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energies
Article
A Fuzzy-Based Product Life Cycle Prediction for
Sustainable Development in the Electric
Vehicle Industry
Yung Po Tsang 1, Wai Chi Wong 2, G. Q. Huang 2, Chun Ho Wu 3,4, * , Y. H. Kuo 2and
King Lun Choy 1
1Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University,
Kowloon, Hong Kong, China; p.tsang@connect.polyu.hk (Y.P.T.); kl.choy@polyu.edu.hk (K.L.C.)
2Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong,
Pokfulam, Hong Kong, China; u3007331@connect.hku.hk (W.C.W.); gqhuang@hku.hk (G.Q.H.);
yhkuo@hku.hk (Y.H.K.)
3Department of Supply Chain and Information Management, The Hang Seng University of Hong Kong,
Shatin, Hong Kong, China
4Big Data Intelligence Centre, The Hang Seng University of Hong Kong, Shatin, Hong Kong, China
*Correspondence: jackwu@ieee.org
Received: 30 June 2020; Accepted: 30 July 2020; Published: 31 July 2020
Abstract:
The development of electric vehicles (EVs) has drawn considerable attention to the establishment
of sustainable transport systems to enable improvements in energy optimization and air quality. EVs are
now widely used by the public as one of the sustainable transportation measures. Nevertheless, battery
charging for EVs create several challenges, for example, lack of charging facilities in urban areas and
expensive battery maintenance. Among various components in EVs, the battery pack is one of the core
consumables, which requires regular inspection and repair in terms of battery life cycle and stability.
The charging efficiency is limited to the power provided by the facilities, and therefore the current business
model for EVs is not sustainable. To further improve its sustainability, plug-in electric vehicle battery pack
standardization (PEVBPS) is suggested to provide a uniform, standardized and mobile EV battery that is
managed by centralized service providers for repair and maintenance tasks. In this paper, a fuzzy-based
battery life-cycle prediction framework (FBLPF) is proposed to effectively manage the PEVBPS in the
market, which integrates the multi-responses Taguchi method (MRTM) and the adaptive neuro-fuzzy
inference system (ANFIS) as a whole for the decision-making process. MRTM is formulated based on
selection of the most relevant and critical input variables from domain experts and professionals, while
ANFIS takes part in time-series forecasting of the customized product life-cycle for demand and electricity
consumption. With the aid of the FPLCPF, the revolution of the EV industry can be revolutionarily boosted
towards total sustainable development, resulting in pro-active energy policies in the PEVBPS eco-system.
Keywords:
sustainable development; electric vehicle; decision making; multi-response Taguchi
method; ANFIS
1. Introduction
In recent years, sustainable development in transportation has been dramatically advocated, in which
it is believed that the use of electric vehicles (EVs) replacing existing gasoline-powered vehicles is relatively
sustainable, without air pollution, and better energy consumption performance [
1
,
2
]. Also, the adoption of
EVs can effectively facilitate the green energy economy approach in the automotive industry [
3
]. EVs have
been widely used by the public as a sustainable and environmentally-friendly transport measure. Therefore,
the operations management of typical EVs is well-established and sophisticated in the market. According
Energies 2020,13, 3918; doi:10.3390/en13153918 www.mdpi.com/journal/energies
Energies 2020,13, 3918 2 of 23
to the report on the global EV outlook 2019 [
4
], the number of EVs increased rapidly with an increase of 63%
in 2019 where more than five million EVs were recorded. Although there were about 5.2 million charging
facilities globally in 2018, only 0.54 million facilities were publicly available, and the rest (4.66 million units)
were private chargers. Thus, charging infrastructure and support are deemed to be the major obstacles in
the current EV industry.
On the other hand, end users cannot manage the product life cycle of vehicle batteries, which
gradually deteriorate along with the usage of EVs. Thus, EVs are disadvantaged in the second-hand
market when being sold. Also, the repair and maintenance of the batteries should be conducted
regularly, and are relatively expensive to end-users. Therefore, the current EV eco-system cannot be
effectively sustainable in attracting more end-users to adopt EVs, particularly estimating the product
life cycle for the batteries.
In view of the above issues, plug-in electric vehicle battery pack standardization (PEVBPS) is
being actively researched and developed to further enhance the sustainability of the EV industry [
5
,
6
].
The PEVBPS, which can be the next revolution of the EV industry, provides high flexibility and ease of
use in inserting and removing batteries in EVs, as shown in Figure 1. Also, a new energy policy for the
EVs can be formulated by adopting PEVBPS in the market. EV battery management hub manages the
PEVBPS produced from the battery manufacturers, including charging, recycling, maintenance, and service
support. PEVBPS is then ready in the battery exchange stations for the replacement of the vehicle batteries
by end-users. Since the research and development of PEVBPS are still at the preliminary stage and not
ready to launch to market, product life-cycle management (PLM) for the PEVBPS is essential to determine
the appropriate energy policy and to facilitate sustainable development. To effectively establish the PLM
for the PEVBPS, a fuzzy-based battery life cycle prediction framework (FBLPF) is proposed in this paper for
forecasting time-series demand and electricity consumption for the PEVBPS to support decision-making
on inventory management and production planning. Although there are several studies related to product
life cycle prediction, the selection of most critical input variables to minimize error and time used for the
training forecasting engine is under-researched. Consequently, the multi-response Taguchi method (MRTM)
is adopted to guide the training and operations of the adaptive neuro-fuzzy inference system (ANFIS) such
that a minimal number of the input variables, forecasting errors and training times can be obtained to build
the prediction engine. As multiple objectives are considered in the Taguchi method, the MRTM, which is
one of the techniques for the design of experiments (DoE), is deemed to be promising to aggregate the
quality characteristics from multiple objectives [
7
]. Therefore, the input variables and process settings
can be determined to guide the deployment of ANFIS to forecast the time-series demand and electricity
consumption for achieving the product life cycle prediction. Also, since the input variables to formulate the
product life-cycle prediction are relatively subjective and vague, ANFIS is selected for time-series demand
forecasting, which uses the advantages of artificial neural network and fuzzy inference system, for example,
non-linear structure computation and rapid learning capability [
8
]. Overall, the MRTM-guided ANFIS
approach is developed in this paper to achieve useful battery life cycle prediction for the PEVBPS in the
EV industry.
The contributions of this research work are summarized in two aspects. First, this study presents a
systematic development for the product life-cycle management for the novel product, namely PEVBPS,
in the EV industry. The development of the PEVBPS is a sustainable measure in the EV industry so that
effective product life-cycle management is essential to reduce the likelihood of failure in implementing
such a new product. To structure the PLM of the PEVBPS, eight essential parameters; time sequence,
fuel price, electricity price, project investment, market size, market competition, customer satisfaction,
and policy support and subsidies, are considered for enriching the research domain of the EV. Second,
in order to effectively forecast the time-series demand and electricity consumption, integration of
MRTM and ANFIS is proposed, in which MRTM is functioned to obtain the optimal parameter settings
in the ANFIS. Subsequently, the aim is to improve the forecasting accuracy and efficiency, and to obtain
a scalable and adaptive forecasting approach for various application domains.
Energies 2020,13, 3918 3 of 23
Energies 2020, 13, x FOR PEER REVIEW 3 of 23
forecasting accuracy and efficiency, and to obtain a scalable and adaptive forecasting approach for
various application domains.
Figure 1. Graphical illustration of the plug-in electric vehicle battery pack standardization (PEVBPS)-
based electric vehicle (EV) industry.
The remainder of this paper is organized as follows. Section 2 reviews research studies
concerning electric vehicle management, product life cycle management, and artificial intelligence
techniques, such that, the motivation and research gap can be formulated accordingly. Section 3
describes the methodology of the proposed FBLPF. Section 4 presents a case study of implementing
the proposed framework in an EV management company to examine its feasibility and performance.
Section 5 includes the results and discussion after the implementation of the proposed framework.
Finally, the concluding remarks are drawn in Section 6.
2. Literature Review
In this section, a literature review is conducted on the aspects of (i) electric vehicle management,
(ii) product life cycle management, and (iii) artificial intelligence techniques to support the research
motivation.
The development of EVs in the community is regarded as a sustainable strategic measure in
transport systems [2], leading to a sustainable society and a green economy. Differing to traditional
vehicles, EVs have the advantages of zero-emission of greenhouse gases (GHGs), such as carbon
dioxide, nitrogen oxide, and hydrocarbon, which arise due to the heat generated from the combustion
engines. To sustain the community and improve environmental awareness, EVs were invented with
higher engine efficiency and minimizing pollutants during driving [9]. For typical EVs, the electricity
that is generated from electricity plants and renewable energy sources is stored in EVs’ batteries, fuel
cells, and ultracapacitors. Typically, there are five types of EVs in the market in recent years, namely
hybrid EV, battery-powered EV, plug-in hybrid EV, photovoltaic EV, and fuel cell EV [10,11]. The
hybrid EV, battery-powered EV, plug-in hybrid EV are widely used in modern society, in which only
the battery-powered EVs purely rely on rechargeable batteries to run the electric motors. For the
battery charging on the battery-powered EVs, three classifications from levels 1 to 3 were developed,
in which level-1 chargers are typically for charging at home; level-2 chargers are for charging at
workplaces and public charging stations; level-3 chargers are dedicated charging stations by EV
companies. Instead of considering the environmental incentives from EVs, industrial sustainability
should be further considered, in which a profitable business model is required for the industry to
sustain the development of EVs [12]. To classify the EVs, nine scenarios, namely type of power
supply, technology, power, accessibility, payment, information flow, identification, roaming, and
contents of charging service, are considered for managing EVs in society. However, the current EV
Figure 1.
