Gm Global Technolgoy Operations, Inc.

Garrett M Seeman

Yue-Yun Wang

Jeffrey S Piasecki

Justin Bunnell

Chaitanya Sankavaram

Brian James Koch

Shifang Li

Alfred Zhang

Andres V Mituta

Ciro A Spigno

Calvin Wang

Chandra S Namuduri

Suresh Gopalakrishnan

Lei Hao

Jun-Mo Kang

Daniel P Grenn

Azeem Sarwar

James C Gibbs

Jing Gao

Charles Weldon Wampler, Ii

Russell K Steele

Shaochun Ye

Brandon R Jones

Mohamed Kamel

Emil Francu

Justin R McDade

Jeffrey A Bednar

Joseph Cho

Xiumei Guo

Zining Zhang

An example vehicle battery control system includes an electric motor, a vehicle battery pack configured to supply power to the electric motor, the vehicle battery pack including multiple battery modules each including multiple rechargeable battery cells, and a vehicle control module configured to measure an output current of the vehicle battery pack, estimate a state of charge of the vehicle battery pack using a coulomb counter, and for each of the multiple battery modules, measure an output voltage of the battery module, determine a state of charge shifting bias value for the battery module, estimate an individual state of charge of the battery module by applying the state of charge shifting bias value to the state of charge of the vehicle battery pack, and control at least one of a charging current and a discharging current according to the individual state of charge of the battery module.

State of charge control system for vehicle battery modules

A system for monitoring a battery of a vehicle includes a current measuring circuit to measure a current of the battery comprising a plurality of cell groups connected to each other. A voltage measuring circuit is to measure voltages of the cell groups. A controller is configured to define a plurality operating regions in a current profile of the battery during a drive cycle of the vehicle. The controller is configured to filter the current and the voltages measured in the operating regions and calculate internal resistances of the cell groups in the operating regions based on the filtered current and voltages. The controller is configured to generate statistical values based on the internal resistances of the cell groups and determine whether one or more of the cell groups is faulty based on differences between maximum and minimum values of one of the statistical values across the cell groups.

Resistance estimation of high voltage battery packs during vehicle driving operation

A supervisory computer is used with an energy management system of an electric vehicle. The energy management system includes a battery system having a plurality of battery subsystems. One of the battery subsystems is an abnormal battery subsystem while the remaining battery subsystems are normal battery subsystems. The supervisory computer includes at least one processor and at least one non-transitory computer-readable medium. The processor monitors the remaining state of charge, capacity, and resistance of the battery system and monitor the remaining state of charge and capacity of the abnormal battery subsystem, calculate a low integration bound value, calculate a remaining energy value for all of the normal battery subsystems with respect to the low integration value, calculate a remaining energy value for the abnormal battery subsystem, and summate the remaining energy values of the normal and abnormal battery subsystems to determine a global remaining energy value for the battery system.

Energy management system for an electric vehicle

A current measuring circuit measures a charging current of a battery comprising a plurality of cell groups while the battery is being charged using one of a plurality of charging systems. A voltage measuring circuit measures voltages of the cell groups. A controller defines a plurality operating regions in a charging current profile of the battery during a charging cycle, filters the charging current and the voltages measured in the operating regions, calculates internal resistances of the cell groups in the operating regions based on the filtered current and voltages, generates at least one of statistical values and distance metrics based on the internal resistances of the cell groups, and determines whether one or more of the cell groups is faulty based on the at least one of the statistical values and the distance metrics irrespective of the plurality of charging systems used to charge the battery.

Resistance estimation of high voltage battery packs during vehicle charging operation

A method is disclosed for determining the state of health of an electric battery that includes a plurality of battery cells. The method includes the steps of measuring the cell voltage of each individual cell of the plurality of battery cells, and analyzing each measured battery cell voltage to determine the state of health of the corresponding battery cell.

Drooping cell detection and state of cell health monitoring

A method for battery capacity estimation is provided. The method includes monitoring a sensor, collecting a plurality of data points including a voltage-based state of charge value and an integrated current value, defining within the data points a first data set collected during a first time period and a second data set collected during a second time period, determining an integrated current error related to the second data set, comparing the integrated current error related to the second data set to a threshold integrated current error. When the error related to the second data set exceeds the threshold, the method further includes resetting the second data set based upon an integrated current value from the first time period. The method further includes combining the data sets to create a combined data set and determining a voltage slope capacity estimate as a change in integrated current versus voltage-based state of charge.

Method and system for battery capacity estimation using voltage slope capacity and dynamic anchors

Powerflow of a rechargeable energy storage system (RESS) is managed according to a method. The RESS has series-connected first and second battery elements with different characteristics. Each element, e.g., a pack, has a corresponding maximum or minimum voltage or current limit. Currents are predicted for each of the first and second battery elements via a controller using a corresponding voltage limit. A requested operating mode of the RESS is used to select a current for the elements. A voltage across the elements is predicted using the selected current and a corresponding battery state space model. The method predicts a total power capability of the RESS over a prediction horizon using the selected current to generate predicted power capability values. The requested operating mode is controlled over the horizon using the power capability values. A powertrain system includes the RESS, an inverter, an electric machine, and the controller.

