energy- and power-split management of dual energy storage

0018-9545 (c) 2016 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.

This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TVT.2016.2636282, IEEETransactions on Vehicular Technology

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 1

Abstract— Specific applications, such as recreational vehicles

require new developments with respect to their energy storage

system. Despite some recent trends in battery development, the

ratio between power and energy has not yet meet the

requirements of this specific kind of vehicles. This paper presents

the integration of a SuperCapacitors pack in a three-wheel

electric vehicle considering the energy- and power-split

management strategy. An energy management strategy based on

a comprehensive fuzzy logic controller approach is fully

addressed to increase the global efficiency and performance of

the studied vehicle. The proposed control and management

strategy ensures that the battery supplies the average portion of

the power demand, while the energy level of the SuperCapacitors

is smartly handled. The proposed strategy is easily adaptable to

other vehicles or different driving modes. The approach was

validated with a power-level hardware-in-the-loop platform for a

reduced-scale hybrid dual energy storage system. This

experimental test allows a real-time verification of the proposed

energy management and evaluates the ability to coordinate more

efficiently the energy flow. The proposed approach enhances the

battery lifetime by reducing the battery current root mean

square value by 12% compared to a battery-only architecture.

Index Terms—Three-wheel Electric Vehicle, Fully-active

Parallel Topology, Batteries, SuperCapacitors, Energy

Management System.

I. INTRODUCTION

LECTRIC VEHICLES (EVS) have several advantages over

vehicles with internal combustion engines, as they are

more energy efficient, more environmentally-friendly, quieter

and their use reduces energy dependence [1 - 3]. There are,

however, some battery-related challenges, such as driving

range, recharge time, battery cost and weight. There is

currently a lot of research, aiming at improving battery

technologies and architecture, as to increase the driving range

Manuscript received Mars 7, 2016; revised August 12; accepted xxx.

Copyright © 2016 IEEE. Personal use of this material is permitted. However, permission to use this material for any other purposes must be

obtained from the IEEE by sending a request to [email protected].

This work was supported in part by the by the Canada Research Chairs Program and the Natural Sciences and Engineering Research Council of

Canada.

J. P. F. Trovão and M. R. Dubois are with the Department of Electrical Engineering and Computer Engineering, Université de Sherbrooke,

Sherbrooke, QC, J1K 2R1, Canada. J. P. F. Trovão is also with the Institute

for Systems and Computers Engineering at Coimbra, Portugal (e-mail: [email protected]; [email protected]).

M.-A. Roux and É. Ménard are with Centre de Technologies Avancées,

CTA – BRP – UdeS, Sherbrooke, QC, J1K 0A5, Canada (e-mail: [email protected]; [email protected]).

and decrease the recharging time, weight, and cost [3].

Globally, the manufacturing of motorized recreational vehicles

produces a wide range of personal vehicles used primarily in

sporting or leisure activities on the water or on land [5].

Nonetheless, the shift of this specific type of vehicles to full

EVs is slow due to the consumers’ attraction for performance

combining wide driving range (feeling of freedom) and

aggressive riding (thrill feeling). However, electric propulsion

can still deliver a great potential to reinforce the aggressive

behavior of these vehicles [5]. Moreover, the widespread

adoption of EVs, comprising this particular segment, also

represents a source of opportunities for the energy utilities and

the automotive industry [6].

In pure EVs for recreational purposes, higher focus is

placed not only on specific power, related to acceleration and

climbing capability, but also to the life of the main Energy

Storage System (ESS). Batteries are the most suitable ESS for

EVs although they are limited in terms of power/energy

density, as presented in Fig. 1. Furthermore, batteries with

high power and energy density characteristics still have a high

cost [4]. Coupling batteries with other energy sources can

allow attaining better global performances [2, 4]. Thus, the

hybridization of different energy sources, such as batteries,

SuperCapacitors (SCs), flywheels and/or Fuel Cells (FCs) has

been studied [7 - 15]. At the current state of the technology,

the most common hybridization is the use of batteries, with

high specific energy, and SCs, with high specific power.

Therefore, hybrid topologies of energy storage systems,

including batteries and SCs, should be appropriately combined

to improve performance [4]. SCs-assisted EVs are attractive

alternatives to battery-only recreational vehicles. The

particular features of SCs allow energy to be stored and

released without any chemical reaction. Thus energy can

readily be absorbed and released with less losses. Moreover,

SCs are suited to instantaneously feed EVs under high power

demand requirements. Considering the different characteristics

of batteries and SCs, the batteries only need to meet the

average power requirement of the feeding system during the

EV driving, while the SCs are used to respond to high power

requests and load fluctuations. The comparison presented in

Ragone diagram of the Fig. 1 summarizes the power versus

energy characteristics of the most used energy sources in

vehicles [6]. This diagram shows the requirement to all-

electric vehicles and, in particular, to recreational products.

Energy- and Power-Split Management of Dual

Energy Storage System for a Three-Wheel

Electric Vehicle João P. Trovão, Member, IEEE, Marc-André Roux, Éric Ménard, Maxime R. Dubois, Member, IEEE

E




Fig. 1. Energy and power densities of different options of ESS in transportation systems (based on [6]).

In the literature, several kinds of coupling topologies (active

and passive) have already been proposed and evaluated [4, 7,

10, 16]. Passive coupling approaches are the lowest in cost

and no power converters are needed, but they have some

limited performance for road applications [4]. Instead, full-

active coupling approach can do the best use of the each ESS,

but has some restriction in size, cost, and complexity [4]. The

use of one DC/DC converter per ESS allows an improved

management of energy flows and keep a stable system

independently of the state-of-charge (SoC) in the energy

element [9 - 11]. The major challenge is the complexity of the

energy management algorithm, which needs to deal with the

EV power demand without compromising the vehicle’s

security and reliability.

Energy management for hybrid coupling of battery and SCs

have been widely investigated in recent literature (e.g. [7 -

22]). Algorithms based on the a priori knowledge of the

driving cycle have been successfully implemented [17, 18].

Nevertheless, the specification of recreational vehicle is not

suitable with the a priori known environments and power

requirements. Some real-time approaches have already been

depicted in the literature [7 - 11]. For instance, a switching

strategy and a frequency strategy were tested and validated in

real-time for hybrid-ESS using the same control scheme under

a hardware-in-the-loop (HIL) approach [10]. A simple and

well-known rule-based strategy (state machine control) is used

for a fuel-cell/battery hybrid system [8, 20]. Therefore, the

performance of this strategy also depends on the knowledge of

the operation of each element used in the feeding system. In

[21], a fuzzy logic supervisor was used for on-line energy

management of an EV power supply system using parallel

active topology. Also, a dynamic strategy to manage the

energy in a testbed heavy hybrid EV using a fuzzy logic

controller that considers the slow dynamics and the SoC of the

ESS is proposed in [11]. In [23], the authors compare an EMS

based on a frequency sharing and proposed both symmetric

and asymmetric frequency energy management to study the

gains in terms of system weight and life cycle analysis. Other

real-time management techniques are previously realized, such

as flatness control [24], optimal control [16, 25], model

predictive techniques [26, 27], stochastic dynamic

programming [28], and H-infinity control [29]. However,

some of them are very complex strategies, requiring large

computational resource, and are not designed to automotive

controller implementation. Regarding, mathematical

complexity and embarked suitability, fuzzy logic approaches

are well positioned, and they have widely been demonstrated

to be implemented in real-world applications [11].

