Battery model with robustness to cloud-specific communication issues

• US 9,446,678 B2
• Filed: 03/05/2014
• Issued: 09/20/2016
• Est. Priority Date: 03/05/2014
• Status: Active Grant

• Current Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Original Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Inventors: Meyer, Jason
• Primary Examiner(s): Tso, Edward
• Assistant Examiner(s): Bui, Dung V
• Application Number:
  US14/197,725
• Publication Number:
  US 20150251556A1
• Time in Patent Office: 930 Days
• Field of Search: 324/427, 320/132, 320/149, 702/63
• US Class Current: 1/1
• CPC Class Codes: B60K 6/442 Series-parallel switching type
• CPC Class Codes: B60L 15/2009 for braking
• CPC Class Codes: B60L 2210/10 DC to DC converters
• CPC Class Codes: B60L 2210/40 DC to AC converters
• CPC Class Codes: B60L 2240/545 Temperature
• CPC Class Codes: B60L 2240/547 Voltage
• CPC Class Codes: B60L 2240/549 Current
• CPC Class Codes: B60L 2240/70 Interactions with external data bases, e.g. traffic centres
• CPC Class Codes: B60L 2240/80 Time limits
• CPC Class Codes: B60L 2260/44 by parameter estimation
• CPC Class Codes: B60L 3/0046 relating to electric energy storage systems, e.g. batteries or capacitors
• CPC Class Codes: B60L 3/12 Recording operating variables ; Monitoring of operating variables
• CPC Class Codes: B60L 50/16 with provision for separate direct mechanical propulsion
• CPC Class Codes: B60L 50/61 by batteries charged by engine-driven generators, e.g. series hybrid electric vehicles
• CPC Class Codes: B60L 50/66 Arrangements of batteries
• CPC Class Codes: B60L 58/12 responding to state of charge [SoC]
• CPC Class Codes: B60L 58/16 responding to battery ageing, e.g. to the number of charging cycles or the state of health [SoH]
• CPC Class Codes: B60L 58/21 having the same nominal voltage
• CPC Class Codes: B60L 58/24 for controlling the temperature of batteries
• CPC Class Codes: B60L 7/14 for vehicles propelled by ac motors
• CPC Class Codes: B60W 10/26 : for electrical energy, e.g. batteries or capacitors
• CPC Class Codes: B60W 2510/244 : Charge state
• CPC Class Codes: G01R 31/3648 : comprising digital calculation means, e.g. for performing an algorithm
• CPC Class Codes: G01R 31/367 : Software therefor, e.g. for battery testing using modelling or look-up tables
• CPC Class Codes: G01R 31/382 : Arrangements for monitoring battery or accumulator variables, e.g. SoC
• CPC Class Codes: Y02T 10/62 : Hybrid vehicles
• CPC Class Codes: Y02T 10/64 : Electric machine technologies in electromobility
• CPC Class Codes: Y02T 10/70 : Energy storage systems for electromobility, e.g. batteries
• CPC Class Codes: Y02T 10/7072 : Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
• CPC Class Codes: Y02T 10/72 : Electric energy management in electromobility
• CPC Class Codes: Y02T 90/16 : Information or communication technologies improving the operation of electric vehicles

Abstract

A vehicle may include battery cells and a controller configured to control the vehicle, based on a state observation of the battery cells, using model parameters computed according to controller-determined cell nominal parameters adjusted by remotely-determined cell delta parameters, the delta parameters received from an external computing device responsive to cell measurements relating to a cell battery model sent to the external computing device, the delta parameters indicating adjustments that improve accuracy of the nominal parameters.

Claims

1. A vehicle comprising:

battery cells; and

a controller configured to control the vehicle, based on a state observation of the battery cells, using model parameters computed according to controller-determined cell nominal parameters adjusted by remotely-determined cell delta parameters, the delta parameters received from an external computing device responsive to cell measurements relating to a cell battery model sent to the external computing device, the delta parameters indicating adjustments that improve accuracy of the nominal parameters.

2. The vehicle of claim 1, wherein the controller is further configured to estimate the nominal parameters according to a function of the cell measurements, and the cell measurements include cell voltage, cell current and cell temperature.

3. The vehicle of claim 1, wherein each nominal parameter has an associated delta parameter, and the controller is further configured to identify each of the model parameters as one of (i) a sum of a nominal parameter and an associated delta parameter, and (ii) a product of the nominal parameter and the associated delta parameter.

4. The vehicle of claim 1, wherein the battery model includes a battery equivalent circuit model, and the controller is further configured to perform the state observation to estimate battery state of charge (SOC).

5. The vehicle of claim 4, wherein the measurements further include a previous estimate of battery SOC.

6. The vehicle of claim 4, wherein the controller is further configured to compute an initial estimate of SOC according to ampere-hour integration.

7. A system comprising:

a computing device configured to

receive, from a vehicle via a communication network, battery cell measurements corresponding to a battery model of battery cells of the vehicle; and

send, to the vehicle via the communication network responsive to the measurements, cell delta parameters indicating adjustments to cell nominal parameters determined by the vehicle for the cells, the delta parameters indicating adjustments that improve accuracy of the nominal parameters.

8. The system of claim 7, wherein the measurements include measurements of cell voltage, cell current and cell temperature.

9. The system of claim 7, wherein the battery model includes a battery equivalent circuit model configured to supply battery equivalent circuit model parameters to estimate a battery state of charge (SOC).

10. The system of claim 9, wherein the measurements further include a previous estimate of battery SOC.

11. The system of claim 7, wherein the computing device is further configured to

estimate the nominal parameters according to a function of the cell measurements;

and

compute the cell delta parameters as a difference of model parameters identified according to a battery equivalent circuit model and the nominal parameters.

12. The system of claim 7, wherein the computing device is further configured to compute the cell delta parameters according to direct identification of model parameters identified according to a battery equivalent circuit model.

13. A method comprising:

controlling a vehicle by a controller, based on a state observation of battery cells, using model parameters computed according to controller-determined cell nominal parameters adjusted by remotely-determined cell delta parameters, the delta parameters received by the controller from an external computing device responsive to cell measurements relating to a battery model sent to the computing device, the delta parameters indicating adjustments that improve accuracy of the nominal parameters.

14. The method of claim 13, further comprising estimating the nominal parameters according to a function of the cell measurements, the cell measurements including cell voltage, cell current and cell temperature.

15. The method of claim 13, wherein each nominal parameter has an associated delta parameter, and further comprising identifying each of the model parameters as one of (i) a sum of a nominal parameter and an associated delta parameter, and (ii) a product of the nominal parameter and the associated delta parameter.

16. The method of claim 13, wherein the battery model includes a battery equivalent circuit model, and further comprising performing the state observation to estimate battery state of charge (SOC).

17. The method of claim 13, wherein the measurements further include a previous estimate of battery SOC.

18. The method of claim 13, further comprising computing an initial estimate of SOC according to ampere-hour integration.