Power prediction for reconfigurable series-connected battery with mixed battery chemistry

A battery system and a method include an auxiliary power module configured to support auxiliary loads. A first contactor switch connected between first and second battery packs, and a second contactor switch is in series with the first contactor switch. A controller determines whether to open or close first and second contactor switches depending on whether the battery packs are being charged in a high voltage mode or a low voltage mode. The contactor switches are both closed when in the high voltage mode and at least one of the contactor switches is opened when in the low voltage mode. At least one of the first and second battery packs operate to power the auxiliary power module while charging at least one of the first and second battery packs regardless of whether the battery packs are in the high voltage mode or the low voltage mode.

Battery system and a method for use in the battery system

A distributed battery power system having a battery pack and a battery controller. The battery pack has: a plurality of cells configured to generate a plurality of cell voltages; a voltage current temperature module electrically connected to the plurality of cells; and a plurality of isolation switch sets electrically connected between the plurality of cells. The battery controller is in communication with the voltage current temperature module, and operable to: send a status request to the voltage current temperature module; receive the plurality of cell voltages from the voltage current temperature module in response to the status request; determine if the plurality of cells includes one or more problem cells in response to the plurality of cell voltages; and perform an action in response to determining that the one or more problem cells are present to prevent damage to the one or more problem cells.

Distributed battery power estimation with weaker cell monitoring

A battery system is electrically connected to a power bus that is arranged to supply electric power to an on-vehicle actuator such as an electric traction machine. Controlling electric power flow in the battery pack includes determining states of charge for the plurality of battery packs, identifying one of the battery packs as a weakest battery pack based upon the states of charge, and determining an internal circulating current being transferred via the wiring harness that is associated with charging of the weakest battery pack. Electric power transfer through the wiring harness and the power bus is controlled such that the internal circulating current is less than an internal circulating current limit. In one embodiment, the internal circulating current limit is determined in relation to battery temperature.

Method and apparatus for controlling electric power flow in a battery system

Management system for a rechargeable energy storage device in an electric vehicle and corresponding method is disclosed. The rechargeable energy storage device has one or more battery packs each having a plurality of modules with one or more respective cells. A respective module management unit is embedded in each of the plurality of modules through respective microcircuits and configured to determine one or more local parameters. A supervisory controller is configured for two-way communication with the respective module management unit. The supervisory controller is configured to receive the local parameters, determine one or more global pack parameters based in part on the local parameters and transmit the global pack parameters back to the respective management unit. The supervisory controller is configured to control operation of the rechargeable energy storage device based in part on the global pack parameters and the local parameters.

Management system with supervisory control for rechargeable energy storage device in electric vehicle

A system and associated method are described for balancing an electrical parameter associated with stored electric power in a multi-cell battery. A cell charge balancing system determines a first plurality of the electrical parameters associated with respective ones of the plurality of cells, a plurality of first charge parameters associated with the cells, and a first quality index. A second plurality of the electrical parameters are determined, and second charge parameters are determined to determine a second quality index. The first and second quality indices are compared, and a cell charge balancing routine controls the balancing system based upon the first quality index when the first quality index is greater than the second quality index. The cell charge balancing routine controls the cell charge balancing system based upon the second quality index when the first quality index is less than the second quality index.

Method and apparatus for battery cell charge balancing

System and method of controlling operation of a device having multiple rechargeable packs configured to store energy. The multiple rechargeable packs include at least one primary pack characterized by a first capacity and at least one auxiliary pack characterized by a second capacity, the first capacity being greater than the second capacity. A controller is configured to selectively command one or more of the multiple rechargeable packs to begin at least one of discharging and charging. When an estimated end of trip time is less than or equal to a discharging time of the at least one auxiliary pack, the discharging of the at least one auxiliary pack is begun. When the estimated end of trip time is greater than the discharging time, the discharging is delayed until the respective state of charge of the at least one primary pack reaches a first threshold.

Control of device having multiple rechargeable packs with different capacities for optimal energy delivery

A method for estimating remaining energy in a battery pack having series-connected cells/cell groups includes measuring battery parameters, including a battery voltage, current, and temperature. The controller estimates a static state of charge difference (&#x394;SOC) value and a current-dependent &#x394;SOC value in real-time using the parameters, including calculating the static &#x394;SOC value as a difference between an average SOC of the battery pack and an SOC of a weakest/lowest energy cell group. The current-dependent &#x394;SOC value is a percentage SOC per unit of the current. The static &#x394;SOC value and current-dependent &#x394;SOC values are filtered via a multi-parameter state estimator block. Using the filtered state values, the controller executes a control action responsive to the estimated remaining energy, including displaying the remaining energy and/or a quantity derived from the remaining energy via a display device. A powertrain system includes the controller, electric machine, and battery pack.

Method and system for estimating remaining battery pack energy using cell-group state of charge spread

Powerflow is managed using a method, e.g., in a powertrain system having a multi-pack rechargeable energy storage system (RESS) with parallel battery packs. Each pack has a corresponding maximum electrical (current or voltage) limit. The method includes predicting a corresponding terminal voltage for each pack using the corresponding maximum electrical limit. The method includes selecting a terminal voltage as a selected voltage based on a requested operating mode, including selecting a maximum of the terminal voltages when the requested operating mode is a discharging mode and a minimum of the same when the requested operating mode is a charging mode. A pack current through each pack is predicted using the selected voltage and a corresponding battery state space model. A total power capability of the RESS is predicted over a predetermined prediction horizon using the selected voltage, with the operating mode controlled over the prediction horizon via the controller.

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Novi, MI, United States of America

Garrett M Seeman