Rule-based fuzzy logic controller can be easily set using

engineering expertise and adaptable to structural change of the

system. In energy management domain, fuzzy logic

approaches use real-time input parameters to compute near

optimal power splits. More recently, frequency decoupling and

fuzzy logic controller are proposed in [30]. The power demand

is filtered and used as an input of the controller. In [31], a

fuzzy logic supervisory wavelet-transform is developed to

manage the power between batteries and SCs.

As recognized in multi-source vehicles, energy management

problem is complex because it was simultaneously to control

the power flow regulating the ESSs’ SoC under several

constraints. As reactional vehicles have different patterns in

behavior and purposes, the motivation of this paper is

proposing an improved fuzzy logic controller strategy which

includes a battery current range control and easily adaptable

for different vehicles and driving’s modes. A filtering

technique integration is motivated by its complementary

features to introduce a decoupling-based frequency on the

power demand and strongly reduce the stress on the battery

current. This proposal provides the specific compromise

between computational simplicity, real-time operation and

efficiency maximization, and perfectly suitable for current

automotive microprocessors.

The end objective of this work is to develop an upgraded

version of a battery-only powered three-wheel vehicle

prototype, presented in Fig. 2. The addition of a SCs pack

combined to high specific energy batteries could be a first step

to address these issues. An initial study of this improvement

was presented in [32] and it is hereby extended using a

validated model based on energetic macroscopic

representation (EMR) of the three-wheel electric vehicle. The

model used has been instrumental in improving the inner-

control loop, introducing a low pass filter, and the energy

management. The proposed coupled strategy is truly validated

with a reduced-scale HIL simulation approach and full-scale

simulations under different test patterns in terms of initial

SCs’ SoC and maximum battery power for a real on road

driving cycle. A complete analysis is carried out in the paper,

with an assessment of battery-SCs hybridized topology

compared to the battery-only configuration using battery

current Root Mean Square (RMS) values.

The remainder of the paper is structured as follows. The

studied battery-only three-wheel vehicle characteristics, on-

road results and its validation with the real-world experimental

results are described in Section II. In Section III, the dual-ESS

for new vehicle configuration, the energy management

strategy framework and full-scale simulation results for real

on-road driving cycle are presented. Section IV introduces the

reduced-scale setup and experimental validation, while the

concluding remarks are outlined in Section V.




II. THREE-WHEEL ELECTRIC VEHICLE

The studied vehicle is a three-wheel electric vehicle

prototype, currently used as a workbench at the Centre de

Technologies Avancées - BRP - Université de Sherbrooke

(CTA-BRP-UdeS) for research and developments [5, 32]. The

original architecture of the EV powertrain under study is

presented in Fig. 2.

A. Original Configuration

For this prototype development, a 28 kW-96 V Permanent

Magnet Synchronous Motor (PMSM) was selected and is

directly connected to the rear wheel through a belt

transmission drive. The specifications of the vehicle are

presented in Table I.

TABLE I. PROTOTYPE SPECIFICATIONS

Variable Symbol Value Units

Vehicle mass (without driver and storage systems) 𝑚 350 kg

Typical rolling resistance coefficient 𝜇𝑟𝑟 0.02 -

Typical aerodynamic drag coefficient (with driver) 𝐶𝐷 0.75 -

Vehicle front area 𝐴𝑓 1.25 m2

Wheels radius 𝑟 0.305 m

Belt transmission drive ratio 𝐺𝑔𝑏 5.033

(30:151) -

Belt transmission drive efficiency 𝜂𝑔𝑏 95 %

The traction system is directly powered by a 96 V – 130 Ah

Li-Ion battery pack. The main characteristics of the used

battery pack based on Lithium Ion ICR C2 2800 mAh cells are

presented in Table II. The battery pack primary fed the

voltage-source-inverter (VSI). The three-wheel electric

vehicle is limited at the maximum speed of 140 km/h.

Before future improvements, a complete characterization of

the vehicle is mandatory. To evaluate the accuracy of the

proposed mathematical model proposed in [32], an on-road

test drive is described to completely characterize the vehicle’s

powertrain.

For that, the vehicle is instrumented with a precision eDAQ

acquisition system. The on-board acquisition system is placed

into the front vehicle trunk storage (see Fig. 2) and acquires

synchronously the data with a frequency rate of 10 Hz. The

data are acquired directly through the vehicle CAN bus and

battery management system.

TABLE II. CHARACTERISTICS OF THE BATTERY SYSTEM


Battery pack Power @2C 𝑃𝐵𝑎𝑡 [ - 11.2, 24.2 ] kW

Battery pack SoC Limits 𝑆𝑜𝐶𝐵𝑎𝑡 [ 0.2, 1 ] -

Min. cell open-circuit voltage 𝑉𝐵𝑎𝑡𝑂𝐶_𝑚𝑖𝑛 3.4 V

cell no-load voltage drop 𝛿𝐵𝑎𝑡 0.7 V

Max. cell open-circuit voltage 𝑉𝐵𝑎𝑡𝑂𝐶_𝑚𝑎𝑥 4.1 V

Number of cells in series 𝑁𝐵𝑎𝑡 27 -

Num. of cells bank in parallel 𝑛𝐵𝑎𝑡 45 -

Cell mass 𝑀𝐵𝑎𝑡 50 g

The considered test is composed of an extra urban part with

two hard acceleration (0 to 70 km/h in 8 s) and two hard

decelerations in order to exploit the specific particularity of a

reactional vehicle. The maximum speed is around the 90 km/h

and the total distance covered by the prototype is 30 km. The

driving cycle is performed with an altitude variation between

150 and 290 m. The slope rate is computed from altitude

information coming from the GPS data to further inclusion in

the EV model (see (4)). The complete driving cycle and the

altitude evolution are presented in Fig. 3.

The battery pack current, voltage and SoC were acquired by

the eDAQ system directly from the EV battery management

system, and are plotted in Fig. 4 (red lines). After, using the

voltage and the current values, the power demand to the

battery pack is computed and its trend is presented in the first

row of the same figure.

For further comparison, a per-unit (pu) representation is

selected to have the same base of system quantities as

fractions of the rated value of the motor-drive system: 96 V

(for voltage values); 28 kW (for power and current values).