Graphical illustration of the plug-in electric vehicle battery pack standardization (PEVBPS)-based
electric vehicle (EV) industry.
The remainder of this paper is organized as follows. Section 2reviews research studies concerning
electric vehicle management, product life cycle management, and artificial intelligence techniques, such
that, the motivation and research gap can be formulated accordingly. Section 3describes the methodology
of the proposed FBLPF. Section 4presents a case study of implementing the proposed framework in an
EV management company to examine its feasibility and performance. Section 5includes the results and
discussion after the implementation of the proposed framework. Finally, the concluding remarks are drawn
in Section 6.
2. Literature Review
In this section, a literature review is conducted on the aspects of (i) electric vehicle management,
(ii)product lifecycle management, and (iii) artificial intelligence techniques to support the research motivation.
The development of EVs in the community is regarded as a sustainable strategic measure in
transport systems [
2
], leading to a sustainable society and a green economy. Differing to traditional
vehicles, EVs have the advantages of zero-emission of greenhouse gases (GHGs), such as carbon
dioxide, nitrogen oxide, and hydrocarbon, which arise due to the heat generated from the combustion
engines. To sustain the community and improve environmental awareness, EVs were invented with
higher engine efficiency and minimizing pollutants during driving [
9
]. For typical EVs, the electricity
that is generated from electricity plants and renewable energy sources is stored in EVs’ batteries, fuel
cells, and ultracapacitors. Typically, there are five types of EVs in the market in recent years, namely
hybrid EV, battery-powered EV, plug-in hybrid EV, photovoltaic EV, and fuel cell EV [
10
,
11
]. The hybrid
EV, battery-powered EV, plug-in hybrid EV are widely used in modern society, in which only the
battery-powered EVs purely rely on rechargeable batteries to run the electric motors. For the battery
charging on the battery-powered EVs, three classifications from levels 1 to 3 were developed, in which
level-1 chargers are typically for charging at home; level-2 chargers are for charging at workplaces and
public charging stations; level-3 chargers are dedicated charging stations by EV companies. Instead
of considering the environmental incentives from EVs, industrial sustainability should be further
considered, in which a profitable business model is required for the industry to sustain the development
of EVs [
12
]. To classify the EVs, nine scenarios, namely type of power supply, technology, power,
accessibility, payment, information flow, identification, roaming, and contents of charging service,
are considered for managing EVs in society. However, the current EV business model is facing critical
challenges related to charging infrastructures and battery management, which hinder the sustainable
development of the EV industry. Bosshard and Kolar [
13
] presented technological barriers on further
wide-spreading EV adoption, where the limited driving range and long battery charging time still exist
Energies 2020,13, 3918 4 of 23
in the market. Innovative solutions for battery charging are encouraged to mitigate the above existing
challenges to boost the EV acceptance and the development of electric mobility. Also, Hannan et al. [
14
]
summarized seven challenges in the current EV industry, (i) cell unbalancing, (ii) battery modelling,
(iii) ageing, (iv) degradation, (v) monitoring battery health, (vi) estimation of maximum capacity, and
(vii) communication method, which should be addressed in future research and development. Because
of the number of challenges in the EV industry, some research focused on considering the development
of battery packs for EVs, instead of charging EVs at a fixed station [15,16].
Therefore, one of the innovative solutions, i.e., PEVBPS, has been introduced to draw significant
attention from the public and industry. The use of PEVBPS can mitigate the challenge of long charging
times at the fixed stations, in which a self-service for battery insertion and removal is now made.
The PEVBPS provides a standardized battery pack for various types of vehicles, such as private cars
and trucks, and the time to be fully charged depends on the battery switching process. In addition to
the efficient charging process, the most significant benefit from the PEVBPS is to share the risk and
responsibility of battery repair and maintenance with the centralized service providers. Therefore, end
users can drive their EVs in a safe and reliable condition. In Taiwan, a ridesharing platform has been
launched by Gogoro, Yamaha, Aeon Motor, and PGO, where all scooters can exchange batteries in the
battery switching stations [
17
]. Such a deployment can be further extended to other types of vehicles in
the market to achieve better sustainability development. To launch the PEVBPS, effective product life
cycle management should be formulated to estimate the average demand for battery packs in different
periods, which is important for the infrastructure setup, inventory management, and service support.
Product life-cycle management (PLM) is generally referred to the business of managing a
company’s products effectively across their life-cycles [
18
]. Effective PLM can maximize product
revenue, the value of product portfolios, and customer values, while product-related costs can be
reduced. Particular to new product development, the PLM plays an essential role in providing strategic
support for assessing market opportunities, product design, and manufacturing prior to launching in
the market. Orcik et al. [
19
] illustrated five significant stages to represent the generic product life-cycle,
namely (i) research and development, (ii) introduction, (iii) growth, (iv) maturity, and (v) decline.
A non-linear characteristic was used to express the relationship between sales and time during the
above five stages. Subsequently, an overview of the performance of new products can be examined
with the structured paradigm. In the EV industry, PLM has also been widely applied to formulate
various strategic decision-making methods, which are essential to establish healthy and sustainable
development [
20
]. The environmental burdens can also be assessed from the products, processes,
and activities involved in the eco-system. Zwolinski and Tichkiewitch [
21
] defined a set of critical
project life-cycle parameters in considering the environmental view, associated to the energy policy
and material usage, contributing to maximizing the sustainability in the whole life-cycle. To further
boost sustainability in the EV industry, the launch of the PEVBPS requires an in-depth evaluation of
product life-cycle prediction. The adoption of machine learning and artificial intelligence in the PLM
is an emerging research area to minimize obsolescence risk and prediction inconsistency [
22
]. Apart
from the historical sales data, additional data related to technical characteristics and processes can be
integrated to generate a ballpark estimate of the PLM.
With artificial intelligence (AI), industrial forecasting problems can be addressed adequately,
where the artificial neural network (ANN) and the support vector machine (SVM) are well-known
for handling non-linear forecasting problems [
23
,
24
]. Although ANN requires considerable training
time and resources to build the neural network, the flexibility and adaptability in implementing ANN
are relatively high in the various application domains. The principles of ANN and SVM are based
on the empirical risk minimization and structural risk minimization, respectively. In [
25
], the ANN
approach was adopted to simulate time-series demand forecasting in supply chain management, where
only historical sales were considered. Also, ANN was applied to forecast electricity demand, which
demonstrated the feasibility of the ANN in addressing the forecasting problems [
26
]. However, the
typical ANN models cannot handle subjective and uncertain datasets during the decision-making
Energies 2020,13, 3918 5 of 23
process, and therefore the adaptive neuro-fuzzy inference system (ANFIS) was developed to integrate
ANN and the Takagi–Sugeno fuzzy inference system as a whole [
27
,
28
]. By combining learning
capability and relational structure, the practicality of ANFIS outperforms the typical ANN approach in
solving many real-life problems. In addition, hybridization of the optimization method and ANFIS
becomes an emerging trend in current research studies to determine the most appropriate architecture
and input variables for problem domains. Jadidi et al. [
29
] integrated the non-dominated sorting
genetic algorithm II (NSGA-II) and ANFIS to achieve the short-term electric power demand forecasting.
Among several optimization methods, including particle swarm optimization, ant colony optimization,
differential evolution, and imperialistic competitive algorithm, the combination of NSGA-II and ANFIS
provided the most accurate forecasting results. Apart from optimization methods, design of experiment
(DoE) techniques, such as the Taguchi method, are also able to optimize the quality characteristics by
considering the responses from various experimental studies [
30
]. To further extend the capability of
the Taguchi method, a multi-response Taguchi method was proposed to combine various responses for
different objectives [
31
]. However, the research using DoE methods and ANFIS is limited, and needs
to be investigated to improve the learning efficiency and forecasting accuracy.
After reviewing the above literature, it is summarized that the development of the PEVBPS is
the emerging trend in the EV industry for strengthening the sustainability of EVs and the market.
For launching the PEVBPS in the market effectively, the corresponding PLM is essential for estimating
its time-series demand to support the evaluation of electricity consumption and amount of PEVBPS
to be produced. To achieve the above objectives, the ANFIS is found to be promising in product
life-cycle prediction, while the MRTM is adopted to guide the formulation of the ANFIS. Therefore,
an integrated approach for the product life-cycle prediction for demand and electricity consumption
can be established in our study.
