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Virtual for and After-treatment Management

Federico Covassin Magneti Marelli S.p.A. - Powertrain

Reggio Emilia June 27th 2016

Powertrain Index

■ Company Overview ■ Magneti Marelli Powertrain (Diesel) System ■ Standard Emission trend and technologies ■ O2 Virtual for Diesel engine management ■ NOx Virtual Sensor for after-treatment management ■ NOx Virtual Sensor adaptivity concepts ■ Conclusions

Jun 27th, 2016 2 Company Overview

Magneti Marelli is an international company committed to the design and production of hi-tech systems and components for the automotive sector.

AUTOMOTIVE POWERTRAIN ELECTRONIC SUSPENSION LIGHTING ( and Diesel SYSTEMS SYSTEMS engine control, Electric Motor and Transmission) (Headlamp, Rearlamp and Body (Instrument clusters, Infotainment & (Suspension Systems, Shock Computer) Telematics) Absorbers, Dynamic Systems)

EXHAUST PLASTIC AFTERMARKET MOTORSPORT PARTS & SERVICES (Injectors, ECU, KERS, DST, SYSTEMS COMPONENTS Hydraulic Gearbox Control (Mechanical, Body Work, Electrics and Electronic (Manifolds, , AND MODULES Unit…) DPF and ) and consumables) (Bumper, Dashboard, Central Consol, and Fuel system)

Jun 27th, 2016 3 Magneti Marelli Worldwide Presence

Sales 2015 7.3 bn €

R&D Centers 12

R&D (of sales) 4.6%

Production units 89

Application Centers 30

Investments (of sales) 5.7%

Employees 40,418

Jun 27th, 2016 4 Magneti Marelli Powertrain Worldwide Presence

BOLOGNA (I)

CREVALCORE(I) SHANGHAI (CN) VENARIA (I) CHANGCHUN(CN) CORBETTA (I) SMMP (CN) BARI (I) GUANGZHOU(CN) TRAPPES (F) WUHU(CN) ARGENTAN (F) MANESAR (IN) WOLFSBURG (D) PUNE(IN) KECHNEC

AUBURN HILLS (USA)

SANFORD (USA)

SALTILLO (MEX)

HORTOLANDIA (BR)

Production plant R&D Center Application Center

Sales 2015 919 mio € Production units 15 R&D Centers 4 Application Centers 9 R&D* 5.7% Investments* 13.7% Employees worldwide 5.293

* % of “make” sales Jun 27th, 2016 5 Customer Brand Portfolio

Jun 27th, 2016 6 Magneti Marelli Powertrain – Diesel Systems

CONTROL SYSTEMS SENSORS Hybrid Propulsion

ECU Smart Sensors Power Motor-Generator Inverter

Controls Transmission & SW

AMT DCT

Engine Management Calibration

Components for Combustion Models SCR/SCRoF System & Spray targeting ACTUATORS Intake man. w var. Body VGT Actuator Swirl Actuator Jun 27th, 2016 7 Emission Standard trend

EPA European Other main legislation Union Japan

HC India CO Brazil

NOX China NH3 PM PM Other main requirements Number limit Pollutant SO2 On Board Diagnostic Requirement Pb (OBD II , EOBD)

Fuel consumption, years CO2 Reduction

Euro1 Euro2 Euro3 Euro4 Euro5 Euro6b Euro6d PC Euro I Euro II Euro III/IV Euro V Euro VI LDT LCV Stage I Stage II Stage III Stage IV Stage V

Tier 0 Tier 1 Tier 2 Tier 3 Tier 4 NRMM HD LEV I LEV II LEV III Jun 27th, 2016 8 Technologies for reaching the target

Two main ways for NOx reduction on Diesel Engine ‹ NOx Engine Out reduction ‰ Combustion improvement (e.g. EGR HP, EGR LP) ‹ After-treatment technologies (e.g. LNT, SCR, SCRonF) could be combine for reach the target

NOx VS LNT DPF

Tsensor Tsensor

UEGO UEGO

∆PTrap

NOx VS NOx VS SCR DOC DPF (or LNT) NOx Sensor Tsensor DEF NOx Sensor Tsensor (opt) Injector

Mixer UEGO ∆PTrap

NOx VS DOC SCRoF ufSCR (or LNT) DEF NOx Sensor NOx Sensor NO Sensor Injector T x sensor (opt) O2 Virtual Sensor

