Smart electric vehicle (EV) charging and grid integration apparatus and methods

• US 9,026,347 B2
• Filed: 12/04/2012
• Issued: 05/05/2015
• Est. Priority Date: 06/10/2010
• Status: Active Grant

• Current Assignee: Regents of the University of California (University of California)
• Original Assignee: Regents of the University of California (University of California)
• Inventors: Gadh, Rajit, Mal, Siddhartha, Prabhu, Shivanand, Chu, Chi-Cheng, Sheikh, Omar, He, Lei, Xiao, Bingjun, Shi, Yiyu, Chung, Ching-Yen
• Primary Examiner(s): To, Tuan C.
• Assistant Examiner(s): Kan, Yuri
• Application Number:
  US13/693,747
• Publication Number:
  US 20130179061A1
• Time in Patent Office: 882 Days
• Field of Search: 701/22, 701/123, 700/297, 705/1.1, 705/39, 307/48, 320/109, 320/132, 320/136, 370/254, 180/242, 180/272, 361/62, 380/277
• US Class Current: 701/123
• CPC Class Codes: B60L 53/30 Constructional details of charging stations
• CPC Class Codes: B60L 53/305 Communication interfaces
• CPC Class Codes: B60L 53/51 Photovoltaic means
• CPC Class Codes: B60L 53/63 in response to network capacity
• CPC Class Codes: B60L 53/67 Controlling two or more charging stations
• CPC Class Codes: B60L 55/00 Arrangements for supplying energy stored within a vehicle to a power network, i.e. vehicle-to-grid [V2G] arrangements
• CPC Class Codes: G01R 31/3842 combining voltage and current measurements
• CPC Class Codes: H02J 2310/64 The condition being economic, e.g. tariff based load management
• CPC Class Codes: H02J 3/14 by switching loads on to, or off from, network, e.g. progressively balanced loading
• CPC Class Codes: Y02B 70/3225 Demand response systems, e.g. load shedding, peak shaving
• CPC Class Codes: Y02E 60/00 Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
• 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 90/12 Electric charging stations
• CPC Class Codes: Y02T 90/14 Plug-in electric vehicles
• CPC Class Codes: Y02T 90/16 Information or communication technologies improving the operation of electric vehicles
• CPC Class Codes: Y02T 90/167 Systems integrating technologies related to power network operation and communication or information technologies for supporting the interoperability of electric or hybrid vehicles, i.e. smartgrids as interface for battery charging of electric vehicles [EV] or hybrid vehicles [HEV]
• CPC Class Codes: Y04S 10/126 the energy generation units being or involving electric vehicles [EV] or hybrid vehicles [HEV], i.e. power aggregation of EV or HEV, vehicle to grid arrangements [V2G]
• CPC Class Codes: Y04S 20/222 Demand response systems, e.g. load shedding, peak shaving
• CPC Class Codes: Y04S 30/12 Remote or cooperative charging
• CPC Class Codes: Y04S 30/14 : Details associated with the interoperability, e.g. vehicle recognition, authentication, identification or billing
• CPC Class Codes: Y04S 50/10 : Energy trading, including energy flowing from end-user application to grid

Abstract

An expert system manages a power grid wherein charging stations are connected to the power grid, with electric vehicles connected to the charging stations, whereby the expert system selectively backfills power from connected electric vehicles to the power grid through a grid tie inverter (if present) within the charging stations. In more traditional usage, the expert system allows for electric vehicle charging, coupled with user preferences as to charge time, charge cost, and charging station capabilities, without exceeding the power grid capacity at any point. A robust yet accurate state of charge (SOC) calculation method is also presented, whereby initially an open circuit voltage (OCV) based on sampled battery voltages and currents is calculated, and then the SOC is obtained based on a mapping between a previously measured reference OCV (ROCV) and SOC. The OCV-SOC calculation method accommodates likely any battery type with any current profile.

