Rule-based Control Strategy Research Articles

Smart grid energy flexible buildings through the use of heat pumps and building thermal mass as energy storage in the Belgian context

The management of electricity grids requires the supply and demand of electricity to be in balance at any point in time. To this end, electricity suppliers have to nominate their electricity bids on the day-ahead electricity market so that the forecast supply and demand are in balance. One way to reduce the cost of electricity supply is to minimize the procurement costs of electricity by shifting flexible loads from peak to off-peak hours. This can be done by offering consumers time-of-use variable electricity tariffs as an incentive to shift their demand. This study provides typologies of smart grid energy ready buildings within the context of the Belgian residential building stock and the Belgian day-ahead electricity market. Typical new residential buildings are considered, equipped with air-to-water heat pumps that supply either radiators or a floor heating system. Five heating control strategies are compared in terms of thermal comfort, energy use, cost, and flexibility. Flexibility is quantified in terms of load volumes shifted and in terms of procurement costs avoided. The first three are rule-based control strategies, whereas the last two are a smart grid-oriented optimal predictive control strategy responding to a time-varying electricity price profile. The results show that the smart grid control strategies allow reduction of procurement costs by up to 15% and the consumer's cost by 13%. The flexibility, defined in terms of loads volume shifted, is increased by 3% to 14% with the same thermal comfort. The impact of building insulation level and thermal mass is also evaluated. The flexibility for load shifting is higher when shifting from a low-energy (average U-value of 0.458 W/m2K) to a very-low-energy house (average U-value of 0.152 W/m2K).

Study on Energy Management Strategies for Series-parallel Plug-in Hybrid Electric Buses

Abstract The power flow between the power sources and the performance of plug-in-hybrid electric vehicles (PHEV) significantly in fuel economy are mainly determined by energy management strategy. To research the energy management of the PHEV, the control strategy of a plug-in series-parallel hybrid electric bus (PHEB) is studied in this paper. Firstly, the simulation model of the PHEB is built with CRUISE software. Secondly, to explore the fuel-saving potential of the PHEB, the global optimal control strategy which adopts dynamic programming (DP) algorithm is studied and built with MATLAB software, although the minimum fuel consumption of certain driving cycle can be a benchmark for evaluating the energy management strategy of PHEB, the global optimal control strategy based on DP is rarely directly applied in real vehicle due to its long online computation time. At last, to ensure control timely, a rule-based control strategy is calibrated according to the computation results of global optimal control strategy. The simulation results show that, the fuel consumption per 100 km decreased by 9.11% and the electric power consumption per 100 km decreased by 6.27%.

SEMI-ACTIVE FUZZY CONTROL OF STRUCTURES SUBJECTED TO NEAR-FAULT GROUND MOTIONS HAVING FORWARD DIRECTIVITY AND FLING STEP USING FRICTION DAMPING SYSTEM WITH AMPLIFYING BRACES (FDSAB)

In this paper, the consequences of well-known characteristics of near-fault ground motions, forward directivity and fling step, on the seismic response control is investigated. An integrated fuzzy rule-based control strategy for building structures incorporated with semi active friction damping system with amplifying braces (FDSAB) is developed. The membership functions and fuzzy rules of fuzzy controller were optimized by Genetic Algorithm (GA). The main purpose of employing a GA is to determine appropriate fuzzy control rules as well to adjust parameters of the membership functions. Numerical study is performed to assess the effects of near-fault ground motions on a building that is equipped with FDSABs. To demonstrate the effectiveness of the fuzzy logic algorithm, it is compared with that of a conventional linear quadratic regulator (LQR) controller, while the uncontrolled system response is used as the base line. Results reveal that the fuzzy logic controller with FDSAB is capable of improving the structural responses and is promising for reducing seismic responses during near-fault earthquakes. It is also shown that, the near-fault earthquakes require much more control force than the far-field earthquakes and result in less response mitigation.