3. Design of a Fuzzy-Based Battery Life Cycle Prediction Framework
In this section, the research methodology is presented through the design of the fuzzy-based
battery life-cycle prediction framework (FBLPF), which consists of three main tiers, namely (i) data
collection, (ii) an MRTM-guided ANFIS approach, and (iii) PLM for the PEVBPS. Figure 2shows the
modular framework of the FBLPF to illustrate the interactions among the three tiers to support the
PLM and decision-making process for the PEVBPS.
3.1. Tier 1: Data Collection
This tier describes the selection and collection of data as the input in the MRTM-guided ANFIS
approach to estimate the time-series demand and electricity consumption effectively. In regard to
the PLM, Tao et al. [
32
] summarized the product life-cycle data in nine aspects, i.e., product concept,
design, raw material purchase, manufacturing, transportation, sales, utilization, after-sale services, and
recycling/disposal, which were extended from the basic product life-cycle model to be engineering-oriented.
Among the stages of introduction, growth, maturity, and decline, the engineering activities can be specified
such that various data can be collected during the entire product life-cycle in terms of the nine defined
aspects. Since the PEVBPS is still at a preliminary development stage, merely the data in the product
concept needs to be considered to construct the model, and the proposed framework can be further
extended in the future to incorporate additional features of the product life-cycle. Therefore, a set of
product life-cycle data
D
={d
1
,d
2
,
. . .
,d
t
} can be formulated where trefers to maximum number of
product life-cycle data, for example, historical demand, investment planning, and customer satisfaction,
which may affect the prediction of future demand and electricity consumption. All the collected data are
then centralized in the cloud database for effective evaluation and analytics. Among set D, the data are
separated into input and output datasets; namely, I=[i
1
,i
2
,
. . .
,i
n
]
⊂
Dand O=[o
1
,o
2
,
. . .
,o
m
]
⊂
D,
where nand mrefer to the maximum numbers of input and output. Regarding the input, the relevant
parameters to achieve the battery life-cycle prediction are considered with the defined time interval.
The output in this study is to focus on time-series demand and electricity consumption for the PEVBPS.
Energies 2020,13, 3918 6 of 23
From the cloud database, the historical dataset, which is used to train the ANFIS engine is partitioned
into two sub-sets, namely training data and validation data. The training data is applied to facilitate the
training process of the prediction engine, while the validation data is used to measure the errors between
the observed and predicted values. On the other hand, the real-time data are used to predict the battery
life-cycle through considering the application dataset through the novel MRTM-guided ANFIS approach.
Energies 2020, 13, x FOR PEER REVIEW 6 of 23
Figure 2. The modular framework of the fuzzy-based battery life-cycle prediction framework
(FBLPF). MRTM denotes multi-response Taguchi method; ANFIS denotes adaptive neuro-fuzzy
inference system; PLM denote product lifecycle management.
3.1. Tier 1: Data Collection
This tier describes the selection and collection of data as the input in the MRTM-guided ANFIS
approach to estimate the time-series demand and electricity consumption effectively. In regard to the
PLM, Tao et al. [32] summarized the product life-cycle data in nine aspects, i.e., product concept,
design, raw material purchase, manufacturing, transportation, sales, utilization, after-sale services,
and recycling/disposal, which were extended from the basic product life-cycle model to be
engineering-oriented. Among the stages of introduction, growth, maturity, and decline, the
engineering activities can be specified such that various data can be collected during the entire
product life-cycle in terms of the nine defined aspects. Since the PEVBPS is still at a preliminary
development stage, merely the data in the product concept needs to be considered to construct the
Figure 2.
The modular framework of the fuzzy-based battery life-cycle prediction framework (FBLPF).
MRTM denotes multi-response Taguchi method; ANFIS denotes adaptive neuro-fuzzy inference system;
PLM denote product lifecycle management.
3.2. Tier 2: A MRTM-Guided ANFIS Approach
Since the previous tier has defined the set of input and output parameters for the battery life-cycle
prediction, the input variables and system settings are selected in this tier to construct an optimal
ANFIS approach for the prediction purpose, as shown in Figure 3. Regardless of the input attributes,
Energies 2020,13, 3918 7 of 23
three control parameters are considered in this approach, namely (i) selection of input variables,
(ii) type of membership functions, and (iii) epoch number for training. According to the independent
variables X=[x
1
, x
2
,
. . .
, x
p
] set in the above three categories, the orthogonal array can be formulated
for multiple factors Xat two levels, where prefers to the maximum number of independent variables.
The minimum number of experiments N
min
can be measured as in Equation (1), where L
i
refers to
the specific factor level of each independent variable. Together with the balancing property of the
orthogonal array, the specific size of the orthogonal array Ncan be selected accordingly.
Nmin =1+Xp
i=1Li−1 (1)
Energies 2020, 13, x FOR PEER REVIEW 7 of 23
model, and the proposed framework can be further extended in the future to incorporate additional
features of the product life-cycle. Therefore, a set of product life-cycle data D = {d1, d2, …, dt} can be
formulated where t refers to maximum number of product life-cycle data, for example, historical
demand, investment planning, and customer satisfaction, which may affect the prediction of future
demand and electricity consumption. All the collected data are then centralized in the cloud database
for effective evaluation and analytics. Among set D, the data are separated into input and output
datasets; namely, I = [i1, i2, …, in] ⊂ D and O = [o1, o2, …, om] ⊂ D, where n and m refer to the maximum
numbers of input and output. Regarding the input, the relevant parameters to achieve the battery
life-cycle prediction are considered with the defined time interval. The output in this study is to focus
on time-series demand and electricity consumption for the PEVBPS. From the cloud database, the
historical dataset, which is used to train the ANFIS engine is partitioned into two sub-sets, namely
training data and validation data. The training data is applied to facilitate the training process of the
prediction engine, while the validation data is used to measure the errors between the observed and
predicted values. On the other hand, the real-time data are used to predict the battery life-cycle
through considering the application dataset through the novel MRTM-guided ANFIS approach.
3.2. Tier 2: A MRTM-Guided ANFIS Approach
Since the previous tier has defined the set of input and output parameters for the battery life-
cycle prediction, the input variables and system settings are selected in this tier to construct an
optimal ANFIS approach for the prediction purpose, as shown in Figure 3. Regardless of the input
attributes, three control parameters are considered in this approach, namely (i) selection of input
variables, (ii) type of membership functions, and (iii) epoch number for training. According to the
independent variables X = [x1, x2, …, xp] set in the above three categories, the orthogonal array can be
formulated for multiple factors X at two levels, where p refers to the maximum number of
independent variables. The minimum number of experiments Nmin can be measured as in Equation
(1), where Li refers to the specific factor level of each independent variable. Together with the
balancing property of the orthogonal array, the specific size of the orthogonal array N can be selected
accordingly.
=1+
−1
(1)
Figure 3. Architecture of the MRTM-guided ANFIS.
Figure 3. Architecture of the MRTM-guided ANFIS.
For the selection of input variables, a binary number, i.e., 0 or 1, is set to each input variable
randomly to construct its 2-level factor. Moreover, the number of membership functions (e.g., 2 and 4)
and membership function types (e.g., triangular and trapezoid types) are designed to be 2-level factors.
Subsequently, experimental studies following the orthogonal array can be conducted to examine the
responses r
j
of (i) execution time and (ii) root mean squared errors for prediction, in which the entire
experimental studies take place in the offline training phase of ANFIS. To express the structure of the
ANFIS, there are five layers to illustrate the computations of model training, as shown in Figure 3.
Layer 1 refers to the corresponding fuzzy class C
(n,h)
, called the linguistic label, of the input variable I,
where nis the maximum number of input variables and his the maximum number of fuzzy classes for
the specific input variable. Subsequently, the membership function M
k
of the input variable I={i
k
} can
be defined, as in Equation (2). The value of the membership functions ranges between 0 and 1 with the
specific membership shapes, such as triangular and trapezoid types.
Mk=µC(n,h)(ik)(2)
In layer 2, the incoming signals from layer 1 are combined through multiplying the membership
values to compute the firing strength of a rule w
j
, as in Equation (3). Subsequently, the firing strength
of a fuzzy IF-THEN rule is then normalized by calculating the ratio between all the potential rules in
layer 3, as in Equation (4). Therefore, the output of this layer is called the normalized firing strength.
ωj=
n
Y
i=1
µC(i,j)(ik),∀j∈[1, m](3)
Energies 2020,13, 3918 8 of 23
ωJ=ωj
Pm
j=1ωj
(4)
In layer 4, a square node is considered with the node function as in Equation (5). The normalized
firing strength is then multiplied with the output parameters of the ANFIS, where x
0
is equal to 1.
