1. Introduction
Calculating the global number of vehicles on the planet is an inexact science, but according to some approximate statistics, it could double from 1.2 billion in 2014 to 2.5 billion by 2050. In such a situation, reducing or even keeping pollutant emissions at today’s level needs special efforts from car manufacturers. These environmental issues together with the necessity to preserve petroleum resources have conducted scientists to propose hydrogen as a promoting alternative fuel [
1].
From hydrogen, a Fuel Cell (FC) can itself generate electricity via an electrochemical reaction with the oxygen molecules and releases only pure water [
2]. Clean and silent at any size, the Proton Exchange Membrane Fuel Cell (PEMFC) has significantly affected electric propulsion from scooter to aircraft [
3,
4,
5,
6,
7] and especially Electric Vehicles (EV) [
8,
9,
10,
11,
12]. In fact, the Fuel Cell Electric Vehicle (FCEV) is already being marketed in several marks and designs such as Mercedes-Benz F-Cell, Hyundai Tucson Fuel Cell Electric Vehicle (FCEV), Toyota Mirai, Honda Clarity, etc. [
13]. However, a stand-alone fuel cell-based source is not always sufficient to meet vehicle demands, this is mainly due to its slow dynamic of operation and starting [
14]. Hybridization of the FC with one or various auxiliary sources is in fact crucial to assure great driving range and speed for the EV.
A hybrid Electric Storage System (ESS) consisting of a Battery (BAT) and a pack of Ultracapacitors (UC) is used in this paper. It offers the advantages to assist the fuel cell and to recover regenerative energy at braking [
15,
16,
17]. This hybrid ESS could be replaced by the new technology of lithium-ion capacitors in the next few years [
18,
19].
The FC represents the main source of the system. It supplies the majority of the demand whereas battery provides the complement of the required energy during FC start up and high load demand (acceleration, high road slope). For the ultracapacitors, known to have a high dynamic of operation, they are requested to provide pulse load requirements in order to ensure the power balance between the demand and the generation and to maintain the system output voltage constant during operation.
Although FC/BAT/UC hybrid system exhibits high efficiency and good energetic capability [
20], performance of FCEVs depends essentially on how to manage the energy between the various components of the traction string. Otherwise, a Strategy of Energy Management (EMS) is quite necessary to optimally split the power between the sources and the load. The main objectives of such a strategy are to minimize the fuel-hydrogen-consumption during missions, to secure the sources from critical operating conditions and to ensure the higher reliability and durability for the hybrid system. A variety of EMSs has been employed in automotive research from which we cite fuzzy logic control proposed in [
21], neural network technique treated in [
22], dynamic programming given in [
23], predictive control strategy illustrated in [
24], adaptive energy management based on a fuzzy logic system and optimal sizing developed in [
25] and the load-following approach proposed in [
26] to adapt the FC net power to load demand. More approaches and details are provided in [
27].
In fuzzy logic approach, fuzzy rules usually stem from engineering intuition and unfortunately cannot be optimized for each mission profile. Furthermore, neural network and dynamic programming techniques require an advanced information on the entire load profile and an extensive computational efforts, while a compromise between accuracy and simplicity should be considered in on-board energy management applications.
This paper proposes a strategy of energy management based on a frequency separation approach. Indeed, basing on the frequency domain specialization of each source technology, demanded power can be decomposed into three components with three frequency ranges: higher frequencies are allowed by UCs thanks to their higher dynamic during charge and discharge modes, lower frequencies would be provided by the FC since it is expected to present the lower power density and intermediate frequencies are allowed by the battery to avoid harmful current solicitation.
The filtering-based energy management strategy was previously proposed and validated via predefined driving cycle in Refs. [
28,
29]. However, using fixed separation frequency, optimum power splitting may not be guaranteed in real driving conditions in the way that some recommended ranges of security can be violated during harsh mission requirements e.g., lower and upper limits of ESS States Of Charge (SOC).
The contribution of this work is to develop an adaptive filtering-based energy management allowing to share the energy between the sources according to the UC state of charge -expected to present higher variation than battery one- and dynamic constraints of the sources. The filtering frequency is automatically adapted to the SOC evolution using an adaptive filter and a fuzzy logic control system. This algorithm can optimally explore the strength of each source without any detailed or advanced information on the vehicle trajectory.
The paper is organized as follows:
Section 2 provides the topology of the FCEV propulsion system and the hybrid power source characteristics.
Section 3 illustrates the models of the sources, the converters and the traction load.
Section 4 explains the proposed energy management strategy based on the adaptive frequency approach.
Section 5 details the sliding mode control strategy and
Section 6 provides and discusses the simulation results obtained under MATLAB
®/SimPowerSystems
®.
6. Simulation Results
To simulate the behaviour of the hybrid power source, the overall system is modelled in MATLAB
®/Simulink
® using the SimPowerSystems library. The used vehicle parameters are given in
Table 6.
In order to validate the approach under various driving conditions (urban, extra urban, etc.), simulations are carried out for four different driving cycles. The first is the New European Driving Cycle (NEDC) and represents the typical usage of a vehicle in Europe including four repeated urban driving cycles (ECE-15) and one Extra-Urban Driving Cycle (EUDC). The second is the EPA New York City Cycle (NYCC), it simulates a low speed driving with frequent stops in US city areas. The third is the SC03 Supplemental Federal Test Procedure (SFTP). The last is the new Worldwide harmonized Light vehicles Test Procedure (WLTP) including three different sub-cycles: a low speed cycle, a medium speed cycle and a high speed cycle. The considered hybrid system is simulated using a small scale load (all of the driving cycles are divided by three).
