Cooling strategy for battery systems

• US 10,099,562 B2
• Filed: 06/30/2015
• Issued: 10/16/2018
• Est. Priority Date: 10/15/2014
• Status: Active Grant

• Current Assignee: CPS Technology Holdings, LLC
• Original Assignee: Johnson Controls Technology Company (Johnson Controls International plc)
• Inventors: Jin, Zhihong H., Sisk, Brian C., Obasih, Kem M., Johnson, Mark R., Wyatt, Perry M., Aliyev, Timur L., Zhang, Zhenli
• Primary Examiner(s): Chen, Sibin
• Application Number:
  US14/788,223
• Publication Number:
  US 20160107526A1
• Time in Patent Office: 1,204 Days
• CPC Class Codes: B60L 2240/36 Temperature of vehicle components or parts
• CPC Class Codes: B60L 2240/423 Torque
• CPC Class Codes: B60L 2240/545 Temperature
• CPC Class Codes: B60L 2260/40 Control modes
• CPC Class Codes: B60L 2260/44 by parameter estimation
• CPC Class Codes: B60L 50/10 using propulsion power supplied by engine-driven generators, e.g. generators driven by combustion engines
• CPC Class Codes: B60L 50/60 using power supplied by batteries in combination with fuel cells B60L50/75
• CPC Class Codes: B60L 58/15 Preventing overcharging
• CPC Class Codes: B60L 58/24 for controlling the temperature of batteries
• CPC Class Codes: B60L 58/26 by cooling
• CPC Class Codes: B60L 7/10 Dynamic electric regenerative braking B60L7/22 takes precedence
• CPC Class Codes: G01R 31/36 Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
• CPC Class Codes: G01R 31/382 Arrangements for monitoring battery or accumulator variables, e.g. SoC
• CPC Class Codes: G01R 31/3842 combining voltage and current measurements
• CPC Class Codes: G01R 31/392 Determining battery ageing or deterioration, e.g. state of health
• CPC Class Codes: H05K 7/20145 Means for directing air flow, e.g. ducts, deflectors, plenum or guides
• 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

Abstract

Embodiments describe a battery system that includes a first battery module coupled to a regenerative braking system and a control module that controls operation of the battery system by: determining a predicted driving pattern over a prediction horizon using a driving pattern recognition model based in part on a battery current and a previous driving pattern; determining a predicted battery resistance of the first battery module over the prediction horizon using a recursive battery model based in part on the predicted driving pattern, the battery current, a present bus voltage, and a previous bus voltage; determining a target trajectory of a battery temperature of the first battery module over a control horizon using an objective function; and controlling magnitude and duration of electrical power supplied from the regenerative such that a predicted trajectory of the battery temperature is guided toward the target trajectory of the battery temperature during the control horizon.

Claims

1. A battery system configured to be used in an automotive vehicle, wherein the battery system comprises:

a first battery module electrically coupled to a regenerative braking system; and

a control module configured to control operation of the battery system by;

determining a predicted driving pattern of the automotive vehicle over a prediction horizon using a driving pattern recognition model based at least in part on a battery current and a previous driving pattern of the automotive vehicle;

determining a predicted battery resistance of the first battery module over the prediction horizon using a recursive battery model based at least in part on the predicted driving pattern, the battery current, a present bus voltage, and a previous bus voltage;

determining a target trajectory of a battery temperature of the first battery module over a control horizon using an objective function to balance effects of the battery temperature on aspects of the first battery module; and

controlling magnitude and duration of electrical power supplied from the regenerative braking system to the first battery module such that a predicted trajectory of the battery temperature is guided toward the target trajectory of the battery temperature during the control horizon.

2. The battery system of claim 1, comprising a second battery module electrically coupled in parallel with the first battery module, wherein the second battery module is configured to be electrically coupled to the regenerative braking system and has a different battery chemistry from the first battery module.

3. The battery system of claim 2, wherein the first battery module comprises a lithium ion battery and the second battery module comprises a lead-acid battery.

4. The battery system of claim 1, wherein the control module is configured to determine the predicted driving pattern of the automotive vehicle by:

identifying a portion of the previous driving pattern as a basis of the predicted driving pattern based at least in part on a profile of the battery current, wherein the previous driving pattern comprises previously determined battery currents of the first battery module; and

adjusting the portion of the previous driving pattern based on operational parameters of the automotive vehicle.

5. The battery system of claim 1, wherein the control module is configured to determine the predicted battery resistance of the first battery module by:

determining a change between the previous bus voltage and the present bus voltage;

determining the predicted driving pattern as an average current; and

determining the predicted battery resistance as an average resistance based at least in part on the change between the previous bus voltage and the present bus voltage, the average current, and the battery current.

