U.S. patent application number 12/420643 was filed with the patent office on 2009-10-15 for power management of a hybrid vehicle.
This patent application is currently assigned to THE UWM RESEARCH FOUNDATION, INC.. Invention is credited to Yaoyu Li.
Application Number | 20090259355 12/420643 |
Document ID | / |
Family ID | 41164650 |
Filed Date | 2009-10-15 |
United States Patent
Application |
20090259355 |
Kind Code |
A1 |
Li; Yaoyu |
October 15, 2009 |
POWER MANAGEMENT OF A HYBRID VEHICLE
Abstract
A system and method of determining and applying power split
ratios to power sources within hybrid vehicles. The power split
ratio is determined using a two-scale dynamic programming technique
to achieve optimal state of charge depletion over the course of a
trip. On the macro-scale level, a global state of charge profile is
created for the entire trip. On the micro-scale level, the state of
charge profile and accompanying power split ratio is recalculated
at the end of each segment as the vehicle proceeds along the trip.
Various trip modeling techniques are used to provide constraints
for the dynamic programming.
Inventors: |
Li; Yaoyu; (Franklin,
WI) |
Correspondence
Address: |
MICHAEL BEST & FRIEDRICH LLP
100 E WISCONSIN AVENUE, Suite 3300
MILWAUKEE
WI
53202
US
|
Assignee: |
THE UWM RESEARCH FOUNDATION,
INC.
Milwaukee
WI
|
Family ID: |
41164650 |
Appl. No.: |
12/420643 |
Filed: |
April 8, 2009 |
Related U.S. Patent Documents
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
|
|
61044983 |
Apr 15, 2008 |
|
|
|
Current U.S.
Class: |
701/22 ; 703/6;
903/930 |
Current CPC
Class: |
B60W 2556/50 20200201;
B60W 50/0097 20130101; B60W 2720/103 20130101; G01C 21/26 20130101;
B60K 6/445 20130101; B60W 2510/244 20130101; Y02T 10/62
20130101 |
Class at
Publication: |
701/22 ; 703/6;
903/930 |
International
Class: |
G06F 19/00 20060101
G06F019/00; G06G 7/48 20060101 G06G007/48 |
Claims
1. A hybrid vehicle comprising: a drive train; an electric power
source coupled to the drive train and including an electric energy
storage device having a state of charge; a non-electric power
source coupled to the drive-train; and a control system for
controlling the transfer of power from the electric power source
and the non-electric power source to the drive train, the control
system comprising software stored on a computer readable medium for
effecting the steps of: establishing a power split ratio between
the electric power source and the non-electric power source for a
defined trip route so that the state of charge reaches a defined
threshold at the end of the trip route; determining the state of
charge at various points along the trip route as the vehicle
proceeds along the trip route; and recalculating the power split
ratio at the various points along the trip route to ensure that the
state of charge approximately reaches the defined threshold when
the vehicle reaches the end of the trip route.
2. The hybrid vehicle of claim 1, further comprising software
stored on the computer readable medium for effecting the steps of:
segmenting the trip route into (n) segments, and modeling the trip
route to create a driving cycle that includes a velocity profile of
the hybrid vehicle for the trip route.
3. The hybrid vehicle of claim 2, wherein the modeling the trip
route comprises selecting a trip model to use to model each of the
(n) segments, wherein the trip model is one of: a gas-kinetic trip
model, a Gipps car following model, a neural network model, a trip
model using historical or real-time traffic data and constant
acceleration and deceleration rates, and a simple trip model using
constant acceleration, constant deceleration, and speed limits as
velocity rates.
4. The hybrid vehicle of claim 1, wherein the power split ratio is
established using dynamic programming and is recalculated at the
various points along the trip route using dynamic programming.
5. The hybrid vehicle of claim 4, wherein the dynamic programming
uses a driving cycle for the trip route created by trip modeling,
and wherein the driving cycle for the trip route comprises a
velocity profile of the hybrid vehicle.
6. The hybrid vehicle of claim 4, wherein the dynamic programming
is performed in the spatial domain.
7. The hybrid vehicle of claim 1, further comprising software
stored on the computer readable medium for effecting the step of:
recognizing driving patterns at various points along the trip route
as the vehicle proceeds along the trip route, and wherein
recalculating the power split ratio is performed based on
recognized driving patterns.
8. A method of controlling a hybrid vehicle comprising the steps
of: retrieving trip data; determining a trip route based on the
trip data; dividing the trip route into (n) segments; modeling each
of the (n) segments of the trip route to determine a driving cycle
along the trip route for the hybrid vehicle; determining a global
state of charge profile estimating the state of charge at the end
of each of the (n) segments such that the state of charge
approximately reaches the defined threshold when the vehicle
reaches the end of the trip route; determining a power split ratio
for each of the (n) segments based on the actual state of charge at
the beginning of a segment about to be traversed and the estimated
state of charge at the end of the segment about to be traversed,
such that the determined power split ratio causes the state of
charge to approximately reach the estimated state of charge at the
end of the segment about to be traversed; and applying the
determined power split ratio for each of the (n) segments.
