Gm Global Technology Operations LLC

Carnegie Mellon University

Junfeng Zhao

Yiran Hu

Dongxu Li

Chen-Fang Chang

Steven E Muldoon

Jun-Mo Kang

Suresh Gopalakrishnan

Chunhao J Lee

Yue-Yun Wang

Chen-fang Chang

Bharatkumar Hegde

Ragunathan Rajkumar

Norman K Bucknor

Lei Hao

Vivek Vijaya Kumar

Yongjie Zhu

Insu Chang

Jay H Sim

Lung En Jan

Matthew Rademacher

A system in an electric or hybrid vehicle includes two or more battery packs to power one or more motors of the vehicle to move the vehicle. A controller dynamically activates an operational mode of the vehicle during vehicle operation, the operational mode being one of a plurality of possible operational modes. Each of the possible operational modes defines which of the two or more battery packs are connected to the one or more motors and, based on two or more of the two or more battery packs being connected to the one or more motors, also defines an interconnection among the two or more of the two or more battery packs. The plurality of possible operational modes includes a first operational mode defining a series connection between the two or more battery packs and second operational mode defining a parallel connection between the two or more battery packs.

Multi-voltage vehicle operation

A mixed chemistry battery including a sensing cell having a first chemistry, a battery cell having a second chemistry that is different than the first chemistry, where the battery cell is connected to the sensing cell in series. The mixed chemistry battery also includes a battery monitoring system configured to monitor a current flow through the sensing cell and the battery cell and to calculate a state-of-charge (SOC) of the sensing cell. The battery monitoring system is further configured to calculate a SOC of the battery cell based at least in part on the SOC of the sensing cell.

State of charge sensing for a mixed chemistry battery

Examples described herein provide a method that includes determining whether a vehicle is operating in a first high voltage mode or a second high voltage mode. The method further includes, responsive to determining that the vehicle is operating in the first high voltage mode, providing electric power to an electric motor at a first high voltage and providing electric power to an auxiliary device at a second high voltage that is different than the first high voltage. The method further includes, responsive to determining that the vehicle is operating in the second high voltage mode, providing electric power to the electric motor at the second high voltage and providing electric power to the auxiliary device at the second high voltage.

High voltage switching for an electric vehicle

A control system of a vehicle includes: a target speed module configured to, using a parametric driver model and based on first driver parameters, second driver parameters, and vehicle parameters, determine a target vehicle speed trajectory for a future predetermined period; a driver parameters module configured to determine the first driver parameters based on conditions within a predetermined distance in front of the vehicle; and a control module configured to adjust at least one actuator of the vehicle based on the target vehicle speed trajectory and a present vehicle speed.

Control systems and methods using parametric driver model

A method for controlling automated driving operations of a vehicle includes determining vehicle origin and destination data, and generating a graphical representation of a road network with multiple candidate routes between the vehicle's origin and destination. Road-level data, including speed, turn angle, and/or gradient data, is received for each candidate route, and respective total energy uses are estimated for the vehicle to traverse across the candidate routes. Multiple candidate driving strategies, each having respective speed and acceleration profiles, are determined for the candidate route with the lowest estimated total energy use. An optimal candidate driving strategy is selected through a cost evaluation of the associated speed and acceleration profiles and forward movement simulations of the vehicle over a prediction horizon. Command signals are transmitted to the vehicle's steering and/or powertrain systems to execute control operations based on the optimal driving strategy and the candidate route with the lowest energy use.

Connected and automated vehicles, driving systems, and control logic for info-rich eco-autonomous driving

Operating a subject vehicle equipped with an adaptive cruise control system includes setting initial states for control parameters, including setting a desired vehicle speed and determining a desired following gap range, wherein the desired following gap range is associated with a lead vehicle. Operation is controlled via the adaptive cruise control system based upon the initial states for the control parameters. Operation also includes monitoring for presence of the lead vehicle. Upon detecting presence of the lead vehicle, an actual following gap is determined between the subject vehicle and the lead vehicle, and the initial states of the control parameters associated with the adaptive cruise control system are adjusted based upon the actual following gap between the subject vehicle and the lead vehicle, and the desired following gap range. Operation is controlled via the adaptive cruise control system based upon the adjusted initial states of the control parameters.

