Method for controlling energy flow in a hybrid electric vehicle

• US 5,820,172 A
• Filed: 02/27/1997
• Issued: 10/13/1998
• Est. Priority Date: 02/27/1997
• Status: Expired due to Term

• Current Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Original Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Inventors: Brigham, David Richens, Romlein, Timothy, Lev, Amos, Giardini, Sandra, Tamor, Michael Alan
• Primary Examiner(s): Stephan, Steven L.
• Assistant Examiner(s): Ponomarenko, Nicholas
• Application Number:
  US08/807,100
• Time in Patent Office: 593 Days
• Field of Search: 290/11, 290/16, 290/27, 290/40 C, 318/139, 318/143, 322/14, 322/15, 322/16, 180/65.1, 180/65.2, 180/65.4, 180/65.8
• US Class Current: 290/40.00C
• CPC Class Codes: B60K 6/485 Motor-assist type
• CPC Class Codes: B60L 15/2045 for optimising the use of energy
• CPC Class Codes: B60W 10/06 including control of combustion engines
• CPC Class Codes: B60W 10/26 for electrical energy, e.g. batteries or capacitors
• CPC Class Codes: B60W 20/00 Control systems specially adapted for hybrid vehicles hybrid vehicle design, B60K6/00; electric vehicles B60L
• CPC Class Codes: B60W 20/15 Control strategies specially adapted for achieving a particular effect
• CPC Class Codes: B60W 50/0097 Predicting future conditions
• CPC Class Codes: Y02T 10/40 Engine management systems
• CPC Class Codes: Y02T 10/62 Hybrid vehicles
• CPC Class Codes: Y02T 10/64 Electric machine technologies in electromobility
• CPC Class Codes: Y02T 10/72 Electric energy management in electromobility
• CPC Class Codes: Y02T 10/84 Data processing systems or methods, management, administration

Abstract

An operating strategy for a hybrid electric vehicle (HEV) manages the flow of energy to both supply the motive demand power of the HEV and maintain the charge of the energy storage system (ESS). A controller operates the main power unit (HPU) and ESS and, using an optimal fuel cost strategy, scans all possible combinations of power from the HPU and ESS that satisfy the motive demand power. The combination with the lowest fuel cost is selected and the ESS is charged, when possible, using marginal charging; but, if the state of charge of the ESS falls below a certain level, fast charging is invoked. A minimum power threshold strategy can be used rather than the fuel cost strategy. The minimum power threshold strategy determines the optimal compromise of HPU operation and ESS operation to maximize fuel economy by using a motive power threshold below which the HPU is not operated except to recharge the ESS.

Claims

1. An optimal, adaptive method for controlling energy flow in a hybrid electric vehicle (HEV) having a main power unit (HPU) and a supplementary bi-directional energy storage system (ESS) to satisfy power demand for vehicle propulsion and auxiliary systems and to maintain state of charge (SOC) of said ESS within a selected range while minimizing fuel consumption, comprising the steps of:

making an initial estimate of an equivalent amount of fuel to be associated with energy stored in said ESS;

determining motive demand power (MDP) of said vehicle;

determining an effective power dependent fuel consumption rate for withdrawal of energy from said ESS using known output characteristics of said ESS and the effective fuel value of the energy stored in said ESS;

scanning possible combinations of HPU and ESS output power that satisfy MDP;

selecting the combination of HPU and ESS output power providing the lowest fuel cost, said fuel cost being the sum of HPU fuel consumption and effective ESS fuel consumption;

determining whether the lowest fuel cost combination requires energy from both the HPU and ESS;

satisfying MDP and decrementing the fuel value of ESS energy at a rate proportional to the rate of energy withdrawal when the lowest fuel cost combination requires energy from both the HPU and ESS;

determining whether the ESS should be charged;

operating the HPU at a power level higher than the power demand chosen so as to minimize the fuel cost of energy delivered to and stored in the ESS and incrementing the fuel value associated with energy stored in the ESS at a rate proportional to the rate of fuel usage of the HPU multiplied by the fraction of HPU output diverted to the ESS when the ESS should be charged; and

satisfying MDP with the lowest fuel cost combination of HPU and ESS energy and decrementing the fuel value of ESS energy at a rate proportional to the rate of energy withdrawal when the ESS should not be charged.

