In one embodiment, there is provided a device for a vehicle, having: a first interface configured to couple to at least one replaceable fluid container for a vehicle comprising a battery, the first interface comprising at least one fluid port configured to couple to at least one fluid port of the replaceable fluid container; a second interface configured to couple to an engine of the vehicle, the second interface comprising at least one fluid port configured to couple to at least one fluid port of a fluid circulation system of the vehicle; a fluid path coupled to at least one fluid port of the first interface and at least one fluid port of the second interface; and at least one electrical pump configured to be powered and/or driven by the battery of the vehicle and to cause fluid flow.

Systems, methods, and computer-program products for fluid analysis and monitoring are disclosed. Embodiments include a removable and replaceable sampling system and an analytical system connected to the sampling system. A fluid may be routed through the sampling system and data may be collected from the fluid via the sampling system. The sampling system may process and transmit the data to the analytical system. The analytical system may include a command and control system to receive and store the data in a database and compare the data to existing data for the fluid in the database to identify conditions in the fluid. Fluid conditions may be determined using machine learning models that are generated from well-characterized known training data. Predicted fluid conditions may then be used to automatically implement control processes for an operating machine containing the fluid.

Systems, methods, and computer-program products for fluid analysis and monitoring are disclosed. Embodiments include a removable and replaceable sampling system and an analytical system connected to the sampling system. A fluid may be routed through the sampling system and data may be collected from the fluid via the sampling system. The sampling system may process and transmit the data to the analytical system. The analytical system may include a command and control system to receive and store the data in a database and compare the data to existing data for the fluid in the database to identify conditions in the fluid. Fluid conditions may be determined using machine learning models that are generated from well-characterized known training data. Predicted fluid conditions may then be used to automatically implement control processes for an operating machine containing the fluid.

Various embodiments include a method for determining the temperature of an engine oil in an internal combustion engine comprising: acquiring a value of a parameter characterizing a current operating point of the internal combustion engine; and calculating the temperature of the engine oil using an oil temperature model. The oil temperature model depends at least in part on dilution of the engine oil caused by different components in the engine oil and accounts for modified heating behavior of the engine oil based on the dilution.

Systems, methods, and computer-program products for fluid analysis and monitoring are disclosed. Embodiments include a removable and replaceable sampling system and an analytical system connected to the sampling system. A fluid may be routed through the sampling system and data may be collected from the fluid via the sampling system. The sampling system may process and transmit the data to the analytical system. The analytical system may include a command and control system to receive and store the data in a database and compare the data to existing data for the fluid in the database to identify conditions in the fluid. Fluid conditions may be determined using machine learning models that are generated from well-characterized known training data. Predicted fluid conditions may then be used to automatically implement control processes for an operating machine containing the fluid.

Methods and systems are provided for estimating engine oil temperature during conditions when sensors used for engine oil temperature measurement are not functional or not reliable. A null duty cycle of a solenoid spool valve of a variable cam timing mechanism is estimated. Then, a calibrated relationship between the duty cycle, an angular velocity of the associated cam, and the engine oil temperature is used to estimate an engine oil temperature when existing sensors used in the calculation of engine oil temperature calculation become unreliable.

Systems and methods are described for predicting a remaining oil life in an engine of a vehicle. One or more parameters of the engine can be monitored over a period of time. A hot compartment temperature can be determined from the parameter. A condition of the oil can be determined based on the hot compartment temperature, and the condition of the oil can be displayed.

A system and method for activating on a user interface an indicator of a condition of a lubricant in a machine module. The system may comprise a controller and a user interface. The controller may be configured to, for each of a first time window and a second time window: receive a plurality of lubricant characteristics and measured lubricant temperatures, calculate an adjusted lubricant characteristic, and determine a slope based on the plurality of adjusted lubricant characteristics. The controller may further determine a change in slope, and generate a signal to activate the indicator on the user interface based on the change in slope, wherein, if the change in slope exceeds a threshold, the indicator is a lubricant changed indicator.

A system includes an electronic fuel injection system of an engine, the electronic fuel injection system including an electronic governor control unit for controlling various functions of the engine.

Systems, methods, and computer-program products for fluid analysis and monitoring are disclosed. Embodiments include a removable and replaceable sampling system and an analytical system connected to the sampling system. A fluid may be routed through the sampling system and data may be collected from the fluid via the sampling system. The sampling system may process and transmit the data to the analytical system. The analytical system may include a command and control system to receive and store the data in a database and compare the data to existing data for the fluid in the database to identify conditions in the fluid. Fluid conditions may be determined using machine learning models that are generated from well-characterized known training data. Predicted fluid conditions may then be used to automatically implement control processes for an operating machine containing the fluid.