The control strategy for improving the stability of a powertrain for a compound hybrid power excavator

Nowadays, hybrid power excavators are an active research subject in the construction machinery area. The primary focus of this study is to investigate the control strategy for improving the stability of a powertrain for compound hybrid power excavators. First, the power flow and the dynamics of the powertrain of compound hybrid power excavators are analysed. It is concluded that the control strategy for compound hybrid power excavators has to guarantee the charge and discharge priority for operation of the swing and the boom lowering. Otherwise, large-scale switches of the operation point of the engine have to be performed in order to keep the state of charge in the appropriate range, which results in worse stability of the powertrain. Then, an improved rule-based control strategy is presented, which incorporates a load torque distributor, a charge-sustaining strategy and a dynamic torque compensator. The simulation model of a 20 ton compound hybrid power excavator, namely the ZE205E hybrid, is established. The improved rule-based control strategy, the thermostat control strategy and the control strategy presented by Yoo in 2009 are compared with respect to the stability of the powertrain and the fuel consumption through simulations. The simulation results show that the improved rule-based control strategy is best for the stability; however, for the fuel consumption, the improved rule-based control strategy is better than the thermostat control strategy but poorer than the control strategy described by Yoo. Taking into overall consideration the stability of the powertrain and the fuel consumption, the improved rule-based control strategy presented in this paper is more applicable for compound hybrid power excavators.

A novel multimode hybrid energy storage system and its energy management strategy for electric vehicles

This paper proposes a novel topology of multimode hybrid energy storage system (HESS) and its energy management strategy for electric vehicles (EVs). Compared to the conventional HESS, the proposed multimode HESS has more operating modes and thus it could in further enhance the efficiency of the system. The rule-based control strategy and the power-balancing strategy are developed for the energy management strategy to realize mode selection and power distribution. Generally, the DC–DC converter will operate at peak efficiency to convey the energy from the batteries to the UCs. Otherwise, the pure battery mode or the pure ultracapacitors (UCs) mode will be utilized without the DC–DC converter. To extend the battery life, the UCs have the highest priority to recycle the energy and the batteries are isolated from being recharged directly during regenerative braking. Simulations and experiments are established to validate the proposed multimode HESS and its energy management strategy. The results reveal that the energy losses in the DC–DC converter, the total energy consumption and the overall system efficiency of the proposed multimode HESS are improved compared to the conventional HESS.

Performance analysis of a novel coaxial power-split hybrid powertrain using a CNG engine and supercapacitors

Energy conservation is a very important task for the automotive industry. The use of hybrid electric vehicles can improve energy efficiency, thus reducing fuel consumption and carbon emissions. In this research, the performance characteristics of a novel coaxial power-split hybrid powertrain for a transit bus are presented. The power sources are a combination of a compressed natural gas (CNG) engine and supercapacitors. A mathematical model for the coaxial power-split hybrid powertrain is established. Subsequently, an analysis program is developed based on Matlab and Advisor. The parameters are specified using experimental data. Afterwards, a rule-based control strategy is designed and optimized from the viewpoint of energy efficiency. Later, the system performance is evaluated using the Chinese Transit Bus City Driving Cycle and compared to a conventional powertrain. The results indicate that the proposed coaxial power-split hybrid powertrain can fulfill the requirements of the transit bus and enhance the energy efficiency dramatically. Moreover, the average energy efficiency of the supercapacitors was found to be above 97% over the entire driving cycle. Using supercapacitors as energy storage devices for the coaxial power-split hybrid powertrain can effectively recover the kinetic energy during regenerative braking and is a good solution for transit buses that require frequent acceleration and deceleration.

Modelling and control of a two-mode power-split hybrid powertrain

As one of the most efficient configurations hybrid powertrains, the power-split hybrid powertrain introduced in this paper has two electrically variable transmission modes. By controlling the speed and torque of each component, the powertrain can generate adequate output for the optimal operation of the vehicle. In this paper, detailed component models are first established and then integrated to an overall vehicle simulate model on Matlab/Simulink platform. Then, the mode selection scheme, rule-based power distribution control strategy, and mode transition control scheme are introduced. Finally, the performance of hybrid powertrain is studied by using the developed platform under typical operation condition. The work provides a support to design, test and development of hybrid powertrain through the implementation of a mathematical model to simulate the operation and predict the performance of the hybrid powertrain, and the platform enables users to estimate the impact of various control strategies on vehicle performance.

