Content uploaded by Shobhit Gupta
Author content
All content in this area was uploaded by Shobhit Gupta on Jul 09, 2021
Content may be subject to copyright.
2021-01-0430 Published 06 Apr 2021
In-Vehicle Test Results for Advanced Propulsion
and Vehicle System Controls Using Connected
andAutomated Vehicle Information
Shreshta Rajakumar Deshpande, Shobhit Gupta, Dennis Kibalama, Nicola Pivaro, Marcello Canova,
andGiorgio Rizzoni The Ohio State University
Karim Aggoune and Pete Olin BorgWarner Inc.
John Kirwan Stoneridge Inc.
Citation: Rajakumar Deshpande, S., Gupta, S., Kibalama, D., Pivaro, N. et al., “In-Vehicle Test Results for Advanced Propulsion
andVehicle System Controls Using Connected and Automated Vehicle Information,” SAE Technical Paper 2021-01-0430, 2021,
doi:10.4271/2021-01-0430.
Abstract
A
key enabler to max imizing the benets f rom advanced
powertrain technologies is to adopt a systems integra-
tion approach and develop optimized controls that
consider the propulsion system and vehicle as a whole. is
approach becomes essential when incorporating Advanced
Driver Assistance Systems (ADAS) and communication tech-
nologies, which can provide information on future driving
conditions. is may enable the powertrain control system to
further improve the vehicle performance and energy e-
ciency, shiing from an instantaneous optimization of energy
consumption to a predictive and “look-ahead” optimization.
Benets from this approach can be realized at all levels of
electrication, from conventional combustion engines to
hybrid propulsion systems and full electric vehicles, and at all
levels of vehicle automation.
is paper documents an extensive simulation and experi-
mental campaign that provides a systematic quantication of
fuel economy and mobility benets resulting from leveraging
Vehicle-to-Everything (V2X) connectivity, longitudinal (Level
1+) automation, and advanced cylinder deactivation to improve
the propulsion system and vehicle efficiency. A Vehicle
Dyna mics and Powertrain (VD&PT) optimization framework,
developed jointly by e Ohio State University a nd BorgWa rner,
utilizes advanced route information avai lable from the naviga-
tion system and GPS, and Vehicle-to-Infrastructure (V2I)
communication when available to minimize the cumulative
fuel consumption over a driver-selected route.
To assess the benets of these technologies in real-world
conditions, a Monte Carlo simulation framework was devel-
oped to quantify the impact of Signal Phase and Timing
(SPaT), variability in traffic conditions, and behavior of
dierent drivers on the fuel consumption and vehicle travel
time. Further, verication through in-vehicle testing was
conducted by reconstructing a subset of these scenarios on a
proving ground.
Introduction
Connected and Automated Vehicle (CAV) technologies
oer the potential for improving the vehicle energy
eciency, safety, and comfort by leveraging informa-
tion from advanced mapping and GPS, Vehicle-to-Vehicle
(V2V) and Vehicle-to-Infrastructure (V2I) communication
[1,7]. Research in this area has been growing substantially in
recent years, and there is abundant literature showing the
impact of CAV technologies on energy consumption, travel
behavior and network performance [10,11]. Emerging tech-
nologies such as Machine-to-Machine (M2M) or Internet of
ings (IoT) connectivity protocols and their use in vehicular
systems have further accelerated the deployment of Vehicle-
to-Everything (V2X) communication technologies, a signi-
cant step towards autonomous driving and connected road
infrastructure [11]. is interconnectivity of mobility systems
gives rise to new challenges, for instance how to evaluate
methodica lly the performance of CAVs in a “real-world” envi-
ronment, where considerable variability is induced by route
features, trac, and Signal Phasing and Timing (SPaT) at
intersections. Some of the existing studies propose various
simulation and virtual verication frameworks to model the
interaction of the CAV with the environment and perform
fuel economy evaluation in a deterministic fashion [12].
However, a deterministic approach limits the number of
scenarios that can beevaluated, preventing a comprehensive
evaluation of the combined impact of route and environment-
based uncertainties such as trac and SPaT variability on the
fuel economy and travel time of CAVs.
e development, verication and testing of optimization
algorithms for CAVs involve real-world testing and ex haustive
verication over several possible scenarios. Currently, CAV
Downloaded from SAE International by Shobhit Gupta, Tuesday, March 23, 2021
2 IN-VEHICLE TEST RESULTS FOR ADVANCED PROPULSION AND VEHICLE SYSTEM CONTROLS
verication and testing can bedone using three main plat-
forms: simulation environments, in-vehicle testing on a closed
track and controlled conditions, and on public roads.
Advanced vehicle simulation tools oer the opportunity to
conduct virtual verication over a considerable number of
scenarios in a short time and in a relatively cost-eective
manner [4,5,20]. However, t his method suers from modeling
errors, which may impact the quality of the results [1].
Conversely, vehicle testing in a closed and controlled testing
facility can provide an accurate representation of the real-
world environment, and also allows for the construction of
specialized test scenarios for verication and evaluation of
specic maneuvers or usage cond itions [5]. Some limitations
of this approach are the scheduling and availability of the
testing facility, and the potential diculties in scalability (for
recreating and testing large number of scenarios) [2]. Finally,
testing on public roads oers the opportunity of a realistic
evaluation, but is considerably limiting in terms of the ability
to execute repeatable test cases [3].
e work presented in this paper summarizes the results
of a research activity conducted as part of the ARPA-E Next-
Generation Energy Technologies for Connected and
Automated On-Road Vehicles (NEXTCAR) program [8]. As
part of this program, the team developed a novel vehicle
dynamic and powertrain (VD&PT) control strategy that is
able to signicantly reduce the energy consumption of future
vehicles in real-world driving conditions, through the use of
connectivity and longitudinal (Level 1+) vehicle automation.
e paper outlines a testing and verication framework
in which various optimization features are assessed using a
systematic approach that includes simulation (in deterministic
and stochastic conditions), and proving ground testing on
reconstructed routes. A Monte Carlo simulation is initially
performed to evaluate the fuel economy and travel time distri-
butions of optimized CAV control strategies by varying
several factors, such as the driver aggressiveness, SPaT at
signalized intersections, and trac density. e evaluation is
performed by benchmarking the results against a baseline
controller representing a real-world driver. Cases representing
the maximum likelihood from the resulting distributions
(both the baseline and the VD&PT optimizer) are selected
and demonstrated on a closed test track at the Transportation
Research Center (TRC) in East Liberty, Ohio. e test results
were veried to besuciently close to the initial simulation
results, validating the proposed toolchain and
verication framework.
Vehicle Model:
Development and
Validation
e demonstration vehicle employed for this research, shown
in Figure 1, is a 2016 VW Passat with the EA888 engine.
The powertrain was upfitted with a P0 mild-hybrid
(mHEV) system, which includes a 48V Belted Starter
Generator (BSG) performing torque assist, regenerative
braking and start-stop functions. In addition, the internal
combustion engine was modied to incorporate an advanced
cylinder deactivation technology known as Dynamic Skip
Fire (DSF) [9]. With DSF, engine ring decisions are made
independently on a cylinder-by-cylinder basis to manage
engine torque while ring cylinders at an increased load. is
results in a signicant reduction in engine pumping losses
and improved combustion eciency compared with standard,
throttled engine operation.
