# US 12,005,802 B1 · Bilevel coordinated optimization method for fixed and mobile charging facilities on highways

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- Number: US 12,005,802 B1
- Document ID: US12005802B1
- Status: Granted
- Kind: B1
- Application number: 18/391,662
- Filed: 2023-12-21
- Published: 2024-07-04
- Granted: 2024-06-11
- Earliest claimed: 2022-12-30
- Assignees: Tianjin University
- Applicants: Tianjin University
- Inventors: Hongjie Jia; Kecheng He; Yunfei Mu; Xiaodan Yu; Xiaohong Dong; Xiandong Xu
- Web page: https://patentgrep.com/patent/US12005802B1

## CPC

- B60L 53/64: Optimising energy costs, e.g. responding to electricity rates
- B60L 53/63: in response to network capacity
- G06Q 10/04: Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" (market predictions or forecasting for commercial activities G06Q30/0202)
- B60L 2200/44: Industrial trucks or floor conveyors
- Y02T 10/40: Engine management systems
- Y02T 10/70: Energy storage systems for electromobility, e.g. batteries
- B60L: PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES (arrangements or mounting of electrical propulsion units or of plural diverse prime-movers for mutual or common propulsion in vehicles B60K1/00, B60K6/20; arrangements or mounting of electrical gearing in vehicles B60K17/12, B60K17/14; preventing wheel slip by reducing power in rail vehicles B61C15/08; dynamo-electric machines H02K; control or regulation of electric motors H02P); SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES (electric coupling devices combined with mechanical couplings of vehicles B60D1/64; electric heating for vehicles B60H1/00); ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL (control or regulation of electric motors H02P); MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- Y02T: CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION

## Abstract

A bilevel coordinated optimization method for fixed and mobile charging facilities on highways, includes: constructing an optimization model framework which includes an upper-layer coordinated location optimization model and a lower-layer coordinated capacity optimization model, where the upper-layer coordinated location optimization model is used to optimize locations of charging stations and determine locations and time points of charging demands that require truck mobile charger (TMC) deployment, while the lower-layer coordinated capacity optimization model is used to optimize TMC and fixed charger (FC) capacities at candidate sites, improving an utilization rate of FCs; and performing equivalent linearization on a nonlinear problem using a big-M method and converting the problem into a mixed-integer linear programming model, and implementing a data exchange process between upper and lower layers using analytical target cascading. The present disclosure enhances the utilization and flexibility of charging facilities on highways.

## Claims

### Claim 1

A bilevel coordinated optimization method for fixed and mobile charging facilities on highways, comprising:
constructing an optimization model framework, w % herein the optimization model framework comprises an upper-layer coordinated location optimization model and a lower-layer coordinated capacity optimization model;
wherein the upper-layer coordinated location optimization model is used to optimize locations of charging stations and determine locations and time points of charging demands that require truck mobile charger (TMC) deployment; and
the lower-layer coordinated capacity optimization model is used to optimize TMC and fixed charger (FC) capacities at candidate sites, improving an utilization rate of FCs; and
performing equivalent linearization on a nonlinear problem using a big-M method and converting the problem into a mixed-integer linear programming model, and implementing a data exchange process between upper and lower layers using an analytical target cascading (ATC) technique;
the upper-layer coordinated location optimization model comprises an electric vehicle (EV) model, an improved income approach (IIA) model, a TMC location optimization model, an FC location optimization model, and a TMC depot optimization model; and
the EV model is as follows;
simulations end when the following convergence condition is met or a maximum simulation count is reached, resulting in a spatial-temporal distribution of charging demands;
wherein PjL represents charging demands in each section of a highway network; and M and ε3 are constants.

### Claim 2 (depends on 1)

The bilevel coordinated optimization method according to claim 1, wherein the lower-layer model comprises a TMC capacity optimization model and an FC capacity optimization model.

### Claim 3 (depends on 1)

The bilevel coordinated optimization method according to claim 1, wherein the IIA model is as follows: probabilities for TMC charging and FC charging are shown as follows:
Pr{select TMC mode at site z}=exp(czifc)/{exp(czitmc)+exp(czifc)}
Pr{select FC mode at site z}=exp(czitmc)/{exp(czitmc)+exp(czifc)}
wherein czitmc and czifc are costs when user i chooses TMC and FC charging at site z respectively.

### Claim 4 (depends on 1)

The bilevel coordinated optimization method according to claim 1, wherein the TMC location optimization model is as follows:
t
st
,
z
tmc
=
∑
i
=
1
χ
t
,
z
tmc
m
ci
tmc
/
∑
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=
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24
∑
i
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tmc
≥
φ
,
∃
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[
1
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st
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tmc
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t
e
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tmc
=
∑
i
=
1
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tmc
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=
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∑
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tmc
m
ci
tmc
≥
φ
,
∃
t
∈
[
t
e
,
z
tmc
,
24
]
wherein xt,ztmc represents a total number of EVs that choose TMC charging at candidate site z at time point I; tst,ztmc and tc,ztmc respectively represent a service start time and a service end time of a TMC; and q represents a power-weighted coefficient; and
a utilization rate constraint is as follows:
x
z
=
{
1
,
if
⁢
∑
t
=
t
st
,
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tmc
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e
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(
ρ
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t
⁢
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≥
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≥
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,
∀
z
∈
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t
⁢
m
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c
0
,
othe
⁢
r
⁢
w
⁢
i
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s
⁢
e
wherein pzitmc represents a utilization rate of charging station z per unit time, which is determined by the lower-layer coordinated capacity optimization model; δ represents a lower bound for the utilization rate considering the economic viability of the TMC, and u represents hours when the utilization rate is less than the lower bound; Sctmc represents a set of TMC candidate sites; and mcltmc represents a charging quantity when EV1 chooses TMC charging.

### Claim 5 (depends on 1)

The bilevel coordinated optimization method according to claim 1, wherein the TMC depot optimization model is as follows: a location of a TMC depot is optimized based on a minimization of construction and operation costs:
min
⁢
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=
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y
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{
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dp
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(
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-
1
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+
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x
y
(
c
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+
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)
+
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0
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∈
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∈
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tmc
x
y
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x
yz
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el
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+
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p
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s
.
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.
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f
t
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x
y
⁢
x
yz
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p
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⁢
E
z
t
⁢
m
⁢
c
≤
E
ymax
D
,
∀
y
∈
S
c
D
wherein vector xD comprises Boolean variables, with xy=1 when a TMC depot is set at depot y, wherein y∈ScD; otherwise, xy=0; rG represents a discount rate; q represents an optimization period; vector xyzD comprises Boolean variables, indicating whether a TMC at site z is assigned to depot y; and EymaxD represents a capacity constraint determined by local renewable energy and power grid conditions;
Wherein CD represents construction and operation costs of the TMC depot;
ScD represents a set of candidate TMC depot locations;
csl,yD represents employee wage costs of the TMC depot;
cmt,xD represents maintenance costs of the TMC depot;
D0 represents the number of operating days per year;
Sc,ftmc represents a set of TMC locations, cel,ytmc represents electricity costs,
dyz represents a round-trip distance from TMC z to depot y; Cztmc represents energy consumption per kilometer for the TMC;
ηpd represents an efficiency parameter corresponding to energy loss; y represents a discharge depth considering round-trip efficiency and battery aging; Eqtmc represents a battery capacity of the TMC, and
xyz is a Boolean variable, and xz=1 when the TMC at site z is assigned to depot y; otherwise, xy=0.