The performance of lithium-ion batteries will decline dramatically with the increase in usage time, which will cause anxiety in using lithium-ion batteries. Some data-driven models have been employed to predict the remaining useful life (RUL) model of lithium-ion batteries. However, there are limitations to the accuracy and applicability of traditional machine learning models or just a single deep learning model. This paper presents a fusion model based on convolutional neural network (CNN) and long short-term memory network (LSTM), named CNN-LSTM, to measure the RUL of lithium-ion batteries. Firstly, this model uses the grey relational analysis to extract the main features affecting the RUL as the health index (HI) of the battery. In addition, the fusion model can capture the non-linear characteristics and time-space relationships well, which helps find the capacity decay and failure threshold of lithium-ion batteries. The experimental results show that: 1) Traditional machine learning is less effective than LSTM. 2) The CNN-LSTM fusion model is superior to the single LSTM model in predicting performance. 3) The proposed model is superior to other comparable models in error indexes, which could reach 0.36% and 0.38e-4 in mean absolute percentage error (MAPE) and mean square error (MSE), respectively. 4) The proposed model can accurately find the failure threshold and the decay fluctuation for the lithium-ion battery.
Citation: Dewang Chen, Xiaoyu Zheng, Ciyang Chen, Wendi Zhao. Remaining useful life prediction of the lithium-ion battery based on CNN-LSTM fusion model and grey relational analysis[J]. Electronic Research Archive, 2023, 31(2): 633-655. doi: 10.3934/era.2023031
[1] | Yao Yu, Chaobo Li, Dong Ji . Fixed point theorems for enriched Kannan-type mappings and application. AIMS Mathematics, 2024, 9(8): 21580-21595. doi: 10.3934/math.20241048 |
[2] | Gunaseelan Mani, Arul Joseph Gnanaprakasam, Choonkil Park, Sungsik Yun . Orthogonal F-contractions on O-complete b-metric space. AIMS Mathematics, 2021, 6(8): 8315-8330. doi: 10.3934/math.2021481 |
[3] | Abdullah Shoaib, Poom Kumam, Shaif Saleh Alshoraify, Muhammad Arshad . Fixed point results in double controlled quasi metric type spaces. AIMS Mathematics, 2021, 6(2): 1851-1864. doi: 10.3934/math.2021112 |
[4] | Xun Ge, Songlin Yang . Some fixed point results on generalized metric spaces. AIMS Mathematics, 2021, 6(2): 1769-1780. doi: 10.3934/math.2021106 |
[5] | Tahair Rasham, Abdullah Shoaib, Shaif Alshoraify, Choonkil Park, Jung Rye Lee . Study of multivalued fixed point problems for generalized contractions in double controlled dislocated quasi metric type spaces. AIMS Mathematics, 2022, 7(1): 1058-1073. doi: 10.3934/math.2022063 |
[6] | Hieu Doan . A new type of Kannan's fixed point theorem in strong b- metric spaces. AIMS Mathematics, 2021, 6(7): 7895-7908. doi: 10.3934/math.2021458 |
[7] | Muhammad Waseem Asghar, Mujahid Abbas, Cyril Dennis Enyi, McSylvester Ejighikeme Omaba . Iterative approximation of fixed points of generalized αm-nonexpansive mappings in modular spaces. AIMS Mathematics, 2023, 8(11): 26922-26944. doi: 10.3934/math.20231378 |
[8] | Mi Zhou, Naeem Saleem, Xiao-lan Liu, Nihal Özgür . On two new contractions and discontinuity on fixed points. AIMS Mathematics, 2022, 7(2): 1628-1663. doi: 10.3934/math.2022095 |
[9] | P. Dhivya, D. Diwakaran, P. Selvapriya . Best proximity points for proximal Górnicki mappings and applications to variational inequality problems. AIMS Mathematics, 2024, 9(3): 5886-5904. doi: 10.3934/math.2024287 |
[10] | Fatemeh Lael, Naeem Saleem, Işık Hüseyin, Manuel de la Sen . ˊCiriˊc-Reich-Rus type weakly contractive mappings and related fixed point results in modular-like spaces with application. AIMS Mathematics, 2022, 7(9): 16422-16439. doi: 10.3934/math.2022898 |
The performance of lithium-ion batteries will decline dramatically with the increase in usage time, which will cause anxiety in using lithium-ion batteries. Some data-driven models have been employed to predict the remaining useful life (RUL) model of lithium-ion batteries. However, there are limitations to the accuracy and applicability of traditional machine learning models or just a single deep learning model. This paper presents a fusion model based on convolutional neural network (CNN) and long short-term memory network (LSTM), named CNN-LSTM, to measure the RUL of lithium-ion batteries. Firstly, this model uses the grey relational analysis to extract the main features affecting the RUL as the health index (HI) of the battery. In addition, the fusion model can capture the non-linear characteristics and time-space relationships well, which helps find the capacity decay and failure threshold of lithium-ion batteries. The experimental results show that: 1) Traditional machine learning is less effective than LSTM. 2) The CNN-LSTM fusion model is superior to the single LSTM model in predicting performance. 3) The proposed model is superior to other comparable models in error indexes, which could reach 0.36% and 0.38e-4 in mean absolute percentage error (MAPE) and mean square error (MSE), respectively. 4) The proposed model can accurately find the failure threshold and the decay fluctuation for the lithium-ion battery.
