Hybrid Optimal Theory and Predictive Control for Power Management in Hybrid Electric Vehicle

This paper presents a nonlinear-model based hybrid optimal control technique to compute a suboptimal power-split strategy for power/energy management in a parallel hybrid electric vehicle (PHEV). The power-split strategy is obtained as model predictive control solution to the power management control problem (PMCP) of the PHEV, i.e., to decide upon the power distribution among the internal combustion engine, an electric drive, and other subsystems. A hierarchical control structure of the hybrid vehicle, i.e., supervisory level and local or subsystem level is assumed in this study. The PMCP consists of a dynamical nonlinear model, and a performance index, both of which are formulated for power flows at the supervisory level. The model is described as a bi-modal switched system, consistent with the operating mode of the electric ED. The performance index prescribing the desired behavior penalizes vehicle tracking errors, fuel consumption, and frictional losses, as well as sustaining the battery state of charge (SOC). The power-split strategy is obtained by first creating the embedded optimal control problem (EOCP) from the original bi-modal switched system model with the performance index. Direct collocation is applied to transform the problem into a nonlinear programming problem. A nonlinear predictive control technique (NMPC) in conjunction with a sequential quadratic programming solver is used to compute suboptimal numerical solutions to the PMCP. Methods for approximating the numerical solution to the EOCP with trajectories of the original bi-modal PHEV are also presented in this paper. The usefulness of the approach is illustrated via simulation results on several case studies

Experimental Demonstration of Model Predictive Control in a Medium-Sized Commercial Building

This paper presents the implementation and experimental demonstration results of a practically effective and computationally efficient model predictive control (MPC) algorithm used to optimize the energy use of the heating, ventilation, and air-conditioning (HVAC) system in a multi-zone medium-sized commercial building. Advanced building control technologies are key enablers for intelligent operations of future buildings, however, adopting these technologies are quite difficult in practice mainly due to the cost-sensitive nature of the building industry. This paper presents the results of implementing optimization-based control algorithm and demonstrates the effectiveness of its energy-saving feature and improved thermal comfort along with lessons-learned. The performance of the implemented MPC algorithm was estimated relative to baseline days (heuristic-based control) with similar outdoor air temperature patterns during the cooling and shoulder seasons (September to November, 2013), and it was concluded that MPC reduced the total electrical energy consumption by more than 20% on average while improving thermal comfort in terms of temperature and maintaining similar zone CO2 levels

INDIVIDUALIZED DRUG RESPONSE RELATED TO GENETIC VARIATIONS OF CYTOCHROME P450 ISOFORMS AND OTHER ENZYMES

Abstract Gene polymorphism and single nucleotide polymorphism (SNPs) called "snips" might play a pivotal role in the future of the clinical therapy. Although not much is known about the distribution of SNPs in population and about the gene polymorphism of the drugmetabolizing enzymes, their investigation could represent an important tool in order to determine the precise therapy for maximum efficacy. SNPs represent a DNA sequence variation of a single nucleotide, variation that could determine and alter the genome sequence. If just one nucleotide (A -adenine, C -citosine, G -guanine, T -thymine) is changed this will definitely bring a change in the DNA sequence. In order to improve public health, since the map of the genome was created, scientists thought about new SNPs maps, which will bring a new vision in diagnosis, biological markers, drug therapy, and human response to disease. This review brings some insights in the knowledge of gene polymorphism regarding their impact on drug therapy and disease. Rezumat Polimorfismul genelor şi polimorfismul unui singur nucleotid (SNPs) ar putea juca un rol central în viitorul terapiei. Deşi distribuţia SNPs în populaţie şi polimorfismul genelor enzimelor cu rol în metabolismul medicamentelor nu sunt foarte cunoscute, investigarea acestora ar putea reprezenta un instrument important pentru determinarea tratamentului adecvat pentru o maximă eficacitate. SNPs reprezintă variaţia unei secvenţe de ADN a unui singur nucleotid, variaţie care ar putea determina şi modifica secvenţa genomului. În cazul în care doar unul dintre nucleotide (A -adenină, C -citozină, Gguanină, T -timină) este schimbat aceasta va duce cu siguranţă la o schimbare în secvenţa ADN-ului. În scopul îmbunătăţirii sănătăţii publice, harta genomului fiind deja creată, oamenii de ştiinţă s-au gândit la hărţi noi, ale SNPs-urilor, care vor aduce o nouă viziune în privinţa diagnosticelor, markerilor biologici, tratamentului medicamentos, şi în general a reacţiilor omului la boli. Acest articol abordează unele aspecte legate de nivelul actual al cunoştiinţelor privind polimorfismul genelor şi a impactului lor asupra tratamentului medicamentos şi a bolilor în general

