EVS28 KINTEX, Korea, May 3-6, 2015 Design Space ... KINTEX, Korea, May 3-6, 2015 Design Space Exploration and Hybridization of the Kiira-EV SMACK R.Madanda1, P.I.Musasizi2, P.Korukundo3,

EVS28

KINTEX, Korea, May 3-6, 2015

Design Space Exploration and Hybridization of the

Kiira-EV SMACK

R.Madanda1, P.I.Musasizi2, P.Korukundo3, A.T.Asiimwe4, J.Africa5, and S.S.Tickodri-Togboa6

1Kiira Motors Project, [email protected],[email protected]

Abstract

Hybridization and electrification of vehicles has seen aggressive research in recent years due to the ever-increasing

fuel economy and efficiency demands. Several hybrid powertrain configurations have been developed in commercial

vehicles with Toyota’s and Ford’s power split configurations giving some of the best fuel efficiency values. Kiira

is a vehicle brand of Kiira Motors Project (KMP), the brainchild of the first electric and hybrid vehicles designed

and built in Uganda. KMP is building a flagship hybrid vehicle, the Kiira-EV SMACK, with the intent of mass

production by 2018. Engineering a functional EV or hybrid powertrain is less cumbersome of recent because of the

availability of numerous off-the-shelf products for quick integration. It is important to also note that to achieve a

properly sized powertrain requires a meticulous search from a myriad of alternatives available. In this paper, the

techniques adopted in efficiently searching the design space for the Kiira-EV SMACK hybrid vehicle powertrain are

presented. To tune parameters for the Kiira-EV SMACK plug-in hybrid vehicle, several combinations of traction

motors, batteries, generators and control strategies are considered. Other design factors considered include vehicle

architecture, cost, component volume and drive regimes. The design space given by such choices is huge. Design

Space Exploration (DSE) and reduction using the Genetic global optimization algorithm is used to quickly search

and reduce the design space. The powertrain problem is represented as a multi-objective design problem. The

optimization criteria used follows energy consumption and vehicle performance aspects including range, top speed

and gradeability. Optimization yields a minimal set of pareto-optimal design solutions which are used in Autonomie

for final vehicle level verification and validation. The final design solutions for the different architectural components

are used in the selection of off-the-shelf Kiira-EV SMACK powertrain components.

Keywords: Design Space Exploration(DSE), Hybridization, Powertrain, Global Optimization

1

[email protected], [email protected]

1 Introduction

Design Space Exploration (DSE) refers to the ac-

tivity of exploring design alternatives prior to im-

plementation [1]. DSE is a powerful tool for rapid

prototyping and system integration. In hybrid

electric vehicle design, several design constraints

should be satisfied simultaneously as illustrated

by Shaiket al [2]. Therefore, the powertrain de-

sign problem is a multi-objective constrained non-

linear optimization problem which can be solved

by the DSE technique as illustrated by Markus et

al [3].

The industry standard for powertrain design

involves use of forward (driver driven) or back-

ward looking (vehicle driven) models [4]. In a for-

ward looking model, the driver model sends an ac-

celerator or brake pedal signal to the powertrain

in order to follow the desired vehicle speed trace.

In a backward-looking model, the desired vehicle

speed is instructed from the vehicle model back to

the powertrain to finally find out how each com-

ponent should be used to follow the speed cycle.

These models capture several design constraints

during but other important factors like cost and

volume are not generally considered because there

is no universal per unit cost or per unit volume

benchmarks for the different off-the-shelf power-

train components. The design process with such

vehicle models also involves iterations of different

vehicle specifications and drive cycles to arrive at

an optimal solution. This process is time consum-

ing.

The iterative nature of manually searching for

an optimum solution using the vehicle models ne-

cessitates application of faster design space reduc-

tion techniques. DSE techniques can be applied in

tandem with the vehicle models to arrive at a high

quality design solution in a shorter time. It should

be noted that the choice of the DSE method to be

applied highly depends on the nature of the alter-

natives available and their impact on results. If a

component’s impact on the output is predictable,

then application of a search strategy is possible.