Modular metric spaces were introduced in [4,5]. Behind this new notion, there exists a physical interpretation of the modular. A modular on a set bases on a nonnegative (possibly infinite valued) “field of (generalized) velocities”: to each time λ>0 (the absoulute value of) an averge velocity ωλ(ρ,σ) is associated in such that in order to cover the distance between points ρ,σ∈M, it takes time λ to move from ρ to σ with velocity ωλ(ρ,σ), while a metric on a set stands for non-negative finite distances between any two points of the set. The process of access to this notion of modular metric spaces is different. Actually we deal with these spaces as the nonlinear version of the classical modular spaces as introduced by Nakano [12] on vector spaces and modular function spaces introduced by Musielack [11] and Orlicz [13]. In [1,2] the authors have defined and investigated the fixed point property in the context of modular metric space and introduced several results. For more on modular metric fixed point theory, the reader may consult the books [7,8,9]. Some recent work in modular metric space has been represented in [14,15]. It is almost a century where several mathematicians have improved, extended and enriched the classical Banach contraction principle [1] in different directions along with variety of applications. In 1969, Kannan [6] proved that if X is complete, then a Kannan mapping has a fixed point. It is interesting that Kannan’s theorem is independent of the Banach contraction principle [3].
In this research article, fixed point problem for Kannan mappings in the framework of modular metric spaces is investigated.
Let M≠∅. Throughout this paper for a function ω:(0,∞)×M×M→[0,∞], we will write
ωλ(ρ,σ)=ω(λ,ρ,σ), |
for all λ>0 and ρ,σ∈M.
Definition 1. [4,5] A function ω:(0,∞)×M×M→[0,∞] is called a modular metric on M if following axioms hold:
(ⅰ) ρ=σ⇔ωλ(ρ,σ)=0, for all λ>0;
(ⅱ) ωλ(ρ,σ)=ωλ(σ,ρ), for all λ>0, and ρ,σ∈M;
(ⅲ) ωλ+μ(ρ,σ)≤ωλ(ρ,ς)+ωμ(ς,σ), for all λ,μ>0 and ρ,σ,ς∈M.
A modular metric ω on M is called regular if the following weaker version of (ⅰ) is satisfied
ρ=σif and only ifωλ(ρ,σ)=0, for some λ>0. |
Eventually, ω is called convex if for λ,μ>0 and ρ,σ,ς∈M, it satisfies
ωλ+μ(ρ,σ)≤λλ+μωλ(ρ,ς)+μλ+μωμ(ς,σ). |
Throughout this work, we assume ω is regular.
Definition 2. [4,5] Let ω be a modular on M. Fix ρ0∈M. The two sets
Mω=Mω(ρ0)={ρ∈M:ωλ(ρ,ρ0)→0asλ→∞}, |
and
M∗ω=M∗ω(ρ0)={ρ∈M:∃λ=λ(ρ)>0such thatωλ(ρ,ρ0)<∞} |
are called modular spaces (around ρ0).
It is obvious that Mω⊂M∗ω but this involvement may be proper in general. It follows from [4,5] that if ω is a modular on M, then the modular space Mω can be equipped with a (nontrivial) metric, generated by ω and given by
dω(ρ,σ)=inf{λ>0:ωλ(ρ,σ)≤λ}, |
for any ρ,σ∈Mω. If ω is a convex modular on M, according to [4,5] the two modular spaces coincide, i.e. M∗ω=Mω, and this common set can be endowed with the metric d∗ω given by
d∗ω(ρ,σ)=inf{λ>0:ωλ(ρ,σ)≤1}, |
for any ρ,σ∈Mω. These distances will be called Luxemburg distances.