Claims

1. An apparatus for determining instantaneous state of charge (SOC) for managing a battery in an electric vehicle, the apparatus comprising:

(a) a battery management circuit having a computer processor and memory configured for controlling battery charging in an electric vehicle;

(b) a voltage sensor connected to said battery management circuit and configured for measuring battery voltage;

(c) a current sensor connected to said battery management circuit configured for measuring battery current; and

(d) programming executable from said computer processor of said battery management circuit for performing steps comprising;

(i) mapping a reference open circuit voltage (ROCV) to a reference state of charge (RSOC) of the battery through a discharge cycle to produce an ROCV-RSOC mapping;

(ii) measuring a current and a voltage over a period of time from the battery;

(iii) determining an open circuit voltage (OCV) of the battery under a load in response to;

(iii)(A) performing a time window based OCV extraction process in which over a plurality of short time windows OCV is extracted at multiple sample points in each of said short time windows;

(iii)(B) estimating battery terminal voltage at time t from a combination of zero-input response corresponding to a terminal voltage with no discharge current, plus a zero state response corresponding to battery terminal voltage with a discharge current i as input and voltage source shorted as determined from convolving an impulse response of the linear system with discharge current i;

(iii)(C) wherein zero-input response in a first of said short time windows as being actual open circuit battery terminal voltage;

(iii)(D) wherein in each short time window, after said first of said short time windows, impulse response is determined and a voltage removed which corresponds to voltage caused by current flow in previous windows;

(iv) determining an instantaneous state of charge (SOC) for the corresponding estimated open circuit voltage (OCV) in response to using said ROCV-RSOC mapping, without relying on a synthesizing circuit model of the battery which mimics specific battery behavior; and

(v) outputting SOC on a display device.

2. The apparatus recited in claim 1, further comprising:

a transceiver configured to wirelessly communicate between the electric vehicle (EV) and a power grid expert system (PGES) configured for performing grid balancing, grid management and prediction of peak and off-peak hours to store excess capacity, and demands for EV charging.

3. The apparatus recited in claim 2, further comprising:

a portal display device in said electric vehicle (EV);

a computer processor in said EV, and coupled to said portal display device for receiving inputs and displaying outputs on said portal display device;

programming executable on said computer processor for performing steps comprising;

determining a time averaged rate of battery discharge from the measuring of the current over the period of time from said battery;

determining a projected range of the EV by using the determined state of charge (SOC) in relation to maximum capacity of the battery and time averaged rate of battery discharge;

acquiring a cost per kWh of charge at a charging station; and

displaying on the client portal display device one or more data selected from the group consisting of;

the SOC of the electric vehicle;

the projected range of the electric vehicle;

the cost per kWh of charge at a location of the charging station; and

the location of the charging station.

4. The apparatus recited in claim 1, wherein said programming in the battery management circuit performs steps comprising:

determining a maximum capacity of the battery in response to a relation between measured current and voltage over a period of time from the battery; and

tracking over time maximum capacity of the battery as a state of health of the battery.

5. The apparatus recited in claim 1, wherein said programming is further configured to performs steps comprising:

determining a time-averaged current draw by averaging the measured current over the period of time from the battery;

determining a time-averaged electric vehicle speed by averaging a speed of the electric vehicle over another period of time;

determining a remaining battery capacity by multiplying the state of charge (SOC) by a maximum capacity of the battery; and

determining a remaining range by dividing the remaining battery capacity by the time-averaged current draw, and multiplying by a time-averaged electric vehicle speed.

6. The apparatus recited in claim 5, wherein said programming is further configured for performs steps comprising transmitting from the electric vehicle (EV) the remaining range to a client portal.

7. The apparatus recited in claim 6, wherein said programming is further configured for performs steps comprising controlling the display of remaining range on said client portal.

8. The apparatus recited in claim 6, wherein said transmitting is performed either directly, or is a relayed transmission through a power grid expert system (PGES).

9. The apparatus recited in claim 6, wherein said client portal comprises:

a display device; and

a transceiver configured to wirelessly communicate with the electric vehicle and the power grid expert system (PGES).

10. The apparatus recited in claim 2, wherein interoperation between said electric vehicle (EV) and said power grid expert system (PGES) aggregates state of the charge information from vehicles with demand dispatch and demand response signals to automatically achieve grid-to-vehicle charging or reverse charging from vehicle-to-grid.

11. The apparatus recited in claim 2, wherein the power grid expert system (PGES) comprises:

a charging station configured for charging a battery in an electric vehicle from an electric power grid;

said charging station optionally including a grid tie inverter configured for backfilling power from an electric vehicle connected to the charging station into the electric power grid;

a PGES computer processor; and

programming executable on the PGES computer processor for performing steps comprising;

selectively controlling a charging of the battery in the electric vehicle (EV) by the charging station; and

selectively controlling discharging of the battery for backfilling power to the electric power grid from the battery in the electric vehicle if the charging station has the grid tie inverter.