This is a pronounced advantage in powertrain analysis where

large number of subsystems are connected to perform a more

global power system analysis and an upgrade is envisaged.

Thus, representation of elements in the system with pu values

become more uniform and there is more facility in comparison

of the system improvement.

As can be seen in Fig. 4, during the majority part of the

driving, the current of the battery pack is under 1C (0.87 pu)

even though in four occasions the current reaches values up to

2.5C. These current peaks induce voltage drops at the output

of the feeding system and these increase the losses in the

motor-drive.

Fig. 2. CTA battery-only electric three-wheel vehicle prototype: architecture and on-board acquisition system.




Fig. 3. Real driving cycle and altitude evolution for the EV prototype.

The variations on the speed references bring fast dynamics

in the power demanded to the battery pack that introduce

stress losses in the Li-Ion cells. The thermal effect of these

losses is typically followed by premature degradation and

aging of the battery pack. The SoC decreases linearly to

perform the considered displacement, using 29% of the total

energy capacity of the battery.

These on-road results are used as references to validate the

EV mathematical model herein used (see Fig. 4) and presented

in [32] (see Table VI of Appendix). In order to organize the

simulation and connect each sub-model, the EMR formalism

[10, 16] is used. EMR is a graphical description (see Fig. 15 of

Appendix) that can easily interconnect models of different

domain subsystems in a unified way. All subsystems are

connected according to interaction principle (i.e. action times

reaction variables is equal to transferred power) [10, 16].

Furthermore, EMR is based on the physical causality (i.e.

exclusive integral causality) [10, 16]; that allows a better

understanding of the physical power flows (fundamental in

energy management developments). Only four energetic

elements are sufficient to describe the system (energy source,

energy conversion, energy accumulation and energy

distribution), resulting into a more global synthetic power

flows view. Moreover, the control loops can be systematically

deduced from the EMR of the model [10]. EMR approach has

been successfully used for various EVs and HEVs, from

simulation to real-time control, both in experimental set-ups

and prototypes [10, 11, 16, 32, 35]. In this work EMR is used

firstly, to organize a valid model of battery-only and dual-ESS

three-wheel electric vehicle. Secondly, to develop the control

organization of the global models and better design the

improved energy management strategy. Finally, it is also used

in comparison of on-road results and simulation results for the

on-road driving cycle.

B. Simulation Result and Model Validation

A comparative study of the real and mathematical model

simulation is done and presented in Fig. 5. The proposed

model of the battery-only three-wheel electric vehicle (see Fig.

4) was developed using EMR approach and based on the

model previously presented in [32] (see Table V and Table VI

of Appendix), and implemented in Matlab/Simulink

environment.

The used simulation parameters are given in Tables I and II.

The same variables are plotted in the same graphs of Fig. 5.

As the simulation uses the real driving cycle (Fig. 3) as speed

reference (red line), the first row presents the speed reference

and the vehicle speed simulation results (blue line).

Fig. 4. EMR and inversion based control for battery-only three-wheel electric

vehicle.

As can be seen, the vehicle speed follow perfectly the

reference and that shows the correct tuning of the developed

speed control loop.

Regarding the other variables, the voltage evolution during

the simulations performed is absolutely in line with the

experimental results and presents a maximum error of 1.9% on

the presented driving cycle. For the current, we have exactly

the same pattern and does not perfectly fit with the data

collected in the hard current demands and, also, due to

presence of same sensor’s noise. As the power is computed

using the voltage and current signal, the pattern follow

correctly the experimental on-road results, and the maximum

errors are identified in the same points. Finally, the

experimental and simulated SoC evolution have the same

decrease on the studied driving cycle, using precisely the same

energy to perform the requested displacement (Fig. 5).

Considering the presented simulation results, the main

variables follow correctly the evolutions of the experimental

results. The very small error between the simulation and

experimental results indicate that the proposed model, based

on EMR approach (Fig. 4), meets all the steady-state and

transient behaviors of the real studied EV prototype. Hence, it

can be deduced that, the proposed model can be considered

accurately tuned for power and energy study purposes and can

used to extend the work to include a SCs pack into a global

ESS in order to increase the performances of the EV under

study.

Fig. 5. Experimental (red lines) and simulation (blue lines) results for the

battery-only three-wheel electric vehicle prototype during the considered real

driving cycle.

III. DUAL ENERGY STORAGE SYSTEM CONFIGURATION

In the three-wheel vehicle application, space is critical and

the use of Li-ion batteries with high specific energy is




necessary to reach higher ranges. The purpose of the SCs is to

provide the electrical power that the battery pack is unable to

deliver in many acceleration scenarios. In this sense, the

integration topology that allows the highest degree of freedom

is the fully-decoupled configuration [4, 9, 10]. However, the

control of this topology is complex and may be different,

depending on the vehicle operating modes [9, 10], hence

requiring a robust energy management strategy. Then, the

combination of the two selected ESS, under fully-decoupled

configuration, is performed by two DC/DC converters for

battery and SCs packs, respectively. The input voltage of each

DC/DC converter can therefore assume different values, being

its output adjusted based on the shared DC bus voltage value

[7]. Although other suitable topologies have previously been

discussed in the literature, the fully-decoupled configuration

has been chosen in the paper. This configuration has the

ability to maintain the DC bus voltage close to its nominal

value and reduce the losses in the motor-drive [33]. This

topology is presented in Fig. 6.

Fig. 6. A dual-ESS (batteries and SCs) connection using a fully-decoupled

topology.

A. Electric Vehicle Model Upgrade

Regarding the model upgrade of the three-wheel electric

vehicle to include the hybrid ESS, several parts should be

adapted as presented in Table VI of Appendix Section [32].

The upgraded model of the vehicle is presented in Fig. 7.

Fig. 7. EMR and control of the dual-ESS three-wheel vehicle.

As a result of EMR organization of the model, 𝐾𝐷 defines

the part of the total current that will be imposed to each

source, and that will control the DC voltage. The strategy

defined has to provide continuously changing values of 𝐾𝐷

during the vehicle’s journeys.

To minimize stresses on the battery pack, two compensating

elements are introduced at the control level in the definition of

the current reference: a low-pass filter (𝜏) and saturation

limiters to prevent excessive discharge and charge currents

based on the maximum battery current pack (𝐼𝐵𝑎𝑡𝑚𝑎𝑥). The

resulting expression of saturation and low-pass filter usage in

the reference current 𝑖𝐵𝑎𝑡_r′ computation is given by:

𝑖𝐵𝑎𝑡_r′ = max(−

1

2𝑚𝐵𝑎𝑡𝐼𝐵𝑎𝑡

𝑚𝑎𝑥, min(𝑚𝐵𝑎𝑡𝐼𝐵𝑎𝑡𝑚𝑎𝑥,

𝑖𝐵𝑎𝑡_r′∗

𝜏. 𝑠 + 1 )) (1)

and the ESS current references are computed using (16).

