diesel controls and diagnostics 1 powertrain controls r&a...traffic flows congestions...

Diesel Controls and DiagnosticsPowertrain Controls R&A

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Disruptive Technology

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Powertrain control is exciting once again !!Thanks to Disrupters

Environment

EnergyUser Experience


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Picone et.al

Connectivity:V2V Vehicle to VehicleV2I Vehicle to InfrastructureV2C Vehicle to CloudV2X Vehicle to X (…)

Enables ubiquitous, on-demandaccess to a shared pool ofconfigurable computing resources

SPAT


– GPS position– INS position

• IMU’s– LIDAR 3D mapping … object detection– RADAR motion detection …collision– ULTRASONICS proximity… low grade collision– CAMERAS classification … images

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LIDAR ($90 ~$8000), monitor vehicle surrounding

Video Camera (mono: $150, stereo: $~200), monitor vehicle surrounding + read traffic lights

GPS ($100~$6000), with tachometer, altimeters and gyros, provide accurate positioning

Ultrasonic ( !$20), position of objects close to the vehicle

Odometry: ( Long Range: ~$100), compliments and improves GPS information

Radar: ( Long Range: $150, Short Range: $100), monitor vehicle surrounding


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Connectivity and Autonomy provide information Electronic Horizon E-Horizon is the fundamental enabler of preview based control for CAV’s.E-Horizon is an integrated information source comprising of the following classes of information:

Map information Position

Route information Object/s recognition in vehicular environment and object size. Object Classification (meta-data in objects: tree, vehicle, pedestrian, etc.)

Traffic and Vehicle speed information Vehicle Speed and Acceleration Traffic flows

Congestions Bottlenecks

3D Landscape (topographical) data Road Surface Road Grade

Position and Navigation information (GPS) Weather

Humidity Rain, Snow

Anything else you may need !!dieselnet


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Operating Condition

Reference generator

Yd(t) Desired trajectory

e(t) Error

𝑓𝑓(𝒪𝒪 𝑡𝑡 )

𝒪𝒪 𝑡𝑡 Preview horizon, tf may be finite (tf < Tcycle ) or infinite (tf = Tcycle), Tcycleis the horizon duration. Yd the desired trajectory is either• perfectly known over horizon (idealization)• only stochastically known over the horizon (real world)

Controller Plantu(t) y(t)e(t)𝑌𝑌𝑑𝑑 𝑡𝑡0 … 𝑡𝑡𝑓𝑓Σ+-

𝑓𝑓𝑝𝑝(𝒪𝒪𝑝𝑝)𝒪𝒪𝑝𝑝 𝑡𝑡0 … 𝑡𝑡𝑓𝑓

t0 t0 + tf

𝑌𝑌 𝑑𝑑𝑡𝑡 0

…𝑡𝑡 𝑓𝑓

Preview

Preview based Control is pro-active

𝛿𝛿 𝑡𝑡0+

Controller Plantu(t) y(t)e(t)Yd(t) Σ+-𝑓𝑓(𝒪𝒪 𝑡𝑡 )𝒪𝒪 𝑡𝑡

Conventional Control is Reactive

Tomizuka


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• Preview information must be generalized as a triplet: – [information time series, Preview Length, Confidence] 𝑖𝑖1 … 𝑖𝑖𝑛𝑛 𝑇𝑇𝑙𝑙 𝐶𝐶𝑖𝑖

• Information 𝑖𝑖 is a vector of variables necessary for control decision.• Confidence of information "𝑖𝑖“ “forecasting reliability” and is critical for control

decision. – Confidence is determined by heteroscedasticity of data. Traffic patterns are generally

heteroscedastic… forcing the need for the forecasting of short term conditional variance of traffic flows and therefore of related variables.

• Sensitivity to errors in preview information must be quantified and managed.• Generating reference trajectories from preview information is critical

– System knowledge, Markov Processes, POMDP’s, Bayesian Beliefs or Model free ….

• The minimum preview length (problem dependent), 𝑇𝑇𝐿𝐿𝑚𝑚𝑖𝑖𝑛𝑛, necessary to realize control decisions, must be characterized.

• Optimal solutions must be sought. Stochasticity cannot be avoided.• Problems supporting Sequential Decision Processes are typically better suited.• Pick your problem statements carefully.


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0 1 2 3 4 5 6 7 8 9 10-8

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Error [%]Im

prov

emen

t [%

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Performance Improvement vs Preview Error

Preview error can hurt. This system was tolerant to an error of up

to ~ 7% in preview information. Increased preview error resulted in

performance deterioration.

With driver in the loop, benefit from preview based control is impacted by driver compliance to “coaching”.

Cannot be Coached

High Conf

Reducing Conf

Dis

tanc

e

Poor FE Poor EmissionsPoor Regen performance

Preview confidence is generally poor over extended horizons.

Receding horizon preview is typically adequate.

Inf Preview may however be used to solve DP by backpropagation.

timeEco-drive

Space time diagram at an intersection• Eco driving allows passing the

intersection without a stop.• Most drivers will end up in a stop-

start situation.


