Gm Global Technology Operations LLC

Carnegie Mellon University

Chen-fang Chang

Bharatkumar Hegde

Chunhao J Lee

Jun-Mo Kang

Ibrahim Haskara

Yongjie Zhu

Dongxu Li

Yue-Yun Wang

Hanho Yun

Dave G Rich

Insu Chang

Brandon D Mazzara

Suresh Gopalakrishnan

Nicholas W Pinto

Shailendra Kaushik

Junfeng Zhao

Ragunathan Rajkumar

Jonathan Wilson

Lung En Jan

Matthew Rademacher

Xueyu Zhang

Madhumita Ramesh Babu

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 battery system includes: at least two battery modules, each including at least three strings of battery cells that are configured to, at different times be: connected in series and to a first positive terminal via first switches; connected in parallel and to a second positive terminal via second switches; and disconnected from both of the first and second positive terminals; and a switch control module configured to: during operation in a first power mode: determine periods of phases, respectively; and determine periods for the strings, respectively, to be connected to the second positive terminal during the phases; during operation in a second power mode: set the periods of the phases, respectively, to be equal in length; and set the periods for the strings, respectively, to be equal in length; and selectively actuate the switches based on the periods of the phases and the periods for the strings.

String and module switching control systems and methods for mode transitions

A method for controlling a vehicle charging and thermal operation includes monitoring at least a charge characteristic of a battery system and a thermal characteristic of the battery system. The method predicts a battery system temperature at a future time (t) based at least in part on the monitored characteristics using a predictive physics model and maintains the battery system temperature above a minimum temperature threshold and below a maximum temperature threshold by adjusting a charging profile based at least in part on the predicted battery system temperature.

Predictive control for proactive thermal and energy management charging

Aspects of the disclosure include the detection of battery cell degradation. An exemplary battery management system can include a memory, computer readable instructions, and one or more processors that perform operations that include: responsive to determining that a charging state has completed, measuring a first cell voltage of a cell of the battery pack; responsive to the first cell voltage exceeding a reference voltage, measuring a second cell voltage of the cell of the battery pack; responsive to the second cell voltage being equal to the reference voltage, measuring a cell pressure of the cell of the battery pack at the reference voltage; determining a difference between the cell pressure and a prior cell pressure at the reference voltage; and responsive to the difference between the cell pressure and the prior cell pressure exceeding a threshold, identifying the cell of the battery pack as a degraded cell.

Early detection of battery cell performance degradation

A battery system includes: a first positive terminal, a second positive terminal, and a negative terminal; at least two battery modules, each including switches and at least three strings of battery cells configured to, via the switches, at different times be: connected in series and to the first positive terminal; connected in parallel and to the second positive terminal; and disconnected from both of the first and second positive terminals; and a switch control module configured to, when a fault is diagnosed in one of the strings of one of the battery modules: at least one of: actuate the switches and isolate the one of the battery modules from the first and second positive terminals; and actuate the switches and isolate the one of the strings from the first and second positive terminals; and selectively actuate the switches of the remainder of the battery modules using model predictive control.

Switching of battery strings and modules when a fault is diagnosed in a battery string

A vehicle includes a propulsion system having multiple electric motors. A battery system is connected to the electric motors via a direct current to direct current (DC-DC) converter and an inverter. The battery system includes a battery assembly having a plurality of battery modules. Each of the battery modules has multiple individual battery cells arranged in battery packs, a set of sensors configured to monitor a set of parameters of each battery pack, and a plurality of battery pack mounts. Each battery pack mount includes a base plate and a top plate and an actuator configured to alter a distance between plates. A monitoring unit is configured to monitor a value representative of a battery pack pressure, determine whether the value falls within a pressure threshold window, and maintain the value within the pressure threshold window by actively altering the battery pack pressure.

Battery pressure active control

A vehicle operates a system and method of predicting a thermal runaway event in a battery pack of the vehicle. The battery pack includes a battery cell. The sensor obtains measurements of a parameter of the battery cell at a plurality of times. The processor is configured to determine a value of at least one feature of the battery cell from the measurements of the parameter, determine a likelihood of the thermal runaway event from the value of the at least one feature, and take an action to prevent the thermal runaway event from occurring based on the likelihood.

Architecture and method of thermal runaway prognostics with multi-parameter data fusion

A trip energy estimation system for a vehicle includes a traffic speed module configured to determine an average traffic speed along a projected route, a path information module configured to output path information indicating route features along the projected route, a perceived speed module configured to output a perceived vehicle speed along the projected route based on the average traffic speed and the path information, and a dynamic driving module configured to calculate and output a predicted driver speed based on the perceived vehicle speed and a feedback input indicative of the predicted driver speed. The dynamic driving module is configured to execute a machine learning algorithm to calculate the predicted driver speed.

Traffic-info-incorporated NNT driver model for EV trip energy prediction

A distributed learning system of a vehicle includes: a control module configured to: control a plant of the vehicle using a policy; send signals to a learning module including information on an impact of the control on the plant; and selectively control the plant using exploratory control; and the learning module, where the learning module is separate from the control module and is configured to selectively update the policy based on (a) the signals from the control module, (b) state parameters resulting from the control of the plant using the policy, and (c) performance feedback determined based on the control of the plant using the policy and the selective control of the plant using exploratory control, where the control module is configured to receive the exploratory control from the learning module.

Distributed embedded controller implementation for self-learning controls

A solar loading-based system includes a memory, a disturbance prediction module, a cabin temperature estimation module and a thermal control module. The memory stores a cabin thermal load model of an interior cabin of a host vehicle and a solar load prediction model. The disturbance prediction module: receives signals indicative of states of cabin thermal actuators and comfort metrics; and predicts an effect of solar loading over a known portion of a predicted route including predicting cabin temperatures based on the solar load prediction model, the states of the cabin thermal actuators, and the comfort metrics. The cabin temperature estimation module, based on the cabin thermal load model, determines a first comfort metric based on the predicted cabin temperatures. The thermal control module controls cabin thermal actuators to adjust cabin states, including the first comfort metric, to respective target values based on the predicted effect of solar loading.

Solar-load prediction for vehicular cabin thermal actuator control

A trip energy estimation system includes a memory and a control module. The memory stores average traffic speed, average traffic acceleration, driver speed, and driver acceleration data. The control module executes an algorithm to estimate an amount of energy for an electric vehicle to travel between two locations. The algorithm includes: determining, based on the average traffic speed data and the average traffic acceleration data, a baseline amount of energy for the electric vehicle to travel between the two locations; determining, based on the driver speed data and the driver acceleration data, a dynamic amount of energy corresponding to at least one of stop and go events, over speeding, or under speeding; and determining a total amount of energy based on the baseline amount of energy and the dynamic amount of energy. The control module, based on the total amount of energy, performs an operation including indicating a trip estimate.

Electric vehicle trip energy prediction based on baseline and dynamic driver models

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.

Energy management system for an electric vehicle

A method for generating energy-optimized travel routes for a motor vehicle includes one or more of the following: receiving an origin destination (OD) of the motor vehicle and an encrypted energy consumption database of the motor vehicle; generating N candidate routes for the OD; evaluating encrypted energy consumption over a route using an encrypted energy consumption database; applying at least one of homomorphic addition function or homomorphic multiplication function to the encrypted energy consumption data; and returning N candidate routes and their encrypted energy consumption to a client.

Secure eco-routing with databases under homomorphic encryption

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.

Growing community of inventors

Bloomfield Hills, MI, United States of America

Chen-fang Chang