A forward-looking dynamic powertrain model was devel-
oped for the demonstration vehicle and utilized to predict the
performance and fuel economy over user-dened routes [7].
e simulator includes a low frequency, quasi-static model of
the engine (fuel map), BSG (torque and eciency maps), trans-
mission (eciency maps), and 48V battery pack. e vehicle
longitudinal dynamics are based on the road load equation
[27]. e eects of DSF are captured in the model by modi-
fying the engine fuel consumption maps and the torque
converter slip, which are implemented as gear-dependent
look-up tables.
As shown in Figure 2, the inputs to the plant model are
obtained from a simplied model of the Electronic Control
Module (ECM), which contains the essential functions to
convert the driver’s input (pedal position) to torque
commands. e outputs of the ECM, the desired BSG torque
(
T
bsg
des
) and desired engine torque (
T
eng
des
), are obtained from a
production-level torque split strategy, which is used as the
baseline for fuel economy evaluation. Here,
FIGURE 1 Schematic of P0 mild-hybrid powertrain.
© SAE International.
FIGURE 2 Block diagram of mHEV powertrain and vehicle
dynamics model.
© SAE International.
Downloaded from SAE International by Shobhit Gupta, Tuesday, March 23, 2021
IN-VEHICLE TEST RESULTS FOR ADVANCED PROPULSION AND VEHICLE SYSTEM CONTROLS 3
“production-level” refers to the rule-based energy manage-
ment strategy that is provided by the supplier along with the
48V mHEV system. This strategy also determines the
powertrain operating modes (load shiing, drive assist, or
regenerative braking).
e vehicle model was validated based on experimental
data collected on a chassis dynamometer. e key variables
used for evaluating the model are vehicle velocity, battery State
of Charge (SOC), gear number, engine speed, desired engine
and BSG torque proles, and the fuel consumption.
Figure 3 shows a verication over the FTP drive cycle,
where the vehicle velocity, battery SOC and fuel consumption
are compared against experimental data. e fuel consump-
tion over the FTP cycle is well predicted, with cumulative
error less than 4%. Small mismatches in the battery SOC
proles are attributed to simplication of the 12V electrical
system, where the power demand from the auxiliary loads
was approximated by a constant current bias, which does not
capture the inherent dynamics.
VD&PT Optimization
Algorithm for CAVs
The main objective of the VD&PT optimization framework
developed is to minimize a trade-off between fuel
consumption and travel time over an entire trip, while
satisfying route-dependent and state-dependent
constraints. This is achieved through jointly optimizing
the vehicle velocity and the torque split strategy of the
mHEV powertrain, and by making extended use of the DSF
capability of the engine. Note that the firing frequency in
DSF is not assumed as a degree of freedom for the optimi-
zation but is indirectly controlled by shaping the vehicle
velocity and torque split trajectories [9]. Considering the
information available via current-generation V2I commu-
nication modules, the VD&PT optimization is formulated
as a receding horizon optimal control problem, performed
over a look-ahead horizon of NH steps, where NH is signifi-
cantly sma ller than the route lengt h. Dynamic Prog ramming
(DP) is selected as the method of choice to solve the
constrained optimization problem. To this extent, the state
equations of the vehicle and powertrain models were
discretized and expressed in distance-based coordinates
(instead of time), which is necessary to consider the
presence of stop signs, traffic lights and other route features
whose positions along the route remain fixed. A more
comprehensive description of the VD&PT optimizer is
available in [12].
e cost function of the receding horizon optimization
problem is dened as:
Jx
cx cx x
sN
ss sN sN
ks
sN
kk
kk
sHH
H
∗++
=
+−
()
=
()
+
()
()
∀= …−
∑
min
,,
M
1
1
,
µ
NN
H
(1)
where x is the state vector in state-space
Xs
,
Ms
ss sN
H
:
=…
()
++
−
µµ µ
,,,
11
is denoted as the set of admissible
control maps policies of the controller for each s=1, …,
N−NH, cs+ NH(xs + NH) refers to the terminal cost, and
ckk k
:XU×→R
is the per stage cost function dened as a
trade-o bet ween fuel consumption and travel time associated
to a given route:
cx
umxsuss
m
kkk
fk kk kk k
f
norm
,,,
()
=
() ()
()
+−
()
γγ
··
1∆∆s
vs
kk
()
(2)
where u is the input vector, Δs=sk+1−sk is the distance
traveled over one step and
vs
vs vs
kk
kk kk
()
=
()
+
()
++
max.01 2
11
,
is the average velocity over one step in meters per second. e
weighting factor γ∈(0, 1) can beinterpreted as a tunable
“aggressiveness” parameter, used to trade-o the amount of
fuel consumed and time taken to complete the route.
An appropriate termina l cost in this case eectively repre-
sents the residual cost (or cost to complete the remaining
route) in the optimization. is can bederived from a base-
heuristic or a base-policy evaluated oine [25]. Finally, the
following constraints are imposed on the NH step optim ization
problem ∀k=1, …, N−NH:
FIGURE 3 Validation of vehicle and powertrain model over
FTP cycle.
© SAE International.
Downloaded from SAE International by Shobhit Gupta, Tuesday, March 23, 2021
4 IN-VEHICLE TEST RESULTS FOR ADVANCED PROPULSION AND VEHICLE SYSTEM CONTROLS
vv vvv
v
ss sEAND ss
EAND
slead
∈+ −
−
∀
minmin,max max,
max, ,
∆∆
∆
,
ssk kN
SOCSOC SOCskkN
vv SO
H
sH
=+…+
∈
∀= +…+
=
1
1
11
,
,,
,
minmax
min
,
CCSOC SOCSOC SOC
aaaskkN
N
sH
11
∈
=
∈
∀= …+
minmax
minmax
,
,,
,
,−−
∈
() ()
∀= …+ −
1
1TTvT vskkN
T
engs engs seng ss H
bsg
,,
min,
max,,,
,, ,
min,
max,,
sbsg ss bsgs
sH
sPTs
TvTv sk kN
xfx
∈
() ()
∀= …+ −
=
+
,1
1,, uskkN
sH
()
∀= +…+,,1
(3)
where
vv
ss
minmax
,
refer to route minimum and maximum
speed limit respectively, SOCmin, SOCmax are the battery SOC
limits, amin, ama x are acceleration limits imposed for drive
quality,
TT
engs engs,
min,
max
,
are the state-dependent minimum and
maximum torque limits of the engine respectively, and
TT
bsgs bs
gs
,
min
,
max
, are the state-dependent minimum and maximum
BSG torque limits respectively. To ensure SOC-neutrality over
the global optimization, a terminal constraint is applied on
the battery SOC at the end of the route.
A rule-based strategy for Eco-Approach and Departure
(Eco-AND) is developed and implemented to handle vari-
ability in SPaT information in a realistic manner and
maximize t he chance to pass through a signalized intersection
in the green phase. e Eco-AND algorithm uses the ego
vehicle velocity, distance to the upcoming intersection, and
the SPaT information to calculate the offsets
∅ ∅vv
sEAND sEANDmin, max,
,
, applied to the minimum and ma ximum
speed limit respectively. ese reshaped velocity constraints
are then used by the VD&PT optimizer.
e presence of a lead vehicle is also accounted for in a
similar manner by determining and applying an appropriate
oset
∅vsleadmax,
to the maximum speed limit. Figure 4 shows a
scenario where the lead vehicle travelling at a speed lower than
the ego vehicle is within the NH horizon of the ego. To avoid
a collision, the ego vehicle must slow down to a speed less than
or equal to the lead vehicle speed. Under this scenario, the ego
would eva luate
∆vsleadmax, >0
by imposing max imum decelera-
tion constraints (shown in red) in an attempt to maintain a
safe gap, s0 from the lead vehicle. e route maximum speed
constraint would then belowered by
∅vsleadmax,
at each distance
step to perform this maneuver. However, if the lead vehicle is
outside the NH horizon of ego, then
∅vsleadmax,
is set to zero and
the ego continues to travel in free ow conditions.
