Adaptive charging algorithms for a network of electric vehicles

• US 10,320,203 B2
• Filed: 10/17/2016
• Issued: 06/11/2019
• Est. Priority Date: 10/16/2015
• Status: Active Grant

• Current Assignee: Energy Adaptive Networks Corporation
• Original Assignee: California Institute of Technology
• Inventors: Low, Zhi H., Low, Steven H.
• Primary Examiner(s): Wathen, Brian W
• Assistant Examiner(s): Shafayet, Mohammed
• Application Number:
  US15/295,877
• Publication Number:
  US 20170110895A1
• Time in Patent Office: 967 Days
• Field of Search: 700296
• CPC Class Codes: B60L 53/63 in response to network capacity
• CPC Class Codes: B60L 53/68 Off-site monitoring or control, e.g. remote control
• CPC Class Codes: G05B 13/041 in which a variable is automatically adjusted to optimise the performance
• CPC Class Codes: H02J 2310/48 for electric vehicles [EV] or hybrid vehicles [HEV]
• CPC Class Codes: H02J 7/00034 Charger exchanging data with an electronic device, i.e. telephone, whose internal battery is under charge
• CPC Class Codes: H02J 7/0013 acting upon several batteries simultaneously or sequentially H02J7/1423 takes precedence
• CPC Class Codes: H02J 7/0047 with monitoring or indicating devices or circuits
• CPC Class Codes: H02J 7/007 Regulation of charging or discharging current or voltage
• CPC Class Codes: H02J 7/04 Regulation of charging current or voltage
• 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 30/12 Remote or cooperative charging

Abstract

Electric vehicle node controllers in accordance with embodiments of the invention enable adaptive charging. One embodiment includes one or more centralized computing systems; a communications network; a plurality of electric vehicle node controllers, where each electric vehicle node controller in the plurality of node controllers contains: a network interface; a processor; a memory containing: an adaptive charging application; a plurality of electric vehicle node parameters describing charging parameters of an electric vehicle node in the electric vehicle charging network; where the processor is configured by the adaptive charging application to: send electric vehicle node parameters to the one or more centralized computing systems; and charge the electric vehicle node using a charging rate received from the one or more centralized computing systems; where the one or more centralized computing systems is configured to: receive the electric vehicle node parameters from the plurality of electric vehicle node controllers; calculate a plurality of charging rates for the plurality of electric vehicle node controllers using the electric vehicle node parameters, a plurality of adaptive charging parameters, and a cost function; and send the charging rates to the plurality of electric vehicle node controllers.

Claims

1. An electric vehicle charging network, comprising:

one or more centralized computing systems;

a communications network;

a plurality of electric vehicle node controllers, where each electric vehicle node controller in the plurality of node controllers contains;

a network interface;

a processor;

a memory containing;

an adaptive charging application;

a plurality of electric vehicle node parameters describing charging parameters of an electric vehicle node in the electric vehicle charging network;

where the processor is configured by the adaptive charging application to;

send electric vehicle node parameters to the one or more centralized computing systems; and

charge the electric vehicle node using a charging rate received from the one or more centralized computing systems;

where the one or more centralized computing systems is configured to;

receive the electric vehicle node parameters from the plurality of electric vehicle node controllers;

calculate a plurality of charging rates for the plurality of electric vehicle node controllers using the electric vehicle node parameters, a plurality of adaptive charging parameters, and a cost function; and

send the charging rates to the plurality of electric vehicle node controllers;

wherein;

the charging rates for a first set of electric vehicle node controllers are a maximum charging rate;

the charging rates for a second set of electric vehicle node controllers are zero charge; and

the charging rate for one electric vehicle node controller is less than the maximum charging rate;

wherein distributing charging rates into the first set of electric vehicle node controllers, the second set of electric vehicle node controllers, and one electric vehicle node controller is a least laxity first process;

wherein the least laxity first process further includes calculating a plurality of laxity parameters corresponding to the plurality of electric vehicle nodes, where the electric vehicle with a smallest laxity parameter value is charged first;

wherein a laxity parameter in a plurality of laxity parameters is a slack time the corresponding electric vehicle node has before it must charge at its peak rate in order to meet a deadline;

wherein a laxity parameter in the plurality of laxity parameters can be evaluated by the one or more centralized computing systems using the following expression;

2. The electric vehicle charging network of claim 1, wherein the charging rates meet an energy demand of the plurality of electric vehicle nodes by a plurality of departure times.