2. The method of claim 1, including the step of storing regenerative braking energy in the ESS and incrementing the fuel value of energy in the ESS at a rate equivalent to that of ESS charging from the HPU at its peak efficiency.

3. The method of claim 2, including the step of reducing the fuel value of regenerated energy to assure complete consumption of the regenerated energy in future driving.

4. The method of claim 1, wherein the MDP is instantaneous demand power.

5. The method of claim 1, wherein the step of determining MDP includes averaging instantaneous motive power over an integration time period.

6. The method of claim 1, including the step of charging said ESS at a higher than optimal charge rate when SOC is below said selected range.

7. The method of claim 1, including the step of replacing the fuel cost associated with stored energy with a weighted sum thereby allowing adaptive minimization of the weighted total cost of driving the HEV, said weighted sum including costs from a group containing mechanical wear costs and emission costs.

8. The method of claim 7, including the step of varying individual weight of selected components of said weighted sum and determining the fuel economy value of reduction of other components.

9. The method of claim 1, including the step of determining the fuel value of stored energy by estimation or computational means other than storing regenerative braking energy in the ESS and incrementing the fuel value of energy in the ESS at a rate equivalent to that of ESS charging from the HPU at its peak efficiency;

reducing the fuel value of regenerated energy to assure complete consumption of the regenerated energy in future driving;

replacing the fuel cost associated with stored energy with a weighted sum thereby allowing adaptive minimization of the weighted total cost of driving the HEV wherein said weighted sum includes costs from a group containing mechanical wear costs and emission costs;

or varying individual weight of selected components of said weighted sum and determining the fuel economy value of reduction of other components.

10. A near optimal, nonadaptive method for controlling energy flow in a hybrid electric vehicle (HEV) having a main power unit (HPU) and a supplementary bidirectional energy storage system (ESS) to satisfy power demand for vehicle propulsion and auxiliary systems and to maintain state of charge (SOC) of said ESS within a selected range, said method comprising the steps of:

selecting a motive power threshold (MPT);

determining motive power demand (MDP) of said vehicle;

determining whether the ESS SOC is below a critical value lower than a lower end of said selected range for normal operation;

operating the vehicle with the HPU on while charging the ESS at a higher than optimal rate when the ESS SOC is below the critical value;

determining whether the ESS should be charged when the ESS SOC is not below the critical value;

operating the vehicle on energy stored in the ESS alone when the ESS should not be charged;

determining whether MDP is less than MPT when the ESS should be charged and operating the HPU at a constant output power equal to MPT when MDP is less than MPT; and

determining whether MDP is less than MPT when the ESS should be charged and operating the HPU in a load following mode where the HPU power is equal to MDP when MDP is not less than MPT.

11. The method of claim 10, wherein the MDP is instantaneous demand power.

12. The method of claim 10, wherein the step of determining MDP includes averaging instantaneous motive power over an integration time period.

13. The method of claim 10, including the step of adjusting MPT for optimal fuel economy under various driving conditions.

14. The method of claim 10, including the step of continuously determining the fuel value of stored energy.

15. The method of claim 10, including the step of determining MPT from the efficiency characteristics of the HPU and ESS, and an external estimate of the fuel value of ESS energy.

16. The method of claim 15, including the step of charging said ESS in a load following mode by operating the HPU at power level higher than the power demand chosen so as to minimize the fuel cost of energy delivered to and stored in the ESS and by incrementing the fuel value associated with energy stored in the ESS at a rate proportional to the rate of fuel usage of the HPU multiplied by the fraction of HPU output diverted to the ESS.

17. The method of claim 10, including the step of determining MPT from the efficiency characteristics of the HPU and ESS, and a running estimate of fuel value of stored energy.

18. The method of claim 10, including the step of charging said ESS in a load following mode by operating the HPU at power level a predetermined amount higher than MDP.

19. The method of claim 10, including the step of minimizing fuel consumption by sweeping the value of MPT over the selected range.

20. The method of claim 10, including the step of adjusting MDP averaging time and MPT and holding HPU cycling frequency and associated restart fuel penalty within predetermined limits.

21. The method of claim 10, including the step of reaching an optimal compromise between fuel economy and HPU cycling frequency by adjusting MDP averaging time and MPT.