A vehicle air conditioning system, including: a vehicle interior environmental information acquisition unit that acquires environmental information of a vehicle interior; a vehicle exterior environmental information acquisition unit that acquires environmental information of surroundings of the vehicle; a sensible temperature prediction unit that predicts a sensible temperature of an occupant of the vehicle after a predetermined amount of time has elapsed, based on the environmental information acquired by the vehicle cabin interior environmental information acquisition unit and the vehicle cabin exterior environmental information acquisition unit; and an air conditioner controller that controls an air conditioner based on information regarding a future sensible temperature of the occupant which has been predicted by the sensible temperature prediction unit and a comfortable sensible temperature of the occupant which is stored in a storage unit.

Disclosed is a temperature control method which includes acquiring temperature data of a plurality of temperature detection points in a target environment; calculating, according to the temperature data, an average temperature value of the plurality of temperature detection points and a first temperature difference between the average temperature value and a target temperature value; determining whether an absolute value of the first temperature difference exceeds a first temperature difference threshold; and in response to the absolute value of the first temperature difference exceeding the first temperature difference threshold, controlling the temperature of the target environment by a variable universe fuzzy proportional integral derivative control algorithm.

A computer includes a processor and a memory, the memory storing instructions executable by the processor to collect (a) ambient weather data, (b) vehicle speed data including at least one of a vehicle speed or an engine speed, and (c) operation data of a climate control subsystem of a vehicle, input the collected data to a regression program trained to output a predicted pressure of refrigerant of the climate control subsystem, the regression program trained with previously determined ambient weather data, data of a previous vehicle speed or a previous engine speed, and previous operation data of the climate control subsystem, determine an actual pressure of the refrigerant in the climate control subsystem, and actuate a component upon determining that a difference between the predicted pressure and the actual pressure falls below threshold.

Methods and apparatus for vehicle climate control using distributed sensors are disclosed. A disclosed example method includes receiving data from sensors distributed within a vehicle at a first controller, processing the data at the first controller to identify an event associated with the interior of the vehicle and sending an instruction based on the event from the first controller to a second controller of the vehicle to affect an operation of a climate control system of the vehicle.

A method of controlling a temperature altering element within a seating assembly of a vehicle comprising: presenting a vehicle including a seating assembly including a temperature altering element, a controller in communication with the temperature altering element, the controller including a Pre-established Predictive Activation Model setting forth rules governing the activation of the temperature altering element as a function of data relating to Certain Identifiable Conditions, and a user interface configured to allow the temperature altering element to be manually activated or deactivated; occupying the seating assembly with a first occupant; collecting data relating to the Certain Identifiable Conditions while the first occupant is occupying the seating assembly; determining, by comparing the collected data to the rules of the Pre-established Predictive Activation Model, whether the collected data satisfies the rules of the Pre-established Predictive Activation Model so as to activate the temperature altering element; and activating the temperature altering element.

Systems and methods are disclosed for supporting and executing automated control of climate comfort settings based on user classification within a group identity database. Methods of generating and employing the group identity database are also disclosed.

Operating a motor vehicle (1) by adjusting the temperature of one or more vehicle parts (3, 4). One method includes specifying (S2) an indication of an expected beginning-of-driving time (FBZ) of the motor vehicle (1) and a desired operating temperature (BT) of the one or more vehicle parts (3, 4); and waking up (S6) a controller (22), which is configured to control adjustment of the temperature of the respective vehicle part (3, 4), at a temperature-adjustment starting time (A2) which is chosen such that, if heating up or cooling down begins at the temperature-adjustment starting time (A2), the heating up or cooling down ends, by reaching the desired operating temperature (BT) of the corresponding vehicle part (3, 4), at a time which corresponds to the expected beginning-of-driving time (FBZ) or is just before the expected beginning-of-driving time (FBZ).

A computer for an energy management system of an electric vehicle includes a processor. The computer further includes a memory including instructions such that the processor is programmed to determine a value function V based on a plurality of actions U in a plurality of states S. The processor is further programmed to select an action associated with a highest reward value at a current state S. The action U is an HVAC subsystem variable. The state S is a traction power drawn from a rechargeable energy storage system (RESS) to operate a traction subsystem, a base power input drawn from the RESS to operate an HVAC subsystem, a nominal reference cabin heat input set-point determined by the local HVAC processor, an acceleration of the electric vehicle, a current vehicle speed, an average vehicle speed, and a calibrated average vehicle speed estimate.

A method for controlling an occupant microclimate system includes determining an occupant personal parameter, determining a vehicle environmental condition, predicting an initial set point value for a plurality of thermal effectors associated with the occupant from a portion of a master dataset based on the occupant personal parameter3 and the vehicle environmental condition, and regulating the plurality of thermal effectors based upon the initial set point values.

A method for predicting an impending climate control failure for a transport temperature control system (TCCS) is provided. The method includes a backend obtaining one or more operational parameters and/or one or more control parameters of transport temperature control systems including the TCCS. The method also includes obtaining warrantee data and/or service records for the transport temperature control systems. The method further includes training a machine learning model with the warrantee data and/or service records for the transport temperature control systems, and at least one of the operational parameters of the transport temperature control systems or the control parameters of the transport temperature control systems. Also the method includes deploying the trained machine learning model. The method further includes predicting the impending climate control failure for the TCCS based on the trained machine learning model, operational parameters of the TCCS, and/or control parameters of the TCCS.