RESEARCH ON DESIGN OPTIMIZATION OF ENERGY SOURCE SIZES AND CONTROL PARAMETERS OF HYBRID ELECTRIC VEHICLE POWERTRAIN SYSTEMS

MINISTRY OF EDUCATION AND TRAINING NHA TRANG UNIVERSITY VU THANG LONG RESEARCH ON DESIGN OPTIMIZATION OF ENERGY SOURCE SIZES AND CONTROL PARAMETERS OF HYBRID ELECTRIC VEHICLE POWERTRAI

MINISTRY OF EDUCATION AND TRAINING

NHA TRANG UNIVERSITY

VU THANG LONG

RESEARCH ON DESIGN OPTIMIZATION OF ENERGY SOURCE SIZES AND CONTROL PARAMETERS OF HYBRID ELECTRIC VEHICLE POWERTRAIN SYSTEMS

Mayor : Transportation Engineering

Major Code : 62520116

DOTOCRAL DISSERTATION SUMMARY

Khanh Hoa – 2015

1

Research was accomplished at Nha Trang University

Supervisor: Prof Nguyen Van Nhan

The optimization method of key component sizes and control strategy parameters of HEV powertrain system is one of prerequisite conditions to design and fabricate HEVs

Study on theory and hybridisation applications in order to base for the design and fabrication of HEVs and evaluate the advantages of HEVs using in Vietnamses

road conditions, the research topic “Research on design optimization of energy source sizes and control strategy parameters of hybrid electric vehicle powertrain systems” has been carried out

2 Research Objectives

Building a simultaneous multi-objective optimization model of energy source sizes and control strategy parameters of HEV powertrain systems in order to reduce fuel consumption (FC) and improve toxic emissions of ICE

(2) Design optimization of powertrain energy source sizes of parallel and parallel HEVs

(3) Optimization of energy source control parameters of parallel and parallel HEV powertrain

series-3

5 Reseach Contents

The research contents include: HEV overview and optimization of HEV powertrain systems; Application of a Bees Algorithm as an optimizer for powertrain component sizing and control strategy parameter optimization for hybrid electric vehicles; Experimental simulation of key component size and control strategy parameter optimization for powertrain system; Conclusions and recomendations

6 Limitations

The dissertation mainly focuses on the powertrain component sizing and control strategy parameter optimization for hybrid electric vehicles in order to reduce fuel consumption and emissions of ICE without sacrificing on-road performances The design optimization method of energy source sizes and control strategy parameters of HEV powertrain systems by using bees algorithms has not been experimented on physical HEVs in laboratory or in real operating conditions

7 Key Findings

(1) Development of multi-objective optimization model of energy source sizes and control strategy parameters of hybrid electric vehicle powertrain systems;

(2) Developing equations fs(S j ) (2.30), n b (S j ,t) (2.32) and ph(S j ,t) (2.34) of

pheromone-based Bees Algorithm;

(3) Building penalty functions to optimize energy source sizes and control strategy parameters of hybrid electric vehicles

(4) Building a software module to optimize energy source sizes and control strategy parameters of hybrid electric vehicles by means of ADVISOR software

in which Vietnamese dynamic performances and driving cycle are taken into account

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Chapter 1 OVERVIEW OF HYBRID ELECTRIC VEHICLES AND HYBRID ELECTRIC VEHICLE POWERTRAIN SYSTEM OPTIMIZATION

Design optimization of energy source sizes of hybrid electric vehicle powertrain system: The HEV powertrain system has at least two kinds of different power sources

including an ICE and an or more EMs They play the main role of providing kinematic energy for driving wheels, however, the powertrain needs to have a battery system and

an EG for storing and producing electric energy The size of ICE and EM means maximum power The size of battery system is its total capacity Design optimization

of energy source sizes of hybrid electric vehicle powertrain systems is the determination of maximum suitable power of ICE, EM, EG and battery capacity to satisfy optimization objectives

Optimization of control strategy parameters of hybrid electric vehicle powertrain systems: That is a working mode determination and supervision of energy sources to

get optimization objectives of HEV inherent advantages

Most of researches on HEV optimization focus on :

- Design and fabrication of HEV models and some of key components of HEV powertrain systems

- Research on a control strategy parameter optimization: Particle swarm

optimization (PSO) method; Genetic algorithm (GA) method and Simulated annealing (SA) method

- Research on design optimization of power source sizes: Power source size

optimization for series HEVs using a combination between GA and Sequential Quadratic Programming (SQP); Power source size optimization for parallel HEVs using three algorithms including DIRECT algorithm, SA and GA to improve FC

