LPV control for power source coordination - application to electric vehicles energy management systems

International audienceThis paper presents an LPV/Hinf control strategy applied to power source coordination on board of average power electric vehicles. The proposed approach concerns separation in frequency responses between three power sources in order to satisfy power demand of the vehicle's electrical motor, taking into account that sources are devoted to work within distinct frequency ranges. The three sources - fuel cell, battery and ultracapacitor - are connected in parallel to a common DC-bus which supplies the electrical motor. The idea is to use the weighting function associated to the LPV/Hinf controller to determine the auxiliary power source behaviors - battery and ultracapacitor - and to minimize the variation in fuel cell current and the DC-bus voltage. As a result, DC-bus voltage is regulated to 150 V, while the fuel cell provides mean power to the electrical motor. The proposed approach is validated by MATLAB/Simulink numerical simulation by using two driving scenarios, namely Normalized European Driving Cycle (NEDC) and the driving cycle proposed by IFSTTAR (Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux)

Optimal frequency separation of power sources by multivariable LPV/Hinf control: application to on-board energy management systems of electric vehicles

International audienceIn this paper a multi-variable LPV/Hinf control approach is applied to design a strategy for power source coordination within a multi-source energy system. Three different kinds of power sources - fuel cell, battery and ultracapacitor - compose the power supply system of an electric vehicle. All sources are current-controlled and paralleled together with their associated DC-DC converters on a common DClink coupled to vehicle's electrical motor and its converter. DC-link voltage must be regulated in spite of load power variations representing the driving cycle image. To this end, a MIMO LPV/Hinf provides the three current references so that each source operates in its most suitable frequency range as either high-energy-density or high-power-density source: lowfrequency, mean power is provided by fuel cell, ultracapacitor supplies/absorbs the instantaneous variations of power demand and battery operates in between the two other sources. Selection of Hinf weighting functions is guided by a genetic algorithm whose optimization criterion expresses the frequency separation requirements. The nonlinear multi-source system is simulated in MATLAB®/Simulink® using the driving cycle of IFSTTAR (Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux) as load profile, whose frequency content is richer than that of Normalized European Driving Cycle (NEDC). Simulation results show good performance in supplying the load at constant DC-link voltage according to user-configured frequency-separation power sharing strategy

MIMO Hinf control for power source coordination - application to energy management systems of electric vehicles

International audienceThis paper deals with a control strategy used for designing energy management systems within average-power electric vehicles. The power supply system is composed of three sources, namely a fuel cell, a battery and an ultracapacitor - specialized within distinct frequency ranges - which must be coordinated in order to satisfy power demand of the vehicle's electrical motor. The three sources with their associated DC-DC converters are paralleled on a common DC-bus supplying the electrical motor. The DC-bus is required to be constant regardless of the load state thanks to the fuel cell which provides the mean power and to the other two sources - auxiliary sources - which are controlled to supply the high-frequency variations of power demand according to an H1 optimization strategy. MATLAB/ Simulink numerical simulation is used to validate the proposed strategy under real driving cycle condition proposed by IFSTTAR (Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux), and this approach is assessed against another optimal strategy that uses LQR as control design

Power sources coordination through multivariable LPV/Hinf control with application to multi-source electric vehicles

International audienceIn this paper the problem of multi-source power sharing strategy within electric vehicles is considered. Three different kinds of power sources - fuel cell, battery and supercapacitor - compose the power supply system, where all sources are current-controlled and paralleled together with their associated DC-DC converters on a common DC-link. The DC-link voltage must be regulated regardless of load variations corresponding to the driving cycle. The proposed strategy is a robust control solution using a MIMO LPV/H-inf controller which provides the three current references with respect to source frequency characteristics. The selection of the weighting functions is guided by a genetic algorithm whose optimization criterion expresses the frequency separation requirements. A reduced-order version of the LPV/H-inf controller is also proposed to handle an embedded implementation with limited computational burden. The nonlinear multi-source system is simulated in MATLAB® / Simulink® using two different types of driving cycles: the driving cycle of IFSTTAR (Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux) and a constant load profile used in order to illustrate system steady-state behaviour. Simulation results show good performance in supplying the load at constant DC-link voltage according to user-configured frequency-separation power sharing strategy. When assessed against the classical-PI-based filtering strategy taken as base-line, the proposed strategy offers the possibility of integrating a variety of constraints into a systematic design procedure, whose result guarantees stability and performance robustness