Verification Framework for
VD&PT Optimization
Algorithm
e process for verication of the VD&PT optimization
strategy is summarized in Figure 5.
The virtual Evaluation of VD&PT Optimizer for
Eco-Driving case involves the generation of multiple simula-
tion scenarios, where the parameter γ in the optimization is
varied to determine and quantify the Pareto-optimal fronts
among the objectives (total fuel consumption and trip travel
time). is evaluation is initially performed by assuming that
all the trac lights along the route are stop signs. e VD&PT
optimizer is benchmarked against a realistic baseline, which
is assumed as the same demonstration vehicle, however
without DSF. is assumption is introduced because one of
the objectives of the VD&PT optimizer is to synergistically
integrate DSF with the rest of the hybrid powertrain in such
a way that the optimized controls are able to push more engine
operating points into the DSF y-zone than would bepossible
without the optimization. Here, the DSF y-zone refers to the
region of the engine speed-brake torque map in which DSF is
FIGURE 4 Speed constraint shaping in the presence of
a leader.
© SAE International.
FIGURE 5 Process for verification of VD&PT
optimization strategy.
© SAE International.
Downloaded from SAE International by Shobhit Gupta, Tuesday, March 23, 2021
IN-VEHICLE TEST RESULTS FOR ADVANCED PROPULSION AND VEHICLE SYSTEM CONTROLS 5
feasible and active. Note that for the baseline case, the 48V
mHEV system is enabled. Furt her, no longitudinal automation
is assumed, and for this reason all the simulations were
conducted by including a validated Enhanced Driver Model
(EDM) to mimic the response of a human driver [22].
Following this initial virtual verication step, experi-
mental testing was conducted on a closed test track at TRC,
where the VD&PT optimizer was demonstrated through the
real-time implementation in the demonstration vehicle. To
obtain a realistic baseline for benchmarking the results, the
vehicle was tted with a Brake and rottle Robot (BTR) [28],
programmed to follow pre-established velocity proles gener-
ated in simulation with the EDM. is deterministic scenario
was used to validate the simulation tools on specic scenarios
and oered an initial estimate of the fuel saving potentials of
the developed VD&PT optimizer.
Following this initial verication step, a more compre-
hensive virtual verication (Evaluation of VD&PT Optimizer
for Eco-AND, in Figure 5) was performed using a Monte Carlo
simulation framework, in which t he driver aggressiveness and
the SPaT information are treated as random variables. From
the results of this simulation, in-vehicle verication at TRC
was conducted by extracting and testing sample cases from
the corresponding Monte Carlo simulations. ese selected
cases were used to conrm the results obtained in the Monte
Carlo simulation. Here, SPaT variability, including commu-
nication latencies, was realistically emulated during testing.
Benchmarking is performed using a BTR that follows the
corresponding Line-of-Sight (LoS) based EDM-generated
velocity proles in a repeatable manner.
Finally, the virtual Evaluation of VD&PT Optimizer with
Eco-AND and Trac case involves the development of a more
complete simulation framework where the trac dynamics
are considered in a mesoscopic sense, and the VD&PT opti-
mizer is modied to handle the presence of trac without
increasing its computational complexity. Verication for this
case was performed using a Monte Carlo simulation frame-
work, in which the driver aggressiveness, SPaT information
and trac density (or an equivalent trac stream metric) are
treated as random variables. e VD&PT optimizer was
benchmarked against the EDM, which was suitably modied
to preview the presence of a lead vehicle or the current state
of the upcoming trac light, by dening a human-vision
distance parameter, namely the LoS [23].
Inclusion of Trac in
Baseline
To establish a baseline for benchmarking fuel consumption
and travel time, the EDM was used to generate a reference
velocity prediction, starting from known route characteristics
and considering different levels of driver aggressiveness
[17,18].
As explained in [12], the EDM consists of three operating
modes: Car Following, Freeway Driving and Stop Mode. As
depicted in Figure 6, the car following mode is activated in
the presence of a leader.
In this paper, the car following mode is augmented to
incorporate a dynamic intravehicular spacing term (s∗). e
following equations describe in particular the car following
model used in the EDM. Interested readers are encouraged to
refer to [12], in which the complete EDM and its governing
equations are detailed.
dv
dt av
v
svv
s
svv
lead
=−
−
()
()
=
∗
∗
12
2
δ
,
,
∆
∆ssvTvv
ab
0
2
++
∆
(4)
where v is the ego vehicle velocity, a, b are the maximum
acceleration and deceleration limits respectively, vlead is the
velocity of the lead vehicle, and δ refers to the acceleration
exponent that denes how aggressively the driver accelerates
or decelerates. In the presence of a lead vehicle, the ego decel-
erates according to the prescribed repulsive braking strategy.
is ensures that the actual distance to the leader (s) is no less
than s∗. is dynamic intravehicular gap term comprises a
safe gap to the leader in a trac jam (s0), the headway time
separation (T), and relative velocity (Δv) between the ego and
the leader.
Virtual Verification in
Mixed Trac Conditions
Trac Modeling and
Environment Setup
e eect of trac was evaluated on the fuel consumption
and travel time of the demonstration vehicle equipped with
Level 1 longitudinal automation. This was achieved by
modeling trac at a mesoscopic level (i.e., macroscopic +
microscopic). At a macroscopic level, the trac is controlled
by varying the Trac Flow (F) that determines the number
of vehicles (Nveh) passing through a g iven point on the roadway
and is represented in terms of vehicles per hour [13]. At a
microscopic level, the individual vehicles are assumed as
point-wise elements, whose velocities and spacing can
becontrolled. For simplicity, it is assumed that the ego vehicle
FIGURE 6 Overview of the EDM model with the freeway
and car- following mode.
© SAE International.
Downloaded from SAE International by Shobhit Gupta, Tuesday, March 23, 2021
6 IN-VEHICLE TEST RESULTS FOR ADVANCED PROPULSION AND VEHICLE SYSTEM CONTROLS
travels in a given lane but surrounding vehicles can perform
cut-in or cut-out maneuvers in front of the ego vehicle.
SUMO (Simulation of Urban MObility) is used as the
trac simulator to generate real-world scenarios. SUMO is
an open source, microscopic, multi-modal trac simulator
that contains a suite of applications to create and import road
networks and dene its corresponding trac demand using
additional infrastructure such as trac lights. e simulation
environment contains an Intelligent Driver Model (IDM) [14]
that generates the speed trajectories with varying aggressive-
ness for the surrounding vehicles in the network [15,16,17].
is provides the capability to model individual vehicles in a
microscopic fashion.
e benets from the VD&PT optimization will beroute
dependent, varying from pure highway routes to pure urban
routes. For the testing reported on in this paper, a mixed
urban-highway route (termed Route 19) in the central
Columbus, OH region (pictured on OpenStreetMap (OSM)
in Figure 8 is selected for virtual verication. e speed limits,
position of trac lights and stop signs along Route 19 are
shown in Figure 7. is route is used to setup the macroscopic
simulation by dening a ow of vehicles between an origin
node and a destination node, using the duarouter package in
SUMO. In the context of trac simulation, the probability of
an agent or vehicle vi entering or exiting a node ni is given by
a Poisson Distribution:
Pv
vn nPois
vn ii|)~(|
==
()
λ
(5)
e magnied view in Figure 8 shows the vehicles (green
dots) having dierent driver aggressiveness levels entering
into the trac stream at a particular intersection. e depar-
ture time of these vehicles aects the interactions of the trac
stream with signalized intersections along the route, as they
would encounter dierent SPaT information.