In summary, the inner-control loop of the hybrid feeding

system requires three controllers and a distribution ratio,

keeping the traction system part as presented in Section II

(Fig. 4). The distribution ratio has to be defined by a strategy

level in order to distribute on-line the power from the batteries

and SCs. The strategy approach is presented in next section.

B. Energy Management Strategy

The energy management problem consists of an on-line

search of an energy flow leading to the optimal use of the

battery stored energy, while keeping SCs SoC within

acceptable range. The energy management strategy needs to

consider the available energy of both storage elements and

instantaneously determines the power contribution between

the two sources. The inversion-based control described

previously is able to receive references for different energy

strategies [10]. Using a high-level macroscopic energy view

for the studied EV, as presented in Fig. 8, where each storage

element feed perfectly the power reference computed by the

strategy algorithm. The power flows between the different

sources (environment, batteries and SCs) are highlighted and

allow conveniently the definition of the membership functions

of the proposed fuzzy logic controller. For the high-level

representation a DC bus constant voltage is assumed, and the

current state variable can be directly deduced from the power

references computed to each sources.

Fig. 8. High-level macroscopic energy view for the studied EV.

Fuzzy logic systems provide several strategy laws using

linguistic labels in a simpler way. This approach has been used

in several other works to manage the power demand in hybrid

electric vehicles [11, 21, 30, 31, 34]. As the behavior of

exogenous variables like route slope, traffic, user

requirements, weather, etc. are unknown. Hence, the fuzzy

logic approach can be a suitable method for the energy

management of such systems. One of the main aspects

involved in fuzzy-rule based energy management approaches

are their effectiveness in real-time supervisory control of

power and energy sharing [30, 31, 34]. The fuzzy-rules are

supported by heuristics, engineering expertise and even

models without a prior knowledge about the future power

demands. The rules are typically easy to understand and

modify by simple expressions and could be adaptable to




different vehicles or driving’s dynamics. Finally, the fuzzy

logic systems are quite appreciated by the industry. Due to

these reasons, the fuzzy approach has been adopted to manage

the hybrid ESS in this study. The global fuzzy logic system is

presented in Fig. 9 a). The overall idea of the implemented

energy strategy is that the batteries will supply the propulsion

power in steady state operations. For this reason, one input of

the fuzzy logic controller is the ratio between the powers

demanded by the powertrain 𝑃𝐷𝐶_𝑏𝑢𝑠 and the rated power that

the batteries could provide to the DC bus (see Fig. 9. a)). For

an effective SCs operation, the energy strategy should regulate

the SCs SoC. The second input of the fuzzy controller is the

estimated value of SCs SoC (see Fig. 9. a)).

Fig. 9. a) Fuzzy logic strategy scheme; b) 𝐾𝐷 Output SCs contributions.

The fuzzy controller therefore converts two clearly defined

inputs into an output using a normalized fuzzy logic strategy.

Typically, fuzzy logic systems are designed form experience

and the use of corresponding rules allow finding the

membership function parameters. Herein, the rules are based

on the truly analysis of the high-level EMR presented in Fig.

8. For the management point of view, the variable that should

be controlled is the SCs’ energy, represented here by its SoC.

The variable that should be restricted is the battery current. In

the proposed approach, this restriction is evaluated by the ratio

between the DC bus power demand (𝑃𝐷𝐶_𝐵𝑢𝑠) and the capacity

of the battery (𝑃𝐵𝑎𝑡𝑚𝑎𝑥) to supply this demand. These two

variables (𝑆𝑜𝐶𝑆𝐶𝑠 and 𝑃𝐷𝐶_𝐵𝑢𝑠 𝑃𝐵𝑎𝑡𝑚𝑎𝑥⁄ ) are used as inputs of

fuzzy logic controller associated to two memberships

functions presented in Fig. 10. For better evaluation purpose,

the fuzzy membership functions presented in Fig. 10, are

validated by extended simulation processes, and the output

SCs contributions are illustrated in Fig. 9 b). The distribution

ratio, 𝐾𝐷, has been defined by (2):

𝐾𝐷 = 𝑓(𝑆𝑜𝐶𝑆𝐶𝑠, 𝑃𝐷𝐶_𝐵𝑢𝑠 𝑃𝐵𝑎𝑡𝑚𝑎𝑥⁄ ) (2)

The fuzzy controller output (a relative change of SCs

contributions) is computed based on the rated limit of the

battery pack power (𝑃𝐵𝑎𝑡𝑚𝑎𝑥, defined according to the battery

capacity evolution) and the SCs SoC. The charging and

discharging rates will depend on 𝑃𝐷𝐶_𝐵𝑢𝑠 𝑃𝐵𝑎𝑡𝑚𝑎𝑥⁄ and 𝑆𝑜𝐶𝑆𝐶𝑠.

On the one hand a lower value of 𝑆𝑜𝐶𝑆𝐶𝑠 will originate higher

charge rates, but on the other hand a higher 𝑆𝑜𝐶𝑆𝐶𝑠 levels will

result in higher discharge rates.

Fig. 10. Mamdani-type membership functions (based on [34]): input

𝑃𝐷𝐶_𝐵𝑢𝑠 𝑃𝑏𝑎𝑡𝑚𝑎𝑥⁄ (7), input 𝑆𝑜𝐶𝑆𝐶𝑠 (6), and output SCs Contributions (7).

For instance, if 𝑃𝐷𝐶_𝐵𝑢𝑠 𝑃𝐵𝑎𝑡𝑚𝑎𝑥⁄ is negative, the powertrain

operates in regenerative braking mode and if the 𝑆𝑜𝐶𝑆𝐶𝑠 is

lower than a defined threshold, the fuzzy controller induces a

𝐾𝐷 that tends to zero, meaning that all the braking energy will

be absorbed by the SCs. However, if the electric braking phase

persists, the 𝑆𝑜𝐶𝑆𝐶𝑠 may reach a higher level. When it is close

to the threshold value, 𝐾𝐷 is raised to positive values in order

to decrease the quantity of energy going to the SCs, to prevent

an “over voltage”. When 𝑃𝐷𝐶_𝐵𝑢𝑠 𝑃𝐵𝑎𝑡𝑚𝑎𝑥⁄ is in “veryLow”,

“Low” and “Normal” regions, if 𝑆𝑜𝐶𝑆𝐶𝑠 is lower than

“Optimal”, the output membership functions induce a

controlled charge of the SCs (negatives 𝐾𝐷) to reach the

designed threshold value. But, if the 𝑆𝑜𝐶𝑆𝐶𝑠 is higher than

“Optimal”, the output membership functions tend to discharge

the SCs (positives 𝐾𝐷) and return their state to the threshold

value. For higher positive values of 𝑃𝐷𝐶_𝐵𝑢𝑠 𝑃𝐵𝑎𝑡𝑚𝑎𝑥⁄ the SCs

should assist the batteries to supply the power demanded by

the powertrain, until the 𝑆𝑜𝐶𝑆𝐶𝑠 does not reach lowers values

of energy.