Control Strategy for Power Distribution in Dual Motor Propulsion System for Electric Vehicles

Electric Vehicles with more than one electric motor can offer advantages in saving energy from the batteries. In order to do that, the control strategy plays an important role in distributing the required torque between the electric motors. A dual motor propulsion system with a differential transmission is simulated in this work. A rule based control strategy for this propulsion system is proposed and analyzed. Two parameters related to the output speed of the transmission and the required torque are used to switch the two modes of operation in which the propulsion system can work under acceleration. The effect of these parameters is presented over the driving cycles of NEDC, UDDS, and NYCC, which are followed using a PID controller. The produced energy losses are calculated as well as an indicator of drivability, which is related to the difference between the desired speed and the actual speed obtained. The results show that less energy losses are present when the vehicle is maintained with one electric motor most of the time, switching only when the extended speed granted by the second motor is required. The propulsion system with the proposed control strategy represents a feasible alternative in the spectrum of sustainable transportation architectures with extending range capabilities.

Route-based adaptive optimization for energy management of hybrid electric vehicles

Compared with real-time control, global optimization has a great advantage to improve the fuel economy due to considering the whole drive condition in advance. However, on-board controller doesn’t support the global optimization with a large amount of calculation. Remote data communicating technology provides a platform to make global optimization applied to on-board control at a pre-known driving cycle. In this paper, a route-based optimal control strategy for the real-time energy management of parallel hybrid electric vehicles is developed. The proposed control strategy employs computers to optimize the power-split and engine stop-start control based on the minimum principle at a predicted driving cycle. Guiding controller provides real-time control with CAN bus communication. The experiment results prove the proposed strategy has a 10% fuel economy improvement than rule-based control strategy.

A hybrid dynamic programming-rule based algorithm for real-time energy optimization of plug-in hybrid electric bus

The optimization of the control strategy of a plug-in hybrid electric bus (PHEB) for the repeatedly driven bus route is a key technique to improve the fuel economy. The widely used rule-based (RB) control strategy is lacking in the global optimization property, while the global optimization algorithms have an unacceptable computation complexity for real-time application. Therefore, a novel hybrid dynamic programming-rule based (DPRB) algorithm is brought forward to solve the global energy optimization problem in a real-time controller of PHEB. Firstly, a control grid is built up for a given typical city bus route, according to the station locations and discrete levels of battery state of charge (SOC). Moreover, the decision variables for the energy optimization at each point of the control grid might be deduced from an off-line dynamic programming (DP) with the historical running information of the driving cycle. Meanwhile, the genetic algorithm (GA) is adopted to replace the quantization process of DP permissible control set to reduce the computation burden. Secondly, with the optimized decision variables as control parameters according to the position and battery SOC of a PHEB, a RB control is used as an implementable controller for the energy management. Simulation results demonstrate that the proposed DPRB might distribute electric energy more reasonably throughout the bus route, compared with the optimized RB. The proposed hybrid algorithm might give a practicable solution, which is a tradeoff between the applicability of RB and the global optimization property of DP.

Rule-Based Control Strategy With Novel Parameters Optimization Using NSGA-II for Power-Split PHEV Operation Cost Minimization

One of the major considerations in the automotive industry is the reduction of hybrid electric vehicle fuel consumption and operation cost. This paper is the first to use the nondominated sorting genetic algorithm-II (NSGA-II) for power-split plug-in hybrid electric vehicle (PHEV) applications. The NSGA-II, one of the most efficient multiobjective genetic algorithms (MOGAs), simultaneously optimized operation cost, including gasoline and electricity consumption. The Pareto optimal solutions are discussed for the parameter calibrations of the rule-based control strategy as a useful guide in PHEV development, particularly in the earlier phases. The optimized operation cost at the different power-split device (PSD) gear ratios is used to determine the ideal PSD gear ratio to further minimize the operation cost. To validate the proposed strategy, dynamic PSD and powertrain models of PHEV are developed in the numerical analysis. The two typically different driving cycles, namely, the Urban Dynamometer Driving Schedule (UDDS) and the Highway Fuel Economic Drive Schedule (HWFET), with different numbers of driving cycles, are used for control strategy optimization.

