Abstract: A system for controlling an operation of an air-conditioning system including a heat exchanger is provided. The system comprises a processor that executes a neural network trained to simulate an operation of the heat exchanger for a test control input, to produce an output of the simulation based on historical data defining a state of the heat exchanger. The historical data includes a sequence of historical control inputs provided to the heat exchanger and a sequence of historical outputs of the operation of the heat exchanger corresponding to the sequence of historical control inputs. The processor determines a control command to the air-conditioning system based on the predicted test output of the simulation of the operation of the heat exchanger for the test control input and transmits the determined control command to an actuator of the air-conditioning system.

Abstract: A system for controlling an operation of an air-conditioning system including a heat exchanger is provided. The system comprises a processor that executes a neural network trained to simulate an operation of the heat exchanger for a test control input, to produce an output of the simulation based on historical data defining a state of the heat exchanger. The historical data includes a sequence of historical control inputs provided to the heat exchanger and a sequence of historical outputs of the operation of the heat exchanger corresponding to the sequence of historical control inputs. The processor determines a control command to the air-conditioning system based on the predicted test output of the simulation of the operation of the heat exchanger for the test control input and transmits the determined control command to an actuator of the air-conditioning system.

Abstract: A battery charging system and method that take different sources of information regarding the battery status and user needs, assesses the charging objectives and the battery state, derives the charging decisions, and then charges the battery accordingly. A controller interprets the information and, in connection with inputs from user, determines an optimal charging application for the battery. The battery charging system and methods operate in a either real-time or approximately real-time fashion. It can collect the data information, makes the charging decisions based on rules, principles, algorithms and computation, and implements the decisions. It can also be triggered by time or event related with the battery status.

Abstract: A battery charging system and method that take different sources of information regarding the battery status and user needs, assesses the charging objectives and the battery state, derives the charging decisions, and then charges the battery accordingly. A controller interprets the information and, in connection with inputs from user, determines an optimal charging application for the battery. The battery charging system and methods operate in a either real-time or approximately real-time fashion. It can collect the data information, makes the charging decisions based on rules, principles, algorithms and computation, and implements the decisions. It can also be triggered by time or event related with the battery status.

Abstract: A method estimates a state-of-charge (SoC) of a battery by constructing a set of two or more battery models. Each battery model is associated with an adaptive SOC estimator. A set of intermediate SOCs is estimated using the models and the associate adaptive SoC estimators. Then, the set of intermediate SoCs are fused to obtain a final SoC of the battery.

Abstract: A state of charge (SoC) a lithium-ion (Li+) battery is estimated at a time instant by first constructing a battery model of the Li+ battery based on a single particle operation. The battery model describes a relationship between the SoC, a charge current, a discharge current, and an output voltage of the Li+ battery. Using a nonlinear optimization, a function expressing a relationship between the SoC and an open circuit voltage of the Li+ battery is determined. The relationship is based on off-line measurements of the charge current, the discharge current, and the open-circuit voltage of the Li+ battery. Then, the SoC and parameters of the battery model are estimated concurrently, wherein the SoC and the parameters are estimated based on the battery model, the function, and on-line measurements of the charge current, the discharge current, and the output voltage.

Abstract: A method of determining the location of actuators and sensors for climate control that includes providing a model of temperature and airflow within a room. A matrix for the placement of sensors is calculated using a Lyapunov equation. A Lyapunov equation includes a matrix for the transition state from the model of temperature and airflow. A trace of the matrix for the placement of sensors is maximized to provide optimum placement of the sensors. A matrix for the placement of actuators within the model is calculated using the Lyapunov equation. A variable for the Lyapunov equation includes the matrix for the transition state obtained from the model of temperature and airflow. A trace of the matrix for the placement of actuators is maximized to provide optimum placement of the actuators within the room.

Abstract: A state of charge (SoC) a lithium-ion (Li+) battery is estimated at a time instant by first constructing a model of the Li+ battery based on a single particle operation. The model describes a relationship between the SoC, a charge current, a discharge current, and an output voltage. Using a nonlinear optimization, a function expressing a relationship between the SoC and an open circuit voltage is determined. The relationship is based on off-line measurements of the charge current, the discharge current, and the open-circuit voltage. Then, the SoC and parameters of the model are estimated concurrently, wherein the SoC and the parameters are based on the model, the function, and on-line measurements of the charge current, the discharge current, and the output voltage.

Abstract: A method estimates a state-of-charge (SoC) of a battery by constructing a set of two or more battery models. Each battery model is associated with an adaptive SOC estimator. A set of intermediate SOCs is estimated using the models and the associate adaptive SoC estimators. Then, the set of intermediate SoCs are fused to obtain a final SoC of the battery.