9. The method of controlling a hybrid vehicle of claim 8, wherein
modeling each of the (n) segments of the trip route further
comprises selecting a trip model to use to model each of the (n)
segments, wherein the trip model is one of: a gas-kinetic trip
model, a Gipps car following model, a neural network model, a trip
model using historical or real-time traffic data and constant
acceleration and deceleration rates, and a simple trip model using
constant acceleration, constant deceleration, and speed limits as
velocity rates.
10. The method of controlling a hybrid vehicle of claim 8, wherein
determining a power split ratio for each of the (n) segments is
performed using dynamic programming.
11. The method of controlling a hybrid vehicle of claim 10, wherein
the dynamic programming uses the driving cycle, and wherein the
driving cycle comprises a velocity profile of the hybrid
vehicle.
12. The method of controlling a hybrid vehicle of claim 10, wherein
the dynamic programming is performed in the spatial domain.
13. The method of controlling a hybrid vehicle of claim 8, and
further comprising: recognizing driving patterns at various points
along the trip route as the vehicle proceeds along the trip route,
and wherein determining a power split ratio for each of the (n)
segments is based on recognized driving patterns.
14. The method of controlling a hybrid vehicle of claim 8, wherein
determining a power split ratio for each of the (n) segments is
based on real-time traffic data received from an information
database.
Description
RELATED APPLICATIONS
[0001] This application claims priority to provisional application
61/044,983 filed Apr. 15, 2008.
BACKGROUND
[0002] The present invention relates to hybrid vehicles and systems
and methods of determining and applying power split ratios to power
sources within hybrid vehicles.
SUMMARY
[0003] In one embodiment, the invention provides a hybrid vehicle
comprising a drive train, an electric power source coupled to the
drive train and including an electric energy storage device having
a state of charge, a non-electric power source coupled to the
drive-train, and a control system for controlling the transfer of
power from the electric power source and the non-electric power
source to the drive train. The control system comprises software
stored on a computer readable medium for effecting the steps of:
establishing a power split ratio between the electric power source
and the non-electric power source for a defined trip route so that
the state of charge reaches a defined threshold at the end of the
trip route, determining the state of charge at various points along
the trip route as the vehicle proceeds along the trip route, and
recalculating the power split ratio at the various points along the
trip route to ensure that the state of charge approximately reaches
the defined threshold when the vehicle reaches the end of the trip
route.
[0004] In another embodiment the invention provides a method of
controlling a hybrid vehicle comprising the steps of retrieving
trip data, determining a trip route based on the trip data,
dividing the trip route into (n) segments, modeling each of the (n)
segments of the trip route to determine a driving cycle along the
trip route for the hybrid vehicle, determining a global state of
charge profile estimating the state of charge at the end of each of
the (n) segments such that the state of charge approximately
reaches the defined threshold when the vehicle reaches the end of
the trip route, determining a power split ratio for each of the (n)
segments based on the actual state of charge at the beginning of a
segment about to be traversed and the estimated state of charge at
the end of the segment about to be traversed, such that the
determined power split ratio causes the state of charge to
approximately reach the estimated state of charge at the end of the
segment about to be traversed, and applying the determined power
split ratio for each of the (n) segments.
[0005] Other aspects of the invention will become apparent by
consideration of the detailed description and accompanying
drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0006] FIG. 1 illustrates an exemplary powertrain for a hybrid
vehicle according to an embodiment of the invention.
[0007] FIG. 2 illustrates an exemplary control system for a hybrid
vehicle according to an embodiment of the invention.
[0008] FIGS. 3a-c include graphs depicting the change in a
battery's state of charge over the course of a trip for a hybrid
vehicle.
[0009] FIG. 4 illustrates an exemplary process for determining and
applying a power split ratio according to an embodiment of the
invention.
[0010] FIG. 5 illustrates a typical driving cycle for a vehicle on
a on/off ramp of a freeway.
[0011] FIG. 6 illustrates an exemplary Neural Network Module
according to an embodiment of the invention.
[0012] FIGS. 7a-b illustrate estimated and actual state of charge
depletion over the course of a trip according to an embodiment of
the invention.
[0013] FIG. 8 illustrates an exemplary process for simplified
dynamic programming in the spatial domain according to an
embodiment of the invention.
DETAILED DESCRIPTION
[0014] Before any embodiments of the invention are explained in
detail, it is to be understood that the invention is not limited in
its application to the details of construction and the arrangement
of components set forth in the following description or illustrated
in the following drawings. The invention is capable of other
embodiments and of being practiced or of being carried out in
various ways.
[0015] As is apparent to those of ordinary skill in the art, the
systems shown in the figures are models of what actual systems
might be like. Many of the modules and logical structures described
are capable of being implemented in software executed by a
microprocessor or a similar device or of being implemented in
hardware using a variety of components including, for example,
application specific integrated circuits ("ASICs"). Terms like
"controller" or "module" may include or refer to both hardware
and/or software. Furthermore, throughout the specification
capitalized terms are used. Such terms are used to conform to
common practices and to help correlate the description with the
coding examples, equations, and/or drawings. However, no specific
meaning is implied or should be inferred simply due to the use of
capitalization. Thus, the claims should not be limited to the
specific examples or terminology or to any specific hardware or
software implementation or combination of software or hardware.