In the context of fuel economy and travel time eva luation
of a CAV, it is crucial to appropriately dene and classify the
trac density across a trip [18,19]. To systematically evaluate
the impact of dierent trac densities, the generated trac
streams are classied into low, medium and high. To perform
this classication, a uniform metric termed the queue density
(qD,t) is created, and is dened as the ratio of the number of
lead vehicles (Nlead,t) in a queue (shorizon,t) ahead of the ego
vehicle as shown in Figure 9. shorizon,t refers to the bumper-to-
bumper distance between the ego and the farthest lead vehicle
within the sensing range.
qN
s
Dt
lead t
horizont
,,
,
= (6)
e advantage of dening shorizon,t as the bumper-to-
bumper gap instead of a constant value is seen when the lead
vehicles are stationary at a traffic light and the shorizon,t
continues to decrease until the safe gap to the leader is reached.
In this scenario, even if there is a single lead vehicle at the red
light, the higher queue density value allows the ego to reduce
its velocity, eventually coming to a stop and avoiding
a collision.
As qD(t) varies over the route, the mean (μqD) and
standard deviation (σqD) are dened to capture the average
behavior of queue density over a given route. Figur e 11 shows
a cloud of points representing μqD and σqDfor 500 random trips
generated in SUMO. Lower μqD,σqD represent trips with lower
average queue density, eventually leading to higher average
velocity over the route and lower travel times as shown in the
corresponding velocity prole plot (highlighted in green). In
contrast, points with higher μqD,σqD represent trips with
higher average queue density, having lower average velocity
over the route and increased travel times. is is visualized
in the corresponding velocity plot (highlighted in red).
Simulation Architecture
Analyzing the impact of varying trac conditions on the fuel
consumption and travel time of a CAV calls for a simulation
framework that allows the ability to simulate both ego and
lead vehicle in a single environment. Most of the existing
literature is focused on developing integrated solutions where
the benchmarking of fuel economy of CAVs is generally
performed oine using an optimizer-in-the-loop with the
FIGURE 7 Route features of route 19.
© SAE International.
FIGURE 9 Description of queue density.
© SAE International.
FIGURE 8 Route 19 on OSM.
© SAE International.
Downloaded from SAE International by Shobhit Gupta, Tuesday, March 23, 2021
IN-VEHICLE TEST RESULTS FOR ADVANCED PROPULSION AND VEHICLE SYSTEM CONTROLS 7
trac simulator [20]. In this work, a two-phase approach of
trac simulation and information extraction is adopted; this
benets the testing and development of the control algorithms
and can also beeasily scaled for use in ADAS via a speed
advisory module. Further, as the VD&PT optimizer leverages
a distance-based optimal control problem formulation, the
proposed methodology elegantly solves the challenge of inte-
grating a time-based traffic simulator with a distance-
based optimizer.
Figure 10 shows the methodology followed to integrate
trac information (generated via simulations in SUMO) with
the baseline model and the VD&PT optimizer. At a micro-
scopic level, the model parameters for the IDM in SUMO are
defined to model individual vehicles and includes the
maximum acceleration (amax), deceleration (bmax), speed oset
(θoset) from the speed limit and acceleration exponent (δ). At
a macroscopic level, the ow is dened by the origin (O), desti-
nation (D) and the probability of entry of vehicles into the
route (Pv∣n). ese parameters are then calibrated in SUMO
to achieve a certain ow and driver aggressiveness. A route,
imported from OSM into SUMO, is then used to dene the
road networks and develop a multi-lane trac environment.
e time-varying velocity prole of the ego vehicle (vego,t)
captures the eect of trac in the microscopic simulation and
justies its use as the leader velocity constraint source for the
baseline and VD&PT optimizer simulations. is process of
using trac simulations to generate lead vehicle velocity
constraints is referred to as the extraction phase of the trac
modeling. e optimizer and the baseline are then used to
evaluate the corresponding fuel (
mm
f
base
f
opt
,) and travel time
(Tbase,Topt) metrics of a CAV.
Monte Carlo Simulation
Several studies have shown that driving behavior and trac
have a large impact on vehicle fuel consumption and travel
times (see for instance [20,21]). Information from vehicle
connectivity via V2X can also impact driver decisions, such
as following an advisory speed profile for optimal fuel
consumption [24]. ese external factors can beconsidered
as uncertainties to the standard fuel consumption estimation
procedures, motivating the quantication of the eects of
driver aggressiveness and CAV information on the fuel
consumption and travel time for a given route. In this work,
a large-scale Monte Carlo simulation has been performed to
analyze the impact of trac on the fuel consumption and
travel time of a CAV.
FIGURE 11 Variation of average queue density with
dierent departure times.
© SAE International.
FIGURE 10 Overview of simulation architecture.
© SAE International.
Downloaded from SAE International by Shobhit Gupta, Tuesday, March 23, 2021
8 IN-VEHICLE TEST RESULTS FOR ADVANCED PROPULSION AND VEHICLE SYSTEM CONTROLS
Procedure
e entry points of lead vehicles into the route aect the SPaT
information encountered by the ego vehicle. erefore, it is
important to quantify the eects of the SPaT and trac vari-
ability on the fuel consumption and travel time of a CAV. To
analyze their marginal (in a probabilistic sense) impact, two
sets of Monte Carlo simulations are run. e rst case is a no
trac case with V2I enabled. Here, the source of randomness
stems from the route departure time, which aects the initial
state of the trac lights along the route. e second case
involves the presence of lead vehicles that are generated using
the SUMO model and the extraction methodology described
previously. In this case, the random variables are represented
using a joint distribution of the uniform departure time and
the Poisson distribution assumed to consider the presence
of trac.
A comparison is then performed between the trac and
no trac case. For both these cases, Monte Carlo simulations
are performed for the baseline and the optimizer. For the
optimizer, three values of driver aggressiveness (captured by
γ) representing an aggressive (γ=0.4), normal (γ=0.7) and
relaxed driver (γ=0.82) are considered. For fair comparison,
three corresponding sets of EDM parameters are tuned such
that the baseline has comparable travel times in a determin-
istic scenario where all trac lights are assumed to bered.
For the no trac case, 500 cases are generated by sampling
from the uniform distribution of the departure time, where
the SPaT cycle time is taken to be90s. For the trac case, 500
trips are randomly generated in SUMO to capture the joint
randomness of the SPaT information and trac conditions.
Results and Analysis
Figure 12 illustrates the distributions of travel time and fuel
consumption for the no trac case. e simulation results
indicate that the VD&PT optimizer achieves mean fuel
savings within 13-25%, with 5-7% reduction in the mean travel
time across dierent γ values. As mentioned previously, the
fuel consumption benets obtained by the VD&PT optimizer
result from the joint optimization of vehicle velocity and
energy management strategy of the mHEV powertrain. In
determining the optimal speed and torque split trajectories,
the optimizer utilizes the DSF model to determine the most
suitable way to displace the engine operating points into the
y-zone, hence taking advantages of the reduced pumping
losses and, ultimately, improving the fuel savings. In the
baseline case (without preview), issues related to drive quality
and Noise Vibration Harshness (NVH) limit the DSF y-zone,
hence its eect on fuel consumption is minimal.
e inclusion of variability from trac conditions (as
shown in Figure 13) decreases the range of mean fuel savings
to 9-21% (relative to the baseline, and across all values of γ
considered). An important eect of the trac variability is
the noticeably higher variance in the fuel consumption and
the travel time, both for the baseline and the optimizer. For
conciseness, detailed results are presented in this paper only
for the γ=0.7 case.