Connectivity set up for RealTime implementation

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NavigationsystemVehicle GPS

module

EngineControl (ECU)

Wirelessconnectivity

On-boardDiagnostic(OBD) port

Rapid Prototyping

CAN Gateway

CAN

Calibration Tool BusPrivate CAN


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• Assisted boosting systems, TC arrangements and ECompressor + TC configurations will benefit frompreview information.

• For such systems, preview allows proper synchronization between the various high speed rotatingmachines during energy balancing.

9.75 10.00 10.25 10.500.6

0.7

0.8

2.02.53.03.5

3.15

3.20

3.259.75 10.00 10.25 10.50

u VGT

Time (s)

uVGT

P T (kW

)

PT

(g/s)

100 200 300 400 5000.1

0.2

0.3

0.4

Tim

e C

ost (

ms)

Prediction Horizon (ms)

Time cost

Optimal uVGT based on numerical search0.5

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Opt

imal

uVG

T

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Time [s]

Veh

icel

Spe

ed [m

ph]

Vehicel Speed [mph]

Ass

ist

Reg

en For E-TC anticipatory knowledge on tip-in , tip-out allowsefficient engagement of assist and regenerative functions. ….Need driver models, path forecasting

For an E-TC system the NMPbehavior of the TC (VGT) forces anincreased horizon for optimalsolution …. This drives previewduration considerations.


• After-treatment systems, where the catalytic kinetics are rate limited and/or catalyst controltime scales are compatible with preview information updates, can benefit from preview basedcontrol decisions.

• Examples:1. DPF Regeneration:

1. Manage Soot load Regeneration for optimal regeneration fuel penalty.2. Suggestion of optimal routes for achieving optimal regeneration (driver coaching) .

2. SCR storage control1. Managing NH3 storage is difficult in the context of managing both high levels of NOx reduction and maintaining 0

NH3 slip.2. The difficulty is amplified in the presence of thermal transients. Under increasing temperature gradients the rate of

NH3 desorption is the fastest kinetic and the rate of NH3 storage depletion from NOx , while quite fast, is notadequate to reduce the excess storage so as to limit NH3 slip.

3. Preview of upcoming load increases will provide additional bandwidth for slip control.

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O D

Traffic flow disturbances: 𝜹𝜹𝒊𝒊Disturbances reduce free speed �̇�𝑽 < 𝟎𝟎𝛿𝛿1

𝛿𝛿2 𝛿𝛿𝑛𝑛

𝛿𝛿3�̇�𝑽 < 𝟎𝟎 �̇�𝑽 < 𝟎𝟎

�̇�𝑽 < 𝟎𝟎

�̇�𝑽 < 𝟎𝟎

DPF regen case studyRegen Efficiency impediments

• Short trips• Frequent stop-start• Transitions to low velocities


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1

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1.2 1.25 1.3 1.35 1.4 1.45 1.5

x 104

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Fuel

Use

d fo

r Filt

er R

egen

erat

ion

[gal

lons

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Vehicle Mileage [miles]

BaselineCloud Optimized

Cost to regen is always > cost to fill so don’t regen

0 500 1000 1500 20000

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40

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Drive Time [sec]

Vehi

cle

Spee

d [M

PH]

1

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Regen Opportunity exist

Initiate Regen

RegenEnd Regen

Cloud optimized regen saves fuel (sims)


Find the Gaps

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20 % = HOW ?= ∑∆𝑖𝑖

20%


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Drive Classifiers

Control/Diagnostics

Classification Techniques


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Connectivity and Autonomy provide abundant data. Data analytics is critical for “mining” out opportunity areas for realizing, often,

only incremental benefits. Causalities are usually not readily obvious Factor analysis Latent

variables Understanding driver groups (classifications) is useful.

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FE [mpg]

Vs <= 10 mph

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FE [mpg]

10 < Vs <= 20 mph

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FE [mpg]

20 < Vs <= 50 mph

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Am

beitn

Tem

pera

ture

[C]

Ambient Temperatures over all trips

Geographic clustering may revealinteresting system behaviors underglobal control.

Trip distance based clusters offer insights into drive patterns ,, FE etc

Ambient temperature based clusters offer insights on performance.

Data can be: Dense Sparse UnbalancedAutomotive data is typically multivariate latent variables.


• CV-AV’s provide information that had to be estimated/predicted forconventional vehicles.

• This information is available as preview which allows trajectory planning andexogenous disturbance rejection.

• In conjunction with techniques such as driver modeling, path forecasting,traffic flows … etc several useful applications can be devised to extractadditional efficiencies from powertrain functions.

• CV-AV applications will also provide abundant data which must be minedfor opportunities find the gap !

• Defining FE metrics is typically quite tricky… there can be severalbaselines.

• Other major applications include Diagnostics and Prognostics.• Powertrain control in the CAV domain is currently a lesser known emergent

automotive technology (relative to AV) but, will become a major aspect ofthe smart/connected vehicles concept regardless of the powertraindefinition.

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diesel controls and diagnostics 1 powertrain controls r&a...traffic flows congestions...

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