[0016] Hybrid vehicles use more than one type of power source for
providing power to the vehicle's drive train. Different types of
power sources include, for example, internal combustion engines,
electric motors, and hydraulic accumulators. These power sources
can be fueled by various types of batteries, fuel cells, petroleum
products (e.g., gasoline), biofuels, etc.
[0017] In power-split hybrid vehicles, the power sources work
together to directly provide driving power to the drive train. In
contrast, series hybrid vehicles have a first source directly
providing driving power to the drive train, and a second source
providing power to the first source. For power-split hybrid
vehicles, the relative amounts of power provided from the multiple
power sources to the drive train is referred to as the power split
ratio ("PSR"). In a power splitting hybrid vehicle with two power
sources, a PSR of 60%, and a total power demand Ptotal, the
following equations apply:
P.sub.total=P.sub.source 1+P.sub.source 2
P.sub.source 1=60%.times.P.sub.total
P.sub.source 2=40%.times.P.sub.total
Determining whether to use PSR (e.g., 60%) or 1-PSR (e.g., 40%) in
the P.sub.source 1 equation or the P.sub.source 2 equation is an
implementation decision. The selection of a PSR can alter the
performance of the vehicle, for instance, the fuel efficiency,
torque output, and emission levels.
[0018] FIG. 1 depicts a powertrain 100 of an exemplary power-split
hybrid vehicle of the invention. A fuel tank 110 provides fuel for
an internal combustion engine ("ICE") 105. The ICE 105 is coupled
to a transmission 140 that enables the ICE 105 to provide
mechanical power to a generator 135 and transmission 145. The
generator may provide electrical power to both a battery 125 and an
electric motor 115. The battery is capable of receiving and storing
electrical power from the generator 135 to increase its total state
of charge ("SOC"). The battery 125 is also capable of outputting
electrical power to the electric motor 115, which decreases the SOC
of the battery 125. The electric motor 115 receives electrical
power from the generator 135 and/or the battery 125 and converts it
to mechanical power to drive the transmission 145. Thus, the
transmission 145 may receive mechanical driving power from both the
ICE 105 and the electric motor 115. Thereafter, the transmission
145 provides mechanical driving power to the wheels 160 via
transmission 150 and axles 155, which propels the hybrid vehicle.
In alternative embodiments, the powertrain provides power to two or
more axles. In other embodiments, the powertrain 100 does not
include a generator 135 or transmission 140. Therefore, the battery
125 can not be recharged by the ICE 105. Instead, the battery 125
is recharged by solar panels, a main power grid (e.g., via a
plug-in connection), or other power sources.
[0019] FIG. 2 depicts a control system 200 to be used with a
powertrain of a power-split hybrid vehicle, such as powertrain 100.
The control system 200 includes a Control Module 205 with a Power
Management Module 210, Trip Information ("Info") Module 225, and
Power Split Signal Generator Module 215. The Control Module 205
receives input from the Power Request Module 220. The Power Request
Module 220 can include, for example, an accelerator pedal operated
by a driver of the hybrid vehicle. The Power Request Module 220 can
convert a mechanical action, such as a depression of the
accelerator or brake pedal, into an electronic signal indicating
the driver's desired acceleration or deceleration level. The Trip
Info Module 225 provides information about the driver's intended
and on-going trip. Information received and provided by the Trip
Info Module 225 can include destination information, current
location information, time of day information, speed information,
route information, traffic information, construction information,
and a battery's current state of charge ("SOC").
[0020] The Power Management Module 210 receives the information
output from the Power Request Module 220 and the Trip Info Module
225. The Power Management Module 210 uses the information received
to calculate a PSR, which is output to the Power Split Signal
Generator 215. The Power Split Signal Generator 215, in turn,
calculates the power request amount for each of the ICE 105 and the
electric motor 115. The ICE 105 power request can be calculated by
multiplying the PSR by the total power request (e.g.,
40%.times.total power request=power request for ICE 105). The
electric motor 115 power request can be calculated by multiplying
(1-PSR) by the total power request (e.g., 60%.times.total power
request=power request for electric motor 115. Therefore,
calculating and applying a PSR to the ICE 105 and electric motor
115 causes the ICE 105 to provide the same power, more power, or
less power than the electric motor 115 to propel the hybrid
vehicle. In other embodiments, the Power Split Signal Generator
Module 225 multiplies the PSR by the total power request to
determine the electric motor 115 power request, and multiplies
(1-PSR) by the total power request to determine the ICE 105 power
request.