It is worth noting that the variance in the fuel consump-
tion obtained for the VD&PT optimizer is much lower than
that for the baseline. Another interesting observation is that
the mean fuel consumption of the baseline vehicle reduces
upon adding trac variability. is trend can beexplained
by examining the behavior of the baseline in the absence of
trac. Under free ow conditions, the baseline EDM velocity
prole tends to remain close to the speed limit, and sharp
velocity transients are present when the speed limit changes.
In presence of a leader, which can have dierent levels of
aggressiveness, the baseline ego vehicle has to maintain a safe
distance, oen traveling at lower speeds. is eectively
“lters” its velocity relative to the free ow conditions, inad-
vertently improving its fuel economy. Overall, in spite of the
added trac streams, the VD&PT optimizer with Eco-AND
still manages to reduce the fuel consumption by 21%. is
serves as a verication of the robustness of the optimization
framework developed.
FIGURE 12 Fuel consumption and travel time distribution from Monte Carlo simulations (no trac case).
© SAE International.
Downloaded from SAE International by Shobhit Gupta, Tuesday, March 23, 2021
IN-VEHICLE TEST RESULTS FOR ADVANCED PROPULSION AND VEHICLE SYSTEM CONTROLS 9
Under free ow conditions, the Eco-AND algorithm that
uses the VD&PT optimizer allows for overall faster travel
times, by maximizing the pass-in-green events at intersec-
tions. As a result, the vehicle travels 5% faster than the baseline.
In the proposed VD&PT optimization framework, it is not
possible to directly optimize or control the vehicle separation
without adding time as an additional state variable. is,
coupled with the limited V2I range (considered as 190m),
reduces the number of scenarios in which the Eco-AND can
determine feasible reshaped constraints for passing-in-green
phase. As a result, both the optimizer and the baseline have
simila r/comparable distributions of the respective travel times.
is is further veried by the time-space plots for a repre-
sentative case in the Monte Carlo simulations with γ=0.7,
shown in Figure 14 and Figure 15. Here, for fair comparison,
the characteristics and velocity prole of the injected leaders
are identical. Both the baseline and the optimizer have compa-
rable travel times, as they both pass through the same number
of signalized intersections during the green phase. Note that
a suciently safe separation is always maintained from the
lead vehicle in both the cases presented.
In future work, this limitation could be addressed by
suitably reformulating t he optimization problem, for example,
by augmenting its state space to include time. is would allow
the denition of a desired range of headway times in the
presence of a leader about which the velocity and separation
can beoptimized.
In-Vehicle Tests at TRC
e experimental tests conducted at TRC focused primarily
on verifying the implementation of the VD&PT optimizer
and comparing the performance the results obtained by the
demonstration vehicle against the simulation models and a
realistic baseline. e tests conducted allow one to evaluate
FIGURE 13 Fuel consumption and travel time distribution from Monte Carlo simulations with trac variability.
© SAE International.
FIGURE 14 Time-space diagram, baseline case (with EDM).
© SAE International.
FIGURE 15 Time-space diagram, VD&PT optimizer with
Eco-AND.
© SAE International.
Downloaded from SAE International by Shobhit Gupta, Tuesday, March 23, 2021
10 IN-VEHICLE TEST RESULTS FOR ADVANCED PROPULSION AND VEHICLE SYSTEM CONTROLS
the real-world benets of fuel consumption and travel time of
the optimizer over reconstructed routes.
In-Vehicle Implementation of
Optimizer
e VD&PT optimizer solves the receding horizon optimiza-
tion problem in real-time using Approximate Dynamic
Programming (ADP). is Model Predictive Control (MPC)
framework is implemented on the test vehicle, a 2016 VW
Passat 48V mHEV, equipped with CAV technologies and DSF.
For all baseline testing the 48V mild-hybrid system is active
and DSF is disabled, while for all Eco-Driving testing both
the 48V mild-hybrid system and DSF are enabled. The
on-board CAV technologies include an onboard e-Horizon
module and GPS antenna for enh anced navigation, a Dedicated
Short Range Communication (DSRC) module that enables
V2X communication, camera and radar modules to support
Adaptive Cruise Control (ACC) functionality. ese CAV
technologies enable the ability of full longitudinal control of
the vehicle to achieve SAE Level 1+ functionality (in accor-
dance with SAE J3016) and Cooperative Driving Automation.
e real-time VD&PT Optimizer integrated with V2X
communication and ACC have been implemented using rapid
prototyping hardware (dSPACE MicroAutoBox II or MABx
in short) in the demonstration vehicle with the capability of
utilizing look-ahead information to perform real-time
combined optimization of the vehicle velocity and powertrain
torque split. Online calibration of control parameters and data
logging are performed using dSPACE ControlDesk soware
via the Host Ethernet interface.
Experimental Test Setup
All vehicle tests are performed in a single lane with 0% grade
on the 7.5-mile High-Speed Test Track located at TRC over
the reconstructed routes extracted from the central Ohio
region. e results presented in this section are related to tests
run over the mixed Route 19.
For each of the tests conducted, the measured and calcu-
lated variables are vehicle speed, battery SOC, cumulative
fuel consumed, and travel time. Each test case is repeated at
least ve times while ensuring that the controllable test
conditions (e.g. payload, initial battery SOC, calibration
values for drivability, wait time at a stop) along each trip are
maintained similar. Uncontrollable factors (such as track
conditions, ambient temperature and humidity) are noted
throughout the tests. is is used to evaluate the variability
in travel time and fuel consumption for each case and
quantif y the possible inherent variability in fuel consumption
measurement error due to the fuel measurement. In this
study, the Air-Fuel Ratio (AFR) and airow estimates from
the ECM, obtained over CAN, are used to calculate the
instantaneous fuel ow rate.
Procedure for VD&PT Optimizer Testing in
Eco-Driving For the Eco-Driving test scenario, all the
trac lights along the route are assumed to beashing red,
i.e. treated as stop signs. A subset of the trade-o penalty
factor, γ={0.3,0.7,0.75} is considered for executing the tests
with the demonstration vehicle. e selected values represent
a relatively aggressive, an average, and a relaxed driving
behavior, respectively. e number of steps in the receding
horizon NH is set to 30, which corresponds to a variable
preview window of length 150m to 750m. e formulation of
the optimization algorithm considers the minimum and
maximum acceleration limits (amin, amax) that can beachieved
by the ACC system and these are both set to 2.4 m/s2. Table 1
summarizes the test setup for the Eco-Driving test scenario.
Note that the DSF is activated only for the tests where the
VD&PT optimizer is used, while the advanced cylinder deac-
tivation is switched o for the baseline testing.
Procedure for VD&PT Optimizer Testing in
Eco-AND In order to verify and evaluate the Eco-AND
algorithm, a test environment was designed where trac light
SPaT information is broadcast to the VD&PT optimizer as
the vehicle approaches a signalized intersection. e introduc-
tion of SPaT variability makes evaluation of the VD&PT opti-
mizer with Eco-AND dierent from the aforementioned
Eco-Driving case.
