Rule-based Control Strategy Research Articles

CONTROL DEL PROCESO DE CICLOS ALTERNADOS PARA LA ELIMINACIÓN EFICIENTE DE CONTAMINANTES EN UNA EDAR

RESUMEN: En el presente trabajo se propone una estrategia de control basada en reglas para actualizar la gestion de una Estacion Depuradora de Aguas Residuales (EDAR) mediante el proceso de alternancia de ciclos de aireacion/no aireacion con el objetivo de mejorar el rendimiento en la eliminacion de la carga contaminate de nitrogeno y fosforo que la planta recibe, asi como obtener una reduccion de su consumo energetico. Todo ello sin comprometer la eficiencia en la eliminacion de materia organica y solidos en suspension que exige la normativa vigente de calidad del agua vertida a los cauces receptores. El proceso ha sido probado en una planta real obteniendose una eficiencia en la eliminacion del nitrogeno del 85% frente al 35% original y del 55 % frente al 13 % para el fosforo. Se consiguio tambien un ahorro del consumo electrico del 31%. La eficiencia en la eliminacion de la materia organica paso del 90% original al 92%.

Model predictive control strategies for buildings with mixed-mode cooling

The paper presents model predictive control (MPC) strategies for buildings with mixed-mode cooling (window opening position, fan assist, and night cooling schedule) and demonstrates their potential performance bounds in terms of energy savings within thermal comfort constraints, in comparison with standard heuristic rules used in current practice. The study also identifies optimal control sequences coordinated with shading, for the control of solar gains. A transient, multi-zone building energy prediction model, with a coupled thermal and airflow network, is developed in MATLAB, and it is used within an offline MPC framework with Particle Swarm Optimization (embedded in GenOpt) as an optimizer. Simulations are performed for a period of six consecutive summer days with mixed-mode cooling strategies decided by the predictive controller, based on weather forecast and cooling load anticipation over a 24 h planning horizon. The results show that MPC can significantly reduce the cooling requirements compared to baseline night setback control while maintaining the operative temperature during the occupied period within acceptable limits. On the contrary, rule-based control strategies for the window opening position, based on simple heuristics for the outdoor conditions, create an increased risk of overcooling with lower thermal comfort acceptability.

System Dynamics Analysis and Mode-Switch Control for a 3-Mode Range-Extender Electric Vehicle

This paper studies the system modeling and mode-switch control for a novel 3-mode (serial/parallel/pure electric) range-extender electric vehicle (REEV). The REEV is modeled with low-order dynamics of 8 subsystems: the driving pattern, drivers behavior, lithium batteries, a spark-ignition engine, a traction motor, a generator, a 6-speed transmission, and a longitudinal vehicle dynamics. Dynamics of the REEV is the integration of above subsystems. To properly evaluate the system performance of the REEV, a rule-based mode-switch control rule is designed with 7 operation modes (System Ready, EV, Serial, Coast Down, Coast-Down Regen., Idle Regen., Parallel). By applying the control rules for the 3-mode REEV, the vehicle can properly operate. Simulation is conducted on the Matlab/Simulink platform. The results show that this study details the system dynamics of subsystems and the vehicle. Meanwhile the rule-based control strategy governs the subsystems well. The developed simulator can be utilized for specification designs of real vehicles and for vehicle control unit designs in the near future.

Analysis of Short-Term Energy Storage for Midsize Hydrostatic Wind Turbine1

This paper presents a novel method of capturing more energy from the wind using short-term energy storage in a hydrostatic wind turbine. A hydrostatic transmission (HST) not only provides reliable operation but also enables energy management features like energy regeneration using hydraulic accumulators. In this study, turbulence-induced wind transients occurring near the rated power are exploited to extract more energy from the wind. Wind characteristics are analyzed to develop models to quantify the energy losses due to the wind turbulence and the potential energy gains from the short-term energy storage. A dynamic simulation model of the hydrostatic wind turbine and the proposed energy storage system is developed. A rule-based control strategy for the energy storage is proposed. Results show that in a 50 kW hydrostatic wind turbine, the annual energy production (AEP) can be increased by 4.1% with a 60 liter hydraulic accumulator.