[0021] Graphs 300, 320, and 340 of FIGS. 3a-c depict SOC values for
a power-split hybrid vehicle battery, such as battery 125, over the
course of a trip. The power-split hybrid vehicle for FIGS. 3a-c
includes generator 135 to maintain the battery level once it
reaches it's lowest healthy SOC level (SOC.sub.m). At the beginning
of a trip, the initial battery level is at SOC.sub.i. In one
embodiment, SOC.sub.m=0.3 and SOC.sub.i=0.8. In FIG. 3a, the
battery's SOC is reduced to SOC.sub.m before the end of the trip,
forcing the hybrid vehicle to rely more on the ICE 105 to power the
vehicle and maintain the battery's SOC. In FIG. 3b, the battery's
SOC is not reduced to an SOC.sub.m level at the end of the trip.
Therefore, the hybrid vehicle relied on the ICE 105 more than
necessary, using more fuel from fuel tank 105.
[0022] FIG. 3c depicts the ideal SOC usage over the course of a
trip, such that the vehicle will have the most efficient fuel
usage. In FIG. 3c, the SOC reaches its lowest healthy level at the
end of the trip. Properly chosen PSR levels in accordance with
embodiments of this invention will optimize the battery usage such
that the battery reaches the SOC.sub.m level at the end of the trip
as shown in FIG. 3c.
[0023] FIG. 4 shows a method 400 that implements two-scale dynamic
programming to dynamically calculate optimal PSR levels for a trip
in order to achieve the ideal SOC.sub.m level at the end of the
trip. The method 400 can be used, for example, by the control
system 200 of FIG. 2, and is described with reference thereto.
Before starting a trip, a user, such as a driver, passenger, or
third party, enters trip data into the Trip Info Module 225 (step
405). The data can include one or more trip destinations (e.g.,
through longitude and latitude coordinates, cross streets, an
address, etc.) and an estimated departure time (which can be
assumed the current time unless otherwise specified).
[0024] Next, the Trip Info Module 225 performs trip modeling to
find the driving cycle for the trip given the origin, destination,
and estimated departure time of the trip (step 410). The driving
cycle includes, for example, vehicle speed, trip time, and
acceleration/deceleration rates at each point along the trip. A
path-finding algorithm, such as those available via Geographic
Information Systems (GIS), will be used to find a route from the
origin to the destination. The path-finding algorithm will
determine a route based on some or all of the following: road
segment lengths, speed limits, historical and real-time traffic
data, road slope, intersection/traffic light distribution, and
estimated time of departure.
[0025] In one embodiment, once a route is determined, the trip is
segmented into a number (n) of segments. There are different ways
to segment the trip. For instance, a new segment can be created at
each traffic signal (e.g., stop light and stop sign), at each speed
limit change (e.g., from 30 mph to 40 mph), at each turn along the
route, at any combination of these, or at equidistant locations
along the route. The vehicle speed, segment time, and
acceleration/deceleration rates are determined for each segment
according to a chosen trip modeling approach. Different trip
modeling schemes include a simple model, a Gipps car following
model, an actual or historic data model, a gas-kinetic model, and a
neural network model.
[0026] In step 415, the control system 200 calculates a macro-scale
optimal SOC profile for the entire trip, an example of which is
shown in FIG. 7a. In FIG. 7a, SOC.sub.i is 0.8 and SOC.sub.m is
0.3. The resulting macro-scale SOC trajectory will include an
estimated ending SOC level (SOC(x)) for each of the n segments
(see, e.g., SOC(i) and SOC(i+1) in FIG. 7b). The SOC(x) level for
each segment end will be used as reference points throughout the
trip to ensure the SOC decreases approximately at an optimal rate
(i.e., like that shown in FIG. 3c). Calculating the macro-scale
optimal SOC profile will be described in more detail below with
respect to FIG. 8.
[0027] In another embodiment, one or both of steps 410 and 415 are
implemented by a computational device that is not onboard the
hybrid vehicle. That is, the trip information may be sent from the
control system or another device to a computational device that
performs the trip modeling (step 410), calculates a macro-scale
optimal SOC profile (step 415), and then transmits the resulting
data to the hybrid vehicle control system 200 wirelessly.
[0028] In step 420, real-time optimization with a micro-scale
dynamic programming ("DP") occurs with respect to the first segment
of the trip. The initial SOC value (soc(0)) and the predicted SOC
value for the end of segment 1 (SOC(1)), along with updated route
information, will be used to calculate an optimal PSR value for the
first segment such that the predicted SOC(1) is met as the hybrid
vehicle reaches the end of that trip segment. The updated route
information can include historical or, preferably, real-time
vehicle speed information along the segment in question (in this
case, segment 1). With the updated driving cycle information, a
dynamic programming optimization algorithm is executed to calculate
the optimal PSR level for that segment. In step 425, the control
system 200 applies the calculated PSR value and the hybrid vehicle
travels the first segment of the trip. If (while the hybrid vehicle
is traveling) the control system determines that the user has
altered the trip destination or the trip route has changed (step
430), the method restarts at step 405.