Ideally, to experimentally verify the VD&PT optimizer
with Eco-AND it would benecessary to run a statistically
signicant number of tests, treating γ and SPaT as random
variables. However, to signicantly reduce the number of tests
and the resulting testing time without compromising the
quality of the results, sample cases from the Monte Carlo
simulations (Figure 12) for the no trac case were selected
and reconstructed at TRC.
For each tested condition, the values of the parameter γ
and the SPaT sequence were selected to represent a dominant
mode of the respective Monte Carlo simulation over that
route. In this work, γ=0.7 is chosen for the reconstructed
route as it compactly represents “normal” driver aggressive-
ness. e SPaT sequences are replicated in real-time by broad-
casting them on a Roadside Unit (RSU). For convenience in
setup and testing, this RSU is mounted on the rear seat and
connected to a supplementary 12V battery as shown in
Figure 16.
e V2I communication is emulated via the in-vehicle
RSU that broadcasts SPaT and MAP information in accor-
dance with the SAE J2735 standard to the On-Board Unit
(OBU) mounted on the vehicle. A custom application was
TAB LE 1 Summary of testing setup for verification in
Eco-Driving mode.
Variable Description
HEV State 48V mHEV
DSF State ON
Vehicle Mass ≈1850kg (including payload)
Grade 0%
Initial SoC 50%
Controls VD&PT Optimizer
Driver ACC
NH30 steps (150- 750m)
γ{0.3, 0.7, 0.75}
{amin, amax} {-2.4, 2.4} m/s2
© SAE International.
Downloaded from SAE International by Shobhit Gupta, Tuesday, March 23, 2021
IN-VEHICLE TEST RESULTS FOR ADVANCED PROPULSION AND VEHICLE SYSTEM CONTROLS 11
developed to input specic SPaT IDs, dened by a duration
of phase for each trac light, and initial parameters corre-
sponding to a particular SPaT scenario (i.e. initial phase and
time remaining in phase). A custom decoder, designed and
implemented on a PC connected to the OBU, then unpacks
the SPaT messages and makes it available on the MABx, which
contains the VD&PT optimizer with Eco-AND algorithm.
Within a predened range (assumed to be 190m) from the
upcoming trac light, an arbitration strategy implemented
within the MABx model selects the corresponding
SPaT information.
It is to benoted that the travel times recorded for testing
the VD&PT optimizer with Eco-AND include the time spent
at stop signs and the wait time at red trac lights along
the route.
Baseline Testing Setup e baseline should berepre-
sentative of the driving behavior of a conventional driver,
against which the travel time and fuel consumption benets
of the VD&PT optimizer are benchmarked. However, baseline
testing with human drivers over reconstructed routes (in
which upcoming speed limits are available to the driver) poses
a challenge. Specically, utilizing a human driver limits the
repeatability of the tests, inevitably introducing undesired
variability in the fuel consumption and travel time.
For this reason, a BTR is programmed and calibrated to
follow reference velocity proles which are generated in simu-
lation using the EDM for various driver aggressiveness param-
eters. is ensures repeatability and controlled variability
across baseline tests. e BTR accurately tracks the reference
velocity prole by actuation of the accelerator and brake
pedals with a human driver only responsible for the steering
action. Table 2 summarizes the test setup adopted to evaluate
the baseline with the vehicle powertrain conguration as a
mHEV with DSF disabled.
Testing Observations Despite ensuring similar trip
conditions and calibration values at the start of each test, some
variability in the fuel consumption and travel time is noted
even for the same γ. Although the VD&PT optimizer is imple-
mented as a state feedback controller (via MPC), uncertainties
are inevitably introduced due to variability along the route
and modeling errors. Some of the factors causing variability
across dierent trips are:
•Dierences in ambient and track conditions across
multiple trips/routes, such as auxiliary loads, engine
thermal management system response to ambient
conditions resulting in various parasitic loads on
the engine.
•Higher than usual ambient temperature and humidity
during summer testing periods, resulting in longer on-
times of the cooling fans.
•Change in fuel mass across various trips contributing to
payload changes.
•Inherent limitations in the tracking performance of the
ACC, which is not designed for precise velocity tracking
at low vehicle speeds or during launches and stops.
•During stops along the route, the engine is shut o and
all the electric auxiliary loads draw power from the 48V
system, depleting the battery SOC at a higher rate.
Variability in the time spent at stops leads to dierences
in the SOC prole across multiple trips.
Experimental Results for
Eco-Driving Case
Comparison Between Experimental Tests and
Simulation To validate the simulation models and
in-vehicle implementation of the VD&PT optimizer, the
Pareto curves from simulation studies are compared against
the test data for a selected subset of γ (0.3, 0.7, 0.75). e
comparison shown in Figure 17 shows the error bars for each
γ tested over Route 19. ese error bars represent t he maximum
deviation of the data points from their respective mean and
are determined using data from at least ve test runs. For each
γ, the dierence in both mean fuel consumption and mean
travel time between the simulation and optimizer data is less
than 3%.
For γ=0.7, representative of “normal” driving behavior,
the optimal vehicle states (vehicle velocity and battery SOC)
and the cumulative fuel consumption trajectories are shown
for both simulation and the corresponding experimental
test run. e trends for each of the parameters closely follow
each other, with the resulting total fuel consumption and
travel time diering by less than 2% over Route 19. e
FIGURE 16 RSU apparatus mounted in the rear seat of the
test vehicle.
© SAE International.
TAB LE 2 Summary of baseline testing setup.
Variable Description
HEV State 48V mHEV
DSF State OFF
Vehicle Mass ≈1850kg (including payload)
Grade 0%
Initial SOC 50%
Controls Production ECM
Driver BTR
© SAE International.
Downloaded from SAE International by Shobhit Gupta, Tuesday, March 23, 2021
12 IN-VEHICLE TEST RESULTS FOR ADVANCED PROPULSION AND VEHICLE SYSTEM CONTROLS
VD&PT optimizer results in a charge-sustaining torque
split strategy over the route for both simulation and
in-vehicle tests.
Comparison Between VD&PT Optimizer and
Baseline To evaluate the benets of the optimizer over a
representative baseline, multiple trips of the VD&PT opti-
mizer are executed over the same reconstructed route for the
chosen subset of γ (selected among 0.3, 0.7, 0.75). e travel
times from the VD&PT optimizer tests are used to generate
EDM parameter sets with similar aggressiveness and travel
times for each γ. e generated EDM velocity proles are then
used as reference inputs to the BTR. e measured or derived
variables (vehicle speed, battery SOC, cumulative fuel
consumed and travel time) are compared against each other
for both the baseline and the VD&PT optimizer for the
Eco-Driving cases.
Figure 20 compares the vehicle testing results for the
baseline case (executed using the BTR) against the VD&PT
optimizer, for each γ. Over the mixed urban-highway Route
19, featuring six stops, the VD&PT optimizer with DSF active
reduces the fuel consumption by over 10% (and up to 25%),
relative to the baseline, with comparable travel time.
A run-to-run comparison of the states (vehicle velocity
and battery SOC) and cumulative fuel consumption along
Route 19 for γ=0.7 showcases a noticeable contrast between
baseline and optimizer tests, with the optimizer with DSF
active resulting in 20% fuel savings for similar travel time
with a resulting energy management strategy that has a
charge-sustaining SOC prole over the entire route.