If a component’s impact on performance is unpre-

dictable then an optimum search strategy may not

be possible. The classes of common DSE methods

include;

1. Exhaustive search. This involves trying

out all possible scenarios e.g branch and

bound and depth first search. This is only

feasible for small search spaces.

2. Random search. Where large irregular

and unpredictable search spaces exist, this

is the method of choice. In such a case, it is

impossible to predict the next best solution

once you have an initial solution, therefore

the choices are chosen at random and evalu-

ated.

3. Guided search. This may be used where

there is sufficient knowledge of the design

space and the trend of choices made in the

future. Such design spaces may be traversed

using heuristic algorithms e.g the greedy al-

gorithm.

4. Simulation based search. This is a tech-

nique for evaluating performance of single

points in design space using an executable

model.

5. Analytical methods. Here by reasoning or

by using a suitable algorithm, different solu-

tions are examined and pruned. Analytical

techniques and simulation based calibration

can be combined to arrive at a solution.

The aforementioned approaches are generic and

used in several science disciplines for DSE. In

the next section, a review of common design

space traversal and optimization methods applied

specifically to electric and hybrid vehicles are dis-

cussed. The rest of this paper is organised as fol-

lows. In section 2, a review of the current opti-

mization approaches applied to hybrid vehicles is

EVS28 International Electric Vehicle Symposium and Exhibition

2

elaborated, in section 3 and 4, the high level ve-

hicle definition and constraints of the Kiira EV

SMACK are stated, in section 5 the application

of the DSE technique to the Kiira EV SMACK is

discussed and finally in section 6, the results from

the DSE approach are presented.

2 Related Work

The impact of a particular component choice

on powertrain performance is always predictable.

The search space for solutions is also regu-

lar i.e there is a mathematical relationship be-

tween specifications and performance. This makes

exhaustive-based search and random-based search

methods unattractive DSE methods of choice.

Previous researchers have mainly used analytical-

based or a hybrid of simulation-based, guided

search and analytical-based. The unique meth-

ods in literature are presented hereafter. Sharer

et al [5] presents an iterative component sizing

approach for hybrid vehicle design space search.

The iterative component sizing algorithm simply

searches for a vehicle mass which solves the con-

straint Equation 1.

Pm(Mveh)

σm

+Peng(Mveh

σeng

+Ncell(1.3Pbatt...

2Ebatt)XMcell(1.3Pbatt, .2Ebatt) +Mglider = Mveh

(1)

where Pm is the motor peak power, Peng is

the engine peak power, Pbatt is the battery peak

power, Ebatt is the battery total energy capacity,

Mveh is the vehicle total mass, Mglider is the mass

of the vehicles glider, Ecell is the mass of a bat-

tery cell, σm is the specific power of the motor,

σeng is the specific power for the engine,and Ncell

is the number of cells connected in series to form

the battery pack.

Jainet al [6], Shahirinia et al [7], Morteza et

al [8], Jianping et al [9], Kumaret al [10] use a ge-

netic algorithm (GA) for design space search. A

GA is a stochastic global search technique which

mimics the process of natural biological evalua-

tion(”survival of the fittest”) used for solving both

constrained and unconstrained optimization prob-

lems based on a natural selection process that

mimics biological evolution. The algorithm re-

peatedly modifies a population of individual solu-

tions at each iteration. At each step, the GA ran-

domly selects individuals from the current popu-

lation and uses them as parents to produce the

children for the next generation. Over successive

generations, the population ”evolves” toward an

optimal solution. It is important to note that the

GA is derivative free and good at arriving at a

global optima rather than local minima as a final

solution. This motivates the choice of its applica-

tion in this paper.

Murgovski et al [11] uses convex optimization

to search the design space. In convex optimiza-

tion, a multivariable function is optimized sub-

ject to constraints which have a convex constraint

function.

Xiaolan et al [12], use a parallel chaos opti-

mization algorithm(PCOA) where a multiobjec-

tive function is defined so as to minimize the driv-

etrain cost considering the drive performance re-

quirements as constraints.

3 Vehicle Definition

Table 1 lists the initial Kiira-EV SMACK vehicle

level goals. The rest of the vehicle definition pa-

rameters are stated in the Table 2, where GVW

is the gross vehicle weight and AER, is the all

electric range.


