Abstract: A vehicle includes an electric machine and a controller. The controller is programmed to, in response to releasing an accelerator pedal during a first driving scenario that is based on a first set of navigation data, increase regenerative braking torque of the electric machine to a first value. The controller is further programmed to, in response to releasing the accelerator pedal during a second driving scenario that is based on a second set of navigation data, increase the regenerative braking torque of the electric machine to a second value that is less than the first value.

Abstract: A powertrain system includes a powertrain and a controller. The controller commands a change in torque produced by the powertrain for a given change in pedal position according to a default calibration schedule that is defined by a driving style of a driver such that the change in torque is different for different drivers.

Abstract: A vehicle includes an electric machine and a controller. The controller is programmed to, in response to releasing an accelerator pedal during a first driving scenario that is based on a first set of navigation data, increase regenerative braking torque of the electric machine to a first value. The controller is further programmed to, in response to releasing the accelerator pedal during a second driving scenario that is based on a second set of navigation data, increase the regenerative braking torque of the electric machine to a second value that is less than the first value.

Abstract: A powertrain control system includes a controller that, when attribute data is indicative of an expected deceleration having a magnitude that exceeds a threshold within a predefined duration of time after receipt of an engine on request, inhibits start of an engine, and when the attribute data is indicative of an expected deceleration having a magnitude that does not exceed the threshold within the predefined duration, permits start of the engine.

Abstract: A vehicle such as a hybrid electric vehicle (HEV) and method of operation, which include controller(s) coupled to a communication unit, configured to respond to a trip signal, and in response, to generate a driver notification to adjust vehicle performance parameters, such as acceleration, speed, braking, and others. The driver notification is generated to reduce fuel and/or battery consumption, and according to a recommendation signal that is received from and generated by a remote fleet server, and in response to instantaneous vehicle operating conditions that are communicated in real-time from the vehicle to the remote server. The recommendation signal includes a fuel and/or a battery consumption estimate, among other data. The vehicle controller is also responsive to detecting the one or more adjusted vehicle performance parameters, and to generate and store one or more estimate errors, which are respective differences between the estimates and actual fuel and battery consumption.

Abstract: Methods and systems are provided for diagnosing an engine thermal encapsulation positioned around a powertrain of a vehicle. In one example, degradation of the thermal encapsulation may be determined by comparing a temperature sampled via a sensor during engine off condition with an expected temperature.

Abstract: Methods and systems are provided for diagnosing an engine thermal encapsulation positioned around a powertrain of a vehicle. In one example, degradation of the thermal encapsulation may be determined by comparing a temperature sampled via a sensor during engine off condition with an expected temperature.

Abstract: A system and method for powertrain testing of a powertrain under load using a computer network includes a plurality of computer nodes for monitoring and adjustably controlling the powertrain and applied load for acquiring powertrain data relating to powertrain performance. A shared memory is connected to the plurality of computer nodes for receiving in real time the powertrain data generated by each of the plurality of computer nodes. An integrating computer node is connected to the shared memory for receiving in real time powertrain data and is arranged to send different commands to the plurality of computer nodes as part of a powertrain testing process. Further, the present invention allows for the powertrain testing of a powertrain in deterministic real time to develop the powertrain, as well as create a powertrain calibration.

Abstract: A system and method for powertrain testing of a powertrain under load using a computer network includes a plurality of computer nodes for monitoring and adjustably controlling the powertrain and applied load for acquiring powertrain data relating to powertrain performance. A shared memory is connected to the plurality of computer nodes for receiving in real time the powertrain data generated by each of the plurality of computer nodes. An integrating computer node is connected to the shared memory for receiving in real time powertrain data and is arranged to send different commands to the plurality of computer nodes as part of a powertrain testing process. Further, the present invention allows for the powertrain testing of a powertrain in deterministic real time to develop the powertrain, as well as create a powertrain calibration.

Abstract: In one embodiment of the present invention, a fault detection system for a dynamometer includes circuitry for measuring the armature voltage of the dynamometer. The voltage can used as an indicator of dynamometer speed to prevent reverse rotation of the dynamometer and to prevent overspeed operation of the dynamometer.

Abstract: A motor vehicle related operating signal, such as a torque signal produced by operation of an engine of the motor vehicle, is monitored to produce a frequency signature for the vehicle. The frequency signature is filtered by a fuzzy spectral filter to extract a frequency membership for the vehicle which is utilized to generate characteristic signals representative of the vehicle. Signals representative of vehicle inertia (J), vehicle horsepower (HP) and relative vehicle temperature (RVT) are extracted by a radial basis function (RBF) neural network. These characteristic signals are then used to control the vehicle directly via a powertrain control module (PCM) or via a robot driver for vehicle test purposes. For robot control, an anticipated throttle lag and an anticipated brake lag are generated and used to more accurately and repeatedly control the robot to simulate a human driver in following acceleration curves for vehicle testing purposes.