Following example presented by Abdou and Khamsi [1,2] is an important motivation of the concept modular metric spaces.
example 3. Let Ω be a nonempty set and Σ be a nontrivial σ-algebra of subsets of Ω. Let P be a δ-ring of subsets of Ω, such that E∩A∈P for any E∈P and A∈Σ. Let us assume that there exists an increasing sequence of sets Kn∈P such that Ω=⋃Kn. By E we denote the linear space of all simple functions with supports from P. By N∞ we will denote the space of all extended measurable functions, i.e. all functions f:Ω→[−∞,∞] such that there exists a sequence {gn}⊂E, |gn|≤|f| and gn(ω)→f(ω) for all ω∈Ω. By 1A we denote the characteristic function of the set A. Let ρ:N∞→[0,∞] be a nontrivial, convex and even function. We say that ρ is a regular convex function pseudomodular if:
(ⅰ) ρ(0)=0;
(ⅱ) ρ is monotone, i.e. |f(ω)|≤|g(ω)| for all ω∈Ω implies ρ(f)≤ρ(g), where f,g∈N∞;
(ⅲ) ρ is orthogonally subadditive, i.e. ρ(f1A∪B)≤ρ(f1A)+ρ(f1B) for any A,B∈Σ such that A∩B≠∅, f∈N;
(ⅳ) ρ has the Fatou property, i.e. |fn(ω)|↑|f(ω)| for all ω∈Ω implies ρ(fn)↑ρ(f), where f∈N∞;
(ⅴ) ρ is order continuous in E, i.e. gn∈E and |gn(ω)|↓0 implies ρ(gn)↓0.
Similarly, as in the case of measure spaces, we say that a set A∈Σ is ρ-null if ρ(g1A)=0 for every g∈E. We say that a property holds ρ-almost everywhere if the exceptional set is ρ-null. As usual we identify any pair of measurable sets whose symmetric difference is ρ-null as well as any pair of measurable functions differing only on a ρ-null set. With this in mind we define
N(Ω,Σ,P,ρ)={f∈N∞;|f(ω)|<∞ ρ−a.e}, |
where each f∈N(Ω,Σ,P,ρ) is actually an equivalence class of functions equal ρ-a.e. rather than an individual function. Where no confusion exists we will write M instead of N(Ω,Σ,P,ρ). Let ρ be a regular function pseudomodular.
(a) We say that ρ is a regular function semimodular if ρ(αf)=0 for every α>0 implies f=0 ρ−a.e.;
(b) We say that ρ is a regular function modular if ρ(f)=0 implies f=0 ρ−a.e.
The class of all nonzero regular convex function modulars defined on Ω will be denoted by ℜ. Let us denote ρ(f,E)=ρ(f1E) for f∈N, E∈Σ. It is easy to prove that ρ(f,E) is a function pseudomodular in the sense of Def.2.1.1 in [10] (more precisely, it is a function pseudomodular with the Fatou property). Therefore, we can use all results of the standard theory of modular function space as per the framework defined by Kozlowski in [10], see also Musielak [11] for the basics of the general modular theory. Let ρ be a convex function modular.
(a) The associated modular function space is the vector space Lρ(Ω,Σ), or briefly Lρ, defined by
Lρ={f∈N;ρ(λf)→0 as λ→0}. |
(b) The following formula defines a norm in Lρ (frequently called Luxemburg norm):
‖f‖ρ=inf{α>0;ρ(f/α)≤1}. |
A modular function spaces furnishes a wonderful example of a modular metric space. Indeed, let Lρ be modular function space.
example 4. Define the function ω by
ωλ(f,g)=ρ(f−gλ) |
for all λ>0, and f,g∈Lρ. Then ω is a modular metric on Lρ. Note that ω is convex if and only if ρ is convex. Moreover we have
‖f−g‖ρ=d∗ω(f,g), |
for any f,g∈Lρ.
For more examples readers can see [4,5]
Definition 5. [1]
(1). A sequence {ρn}⊂Mω is ω -convergent to ρ∈Mω if and only if ω1(ρn,ρ)→0.
(2). A sequence {ρn}⊂Mω is ω -Cauchy if ω1(ρn,ρm)→0 as n,m→∞.
(3). A set K⊂Mω is ω-closed if the limit of ω1-convergent sequence of K always belongs to K.
(4). A set K⊂Mω is ω-bounded if
δω=sup{ω1(ρ,σ);ρ,σ∈K}<∞. |
(5). If any ω-Cauchy sequence in a subset K of Mω is a convergent sequence and its limit is in K, then K is called an ω-complete.
(6). The ρ-centered ω-ball of radius r is defined as
Bω(ρ,r)={σ∈Mω;ω1(ρ,σ)≤r}, |
for any ρ∈Mω and r≥0.
Let (M,ω) be a modular metric space. In the rest of this work, we assume that ω satisfies the Fatou property, i.e. if {ρn}ω-converges to ρ and {σn}ω -converges to σ, then we must have
ω1(ρ,σ)≤lim infn→∞ω1(ρn,σn), |
for any ρ∈Mω.
Definition 6. Let (M,ω) be a modular metric space. We define an admissible subset of Mω as intersection of modular balls.
Note that if ω satisfies the Fatou property, then the modular balls are ω-closed. Hence any admissible subset is ω-closed.