To compute the overall output of the ANFIS, the summation of all incoming signals from layer 4 is
conducted, as in Equation (6). Overall, the training data is used to train the architecture of the ANFIS
according to the above steps so that the total training time T
training
can be measured. Also, making
use of the validation data can, therefore, allow evaluation of the root mean squared error
ε
of the
trained ANFIS.
O4
j=ωJfj=ωJXn
k=0rjkik(5)
O5
j=Xm
j=1ωJfj=Pm
j=1ωjfj
Pm
j=1ωj
(6)
Subsequently, the responses of experimental studies following the defined orthogonal array can
be analyzed through the computation of the corresponding signal-to-noise ratios. When considering
minimizing the training time and errorsof the ANFIS approach, the quality characteristic “smaller-the-better”
is applied to calculate the signal-to-noise ratio (
η
) as in Equation (7). To optimize the above two objectives,
weight assignment and normalization are adopted in this paper for effectively aggregating the responses
as a whole. Since the analyses of the responses are independently conducted, a min–max normalization is
applied to the signal-to-noise ratios for every control parameter in order to generate a normalized weight
ωijk
, as in Equation (8), where i,jand kdenote the factors, factor levels, and responses for the MRTM.
Compared with other normalization techniques, such as z-score normalization, min-max normalization is
found to be effective in confining different scales and ensuring that all features are on a common scale
for further comparison and combination. Afterwards, the normalized weights from different responses
are then combined as a whole, while the combination weight
αk
is applied such that the resultant weight
ωij
*=
α1ωij1
+
α2ωij2
+
. . .
+
αkωijk
.The best feature for the ANFIS in terms of input variables, number of
membership functions and membership function types can be selected. Consequently, the MRTM guides
the development of the ANFIS approach for achieving the PLM for the PEVBPS.
η=−10·log(1
nXN
j=1rj)(7)
ωijk =ηijk−min
kηijk
max
kηijk−min
kηijk(8)
3.3. Tier 3: PLM for the PEVBPS
Based on the above MRTM-guided ANFIS approach, the application data can be inputted to
formulate the prediction of the battery life-cycle in the aspects of time-series demand and time-series
electricity consumption. Also, the above prediction results are utilized for the PLM according to the
stages of introduction, growth, maturity, and decline [
23
]. With the predicted findings of demand and
electricity consumption over time, decision-making support for the development of the PEVBPS can
be achieved. First, the production planning for the PEVBPS modules can be estimated according to the
forecasted demand so that any unnecessary waste during the production and inventory obsolescence
can be eliminated. Second, the predicted electricity consumption supports the business negotiations
with the electricity plant for facilitating the entire business model, which ensures that the electricity
grid can adequately support the battery charging activities. Thirdly, instead of managing the EV
battery issues by end-users themselves, the development of the PEVBPS boosts the sustainability
development in the EV market through managing all the EV batteries in a centralized manner. Therefore,
the appropriate PLM strategies can be formulated in this tier to support the development of the PEVBPS.
Energies 2020,13, 3918 9 of 23
4. Case Study
To examine the feasibility of the proposed model in the EV industry, a pilot study was conducted
to predict the product life-cycle of the PEVBPS in the market. A case company in Hong Kong who
are operating the EV business and are eager to develop the PEVBPS in the market was selected,
having the vision of being a leader in the EV market in providing sustainable measures for EV battery
management. To introduce the PEVBPS in the market effectively, accurate PLM is crucial to determine
the consumption of raw materials, production planning, as well as the electricity supply to fulfil
customer demand. Without effective PLM for the PEVBPS, the supply chain for the new EV business
model might be easily broken. In this section, implementation of the proposed model, i.e., FBLPF,
is presented to predict the customer demand and electricity consumption along the entire battery
life-cycle in the market.
4.1. Motivation of the Case Study
In the automotive industry, plug-in electric vehicles (PEVs) are the most common EV types in
modern society, and has become an active research topic recently. Although the use of PEV tends to be
more sustainable and environmentally-friendly than traditional vehicles using combustion engines,
the current charging practice for the PEV is still facing several drawbacks in the market. First, the
popularization of EVs solely relies on the amount of charging infrastructure in urban areas. In contrast,
the installation of charging infrastructure is relatively difficult once the facilities have already been
built. The arrangement of the power supply may need to be re-located to specific regions in the
facilities. Second, for the PEVs, the batteries are pre-installed and fixed inside the vehicles, and are not
removed by the end-users. Due to the phenomenon of battery depreciation and deterioration of PEVs,
the batteries, which are the core power source of the vehicles, have to be repaired and maintained
on a regular basis, and it is relatively expensive for end-users to maintain PEV batteries in good
condition. Therefore, sales in the first-hand market and re-sales in the second-hand market are not
competitive. Consequently, the development of the PEVBPS has started in recent years to enhance
battery management and sustainability for the PEV. To launch a new product in the market, market
analysis is crucial to understand the market niches. At the same time, the entire PLM needs to be
established to support the control and monitoring of the business model. However, an effective model
regarding the PLM of the PEVBPS is lacking, and has become the main barrier for EV companies to
invest in the PEVBPS. Overall, the motivation for the case study can be summarized as follows:
•Selection of the most relevant factors for the development of the PEVBPS
•Establishment of the effective battery life-cycle prediction approach
4.2. Implementation of the FBLPF
To address the above industrial concerns in the EV industry, the proposed model, i.e., FBLPF,
is implemented for the launch of PEVBPS in the market, while its PLM can be measured to support
the business development. The implementation of the proposed model in the case company consists
of five steps, namely (i) variable selection, (ii) formulation of the orthogonal array, (iii) experimental
studies and analysis by the MRTM, (iv) formulation of the optimal ANFIS, and (v) establishment of the
battery life-cycle management. The overall implementation roadmap is presented in Figure 4, which
consists of the above five steps with the corresponding outputs. On the one hand, the evaluation by
using MRTM is conducted in the Minitab 19 environment, where “Taguchi” under DOE is adopted to
execute the single-objective Taguchi method. The results from the Minitab 19 are then aggregated to
obtain the optimal parameter settings for the ANFIS formulation. On the other hand, the neuro-fuzzy
designer and a set of functions, for example, anfis() and genfis(), in the MATLAB environment are used
to build the Sugeno-type fuzzy inference system between the selected inputs and outputs. Therefore,
the MRTM-guided ANFIS can be realized in the case study to obtain the optimal ANFIS design for
forecasting time-series demand and electricity consumption.
Energies 2020,13, 3918 10 of 23
Energies 2020, 13, x FOR PEER REVIEW 10 of 23
model regarding the PLM of the PEVBPS is lacking, and has become the main barrier for EV
companies to invest in the PEVBPS. Overall, the motivation for the case study can be summarized as
follows:
• Selection of the most relevant factors for the development of the PEVBPS
• Establishment of the effective battery life-cycle prediction approach
4.2. Implementation of the FBLPF
To address the above industrial concerns in the EV industry, the proposed model, i.e., FBLPF, is
implemented for the launch of PEVBPS in the market, while its PLM can be measured to support the
business development. The implementation of the proposed model in the case company consists of
five steps, namely (i) variable selection, (ii) formulation of the orthogonal array, (iii) experimental
studies and analysis by the MRTM, (iv) formulation of the optimal ANFIS, and (v) establishment of
the battery life-cycle management. The overall implementation roadmap is presented in Figure 4,
which consists of the above five steps with the corresponding outputs. On the one hand, the
evaluation by using MRTM is conducted in the Minitab 19 environment, where “Taguchi” under
DOE is adopted to execute the single-objective Taguchi method. The results from the Minitab 19 are
then aggregated to obtain the optimal parameter settings for the ANFIS formulation. On the other
hand, the neuro-fuzzy designer and a set of functions, for example, anfis() and genfis(), in the
MATLAB environment are used to build the Sugeno-type fuzzy inference system between the
selected inputs and outputs. Therefore, the MRTM-guided ANFIS can be realized in the case study
to obtain the optimal ANFIS design for forecasting time-series demand and electricity consumption.
Figure 4. Implementation roadmap of the fuzzy-based battery life-cycle prediction framework
(FBLPF).
Moreover, the forecasting horizon was between 24th April, 2019 (epoch time: 1,556,064,000) and
9th April, 2020 (epoch time: 1,586,390,400), in which the data collection was collected once a week.
Afterwards, the datasets were deployed in the MRTM-guided ANFIS so as to formulate the optimal
design of the ANFIS, where the details of the method are given in Section 3.2. By making use of the
MRTM, the features of input variables and parameter settings can be selected in a systematic manner,
and therefore the optimal feature settings can be obtained for building the appropriate ANFIS.
Figure 4.
Implementation roadmap of the fuzzy-based battery life-cycle prediction framework (FBLPF).
Moreover, the forecasting horizon was between 24th April, 2019 (epoch time: 1,556,064,000) and
9th April, 2020 (epoch time: 1,586,390,400), in which the data collection was collected once a week.