Mixer UEGO

∆PTrap NOx Virtual Sensor Jun 27th, 2016 9 Intake O2 and NOx Virtual Sensors

NOx VS DOC SCRoF ufSCR

Adaptive control

Combustion Management – EGR Control Management

Partnership with University of Salerno

Jun 27th, 2016 10 Intake O2 Virtual Sensor

Purpose of intake O2 measure: ‹ Estimation of effective EGR ratio (e.g. EGR Valve Control Management) ‹ Enhancement of conventional and advanced combustion control (PCCI, HCCI,LTC) ‹ Improvement of NOx prediction during engine transients, suitable for both dynamic adjustments of EMS strategies and management of after-treatment devices.

m Pamb Tamb & air

O2exh

n m& f

uEGR

Combustion Mng - EGR Control Mng

Pman Jun 27th, 2016 11 Intake O2 Virtual Sensor

MEAN VALUE MODEL ‹ Filling and Emptying for ‹ Low computational intake/Exhaust manifolds demand and identification issues ‹ Mass and energy conservation in any control volume ‹ Homogeneous pressure, temperature ‹ Suitable for on- and chemical composition in the intake board manifold implementation ‹ Instantaneous and perfect mixing of incoming flows ‹ Model accuracy fully satisfactory ‹ No heat transfer through manifold walls

Jun 27th, 2016 12 Mean Value model Main equation

Oxygen mass fraction in the intake manifold

RairTman O&2man = [()O ,2 exh − O ,2 man m& egr +()O ,2 amb − O ,2 man m& air ] PmanVman Intake manifold temperature

RairTamb T&man = [(kairTegr −Tman )m& egr + ()kairTic,out −Tman m& egr − ()kair −1 Tmanm& cyl,in ] PmanVman

Prediction of Exhaust O2 concentration in place of UEGO measurement

RexhTexh O& ,2 exh = [(O ,2 cyl,out − O ,2 exh )m& cyl,out ] PexhVexh

m& cyl,inO ,2 man −α st m& f O ,2 man O ,2 cyl,out = m& cyl,in + m& f

Jun 27th, 2016 13 O2 Experimental results

Common-Rail Diesel 1.3 – EGR/HP - VGT

60 c Engine Key characteristics

/str] 40

3 60 Rated Power and 95Hp and Max. Torque [email protected] a

[mm 20 Cylinders 4 in line f 3

m /str] 40 Displacement 1248 cm 0 3 Valves per 4 (DOHC) 600 200 400 600 800 1000 1200 1400 Combustion Diesel Direct Injection System

[m m 20 f 16.8:1 40 m Synchronisation • position sensor 0 system • position sensor 20 60 Solenoid Injectors System (160 MPa) EGR EGR POS [%] 0 Intake Air System • Electrical Throttle Body 40 actuator 0.21 • Intake manifold pressure and temperature sensor b 0.2 Turbo charging VGT , vacuum 20 System controlled with vacuum electro- 0.19 modulator with VGT position sensor EGR POS [%] EGR System • DC-Motor EGR valve + 0.18 0 position feedback

man [%] 0.22 • , before Turbo- 2

O 0.17 Compressor c 0.2 Exhaust • 1 linear sensor 0.16 measured System (UHEGO) in the exhaust pipe, predicted downstream of turbine just 0.15 0.18 before catalyst

0 200 400 600 800 1000 1200 1400 m an [% ] After-treatment • Oxidant Catalyst + Diesel 2 measured Time [s] System Particulate Filter (coated soot O 0.16 filter, without additive),predicted close- coupled 560 570 580 • 590DPF Differential600 pressure610 620 Time [s]sensor *O2 intake measured with O2 sensor • DPF inlet temperature sensor

Jun 27th, 2016 14 O2 Experimental results

Common-Rail Diesel 2.3 – EGR/HP - VGT 50 Engine Key characteristics Rated Power and 150Hp and Max. Torque 320Nm 40

Cylinders 4 in line /str] Displacement 2286 cm3 3 Valves per cylinder 4 (DOHC) 30 Combustion Diesel Direct Injection System Qref [mm Qref Compression Ratio 19:1 20 Synchronisation • Crankshaft position sensor system • Camshaft position sensor 10 Fuel Injection Common Rail Solenoid Injectors 0 5 10 15 20 25 30 System (160 MPa) Intake Air System • Electrical Throttle Body actuator • Intake manifold pressure and temperature sensor Turbo charging VGT turbocharger, vacuum System controlled with vacuum electro- modulator with VGT position sensor EGR System • DC-Motor EGR valve + position feedback • Air Flow Meter, before Turbo- Compressor • 1 linear oxygen sensor System (UHEGO) in the exhaust pipe, downstream of turbine just 0 before catalyst After-treatment • Oxidant Catalyst + Diesel 0 System Particulate Filter (coated soot man [%] man

filter, without additive), close- 2

coupled O 0 • DPF Differential pressure sensor 0 • DPF inlet temperature sensor 0 200 400 600 800 1000 1200 1400 0 Time [s]