The heading levels should not be more than 4 levels. The font of heading and subheadings should be 12 point normal Times New Roman. The first letter of headings and subheadings should be capitalized.
It is well-known that every Banach contractive mapping is a continuous function. In 1968, Kannan [6] was the first mathematician who found the answer and presented a fixed point result in the seting of metric space as following.
Theorem 7. [6] Let (M,d) be a complete metric space and J:M→M be a self-mapping satisfying
d(J(ρ),J(σ))≤α (d(ρ,J(ρ))+d(σ,J(σ))), |
∀ρ,σ∈M and α∈[0,12). Then J has a unique fixed point ς∈M, and for any ρ∈M the sequence of itreaive (Jn(ρ)) converges to ς.
Before we state our results, we introduce the defintion of Kannan mappings in modular metic spaces.
Definition 8. Let K be a nonempty subset of Mω. A mapping J:K→K is called Kannan ω -Lipschitzian if ∃α≥0 such that
ω1(J(ρ),J(σ))≤α (ω1(ρ,J(ρ))+ω1(σ,J(σ))), |
∀ρ,σ∈K. The mapping J is said to be:
(1). Kannan ω-contraction if α<1/2;
(2). Kannan ω-nonexpansive if α=1/2.
(3) ς∈K is said to be fixed point of J if J(ς)=ς.
Note that all Kannan ω-Lipschitzian mappings have at most one fixed point due to the regularty of ω.
The following result discusses the existence of fixed point for kannan contraction maps in the setting of modular metric spaces.
Theorem 9. Let (M,ω) be a modular metric space. Assume that K is a nonempty ω-complete of Mω. Let J:K→K be a Kannan ω -contraction mapping. Let ς∈K be such that ω1(ς,J(ς))<∞. Then {Jn(ς)}ω-converges to some τ∈K. Furthermore, we have ω1(τ,J(τ))=∞ or ω1(τ,J(τ))=0 (i.e., τ is the fixed point of J)
Proof. Let ς∈K such that ω1(ς,J(ς))<+∞. Now we establish that {Jn(ς)} is ω-convergent. As K is ω-complete, it suffices to prove that {Jn(ς)} is ω-Cauchy. Since J is a Kannan ω-contraction mapping, so ∃α∈[0,1/2) such that
ω1(J(ρ),J(σ))≤α (ω1(ρ,J(ρ))+ω1(σ,J(σ))), |
for any ρ,σ∈K. Set k=α/(1−α)<1. Furthermore
ω1(Jn(ς),Jn+1(ς))≤α (ω1(Jn−1(ς),Jn(ς))+ω1(Jn(ς),Jn+1(ς))), |
which implies
ω1(Jn(ς),Jn+1(ς))≤α1−α ω1(Jn−1(ς),Jn(ς))=k ω1(Jn−1(ς),Jn(ς)), |
for any n≥1. Hence,
ω1(Jn(ς),Jn+1(ς))≤kn ω1(ς,J(ς)), |
for any n∈N. As J is a Kannan ω -contraction mapping, so we get
ω1(Jn(ς),Jn+h(ς))≤α (ω1(Jn−1(ς),Jn(ς))+ω1(Jn+h−1(ς),Jn+h(ς))), |
which implies
ω1(Jn(ς),Jn+h(ς))≤α (kn−1+kn+h−1)ω1(ς,J(ς)), | (NL) |
for n≥1 and h∈N. As k<1 and ω1(ς,J(ς))<+∞, we conclude that {Jn(ς)} is ω-Cauchy, as claimed. Let τ∈K be the ω-limit of {Jn(ς)}. As K is ω -closed, we get τ∈K. Suppose that ω1(τ,J(τ))<+∞; then we will obtain that ω1(τ,J(τ))=0. As
ω1(Jn(ς),J(τ)))≤α(ω1(Jn−1(ς),Jn(ς))+ω1(τ,J(τ)))≤α(kn−1 ω1(ς,J(ς))+ω1(τ,J(τ))), |
for any n≥1. By the use of Fatou's property, we obtain
ω1(τ,J(τ)))≤lim infn→∞ ω1(Jn(ς),J(τ)))≤α ω1(τ,J(τ)). |
Since α<1/2, we conclude that ω1(τ,J(τ))=0, i.e., τ is the fixed point of J.
The upcoming result is the analogue to Kannan's extention of the classical Banach contraction principle in modular metric space.
Corollary 10. Let K be a nonempty ω-closed subset of Mω. Let J:K→K be a Kannan ω-contraction mapping such that ω1(ρ,J(ρ))<+∞, for any ρ∈K. Then for any ς∈K, {Jn(ς)}ω-converges to the unique fixed point ς of J. Furthermore, if α is the Kannan constant associated to J, then we have
ω1(Jn(ς),τ)≤α(α1−α)n−1ω1(ς,J(ς)), |
for any ρ∈K and n≥1.