In the route segments characterized by higher speed
limits, the feasible opportunities for velocity optimization
increase. Further fuel savings are achieved by DSF being
active, and the optimizer increasing the operation of the
engine within the DSF y-zones and thereby leveraging the
fuel-saving potential of the cylinder deactivation
technology (DSF).
FIGURE 18 Comparison of optimal vehicle states (velocity
and SOC) between simulation and in-vehicle tests (γ=0.7 with
DSF active).
© SAE International.
FIGURE 19 Comparison of cumulative fuel consumption
between simulation and in-vehicle tests for γ=0.7.
© SAE International.
FIGURE 20 Experimental comparison of fuel consumption
and travel time between baseline (without DSF), and optimizer
(with DSF).
© SAE International.
FIGURE 17 Comparison of Pareto curves between
simulation and vehicle tests conducted over route 19.
© SAE International.
Downloaded from SAE International by Shobhit Gupta, Tuesday, March 23, 2021
IN-VEHICLE TEST RESULTS FOR ADVANCED PROPULSION AND VEHICLE SYSTEM CONTROLS 13
e operating modes of the vehicle for both the baseline
test data and VD&PT optimizer test data are shown to better
understand the fuel savings resulting from the optimizer. e
operating modes are Engine only, E-Assist, regenerative
braking, engine-on battery recharge, Deceleration Fuel
Cut-O (DFCO), Deceleration Cylinder Cut-O (DCCO), and
engine start-stop. Figure 22 and Figure 23 show the vehicle
speed trajectory measured on the demonstration vehicle for
Route 19 and indicate the dierent operating modes of the
mHEV powertrain. e results indicate that the optimizer
increases the use of the BSG to expand E-assist and regenera-
tive braking operations.
Experimental Results for
Eco-AND Case
Comparison Between VD&PT Optimizer and
Baseline e specic case (γ and SPaT ID pair) selected
from the Monte Carlo simulations for vehicle testing corre-
sponds to a dominant mode of the statistical distribution.
Over the reconstructed route considered, it is seen that average
(over ve runs) in-vehicle fuel consumption and travel time
lie within the range of their corresponding distributions.
Further, they are closely comparable to their respective mean
values from the Monte Carlo simulations, discussed in Figure
13. e baseline case considered to benchmark the optimizer
is constructed such that the driver aggressiveness (i.e.,
resulting travel time) matches a dominant mode of the statis-
tical distribution obtained through Monte Carlo simulations
while being comparable to the mean travel time from VD&PT
optimizer with Eco-AND testing.
Figure 24 compares the in-vehicle test results from the
baseline (using the BTR) and the VD&PT optimizer for the
case of Eco-AND. For this combination of γ and SPaT ID, both
the baseline and the optimizer arrive at the ve signalized
intersections in the red window (i.e., when the phase of the
trac light is red), as seen from the t ime-space phase diagrams.
However, the wait times at each trac light will not beequal
as this depends on the prior velocity trajectory. e resulting
velocity proles obtained are similar to the “Eco-Driving”
case discussed previously.
e torque split strategies of the baseline and the VD&PT
optimizer with Eco-AND are evaluated by comparing the
resulting battery SOC and cumulative fuel consumption
trajectories. A key benet from the designed VD&PT optimi-
zation with Eco-AND is the resulting charge sustaining torque
split strategy. In contrast, the terminal SOC for the baseline
case is 42%.
e impact of smoother velocity transients, and fuel-
ecient approach and departure from signalized intersec-
tions and stop signs is seen in the relative cumulative fuel
consumption. For the specic route tested here, the VD&PT
optimizer saves over 19% in fuel with a marginal increase of
2.5% in trip time while ensuring SOC neutrality over the
entire trip.
FIGURE 21 Comparison of vehicle velocity and battery
SOC, and fuel consumption between baseline (without DSF),
and VD&PT optimizer (γ=0.7, with DSF active).
© SAE International.
FIGURE 22 Operating modes of mHEV powertrain (VD&PT
optimizer with γ=0.7 with DSF active).
© SAE International.
FIGURE 23 Operating modes of mHEV powertrain
(baseline case without DSF, matched to achieve same travel
time of VD&PT optimizer).
© SAE International.
Downloaded from SAE International by Shobhit Gupta, Tuesday, March 23, 2021
14 IN-VEHICLE TEST RESULTS FOR ADVANCED PROPULSION AND VEHICLE SYSTEM CONTROLS
Conclusions
is paper presents a comprehensive verication framework
for the assessment of CAV technologies with advanced opti-
mization strategies, which includes extensive simulation and
experimental testing. e objective of this work is to provide
a systematic quantication of fuel economy and mobility
benets resulting from leveraging V2X connectivity and
longitudinal (Level 1+) automation to improve the propulsion
system and vehicle efficiency. A Vehicle Dynamics and
Powertrain (VD&PT) optimal control algorithm was devel-
oped and implemented to minimize the cumulative fuel
consumption over a driver-selected route by exploiting
advanced route information from GPS and navigation, and
vehicle-to-infrastructure (V2I) communication
when available.
To assess the benets of CAV technologies in meaningful
conditions, a Monte Carlo simulation framework was devel-
oped to quantif y the impact of variability in Signal Phase and
Timing (SPaT), trac conditions, and behavior of dierent
drivers on the fuel consumption and vehicle travel time.
Further, verication through in-vehicle testing was conducted
by reconstructing selected routes and SPaT conditions on a
closed test track at the Transportation Research Center.
Experimental tests were conducted to quantify the benets
resulting from the predictive VD&PT optimization frame-
work for the case of Eco-Driving and Eco Approach and
Departure. For the route tested, when used in conjunction
with V2I connectivity, L1+ automation, and DSF, the VD&PT
optimizer results into more than 20% fuel economy improve-
ment, compared to the same vehicle operated without the
look-ahead capabilities and DSF.
References
1. Gonder, J., Chen, Y., Lammert, M., and Wood, E., Assessing
the Energy Impact of Connected and Automated Vehicle
(CAV) Technologies. No. NREL/PR-5400-65743 (Golden, CO
(United States): National Renewable Energy Lab.
(NREL), 2016).
FIGURE 24 Run-to-run comparison of vehicle velocity and
time-space plots between baseline (without DSF), and VD&PT
optimizer (γ=0.7 with DSF) for Eco-AND.
© SAE International.
FIGURE 25 Run-to-run comparison of battery SOC and
fuel consumption between baseline (without DSF), and
Optimizer (γ=0.7 with DSF) for Eco-AND.
© SAE International.
TAB LE 3 Comparison of cumulative fuel consumption and
travel time from baseline (without DSF) and VD&PT optimizer
(with DSF) for Eco-AND.
Case Travel Time [s] Fuel Consumed [g]
Baseline (without DSF) 524 357
VD&PT Optimizer with
Eco-AND and DSF active
537 289
© SAE International.
Downloaded from SAE International by Shobhit Gupta, Tuesday, March 23, 2021
IN-VEHICLE TEST RESULTS FOR ADVANCED PROPULSION AND VEHICLE SYSTEM CONTROLS 15
2. Fagnant, D.J., and Kockelman, K., “Preparing a Nation for
Autonomous Vehicles: Opportunities, Barriers and Policy
Recommendations,” Transportation Research Part A: Policy
and Practice 77:167-181, 2015.
3. Schrank, D., Eisele, B., Lomax, T., and Bak, J., “2015 Urban
Mobility Scorecard,” 2015.