3

Table 1: Vehicle Definition

Requirement Specification

Top Speed 180 km

AER 50 kms

Gradeability 17.63 at 30 km/hr

Table 2: Vehicle Specifications

GVW 1500 kg

Length 4900 mm

Track width 2040 mm

Height 1460 mm

Frontal Area 2.66m2

Wheels 215/65R18

Drag Coefficient 0.28

Capacity and Type Passenger Hybrid

3.1 Vehicle Architecture

The Kiira-EV SMACK is a series hybrid electric

vehicle, refer to Fig.1 and Fig. 2 . The drive train

architecture comprises of two energy sources; a

Lithium ion battery bank of 211 V and 40 AH

Winston lithium ion and a generator/engine com-

bination of 100 kW (Remy-HVH generator). The

traction motor of peak power 80 kW (Tm4 motor)

is coupled to a single speed transmission with a

fixed ratio of 8:1.

Figure 1: Kiira-EV SMACK

Figure 2: Vehicle architecture

3.2 Control Strategy

The envisaged control strategy governing the op-

eration of the drive train was the engine turn on

and off . Vehicle start up is supported by the

battery bank for speeds up to 50 km/hr. The

engine is run below 50 km/hr only when the bat-

tery State of Charge(SoC) is below 20 (in this case

the generator charges the batteries) or when the

requested power is much greater than the battery

can sustain for a long period like during hill climb-

ing. Above 50 km/hr, the vehicle is run on the

generator and batteries occasionally switching off

the generator when the battery charge is regained

above a certain point of SoC. To maintain the top

speed, both the generator and the batteries are

operated simultaneously.

4 Design Constraints

All vehicle design constraints are typically de-

rived from the high level requirements. Kumaret

al [10] and Galdi et al [13], use performance con-

straints for PHEV design. In this paper, in addi-

tion to performance constraints, cost and weight

constraints which use the estimation of the mean

unit cost and weight of components from different


4

vendors are added. Obtaining a minimal power-

train cost and weight is key in keeping the overall

vehicle cost and weight low. These requirements

should be satisfied on a typical Kampala road cy-

cle presented in [14]. A typical Urban Dynamome-

ter Drive Schedule (UDDS) cycle, implemented

in Autonomie and, a flat grade road with a con-

stant speed of 50 km/hr are also added. These

constraints form the basis for the mathematical

constraint and objective functions used in the GA

discussed later. In this paper fuel efficiency and

carbon emissions are not considered because they

are of less importance to the design goals of the

project.

• Maximum speed on level road (120 km/hr)

• Acceleration 0 − 100 km/hr in 10s

• Vehicle weight 2000 kg

• AER Range 50 km

• Grade ability 17 % at 30km/hr

• Cost estimate between USD. 20, 000 and

USD. 30, 000

• SOC limit 20 %

5 DSE Approach

The approach employed follows a Y-chart design

process shown in Fig. 3. The Y-Chart approach is

widely used in the DSE of embedded systems. It

involves exploration of the hardware and software

solutions independently before obtaining a single

final solution [15]. In our implementation of the

Y-chart DSE, the processes of adjusting vehicle

constraints models and the search space bound-

aries are independent. By benchmarking vehicles

in the category of the Kiira sedan i.e Honda Civic

GX, Honda Civic Hybrid, Chevrolet Volt, Fisker

Karma and Toyota Prius hybrid, lower and upper

specification bounds are drawn for the different

components i.e motors, batteries and generators.

This is simply achieved by linear extrapolation us-

ing weight and performance.

The models based on the road-load equations

are developed for both the objective function, e.g

the Ampere hour (AH) rating and the constraint

function, e.g the consumption rate constraint.

The objective and constraint functions are op-

timized using the GA, which produces candidate

solutions from a population of solutions. Since GA

is stochastic, each run produces slightly different

results which are all pareto-optimal. It is applied

20 times to increase the population of solutions.

The optimal solutions are used in a suitable model

based simulation (model in the loop or ”out of the

loop”) to arrive at one final candidate.