Simulation Analysis and Study of Energy Control Strategy for an Extended-Range Electric Vehicle

The basic configuration and operation modes of the series extended-range electric vehicle were analyzed and a new method of engine choice and fuel economy evaluation was proposed. A co-simulation platform is set up with Cruise and Matlab/Simulink to evaluate the rule-based engine on-off and optimal operation line control strategies. The simulation results with typical driving cycles illustrate that: firstly, the engine on-off control strategy makes engine and generator operate on their best efficiency points, and its fuel economy is better than optimal operation line control strategy; secondly, when the optimal operation line control strategy is adopted in Extended-Range Mode, the engine downsizing will be beneficial and the extended-range electric vehicle (E-REV) will achieve a better fuel economy and control effect.

Development of New Vehicle Power Integration System

This study developed a new hybrid power system which consisted of two continuous variable transmissions (CVT). By matching between the engine, motor, generator, and dual CVT system, this integrated power system can take advantages of components. The hybrid vehicle can be driven by the internal combustion engine (ICE) or electric motor (M) alone, or by these two power sources together when vehicle is driven in hard acceleration or high load. The integrated hybrid system could provide better fuel economy. The energy management of this integrated system controls the power systems based on rule-based control strategy to achieve better fuel economy. When the vehicle driving power demand is low, the ICE which is operating in the low efficiency region. The ICE is shut down, and the vehicle is driven by motor only. When the vehicle driving power demand is high, ICE would operate in the high efficiency region. The vehicle could be driven by ICE. This strategy would operate ICE only in optimal efficiency region to improve the fuel economy. In this research, the vehicle simulation model was built in Matlab/Simulink environment. The model simulation was according with the vehicle dynamics test, and the analysis and discussion of the simulation results was present.

Hybrid powertrain optimization with trajectory prediction based on inter-vehicle-communication and vehicle-infrastructure-integration

Recent advances in Inter-Vehicle Communications (IVC) and Vehicle-Infrastructure Integration (VII) paved ways to real-time information sharing among vehicles, which are beneficial for vehicle energy management strategies (EMS). This is especially valuable for power-split hybrid electrical vehicles (HEV) in order to determine the optimal power-split between two different power sources at any particular time. Certainly, researches in this area have been done, but tradeoffs between optimality, driving-cycle sensitivity, speed of calculation and charge-sustaining (CS) conditions have not been cohesively addressed before. In light of this, a combined approach of a time-efficient powertrain optimization strategy, utilizing trajectory prediction based on IVC and VII is proposed. First, Gipps’ car following model for traffic prediction is used to predict the interactions between vehicles, combined with the cell-transmission-model (CTM) for the leading vehicle trajectory prediction. Secondly, a computationally efficient charge-sustaining (CS) HEV powertrain optimization strategy is analytically derived and simulated, based on the Pontryagin’s Minimum Principle and a CS-condition constraint. A 3D lookup-map, generated offline to interpolate the optimizing parameters based on the predicted speed, is also utilized to speed up the calculations. Simulations are conducted for 6-mile and 15-mile cases with different prediction update timings to test the performance of the proposed strategy against a Rule-Based (RB) control strategy. Results for accurate-prediction cases show 9.6% average fuel economy improvements in miles-per-gallon (MPG) over RB for the 6-mile case and 7% improvements for the 15-mile case. Prediction-with-error cases show smaller average MPG’s improvements, with 1.6% to 4.3% improvements for the 6-mile case and 2.6% to 3.4% improvements for the 15-mile case.