- Research on a simultaneous optimization of key power source sizes and control

strategy parameters: Optimization for series and series-parallel HEVs using GA

Above analyses and literature reviews about HEVs show that most of domestic researches focus on design and fabrication of HEV models and some key components

in default values

From existing problems in researching on HEVs, in this research, new methods will be used to optimize energy source sizes and control strategy parameters of HEV powertrain systems Bees algorithm is chosen and developed to solve parallel and series-parallel HEV optimization ADVISOR and Matlab software will be applied to simulate optimization experiments for Honda Insight 2000 and Toyota Prius 1998, in which Vietnamese constraint conditions are also taken into account

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Chapter 2 APPLICATION OF A BEES ALGORITHM AS AN OPTIMIZER FOR POWERTRAIN COMPONENT SIZING AND CONTROL STRATEGY PARAMETER OPTIMIZATION FOR HEV POWERTRAIN SYSTEMS

Chapter 2 presents a general optimization model of energy source sizes and control strategy parameters of HEV powertrain systems composed from an ICE and EMs Basic characteristics of BA, some developed innovations about BAs and its applications for only control strategy parameter optimization and simultaneous optimization of control strategy parameters and energy source sizes are fundamental theories of experimental simulations presented in chapter 3

A GENERAL OPTIMIZATION MODEL OF ENERGY SOURCE SIZES AND CONTROL STRATEGY PARAMETERS OF HEV POWERTRAIN SYSTEMS

Fig 2-1: A general optimization model for HEVs using MDO

EQUIPTMENTS / HEV MODEL

0 ) (

X k

X h

q l

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The author has been used Multidisciplinary Design Optimization (MDO) to solve

a problem of multi-parameter optimization of HEV powertrain systems composed from components having different structure characteristics and operating principles as shown in Fig 2-1

1( ) 0

_

max _ min

_ 1

max _ min

_

max _ min

_ 1

m CS m

CS m CS

CS CS

CS

n CZ n

CZ n CZ

CZ CZ

CZ

x x

X

x x

X

x x

X

x x

X

( )( )( )( )

X CS-j (j = 1 ÷ m) is a variable set of control strategy parameters

Y is a output set including FC, contents of HC, CO and NO

In this dissertation, an oriented trial and error method is used for the optimization process of energy source sizes and control strategy parameters of HEV powertrain systems This process includes many iterations, at each iteration, OPTIMIZATION

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UNIT will assign a specific value for each variable of X in a range bounded by lower

and upper limits as shown in Eq (2.2) to create a “optimal candidate” Then, the candidate will be sent to the modeled EQUIPMENT block Control strategy will determine an operating point of all power sources according to each driving cycle

period The output Y of EQUIPTMENT block will be FC, contents of HC, CO and NO

in ICE emissions, and HEV dynamic performances All components of Y will be

returned to OPTIMIZATION UNIT to calculate G(X) and check dynamic performence constraint requirements Basing on results of G(X) and checking constraints,

OPTIMIZATION UNIT will change component values of X through an optimization

algorithm to create a “new candidate” The process will be repeated until optimization conditions are satisfied

Next part, innovations and applications of BAs as an OPTIMIZATION UNIT to optimize key component sizes and control strategy parameters of HEV powertrain systems

BASIC BEES ALGORITHM (BBA)

The optimization process for HEVs using BBA is described as follows:

 Step 1: Initialize the population of n scout bees, each scout bee is a set of specific

value of all variables of key component sizes and control strategy parameters

 Step 2: Evaluate the FC, HC, CO, NO and penalty functions Ci(x) for each scout

bee by combining between BBA and ADVISOR software

 Step 3: Calculate the fitness value of all scout bees according to Eq (2.22) and

choose m scout bees with ranked fitness from the highest to lowest value

 Step 4: Conduct n1 searches in each neighbourhood of the best e sites and choose

the bee with highest fitness for each site

 Step 5: Conduct n2 searches in each neighbourhood of the (m-e) sites and choose the bee with highest fitness at each site

 Step 6: Assign remaining (n-m) bees to search randomly around the search space

for new potential solutions These searches are carried out to avoid a local optimization

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 Step 7: Update new population from best bees of m and (n-m) sites

 Step 8: Stop the program if the convergence criteria is satisfied, otherwise go to step

2 The convergence criterion is that the fitness of the best bee of the new population

is not improved after the specific number of iterations or after N iterations

In order to apply BA to the simultaneous optimization of HEVs, the fitness in step 2 is the inverse of objective function G(X) in Eq (2.1) However, the optimization task is required to maintain Vietnamese dynamic performances according

to TCVN 4054 : 2005 và 22 TCN 307 – 03 Unfortunately, BBA can not work directly with constrained optimization problem To solve this problem, it is necessary to add penalty functions into objective function G(X) as shown from Eq (2.22) to (2.24) The penalty functions are used to penalize the infeasible solutions by reducing their fitness values Ci(X) = 0, if its constrain is satisfied