Proof. From Theorem 9, we can obtain the proof of first part directly. Using the inequality (NK) and since k<1, we get
lim infh⟶∞ω1(Jn(ς),Jn+h(ς))≤α (kn−1)ω1(ς,J(ς)), | (3.1) |
Now, using the fatou's property, we have
ω1(Jn(ς),τ)≤α kn−1 ω1(ς,J(ς))=α (α1−α)n−1ω1(ς,J(ς)), |
for any n≥1 and ς∈K.
Recall that an admissible subset of Mω is defined as an intersection of modular balls.
Definition 11. We will say that:
(ⅰ). if any decreasing sequence of nonempty ω-bounded admissible subsets in Mω have a nonempty intersection, then Mω is said to satisfy the property (R),
(ⅱ). if for any nonempty ω-bounded admissible subset K with more than one point, there exists ρ∈K such that
ω1(ρ,σ)<δω(K)=sup{ω1(a,b); a,b∈K}, |
for any σ∈K, then Mω is said to satisfy ω-quasi-normal property.
Following technical lemma is very useful in the proof of our theorem.
Lemma 12. Suppose that Mω satisfy the both (R) property and the ω-quasi-normal property. Let K be a nonempty ω-bounded admissible subset of Mω and J:K→K be a Kannan ω-nonexpansive mapping. Fix r>0. Suppose that Ar={ρ∈K; ω1(ρ,J(ρ))≤r}≠∅. Set
Kr=⋂ {Bω(a,t);J(Ar)⊂Bω(a,t)}∩K. |
Then Kr≠∅, ω-closed admissible subset of K and
J(Kr)⊂Kr⊂Ar andδω(Kr)≤r. |
Proof. As J(Ar) is strictly contained in each balls and intersection of all balls contained in Kr. Thus J(Ar)⊂Kr, and Kr is not empty. From definition of admissible set, we deduce that Kr is an admissible subset of K. Let us prove that Kr⊂Ar. Let ρ∈Kr. If ω1(ρ,J(ρ))=0, then obviously we have ρ∈Ar. Otherwise, assume ω1(ρ,J(ρ))>0. Set
s=sup {ω1(J(ς),J(ρ));ς∈Ar}. |
From the definition of s, we have J(Ar)⊂Bω(J(ρ),s). Hence Kr⊂Bω(J(ρ),s), which implies ω1(ρ,J(ρ))≤s. Let ε>0. Then ∃ς∈Ar such that s−ε≤ω1(J(ρ),J(ς)). Hence
ω1(ρ,J(ρ))−ε≤s−ε≤ω1(J(ρ),J(ς))≤12(ω1(ρ,J(ρ))+ω1(ς,J(ς)))≤12(ω1(ρ,J(ρ))+r). |
As we are taking ε an arbitrarily positive number, so we get
ω1(ρ,J(ρ))≤12(ω1(ρ,J(ρ))+r), |
which implies ω1(ρ,J(ρ))≤r, i.e., ρ∈Ar as claimed. Since J(Ar)⊂Kr, we get J(Kr)⊂J(Ar)⊂Kr, i.e., Kr is J-invariant. Now we prove that δω(Kr)≤r. First, we observe that
ω1(J(ρ),J(σ))≤12(ω1(ρ,J(ρ))+ω1(ς,J(ς)))≤r, |
for any ρ,σ∈Ar. Fix ρ∈Ar. Then J(Ar)⊂Bω(J(ρ),r). The definition of Kr implies Kr⊂Bω(J(ρ),r). Thus J(ρ)∈⋂σ∈Kr Bω(σ,r), which implies J(Ar)⊂⋂σ∈Kr Bω(σ,r). Again by the definition of Kr, we get Kr⊂⋂σ∈Kr Bω(σ,r). Therefore, we have ω1(σ,ς)≤r, for any σ,ς∈Kr, i.e., δω(Kr)≤r.
Now, we are able to state and prove our result for ω -nonexpansive Kannan maps on modular metric spaces.
Theorem 13. Suppose that Mω satisfies both the (R) property and the ω-quasi-normal property. Let K be a nonempty ω-bounded admissible subset of Mω and J:K→K is a Kannan ω-nonexpansive mapping. Then J has a fixed point.
