Hybrid fuzzy logic control (FLC) strategies for hot water district heating (HWDH) systems are designed. A non-linear dynamic model of a HWDH system (heated floor area 90 000 m ² ) with 21 dynamic equations is developed. The dynamic model consists of a boiler, pipe network, heaters and buildings. Six different hybrid control strategies involving FL and PI control were designed to regulate fuel firing rate, water flow rate or water temperature in the system. Simulation results show that the control strategies, based on the use of return water temperature and indoor air temperature predictor (IATP), give good zone temperature control. Pumping costs were reduced by modulating water flow rate in the system. Results also showed that the proposed hybrid control strategies could save up to 17% energy in buildings.

A study was conducted to assess the energy performance of an optimal predictive control strategy for radiant floor district heating systems. A four-zone radiant floor heating system model was developed. The simulated performance of the optimal predictive control strategy was studied. The results showed 10% energy savings compared to a Proportional-Integral (PI) control strategy. Experiments were conducted in a laboratory radiant floor district heating system test facility. The description of the test facility, its operating conditions, and the results obtained are described. Experimental results further confirm the findings from the simulation study. Being simple and energy efficient, the optimal predictive control strategy is a good candidate control strategy for radiant floor district heating systems. Practical application: Energy efficiency is a major issue of interest in the design and operation of sustainable heating systems. The radiant floor district heating systems have been successfully installed and operated in many countries resulting in significant energy savings. The optimal predictive control strategy proposed in this study further enhances the potential for higher energy savings from the district heating systems. The optimal control strategy is simple to implement as it relies on the predicted outdoor air temperature and computes future temperature set-points for the boiler water temperature. A suitably tuned Proportional-Integral (PI) controller can be used to track the optimal set-point thus realizing potential energy savings. A programmable logic controller or a supervisory control system would be appropriate to implement the designed optimal control strategy. The local control can be realized by using industrial PI feedback controllers.

A dynamic model of a hybrid hydronic heating system has been developed. The overall model consists of a boiler, a heat exchanger, a ground loop heat pump, a ground loop heat exchanger, baseboard heaters (convector–radiators), and radiant floor hydraulic piping system. Two control strategies for improving the overall system performance were explored: (1) a conventional proportional-integral (PI) control and (2) an adaptive gain control. The simulation results showed that the performance of the adaptive controller is better than the fixed gain PI controller in disturbance rejection and stability. Energy simulations under three different operating strategies were conducted: (1) a fixed set-point PI control, (2) an outdoor air temperature reset control, and (3) an optimal set-point adaptive PI control. It was shown that the outdoor temperature reset strategy can save 4.5 and 19.9% energy under cold day and mild day conditions compared to the fixed set-point PI control strategy. The optimal adaptive PI control strategy resulted in higher energy savings of 6.6 and 22% as compared to the PI control under cold and mild day conditions, respectively. Practical application: Energy efficiency and sustainability are a major issue of importance in the design and operation of space heating systems. The proposed hybrid system combines a ground-source heat pump and a hot water boiler for space heating of both residential and commercial building applications. The heating system consists of radiant floor piping for the residential zones and hot water baseboard heaters (convector–radiators) for the commercial zones. An energy optimal adaptive control strategy is designed to improve energy efficiency and temperature control performance of the hybrid system. The control strategy is simple and can be implemented on the existing building control systems.

This research investigated different nonlinear models, state estimation techniques and control strategies applied to rechargeable Li-ion batteries and electric motors powered and adapted to these batteries. The finality of these investigations was achieved by finding the most suitable design approach for the real-time implementation of the most advanced state estimators based on intelligent neural networks and neural control strategies. For performance comparison purposes, was chosen as case study an accurate and robust EKF state of charge (SOC) estimator built on a simple second-order RC equivalent circuit model (2RC ECM) accurate enough to accomplish the main goal. An intelligent nonlinear autoregressive with exogenous input (NARX) Shallow Neural Network (SSN) estimator was developed to estimate the battery SOC, predict the terminal voltage, and map the nonlinear open circuit voltage (OCV) battery characteristic curve as a function of SOC. Focusing on nonlinear modeling and linearization techniques, such as partial state feedback linearization, for “proof concept” and simulations purposes in the case study, a third order nonlinear model for a DC motor (DCM) drive was selected. It is a valuable research support suitable to analyze the performance of state feedback linearization, system singularities, internal and zero dynamics, and solving reference tracking problems.