Afterwards, the datasets were deployed in the MRTM-guided ANFIS so as to formulate the optimal
design of the ANFIS, where the details of the method are given in Section 3.2. By making use of the
MRTM, the features of input variables and parameter settings can be selected in a systematic manner,
and therefore the optimal feature settings can be obtained for building the appropriate ANFIS.
4.2.1. Step 1: Variable Selection
First and the foremost, the product life-cycle data that may affect the demand and electricity
consumption for the PEVBPS in the industry are considered at this product design stage. To be applied
in the MRTM-guided ANFIS, there are eight input parameters and two output parameters that affect
the PLM of the PEVBPS. The eight input parameters which can be changed along the time are expressed
as follows.
(i)
Time sequence (V
1
, in hours): It refers to the timestamp for collecting the product life-cycle data
for the PEVBPS project, while the time is measured in the Unix epoch time in hours. Therefore,
the date can be standardized in the computation process throughout the entire dataset.
(ii) Fuel price (V
2
, in HK$/Liter): It refers to the fuel price of gasoline for the use of traditional vehicles
with combustion engines. The fuel price is one of the significant considerations that influence
drivers’ behavior and EV market size.
(iii)
Electricity price (V
3
, in HK$/kWh): Similar to the fuel price, the electricity price is a core concern
for EV users in continuously adopting EVs. If the electricity price is too high such that the
cost-performance ratio for using EVs cannot be justified, the EV users may be switched to another
type of vehicle.
(iv) Project investment (V
4
, in HK$): It refers to the extent of company investment on the development
of the PEVBPS. The investment planning influences the growth and sustainability of the product
life-cycle in the market. Therefore, the amount of the project investment planning should be
taken into consideration for the PLM.
Energies 2020,13, 3918 11 of 23
(v)
Market size (V
5
, in unit): To introduce the new product, i.e., PEVBPS, in the EV industry, the
number of existing EV users is an essential indicator to examine the market size and potential
users for the PEVBPS. Thus, the number of regional EV users is used to quantify market size.
(vi)
Market competition (V
6
, in unit): Peer competition in the EV industry has to be considered in
the PLM, while the launch of a new product may have to compete with peers in the market.
Consequently, the number of EV companies who are actively engaging the development of the
PEVBPS should be considered.
(vii) Customer satisfaction (V
7
, in a rating of 0–100): It refers to the feedback from EV users, defined as
customer satisfaction. The satisfaction level of the EV users to the current products and services
of EVs can be reflected, which can determine the success of the PEVBPS.
(viii)
Policy support and subsidies (V
8
, in HK$): The incentives from the government to boost EV
development is important to the EV industry. The extent of the policy support and subsidies can
influence the adoption rates of new products in the EV market.
On the other hand, the two output parameters in this study are (i) demand of the PEVBPS (in units)
and (ii) electricity consumption (in kWh). In this case study, the datasets were collected from the
PEVBPS business and existing EV market to construct the battery life-cycle management. When the
development of the PEVBPS goes into another stage of the PLM, additional input parameters are
considered in the proposed model. Nevertheless, effective measures in selecting the most relevant
parameters in the forecasting are lacking. Considering all the parameters in the forecasting approach
may lead to energy- and cost-ineffectiveness in training the forecasting engine. At the same time,
the training time and prediction errors cannot be optimized. Therefore, deployment of the proposed
MRTM-guided ANFIS is illustrated in the following steps of the case study to verify the performance
and feasibility in the battery life-cycle prediction.
4.2.2. Step 2: Formulation of the Orthogonal Array
To conduct the experimental studies for the eight input parameters, the factors and the corresponding
levels are formulated in Table 1. For the input parameters, their factor levels are binary as 0 or 1, which
denotes not preferable and preferable in the forecasting engine, respectively. Also, the process parameters,
namely type of membership functions (F
m
) and number of epoch numbers (N
e
) for the parameters, are
considered. For the factor F
m
, triangular and trapezoid shapes, namely ‘trimf’ and ‘trapmf’, are considered,
while the value N
e
can be either 100 or 1000 for engine training in the ANFIS. It aims at optimizing the
time and error for the forecasting of demand and electricity consumption, while the best combination of
the ten factors is selected.
Table 1. Factors and factor levels for the MRTM.
V1V2V3V4V5V6V7V8FmNe
Level 1
1 1 1 1 1 1 1 1 trimf 100
Level 2
0 0 0 0 0 0 0 0 trapmf 1000
When applying Equation (1), the minimum number of experiments to be conducted is 11, and therefore
the orthogonal design (L
12
) is selected by considering the balancing property. Due to the balancing property,
the total number of experiments to be conducted are multiples of 2. Hence, the orthogonal design (L
12
)
is deemed to be appropriate in the factor optimization. Instead of conducting the experiments for all
combinations, i.e., 2
10
=1024, only 12 experiments are conducted in the MRTM, which are relatively
effective and efficient to optimize the selection of the input variables and characteristics of membership
functions. Table 2shows the orthogonal array (L
12
) regarding the experimental studies for examining
the best combination of the factors in the PLM. After the experiments following the orthogonal array are
conducted, the responses in training time and root mean squared errors (RMSE) for the forecasting can
be collected. When the value of the input parameters is 0, the corresponding parameters opt out in the
Energies 2020,13, 3918 12 of 23
training and validation of the ANFIS. In the future, additional process parameters can be considered in the
proposed MRTM method to build the optimal ANFIS for real-life applications.
Table 2. Orthogonal array (L12) for the feature selection.
Exp# V1V2V3V4V5V6V7V8FmNm
1 1 1 1 1 1 1 1 1 trimf 100
2 1 1 1 1 1 0 0 0 trapmf 1000
3 1 1 0 0 0 1 1 1 trapmf 1000
4 1 0 1 0 0 1 0 0 trimf 100
5 1 0 0 1 0 0 1 0 trimf 1000
6 1 0 0 0 1 0 0 1 trapmf 100
7 0 1 0 0 1 1 0 0 trimf 1000
8 0 1 0 1 0 0 0 1 trimf 100
9 0 1 1 0 0 0 1 0 trapmf 100
10 0 0 0 1 1 1 1 0 trapmf 100
11 0 0 1 0 1 0 1 1 trimf 1000
12 0 0 1 1 0 1 0 1 trapmf 1000
4.2.3. Step 3: Experimental Studies and Analysis by the MRTM
According to the above orthogonal array, the experimental studies to formulate the accurate and
time-efficient ANFIS approach are conducted for the estimations of demand and electricity consumption.
In this study, the size of the dataset is 40 in which each data row contains the input (V
1
to V
8
) and
output (demand and electricity consumption). Thirty out of forty datasets (i.e., 75%) are used to train
the ANFIS, while the rest (25%) are adopted to validate the performance of the trained ANFIS approach.
Also, the number of membership functions is fixed at 2, and the grid partition is selected for building the
fuzzy inference engine. Considering either demand or electricity consumption, the ANFIS can be trained to
measure (i) training time, (ii) RMSE for training, and (iii) RMSE for validation, as shown in Table 3. These
experimental studies attempt to minimize the above three response measurements at the same time, and
the best combination of input variables and process parameters can be formulated in return.
Table 3. Response measurement to the orthogonal array.
Demand Forecasting Electricity Consumption Forecasting
Exp# Training
RMSE (units)
Validation
RMSE (units)
Execution
Time (s)
Training
RMSE (units)
Validation
RMSE (units)
Execution
Time (s)
1 57,648,002 48,571,000 1008.85 415,290 302,486 972.916
2 4,784,477 11,022,400 10.73 134,215 235,646 10.628
3 2,611,253 10,283,300 10.78 96,131 692,502 10.710
4 25,977 41,695 0.29 46 136 0.287
5 25,801 38,368,000 0.54 122 45,978 0.559
6 19,472,111 51,808,100 0.30 128,675 557,182 0.287
7 953 13,323 0.54 13 87 0.584
8 2781 6564 0.30 16 325 0.291
9 342 105,543 0.29 2 298 0.299
10 1885 100,607 0.88 21 298 0.870
11 153 85,245 2.15 0 776 2.177
12 713 16,251 2.16 1 136 2.153
Remarks: RMSE denote root mean square error.
For demand forecasting, the corresponding signal-to-noise ratios in the three responses can be
measured concerning defined factors following the quality characteristic of “smaller-the-better”, as shown
in Figure 5. The signal-to-noise ratios can then be normalized and combined as a whole, in which the
combination weights for the (i) training time, (ii) RMSE for training, and (iii) RMSE for validation are
expressed as
α1
=0.05,
α2
=0.45,
α3
=0.5, respectively. Table 4summarizes the computational results on
the normalization and aggregation process, and thus the resultant weights for 10 factors can be obtained.
Energies 2020,13, 3918 13 of 23
According to Table 4, the variables 3 and 6, i.e., V
3
and V
6
, are selected in the demand forecasting;
the type of membership function is trapezoidal; the number of epoch number is 100. Consequently, the
optimal ANFIS approach is formulated to estimate the demand in the market, in which small values in
training time, training RMSE, and validation RMSE are preferred.