Proof. Set r0=inf {ω1(ρ,J(ρ)); ρ∈K} and rn=r0+1/n, for n≥1. By definition of r0, the set Arn={ρ∈K; ω1(ρ,J(ρ))≤rn} is not empty, for any n≥1. Taking Krn defined in Lemma 12. It is simple to analyze that {Krn} is a decreasing sequence of nonempty ω-bounded admissible subsets of K. The property (R) implies that K∞=⋂n≥1 Krn ≠∅. Let ρ∈K∞. Then we have ω1(ρ,J(ρ))≤rn, for any n≥1. If we let n→∞, we get ω1(ρ,J(ρ))≤r0 which implies ω1(ρ,J(ρ))=r0. Hence the set Ar0≠∅. We claim that r0=0. Otherwise, assume r0>0 which implies that J fails to have a fixed point. Again consider the set Kr0 as defined in Lemma 12. Note that since J fails to have a fixed point and Kr0 is J-invariant, then Kr0 has more than one point, i.e., δω(Kr0)>0. It follows from the ω -quasi-normal property that there exists ρ∈Kr0 such that
ω(ρ,σ)<δω(Kr0)≤r0, |
for any σ∈Kr0. From Lemma 12, we know that Kr0⊂Ar0. From the definition of Kr0, we have
J(ρ)∈T(Ar0)⊂Kr0. |
Hence Obviously this will imply
ω1(ρ,J(ρ))<δω(Kr0)≤r0, |
which is a contradiction with the definition of r0. Hence r0=0 which implies that any point in K∞ is a fixed point of J, i.e., J has a fixed point in K.
In this paper, we have introduced some notions to study the existence of fixed points for contractive and nonexpansive Kannan maps in the setting of modular metric spaces.Using the modular convergence sense, which is weaker than the metric convergence we have proved our results. The proved results generalized and improved some of the results of the literature.
This work was funded by the University of Jeddah, Saudi Arabia, under grant No. UJ-02-081-DR. The author, therefore, acknowledges with thanks the University technical and financial support. The author would like to thank Prof. Mohamed Amine Khamsi for his fruitful discussion and continues supporting of this paper.
The author declares that they have no competing interests.
[1] |
C. Depcik, T. Cassady, B. Collicott, S. P. Burugupally, J. Hobeck, Comparison of lithium-ion ion batteries, hydrogen fueled combustion Engines, and a hydrogen fuel cell in powering a small Unmanned Aerial Vehicle, Energy Convers. Manage., 207 (2020), 112514. https://doi.org/10.1016/j.enconman.2020.112514 doi: 10.1016/j.enconman.2020.112514
![]() |
[2] |
M. Chen, G. Rincon-Mora, Accurate electrical battery model capable of predicting runtime and Ⅰ–Ⅴ performance, IEEE Trans. Power Syst., 21 (2006), 504–511. https://doi.org/10.1109/TEC.2006.874229 doi: 10.1109/TEC.2006.874229
![]() |
[3] |
J. B. Goodenough, K. S. Park, The li-ion rechargeable battery: A perspective, J. Am. Chem. Soc., 135 (2013), 1167–1176. https://doi.org/10.1021/ja3091438 doi: 10.1021/ja3091438
![]() |
[4] |
Z. Liu, B. He, Z. Zhang, W. Deng, D. Dong, S. Xia, et al., Lithium/graphene composite anode with 3D structural LiF protection layer for high-performance lithium metal batteries, ACS Appl. Mater. Interfaces., 14 (2022), 2871–2880. https://doi.org/10.1021/acsami.1c21263 doi: 10.1021/acsami.1c21263
![]() |
[5] |
A. Attanayaka, J. Karunadasa, K. Hemapala, Estimation of state of charge for lithium-ion batteries-A review, AIMS Energy, 7 (2019), 186–210. https://doi.org/10.3934/energy.2019.2.186 doi: 10.3934/energy.2019.2.186
![]() |
[6] |
A. Basia, Z. Simeu-Abazi, E. Gascard, P. Zwolinski, Review on state of health estimation methodologies for lithium-ion batteries in the context of circular economy, CIRP J. Manuf. Sci. Technol., 32 (2021), 517–528. https://doi.org/10.1016/j.cirpj.2021.02.004 doi: 10.1016/j.cirpj.2021.02.004
![]() |
[7] |
C. Julien, A. Mauger, A. Abdel-Ghany, A. Hashem, K. Zaghib, Smart materials for energy storage in Li-ion batteries, AIMS Mater. Sci., 3 (2016), 137–148. https://doi.org/10.3934/matersci.2016.1.137 doi: 10.3934/matersci.2016.1.137
![]() |
[8] |
M. Ge, Y. Liu, X. Jiang, J. Liu, A review on state of health estimations and remaining useful life prognostics of lithium-ion batteries, Measurement, 174 (2021), 109057. https://doi.org/10.1016/j.measurement.2021.109057 doi: 10.1016/j.measurement.2021.109057