C. Simulation Results

The simulation are performed in Matlab/Simulink

environment and PI controllers are used to regulate the ESS

currents, voltage and vehicle speed. The PI controllers are

tuned using the procedure presented in Table V of Appendix.

For better evaluation and comparison to the battery-only

configuration, the driving cycle used for this simulation test is

the real driving cycle presented in Fig. 3. The hybrid

configuration of the ESS has a new battery pack design

operating at 72 V and 224 A@2C and a SCs pack with a rated

voltage of 85.5 V. Their characteristics are shown in Table III.

The SCs and the battery packs are connected to the common

DC bus rated to 108 V with 𝐶 is set to 10 mF. The inductors of

the DC/DC converters are fixed to 𝑅𝐿𝐵𝑎𝑡 = 1.82 mΩ, 𝑅𝐿𝑆𝐶𝑠 =

0.32 mΩ, and 𝐿𝐵𝑎𝑡 = 𝐿𝑆𝐶𝑠 = 1.35 mH. The initial batteries’

SoC is set to 85% for all studied cases presented in Fig. 11: a)

𝑆𝑜𝐶𝑆𝐶𝑠 = 100% and 𝑃𝐵𝑎𝑡𝑚𝑎𝑥 = 100%; b) 𝑆𝑜𝐶𝑆𝐶𝑠 = 75% and

𝑃𝐵𝑎𝑡𝑚𝑎𝑥 = 80%; c) 𝑆𝑜𝐶𝑆𝐶𝑠 = 87.5% and 𝑃𝐵𝑎𝑡

𝑚𝑎𝑥 = 80%.




TABLE III. CHARACTERISTICS OF THE ENERGY STORAGE SYSTEMS


Battery Pack Configuration (Lithium Ion ICR C2 2800 mAh cells)

Battery pack Power@2C 𝑃𝐵𝑎𝑡 [ - 8.1, 16.2 ] kW

Number of cells in series 𝑁𝐵𝑎𝑡 20 -

Num. of cells bank in parallel 𝑛𝐵𝑎𝑡 40 -

Supercapacitors (MAXWELL BC series BCAP0350 cells)

SCs pack Capacitance 𝐶𝑎𝑝𝑆𝐶 175 F

SCs pack Power 𝑃𝑆𝐶 [- 123.8, 123.8 ] kW

SCs pack SoC Limits 𝑆𝑜𝐶𝑆𝐶 [ 0.5, 1 ] -

Min. SCs open-circuit voltage 𝑉𝑆𝐶𝑂𝐶_𝑚𝑖𝑛 0 V

SCs no-load voltage drop 𝛿𝑆𝐶 2.85 V

SCs pack operation range 𝑉𝑆𝐶𝑂𝐶 [ 42.8, 85.5 ] V

Number of SCs module in series 𝑁𝑆𝐶 30 -

Num. of SCs bank in parallel 𝑛𝑆𝐶 15 -

SCs module Mass 𝑀𝑆𝐶 60 g

The 𝑣𝐷𝐶 stability and the evolution of the currents regarding

their references (Fig. 11), show the reliability of the

controllers. Analyzing the power demands from the batteries

and SCs, the lower frequency of the battery current demands

can be seen when compared to the SCs currents. This result

demonstrates that the strategy and decision making algorithm

of the EMS is well tuned.

Analyzing the results of Fig. 11, several general comments

can be made. The batteries currents were kept below 2C rate

when discharging. Higher dynamic transitions were observed

in the SCs in cases of higher power demanded by the

powertrain. The energy management algorithm induces a

behavior that allows the SCs SoC to return to values that can

help in further acceleration phases. During the deceleration

phases, all the power flows to the SCs in order to recharge

them, which demonstrates the importance of the dynamic

control of the SoC level.

In Fig. 11, different behavior of the SCs can be observed

during the first displacement of the vehicle. In Fig. 11 a), the

fully charged assumption at the beginning leads the EMS to

induce their discharge to better prepare possible next operation

(deceleration). In Fig. 11 b), as the SCs’ energy level is around

the stipulated threshold value, the batteries give the total

power without the higher frequency transition and power

demands (guaranteed by the SCs). In Fig. 11 c), as the SCs

SoC is higher, the SCs contribute more for the average power

than the previous case but less than the first.

Globally, when the SCs SoC is near its threshold, the

batteries fed the system up to their nominal power while the

SCs provide the rest. Finally, as the SoC is under its threshold

value (due to the previous displacement), the energy

management algorithm induces to SCs a controlled recharging

process through the batteries in order to reach an acceptable

energy level.

Considering the variation of the maximum power of the

batteries, the EMS permits a higher supplied average power to

the system when considered higher limits, reducing the SCs

contribution on the overall cycle.

Fig. 11 b) shows that the SoC SCs increases to a maximum

value near 520 s, due to the EMS action on the SoC to be

brought back to values up to 85%, without compromising the

DC bus voltage stabilization (second row). This means that the

SCs SoC reaches acceptable values to give its contribution to

the global feeding system. Near 530 s, one of the highest

power demand occurs and a well-balanced contribution of

each source is regulated by the energy management algorithm.

The proposed algorithm was able to determine the adequate

energy- and power-split of the dual-ESS regarding a

controllable return to an acceptable level of the SCs SoC, with

no prior knowledge of the power demanded by the driving

cycle.

Fig. 11. Simulation results of the dual-ESS three-wheel electric vehicle for real driving cycle: a) 𝑆𝑜𝐶𝑆𝐶𝑠 = 100% and 𝑃𝐵𝑎𝑡

𝑚𝑎𝑥 = 100%; b) 𝑆𝑜𝐶𝑆𝐶𝑠 = 75% and

𝑃𝐵𝑎𝑡𝑚𝑎𝑥 = 80%; c) 𝑆𝑜𝐶𝑆𝐶𝑠 = 87.5% and 𝑃𝐵𝑎𝑡

𝑚𝑎𝑥 = 80%.




The results present a variation of 30% of the SoC for the

batteries, but the embarked energy is lower than the original

by 12% (to introduce the SCs pack in the same space). Thus,

the presented variation correspond to an effective variation of

26% regarding the original configuration stored energy.

Therefore, the proposed architecture and energy management

strategy brings a direct reduction of 3% of energy on the

studied real driving cycle. The saved energy increases with the

active usage of the SCs pack, which is the case for more urban

driving cycles.