Energies 2020, 13, x FOR PEER REVIEW 14 of 23
Figure 5. Main effect plots for signal-to-noise ratios in demand forecasting.
Similarly, for electricity consumption forecasting, the signal-to-noise ratios are calculated with
the quality characteristic of “smaller-the-better”, and the same set of combination weights for the
three responses are adopted. Figure 6 shows the main effect plots on the signal-to-noise ratios of the
three measurement dimensions for the electricity consumption forecasting. Also, Table 5 summarizes
the results of normalization and aggregation, and therefore the best combination of factors is obtained
to build the ANFIS approach for electricity consumption forecasting. Consequently, the variables 3
and 6, i.e., V
3
and V
6
, are selected in the electricity consumption forecasting; the type of membership
functions should be trapezoidal; the number of epoch numbers is 1000. Following the results from
the MRTM, the optimal ANFIS can be formulated for demand and electricity forecasting in the PLM.
Figure 5. Main effect plots for signal-to-noise ratios in demand forecasting.
Similarly, for electricity consumption forecasting, the signal-to-noise ratios are calculated with the
quality characteristic of “smaller-the-better”, and the same set of combination weights for the three
responses are adopted. Figure 6shows the main effect plots on the signal-to-noise ratios of the three
measurement dimensions for the electricity consumption forecasting. Also, Table 5summarizes the
results of normalization and aggregation, and therefore the best combination of factors is obtained
to build the ANFIS approach for electricity consumption forecasting. Consequently, the variables 3
and 6, i.e., V3and V6, are selected in the electricity consumption forecasting; the type of membership
Energies 2020,13, 3918 14 of 23
functions should be trapezoidal; the number of epoch numbers is 1000. Following the results from the
MRTM, the optimal ANFIS can be formulated for demand and electricity forecasting in the PLM.
Table 4. Weight normalization and combination for demand forecasting.
Training
Time V1V2V3V4V5V6V7V8FmNm
Level 1 0.0710 0.0705 0.0354 0.0059 0.0022 0.0000 0.0000 0.0453 0.3841 0.0993
Level 2 1.0000 0.9994 0.9644 0.9349 0.9312 0.9290 0.9290 0.9743 0.2394 0.5244
Training
RMSE V1V2V3V4V5V6V7V8FmNm
Level 1 0.0000 0.3633 0.5353 0.4338 0.3454 0.4707 0.5273 0.3565 0.4022 0.5811
Level 2 1.0000 0.6367 0.4647 0.5662 0.6546 0.5293 0.4727 0.6435 0.5978 0.4189
Validation
RMSE V1V2V3V4V5V6V7V8FmNm
Level 1 0.0000 0.4779 0.5600 0.4342 0.3546 0.6188 0.3039 0.4322 0.3887 0.4643
Level 2 1.0000 0.5223 0.4402 0.5660 0.6456 0.3814 0.6963 0.5680 0.6115 0.5359
Resultant V1V2V3V4V5V6V7V8FmNm
Level 1 0.0036 0.4060 0.5226 * 0.4126 0.3328 0.5212 * 0.3893 0.3788 0.3945 0.4986 *
Level 2 1.0000 * 0.5976 * 0.4775 0.5845 * 0.6639 * 0.4754 0.6073 * 0.6223 * 0.5868 * 0.4827
Remarks: The sign of asterisk (*) refers to the preferable level of the considered factors.
Energies 2020, 13, x FOR PEER REVIEW 15 of 23
Table 5. Weight normalization and combination for electricity consumption forecasting.
Training Time V
1
V
2
V
3
V
4
V
5
V
6
V
7
V
8
F
m
N
m
Level 1 0.0637 0.0768 0.0366 0.0000 0.0051 0.0049 0.0058 0.0343 0.3930 0.0939
Level 2 0.9869 1.0000 0.9598 0.9231 0.9284 0.9281 0.9289 0.9574 0.2400 0.5391
Training RMSE V
1
V
2
V
3
V
4
V
5
V
6
V
7
V
8
F
m
N
m
Level 1 0.0000 0.2911 0.5927 0.4221 0.3389 0.4519 0.5259 0.3960 0.3544 0.5627
Level 2 1.0000 0.7090 0.4075 0.5780 0.6611 0.5481 0.4742 0.6041 0.6456 0.4373
Validation RMSE V
1
V
2
V
3
V
4
V
5
V
6
V
7
V
8
F
m
N
m
Level 1 0.0000 0.3792 0.5908 0.4456 0.3600 0.6234 0.3655 0.3128 0.3509 0.4321
Level 2 1.0000 0.6208 0.4092 0.5544 0.6398 0.3766 0.6345 0.6872 0.6491 0.5679
Resultant V
1
V
2
V
3
V
4
V
5
V
6
V
7
V
8
F
m
N
m
Level 1 0.0032 0.3245 0.5639 * 0.4128 0.3328 0.5153 * 0.4197 0.3363 0.3546 0.4739
Level 2 0.9993 * 0.6795 * 0.4359 0.5835 * 0.6638 * 0.4813 0.5771 * 0.6633 * 0.6271 * 0.5077 *
Remarks: The sign of asterisk (*) refers to the preferable level of the considered factors.
Figure 6. Main effect plots for signal-to-noise ratios in electricity consumption forecasting.
4.2.4. Step 4: Formulation of the Optimal ANFIS
Figure 6. Main effect plots for signal-to-noise ratios in electricity consumption forecasting.
Energies 2020,13, 3918 15 of 23
Table 5. Weight normalization and combination for electricity consumption forecasting.
Training
Time V1V2V3V4V5V6V7V8FmNm
Level 1 0.0637 0.0768 0.0366 0.0000 0.0051 0.0049 0.0058 0.0343 0.3930 0.0939
Level 2 0.9869 1.0000 0.9598 0.9231 0.9284 0.9281 0.9289 0.9574 0.2400 0.5391
Training
RMSE V1V2V3V4V5V6V7V8FmNm
Level 1 0.0000 0.2911 0.5927 0.4221 0.3389 0.4519 0.5259 0.3960 0.3544 0.5627
Level 2 1.0000 0.7090 0.4075 0.5780 0.6611 0.5481 0.4742 0.6041 0.6456 0.4373
Validation
RMSE V1V2V3V4V5V6V7V8FmNm
Level 1 0.0000 0.3792 0.5908 0.4456 0.3600 0.6234 0.3655 0.3128 0.3509 0.4321
Level 2 1.0000 0.6208 0.4092 0.5544 0.6398 0.3766 0.6345 0.6872 0.6491 0.5679
Resultant V1V2V3V4V5V6V7V8FmNm
Level 1 0.0032 0.3245 0.5639 * 0.4128 0.3328 0.5153 * 0.4197 0.3363 0.3546 0.4739
Level 2 0.9993 * 0.6795 * 0.4359 0.5835 * 0.6638 * 0.4813 0.5771 * 0.6633 * 0.6271 * 0.5077 *
Remarks: The sign of asterisk (*) refers to the preferable level of the considered factors.
4.2.4. Step 4: Formulation of the Optimal ANFIS
Based on the above resultant factor combination, the optimal ANFIS engines can be established
for demand forecasting and electricity consumption forecasting. From the results of the MRTM,
the electricity price and market competition are selected as the core variables for the forecasting of
demand and electricity consumption. By using the same dataset (i.e., 75% for training and 25% for
validation), the ANFIS for demand forecasting can be established in 0.1576 s with the minimal training
RMSE of 4295.1883 and the minimal validation RMSE of 11195.9 at the epoch 100, as shown in Figure 7.
Subsequently, Figure 8show optimal membership functions for the ANFIS to forecasting time-series
demand of the PEVBPS.
Energies 2020, 13, x FOR PEER REVIEW 16 of 23
Based on the above resultant factor combination, the optimal ANFIS engines can be established
for demand forecasting and electricity consumption forecasting. From the results of the MRTM, the
electricity price and market competition are selected as the core variables for the forecasting of
demand and electricity consumption. By using the same dataset (i.e., 75% for training and 25% for
validation), the ANFIS for demand forecasting can be established in 0.1576 s with the minimal
training RMSE of 4295.1883 and the minimal validation RMSE of 11195.9 at the epoch 100, as shown
in Figure 7. Subsequently, Figure 8 show optimal membership functions for the ANFIS to forecasting
time-series demand of the PEVBPS.
Figure 7. Error measurement for training and validation for demand forecasting.
Figure 8. Optimal membership functions of the ANFIS for demand forecasting.
Figure 7. Error measurement for training and validation for demand forecasting.