6. The battery system of claim 1, wherein the control module is configured to determine the target trajectory of the battery temperature by:

determining a plurality of factor target trajectories of the battery temperature, wherein each of the plurality of factor target trajectories is associated with one aspect of the first battery module; and

determining the target trajectory by weighing each of the plurality of factor target trajectories using the objective function.

7. The battery system of claim 1, wherein the control module is configured to determine the target trajectory of the battery temperature by:

determining a battery life target trajectory of the battery temperature using a battery life model based at least in part on the predicted driving pattern and a current age of the first battery module;

determining a fuel economy target trajectory of the battery temperature using a fuel economy model based at least in part on the predicted driving pattern and the battery current; and

determining the target trajectory of the battery temperature by combining the battery life target trajectory and the fuel economy target trajectory based on the objective function.

8. The battery system of claim 1, comprising:

an electric motor of the regenerative braking system configured to convert mechanical energy produced by movement of the automotive vehicle into electrical energy;

or

an alternator of the regenerative braking system configured to convert mechanical energy produced by an internal combustion engine of the automotive vehicle into electrical energy.

9. The battery system of claim 1, comprising a thermal system configured to facilitate cooling the battery system, wherein the control module is configured to supplement the thermal system by reducing amount of charging and discharging performed by the first battery module.

10. The battery system of claim 1, wherein the control horizon comprises a first plurality of future time steps and the prediction horizon comprises a second plurality of future time steps, wherein the first plurality is less than or equal to the second plurality.

11. A tangible non-transitory, computer readable medium of a lithium ion battery system configured to store instructions executable by a processor in an automotive vehicle, wherein the instructions comprise instructions to:

determine, using the processor, temperature of a lithium ion battery module;

determine, using the processor, a temperature threshold;

instruct, using the processor, an electrical energy generator to output a high electrical power when the temperature of the lithium ion battery module is not greater than the temperature threshold to enable the lithium ion battery system to utilize a first amount of storage capacity to capture generated electrical energy; and

instruct, using the processor, the electrical energy generator to output a low electrical power when the temperature of the lithium ion battery module is greater than the temperature threshold to enable the lithium ion battery system to utilize a second amount of storage capacity to capture generated electrical energy, wherein the second amount is less than the first amount.

12. The tangible non-transitory, computer-readable medium of claim 11, comprising instructions to, when the temperature of the lithium ion battery module is not greater than the temperature threshold:

determine a target trajectory of the temperature of the lithium ion battery module over a control horizon;

determine battery parameter setpoints based at least in part on a thermal predictive model, wherein the thermal predictive model is configured to describe a relationship between the battery parameter setpoints and a predicted trajectory of the temperature over a prediction horizon; and

instruct the lithium ion battery system to implement the battery parameter setpoints such that the predicted trajectory of the temperature is guided toward the target trajectory, maintained below the temperature threshold, or both over the control horizon.

13. The tangible non-transitory, computer-readable medium of claim 12, comprising instructions to instruct a relay to electrically disconnect the lithium ion battery module from the lithium ion battery system when the temperature is greater than the temperature threshold.

14. The tangible non-transitory, computer-readable medium of claim 12, comprising instructions to:

determine, using the processor, a predicted driving pattern of the automotive vehicle over the prediction horizon based at least in part on a driving pattern recognition model;

determine, using the processor, a predicted battery resistance of the lithium ion battery module over the prediction horizon based at least in part on a recursive battery model; and

determine, using the processor, the predicted trajectory of the temperature over the prediction horizon based at least in part on;

the predicted driving pattern of the automotive vehicle over the prediction horizon;

the predicted battery resistance over the prediction horizon;

the temperature of the battery module; and

the target trajectory of the temperature of the lithium ion battery module, the temperature threshold, or both.

15. The tangible non-transitory, computer-readable medium of claim 11, wherein reducing from the first amount of storage capacity to the second amount of storage capacity causes the temperature of the lithium ion battery module to decrease.

16. The tangible non-transitory, computer-readable medium of claim 11, wherein the instructions to instruct the electrical energy generator to output a low electrical power comprise instructions to reduce charging and discharging performed by the lithium ion battery module.

