Hierarchical Model Predictive Control for the Dynamical Power Split of a Fuel Cell Hybrid Vehicle

In order to reduce emissions of the transport sector, fuel cell hybrid vehicles (FCHVs) constitute a promising alternative as they have zero local emissions and overcome the limited range of electric vehicles. The power management of the propulsion system poses many challenges since it is a highly nonlinear, constrained, strongly coupled, multiple-input multiple-output (MIMO) system. The control objectives aim at dynamic power delivery, minimization of hydrogen consumption and charge sustainability of the battery. This thesis presents a hierarchical model predictive control (MPC) with three levels approaching the control problem on different time scales. The high-level control (HLC) implemented as a nonlinear MPC optimizes the static power split between battery and fuel cell system. The intermediate-level control (ILC) uses static optimization to determine the optimal operating point of the air supply. The lowlevel control (LLC) is a nonlinear MPC and tracks the reference trajectories received from the higher levels. The hierarchical MPC is evaluated on a detailed model of an FCHV using the worldwide harmonized light vehicles test cycle. Utilizing predictive information about the power demand, the HLC provides a power split that assures charge sustainability of the battery and only deviates by 0.2% from the optimal solution in terms of hydrogen consumption. Due to the predictive behavior and inherent decoupling capability of an MPC, the LLC achieves dynamic power delivery while explicitly considering the system constraints caused by prevention of oxygen starvation and limited operating range of the compressor. Moreover, the actual hydrogen consumption deviates only by 1% from the hydrogen consumption that is predicted by the HLC. Even for uncertain power demand prediction, the LLC attains dynamic power delivery by deviating from the reference trajectories to relieve the fuel cell system when operating under system constraints.In order to reduce emissions of the transport sector, fuel cell hybrid vehicles (FCHVs) constitute a promising alternative as they have zero local emissions and overcome the limited range of electric vehicles. The power management of the propulsion system poses many challenges since it is a highly nonlinear, constrained, strongly coupled, multiple-input multiple-output (MIMO) system. The control objectives aim at dynamic power delivery, minimization of hydrogen consumption and charge sustainability of the battery. This thesis presents a hierarchical model predictive control (MPC) with three levels approaching the control problem on different time scales. The high-level control (HLC) implemented as a nonlinear MPC optimizes the static power split between battery and fuel cell system. The intermediate-level control (ILC) uses static optimization to determine the optimal operating point of the air supply. The lowlevel control (LLC) is a nonlinear MPC and tracks the reference trajectories received from the higher levels. The hierarchical MPC is evaluated on a detailed model of an FCHV using the worldwide harmonized light vehicles test cycle. Utilizing predictive information about the power demand, the HLC provides a power split that assures charge sustainability of the battery and only deviates by 0.2% from the optimal solution in terms of hydrogen consumption. Due to the predictive behavior and inherent decoupling capability of an MPC, the LLC achieves dynamic power delivery while explicitly considering the system constraints caused by prevention of oxygen starvation and limited operating range of the compressor. Moreover, the actual hydrogen consumption deviates only by 1% from the hydrogen consumption that is predicted by the HLC. Even for uncertain power demand prediction, the LLC attains dynamic power delivery by deviating from the reference trajectories to relieve the fuel cell system when operating under system constraints

PROGNOSTIC AND HEALTH-MANAGEMENT ORIENTED FUEL CELL MODELING AND ON-LINE SUPERVISORY SYSTEM DEVELOPMENT

Of the fuel cells being studied, the proton exchange membrane fuel cell (PEMFC) is viewed as the most promising for transportation. Yet until today, the commercialization of the PEMFC has not been widespread in spite of its large expectation. Poor long term performances or durability, and high production and maintenance costs are the main reasons. For the final commercialization of fuel cells in the transportation field, durability issues must be addressed, while costs should be further brought down. At the same time, health-monitoring and prognosis techniques are of great significance in terms of scheduling condition-based maintenance (CBM) to minimize repair and maintenance costs, the associated operational disruptions, and also the risk of unscheduled downtime for the fuel cell systems. This dissertation presents a comprehensive on-line supervisory system to address the important issues related to the PEMFC durability, including: 1) diagnosis of critical operating conditions, 2) optimization of the operating conditions, and 3) health monitoring (or damage tracking) and remaining useful life (RUL) prediction. In order to design and implement this supervisory system, a comprehensive fuel cell model is developed that integrates a control/diagnostic oriented dynamic fuel cell model and a prognostic oriented fuel cell degradation model, due to a lack of such models in the existing literature. To address the first issue, a model-based on-line diagnostics system is developed for fuel cell flooding and drying diagnosis, thanks to the incorporation of the diagnostic feature in the dynamic fuel cell model. The channel flooding diagnostic problem is decoupled from the gas diffusion layer (GDL) flooding and membrane drying diagnostic problem. Simultaneous state and parameter estimation problems are formulated for both cases. Dual extended Kalman filter (EKF) and dual unscented Kalman filter (UKF) techniques are applied respectively to solve the problems. The second issue is addressed by a diagnostic based control design for the air supply of the fuel cell system. The design concept allows selection of the most suitable controller in a controller bank that delivers the best performance under specific operating conditions and that mitigates the faulty condition based on the feedback of the diagnosis results. The control problem is reformulated as an H-infinity robust control problem, the objective of which is to minimize the difference between the desired and actual excess O2 ratio, thus preventing and minimizing oxidant starvation at the cathode. Finally, an UKF-based health-monitoring and prognostic scheme is proposed and applied to the damage tracking and RUL prediction for the fuel cell. The developed aging model is employed as the kernel for this scheme, which utilizes the fuel cell output voltage as the only feature for the prognostic and health management task