4. Kim, N., Karbowski, D., Jeong, J., and Rousseau, A.,
“Simulation of Heavy-Duty Vehicles in Platooning
Scenarios,” in In 2018 21st International Conference on
Intelligent Transportation Systems (ITSC), pp. 1604-1610,
IEEE, 2018.
5. Do, W., Rouhani, O.M., and Miranda-Moreno, L.,
“Simulation-Based Connected and Automated Vehicle
Models on Highway Sections: A Literature Review,” Journal
of Advanced Transportation 2019, 2019.
6. Olin, P., Aggoune, K., Kirwan, J., Deshpande, S.R. et al.,
“Intelligent Driving through Advanced Propulsion System
Control to Reduce Fuel Consumption,” Aachen Colloquium
Automobile and Engine Technology 28:373-400, 2019.
7. Vahidi, A., and Sciarretta, A., “Energy Saving Potentials of
Connected and Automated Vehicles,” Transportation
Research Part C: Emerging Technologies 95:822-843, 2018.
8. https://arpa-e.energy.gov/?q=arpa-e-programs/nextcar.
9. Olin, P., Aggoune, K., Tang, L., Confer, K. et al., “Reducing
Fuel Consumption by Using Information from Connected
and Automated Vehicle Modules to Optimize Propulsion
System Control,” SAE Technical Paper No 2019-01-1213,
2019, https://doi.org/10.4271/2019- 01-1213
10. Rengarajan, S.B., Hotz, S., Hirsch, C., Lobato, P. et al., “Test
Methodology to Quantify and Analyze Energy Consumption
of Connected and Automated Vehicles,” SAE Technical
Paper No 2019-01-0116, 2019, https://doi.org/10.4271/2019-
01-0116
11. Mahbub, A.M., Karri, V., Parikh, D., Jade, S., and
Malikopoulos, A.A., “A Decentralized Time-and Energy-
Optimal Control Framework for Connected Automated
Vehicles: From Simulation to Field Test,” arXiv preprint
arXiv:1911.01380, 2019.
12. Gupta, S., Deshpande, S.R., Tufano, D., Canova, M. et al.,
“Estimation of Fuel Economy on Real-World Routes for
Next-Generation Connected and Automated Hybrid
Powertrains,” SAE Technical Paper No 2020-01-0593, 2020,
https://doi.org/10.4271/2020-01-0593
13. Haddouch, S., Hachimi, H., and Hmina, N., “Modeling the
Flow of Road Trac with the SUMO Simulator,” In 2018 4th
International Conference on Optimization and Applications
(ICOA), pp. 1-5. IEEE, 2018.
14. Kesting, A., Treiber, M., and Helbing, D., “Enhanced
Intelligent Driver Model to Access the Impact of Driving
Strategies on Trac Capacity,” Philosophical Transactions of
the Royal Society A: Mathematical, Physical and Engineering
Sciences 368(1928):4585-4605, 2010.
15. Krajzewicz, D., Erdmann, J., Behrisch, M., and Bieker, L.,
“Recent Development AND Applications of SUMO-
Simulation of Urban MObility,” International Journal on
Advances in Systems and Measurements 5(3&4), 2012.
16. Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J.,
Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J.,
Wagner, P., and WieBner, E. “Microscopic Trac Simulation
Using Sumo.” In 2018 21st International Conference on
Intelligent Transportation Systems (ITSC), pp. 2575-2582,
IEEE, 2018.
17. Smith, D., Djahel, S., and Murphy, J., “A SUMO Based
Evaluation of Road Incidents’ Impact on Trac Congestion
Level in Smart Cities.” In 39th Annual IEEE Conference on
Local Computer Networks Workshops, pp. 702-710,
IEEE, 2014.
18. Rios-Torres, J., and Malikopoulos, A.A., “Impact of
Connected and Automated Vehicles on Trac Flow,” In 2017
IEEE 20th International Conference on Intelligent
Transportation Systems (ITSC), pp. 1-6, IEEE, 2017.
19. Auld, J., Verbas, O., Javanmardi, M., and Rousseau, A.,
“Impact of Privately-Owned Level 4 CAV Technologies on
Travel Demand and Energy,” Procedia Computer Science
130 :914 -919, 2018.
20. Zhao, J., Wu, H., and Chang, C.F., “Virtual Trac Simulator
for Connected and Automated Vehicles,” SAE Technical
Paper No 2019-01-0676, 2019, https://doi.org/10.4271/2019-
01-0 676.
21. Liu, Z., Ivanco, A., and Filipi, Z.S., “Impacts of Real-World
Driving and Driver Aggressiveness on Fuel Consumption of
48V Mild Hybrid Vehicle,” SAE International Journal of
Alternative Powertrains 5(2):249-258, 2016.
22. Gupta, S., Deshpande, S.R., Tulpule, P., Canova, M., and
Rizzoni, G., “An Enhanced Driver Model for Evaluating Fuel
Economy on Real-World Routes,” IFAC-PapersOnLine
52(5):574-579, 2019.
23. Sun, C., Shen, X., and Moura, S., “Robust Optimal Eco-
Driving Control with Uncertain Trac Signal Timing,” In
2018 Annual American Control Conference (ACC), pp. 5548-
5553. IEEE, 2018.
24. Katsaros, K., Kernchen, R., Dianati, M., Rieck, D., and
Zinoviou, C., “Application of Vehicular Communications for
Improving the Eciency of Trac in Urban Areas,” Wireless
Communications and Mobile Computing 11(12):1657-
1667, 2011.
25. Powell, W.B., Approximate Dynamic Programming: Solving
the Curses of Dimensionality. Vol. 703 (John Wiley &
Sons, 20 07).
26. Di Filippi, A., Stockar, S., Onori, S., Canova, M., and
Guezennec, Y., “Model-Based Life Estimation of Li-Ion
Batteries in PHEVs Using Large Scale Vehicle Simulations:
An introductory Study,” In 2010 IEEE Vehicle Power and
Propulsion Conference, pp. 1-6, IEEE, 2010.
27. Guzzella, L., and Sciarretta, A., Vehicle Propulsion Systems.
Vol. 1 (Berlin Heidelberg: Springer-Verlag, 2007).
28. Coovert, D.A., Heydinger, G.J., Bixel, R.A., Andreatta, D. et
al., “Design and Operation of a Brake and rottle Robot,”
SAE International Journal of Passenger Cars-Mechanical
Systems 2(2009-01-0429):613-621, 2009.
Downloaded from SAE International by Shobhit Gupta, Tuesday, March 23, 2021
© 2021 SAE I nternational. Al l rights reserve d. No part of this p ublication may be re produced, store d in a retrieval system , or transmitted , in any form or by any mean s,
electronic, mechanical, photocopying , recording, or otherwise, without the prior written permission of SAE International.
Positions and opinions advanced in this work are those of the author(s) and not necessarily those of SAE International. Responsibility for the content of the work lies
solely with the author(s).
ISSN 0148-7191
16 IN-VEHICLE TEST RESULTS FOR ADVANCED PROPULSION AND VEHICLE SYSTEM CONTROLS
Contact Information
Shreshta Rajakumar Deshpande
PhD Candidate, Department of Mechanical Engineering
e Ohio State University
rajakumardeshpande.1@osu.edu
Acknowledgments
Our team gratefully acknowledges the support from the
UnitedStates Department of Energy, Advanced Research
Projects Agency - Energy (ARPA-E) NEXTCAR project
(Award Number DE-AR0000794).
Downloaded from SAE International by Shobhit Gupta, Tuesday, March 23, 2021
