Optimized Fuzzy Logic Controller For Energy Management In Micro and Mild Hybrid Electric Vehicles

• US 20140067183A1
• Filed: 08/29/2013
• Published: 03/06/2014
• Est. Priority Date: 08/31/2012
• Status: Active Grant

• Current Assignee: CPS Technology Holdings, LLC
• Original Assignee: Johnson Controls Technology Company (Johnson Controls International plc)
• Inventors: Sisk, Brian C.
• Granted Patent: US 9,669,724 B2
• Time in Patent Office: Days
• US Class Current: 701/22
• CPC Class Codes: B60L 1/003 to auxiliary motors, e.g. for pumps, compressors
• CPC Class Codes: B60L 1/02 to electric heating circuits
• CPC Class Codes: B60L 2200/12 Bikes
• CPC Class Codes: B60L 2200/18 Buses
• CPC Class Codes: B60L 2200/32 Waterborne vessels
• CPC Class Codes: B60L 2200/36 Vehicles designed to transport cargo, e.g. trucks
• CPC Class Codes: B60L 2200/44 Industrial trucks or floor conveyors
• CPC Class Codes: B60L 2210/12 Buck converters
• CPC Class Codes: B60L 2210/14 Boost converters
• CPC Class Codes: B60L 2240/12 Speed
• CPC Class Codes: B60L 2240/34 Cabin temperature
• 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 50/16 with provision for separate direct mechanical propulsion
• CPC Class Codes: B60L 50/40 using propulsion power supplied by capacitors
• 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 58/20 having different nominal voltages
• CPC Class Codes: B60L 58/26 : by cooling
• CPC Class Codes: B60L 7/18 : Controlling the braking effect B60L7/12, B60L7/14, B60L7/16 take precedence
• CPC Class Codes: B60W 10/08 : including control of electric propulsion units, e.g. motors or generators
• 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 20/10 : Controlling the power contribution of each of the prime movers to meet required power demand
• CPC Class Codes: B60W 2510/244 : Charge state
• CPC Class Codes: Y02P 90/60 : Electric or hybrid propulsion means for production processes
• CPC Class Codes: Y02T 10/62 : Hybrid vehicles
• 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

Abstract

An energy storage system of a vehicle includes an energy storage device, a regulation device coupled to the energy storage device, one or more sensing devices for sensing current levels, voltage levels, temperature levels, and/or pressure levels of the energy storage device and/or on components thereof, and a control unit configured to determine dynamically a power flow in/out of the energy storage device using a fuzzy logic approach. The regulation device is configured to regulate at least one of a voltage level, a current level, and any additional state parameter of the energy storage device.

Claims

1. An energy storage system of a vehicle, comprising:

an energy storage device;

a regulation device coupled to the energy storage device, wherein the regulation device is configured to regulate at least one of a voltage level, a current level, and any additional state parameter of the energy storage device;

one or more sensing devices for sensing current levels, voltage levels, temperature levels, and/or pressure levels of the energy storage device and/or on components thereof; and

a control unit configured to determine dynamically a power flow in/out of the energy storage device using a fuzzy logic approach.

2. The energy storage system of claim 1, wherein the control unit is further configured to determine a current state of charge (SOC) of the energy storage device based on data collected from the energy storage device.

3. The energy storage system of claim 2, wherein the control unit is further configured to determine a target SOC of the energy storage device based a current vehicle speed.

4. The energy storage system of claim 3, wherein the control unit is further configured to evaluate a difference between the target SOC and the current SOC in order to determine an amount of available power for energy assistance, and propulsion.

5. The energy storage system of claim 1, wherein the control unit is further configured to power on/off the regulation device, and to regulate a voltage output from the voltage and current regulation device.

6. The energy storage system of claim 1, wherein the control unit is further configured to implement the fuzzy logic approach based on input signals received from a vehicle control unit, wherein the input signals relate to at least one of a vehicle speed, a vehicle acceleration, and a driving route of the vehicle.

7. The energy storage system of claim 6, wherein the input signals received by the control logic unit include signals received internally to the energy storage system, which relate to current, voltage, temperature, state of charge, state of health of the energy storage device and signals external to the energy storage system, such as signals communicated through a vehicle communication network.

8. The energy storage system of claim 1, wherein in addition to the energy storage device, the vehicle includes electrical accessories operating an about twelve volts (12V) voltage and an electric generator operating on a voltage range of about 12V to about 60V.