Figure 10 illustrates the ultracapacitor current, the battery current, the fuel cell current and the dc bus voltage given by applying the proposed adaptive filtering based EMS under the NEDC. The load current is shared between the sources while respecting the frequency domain specialization of each source. The supercapacitors provide the fast fluctuated content of the demand and maintain the dc bus voltage constant at the desired level of
= 42 V. The li-ion battery supplies the smoothed component and the fuel cell provides the lower dynamic component (after several minutes of start-up).
To demonstrate the relevance of the proposed approach against traditional frequency management, simulations are carried out using a fixed separation frequency (three different values of are tested) and then using the proposed adaptive filtering-based EMS.
According to Equation (
8) and the Ragone diagram (
Figure 9b), the splitting frequency
can vary between 0.007 Hz and 0.27 Hz and on the basis of preliminary analysis on the studied system this interval is delimited on (0.01 Hz; 0.07 Hz).
6.1. Fixed Energy Splitting
Figure 11 shows the evolution of ultracapacitor state of charge simulated for
= 0.01 Hz,
= 0.03 Hz and
= 0.07 Hz under the NEDC, the NYCC, the SC03 and the WLTP respectively.
For the NEDC, a deep discharging is occurred ( = 10%) during the extra-urban driving cycle for = 0.01 Hz (over use of the UC pack) while an over charging is occurred ( = 105%) during the urban driving cycle for = 0.01 Hz and = 0.03 Hz.
For the other cycles, we note an overcharging for the three levels of . The UC sate of charge reaches 105% under the NYCC for = 0.01 Hz, 106% under the SC03 for = 0.07 Hz and 115% under the WLTP for = 0.01 Hz.
For the cut-off frequency = 0.03 Hz, the capacity limitation is respected under the SC03 and violated under the other cycles. A part of regenerative energy is lost during this unsafe operation mode and cannot be consequently recovered by the battery. We can conclude, then, that for a fixed splitting frequency, the ultracapcitors can be deeply discharged under some driving cycles and overcharged under others which limits the performance of fixed filtering-based EMS under real driving conditions.
6.2. Adaptive Energy Splitting
The simulation results obtained by applying the adaptive energy splitting are given in
Figure 12,
Figure 13,
Figure 14 and
Figure 15, for the simplified models (SM) developed in
Section 3 and for the MATLAB
®/SimPowerSystems
® detailed models (MM).
The state of charge evolution is given, for the four driving cycles, when the UC module is fully charged ( = 100%) and when the UC module has an initial state of charge of 70% ( = 70%).
The effectiveness of the adaptive filtering approach is validated. The splitting frequencies are adapted, over the time, in accordance with the UC state of charge and the load demand. The resulting states of charge are kept within the admissible limits of 40% and 100% in all of the proposed tests even in harsh driving conditions (EUDC and WLTP) for both simulation models:
Under the NEDC
for .
for .
Under the NYCC
for .
for .
Under the SC03
for .
for .
Under the WLTP
for .
for .
The degradation factor of the li-ion battery is calculated in (%) for 100 h of battery operation using fixed and adaptive splitting frequency (
Figure 16). The results indicate an improvement by 0.5% in the battery longevity during the simulation period.
This value would be increased over the operation period of the battery. The performances of the proposed EMS are also confirmed through battery responses given in
Figure 12c and
Figure 15d. The battery states of charge given for the NEDC and the WLTP when
show the same profiles as given for
. Which means that for the same amount of the energy drawn from the battery, the UC pack gained 10% on its state of charge under the NEDC and 5% under the WLTP.
7. Conclusions
An adaptive-filtering-based EMS for a fuel cell hybrid electric vehicle powered by a PEMFC, a li-ion battery and a pack of ultracapacitors, is proposed in this paper. Basing on the frequency-separation of the mission power, the proposed EMS allows to efficiently explore the strength of the supercapacitors as a peak power source and the battery and fuel cell as perfect energy units.
Simulation results performed by applying the proposed adaptive filtering technique under different driving conditions have proven an effective energy sharing between the sources, submitted to different dynamic and energetic constraints. Higher dynamic content of the demand is routed into the UCs pack with a variable frequency, automatically adapted to the UC state of charge and the current demand. Which protects the system from unsafe operating mode and ensures a better performance and speed for the FCHEV.
Strain on the battery is significantly relieved when a maximum of energy is extracted from the UCs module within the admissible limits of state of charge leading to a battery longevity improvement. The energy consumption (hydrogen consumption) can be reduced when the totality of braking energy is recovered by the UCs when they present a low level of SOC or by the battery and the UCs when a reasonable range of energy is ensured in the UCs module or only by the battery when the UCs are in a fully charged state.
Thereby, durability and autonomy of the hybrid power source can be improved with minimal and low cost changes and a good trade off between performance and simplicity is achieved making possible on-line implementation of the proposed adaptive-filtering energy management strategy on board any hybrid electric vehicle.