The battery current RMS value is typically used as an

indirect method to quantify aging impact [16]. For lower RMS

values, batteries suffer less from high current magnitudes,

reducing SoC variations and heating. The battery current RMS

value for 𝑁 sampling is computed by:

𝐼𝐵𝑎𝑡𝑅𝑀𝑆 = √

1

𝑁∑ |𝐼𝐵𝑎𝑡𝑛|

2𝑁

𝑛=1 (3)

The computed battery current RMS values for all the driving

cycles tests are depicted in Fig. 12. The battery current RMS

value obtained with fuzzy logic approach is about 12 %

smaller than in the battery-only configuration for the same real

driving cycles.

Fig. 12. Battery current RMS values for different configuration.

IV. EXPERIMENTAL VALIDATION

After the full-scale simulation of the studied three-wheel

electric vehicle, the next step is to validate the hybrid feeding

system control loop and the proposed energy management

algorithm using a real-time controller approach. For that, HIL

simulation is used to introduce the real constraints of hardware

devices in the loop. In our case, battery, SCs pack systems,

and common DC bus should be evaluated using an emulation

of the traction system. For this purpose an EV reduced-scale

HIL platform is implemented based on power-level approach

[35].

A. Power-Level Hardware-in-the-Loop Simulator

The main goal of the power-level HIL simulation is to test

the dual-ESS and the validation of the feeding system control

loop including the energy management strategy, before their

implementation on the on-road prototype. This intermediate

step is used to validate the behavior of the proposed energy

storage system hybridization. Considering the full-scale

system simulation approach, the hybrid feeding system and the

traction motor-drive are substituted in the simulation by a

reduced-power system using a power adaptation strategy [35].

Thus, the reduced-scale simulation models of the power

system (energy sources and traction motor-drive) are replaced

by the real system as presented in Fig. 13.

For this, the power-level HIL platform is configured to a

scale of 1:36 regarding the full-scale system. The common DC

bus was set to two-time lower than its nominal value 108 V

and then set to 54 V. The maximum power demanded by the

power system is 1 kW. The reduced-scale setup, illustrated in

Fig. 13, comprises two types of energy storage elements: i) a

battery pack composed of two Yuasa VRLA Battery modules

of (12 V, 10 Ah) in series, and ii) a SCs pack consisting of two

branches in parallel, each one consisting of a series of 18 cells

(each cells with 100 F, 2.7 V and 17 A), manufactured by

Nesscap.

Fig. 13. Reduced-scale power-level HIL platform.

A second battery pack is used to emulate the (reduced)

powertrain load requested by the process emulation model

[35], using an arm of the INFINEON IGBT stack as a

bidirectional DC/DC converter to follow the reference of the

power demanded by the traction part of the system. This

battery pack are based on three branches in parallel of three

NiMH Saft modules in series. The controller board simulates

in real-time the model of the traction system in order to

calculate the DC bus power reference. A control loop of the

current inductor for the emulation system is also used.

To connect each storage element to the common DC bus, a

bidirectional DC/DC converter is employed, whose power

electronics were accomplished by the other two arms of the

IGBT stack, switching at 20 kHz. The smoothing inductances

(1.35 mH) are connected to the low-side of the DC/DC

converters. On the common DC bus, a filter capacitor was

used (3x3300 F). The control strategy was implemented

using National Instruments software and hardware, namely,

LabviewRT and cRIO real-time system [9, 35].

B. Experimental Results

Experimental results for the first portion (t∈[0,520] s) of the

real driving cycle using the reduced-scale power-level HIL

simulation approach are presented in Fig. 14. The test starts

with an initial storage elements’ SoC of 80% for the SCs and

83% for the batteries. In this portion, the load profile has only

two different parts, accelerations and deceleration of the three-

wheel electric vehicle, with direct impact on the behavior of

the energy storage elements. As the battery pack is the main




energy storage element in the feeding system, the SCs should

store and release energy as a function of these different EV

operations. During the first acceleration (0 to 67 km/h in 8 s),

a hard power demand occurs and the current demanded by the

powertrain varies between 0 to 1.5 pu during the same period.

After, a soft acceleration occurs to 90 km/h in 60 s, and the

batteries discharge at a constant rate and the SCs are in charge

with the higher dynamics.

As the SCs pack discharges during this first acceleration

phase, its SoC decreases. During the next 180 s a soft

deceleration happens (not enough to produce braking energy)

and the contribution of the battery could be reduced, but as the

SCs SoC is under its threshold value, the energy management

algorithm induces a controlled charge of the SCs, transferring

energy form the batteries to the SCs, in order to prepare the

feeding system to next hard acceleration. On the next 200 s a

new soft acceleration phase occurs, where the power

demanded to the feeding system is under the nominal power of

the battery pack and the SCs keep under recharge to reach a

correct value to help the system to feed higher power

demands. That happens near to 380 s and to 400 s, where the

vehicle reach 100 km/h, and as the SCs have recovered of

lower SoC values, they can correctly help the battery to feed

the powertrain and keep the battery under its nominal current

value. Again the SCs discharge after these two hard power

demands and the SoC decreases. As it can be seen at the end

of the presented results, the energy management system will

readjust the SCs SoC to correct levels in order to meet new

and more demanding power requests.

Fig. 14. Experimental results using reduced-scale power level HIL platform.

As is shown in Fig. 14, throughout the analyzed period, the

batteries give a constant power to the powertrain while the

SCs compensate the most hard power transition. That is a

benefice to the thermal behavior of the battery pack and has a

positive impact in the aging process of the battery cells.

Finally, on overall cycle, the DC bus voltage is kept very close

to the reference value (56 V).

Furthermore, these results are coherent with the ones

obtained by full-scale simulation, showing that the inner-

control loop and the proposed energy management strategy

attempt to maximize the role of the SCs, transferring energy

from the batteries to the SCs during the lower power requests

of the powertrain system. This strategy enables to readjust

dynamically, without compromising the vehicle operation, the

energy level at the SCs pack on the vehicle journeys. The

results also demonstrate the performance of the proposed

energy management strategy and show its capability for future

full-scale implementation.

V. CONCLUSION

In this paper, an implementation of a dual-ESS system

(batteries and SCs) for recreational electric vehicle based on

fully-decoupled topology has been proposed. A global model

was developed in Matlab/Simulink environment using EMR

approach, starting with a battery-only model extended to the

hybrid configuration. The original configuration was validated

with on-road experimental results. The inner-control loop of

the dual-ESS and an energy management strategy were

proposed to an intelligent sharing of the energy and power

between batteries and SCs. Simulation results showed that the

proposed topology coupled to a fuzzy logic energy

management strategy introduces a size-reduction of the battery

pack comparatively to battery-only configuration, with an

effective decrease of energy consumption of 3% on the studied

on-road driving cycle. This reduction would be larger if the

characteristics of the driving cycle is more urban, with lot of

starts and stops. The use of the SCs has enabled a smooth and

reduced current request to the batteries. The proposed

approach reduced 12% the battery current RMS value

comparatively to battery-only vehicle, which might help to

increase the life time and a lower cost of the battery pack.