[0029] If the trip destination and trip route have not changed, as
the hybrid vehicle nears the end of the first segment, the control
system 200 determines whether any additional trip segments remain
(step 435). The control system 200 can determine that the vehicle
is nearing the end of a segment based on, for example, a GPS device
or other navigation tools. If additional segments remain, the
segment value x is increased by one (step 440). Thereafter, Trip
Info Module 225 performs an update of the trip model for the next
segment of the trip (segment 2) in step 445. Any of the trip
modeling schemes described herein may be used for performing the
update in step 445. The control system then implements step 420 for
segment 2 using the actual SOC(1) value as the initial SOC value
and the predicted SOC(2) value to determine an optimal PSR value
for the second segment. FIG. 7b depicts two segments of the trip,
the segment (i-1) which has been completed, and the segment (i),
which is about to begin. The solid bold SOC(i) line represents the
macro-scale optimal SOC profile. The solid bold SOC(i) line
represents the actual SOC level during the i-1 segment. The dashed
thin SOC(i) line represents the micro scale SOC level over the
segment (i) resulting from the dynamic programming of step 420 for
segment (i).
[0030] The method repeats the steps 420-440 to continuously update
(in other words, recalculate) and apply the PSR value for each
segment until no more segments remain (x=n in step 435) and the
trip is complete (step 450), or the trip destination or trip route
has changed (step 430) and the process restarts.
Trip Modeling
[0031] If historical and real-time traffic flow data are not
available for a given road segment, then a simple modeling scheme
(such as constant acceleration/deceleration and constant speed
(assumed equal to the speed limit)) can be used. Currently,
historical and real-time traffic flow data is often not available
on local roads.
[0032] In this simple modeling scheme, traffic sign and signal
delays can also be considered. Such traffic sign and signal data is
available from local transportation agencies (e.g., Geographical
Information Systems (GIS)), and can be quickly transmitted to the
vehicle control system 200 in real-time or pre-stored in the
on-board memories. In some embodiments, the trip model will assume
the vehicle will stop at each traffic signal for a set amount of
time (e.g., 30 seconds) and each stop sign for a set amount of time
(e.g., 3-5 seconds). In other embodiments, the trip modeling can be
synchronized with traffic signal sequences also available from
local transportation administrations. The synchronization allows a
more accurate model, where the vehicle does not stop at each
traffic signal. The traffic signal sequence provides the trip model
with the timing for green, yellow, and red lights. The trip model
can estimate the vehicle stopping distance on each road segment,
given the speed limit and estimated deceleration rate, and then
determine whether the car will have to stop at any given traffic
signal.
[0033] The microscopic Gipps car following model (the "Gipps
model") can increase the accuracy of the driving cycle relative to
the simple modeling. The Gipps model is well-suited to model local
road segments (road portion between traffic signals) of a trip. In
particular, the Gipps model describes the process by which drivers
follow each other in traffic streams, i.e., the interaction between
vehicles in the same lane. The Gipps model assumes the availability
of position and speed information for all vehicles on a road
segment by way of navigation devices, such as GPS transmitting
devices. The Gipps model, for purposes of this discussion, combines
the safety distance model of Gipps, an action point model (which
considers driver reaction times), and the traffic signal
synchronization modeling as described above. In this Gipps model,
all the drivers are assumed to have the same reaction time and each
vehicle has the same length.
[0034] Using the Gipps model, the following steps are executed to
determine the driving cycle along a road segment for the hybrid
vehicle, where (n) vehicles are on the road segment: [0035] 1) When
the vehicle enters the road segment, update the vehicle map and
traffic signal sequences from a traffic management center (TMC).
K=2. [0036] 2) Predict the trip model of the leading car (vehicle
1) with the traffic signal synchronization. [0037] 3) Predict the
driving cycle for the following vehicle (vehicle k) using the Gipps
car following model. Determine whether the vehicle (k) will stop
before the next traffic light. If so, go to step 4. Otherwise go to
step 5. [0038] 4) Set vehicle (k) to be the new leading car. Go to
step 1. [0039] 5) Check if the trip prediction is done for all (n)
vehicles (k=n?). If so, go to step 6. Otherwise, set (k=k+1), go to
step 3. [0040] 6) After the above steps, all (n) vehicles trip
predictions of the current local road segment are finished. End the
process for the current road segment.
[0041] Historical traffic data or real-time traffic data offer an
alternative to the simple modeling and Gipps modeling schemes.
Historical traffic data may include archived information such as
average speed on a road at a given date and time. Real-time traffic
data may include average speed at the approximate moment of the
information request. Historical and real-time traffic data are
available for most metropolitan freeways, e.g., via the Intelligent
Transportation System (ITS) archives and real-time monitoring
systems. In using the historic and real-time traffic modeling, the
driving cycle velocity of a given point on the road segment is the
average speed retrieved from the historic or real-time data
systems. For the road segment between two data points, a straight
line increase or decrease in velocity is assumed. That is, the
model assumes constant acceleration and deceleration between data
points.