An adaptive autopilot design for an uninhabited surface vehicle

An adaptive autopilot design for an uninhabited surface vehicle Andy SK Annamalai The work described herein concerns the development of an innovative approach to the design of autopilot for uninhabited surface vehicles. In order to fulfil the requirements of autonomous missions, uninhabited surface vehicles must be able to operate with a minimum of external intervention. Existing strategies are limited by their dependence on a fixed model of the vessel. Thus, any change in plant dynamics has a non-trivial, deleterious effect on performance. This thesis presents an approach based on an adaptive model predictive control that is capable of retaining full functionality even in the face of sudden changes in dynamics. In the first part of this work recent developments in the field of uninhabited surface vehicles and trends in marine control are discussed. Historical developments and different strategies for model predictive control as applicable to surface vehicles are also explored. This thesis also presents innovative work done to improve the hardware on existing Springer uninhabited surface vehicle to serve as an effective test and research platform. Advanced controllers such as a model predictive controller are reliant on the accuracy of the model to accomplish the missions successfully. Hence, different techniques to obtain the model of Springer are investigated. Data obtained from experiments at Roadford Reservoir, United Kingdom are utilised to derive a generalised model of Springer by employing an innovative hybrid modelling technique that incorporates the different forward speeds and variable payload on-board the vehicle. Waypoint line of sight guidance provides the reference trajectory essential to complete missions successfully. The performances of traditional autopilots such as proportional integral and derivative controllers when applied to Springer are analysed. Autopilots based on modern controllers such as linear quadratic Gaussian and its innovative variants are integrated with the navigation and guidance systems on-board Springer. The modified linear quadratic Gaussian is obtained by combining various state estimators based on the Interval Kalman filter and the weighted Interval Kalman filter. Change in system dynamics is a challenge faced by uninhabited surface vehicles that result in erroneous autopilot behaviour. To overcome this challenge different adaptive algorithms are analysed and an innovative, adaptive autopilot based on model predictive control is designed. The acronym ‘aMPC’ is coined to refer to adaptive model predictive control that is obtained by combining the advances made to weighted least squares during this research and is used in conjunction with model predictive control. Successful experimentation is undertaken to validate the performance and autonomous mission capabilities of the adaptive autopilot despite change in system dynamics.EPSRC (Engineering and Physical Sciences Research Council

Preview-based techniques for vehicle suspension control: a state-of-the-art review

Abstract Automotive suspension systems are key to ride comfort and handling performance enhancement. In the last decades semi-active and active suspension configurations have been the focus of intensive automotive engineering research, and have been implemented by the industry. The recent advances in road profile measurement and estimation systems make road-preview-based suspension control a viable solution for production vehicles. Despite the availability of a significant body of papers on the topic, the literature lacks a comprehensive and up-to-date survey on the variety of proposed techniques for suspension control with road preview, and the comparison of their effectiveness. To cover the gap, this literature review deals with the research conducted over the past decades on the topic of semi-active and active suspension controllers with road preview. The main formulations are reported for each control category, and the respective features are critically analysed, together with the most relevant performance indicators. The paper also discusses the effect of the road preview time on the resulting system performance, and identifies control development trends

Reduced-order LPV controller for coordination of power sources within multi-source energy systems

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Vector Control of Asynchronous Motor of Drive Train Using Speed Controller H∞

This study proposes the speed control of an asynchronous motor (AM) using the Antiwindup design. First, the conventional vector control based on proportional-integral (PI) controllers is developed for a constant speed set point. Then, a driving cycle is based on measurements on the Safi/Rabat motorway in Morocco using a microcontroller equipped with a GPS device. The collected practical speed is used as a speed reference for conventional vector control. The /Antiwindup controller of the direct rotor flow-oriented control is used to improve the performance of conventional vector control and optimize the energy consumption of the drive train. The effectiveness of the proposed control scheme is verified by numerical simulation. The results of the numerical validation of the proposed scheme showed good performance compared to conventional vector control. The speed control systems are analyzed for different operating conditions. These control strategies are simulated in the MATLAB/SIMULINK environment. The simulation results of the improved vector control of the Asynchronous Machine (AM) are used to validate this optimization approach in the dynamic regime, followed by a comparative analysis to evaluate the performance and effectiveness of the proposed approach. A practical model based on a TMS320F28379D embedded board and its reduced voltage inverter (24V) is used to implement the proposed method and verify the simulation results. Doi: 10.28991/ESJ-2022-06-04-012 Full Text: PD