Model Predictive Control and Fault Detection and Diagnostics of a Building Heating, Ventilation, and Air Conditioning System

The paper presents Model Predictive Control (MPC) and Fault Detection and Diagnostics (FDD) technologies, their on-line implementation, and results from several demonstrations conducted for a large-size HVAC system. The two technologies are executed at the supervisory level in a hierarchical control architecture as extensions of a baseline Building Management System (BMS). The MPC algorithm generates optimal set points for the HVAC actuator loops which minimize energy consumption while meeting equipment operational constraints and occupant comfort constraints. The MPC algorithm is implemented using a new tool, the Berkeley Library for Optimization Modeling (BLOM), which generates automatically an efficient optimization formulation directly from a simulation model. The FDD algorithm detects and classifies in real-time potential faults of the HVAC actuators based on data from multiple sensors. The performance and limitations of FDD and MPC algorithms are illustrated and discussed based on measurement data recorded from multiple tests

Context and driver dependent hybrid electrical vehicle operation

This paper studies the driver and context changes during the operation of a hybrid electric vehicle (HEV) and their influence on fuel consumption. Firstly, a context estimation model to recognize driving styles is developed based on machine learning techniques, for which a realistic scenario with simulation of urban mobility (SUMO) and car modeling platform (IPG Carmaker) integration is designed. Secondly, a novel context-aware control strategy based on model predictive control with extended prediction self-adaptive control (MPC-EPSAC) strategy is proposed. The control objective is to achieve optimal torque-split distribution, while optimizing fuel consumption in the parallel HEV. The simulation results suggest that an improvement in fuel economy can be achieved when the driving style in the control loop is adequately considered. Copyright (C) 2020 The Authors

Optimal control of switched /hybrid systems with applications to the control of hybrid electric vehicles

Established at the confluence of control, communication, and computer science, hybrid systems, where discrete (decision making) events interact with continuous or discrete-time processes, are present in many real world applications. For example, in the automotive area, hybrid electric vehicles are equipped with, besides an internal combustion engine, an electric motor-generator drive, a storage battery and a supervisory controller. The dynamic evolution, and therefore the performance, of this hybrid system significantly depends on the discrete decisions of the supervisor such as: the instants of changing the mode of operation of the electric motor-generator, the instants of coupling (decoupling) the internal combustion engine to (from) the drive shaft, etc. The hybrid system considered in this research consists of a family of dynamical systems and a rule that selects, at each time instant, a member of this family (mode of operation). This research studies a hybrid optimal control problem: determine, if they exist, a sequence of modes of operation and control inputs for each mode that minimize a given performance measure subject to state and input constraints. For example, the optimal control problem for a hybrid electric vehicle is to switch modes for minimizing fuel consumption and pollution subject to constraints on the state of charge of the battery, generated power, and drivability. The proposed approach is to reformulate the hybrid optimal control problem for a larger family of systems constructed through a continuous parameterization of the original hybrid system. The set of trajectories of the original system being dense in the set of trajectories of the more general system validates the approach. The reformulation allows the application of the classical results of optimal control theory which are not applicable to the original problem. This research shows that the optimal solution of the more general problem solves the hybrid control problem of interest, except in a possible small number of cases when suboptimal trajectories can be constructed by appropriate mode switches. The technique developed in this research is used at the supervisory level of a hierarchical control strategy for a hybrid electric vehicle. At this level, the optimal control problem uses a hybrid power flow model capturing the two modes of operation of the electric motor-generator; the results are reference power inputs for each subsystem of the vehicle

A TECHNICAL REPORT ON A POLYTOPIC SYSTEM APPROACH FOR THE HYBRID CONTROL OF A DIESEL ENGINE USING VGT/EGR

This paper develops a hybrid/gain scheduled control to move a diesel engine through a driving profile represented as a set of 12 operating points in the 7-dimensional state space of a 7th order nonlinear state model. The calculations for the control design are based on a 3rd order(reduced) model of the Diesel engine on which state space is projected the 12 operating points. About each operating point, we generate a 3rd order nonlinear error models of the Diesel engine. Using the error model for each operating point, a control design is set forth as a system of LMI\u27s. The solution of each system of LMI\u27s produces a norm bounded controller guaranteeing that x x i d i d - Æ 1 where xi d is the i-th desired operating point in the 3-dimensional state space. The control performance is then evaluated on the 7th order model