[0042] In some embodiments, different trip modeling techniques are
used for on and off ramps for freeways to improve the accuracy of
the resulting driving cycle for the on and off ramps. In one
embodiment, a gas-kinetic trip modeling is implemented along
freeway on/off ramps to provide more accurate driving cycles at
such junctions.
[0043] In another embodiment, the trip model near on and off ramps
uses a Multi-layer Perceptron (MLP) type neural network using field
recorded traffic data. The neural network approach is a less
complex trip model than the gas-kinetic model. FIG. 5 depicts the
typical driving cycle for a vehicle near freeway on and off ramps
in graph 500. The vehicle starts with an approximated speed V.sub.1
(upstream speed), which is reduced to V.sub.3 (valley speed) as the
vehicle approaches other vehicles on the on or off ramp due to the
mixing of inflow traffic. After passing the mixing portion, the
vehicle can accelerate until it reaches V.sub.2 (downstream speed).
D is the distance between two main road detectors, and D.sub.1 is
the distance between the valley speed location and the downstream
main road detector.
[0044] FIG. 6 depicts a diagram for a MLP Neural Network Module 600
for trip modeling on and off ramps. The MLP Neural Network Module
600 has a hidden layer 610 and an output layer 620. The MLP Neural
Network Module also has three inputs (V.sub.1, V.sub.2, and
Q.sub.1) and two outputs (D.sub.1 and V.sub.3), where Q.sub.1 is
ramp flow. The training data for the neural network can be obtained
by combining the freeway portion of the actual speed profile along
with the ramp flow data from traffic sensor data (i.e., from an
ITS) retrieved from sensors near the on and off ramps. The
back-propagation algorithm is then applied to obtain the model
parameters. Thereafter, the model is validated.
[0045] In some embodiments, the trip plan modeling uses a
combination of these techniques, for example, the above-described
simplified approach or application of the Gipps model for local
road segments, the historical traffic data or real-time traffic
data for freeway/highway segments, and the neural network model for
freeway on/off ramps. The simple model, Gipps model, historical
traffic model, and real-time traffic model may be used exclusively
or in any combination for trip modeling systems in other
embodiments of the invention.
Dynamic Programming
[0046] For a given driving cycle (determined by trip modeling), the
goal of the control system 200 is to minimize the fuel consumption,
while meeting the speed and torque demand for the vehicle
operation. Such an optimization process can be performed by dynamic
programming with constraints including the dynamic model for
vehicle propulsion and the operational limits of individual
components.
[0047] In the discrete-time format, the hybrid vehicle model can be
expressed as
x(k+1)=f[x(k),u(k)]
where x(k) is the state vector of the system (e.g., vehicle speed,
transmission gear number, and battery SOC) and u(k) is the vector
of control variables (e.g., desired output torque from the engine,
desired output torque from the motor, and gear shift command to the
transmission). The optimization problem is to find the control
input u(k) to minimize the following cost function:
J = k = 0 N - 1 L [ x ( k ) , u ( k ) ] = k = 0 N - 1 [ fuel ( k )
] ##EQU00001##
where N is the duration of the driving cycle, L is the
instantaneous cost referring to the fuel consumption (engine
emissions are not considered in this equation).
[0048] During the optimization process, the following inequality
and equality constraints are satisfied to meet the speed and torque
demands and to ensure a safe and smooth operation of the engine,
battery, and motor:
Motor Speed:
.omega..sub.m.sub.--.sub.min.ltoreq..omega..sub.m(k).ltoreq..omega..sub.m-
.sub.--.sub.max
Motor Torque:
T.sub.m.sub.--.sub.min[.omega..sub.m(k),SOC(k)].ltoreq.T.sub.m(k).ltoreq.-
T.sub.m.sub.--.sub.max[.omega..sub.m(k),SOC(k)]
ICE Speed:
.omega..sub.e.sub.--.sub.min.ltoreq..omega..sub.e(k).ltoreq..omega..sub.e-
.sub.--.sub.max
ICE Torque:
T.sub.e.sub.--.sub.min[.omega..sub.e(k)].ltoreq.T.sub.e(k).ltoreq.T.sub.e-
.sub.--.sub.max[.omega..sub.e(k)]
State of Charge: SOC.sub.min.ltoreq.SOC(k).ltoreq.SOC.sub.max
Vehicle Speed: v.sub.v(k)=v.sub.v.sub.--.sub.req(k)
Torque Demand: T.sub.m(k)+T.sub.e(k)=T.sub.req(k)
[0049] As mentioned above, this optimization process can be
performed by using a dynamic programming (DP) algorithm. The
dynamic programming (DP) algorithm is used to determine the
macro-scale optimal SOC profile and PSR values. Dynamic Programming
(DP) is a general dynamic optimization approach that can provide a
globally optimal solution to a constrained nonlinear programming
problem. Based on Bellman's Principle of Optimality, the optimal
policy can be obtained by solving the sub-problems of optimization
backward from the terminal condition.