Finally, the experiments for fully-decoupled topology were

conducted under a reduced-scale of the real on-road driving

cycle. The results demonstrated that the inner-control loop was

perfectly tuned to meet the demands of the proposed hybrid

architecture using a fully-decoupled topology of two different

ESSs. The experimental results have revealed very good

performances on following the fuzzy logic EMS references,

when subject to a wide range of vehicle operating conditions,

and show its capability for future full-scale implementation.

APPENDIX

Fig. 15 shows a summary of the EMR pictograms.

Fig. 15. EMR pictograms.

Table IV presents the list of variables. Table VI summarizes

the electric three-wheel vehicle model used on the simulation

section.




TABLE IV. LIST OF VARIABLES

Variable Definition

Powertrain System

𝜃 route slope angle

𝑣𝐸𝑉 vehicle speed

𝐹𝑡𝑟 total traction force

𝐹𝑒𝑛𝑣 traction force resistance

𝑀𝑒𝑞 equivalent mass of the vehicle

𝑇𝑒𝑚 electric motor torque

𝛺𝑚 rotor rotation speed

𝐺𝑔𝑏 fixed gear ratio

𝜂𝑔𝑏 efficiency of the gearbox

𝜂𝑚 motor-drive efficiency

𝑖𝑡 traction current

𝐶𝑆 speed controller

DC Bus

𝐶 capacitance of the capacitor

𝑣𝐷𝐶 capacitor voltage

𝑖𝐶 capacitor current

𝑖𝐸𝑆𝑆′ sum of the sources’ currents

𝐶𝑣 voltage controller

DC/DC Converters

𝑣𝑐ℎ_𝑗 input voltage

𝑖𝑐ℎ_𝑗 output current

𝑚𝑗 modulation ratio

𝜂𝐶𝑜𝑛𝑣 converter efficiency

𝐿𝑗 converter inductance

𝑅𝐿𝑗 converter resistance

𝐶𝐼𝑗 current controller

Subscripts

𝑗 ∈ 𝐵𝑎𝑡; 𝑆𝐶𝑠 relate the variables to batteries or supercapacitors

_𝑟 reference of the variable

TABLE V. PI CONTROLLER TRANSFER FUNCTIONS AND GAINS

Transfer Function 𝑲𝒑 gain 𝑲𝒊 gain

Control Variable: 𝒗𝒆𝒗

𝐾𝑖𝑀𝑒𝑞

𝑠2 + 𝑠𝐾𝑝𝑀𝑒𝑞

+𝐾𝑖𝑀𝑒𝑞

(1 + 𝑠𝐾𝑝𝐾𝑖) 2𝜁𝜔𝑛𝑀𝑒𝑞 𝜔𝑛

2𝑀𝑒𝑞

Control Variable: 𝒗𝑫𝑪

𝐾𝑖𝐶

𝑠2 + 𝑠𝐾𝑝𝐶+𝐾𝑖𝐶

(1 + 𝑠𝐾𝑝𝐾𝑖) 2𝜁𝜔𝑛𝐶 𝜔𝑛

2𝐶

Control Variable: 𝒊𝒋

𝐾𝑖𝐿𝑗

𝑠2 + 𝑠(𝑅𝐿𝑗𝐿𝑗+𝐾𝑝𝐿𝑗) +

𝐾𝑖𝐿𝑗

(1 + 𝑠𝐾𝑝𝐾𝑖) 2𝜁𝜔𝑛𝐿𝑗 −𝑅𝐿𝑗 𝜔𝑛

2𝐿𝑗

Damping ratio: 𝜁 = 1 and the natural frequency: 𝜔𝑛 = 4.744 𝑟𝑎𝑑/𝑠.

ACKNOWLEDGMENT

The authors would like to thank Mr. Mário Silva from IPC-

ISEC, Portugal, for his help in setting up the power-level HIL

platform used in the experimental tests.

REFERENCES

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[2] C. C. Chan, A. Bouscayrol, and K. Chen, “Electric, hybrid, and fuel-cell

vehicles: Architectures and modeling,” IEEE Trans. Veh. Technol., 59(2), 589–598, Feb. 2010.

[3] Paulo G. Pereirinha and Joao P. Trovao (2012). Multiple Energy Sources

Hybridization: The Future of Electric Vehicles?, New Generation of

Electric Vehicles, Prof. Zoran Stevic (Ed.), InTech.

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Energy Storage Systems for Electric, Hybrid Electric, Fuel Cell, and Plug-In Hybrid Electric Vehicles: State of the Art”, IEEE Trans. Veh.

Tec., 59(6), 2806-2814, Jul. 2010.

Table V summarizes the applicable transfer functions and PI

gains. The gains of the voltage controller depend up on the DC

bus capacitor (𝐶) and the current PIs on each inductor (𝐿𝑗 with

𝑗 ∈ 𝐵𝑎𝑡, 𝑆𝐶𝑠).

TABLE VI. MATHEMATICAL MODEL OF ELECTRIC THREE-WHEEL VEHICLE

System Equation (#)

Powertrain System Model

Road 𝐹𝑒𝑛𝑣 = 𝑀𝑒𝑞 𝑔 𝜇𝑟𝑟 𝑐𝑜𝑠(𝜃) +

1

2𝜌 𝐴𝑓 𝐶𝑑𝑣𝐸𝑉

2

+𝑀𝑒𝑞 𝑔 𝑠𝑖𝑛(𝜃) (4)

Chassis 𝑀𝑒𝑞𝑑

𝑑𝑡𝑣𝐸𝑉 = 𝐹𝑡𝑟 − 𝐹𝑒𝑛𝑣 (5)

Gearbox 𝐹𝑡𝑟 =

𝐺𝑔𝑏

𝑟𝑇𝑒𝑚𝜂𝑔𝑏

𝛽

𝛺𝑚 =𝐺𝑔𝑏

𝑟𝑣𝐸𝑉

, with 𝛽 = 1, 𝑓𝑜𝑟 𝑃𝑚𝑒𝑐 ≥ 0𝛽 = −1, 𝑓𝑜𝑟 𝑃𝑚𝑒𝑐 < 0

(6)

Motor-Drive

𝑇𝑒𝑚 = 𝑇𝑒𝑚_𝑟

𝑖𝑡 =𝑇𝑒𝑚𝛺𝑚𝜂𝑚

𝛽

𝑣𝐷𝐶

(7)

Powertrain System Control

Speed Controller 𝐹𝑡𝑟_𝑟 = (𝑣𝐸𝑉𝑟 − 𝑣𝐸𝑉)𝐶𝑆(𝑡) + 𝐹𝑒𝑛𝑣 (8)

Gearbox 𝑇𝑔𝑏_𝑟 =𝑟

𝐺𝑔𝑏𝐹𝑡𝑟_𝑟 (9)

