Integrated System Design Optimisation: Combining ...pre) 05.2014, E...Optimal Sizing Results –...

Integrated System Design

Optimisation: Combining Powertrain

and Control Design

Dr. Ir. Theo Hofman

MSc Emilia Silvas

Wednesday, 25-06-2014, 14:15-14:35

.

Technology Topology

Size Control

Are we harming the planet in the name of

progress?

PAGE 1/16 25-06-2014 / CST Group, Mechanical Engineering. Emails: [email protected], [email protected]

Motivation for Hybrid Powertrains

Today

> 200 % increase


What is a hybrid powertrain?

Vehicles Boats or Yachts

Transmission Combustion

Engine

Battery Pack

Electric

Machine

Conventional Vehicle

Hybrid Electric Vehicle

Combustion

Engine Transmission

Conventional Boat

Hybrid Electric Boat

Combustion

Engine Transmission

Transmission Combustion

Engine

Battery Pack

Electric

Machine


Wide Variety of Hybrid Powertrains

E. Silvas et al., Review of Optimal Design Strategies for Hybrid Electric Vehicles. IFAC Workshop on Engine and

Powertrain Control, Simulation and Modelling, 3(1):57–74, 2012. PAGE 4/16 25-06-2014

Optimal Design of Powertrains


Optimal Design of Powertrains (example)

B.A. Skinner, G.T. Parks and P.R. Palmer “Comparison of submarine drivetrain topologies using

multiobjective genetic algorithms”. IEEE Transactions on Vehicular Technology, 2009

Which topology and sizes will find the best combination of cost, risk

and mission effectiveness for different sea scenarios?

4 sea mission scenarios

Multiobjective Genetic Algorithms

Five objective functions

o Max. propeller efficiency

o Max. electric motor efficiency

o Min. electric motor size

o Min. total energy consumption

o Max. steam turbine efficiency

8% improvement in energy

consumption for the hybrid solution


Optimal Design of Powertrains (benefits)

A hybrid drive train enables:

Maximizing the performance;

Maximizing the fuel efficiency (minimize emissions);

Improving the trade-off between 1 and 2;

Usage of new technologies; e.g., advanced engines, electrical auxiliaries, and

transmissions.

Performance

Fuel efficiency 2

3 1


Find the design variables 𝑥 by solving

min𝑥𝑓(𝑥) 𝑠. 𝑡.

𝑔 𝑥 ≤ 0 ℎ 𝑥 = 0

General Optimization Problem

𝑥 = [𝑥𝑝, 𝑥𝑐]

(∙)𝑝 denote a plant related variable

(∙)𝑐 denote a control related variable

w n =

𝑠(𝑛)𝑑(𝑛)𝑣(𝑛)

,with 𝑛 = [1, 𝑡𝑓]

min𝑥𝑠,𝑥𝑐(t)

𝜙

𝑡𝑓

0

𝑥𝑝, 𝑥𝑐(t), 𝑤 𝑑𝑡

𝑠. 𝑡. 𝑔𝑝 𝑥𝑝 ≤ 0 ℎ𝑝 𝑥𝑝 = 0

𝑔𝑐 𝑥𝑐 ≤ 0

ℎ𝑐 𝑥𝑐 = 0

Plant and Control Optimization Problem

Optimal Design of Powertrains (problem)


Optimal Design of Powertrains (methods)

Optimize the

plant

Optimize the

controller

Optimize the

combined

system by

varying both

plant and

controller Optimize the

controller

Improve plant without

compromising the

controller

Sequential Nested Simultaneous Bi-level /


𝜙

𝑡𝑓

0







Optimal Design of Powertrains (Study Case)

Optimize the

controller

Improve plant without

compromising the

controller

Nested Bi-level /

Optimal

Control

Sizing

Optimization

Optimal Sizing and Control Design of a Hybrid

Electric Vehicle

Genetic Algorithms, Sequential

Quadratic Programming, Particle

Swarm Optimization or

Pattern Search (DIRECT)

Dynamic Programming

Application

Find optimal engine, motor and battery sizes for minimum fuel and costs

Find optimal control inputs (power split signal and gear number) for a given driving profile

Compare nested optimization methods

Scope of the study case:



Full parallel hybrid topology:

Backwards modeling

Scalable quasi-static models

Linear cost-models

𝜙𝑝 = max (𝑝𝑟𝑜𝑓𝑖𝑡)

𝜙𝑐 = 𝑃𝑓

𝑡𝑓

𝑖=0

𝑥𝑝 =

𝑃𝑒𝑃𝑚𝐶𝑏

, 𝑥𝑐 =𝑢𝑝𝑠𝛾

𝑃𝑒 = engine power

𝑃𝑚 = motor power

𝐶𝑏 = battery capacity

𝑢𝑝𝑠 = power-split signal

𝛾 = gear ratio



Full parallel hybrid topology:

Backwards modeling

Scalable quasi-static models

Linear cost-models

150000 yearly mileage

highway typical driving

Heavy duty, 40 ton, vehicle

𝜙𝑝 = max (𝑝𝑟𝑜𝑓𝑖𝑡)

𝜙𝑐 = 𝑃𝑓

𝑡𝑓

𝑖=0


𝜙

𝑡𝑓

0






𝑥𝑝 =

𝑃𝑒𝑃𝑚𝐶𝑏

, 𝑥𝑐 =𝑢𝑝𝑠𝛾



Optimal Sizing Results

The choice of optimization target (fuel, hybridization costs,

profit) strongly influences the optimal design PAGE 13/16 25-06-2014 / CST Group, Mechanical Engineering. Emails: [email protected], [email protected]


Optimal Sizing Results – Pareto Analysis



Optimal Sizing Results – Optimization Algorithms Comparison

E. Silvas et al., Comparison of Bi-level Optimization Frameworks for Sizing and Control of a Hybrid Electric

Vehicle, (submitted to) IEEE VPPC 2014.


Current & Future Work Automatic topology generator for hybrid topologies.

Extend the design framework to include switchable topologies, automatically generated.

Conclusions Nested optimal design achieves improved fuel efficiency (proven to particular

cases), eliminates costly re-design steps, and enables the hybrid powertrains

chance to comply with future exhaust emissions legislations.

Using brute force search, to find the optimal sizing values becomes too computationally

expensive and insufficiently accurate. Optimization algorithms as SQP or DIRECT

should be used instead.


Thank you! Questions?

Integrated System Design Optimisation: Combining ...pre) 05.2014, E...Optimal Sizing Results –...

Documents

Transcript of Integrated System Design Optimisation: Combining ...pre) 05.2014, E...Optimal Sizing Results –...