[0050] The sub-problem for the (N-1) step is to minimize:
J N - 1 * [ x ( N - 1 ) ] = min u ( N - 1 ) { L [ x ( N - 1 ) , u (
N - 1 ) ] + G [ x ( N ) ] } ##EQU00002##
For step k (0<k<N-1), the sub-problem is to minimize:
J k * [ x ( k ) ] = min u ( k ) { L [ x ( k ) , u ( k ) ] + J k + 1
* [ x ( k + 1 ) ] } ##EQU00003##
and the cost function to be minimized is defined by:
J = k = 0 N - 1 L [ x ( k ) , u ( k ) ] = k = 0 N - 1 [ fuel ( k )
+ .mu. NOx ( k ) + v PM ( k ) ] ##EQU00004##
[0051] J.sub.k*[x(k+1)] is the optimal cost-to-go function at state
x(k) starting from time stage k. The above recursive equation is
solved backward to find the control policy. The minimizations are
performed subject to the inequality and equality constraints
imposed by the driving cycle determined via trip modeling and
depicted above.
[0052] An effective way to solve the above cost function
numerically is through quantization and interpolation. For
continuous state space and control space, the state and control
values are first discredited into finite grids. At each step of the
optimization search, the function J.sub.k[x(k)] is evaluated only
at the grid points of the state variables. If the next state x(k+1)
does not fall exactly on a quantized value, then the value of
J.sub.k*[x(k+1)] as well as G[x(N)] are determined through linear
interpolation. At each step, the backward DP with interpolation
method was used. For some cases, the vehicle can be assumed fully
charged to the highest healthy level, typically SOC of 0.8, while
the healthy low level of SOC is 0.3. In these instances, the DP
problem is solved with the initial and terminal values of SOC at
0.8 and 0.3, respectively, as boundary conditions.
[0053] Solving the DP in the time domain, as described above, can
be computationally complex and may require computational power in
excess of that available in some on-board vehicle control systems
200. In these instances, the DP can be solved using an outside or
off-board system, with the resulting optimal macro-scale SOC
profile and PSR levels being transferred wirelessly to the control
system 200.
[0054] In another embodiment, the macro-scale optimal SOC profile
can be determined in step 415 in the spatial domain using a
simplified DP approach. This simplified DP approach is illustrated
in FIG. 8 and is less computationally complex than the time-domain
approach. Thus, the simplified DP approach is more easily computed
using on-board vehicle systems, such as control system 200.
[0055] The simplified DP approach used to obtain the macro-scale
SOC profile (step 415) is depicted in FIG. 8. The control system
first divides each segment into sub-segments of approximately the
same length (step 805). The control system then analyzes the
driving cycle produced through trip modeling to determine which
sub-segments of the trip include significant acceleration or
deceleration (step 810). The vehicle will operate in an electric
vehicle (EV) mode for these sub-segments. In the EV mode, the PSR
ratio is chosen such that electric motor satisfies 100% of the
vehicle's propulsion needs and the ICE provides no power (i.e.,
PSR=0). The control system 200 will also determine the estimated
change in SOC (.DELTA.SOC) for the EV mode sub-segments, (change in
fuel (.DELTA.fuel) will be zero). A look-up-table (LUT) populated
with estimates of .DELTA.SOC based on the driving cycle's
acceleration and deceleration estimates of the EV mode segments can
be used to estimate .DELTA.SOC.
[0056] In step 820, the control system 200 analyzes the non-EV mode
sub-segments of the trip to determine an estimated .DELTA.SOC and
.DELTA.fuel for each sub-segment according to each possible value
of PSR. In one embodiment, PSR is a value between 0 and 1 in
1/10.sup.th increments (e.g., 0.0, 0.1, 0.2, . . . 0.9, 1.0). The
PSR increments can be smaller or larger in other embodiments. To
determine the estimated .DELTA.SOC and .DELTA.fuel for each
sub-segment, the total power demand (speed.times.torque) and
selected PSR is used to determine the power demand from the ICE and
electric motor (for the selected PSR). The fuel rate can be found
from a fuel map for the hybrid vehicle based on the average speed
and the torque. The .DELTA.fuel is equal to the product of the fuel
rate and the predicted driving time of the sub-segment. The
.DELTA.SOC is equal to the numerical integration for the battery
dynamics within the sub-segment driving time. By ignoring the
temperature effect and the internal capacitance, a simplified
battery model in discrete time is:
SOC ( k + 1 ) SOC ( k ) - V oc - V oc 2 - 4 ( R int + R t ) T m
.omega. m .eta. m - sgn ( T m ) 2 ( R int + R t ) Q b
##EQU00005##
where the internal resistance R.sub.int and the open circuit
voltage V.sub.oc are functions of the battery SOC, Q.sub.b is the
maximum battery charge, R.sub.t is the terminal resistance, and
.omega..sub.m*.eta..sub.m.sup.-sgn(Tm) is the efficiency of the
electric motor.
[0057] In another embodiment, a look-up-table is populated with
estimated .DELTA.SOC and .DELTA.fuel values for different
sub-segment driving cycle characteristics. This eliminates the need
to perform algebraic calculations in real-time, as described in the
preceding paragraph. Instead, the algebraic calculations are
performed before a trip occurs and stored in the look-up-table.