![]() |
[9] |
C. Hu, B. Youn, P. Wang, J. K. Yoon, Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life, Reliab. Eng. Syst. Saf., 103 (2012), 120–135. https://doi.org/10.1016/j.ress.2012.03.008 doi: 10.1016/j.ress.2012.03.008
![]() |
[10] |
S. Jarid, M. Das, An electro-thermal model based fast optimal charging strategy for lithium-ion batteries, AIMS Energy, 9 (2021), 915–933. https://doi.org/10.3934/energy.2021043 doi: 10.3934/energy.2021043
![]() |
[11] |
G. Ma, Y. Zhang, C. Cheng, B. Zhou, P. Hu, Y. Yuan, Remaining useful life prediction of lithium-ion batteries based on false nearest neighbors and a hybrid neural network, Appl. Energy, 253 (2019), 113626. https://doi.org/10.1016/j.apenergy.2019.113626 doi: 10.1016/j.apenergy.2019.113626
![]() |
[12] |
L. Wu, X. Fu, Y. Guan, Review of the remaining useful life prognostics of vehicle lithium-ion batteries using data-driven methodologies, Appl. Sci., 6 (2016), 166. https://doi.org/10.3390/app6060166 doi: 10.3390/app6060166
![]() |
[13] |
A. Nuhic, T. Terzimehic, T. Soczka-Guth, M. Buchholz, K. Dietmayer, Health diagnosis and remaining useful life prognostics of lithium-ion batteries using data-driven methods, J. Power Sources, 239 (2013), 680–688. https://doi.org/10.1016/j.jpowsour.2012.11.146 doi: 10.1016/j.jpowsour.2012.11.146
![]() |
[14] |
S. Wang, S. Jin, D. Bai, Y. Fan, H. Shi, C. Fernandez, A critical review of improved deep learning methods for the remaining useful life prediction of lithium-ion batteries, Energy Rep., 7 (2021), 5562–5574. https://doi.org/10.1016/j.egyr.2021.08.182 doi: 10.1016/j.egyr.2021.08.182
![]() |
[15] |
N. Khare, P. Singh, J. K. Vassiliou, A novel magnetic field prob-ing technique for determining state of health of sealed lead-acid batteries, J. Power Sources, 218 (2012), 462–473. https://doi.org/10.1016/j.jpowsour.2012.06.085 doi: 10.1016/j.jpowsour.2012.06.085
![]() |
[16] |
A. Mevawalla, Y. Shabeer, M. K. Tran, S. Panchal, M. Fowler, R. Fraser, Thermal modelling utilizing multiple experimentally measurable parameters, Batteries, 8 (2022), 147. https://doi.org/10.3390/batteries8100147 doi: 10.3390/batteries8100147
![]() |
[17] |
Y. Wang, D. Dan, Y. Zhang, Y. Qian, S. Panchal, M. Fowler, et al., A novel heat dissipation structure based on flat heat pipe for battery thermal management system, Int. J. Energy Res., 46 (2022), 15961–15980. https://doi.org/10.1002/er.8294 doi: 10.1002/er.8294
![]() |
[18] |
Y. Xie, W. Li, X. Hu, M. K. Tran, S. Panchal, M. Fowler, et al., Co-estimation of SOC and three-dimensional SOT for lithium-ion batteries based on distributed spatial-temporal online correction, IEEE Trans. Ind. Electron., 2022 (2022), 1–10. https://doi.org/10.1109/TIE.2022.3199905 doi: 10.1109/TIE.2022.3199905
![]() |
[19] | Y. Xing, N. Williard, K. L. Tsui, M. Pecht, A comparative review of prognostics-based reliability methods for Lithium batteries, in 2011 Prognostics and System Health Managment Confernece, 2011. https://doi.org/10.1109/PHM.2011.5939585 |
[20] |
D. Wang, F. Yang, K. L. Tsui, Q. Zhou, B. S. Bae, Remaining useful life prediction of lithium-ion batteries based on spherical cubature particle filter, IEEE Trans. Instrum. Meas., 65 (2016), 1282–1291. https://doi.org/10.1109/TIM.2016.2534258 doi: 10.1109/TIM.2016.2534258
![]() |
[21] |
M. K. Tran, A. DaCosta, A. Mevawalla, S. Panchal, M. Fowler, Comparative study of equivalent circuit models performance in four common lithium-ion batteries: LFP, NMC, LMO, NCA, Batteries, 7 (2021), 51. https://doi.org/10.3390/batteries7030051 doi: 10.3390/batteries7030051
![]() |
[22] |
Z. Lyu, R. Gao, L. Chen, Li-ion battery state of health estimation and remaining useful life prediction through a model-data-fusion method, IEEE Trans. Power Electron., 36 (2021), 6228–6240. https://doi.org/10.1109/TPEL.2020.3033297 doi: 10.1109/TPEL.2020.3033297
![]() |
[23] |