Model-based predictive control of an ice storage device in a building cooling system

This paper describes an approach to the formulation of a model-based predictive control (MPC) algorithm for the cooling plant of a building under a time-dependent electricity price profile. The mechanical system includes a three-stage chiller and an ice bank used for thermal energy storage (TES). Cooling can be provided to the indoor space either by directly using the chiller or by discharging the ice bank when electricity prices are high. The chiller is also used to charge the ice bank at night. By applying system identification techniques, a simplified linear thermal model for the building was derived from a detailed building simulation previously developed in EnergyPlus. The use of a simplified linear model – along with weather and internal gains forecasts – allows to readily calculate the required cooling power for a given temperature setpoint trajectory. By making use of simple parametric models for the chiller and the ice bank, an optimization algorithm is applied to decide on the optimal combination of chiller and ice bank cooling power contributions at discrete hourly intervals over the prediction horizon. The length of the prediction horizon alternates between 24 and 30h in order to coincide with the beginning or end of charge/discharge periods. The formulation of the optimization problem is considerably facilitated by using cooling power as the main working variable and then writing the equations accordingly. The proposed MPC strategy is compared with two rule-based control strategies: a modified storage-priority algorithm (similar to the one currently used in the case study building) and a chiller-priority algorithm. With the considered pricing structure and mechanical system, the MPC algorithm results in typical savings of about 5–20% with respect to the modified storage-priority strategy and about 20–30% with respect to the chiller-priority strategy.

Evaluation of rule-based control strategies according to process state diagnosis in A2/O process

In this study, rule-based control strategies were proposed according to the result of the process state diagnosis. Multivariate statistical techniques were used to derive the diagnosis results that included comprehensive information for the current process state. Based on the diagnosis result, the quantitative control setpoint could be calculated by using the optimized mathematical model. The developed process diagnosis procedure and the control strategies were applied in a pilot-scale A2/O (anaerobic/anoxic/oxic) process. The target variables in the proposed rule-based control strategies according to the process state diagnosis were the effluent NH4–N and NOX–N components. From the application of these rule-based control strategies according to the process state diagnosis, the percentages of groups 3 and 4, which were considered the abnormal process state, were decreased by about 53.8% compared to the no-control case. In addition, the maintenance interval of the control action ranged from 4h to 25h. Effluent NH4–N and NOX–N concentrations lower than the target values were maintained by applying the proposed rule-based control strategies according to the process state diagnosis. Moreover, frequent changes of the process operating state were minimized and the electrical equipment load was reduced.

Reliability Test of a Rule-Based SCR Control Strategy for Cleaner Heavy-Duty Diesel Engine using Biodiesel Blend

A rule-based SCR control strategy is developed for a 5% biodiesel fueled heavy-diesel engine. The control objective is to reduce the tail-pipe NOx emission while minimizing the urea dosage in a reliable fashion. A total of 32 runs of experimental test in ESC and ETC driving modes are conducted to demonstrate the performance and reliability of the rule-based control strategy. Average NOx reduction rates of 78.5% and 60% are achieved for the ESC and ETC tests respectively. In the mean time, the average urea dosage is 160 gram for the ESC tests and 0.49 % of the fuel consumption for the ETC tests. Variation of less than 7.78% and 12.05% for the ESC and ETC tests respectively demonstrate the reliability of the rule-based control strategy.

Analysis and Experimental Implementation of a Heuristic Strategy for Onboard Energy Management of a Hybrid Solar Vehicle

This paper focuses on the simulation analysis and the experimental implementation of a Rule-Based (RB) control strategy for on-board energy management of a Hybrid Solar Vehicle (HSV), consisting in a series hybrid electric vehicle assisted by photovoltaic panels. The RB strategy consists of two tasks: one external, which determines the final battery State of Charge (SOC) to be reached at the end of the driving schedule to allow full exploitation of solar energy during parking phase; the other internal, whose aim is to define the optimal Electric Generator (ICE-EG) power trajectory and SOC oscillation around the final value. This control strategy has been implemented in a real time NI<sup>®<sup/> cRIO control unit, thus allowing to perform experimental tests for energy management validation on a real HSV prototype developed at the University of Salerno.

D2P Real-Time Simulation of Energy Management Strategy for Parallel Hybrid Electric Vehicle

For investigating the feasibility of control strategy used in parallel hybrid electric vehicle, a D2P real-time simulation is introduced. A new multi-mode rule-based control strategy is proposed. The strategy is based on splitting the torque from the motor and engine so that these power sources can be operated at high efficiency. A real-time simulation platform is built based on D2P system. Real driver inputs and controller are applied while controlled objects are simulated using the model of parallel hybrid electric system computed in D2P module. Strategy is validated by D2P real-time simulation, which results show that the presented strategy is feasible and effective.

Power management of parallel hybrid electric power train

This paper presents an integrated and detailed procedure to improve the power management feature implemented in the integrated starter-generator (ISG) parallel hybrid electric vehicle. First, the configuration of the single-shaft ISG hybrid vehicle model established in MATLAB-Simulink environment is given. The vehicle model then is validated by comparing the experimental measurements and the simulation predictions of the traditional vehicle. The baseline rule based control strategy and the optimal control strategy using the dynamic programming (DP) algorithm are introduced. Finally, a suboptimal control strategy which employs the new control rules extracted from the optimal control strategy is designed with the remarkable fuel consumption performance.

