide prudent insights into HESS sizing with different emphases.