Figure 3: Y chart design space search

5.1 Problem Representation

Using the GA, each candidate solution is looked at

a chromosome with the genes which are design pa-

rameters. The genes of the powertrain were iden-

tified as peak generator power PG, maximum mo-

tor power PM , ampere hour rating AHA, cost of

components, final gear ratio GR, DC bus voltage


5

VDC and motor torque Tm. Equation 2 is typical

in defining the pertinent objective functions for

most of these constraints.

Pout =V

ηtηm(Mvg(fr + cosθ) + ...

1

2ρaCDAfV

2 +MvdV

dt)

(2)

where Pout is the output power from the motor,

V is the velocity of the vehicle, ηt and ηm are

the transmission and motor efficiencies, Mv is the

vehicle weight, V is the vehicle speed, g is accel-

eration due to gravity, Af is the vehicle frontal

surface area, CD is the coefficient of drag, and ρa

is the air density. Sections 5.1.1 to 5.1.3 elaborate

further the model representation of the different

components.

5.1.1 Battery Pack Representation

The battery pack minimization problem is simpli-

fied by using only series connected batteries. A

separation of concerns is applied where the vehi-

cle is treated first as purely electric for a speed

range 0-50 km. This criteria is employed so as

to independently design the electric version of the

vehicle and also the conventional version without

paying much attention to the vehicle level control

strategy in the initial stages. Equations 3, 4 ,5

and 6 are used as the objective functions for the

battery pack design.

AHA =CR ·AER

VDC

(3)

where AHA is the overall ampere hour Rating

of the battery system, CR the consumption rate

, AER is the all electric range. The quantity

CR · AER is the energy content of the energy

storage system (ESS). It is assumed that from the

control requirements, electric only system is able

to support the vehicle at top constant speed of 50

km/hr for 50 km on a level road.

CR =Pout

V(4)

Where V is the vehicle velocity.

Cb =V ·AHA · unitCost

3.2(5)

Where Cb is the cost of the entire battery pack

and unit cost is the prevailing cost per AH.

Wb = AHA · (W/AHA) (6)

Where Wb is the weight of the total battery pack

and W/AHA is the mean weight per AHA.

The consumption rate at 50 km/hr is at least

equal to or greater than Pout/V and the range

is not more than 50 km at constant speed of 50

km/hr. These restrictions form the constraints

for the battery pack design. The lower and upper

bounds for CR, AER and VDC in the GA are all

set to values in Table.3. These are obtained by

benchmarking of commercial vehicles.

Table 3: Lower and upper battery variable bounds

CR AER VDC

LB 0 50 100

UB 200 60 400

5.1.2 Motor Specifications

The most important motor parameters are the

peak power, torque and motor speed. The peak

power of the motor is determined by the accel-

eration power required to attain a top speed of

96 km(60 mph) within a given acceleration time.

The power required is defined in Equation 7. The

torque supplied by the motor depends on the

wheel radius and the final effective gear ratio used

as shown in Equation 8. The base speed or the re-

quired motor speed(rpm) is computed according

to Equation 9.

Equation 7 from [16], Equations 8 and 9 are set

as the objective functions for the genetic multi ob-

jective optimization. The lower and upper bounds


6

are informed by benchmarking process of commer-

cial vehicles. Acceleration timeta), final speed Vf

and gear ratio GR are taken as the variable pa-

rameters for optimization. The bounds on these

are set to values in Table.4

PM =1

ηtηm

(Mv

2ta+

2

3MvgfrVf +

1

5ρaCDAfV

3f

)(7)

where Vf is the final vehicle speed, Vb is the vehi-

cle base speed, ta is the time required to accelerate

from the base speed to the final vehicle speed.

Tm =Pout · rdGRmin

torque) (8)

Where Tm is the required motor torque, rd the

radius of the vehicle wheels and GRmin is the low-

est applicable gear ratio.

GRmin =πNmmaxrd

30Vmax

(9)

Where Vmax is the vehicle maximum speed, Nm

is the motor torque.