Fuzzy multi-objective control strategy for parallel hybrid electric vehicle

This study presents a design method of an energy control strategy for parallel hybrid electric vehicles by using fuzzy multi-objective optimisation. By converting the electric energy consumed by the electric motor into an equivalent fuel consumption, the overall vehicle fuel economy and the corresponding emissions could be treated as the optimisation objectives. Then a minimum average weighted deviation method is proposed and intends to find the Pareto optimal solution, considering that its flexibility could content the variations in emission requirement in different districts. The simulation results reveal that, compared with the conventional rule-based control strategy and fuzzy logical control strategy, the proposed fuzzy multi-objective control strategy not only improves fuel economy and emission level but also maintains the battery state of charge within its operation range effectively.

Optimization of Rule-Based Control Strategy for a Hydraulic-Electric Hybrid Light Urban Vehicle Based on Dynamic Programming

Optimization of Rule-Based Control Strategy for a Hydraulic-Electric Hybrid Light Urban Vehicle Based on Dynamic Programming

Different architectures and modes of operation of HEV based on permanent magnet-electric variable transmission with rule-based and fuzzy logic global control strategy

In this paper, permanent magnet–dual mechanical ports (PM–DMP) machine is used as an electrical variable transmission (EVT), instead of the Toyota hybrid system (THS) transmission, in hybrid electric vehicle (HEV). In this paper, a new tactic to strategise the power management and torque distribution in the studied HEV system is developed. This strategy is based on the combination of rule–based and fuzzy logic control (RB–FLC). According to this strategy, different architectures with all modes of operation of the series/parallel HEV based on PM–EVT have been achieved. Using the vehicle specifications, engine, battery, and other control parameters of the Prius HEV, the appealing features of PM–EVT unit and the proposed RB–FLC power management strategy are explored in comparison to the SPHEV obtained from ADVISOR 2002. The obtained results give a specific conclusion that the Prius SPHEV can be successfully replaced by the HEV based on the PM–EVT with the proposed control strategy.

Analysis of a rule-based control strategy for on-board energy management of series hybrid vehicles

In the paper, the performances of a rule-based (RB) control strategy for series hybrid vehicles are assessed via comparison with a batch Genetic Algorithm-based (GA) optimization. The suitability of GA optimization as reference benchmark for series architecture is demonstrated through comparison with Dynamic Programming technique. Specifically in this paper, a hybrid solar vehicle (HSV) was considered, thus requiring to define the heuristic rules as function of both average traction power and current solar irradiation. The comparison with the reference GA benchmark confirms the suitability of the proposed RB strategy for HSV on-board energy management. Extensive simulations were performed to test the influence of driving cycle features, power-prediction time-horizon and solar irradiation on HSV fuel economy. Such simulation analysis, beyond providing useful indications about correct implementation of the RB strategy on both hybrid and solar hybrid cars, also demonstrates the potentialities offered by HSV powertrains in both urban and highway driving conditions.

Adaptive control for hybrid electric vehicles

Existing controllers of hybrid electric vehicles (HEV) are mostly tuned to achieve higher fuel economy for specific driving conditions. In this paper, a brief review of control strategies is presented followed by new approaches to implementing an optimisation based controller. First, a control strategy based on stochastic dynamic programming that can be used for real time applications is presented. This strategy achieves about 10% increase in fuel efficiency in most drive cycles over a rule-based control strategy. The second analysis uses an artificial neural network (ANN) to develop an optimised control strategy. Several existing hybrid control strategies are used to model a base for city and highway driving. Elite simulation data was extracted to form a training set for the ANN. The training set was then used to develop a new ANN control strategy that was superior to all the existing strategies used to develop the training set.

Advanced HVAC Control: Theory vs. Reality

AbstractIntelligent control of HVAC equipment is a key step towards improving the energy efficiency of commercial buildings. Although advanced control techniques have been developed and validated under real conditions, numerous buildings are still being poorly controlled due to wrong setpoints, incorrect PID settings, no coordination of individual PID loops, and other practical problems. This paper aims to summarize the major contributors to inefficient HVAC control and outline possible approaches towards better control strategies. Three areas are discussed: performance monitoring tools, rule-based control strategies and model-based predictive control (MPC). Performance monitoring tools help control engineers to quantify the performance of a particular control strategy, compare multiple control strategies among themselves, and define a baseline for such comparisons. Rule-based control strategies utilize various setpoint resets, rules and other heuristics to reduce HVAC energy consumption; however, such methods yield sub-optimal solutions only. Finally, MPC is a powerful and industrially-proven technology for optimal control of complex systems, but its use in building control seems to be so far limited. The paper analyzes challenges and constraints when implementing a control strategy in real projects, and covers topics such as missing sensors, legacy controllers and legislative changes needed to motivate building owners towards more efficient facility management.