[0058] In step 830, after the sub-segment-wise .DELTA.SOC and
.DELTA.fuel are calculated for the non-EV mode sub-segments with
all possible PSR values, DP is applied to the corresponding spatial
domain optimization. DP is applied to the non-EV mode sub-segments
of the trip using (.DELTA.SOC.sub.NET+.DELTA.SOC.sub.t) as the
initial SOC value and .DELTA.SOC.sub.t as the terminal SOC value.
SOC.sub.s is the initial SOC value for the trip (e.g., 0.8 if at
the typical highest healthy SOC level) and
.DELTA.SOC.sub.NET=SOC.sub.s-SOC.sub.t+the sum of each .DELTA.SOC
for all EV-mode sub-segments.
[0059] Performing DP provides the estimated .DELTA.SOC for each
non-EV sub-segment, which can then be combined with the estimated
.DELTA.SOC for each EV sub-segment. Thus, a macro-scale SOC profile
across the entire trip results, which is divided according to the
original (n) segments from the trip model.
[0060] In step 420, a micro-scale SOC profile is determined for the
upcoming segment (x) using DP. The DP can use an updated driving
cycle resulting from step 445 that uses real-time traffic data
(when available), or updates already-retrieved historic traffic
data based on estimated trip times with historical traffic data
based on actual/current trip times. Updating (also referred to as
recalculating) the driving cycle allows a more accurate DP solution
because the driving cycle constraints are more accurate. Using the
updated driving cycle, the power split ration is updated
(recalculated) in step 425.
[0061] Also, the micro-scale DP algorithm uses updated SOC
constraints to more accurately determine a micro-scale SOC profile
and PSR values. During the trip, the actual .DELTA.SOC may differ
from that in the macro-scale SOC profile, as the macro-scale SOC
profile is merely an estimation. For instance, the driver may brake
or accelerate more or less than expected, changing the demand from
the battery, and, thus, the battery's SOC at the end of a segment
may not be as expected. Therefore, as discussed above with
reference to FIG. 7(b), the initial SOC value used is the actual
SOC at the end of the current segment (soc(i)). The terminal SOC
value used is the estimated SOC level at the end of the next
segment (SOC(i+1)).
[0062] Similar to the macro-scale DP algorithm, the micro-scale DP
algorithm can be solved either in the time or spatial domain.
However, the time domain micro-scale DP is less complex than the
macro-scale DP problem; therefore, an on-board control system is
more likely to be able to perform the micro-scale DP than the
macro-scale DP in the time domain. The spatial domain micro-scale
DP is less complex than the micro-scale DP in the time domain.
[0063] In another embodiment, pattern recognition is used to
account for driver behavior that is inconsistent with the trip
models' driving cycle predictions. For instance, the
acceleration/deceleration rates may be higher for a more "sporty"
driver (thus shorter time periods for acceleration/deceleration),
or lower for a more conservative driver (thus longer time periods
for acceleration/deceleration). By better predicting the transition
period from an acceleration to approximate constant speed segment
and from a constant speed segment to deceleration, better fuel
efficiency is achieved. The pattern recognition will be applied,
for example, in step 425, to more accurately transition between the
EV mode and the PSR values determined via micro-scale DP for local
road segments.
[0064] To determine the time to transition from an acceleration
EV-mode to the DP micro-scale-determined PSR value for
approximately constant speed, the following criteria is used:
[0065] 1) a<a.sub.threshold
[0066] 2)
V.sub.lim-V.sub.threshold<V>V.sub.lim+V.sub.threshold
[0067] 3) Transition region: [S.sub.i+S.sub.1, S.sub.i+S.sub.2]
Where (a) is the acceleration rate of the vehicle,
(a.sub.threshold) is the threshold value of the transition,
(V.sub.lim) is the speed limit of the segment, (S.sub.i) is the
location of the (i.sup.-th) traffic stop, (S.sub.1) is the lower
bound of the transition region, and (S.sub.2) is the upper bound of
the transition region.
[0068] To determine the time to transition from the DP
micro-scale-determined PSR value for approximately constant speed
to a deceleration EV-mode to, the following criteria is used:
[0069] 1) b<b.sub.threshold
[0070] 2)
V.sub.lim-V.sub.threshold<V<V.sub.lim+V.sub.threshold
[0071] 3) Transition region: [S.sub.i+1-S.sub.3, S.sub.i-1]
Where (b) is the deceleration/braking rate of the vehicle,
(b.sub.threshold) is the threshold value of the transition,
(V.sub.lim) is the speed limit of the segment, (S.sub.i+1) is the
location of the (i+1.sup.-th) traffic stop, and (S.sub.3) is the
lower bound of the transition region.
[0072] Thus, the invention provides, among other things, systems
and methods of determining and applying power split ratios to power
sources within hybrid vehicles to improve fuel efficiency and
battery usage. Various features and advantages of the invention are
set forth in the following claims.
* * * * *