Table 4: Lower and upper bounds of motor variables

ta Vf GR

LB 0 0 0

UB 20 100 12

5.1.3 Peak Generator Power PG

The peak generator rating is derived from the re-

quired power to maintain the vehicle at a particu-

lar speed or grade. According to the control strat-

egy, it is required that the generator is started

at speeds above 50 km/hr. In a series hybrid,

the engine is considered as another power source

which delivers power to the DC bus. Therefore

its optimization is similar to the optimization of

the battery. Other parameters like weight, cost

can also be optimized. It is assumed that above

50 km/hr, the battery delivers all the available

power with the generator assistance. This leads

to a global optimization function given according

to Equation 10. Optimization for the generator is

subject to the same constraints as the motor.

PG = Pout− Pbatt (10)

Where Pgen is the required engine rating at a given

design top speed, Pout is the required power at the

driven wheel and Pbatt is the maximum continuous

power from the batteries.

PG = ηePE (11)

The generator and engine specification are related

because the engine rating depends on the calcu-

lated generator requirement where ηe is the con-

version efficiency of the engine

The cost associated with the particular gener-

ator engine combination is given as

CG,E,M =∑

i=G,E,M

Pi · Unitcost (12)

Where CG,E,M Is the cost of the generator, engine

and motors.

5.2 Matlab Implementation

The the optimization algorithm is applied as illustrated in

Table.5.

Table 5: Optimization method

Optimization Process

Step 1:Set Vehicle constraints ie Range and Top Speed

Step 2:Compute and optimize the consumption Rate

Step 3:Apply GA to AH, Weight,

cost and volume ,PM , RPM, Tm and Pg

Step 4:Extract the population of points

Step 5: Apply pareto front to the population of points

Step 6: Extract pareto point and run a vehicle model

in Autonomie

Step7: Compare SoC and Top speed results

and percentage of road cycle missed


7

6 Results and Discussion

2D and 3D pareto graphs are plotted for the dif-

ferent objective functions. The GA generally gives

different optimal results during each run because

it is stochastic, therefore, it is applied within 20

iterations to obtain different optimal results which

are later used in the simulink model for verifica-

tion.

In Fig.4, Fig.6 and Fig.7 most of the points

from the design space population appear in the

pareto front. This is because GA is probabilis-

tic and results in different points but most of the

points are always close to the optimal solutions

in one or more objectives. The values at these

pareto points are reproduced in Table 6, Table7

and Table 8. In Fig.4, the battery pack Energy

capacity can be designed to values between 41 AH

and 121 AH, for a DC voltage rating between 119

VDC and 328 VDC. The final solution is subject

to other factors which cannot be explored exten-

sively by the optimisation. These factors are in-

vestigated in Autonomie.

In Fig.7, the traction motor design values

ranges between 56 kW and 172 kW, for accel-

eration times ranging between 18 and 3 seconds

respectively. The engine specifications range be-

tween 15 kW and 88 kW.

Figure 4: Battery Energy Vs DC bus Voltage

Figure 5: Engine power vs vehicle speed

Figure 6: Motor power vs acceleration time

Figure 7: Motor power vs speed vs torque


8

6.1 Vehicle Level Verification

The genetic optimization does not produce one

final set of specifications which can be used as a

blue print for the powertrain but prunes down the

design choices to a few viable results. To arrive at

one set of design specifications, a suitable vehicle

model in Autonomie is run with a combination

of several parameter specifications from the op-

timization. The specifications obtained from the

pareto graphs are used. The motor, battery and

engine specifications are given in the Table 6, Ta-

ble 7 and and Table 8 respectively. To test for bat-

tery performance, an all electric drive is used with

50 km/hr top speed, 50 km range and the mo-

tor specifications obtained from the pareto graphs.

Two versions of the same vehicle are developed, a

purely electric vehicle and a hybrid vehicle. The

most important statistics obtained from the sim-

ulations are the battery SoC, battery voltage de-

cline, percentage of drive cycle missed, the maxi-

mum attainable speed and the acceleration times

for the different vehicle configurations.