9. The energy storage system of claim 1, wherein the energy storage system is configured for a hybrid electric vehicle application.

10. The energy storage system of claim 1, wherein the fuzzy logic approach is combined with a supervised learning approach to determine dynamically the target SOC of the energy storage device based on the current vehicle speed.

11. The energy storage system of claim 10, wherein the supervised learning approach include one of artificial neural networks (ANNs), discriminant methods, nearest neighbor methods, support vector machines (SVMs), genetic algorithms, Bayesian algorithms, or others approaches.

12. A micro-hybrid vehicle, comprising:

a vehicle control unit for determining operation modes of the micro-hybrid vehicle, and generating controlling signals corresponding to the operation modes;

an energy storage system configured to receive the controlling signals, and comprising;

a first energy storage device and a second energy storage device;

a regulation device coupled to the first and second energy storage devices, wherein the regulation device is configured to regulate at least one of a voltage level, a current level, and any additional state parameter of the first and second energy storage devices;

one or more sensing devices for sensing current input signals and voltage input signals to at least one of the first and second energy storage devices and/or components thereof; and

a control unit configured to determine dynamically a target state of charge (SOC) of each of the first and second energy storage devices based on a current vehicle speed using a fuzzy logic approach.

13. The micro-hybrid vehicle of claim 12, further comprising:

an electro-mechanical motor, a generator, an alternator, and/or a combination of one or more of these functions; and

a plurality of electrical accessories coupled to the first energy storage device.

14. The micro-hybrid vehicle of claim 12, wherein the regulation device is a DC/DC converter.

15. The micro-hybrid vehicle of claim 12, wherein the control unit is further configured to determine a current SOC of each of the first and second energy storage devices based on data collected from the first and second energy storage devices.

16. The micro-hybrid vehicle of claim 15, wherein the control unit is further configured to evaluate a difference between the target SOC and the current SOC in order to determine an amount of available power for energy assistance, and propulsion.

17. The micro-hybrid vehicle of claim 12, wherein, using the fuzzy logic approach, the control unit is further configured to control a power flow between the first and second energy storages devices, to power on/off the regulation device, and to regulate one or more of a power output, a current output, and a voltage output from the regulation device.

18. The micro-hybrid vehicle of claim 12, wherein, using the fuzzy logic approach, the control unit is further configured to control the regulation device using a modulation technique, which can include a pulse width modulation.

19. The micro-hybrid vehicle of claim 12, wherein the control unit is further configured to implement the fuzzy logic approach based on input signals received from a vehicle control unit.

20. The micro-hybrid vehicle of claim 19, wherein the input signals relate to the speed of the vehicle, an acceleration of the vehicle, and/or a current route of the vehicle.

21. The micro-hybrid vehicle of claim 19, the input signals received by the control logic unit include signals received internally to the energy storage system, which relate to current, voltage, temperature, state of charge, state of health of the first and second energy storage devices and signals external to the energy storage system, such as signals communicated through a vehicle communication network.

22. The micro-hybrid vehicle of claim 12, wherein in addition to the first and second energy storage devices, the vehicle includes electrical accessories operating on an about twelve volts (12V) voltage and an electric motor/generator operating on a voltage range of about 12V to about 60V.

23. The micro-hybrid vehicle of claim 12, wherein the energy storage system is configured for a hybrid electric vehicle application.

24. A computer-implemented method for controlling an operation of an energy storage system of a micro-hybrid vehicle, the energy storage system having a vehicle operation unit, a control unit, and a voltage and current regulator device, first and second energy storage devices, the method comprising:

determining whether the micro-hybrid vehicle is in a driving mode, and determining a speed of the micro-hybrid vehicle when in the driving mode;

determining a target SOC of each of the first and second energy storage devices based on the hybrid vehicle speed;

determining a current SOC of each of the first and second energy storage devices;

evaluating a difference between the target SOCs and the current SOCs; and

determining dynamically using a fuzzy logic approach an amount of power available for regeneration, energy assistance, and propulsion.

25. The computer-implemented method of claim 24, wherein, using the fuzzy logic approach, the control unit is further configured to control a power flow between the first and second energy storages devices, to power on/off the regulation device, and to regulate one or more of a power output, a current output, and a voltage output from the regulation device.

26. The computer-implemented method of claim 24, wherein, using the fuzzy logic approach, the control unit is further configured to control the regulation device using a modulation technique, which can include a pulse width modulation.