Rule-Based Control of Battery Energy Storage for Dispatching Intermittent Renewable Sources

Integrating a battery energy storage system (BESS) with a solar photovoltaic (PV) system or a wind farm can make these intermittent renewable energy sources more dispatchable. This paper focuses on the development of a control strategy for optimal use of the BESS for this purpose. The paper considers a rule-based control scheme, which is the solution of the optimal control problem defined, to incorporate the operating constraints of the BESS, such as state of charge limits, charge/discharge current limits, and lifetime. The goal of the control is to have the BESS provide as much smoothing as possible so that the renewable resource can be dispatched on an hourly basis based on the forecasted solar/wind conditions. The effectiveness of this control strategy has been tested by using an actual PV system and wind farm data and it is shown that the BESS can indeed help to cope with variability in wind's and solar's generation.

Economic value and utility of a powertrain system for a plug-in parallel diesel hybrid electric bus

This research is the first to develop a design for a powertain system of a plug-in parallel diesel hybrid electric bus equipped with a continuously variable transmission (CVT) and presents a new design paradigm of the plug-in hybrid electric bus (HEB). The criteria and method for selecting and sizing powertrain components equipped in the plug-in HEB are presented. The plug-in HEB is designed to overcome the vulnerable limitations of driving range and performance of a purely electric vehicle (EV) and to improve fuel economy and exhaust emissions of conventional bus and conventional HEBs. The control strategy of the complicated connected propulsion system in the plug-in parallel HEB is one of the most significant factors in achieving higher fuel economy and lower exhaust emissions of the HEV. In this research, a new optimal control strategy concept is proposed against existing rule-based control strategies. The optimal powertrain control strategy is obtained through two steps of optimizations: tradeoff optimization for emission control and energy flow optimization based on the instantaneous optimization technique. The proposed powertrain control strategy has the flexibility to adapt to battery SOC, exhaust emission amount, classified driving pattern, driving condition, and engine temperature. The objective of the optimal control strategy is to optimize the fuel consumption, electricity use, and exhaust emissions proper to the performance targets. The proposed control strategy was simulated to prove its validity by using analysis simulation tool ADVISOR (advanced vehicle simulator).

Rule-Based Optimization of Intermittent ICE Scheduling on a Hybrid Solar Vehicle

ABSTRACT In the paper, a rule-based (RB) control strategy is proposed to optimize on-board energy management on a Hybrid Solar Vehicle (HSV) with series structure. Previous studies have shown the promising benefits of such vehicles in urban driving in terms of fuel economy and carbon dioxide reduction, and that economic feasibility could be achieved in a near future. The control architecture consists of two main loops: one external, which determines final battery state of charge (SOC) as function of expected solar contribution during next parking phase, and the second internal, whose aim is to define optimal ICE-EG power trajectory and SOC oscillation around the final value, as addressed by the first loop. In order to maximize the fuel savings achievable by a series architecture, an intermittent ICE scheduling is adopted for HSV. Therefore, the second loop yields the average power at which the ICE is operated as function of the average values of traction power demand and solar power. Expected solar contribution can be estimated starting from widely available solar databases and by processing past solar energy data measured on the vehicle. Neural Networks predictors, previously stored data and/or GPS derived information are suitable to estimate average power requested for vehicle traction. Extensive simulation analyses were carried out to test the performance of the RB algorithm, also comparing it to Genetic Algorithms-based optimization strategies previously developed by the authors. The results confirm the high potentialities offered by the proposed RB control strategy to perform real-time energy management on hybrid solar vehicles. The proposed rule-based optimization is currently under-implementation in an NI® cRIO control unit, thus allowing to perform experimental tests on a real HSV prototype developed at University of Salerno.

Analysis of a Rule-Based Control Strategy for On-Board Energy Management of Hybrid Solar Vehicles

In the paper, the performances of a rule-based (RB) control strategy for a series Hybrid Solar Vehicle (HSV) are assessed via comparison with a batch genetic algorithm-based (GA) optimization. The RB strategy relies on heuristic rules defined by optimizing ICE start&stop strategy as function of average traction power and current solar irradiation. The comparison with the reference GA benchmark confirms the suitability of the proposed RB strategy for HSV on-board energy management. Extensive simulations were performed to test the influence of driving cycle features, power-prediction time-horizon and solar irradiation on HSV fuel economy. Such simulation analysis, beyond providing useful indications about correct implementation of the RB strategy, also demonstrates the potentialities offered by HSV powertrains in both urban and highway driving conditions.