US 10,320,203 B2Granted

Adaptive charging algorithms for a network of electric vehicles

AssigneeCalifornia Institute of Technology

InventorsZhi H. Low, Steven H. Low

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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.

Claims16

Dependent claims

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:
l
i
⁡
(
t
)
:=
1
-
e
i
⁡
(
t
)
r
_
i
⁡
(
t
i
-
t
)
where li(t) is the laxity parameter, t is a time, ti is a deadline, ei(t)is a remaining energy demand, and r i is a peak rate.

49 more elements

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:
OPT
⁢
-
⁢
cm
⁢
:
min
r
≥
0
⁢
C
⁡
(
r
)
+
∑
i
⁢
c
i
⁡
(
d
i
)
s
⁢
ubject
⁢
⁢
to
⁢
⁢
r
i
⁡
(
t
)
=
0
⁢
⁢
i
=
1
,
…
⁢
,
N
,
t
<
s
i
⁢
⁢
or
⁢
⁢
t
≥
t
i
r
i
⁡
(
τ
)
<=
r
i
_
,
⁢
i
=
1
,
…
⁢
,
N
,
τ
=
1
,
…
⁢
⁢
T
-
1
d
i
:=
1
e
i
⁢
∑
t
=
s
i
t
i
-
1
⁢
r
i
⁡
(
t
)
≥
d
_
i
,
i
=
1
,
…
⁢
,
N
∑
i
⁢
r
i
⁢
(
t
)
≤
P
,
t
=
1
,
…
⁢
,
T
-
1
where OPT-cm are the adaptive charging parameters, i is an electric vehicle, N is a total number of electric vehicles, C(r) is a linear cost function, ci (di) are cost parameters, ri is a rate, ri(τ) is a calculated charging rate for an electric vehicle node at time τ, si is an arrival time, ti is a deadline, t and T are times, and P is a station power limit.

178 more elements

Dependencies are inferred from the claim text.

Facts

Application: 15/295,877Filed 17 Oct 2016 · us provisional application
Priority: 16 Oct 2015
Published: US 2017/0110895 A120 Apr 2017
Granted: 11 Jun 2019
Term ends: Not estimatedThe source records don’t support an estimate.

Classification24 codes

H02J 7/50
Circuit arrangements for charging or discharging batteries or for supplying loads from batteries: acting upon multiple batteries simultaneously or…
B60L 53/63
Monitoring or controlling charging stations: in response to network capacity
B60L 53/68
Monitoring or controlling charging stations: off-site monitoring or control, e.g. remote control
G05B 13/041
Involving the use of models or simulators: in which a variable is automatically adjusted to optimise the performance

Family

Application62/242,410Us provisional application · filed 16 Oct 2015
US 2017/0110895 A1Pre-grant publication · published 20 Apr 2017
US 10,320,203 B2This patent

From the US filing history. Foreign family members aren’t listed.

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