Cristina Vlad

Explicit model predictive control of buck converter

This article proposes a model predictive control (MPC) design to control a fixed frequency DC-DC power converter in a defined operating domain. The prediction model used for the control synthesis is a piecewise affine approximation (PWA) that covers the whole operating range of the converter. Based on the obtained hybrid model a constrained optimal control problem is formulated and solved. The explicit form of the control law is derived offline as an affine state feedback controller. This explicit controller is stored in a look-up table for implementation. The experimental results show the potential advantages of this control approach in comparison with a static state-feedback or a classic PI controller.

Energy management and control of a fuel cell/supercapacitor multi-source system for electric vehicles

This article presents a control strategy of a multi-source system dedicated to automotive applications, aiming to maximize the global system efficiency. A parallel-architecture of the multi-source system is considered, composed of a fuel cell as a primary power source, a supercapacitor as a secondary storage element, which provides peak power in fast transients, and power converters for the power sources interconnection to the DC bus. The power management is realized as a two-level control structure. The first level contains inner control loops for fuel cell/supercapacitor currents and a DC bus voltage control loop, designed using PI controllers. The higher level consists of an energy management supervision system based on the equivalent consumption minimization strategy. The fuel cell power demand is computed by minimizing the hydrogen mass consumption with respect to system physical constraints. The performances of the proposed control structure are evaluated in simulation, in terms of fuel economy, using an urban driving cycle.

A hybrid model for Buck converter operating in continuous and discontinuous conduction modes

A hybrid modeling technique in CCM (Continuous Conduction Mode) and equally valid in DCM (Discontinuous Conduction Mode) is proposed for the step-down Buck converter. An improved ν-resolution model is obtained which easily allows the deduction of the equivalent PWA (Piecewise Affine) model that is adequate for predictive control design. Simulation results of the open-loop model are presented, proving the prospects of this approach.

Optimization-based energy management strategies for electric vehicles

This article analyses three energy management strategies for an electric vehicle powertrain in order to maximize its global efficiency. The considered electric motor supply system is a multi-source system that consists of a fuel cell as a main energy source and an additional element that supplies peak power (at start-up and during fast transients) and charges by regenerative braking. Energy management is achieved by formulating an optimization problem, aiming to minimize the fuel cell hydrogen mass consumption while satisfying the system physical constraints (strictly positive fuel cell power, limited capacity of the storage element). First, the optimization is realized using dynamic programming, an off-line optimization method that requires the knowledge of the entire power load profile. Secondly, two on-line optimization approaches are used: the equivalent consumption management strategy and the model predictive control strategy, for which only the current power demand or its knowledge over a finite time horizon are demanded. The optimization strategies are tested in simulation using a power load profile that corresponds to an urban driving cycle.

Control Architecture Modeling using Functional Energetic Method - Demonstration on a Hybrid Electric Vehicle.

With the advances on component technology, communication and information, energy systems are becoming more complex. In this context, energy optimization based on various criteria requires the development of relevant and representative models that are able to characterize the system behaviour. Within this study, functional modeling is used to represent a system at a higher level of abstraction, with simple equations, local control loops and a decision manager (DM) for handling the energy flow. The reduced complexity and fast simulation of this model simplify the validation of system architecture and components sizing, as well as the performances evaluation of energy management algorithms according to different criteria. Once this first validation is completed, the following step in the system design process is to test the same algorithms on a more accurate model, represented at multi-physical level, that has its own local controllers and global resource manager (GRM). One way to complete this second validation is to use the information computed using the functional model, to design a high level controller of a more complex multi-physical model. To this purpose, a solution is proposed to interconnect the two models, of the same system, that are represented at different level of abstraction. First, it is shown how the GRM can be extracted from the functional model. Secondly, it is presented how this management system can be adapted in order to be used at multi-physical level. Both models are developed for a plug-in parallel hybrid vehicle (PHEV), and the interconnection solution is illustrated for the considered application.

Decentralized control strategy of a fuel cell/secondary storage element power supply system

This article presents a decentralized control strategy applied to a multi-source power system having a fuel cell (FC) system as a main power source and a secondary storage element (SSE) for peak current supply during transients and energy recovery during braking. The dedicated control structure aims to assure an optimal operation of the FC system and a desired energy level of the SSE. To attain these objectives, a dynamic model of the FC system (stack and auxiliary devices necessary to fuel cell operation) is used, in addition to the SSE dynamics. The FC system regulation and the control of the SSE state of energy are performed separately with two different controllers, both designed using a model predictive control (MPC) approach. A first controller is employed in order to ensure a high efficiency of the FC system while respecting the compressor physical limits (limited voltage of the compressor motor, bounded operation region). The performances of the MPC strategy are evaluated in simulation and compared to the ones of a classic PID regulator. A second controller is used to obtain a desired SSE charge level for a given load profile, by guaranteeing a positive FC current and a bounded state of energy. The decentralized control approach proposed for this multivariable system allows to reduce the computational complexity, compared to a centralized approach. The validation of the control structure is performed in simulation using a nonlinear model of the FC system.