X = (x 1 , x 2 , …, x n) are parameters of key component sizes and control strategy

Ci(X), αi and Fi(X) are penalty function, desired value and evaluated value related to ith constrain

ki is penalty factor chosen by a trial and error method

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PHEROMONE-BASED BEES ALGORITHM

Similar to BBA, the fitness function Fn(X) is also an invert of G’(X) The penalty function at Eq (2.28) is added into Eq (2.27) to consider dynamic constraint requirements of HEVs

The value of fitness at site Sj is calculated as follows :

The optimization process for HEVs using PBA is described as follows:

 Step 1: Initialize the population of n scout bees, each scout bee is a set of specific

values of all variables of key component sizes and control strategy parameters

 Step 2: Evaluate the FC, HC, CO, NO and penalty functions Ci(X) for each scout bee by combining between PBA and ADVISOR software

 Step 3: Calculate the fitness value of all scout bees according to Eq (2.27), (2.28) and (2.30)

 Step 4: Choose e bees with highest fitness

 Step 5: Recruit bees for selected “e” sites according to pheromone levels at those

sites (local search) to conduct searches in the neighborhood of the selected e sites

and choose a bee with the highest fitness for each site The number of bees given by

nb(Sj,t) recruited for a site Sj of e sites at time t is calculated from Equation (2.32)

In this research, equations for n b (S j ,t), fs(S j ) và ph(S j ,t) calculations have been

developed and improved in comparision with Equations of Dr Packianather (Cardiff University, United Kingdom)

1

1 1

f s (S j ) is the fitness score of site Sj Note that the fitness score f s (S j ) is normalized

to smooth noise and suppress systematic variations

m is the average number of bees at each site of e

S e+1 is the best performing site among the non-selected sites

ph(S j ,t) is pheromone value at site Sj

The parameters α and β control the influence of the amount of pheromone that was available at site Sj from the previous iteration, and of the fitness score of site Sj on the bee recruitment The parameter ρ controls the evaporation or decay of pheromone

 Step 6: Assign the remaining (n-e) bees to search randomly around the search space

for new potential solutions These searches are carried out to avoid a local optimization

 Step 7: At the end of the local and global search, the best bees from all sites are

sorted according to their fitness

 Step 8: Update new population

 Step 9: Update pheromone level on each site by using Eq (2.34)

 Step 10: : Stop the program if the convergence criteria is satisfied, otherwise go

back to step 4 The convergence criterion is that the fitness of the best bee of the

new population is not improved after the specific number of iterations or after N iterations

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Chapter 3 EXPERIMENTAL SIMULATIONS OF KEY COMPONENT SIZE AND CONTROL STRATEGY PARAMETER OPTIMIZATION FOR HEV

POWERTRAIN SYSTEMS EXPERIMENTAL SIMULATION OBJECTIVES

Contents of experimental simulation in this dissertation are to get these following objectives :

(1) Solving the problem of HEV powertrain system optimization by using BAs (2) Comparing the HEV powertrain system optimization quality between a basic bees algorithm and a pheromone-based bees algorithm

(3) Comparing HEV powertrain system optimization results according to Vietnamses driving cycle and FTP driving cycle

(4) Evaluating the reliability of HEV powertrain system optimization method using BAs

EXPERIMENTAL SIMULATION CONTENTS

Or

Number List of Experimental Simulation Items

Experimetal Simulations for Honda Insight 2000

1 Optimization of only control strategy parameters by using BBA according

to Vietnamese driving cycle (CECDC)

2 Optimization of only control strategy parameters by using BBA according

to FTP driving cycle

3 Optimization of only control strategy parameters by using PBA according to

CECDC driving cycle

4 Optimization of only control strategy parameters by using PBA according to

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FTP driving cycle

5 Simultaneous optimization of energy source sizes and control strategy

parameters by using BBA according to CECDC driving cycle

6 Simultaneous optimization of energy source sizes and control strategy

parameters by using BBA according to FTP driving cycle

7 Simultaneous optimization of energy source sizes and control strategy

parameters by using PBA according to CECDC driving cycle

8 Simultaneous optimization of energy source sizes and control strategy

parameters by using PBA according to FTP driving cycle

Experimetal Simulations for Toyota Prius 1998

9 Optimization of only control strategy parameters by using PBA according to

CECDC driving cycle with different weighting factors of w i

- BBA and PBA have determined a new value set of control strategy parameters

as shown at columns (1), (2) and (3) in comparision with a current value set of control