Eco-Driving and Complete Vehicle Energy...

Eco-Driving and Complete Vehicle Energy Management

WORKSHOP ON CONNECTED AND AUTOMATED VEHICLES FOR ENERGY EFFICIENCY AND THE ENVIRONMENT, 1 OCTOBER 2019, IFPEN

Tijs Donkers, Assistant Professor

Control Systems group, Department of Electrical Engineering

Acknowledgements

Collaborators: John Kessels, Constantijn Romijn, Paul Padilla, Zuan Khalik, Juan Flores Paredes

Organizers:Giovanni De Nunzio, Antonio Sciarretta

Eco-Driving and Complete Vehicle Energy Management2

Funding:

Outline

Motivation

Eco-Driving

Complete Vehicle Energy Management with Eco-Driving

Experimental Results: Eco-Driving for Electric City Buses

(Energy-Aware Vehicle Coordination)

Conclusions


Motivation


Motivation – Electromobility

Vehicles that are propelled by electricity [1]

Electromobility is expected to be fully adopted by 2030

Limitation: range anxiety [3]


[1] Grauers et al., 2012 [2] International Energy Agency, 2017, Tech. Report [3] McKinsey & Company, 2017, Tech. Report

CO2 emissions

14%

Oil dependency

2014201695%

2016

29%

EV market in Norway [2]

650.000

100.0002014

2016

EV units in China [2]

Motivation – Range Anxiety

Concern (experienced by users) that thevehicle has insufficient energy to reach the next charging station [1]

Mitigation: Light weight and energy dense batteries

Fast charging

Extended charging infrastructure

Vehicle Energy management


EconomicalSimple implementation

Motivation – Energy Management / Eco-Driving

Optimizing the power split in hybrid vehicles: Vehicle Energy ManagementIncluding all auxiliary systems: Complete Vehicle Energy ManagementOptimizing the vehicle speed profile: Eco-Driving


Energy consumption Driving range

Eco-Driving


Eco-Driving

Energy optimal velocity profiles [1]

Changes on driving behavior: • 30% energy savings [2]

Implemented as eDAS: (Eco-Driving Assistance Systems)


A

B

[1] Sciarreta et al., IEEE CS, 2015 [2] Bingham et al., ITS, 2012

Eco-Driving

Problem Formulation


A

B

[1] Petit et al., IFAC WC, 2011 [4] Khalik, …, Donkers, ACC, 2018[2] Vasak et al., ECC, 2015 [5] Padilla, …, Donkers, CDC 2018[3] Johannesson et al., CEP, 2015

Solution methods:• Pontryagin’s max. principle [1]• Dynamic programing [2]• Static optimization [3,4,5]

Forward Euler approximation:

Theorem [1]: one global solution under mild conditions

Sequential Quadratic Program:

(with an approximated Hessian)


Eco-Driving

[1] Padilla, …, Donkers, CDC 2018

Eco-Driving


EV case study as defined in [1]

Simple, but representative example for eco-driving• Solved in [1] using PMP • No state constraints in [1]

State constraints easily added in static optimization approach [2,3]

[1] Petit et al., IFAC WC, 2011 [2] Khalik, …, Donkers, ACC, 2018 [3] Padilla, …, Donkers, CDC 2018

Complete Vehicle Energy Management with Eco-Driving


Vehicle Energy Management

Extend the driving range of a vehicle by maximizing the energy efficiency during operation


Bat.

EGU

EM

Fuel

Complete Vehicle Energy Management (CVEM)

Holistic approach: Consider all energy consumers

Requires scalable, flexible and reconfigurable solution methods


Bat.

EGU

EM

FuelS2

S5

S4

S3S1


Optimal control problem:

subject to local constraints:

and interconnections


Solution methods:• Dynamic programing [1] – Scalability / curse of dimensionality• Pontryagin’s max. principle [2] – State Constraints• Static optimization – Globality / numerical conditioning / scalability

• Scalability achieved using distributed optimization [3]

• Globality for some problem conditions [3,4]

• Numerical conditioning achieved using regularisation [5,6]

[1] Wang et al., IEEE EVC, 2012 [2] Pham et al., ACC, 2013 [3] Romijn et al., CST, 2018 [4] Egardt et al., CSM, 2014 [5] Khalik, et al., ACC,2018 [6] Padilla et al., CDC, 2019

Bat.

EGU

EM

FuelS2

S5

S4

S3S1


Non-Convex Ill-Conditioned CVEM problem:• Solved using Forward-Backward splitting methods• Simple, parallelizable, regularized, provenly convergent algorithms• Based on dualization of the optimization problem (KKT conditions)

Static optimization problem:

subject to



Lagrange dual function:

KKT conditions:

Forward Backward Algorithm:


0CVEM with Ecodriving

Example of series HEV of [1]: 21km over 18mins• Backward: only CVEM: 23.41 l/100km• Forward: CVEM + ecodriving: 22.31 l/100km


[1] Murgovski et al., ACC 2015

4.7%

Experimental Results: Eco-Driving for Electric City Buses


Eco-Driving for City Buses


Exclusive bus lanes

Known starting point and final destination

Fixed travelling time

Limited influence of other traffic

Eco-Driving for City Buses: Implementation

Shrinking Horizon Control (with warm start and reduced number of decision variables)


Horizon

Eco-Driving for City Buses: Implementation

Shrinking Horizon Control (with warm start and reduced number of decision variables)

Worst case computation time: 70 ms


Horizon

Eco-Driving for City Buses: Simulation Results


Logged Data

Hi-Fi Model

Eco-Driving

DriverIdeal

Hi-Fi Model

14%

Assisted Driving?

Eco-Driving for City Buses: Assisted Driving


ECO-DRIVING

DECREASE Speed INCREASE Speed

Human Machine Interface

Flat road: 𝛼𝛼 𝑠𝑠 = 0Distance: 2.5[km]Time: 200[s]Speed limit: 60[km/h]Number of Drivers: 2

Eco-Driving for City Buses: Assisted Driving


Wind velocity: 30[km/h](headwind & tailwind driving)

Disturbances during the experiment

Velocity profiles:

FreeAssistedSp limits

ECO-DRIVING

Eco-Driving for City Buses: Experimental Results


3.18%

10.70%

ENERGYSAVINGS

Headwind(Front)

WINDDIRECTION

Tailwind(Back)

DRIVER

1.27%

5.09%

Driver 1

Driver 2

AVERAGE ENERGY SAVINGS

12.70%

8.70%

Driver 1

Driver 2

AVERAGE ENERGY SAVINGS: 6.94%

Wind disturbances not considered in problem formulation

Savings depend on the driver (still significant)

What if the bus is fully Autonomus?

Eco-Driving for City Buses: Experimental Results


3.18%

10.7%

ENERGYSAVINGS

Headwind(Front)

WINDDIRECTION

Tailwind(Back)

DRIVER

1.27%

5.09%

Driver 1

Driver 2

AVERAGE ENERGY SAVINGS

12.7%

8.70%

Driver 1

Driver 2

AVERAGE ENERGY SAVINGS:

6.89%

15.96%

ENERGYSAVINGS

Experimental Results(Assisted Driving)

Simulated Results(Autonomous)

6.94% 11.43%

Conclusions


Conclusions

Vehicle energy management and/with eco-driving • can mitigate range anxiety• leads to very challenging optimal control problems• can be solved using flexible/scalable/reconfigurable optimization algorithms

Experimental results show a significant energy saving (even with poor models)

Future/Ongoing work:• Traffic-aware eco-driving• Energy-aware vehicle coordination (traffic/intersection) management


Eco-Driving and Complete Vehicle Energy...

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