Table 6: Motor specification

Power 92 67 110 120 69 56

t a 8 12 7 6 12 15

172 75 74

4 10.6 11

Table 7: Battery specification

DC 328 197 259 158 240 203 248

AHA 41 68 53 84 58 66 54 0

119 245 281

121 55 5

Table 8: Engine specifications

Speed (km) 90 100 110 120 130 140 150

Engine(kW) 15 20 25 32 39 47 56

160 170 180

65 76 88

7 Conclusion

In this paper, the design process for the Kiira-

EV SMACK Hybrid powertrain has been demon-

strated. By using Design Space Exploration

techniques with a suitable component optimiza-

tion method, the Genetic algorithm , the design

choices were pruned without missing the optimum

values. These specifications are then used in a

simulation environment which gives a more realis-

tic idea about the impact of the choices on other

parameters like fuel efficiency.

The powertrain specifications for the desired

performance obtained from the optimization and

simulation are; motor power of 110 kW, DC bus

voltage of 259 VDC and AHA of 53 AH with a

generator of 88 kW for a top Speed of 180 km/hr.

Other parameters e.g. gear ratios are not exten-

sively discussed but were considered during the

entire design process. The off-the-shelf compo-

nents used in the final design were informed by

these specifications. A TM4 Motive series MO120

motor of peak power 80 kW was used in the final

drive with WB-LYP 40AH cells connected in se-

ries to give a mean DC bus voltage of 210 VDC.

A Remy HVH250-115-DOM motor of peak power

80 kW was used as the generator.

Future research shall focus on optimization of

the overall vehicle weight, vehicle volume, proto-

typing costs and control strategy.


9

Acknowledgement

The authors would like to thank the government of

the Republic of Uganda for supporting the Kiira-

EV research and development program.

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Authors

Richard Madanda has previ-

ously worked as a researcher pow-

ertrain and charging infrastruc-

ture at the Center for Research in

Transportation Technologies now

Kiira Motors Corporation. He

is currently pursuing Masters de-

gree in Embedded Systems at the

Eindhoven University of Technol-

ogy, the Netherlands.

Paul Isaac Musasizi has

worked on several projects includ-

ing, the Kiira-EV Project, Uganda

Police Force Crime Records Man-

agement Academic Records Man-

agement System for Universities

and Secondary Schools. He is

the Director of Engineering at the

Kiira-EV Project. He has BSc.

and MSc. in Mechanical Engineering from Makerere Uni-

versity, a Certificate in Radical Innovations from MIT,

USA, a Certificate in Managing Engineering and Technical

Professionals from SAE International Detroit, MI USA.

Pauline Korukundo is currently pursuing a Master of

Science in Electrical and Electronics Engineering at the

University of Nottingham. Her present research interests

are in rapid prototyping, control algorithms and perfor-

mance enhancement. She obtained a BSc. in Telecommu-

nications Engineering in 2012 from Makerere University.

She has worked in the Vehicle Electronics department of

the Center for Research in Transportation Technologies.

Arthur Tumusiime Asiimwe

is the Principal Electrical En-

gineer for the KIIRA Motors

Project. He holds a Master of

Science Degree in Electrical En-

gineering of Makerere University.

Arthur has also got professional

Certificates in Managing Engi-

neering and Technical Profession-

als by the SAE International Detroit, MI USA. Arthur has

taught at Makerere University since 2009, currently posted

to the Department of Electrical and Computer Engineer-

ing.

Sandy Stevens Tickodri-

Togboa is an Engineering Scien-

tist and Professor of Electrical and

Computer Engineering at Mak-

erere University, Uganda. He is

the Principal Investigator of the

Kiira-EV Project. He received his

PhD in Digital Communications

in 1985, MSc in Radio Engineer-

ing in 1979 and BSc in Electrical Engineering in 1973.

Junior Africa holds a Bach-

elors Degree in Electrical Engi-

neering from Makerere University

and Master’s Degree in Engineer-

ing Management from Kettering

University. He is a Powertrain

Systems Engineer at Kiira Motors

Project.


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EVS28 KINTEX, Korea, May 3-6, 2015 Design Space ... KINTEX, Korea, May 3-6, 2015 Design Space Exploration and Hybridization of the Kiira-EV SMACK R.Madanda1, P.I.Musasizi2, P.Korukundo3,