http://www.nt.ntnu.no/users/skoge/prost/proceedings/dycops-2010/Papers_DYCOPS2010/WePlenaryT5-01.pdf

Hurdles and challenges for modelling and control of microalgae for CO2 mitigation and biofuel production

The gradual decline in global oil reserves and presence of ever so stringent emissions rules around the world, have created an urgent need for the production of automobiles with improved fuel economy. HEVs (hybrid electric vehicles) have proved a viable option to guaranteeing improved fuel economy and reduced emissions. The fuel consumption benefits which can be realised when utilising HEV architecture are dependent on how much braking energy is regenerated, and how well the regenerated energy is utilised. The challenge in developing an HEV control strategy lies in the satisfaction of often conflicting control constraints involving fuel consumption, emissions and driveability, without over-depleting the battery state of charge at the end of the defined driving cycle. As a result, a number of power management strategies have been proposed in literature. This paper presents a comprehensive review of these literatures, focusing primarily on contributions in the aspect of parallel hybrid electric vehicle modelling and control. As part of this treatise, exploitable research gaps are also identified. This paper prides itself as a comprehensive reference for researchers in the field of hybrid electric vehicle development, control and optimisation.

/pdf/modelling-and-control-of-hybrid-electric-vehicles-a-1hmutec0id.pdf

Modelling and control of hybrid electric vehicles (a comprehensive review)

https://cyberleninka.org/article/n/1424878.pdf

An energy management strategy to concurrently optimise fuel consumption & PEM fuel cell lifetime in a hybrid vehicle

Optimization-based energy management strategy for a fuel cell/battery hybrid power system

This brief evaluates the use of terrain, vehicle speed, and trip distance preview to increase the fuel economy of plug-in hybrid vehicles. Access to future information is classified into full, partial, or no future information and for each case an energy management strategy with the potential for a real-time implementation is proposed. With full knowledge of future driving conditions, dynamic programming (DP) provides a best-achievable benchmark. A partial preview level has access to future trip terrain and requires velocity estimation. Equivalent consumption minimization strategy (ECMS) is deployed as an instantaneous real-time minimization strategy with parameters adjusted by estimated future driving conditions and obtained either from DP or from a backward solution of ECMS. To reduce the requirement for future velocity and detailed terrain information, another partial preview level only assumes known trip distance to the next charging station and elevation changes (if available). In this level, the parameter of the real-time ECMS is estimated based on the remaining trip distance, the battery's state-of-charge, and elevation changes if included. The results are evaluated against cases with no preview. Results from a number of simulation case studies indicate that the fuel economy can be substantially enhanced with only partial preview.

Route Preview in Energy Management of Plug-in Hybrid Vehicles

Supervisory control of hybrid powertrains: An experimental benchmark of offline optimization and online energy management

This paper deals first with the open-loop optimization of a fed-batch bioreactor, using an approach combining direct transcription and collocation techniques. The proposed strategy avoids taking into account singular arcs as it transforms the optimization problem into a nonlinear programming problem (NLP). The state and control variables are both discretized and optimized through classical direct optimization methods. The optimal feeding law is thus computed being less sensitive to initialization of the algorithm. In a second time, the control law is synthesised to provide an optimal tracking of the previous trajectory. This is carried out using a linearizing state-feedback control in an inner loop, in addition to a controller in an outer loop to guarantee a good stability and accuracy. Finally, some numerical results on two case studies are given to illustrate the efficiency of the proposed optimisation and control strategies.

Open-loop optimization and trajectory tracking of a fed-batch bioreactor

This paper presents a nonlinear predictive control (NMPC) strategy applied to the continuous microalgae cultivation process in a closed photobioreactor. The photo-bioreactor system is programmed to operate in a constant biomass density mode, in order to maintain the culture at the optimal population density and sustain high biomass production levels. This objective is achieved by regulating the biomass density while tracking a reference culture medium feeding profile determined off-line. The proposed NMPC approach is validated in simulation, and performances and benefits for the cell cultivations are compared to those obtained when applying a generic model control (GMC) law.

Nonlinear predictive control for continuous microalgae cultivation process in a photobioreactor

A strategy for controlling a fed-batch Escherichia coli culture is described to maintain the culture at the boundary between oxidative and oxido-fermentative regimes. A nonlinear predictive controller is designed to regulate the acetate concentration, constraining the feed rate to follow an optimal reference profile which maximizes the biomass growth. For the sake of simplicity and efficiency, the original problem is converted into an unconstrained nonlinear programming problem, solved by control vector parameterization techniques. The robustness of the structure is further improved by explicitly including the difference between system and model prediction. A robustness study based on a Monte Carlo approach is used to evaluate the performance of the proposed controller. This control law is finally compared to the generic model control strategy.

Nonlinear Predictive Control of Fed‐Batch Cultures of Escherichia coli

Cette these propose une methodologie de commande d’un bioreacteur fed-batch de culture E. Coli. La strategie consiste a maximiser la croissance de la bacterie, c’est-a-dire a maintenir le bioreacteur a un point de fonctionnement « optimal », caracterise par la frontiere entre les regimes oxydatif et oxydo-fermentatif. La demarche proposee comprend une premiere etape de modelisation mathematique et de determination d’un modele parametrique simplifie. Une deuxieme phase consiste en une identification parametrique du modele en se basant sur l’analyse de sensibilite du modele vis-a-vis de ses parametres. Un profil optimal d’alimentation est ensuite elabore pour la maximisation de la croissance de la biomasse. A partir de toutes ces donnees, la synthese et l’application de la commande predictive non-lineaire au bioprocede E. Coli sont mises en œuvre. L’objectif est de reguler la concentration en acetate a une valeur faible donnee, tout en forcant le debit d’alimentation a suivre un profil de reference. La strategie de commande proposee se base sur la transformation du probleme de commande predictive non-lineaire classique en un probleme de programmation non-lineaire non contraint, resolu par des techniques de CVP. Pour une meilleure robustesse de la structure proposee, la difference entre le systeme et le modele est explicitement incluse dans l’algorithme. Enfin, une etude de robustesse par une approche statistique de type Monte Carlo permet de juger de l’applicabilite de la loi de commande proposee sur le systeme reel. Ce travail constitue une etude preliminaire en vue d’une implantation de cette loi de commande a un bioreacteur a l’echelle du laboratoire ou industrielle.

https://tel.archives-ouvertes.fr/tel-00345850/document

Ghizlane Hafidi

Papers

Supervisory control of hybrid powertrains: An experimental benchmark of offline optimization and online energy management

Open-loop optimization and trajectory tracking of a fed-batch bioreactor

Nonlinear predictive control for continuous microalgae cultivation process in a photobioreactor

Nonlinear Predictive Control of Fed‐Batch Cultures of Escherichia coli

Application de la commande prédictive non-linéaire à la commande de culture de bactéries Escherichia coli