Energies 2020,13, 3918 16 of 23
Energies 2020, 13, x FOR PEER REVIEW 16 of 23
Based on the above resultant factor combination, the optimal ANFIS engines can be established
for demand forecasting and electricity consumption forecasting. From the results of the MRTM, the
electricity price and market competition are selected as the core variables for the forecasting of
demand and electricity consumption. By using the same dataset (i.e., 75% for training and 25% for
validation), the ANFIS for demand forecasting can be established in 0.1576 s with the minimal
training RMSE of 4295.1883 and the minimal validation RMSE of 11195.9 at the epoch 100, as shown
in Figure 7. Subsequently, Figure 8 show optimal membership functions for the ANFIS to forecasting
time-series demand of the PEVBPS.
Figure 7. Error measurement for training and validation for demand forecasting.
Figure 8. Optimal membership functions of the ANFIS for demand forecasting.
Figure 8. Optimal membership functions of the ANFIS for demand forecasting.
Similarly, the ANFIS for electricity consumption forecasting can be built in 0.2310 s with the
minimal training RMSE of 52.4142 and the minimal validation RMSE of 152.085 at the epoch 1000,
as shown in Figure 9. Figure 10 shows the optimal membership functions for the ANFIS for the electricity
consumption forecasting. Therefore, the Sugeno-type fuzzy inference system can be established to
manage the PLM of the PEVBPS in the aspects of time-series demand and electricity consumption.
Energies 2020, 13, x FOR PEER REVIEW 17 of 23
Similarly, the ANFIS for electricity consumption forecasting can be built in 0.2310 s with the
minimal training RMSE of 52.4142 and the minimal validation RMSE of 152.085 at the epoch 1,000, as
shown in Figure 9. Figure 10 shows the optimal membership functions for the ANFIS for the
electricity consumption forecasting. Therefore, the Sugeno-type fuzzy inference system can be
established to manage the PLM of the PEVBPS in the aspects of time-series demand and electricity
consumption.
Figure 9. Error measurement for training and validation for electricity consumption forecasting.
Figure 10. Optimal membership functions of the ANFIS for electricity consumption forecasting.
4.2.5. Step 5: Establishment of the Battery Life-Cycle Management
Figure 9. Error measurement for training and validation for electricity consumption forecasting.
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Similarly, the ANFIS for electricity consumption forecasting can be built in 0.2310 s with the
minimal training RMSE of 52.4142 and the minimal validation RMSE of 152.085 at the epoch 1,000, as
shown in Figure 9. Figure 10 shows the optimal membership functions for the ANFIS for the
electricity consumption forecasting. Therefore, the Sugeno-type fuzzy inference system can be
established to manage the PLM of the PEVBPS in the aspects of time-series demand and electricity
consumption.
Figure 9. Error measurement for training and validation for electricity consumption forecasting.
Figure 10. Optimal membership functions of the ANFIS for electricity consumption forecasting.
4.2.5. Step 5: Establishment of the Battery Life-Cycle Management
Figure 10. Optimal membership functions of the ANFIS for electricity consumption forecasting.
4.2.5. Step 5: Establishment of the Battery Life-Cycle Management
With the use of the above optimal ANFIS designs, the battery life-cycle management can be
formulated to estimate the future demand and electricity consumption of the PEVBPS. Instead of using
the dataset for training and validation, another dataset with 20 data rows is considered to examine the
forecasting performance. Figure 11 shows the battery life-cycle management of the PEVBPS in terms
of demand and electricity consumption. On the one hand, the optimal ANFIS engines estimate the
values of demand and electricity consumption along with the selected time duration, which reflects the
accuracy of the proposed MRTM-guided ANFIS approach in the forecasting functionality. On the other
hand, the proposed approach enables prediction of the future demand and electricity consumption.
Therefore, the EV companies can formulate appropriate business plans and energy policies according
to the predicted battery life-cycle of the PEVBPS.
Energies 2020, 13, x FOR PEER REVIEW 18 of 23
With the use of the above optimal ANFIS designs, the battery life-cycle management can be
formulated to estimate the future demand and electricity consumption of the PEVBPS. Instead of
using the dataset for training and validation, another dataset with 20 data rows is considered to
examine the forecasting performance. Figure 11 shows the battery life-cycle management of the
PEVBPS in terms of demand and electricity consumption. On the one hand, the optimal ANFIS
engines estimate the values of demand and electricity consumption along with the selected time
duration, which reflects the accuracy of the proposed MRTM-guided ANFIS approach in the
forecasting functionality. On the other hand, the proposed approach enables prediction of the future
demand and electricity consumption. Therefore, the EV companies can formulate appropriate
business plans and energy policies according to the predicted battery life-cycle of the PEVBPS.
Figure 11. Battery life-cycle management for the plug-in electric vehicle battery pack standardization
(PEVBPS).
5. Results and Discussion
After the case study was conducted, the feasibility of the proposed framework has been verified
such that the EV companies can adapt it to facilitate its new product development, particularly for
the PEVBPS. To further examine the performance of the proposed framework, a comparative analysis
between the MRTM-guided ANFIS, moving average and single exponential smoothing is conducted
in this section. Also, the industrial and managerial implications for the proposed framework are
illustrated to highlight the advantages of this study in the EV industry.
5.1. Comparative Analysis with the MRTM-Guided ANFIS
From the above analysis, it is revealed that a large number of input variables and epoch numbers
cannot guarantee high forecasting accuracy for demand and electricity consumption. Referring to
Table 3, considering all the input variables in the ANFIS makes a long training time compared with
other settings, while the RMSEs for training and validation are relatively large. Therefore, the value
of the feature selection in the ANFIS can be proven. Also, to highlight the effectiveness of the MRTM-
guided ANFIS in the forecasting accuracy, another two forecasting techniques, i.e., moving average
and single exponential smoothing, are considered. Table 6 summarizes the results of the comparative
analysis in the aspects of mean squared error (MSE), mean relative error (MRE), and mean absolute
error (MAE). It is found that the MRTM-guided ANFIS consistently provides better forecasting
accuracy for the trial datasets, collected from the case company. Although the MSE value in
Figure 11.
Battery life-cyclemanagement for the plug-in electricvehicle battery pack standardization (PEVBPS).
Energies 2020,13, 3918 18 of 23
5. Results and Discussion
After the case study was conducted, the feasibility of the proposed framework has been verified
such that the EV companies can adapt it to facilitate its new product development, particularly for
the PEVBPS. To further examine the performance of the proposed framework, a comparative analysis
between the MRTM-guided ANFIS, moving average and single exponential smoothing is conducted
in this section. Also, the industrial and managerial implications for the proposed framework are
illustrated to highlight the advantages of this study in the EV industry.
5.1. Comparative Analysis with the MRTM-Guided ANFIS
From the above analysis, it is revealed that a large number of input variables and epoch numbers
cannot guarantee high forecasting accuracy for demand and electricity consumption. Referring to
Table 3, considering all the input variables in the ANFIS makes a long training time compared with other
settings, while the RMSEs for training and validation are relatively large. Therefore, the value of the
feature selection in the ANFIS can be proven. Also, to highlight the effectiveness of the MRTM-guided
ANFIS in the forecasting accuracy, another two forecasting techniques, i.e., moving average and single
exponential smoothing, are considered. Table 6summarizes the results of the comparative analysis in
the aspects of mean squared error (MSE), mean relative error (MRE), and mean absolute error (MAE).
It is found that the MRTM-guided ANFIS consistently provides better forecasting accuracy for the trial
datasets, collected from the case company. Although the MSE value in predicting energy consumption
by using the moving average is slightly lower than the adoption of the proposed framework, its MRE
value is relatively higher than the proposed framework and the slight difference in MAR values can be
neglected. Overall, the proposed method, namely MRTM-guided ANFIS, is generally better than the
other existing prediction methods in the aspect of demand forecasting and electricity consumption
prediction. Therefore, the proposed MRTM-guided ANFIS outperforms the existing forecasting
methods, such that accurate forecasting results can be obtained. Eventually, the time-series demand
and electricity consumption can be predicted effectively to support the battery life-cycle management.
Table 6. Comparative analysis between MRTM-guided ANFIS and time-series forecasting methods.
MSE MRE MAE
MRTM-guided ANFIS Demand 56,693,630 0.273233 6006.909
Electricity consumption 5846.312 0.393311 64.53847
Moving average Demand 73,697,724 0.390386 7427.82
Electricity consumption 5598.288 0.445295 64.075
Exponential smoothing Demand 81,622,808 0.413367 7901.22
Electricity consumption 8463.286 0.561393 82.668
Apart from the quantitative comparison for the proposed model, a qualitative comparison between
this research work and other existing studies [
23
,
33
] is conducted, as shown in Table 7. This comparison
consists of six aspects: objectives, methods, techniques, forecasting area, considered factors, and fuzziness.
Table 7. Qualitative comparison of the FBLPF.