3. The electric vehicle charging network of claim 1, wherein the charging rates for the plurality of electric vehicle nodes are a time series of timing rates provided to each electric vehicle node controller.

4. The electric vehicle charging network of claim 1, wherein a time series of timing rates is calculated using an online linear program process.

5. The electric vehicle charging network of claim 3, wherein the electric vehicle node parameters include an electric vehicle departure time, a remaining energy demand, and a peak charging rate.

6. The electric vehicle charging network of claim 3, wherein the time series of timing rates can be evaluated by the one or more centralized computing systems using the following expression:

7. The electric vehicle charging network of claim 3, wherein the cost function can be evaluated by the one or more centralized computing systems using the following expression:

r*(t);

=(r*_i(τ

),τ

=t, . . . ,t_i −

1,i=1, . . . ,N(t))

where t is a time, r*(τ

) is an optimal charging rate at time τ

,r*(t) is a vector of charging rates for current and future times, τ

is a departure time, i is an electric vehicle, and N(t) is a number of electric vehicles at time t.

8. The electric vehicle charging network of claim 1, wherein the cost function is a linear function.

9. The electric vehicle charging network of claim 1, wherein the cost function comprises a cost function selected from a group consisting of electricity cost, revenue to a garage, maximum charging delay, asset utilization, and system robustness.

10. The electric vehicle charging network of claim 1, wherein calculating a laxity parameter is a distributed process.

11. The electric vehicle charging network of claim 1, wherein calculating a laxity parameter is a centralized process.

12. An electric vehicle charging network, comprising:

one or more centralized computing systems;

a communications network;

a plurality of electric vehicle node controllers, where each electric vehicle node controller in the plurality of node controllers contains;

a network interface;

a processor;

a memory containing;

an adaptive charging application;

a plurality of electric vehicle node parameters describing charging parameters of an electric vehicle node in the electric vehicle charging network;

where the processor is configured by the adaptive charging application to;

send electric vehicle node parameters to the one or more centralized computing systems; and

charge the electric vehicle node using a charging rate received from the one or more centralized computing systems;

where the one or more centralized computing systems is configured to;

calculate a feasibility of a plurality of adaptive charging parameters and a cost function parameter, where the adaptive charging parameters describe the electric vehicle charging network;

wherein where the plurality of adaptive charging parameters and the cost function parameter are feasible, calculate charging rates for the plurality of electric vehicle node controllers where the charging rates are a time series of timing rates;

wherein where the plurality of adaptive charging parameters and the cost function parameter are not feasible, calculate charging rates for the plurality of electric vehicle node controllers where;

the charging rates for a first set of electric vehicle node controllers are a maximum charging rate;

the charging rates for a second set of electric vehicle node controllers are zero charge; and

the charging rate for one electric vehicle node controller is less than the maximum charging rate; and

send the charging rates to the plurality of electric vehicle node controllers;

wherein the plurality of adaptive charging parameters can be evaluated by the one or more centralized computing systems using the following expressions;

13. The electric vehicle charging network of claim 12, wherein the cost function parameter is a linear function.

14. The electric vehicle charging network of claim 12, wherein the cost function parameter is selected from a group consisting of electricity cost, revenue to a garage, maximum charging delay, asset utilization, and system robustness.

15. The electric vehicle charging network of claim 12, wherein where the plurality of adaptive charging parameters and the cost function parameter are feasible, calculate charging rates using an online linear program process.

16. The electric vehicle charging network of claim 12, wherein where the plurality of adaptive charging parameters and the cost function parameter are not feasible, calculate charging rates using at least laxity first process.