17. A method for controlling temperature of a battery system, comprising:

determining, using a control module, temperature of a lithium ion battery module in the battery system;

determining, using the control module, a temperature threshold and a target trajectory of the temperature over a control horizon, wherein determining the target trajectory of the temperature comprises;

determining a battery life target trajectory using a battery life model, wherein the battery life model is configured to describe a relationship between the battery life target trajectory and a predicted life span of the lithium ion battery module;

determining a fuel economy target trajectory using a fuel economy model, wherein the fuel economy model is configured to describe a relationship between the fuel economy target trajectory and a predicted fuel economy contribution by the lithium ion battery module over a prediction horizon; and

determining the target trajectory using an objective function to weight the battery life target trajectory and the fuel economy target trajectory;

determining, using the control module, battery parameter setpoints based at least in part on a thermal predictive model, wherein the thermal predictive model is configured to describe a relationship between the battery parameter setpoints and a predicted trajectory of the temperature over the prediction horizon; and

controlling, using the control module, operation of the battery system to implement the battery parameter setpoints such that the predicted trajectory of the temperature is guided toward the target trajectory and maintained below the temperature threshold over the control horizon.

18. The method of claim 17, wherein determining the battery parameter setpoints comprises:

determining a predicted driving pattern of an automotive vehicle over the prediction horizon based at least in part on a driving pattern recognition model;

determining a predicted battery resistance of the lithium ion battery module over the prediction horizon based at least in part on a recursive battery model; and

determining the predicted trajectory of the temperature over the prediction horizon based at least in part on;

the predicted driving pattern of the automotive vehicle over the prediction horizon;

the predicted battery resistance over the prediction horizon;

the temperature of the lithium ion battery module;

the target trajectory; and

the temperature threshold.

19. The method of claim 18, wherein determining the predicted driving pattern of the automotive vehicle comprises:

determining a battery current of the lithium ion battery module, wherein the battery current comprises charging current used to charge the lithium ion battery module and discharging current used to supply electrical power to the automotive vehicle;

determining a previous driving pattern of the automotive vehicle, wherein the previous driving pattern comprises previously determined battery currents of the lithium ion battery; and

identifying, using the driving pattern recognition model, a portion of the previous driving pattern as a basis of the predicted driving pattern based at least in part on a profile of the battery current.

20. The method of claim 18, wherein determining the predicted battery resistance comprises:

determining a battery current of the lithium ion battery module, wherein the battery current comprises charging current used to charge the lithium ion battery module and discharging current used to supply electrical power to the automotive vehicle;

determining a first voltage on a bus electrically coupled to the lithium ion battery module associated with a present time step;

determining a second voltage on the bus associated with a previous time step; and

determining the predicted battery resistance using the recursive battery model based at least in part on the predicted driving pattern, the battery current, and a difference between the first voltage and the second voltage.

21. The method of claim 18, wherein:

the predicted driving pattern comprises an average current; and

the predicted battery resistance comprises an average resistance determined based at least in part on the predicted driving pattern.

22. The method of claim 17, wherein determining the battery life target trajectory comprises:

determining, using a driving pattern recognition model, a predicted driving pattern of an automotive vehicle over the prediction horizon;

determining, using a recursive battery model, a current age of the lithium ion battery module based at least in part on the temperature of the lithium ion battery module or battery current of the lithium ion battery module;

determining, using the battery life model, a predicted life span of the lithium ion battery module based at least in part on the current age of the lithium ion battery module and the predicted driving pattern; and

determining the battery life target trajectory based at least in part on a comparison between the predicted life span and a battery life span threshold.

23. The method of claim 22, wherein determining the predicted life span of the lithium ion battery module comprises:

determining a predicted remaining life span of the lithium ion battery module based at least in part on temperature of the lithium ion battery module or number of charging/discharging cycles expected during with the predicted driving pattern; and

combining the current age of the lithium ion battery module with the predicted remaining life span of the lithium ion battery module.

24. The method of claim 17, where determining the fuel economy target trajectory comprises:

determining, using a driving pattern recognition model, a predicted driving pattern of an automotive vehicle over the prediction horizon;

determining a battery current of the lithium ion battery module, wherein the battery current comprises charging current used to charge the lithium ion battery module and discharging current used to supply electrical power to the automotive vehicle;

determining, using the fuel economy model, the predicted fuel economy contribution based at least in part on the predicted driving pattern and the battery current; and

determining the fuel economy target trajectory based at least in part on a comparison between the predicted fuel economy contribution and a fuel economy contribution threshold.

25. The method of claim 17, wherein instructing the battery system to implement the battery parameter setpoints comprises:

de-rating the battery system to reduce the temperature of the lithium ion battery module;

or

re-rating the battery system to increase operation of the lithium ion battery module in the battery system.

26. The method of claim 17, wherein instructing the battery system to implement the battery parameter setpoints comprises:

instructing an electrical energy generator to output a particular charge current to the lithium ion battery module;

instructing the electrical energy generator to output a particular charge voltage to the lithium ion battery module;

instructing the lithium ion battery module to output a particular discharge current to an electrical system;

instructing the lithium ion battery module to output a particular discharge voltage to the electrical system;

or

any combination thereof.