Method and apparatus for controlling a vehicle including an adaptive cruise control system

Presented are intelligent vehicle systems and control logic for predictive charge planning and powertrain control of electric-drive vehicles, methods for manufacturing/operating such systems, and electric-drive vehicles with smart charge planning and powertrain control capabilities. Systems and methods of AI-based predictive charge planning for smart electric vehicles use machine-learning (ML) driver models that draws on available traffic, location, and roadway map information to estimate vehicle speed and propulsion torque requirements to derive a total energy consumption for a given trip. Systems and methods of AI-based predictive powertrain control for smart hybrid vehicles use ML driver models with deep learning techniques to derive a drive cycle profile defined by a preview route with available traffic, geopositional, geospatial, and map data. ML-generated driver models are developed with collected data to replicate driver behavior and predict the drive cycle profile, including predicted vehicle speed, propulsion torque, and accelerator/brake pedal positions for a preview route.

Electric-drive motor vehicles, systems, and control logic for predictive charge planning and powertrain control

A system and method for routing based on a predicted connectivity quality is disclosed. The method includes receiving, by a controller, map information corresponding to a geographical area. The method also includes receiving, by the controller, wireless connectivity information corresponding to the geographical area. The method also includes generating output data of at least one route between a starting location and a destination location within the geographical area. The generating is based on the wireless connectivity information.

Method and system for routing based on a predicted connectivity quality

A system and method are provided for operating a powertrain control system. The method includes receiving data measured from a plurality of sensors, the measured data relating to distance dependent speed values, and receiving information from one or more vehicle modules, the vehicle module information relating to distance independent speed values. The method further includes building a speed trajectory profile for a horizon window that includes a plurality of speed change regions represented by at least some distance dependent speed values or at least some distance independent speed values, and creating a synthesized speed profile for the horizon window by processing the speed trajectory profile. The synthesized speed profile optimizes efficiency of the powertrain control system at each of the plurality of speed change regions.

Powertrain control system and method of operating the same

A multi-cylinder spark-ignition internal combustion engine includes a monitored cylinder and a plurality of non-monitored cylinders, a pressure sensor disposed to monitor in-cylinder pressure in the monitored cylinder and a rotational position sensor. The controller is in communication with the pressure sensor, the rotational position sensor and the spark controller. The controller monitors, via the pressure sensor, in-cylinder pressure for the monitored cylinder during a combustion event, and determines a first spark timing based upon a desired spark timing and the in-cylinder pressure. A spark controller controls spark timing for the monitored cylinder based upon the final spark timing. The controller monitors, via the rotational position sensor, engine rotational speed and crankshaft speed pulsations associated with the monitored cylinder and the non-monitored cylinders, and determines a modal coefficient for the non-monitored cylinders based upon the engine rotational speed and the crankshaft speed pulsations.

Method and apparatus for controlling a multi-cylinder internal combustion engine

Disclosed are model-based combustion timing systems and control logic for engine assemblies, methods for making/operating such engine assemblies, and motor vehicles with spark-ignited engine assemblies implementing model-based combustion timing. A method for controlling torque output of an engine assembly includes receiving a requested torque demand for the engine, and determining a current fuel command and valve timing for the engine's power cylinder(s). A first math model is used to determine a desired CA50 based on the requested torque demand, a power cylinder indicated mean effective pressure (IMEP), an expander cylinder IMEP, and the current fuel command/valve timing. A second math model is used to determine a maximum brake torque (MBT) CA50 based on power cylinder and expander cylinder IMEPs, and current fuel command/valve timing. An engine control unit determines a final spark timing based on a correlation between the desired CA50 and MBT CA50, modified by a spark timing gain.

Model-based combustion timing and torque control for internal combustion engine assemblies

A method for generating energy-optimized travel routes with a vehicle navigation system includes generating candidate travel routes between a route origin and one or more route destinations, and then dividing each candidate travel route into a plurality of route segments. The method includes estimating expected travel speeds along each segment using cloud information and calculating an expected energy efficiency over each of the candidate travel routes using one or more vehicle-specific energy efficiency models. The travel routes are displayed via the navigation system, including a trace of the energy-optimized travel routes and an expected or relative energy efficiency along the energy-optimized travel routes. A vehicle includes the navigation system and a powertrain. A powertrain controller may control vehicle speed over a selected route to maintain an optimally energy-efficient speed.

Energy-optimized vehicle route selection

A vehicle engine system includes a combustion engine configured to provide a propulsion torque to satisfy a propulsion demand in response to a driver input. The engine system also includes a controller programmed to receive data indicative of at least one upcoming driving event, and issue a command to impart a predetermined velocity profile based on the upcoming driving event. The predetermined velocity profile is arranged to optimize a performance attribute of the combustion engine and preempt the driver input.

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Troy, MI, United States of America

Junfeng Zhao