Motor-Drive 𝑇𝑒𝑚_𝑟 = 𝑇𝑔𝑏_𝑟 (10)

DC bus

Bus Capacitor 𝐶𝑑𝑣𝐷𝐶𝑑𝑡

= 𝑖𝐸𝑆𝑆′ − 𝑖𝑡 (11)

Distribution Element

𝑖𝐸𝑆𝑆′ = ∑ 𝑖𝑗

𝑗∈𝐵𝑎𝑡;𝑆𝐶

(12)

DC/DC Converters

Conversion

Element

𝑣𝑐ℎ_𝑗 = 𝑚𝑗𝑣𝐷𝐶

𝑖𝑐ℎ_𝑗 = 𝑚𝑗𝑖𝑗𝜂𝐶𝑜𝑛𝑣𝛽 , with 𝑚𝑗 ∈ 0, 1

and 𝛽 = 1, 𝑓𝑜𝑟 𝑃𝐶𝑜𝑛𝑣 ≥ 0𝛽 = −1, 𝑓𝑜𝑟 𝑃𝐶𝑜𝑛𝑣 < 0

(13)

Inductor 𝐿𝑗𝑑

𝑑𝑡𝑖𝑗 = 𝑣𝑗 − 𝑣𝑐ℎ𝑗 − 𝑅𝐿𝑗𝑖𝑗 (14)

Hybrid Feeding System Control

Voltage

Controller 𝑖𝐶_𝑟 = (𝑣𝐷𝐶_𝑟 − 𝑣𝐷𝐶)𝐶𝑉(𝑡) (15)

Distribution Element

𝑖𝐸𝑆𝑆_𝑟′ = 𝑖𝑡 − 𝑖𝐶_𝑟

𝑖𝐵𝑎𝑡_𝑟 =1

𝑚𝐵𝑎𝑡(𝐾𝐷𝑖𝐸𝑆𝑆_𝑟

′⏟ 𝑖𝐵𝑎𝑡_𝑟′

)

𝑖𝑆𝐶𝑠_𝑟 =1

𝑚𝑆𝐶𝑠(𝑖𝐸𝑆𝑆_𝑟′ − 𝑖𝐵𝑎𝑡_𝑟

′⏟

𝑖𝑆𝐶𝑠_𝑟′

)

, with 𝐾𝐷 ∈ [−1.5, 1.5] (16)

Current

Controller 𝑣𝑐ℎ_𝑗_𝑟 = 𝑣𝑗 − (𝑖𝑗_𝑟 − 𝑖𝑗)𝐶𝐼𝑗(𝑡) (17)

Conversion Element

𝑚𝑗 =𝑣𝑐ℎ_𝑗_𝑟

𝑣𝐷𝐶 (18)

Energy Storage Systems

Voltage 𝑣𝑗(𝑡) = [𝑉𝑗𝑂𝐶_𝑚𝑖𝑛 + 𝛿𝑗 ∙ 𝑆𝑜𝐶𝑗(𝑡)] − 𝑅𝑗𝑖𝑗(𝑡) (19)

SoC 𝑆𝑜𝐶𝑗(𝑡) =𝑄𝑗(𝑡−∆𝑡)−∫ 𝑖𝑗(𝑡)∙𝑑𝑡

∆𝑡0

𝑄𝑗𝑟𝑒𝑓 (20)

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for the energy management of a parallel plug-in hybrid electric vehicle," IET Intel. Transp. Syst., 9(1), 30-37, Fev. 2015.

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João Pedro F. Trovão (S’08, M’13) was born in Coimbra, Portugal, in 1975. He received the MSc

degree and the Ph.D. degree in Electrical Engineering

from the University of Coimbra, Coimbra, Portugal, in 2004 and 2013, respectively. From 2000 to 2014, he

was a Teaching Assistant and an Assistant Professor

with the Polytechnic Institute of Coimbra–Coimbra Institute of Engineering (IPC–ISEC), Portugal. Since

2014, he has been a Professor with the Department of

Electrical Engineering and Computer Engineering, University of Sherbrooke, Sherbrooke, QC, Canada,

where he holds the Canadian Research Chair position in Efficient Electric

Vehicles with Hybridized Energy Storage Systems. His research interests cover the areas of electric vehicles, hybridized energy storage systems, energy

management and rotating electrical machines. J. P. F. Trovão was the General Co-Chair and the Technical Program Committee Co-Chair of the 2014 IEEE

Vehicle Power and Propulsion Conference, as well as the Award Committee

Member for the 2015 and 2016 IEEE Vehicle Power and Propulsion Conferences. He was a Guest Editor for the Special Issue of IET ELECTRICAL

SYSTEMS IN TRANSPORTATION ON ENERGY STORAGE AND ELECTRIC POWER

SUB-SYSTEMS FOR ADVANCED VEHICLES. He is a Guest Editor for the Special Issue of IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY ON ELECTRIC

POWERTRAINS FOR FUTURE VEHICLES.

Marc-André Roux received the B.Sc. degree and the

MSc degree in Mechanical Engineering from the Université de Sherbrooke, Québec, Canada, in 2004

and 2008 respectively. He has 9 years’ work

experience in product development of recreational products as personal watercraft, side-by-side ATV,

roadster and planetary exploration rovers. In addition,

he has 3 years of mechatronics engineering work experience on mobile robotics. He is recognized for

his ability to efficiently simulate and optimize any

system while keeping in mind the project outlook.

Éric Ménard obtained a bachelor's degree in mechanical engineering from the Université de

Sherbrooke in 1996. He has worked for 20 years in

the recreational products industry as an engineer, first at BRP and then at Centre de Technologies Avancées

BRP - Université de Sherbrooke (CTA). For the past

3 years, he has been managing all projects related to electric propulsion systems developed by the CTA.

Maxime R. Dubois (M’99) obtained his B.Sc. in

Electrical Engineering from the Université Laval,

Québec, Canada in 1991. He received a Ph.D cum laude from Delft University of Technology in The

Netherlands in 2004. Between 1993 and 1999 he has

worked in the industry as a power electronics engineer. Between 2004 and 2011, he has been with

the Université Laval. Since 2011, Prof. Dubois has

been Associate Professor at the department of Electrical Engineering at University of Sherbrooke,

Canada. He is the founder of Eocycle Technologies

Inc., a company specialized in the development of TFPM. He is also the founding professor of the company AddEnergie Technologies. His fields of

interest are electrical machines and power electronics applied to the field of

wind energy, energy storage and electric vehicles. He was the Technical

Program Committee Chair of the 2015 IEEE Vehicle Power and Propulsion

Conference and a Guest Editor for the Special Issue of IET ELECTRICAL

SYSTEMS IN TRANSPORTATION ON DESIGN, MODELING AND CONTROL OF

ELECTRIC VEHICLES.

energy- and power-split management of dual energy storage

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