Model Predictive Control for the Operation of Building Cooling Systems

A model-based predictive control (MPC) is designed for optimal thermal energy storage in building cooling systems. We focus on buildings equipped with a water tank used for actively storing cold water produced by a series of chillers. Typically the chillers are operated at night to recharge the storage tank in order to meet the building demands on the following day. In this paper, we build on our previous work, improve the building load model, and present experimental results. The experiments show that MPC can achieve reduction in the central plant electricity cost and improvement of its efficiency

Model Predictive Control of HVAC Systems: Implementation and Testing at the University of California, Merced

A Model Predictive Control algorithm was developed for the UC Merced campus chilled water plant. Model predictive control (MPC) is an advanced control technology that has proven successful in the chemical process industry and other industries. The main goal of the research was to demonstrate the practical and commercial viability of MPC for optimization of building energy systems. The control algorithms were developed and implemented in MATLAB, allowing for rapid development, performance, and robustness assessment. The UC Merced chilled water plant includes three water-cooled chillers and a two million gallon chilled water storage tank. The tank is charged during the night to minimize on-peak electricity consumption and take advantage of the lower ambient wet bulb temperature. The control algorithms determined the optimal chilled water plant operation including chilled water supply (CHWS) temperature set-point, condenser water supply (CWS) temperature set-point and the charging start and stop times to minimize a cost function that includes energy consumption and peak electrical demand over a 3-day prediction horizon. A detailed model of the chilled water plant and simplified models of the buildings served by the plant were developed using the equation-based modeling language Modelica. Steady state models of the chillers, cooling towers and pumps were developed, based on manufacturers performance data, and calibrated using measured data collected and archived by the control system. A detailed dynamic model of the chilled water storage tank was also developed and calibrated. Simple, semi-empirical models were developed to predict the temperature and flow rate of the chilled water returning to the plant from the buildings. These models were then combined and simplified for use in a model predictive control algorithm that determines the optimal chiller start and stop times and set-points for the condenser water temperature and the chilled water supply temperature. The report describes the development and testing of the algorithm and evaluates the resulting performance, concluding with a discussion of next steps in further research. The experimental results show a small improvement in COP over the baseline policy but it is difficult to draw any strong conclusions about the energy savings potential for MPC with this system only four days of suitable experimental data were obtained once correct operation of the MPC system had been achieved. These data show an improvement in COP of 3.1% {+-} 2.2% relative to a baseline established immediately prior to the period when the MPC was run in its final form. This baseline includes control policy improvements that the plant operators learned by observing the earlier implementations of MPC, including increasing the temperature of the water supplied to the chiller condensers from the cooling towers. The process of data collection and model development, necessary for any MPC project, resulted in the team uncovering various problems with the chilled water system. Although it is difficult to quantify the energy savings resulting from these problems being remedied, they were likely on the same order as the energy savings from the MPC itself. Although the types of problems uncovered and the level of energy savings may differ significantly from other projects, some of the benefits of detecting and diagnosing problems are expected from the use of MPC for any chilled water plant. The degree of chiller loading was found to be a key factor for efficiency. It is more efficient to operate the chillers at or near full load. In order to maximize the chiller load, one would maximize the temperature difference across chillers and the chilled water flow rate through the chillers. Thus, the CHWS set-point and the chilled water flow-rate can be used to limit the chiller loading to prevent chiller surging. Since the flow rate has an upper bound and the CHWS set point has a lower bound, the chiller loading is constrained and often determined by the chilled water return temperature (CHWR). The CHWR temperature is primarily comprised of warm water from the top of the TES tank. The CHWR temperature falls substantially as the thermocline approaches the top of the tank, which reduces the chiller loading. As a result, it has been determined that overcharging the TES tank can be detrimental to the chilled water plant efficiency. The resulting MPC policy differs from the current practice of fully charging the TES tank. A heuristic rule could possible avoid this problem without using predictive control. Similarly, the COP improvements from the change in CWS set-point were largely captured by a static set-point change by the operators. Further research is required to determine how much of the MPC savings could be garnered through simplified rules (based on the MPC study), with and without prediction