12. The apparatus recited in claim 1, wherein said state of charge (SOC) comprises a percentage value, indicating an amount of energy available in the battery of an electric vehicle (EV), said SOC utilized for controlling battery charging, dynamic power management, and power backfilling on a power grid.

13. An apparatus for determining instantaneous state of charge (SOC) for managing a battery in an electric vehicle, the apparatus comprising:

(a) a battery management circuit having a computer processor and memory configured for controlling battery charging in an electric vehicle;

(b) a voltage sensor connected to said battery management circuit and configured for measuring battery voltage;

(c) a current sensor connected to said battery management circuit configured for measuring battery current; and

(d) programming executable from said computer processor of said battery management circuit determining state of charge (SOC), as a percentage value of energy available in the battery of the electric vehicle (EV), by performing steps comprising;

(i) mapping a reference open circuit voltage (ROCV) to a reference state of charge (RSOC) of the battery through a discharge cycle to produce an ROCV-RSOC mapping;

wherein said mapping of ROCV-RSOC includes determining a maximum capacity of the battery in response to a relation between measured current and voltage over a period of time from the battery, and tracking maximum capacity of the battery over time as a state of health of the battery;

(ii) measuring a current and a voltage over a period of time from the battery;

(iii) determining an open circuit voltage (OCV) of the battery under a load in response to;

(iii)(A) performing a time window based OCV extraction process in which over a plurality of short time windows OCV is extracted at multiple sample points in each of said short time windows;

(iii)(B) determining battery terminal voltage at time t from a combination of zero-input response corresponding to a terminal voltage with no discharge current, plus a zero state response corresponding to battery terminal voltage with a discharge current i as input and voltage source shorted as determined from convolving an impulse response of the linear system with discharge current i;

(iii)(C) wherein zero-input response in a first of said short time windows as being actual open circuit battery terminal voltage;

(iii)(D) wherein in each short time window, after said first of said short time windows, impulse response is determined and a voltage removed which corresponds to voltage caused by current flow in previous windows;

(iv) determining an instantaneous state of charge (SOC) for the corresponding determined open circuit voltage (OCV) in response to using said ROCV-RSOC mapping, without relying on a synthesizing circuit model of the battery which mimics specific battery behavior; and

(v) outputting SOC on a display device.

14. The apparatus recited in claim 13, further comprising:

a transceiver configured to wirelessly communicate between the electric vehicle (EV) and a power grid expert system (PGES) configured for performing grid balancing, grid management and prediction of peak and off-peak hours to store excess capacity, and demands for EV charging.

15. The apparatus recited in claim 13, further comprising:

a portal display device in said electric vehicle (EV);

a computer processor in said EV, and coupled to said portal display device for receiving inputs and displaying outputs on said portal display device;

programming executable on said computer processor for performing steps comprising;

determining a time averaged rate of battery discharge from the measuring of the current over the period of time from said battery;

determining a projected range of the EV by using the determined state of charge (SOC) in relation to maximum capacity of the battery and time averaged rate of battery discharge;

acquiring a cost per kWh of charge at a charging station; and

displaying on the client portal display device one or more data selected from the group consisting of;

the SOC of the electric vehicle;

the projected range of the electric vehicle;

the cost per kWh of charge at a location of the charging station; and

the location of the charging station.

16. The apparatus recited in claim 15, wherein said programming is further configured to perform steps comprising:

determining a time-averaged current draw by averaging the measured current over the period of time from the battery;

determining a time-averaged electric vehicle speed by averaging a speed of the electric vehicle over another period of time;

determining a remaining battery capacity by multiplying the state of charge (SOC) by a maximum capacity of the battery; and

determining a remaining range by dividing the remaining battery capacity by the time-averaged current draw, and multiplying by a time-averaged electric vehicle speed.

17. The apparatus recited in claim 16, wherein said programming is further configured for performs steps comprising transmitting from the electric vehicle (EV) the remaining range to a client portal.

18. The apparatus recited in claim 17, wherein said programming is further configured for performs steps comprising controlling the display of remaining range on said client portal.

19. The apparatus recited in claim 17, wherein said transmitting is performed either directly, or is a relayed transmission through a power grid expert system (PGES).

20. The apparatus recited in claim 17, wherein said client portal comprises:

a display device; and

a transceiver configured to wirelessly communicate with the electric vehicle and the power grid expert system (PGES).