Prognostic-oriented Fuel Cell Catalyst Aging Modeling and Its Application to Health-Monitoring and Prognostics of a PEM Fuel Cell

Today, poor long-term performance and durability combined with high production and maintenance costs remain the main obstacles for the commercialization of the polymer electrolyte membrane (PEM) fuel cells (PEMFCs). While on-line diagnosis and operating condition optimization play an important role in addressing the durability issue of the fuel cell, health-monitoring and prognosis (or PHM) techniques are of equally great significance in terms of scheduling condition-based maintenance (CBM) to minimize repair and maintenance costs, the associated operational disruptions, and also the risk of unscheduled downtime for the fuel cell systems. The two essential components of a PHM scheme for a general engineering system are 1) an accurate aging model that is capable of capturing the system’s gradual health deterioration, and 2) an algorithm for damage estimation and prognostics. In this paper, a physics-based, prognostic-oriented fuel cell catalyst degradation model is developed to characterize the relationship between the operating conditions and the degradation rate of the electro-chemical surface area (ECSA). The model complexity is kept minimal for on-line prognostic purpose. An unscented Kalman filter (UKF) approach is then proposed for the purpose of damage tracking and remaining useful life prediction of a PEMFC

Online modelling and state-of-charge estimation for lithium-titanate battery

Superior safety, is a promising energy storage element for electric vehicles. Its features can be fully utilised by using a fast charger and a high performance battery management system. Battery model is vital to a battery charger design for characterising the charging behaviours of a battery. Additionally, a robust state-ofcharge (SoC) estimation should be realised for a reliable battery management. This thesis develops a battery model for charger design and a robust method for SoC estimation by using MATLAB. The thesis proposed a transfer function-based battery model which is applicable for small-signal analysis and large-signal simulation of battery charger design, in order to capture the charging profiles of LTO battery. Busse’s adaptive rule, which has simple computations, is applied to improve the accuracy of Kalman filter-based SoC estimation. Busse’s adaptive Kalman filters are also applied for SoC estimation with online battery modelling to eliminate the complicated process of battery modelling. This study was conducted by using 2.4 V, 15 Ah LTO batteries. The batteries were tested with continuous current test and pulsed current test at several ambient temperatures (-25 ºC, 0 ºC, 25 ºC and 50 ºC) and charge/discharge currents (0.5 C, 1 C, 2 C). Additionally, modified dynamic stress tests at several temperatures (-15 ºC, 0 ºC, 15 ºC, 25 ºC, 35 ºC and 50 ºC) were also performed to test the battery under real EV environment. Results of the battery modelling showed that the developed transfer function-based battery model is accurate where the root-mean-square modelling error is less than 30 mV. The results also revealed that the Busse’s adaptive rule has effectively improved the Kalman filter-based SoC estimation for the case of offline battery model by giving a higher accuracy and shorter convergence time. Additionally, Busse’s adaptive Extended Kalman Filter gave a better accuracy in SoC estimation with online battery modelling. The proposed transfer function-based battery model provides a helpful solution for the battery charger design while the proposed Busse’s adaptive Kalman filter offers an accurate and robust SoC estimation for both offline and online battery models

太陽光発電連系直流系統のための蓄電・水素システムに関する予測を基にした出力制御方策ならびに最適容量設計

筑波大学 (University of Tsukuba)201

Advanced multiparametric optimization and control studies for anaesthesia

Anaesthesia is a reversible pharmacological state of the patient where hypnosis, analgesia and muscle relaxation are guaranteed and maintained throughout the surgery. Analgesics block the sensation of pain; hypnotics produce unconsciousness, while muscle relaxants prevent unwanted movement of muscle tone. Controlling the depth of anaesthesia is a very challenging task, as one has to deal with nonlinearity, inter- and intra-patient variability, multivariable characteristics, variable time delays, dynamics dependent on the hypnotic agent, model analysis variability, agent and stability issues. The modelling and automatic control of anaesthesia is believed to (i) benefit the safety of the patient undergoing surgery as side-effects may be reduced by optimizing the drug infusion rates, and (ii) support anaesthetists during critical situations by automating the drug delivery systems. In this work we have developed several advanced explicit/multi-parametric model predictive (mp-MPC) control strategies for the control of depth of anaesthesia. State estimation techniques are developed and used simultaneously with mp-MPC strategies to estimate the state of each individual patient, in an attempt to overcome the challenges of inter- and intra- patient variability, and deal with possible unmeasurable noisy outputs. Strategies to deal with the nonlinearity have been also developed including local linearization, exact linearization as well as a piece-wise linearization of the Hill curve leading to a hybrid formulation of the patient model and thereby the development of multiparametric hybrid model predictive control methodology. To deal with the inter- and intra- patient variability, as well as the noise on the process output, several robust techniques and a multiparametric moving horizon estimation technique have been design and implemented. All the studies described in the thesis are performed on clinical data for a set of 12 patients who underwent general anaesthesia.Open Acces