Similar Existing Approaches
FBLPF [23] [34] [33]
Objectives
To formulate an optimal
fuzzy-based product
life-cycle prediction
framework for managing
PEVBPS effectively
To forecast obsolescence risk
and product life cycle while
minimizing maintenance
and upkeep of the system
To evaluate the product’s
recyclability at the
product design phase in
a time-series manner
To forecast the demand
of short life cycle product
to achieve a sustainable
competitive advantage
Methods DoE-guided machine
learning approach Machine learning approach Time-series forecasting
method Data mining approach
Techniques MRTM and AFNIS ANN, SVMs, and RF Exponential smoothing
forecasting
K-means clustering and
RULES-6
Energies 2020,13, 3918 19 of 23
Table 7. Cont.
Similar Existing Approaches
FBLPF [23] [34] [33]
Forecasting area Time-series demand and
electricity consumption Obsolescence rate Recyclability Sales profiles of new
products
Considered factors
- Time sequence
- Fuel price
- Electricity price
- Project investment
- Market size
- Market competition
- Customer satisfaction
- Policy support
and subsidies
- Status
- Release year
-
Technical specifications
-
Manufacturing cost,
including
maintenance,
repair, insurance,
property, electricity
bill, CO2eq
-
Cost in usage phase
- Price
- Start date of sales
- Product life span
Fuzziness Yes No No No
Remarks: DoE refers to design of experiment; ANN refers to artificial neural network; SVMs refer to support vector
machines; RF refers to random forest.
To the best of our knowledge, this study is the first attempt to utilize the DoE technique, namely
the multi-response Taguchi method, to obtain the optimal design of ANFIS in order to address
the forecasting issues in the product life-cycle management for the PEVBPS. Some similar studies
attempted to adopt machine learning, data mining, and typical time-series forecasting methods to
achieve their objectives in the PLM, while the selected products were not related to the EVs’ batteries
such that this research study can enrich the new product development in the EV industry. In our
study, the DoE-guided machine learning approach provides optimality in the design of machine
learning techniques so that the establishment of the machine learning engine can be more effective
and efficient. Apart from predicting the sales profile of new products, the electricity consumption
can also be estimated to sustain the entire business of PEVBPS. In addition, the proposed framework
considers the fuzziness of the considered factors, which can handle the uncertainties and vagueness for
the variables in the real-life market situation. To summarize, this research study can boost sustainable
development in the EV industry by minimizing the faults and failures in the new product development
(NPD) process, particularly for the PEVBPS.
5.2. Sensitivity Analysis of the MRTM-Guided ANFIS
In order to examine the effectiveness of the proposed method, namely MRTM-guided ANFIS,
a sensitivity analysis is conducted to evaluate the difference before and after embedding MRTM in the
ANFIS. By doing so, the MRTM-guided ANFIS is compared with four scenarios, which consider all the
defined input variables, but the type of membership functions and the number of epochs are different.
Therefore, the training time and prediction accuracy regarding demand and electricity consumption
are evaluated for (i) using MRTM-guided ANFIS (i.e., only V
3
and V
6
are considered in the ANFIS), (ii)
ANFIS with using trimf and 100 epochs, (iii) ANFIS with using trimf and 1000 epochs, (iv) ANFIS with
using trapmf and 100 epochs, and (v) ANFIS with using trapmf and 1000 epochs, as shown in Table 8.
It is found that the proposed MRTM-guided AFNIS outperforms all other scenarios, which implies
that considering all the relevant factors regarding PLM in the forecasting engine cannot guarantee high
forecasting accuracy and computational efficiency. It is proven that the MRTM is well-functioning in
selecting the optimal settings for the variable features and ANFIS parameters, such that the optimal
ANFIS can be established for forecasting time-series demand and electricity consumption in the case
study. When using the proposed MRTM-guided ANFIS, the training and validation errors can be
minimized among all other scenarios, while the training times for forecasting time-series demand and
electricity consumption are also the lowest.
Energies 2020,13, 3918 20 of 23
Table 8. Result summary of the sensitivity analysis in the MRTM-guided ANFIS.
Scenario
(i) (ii) (iii) (iv) (v)
Demand
Training error 4243.206 * 57,648,002 4,613,132 766,055.5 307,835.1
Validation error 11,195.9 * 4.86 ×1072.41 ×106122,056 122,056
Training time (s) 0.374702 * 939.9405 2655.877 956.9371 2666.93
Electricity
Consumption
Training error 52.41422 * 415,290 22,266.8 3888.141 1943
Validation error 152.085 * 302,486 11,319.3 394.146 394.146
Training time (s) 0.308758 * 974.2982 2675.694 962.7536 2734.913
Remarks: The sign of asterisk (*)refers tothe preferred scenario for predictingtime-series demandand electricityconsumption.
5.3. Industrial and Managerial Implications
With the help of the FBLPF, it is found that the life-cycle prediction for the PEVBPS in terms of
time-series demand and electricity consumption can be effectively established. To the EV companies, NPD
is of the utmost importance in attracting the attention of the general public, and maintaining its competitive
edge in the market. Risks of NPDfailures, inventory obsolescence, and energy wastage should be mitigated
when launching new products in the evolution of the EV industry. As the introduction of the PEVBPS is
relatively new to the market, the obstacles to develop the hardware and service support in the market still
exist even though there are several advantages in charging efficiency and battery management. This study
provides the PLM for the PEVBPS not only in the aspect of demand but also electricity consumption.
Industrial practitioners and staff at the management level can facilitate the execution of the business model
and planning in the market, while the market trends and energy consumption can be estimated accurately.
Apart from the industrial contributions, the proposed model aligns with the paradigm of
eco-innovation. The three core modules in the FBLPF can be extended or modified following the
future development of the forecasting engine and product life-cycle data. On the one hand, additional
product life-cycle data, rather than the eight input parameters, for various stages in PLM can be
considered to enrich the data collection (Tier 1) of the proposed model. Subsequently, the proposed
model can be more comprehensive and evolving concerning the business environment and systems.
The MRTM-guided ANFIS is proposed as the core forecasting method for the PLM in this study.
Although the improvements on the method (Tier 2) can be deployed in a flexible and resilient manner,
only if the product life-cycle data can be analyzed in order to predict the key indicators, namely
demand and energy consumption, for the PEVBPS. Overall, the adaptability of the proposed model
ensures providing decision-making functionalities for the establishment of business planning and
energy policies for the EV industry.
6. Concluding Remarks
This study addresses the uncertain decision-making method in the aspect of PLM for sustainable
development, particularly in regard to the introduction of thePEVBPS in the EV industry. The development
of the PEVBPS is regarded as one of the sustainable development measures for EVs, such that the
infrastructure challenges for battery charging, charging efficiency, and battery management can be
eliminated. Subsequently, the eco-system of the EV industry can be elevated into energy- and
customer-oriented EV support. To introduce the PEVBPS in the market, the FBLPF is presented,
combining MRTM and ANFIS as a whole to estimate the time-series demand and electricity consumption.
The results on the PLM can facilitate improvements in the business planning and energy policies in the
EV industry so as to mitigate the risks in introducing such a new product related to EVs in the market.
The design of the proposed model follows the ontology of the eco-innovation such that the proposed
model can be further modified within the defined scope and objectives with minimal wastage of materials
and human resources. In this study, consideration of the product life-cycle data is limited to the product
design stage, which can be further extended in future studies. Also, the MRTM can be further improved
to summarize the normalized weights from various responses in order to make accurate decisions on
Energies 2020,13, 3918 21 of 23
feature selection for the forecasting engine, in which additional datasets can be considered to improve the
forecasting accuracy. Overall, the sustainable development of fuzzy decision-making methods in the EV
industry can be boosted through this study.
Author Contributions:
Conceptualization, W.C.W.; methodology, W.C.W. and Y.P.T.; validation, W.C.W. and
K.L.C.; resources, G.Q.H. and Y.H.K.; data curation, W.C.W.; investigation, Y.P.T. and C.H.W.; writing—original
draft preparation, W.C.W. and Y.P.T.; writing—review and editing, C.H.W.; supervision, G.Q.H. and Y.H.K.; project
administration, G.Q.H. and Y.H.K.; funding acquisition, C.H.W. and K.L.C. All authors have read and agreed to
the published version of the manuscript.
Funding: This research received no external funding.
Acknowledgments:
The authors would like to thank the University of Hong Kong and the Hong Kong Polytechnic
University for supporting the research. Gratitude is extended to Big Data Intelligence Centre of The Hang Seng
University of Hong Kong for supporting the research.
Conflicts of Interest: The authors declare no conflict of interest.
Nomenclature
Acronym Full Name
EV Electric Vehicle
PEVBPS Plug-In Electric Vehicle Battery Pack Standardization
ANFIS Adaptive Neuro-Fuzzy Inference System
AI Artificial Intelligence
DoE Design of Experiment
FBLPF Fuzzy-Based Battery Life-Cycle Prediction Framework
MAE Mean Absolute Error
MRE Mean Relative Error
MSE Mean Squared Error
MRTM Multi-Responses Taguchi Method
NPD New Product Development
PEV Plug-In Electric Vehicles
PLM Product Life-Cycle Management
RF Random Forest
RMSE Root Mean Square Error
SVM Support Vector Machine
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