Jun 27th, 2016 15 NOx Virtual Sensor

Purpose: ‹ After-Treatment Device Management (LNT, SCR, SCRoF, etc.) ‹ Diagnosis After-treatment system: ‹ NOx Sensor diagnosis: plausibility check & functional diagnosis

NOx VS LNT DPF Tsensor Tsensor

UEGO UEGO

∆PTrap

NOx VS DOC SCRoF ufSCR DEF NOx Sensor NOx Sensor Injector T NOx Sensor sensor (opt)

Mixer UEGO

∆PTrap

Jun 27th, 2016 16 Recurrent Neural Network in one slide

Neural Network: • Black Box Model • Basic elements (neurons) are combine together with connections and are placed in different layers depending on the architecture • Right inputs are fundamental for good result • The training and validation phases are important to make more or less strong connections X • A lot of NN parameters impact the final result: 4 number of neuros, number of layers, NN architecture, training algorithm, epoch of training, initial conditions, etc.

Recurrent Neural Network: • The current output also depends on the previous outputs  y ( t − ,1 θ ),..., y ( t − i , θ ),    x ( t ),.., x ( t − j + 1 ), y ( t , θ ) = F  1 1   x ( t ),..., x ()t − j + 1 ,   2 2   x 3 ( t ),..., x 3 ( t − j + )1 

Jun 27th, 2016 17 Recurrent Neural Network training

Recurrent Neural Network NOx m • Delay compensation from measured data & deb

• RNN identification/ training: mf, rpm, ° Parametric analysis and selection of SOI the most suitable neural network structure; ° Deterministic analysis to set RNN weights initial conditions; O2 intake ° RNN Pruning by means of the Optimal Brain Surgeon (OBS) algorithm.

RNN main parameters: • N. of Layers: 3 • N. of neurons in hidden layer: 18 • N. of epoch: 80 • Lag input space: 2 • Lag output space: 2

Jun 27th, 2016 18 NOx Experimental Result

Common-Rail Diesel 1.3 – EGR/HP - VGT

300 40 measured TEST NEDC predicted 35 250 30 200 25

[g/h] 150 20 sp NOx

mf [mm3/str] 15 100 10 50 5

0 0 500 600 700 800 900 1000 1100 1200 0 20 40 60 80 100 120 140 Time [s] 350 measured 300 predicted

250

200

MSE R2 ERR.INT [g/h] sp 150

NEDC 16,14 0,9746 0,0909 NOx 100 TEST 90,21 0,9756 0,1707 50

0 0 20 40 60 80 100 120 140 Time [s] *NOx Engine Out measured with NOx production sensor

Jun 27th, 2016 19 Recursive Least Square based Adaptation

Off/On-line parameters update (adaptivity) • Implementation of RLS methods. • Development of methods for neural network.

• Adaptivity based on gain/offset parameter already implemented. ‰ Low CPU load

Adaptive Control • RNN re-training procedure ‰ more powerful but high CPU load

Jun 27th, 2016 20 Recursive Least Square based Adaptation

NOx measured, plus imposed drift of -30%.

20 k Update Validation 0 k 1 15

10 500 500 Neural network k0=0.701 Neural network k =1 measured measured 400 parameters 0 Least Square 400 k =3.282 5 1 Least Square k1=0 300 300 0 200 200 NOx [ppm] NOx [ppm] 1500 0 NEDC200 profile400 600 Train800 profile1000 1200 1400 Neural1600 network 100 time [s] 100 measured Least Square 0 0 400 450 500 550 600 1550 NEDC1600 profile 1650 1700 1000 time [s] time [s] NEDC RMSE [ppm] IE [%]

Least Square 26, 06 12,7958 NOx [ppm] 500 Neural Network 66,007 55,2771

0 0 200 400 600 800 1000 1200 1400 1600 time [s] Jun 27th, 2016 21 Conclusion

• Emissions standard trend requests strict pollutant limit • Engine Management System and after-treatment will be more complex and more expensive and Virtual Sensors represent a good opportunity • O2 intake estimation with model based approach gives good result and could be implemented on ECU for real-time estimation • NOx engine out estimation with Recurrent Neural Network also present good results with benefits on calibration effort • Parameters update (adaptivity) is implemented with good result recovering the system dispersion

Jun 27th, 2016 22 Thank You Backup