카메라 영상기반 전방향 이동 로봇의 제어

본 논문에서는 카메라를 탑재한 전방향 이동 로봇에서의 표적 추종을 위한 영상기반 시각 서보 제어를 다룬다. 기존 연구에서는 카메라 영상에서 추출한 표적의 영상 좌표로부터 표적 추종을 위한 바퀴의 회전 각속도를 구하기 위하여 카메라의 수학적 모델과 이동 로봇의 기구학 특성으로부터 구한 수학적 영상 자코비안을 널리 활용하였다. 본 논문에서는 표적의 영상 좌표 정보를 이용한 단순한 규칙기반 제어 방식과 영상에 포착된 표적의 크기 정보를 조합하여 바퀴의 회전 각속도를 생성하는 새로운 방식을 제안한다. 카메라 영상을 몇 개의 영역으로 분할하고, 표적이 포함된 영역에 따라 미리 정의한 규칙을 적용하는데, 복잡한 수학적 표현을 사용하지 않으면서도 비교적 적은 수의 규칙을 사용하므로 구현이 용이한 장점이 있다. 제안된 방식은 실제 시스템으로 구현하여 실험하고, 전체 실험 시스템에 대한 설명과 함께 실험 결과를 제시하여 제안하는 방식의 타당성을 입증한다. In this paper, an image-based visual servo control strategy for tracking a target object is applied to a camera-mounted omni-directional mobile robot. In order to get target angular velocity of each wheel from image coordinates of the target object, in general, a mathematical image Jacobian matrix is built using a camera model and a mobile robot kinematics. Unlike to the well-known mathematical image Jacobian, a simple rule-based control strategy is proposed to generate target angular velocities of the wheels in conjunction with size of the target object captured in a camera image. A camera image is divided into several regions, and a pre-defined rule corresponding to the target-located image region is applied to generate target angular velocities of wheels. The proposed algorithm is easily implementable in that no mathematical description for image Jacobian is required and a small number of rules are sufficient for target tracking. Experimental results are presented with descriptions about the overall experimental system.

Fuzzy Control for Food Agricultural Robotics of a Degree

In this study, we have a research of the fuzzy control for food agricultural robotics of a degree. Weeding robots can replace humans weeding activities, since the control system with nonlinear, robustness and a series of complex time-varying characteristics of the traditional PID control of the food agricultural robot end of the operation control effect cannot achieve the desired results, therefore, the design for the traditional use of classical PID control algorithm to control the food agricultural robot end of the operation of a series of drawbacks, combining cutting-edge control theory, fuzzy rule-based adaptive PID control strategy to control the entire system, so as to achieve the desired control effect. Experimental results show that the fuzzy adaptive PID control method for robot end postural control has better adaptability and track-ability.

Control Strategy Influence on the Efficiency of a Hybrid Photovoltaic-Battery-Fuel Cell System Distributed Generation System for Domestic Applications

The full exploitation of locally available renewable resources together with the reduction of system installation and management costs are key issues of diffused Distributed Generation (DG). In the given context, hybrid systems are already at an advanced stage of development which typically integrate several sub-systems. In such hybrid systems, Renewable Energy Sources generation systems (e.g. photovoltaic panels) are coupled to energy storage devices (electric batteries) and with programmable generators (a diesel generator or, more recently, with a sub-system based on fuel cells) allowing stable operations under a wide range of conditions. In this paper a solution which uses hydrogen and fuel cells as a programmable source is presented and is studied by means of a mixed experimental and numerical: a Hardware-In-Loop test bench designed and realized at the Department lab, able to reproduce the behavior of a hybrid system for domestic applications. The system is controlled by means of a rule-based control strategy acting on the common DC-bus whose optimization has a significant influence both on system design and on its overall system energy performances. Results show that Rule-Based strategy have a great potential towards cost reduction and components lifetime increase, while energy efficiency mainly depends on correct system sizing.

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.