S. Wang, P. Ren, P. Takyi-Aninakwa, S. Jin, C. Fernandez, A critical review of improved deep convolutional neural network for multi-timescale state prediction of lithium-ion batteries, Energies, 15 (2022), 5053. https://doi.org/10.3390/en15145053 doi: 10.3390/en15145053
![]() |
[24] |
S. Jin, X. Sui, X. Huang, S. Wang, R. Teodorescu, D. I. Stroe, Overview of machine learning methods for lithium-ion battery remaining useful lifetime prediction, Electronics, 10 (2021), 3126. https://doi.org/10.3390/electronics10243126 doi: 10.3390/electronics10243126
![]() |
[25] |
P. Khumprom, N. Yodo, A data-driven predictive prognostic model for lithium-ion batteries based on a deep learning algorithm, Energies, 12 (2019), 660. https://doi.org/10.3390/en12040660 doi: 10.3390/en12040660
![]() |
[26] |
L. Cai, J. Meng, D. I. Stroe, J. Peng, R. Teodorescu, Multi-objective optimization of data-driven model for lithium-ion battery SOH estimation with short-term feature, IEEE Trans. Power Electron., 35 (2020), 11855–11864. https://doi.org/10.1109/TPEL.2020.2987383 doi: 10.1109/TPEL.2020.2987383
![]() |
[27] |
T. Qin, S. Zeng, J. Guo, Robust prognostics for state of health estimation of lithium-ion batteries based on an improved PSO-SVR model, Microelectron. Reliab., 55 (2015), 1280–1284. https://doi.org/10.1016/j.microrel.2015.06.133 doi: 10.1016/j.microrel.2015.06.133
![]() |
[28] | Y. Cai, Y. Lin, Z. Deng, X. Zhao, D. Hao, Prediction of lithium-ion battery remaining useful life based on hybrid data-driven method with optimized parameter, in 2017 2nd International Conference on Power and Renewable Energy (ICPRE), 2017. https://doi.org/10.1109/ICPRE.2017.8390489 |
[29] |
B. Gou, Y. Xu, X. Feng, State-of-health estimation and remaining useful life prediction for lithium-ion battery using a hybrid data-driven method, IEEE Trans. Veh. Technol., 69 (2020), 10854–10867. https://doi.org/10.1109/TVT.2020.3014932 doi: 10.1109/TVT.2020.3014932
![]() |
[30] |
G. Ma, Y. Zhang, C. Cheng, B. Zhou, Y. Yuan, Remaining useful life prediction of lithium-ion batteries based on false nearest neighbors and a hybrid neural network, Appl. Energy, 253 (2019), 113626. https://doi.org/10.1016/j.apenergy.2019.113626 doi: 10.1016/j.apenergy.2019.113626
![]() |
[31] |
Y. Zhang, R. Xiong, H. He, M. Pecht, Long short-term memory recurrent neural network for remaining useful life prediction of lithium-ion batteries, IEEE Trans. Veh. Technol., 67 (2018), 5695–5705. https://doi.org/10.1109/TVT.2018.2805189 doi: 10.1109/TVT.2018.2805189
![]() |
[32] |
S. Yalçın, S. Panchal, M. S. Herdem, A CNN-ABC model for estimation and optimization of heat generation rate and voltage distributions of lithium-ion batteries for electric vehicles, Int. J. Heat Mass. Tran., 199 (2022), 123486. https://doi.org/10.1016/j.ijheatmasstransfer.2022.123486 doi: 10.1016/j.ijheatmasstransfer.2022.123486
![]() |
[33] |
F. Wang, Z. Zhao, J. Ren, Z. Zhai, S. Wang, X. Chen, A transferable lithium-ion battery remaining useful life prediction method from cycle-consistency of degradation trend, J. Power Sources, 521 (2022), 230975. https://doi.org/10.1016/j.jpowsour.2022.230975 doi: 10.1016/j.jpowsour.2022.230975
![]() |
[34] |
S. Wang, P. Takyi-Aninakwa, S. Jin, C. Yu, C. Fernandez, D. I. Stroe, An improved feedforward-long short-term memory modeling method for the whole-life-cycle state of charge prediction of lithium-ion batteries considering current-voltage-temperature variation, Energy, 254 (2022), 124224. https://doi.org/10.1016/j.energy.2022.124224 doi: 10.1016/j.energy.2022.124224
![]() |
[35] |
M. Xia, X. Zheng, M. Imran, M. Shoaib, Data-driven prognosis method using hybrid deep recurrent neural network, Appl. Soft Comput., 93 (2020), 106351. https://doi.org/10.1016/j.asoc.2020.106351 doi: 10.1016/j.asoc.2020.106351
![]() |
[36] |
A. Kara, A data-driven approach based on deep neural networks for lithium-ion battery prognostics, Neural Comput. Appl., 33 (2021), 13525–13538. https://doi.org/10.1007/s00521-021-05976-x doi: 10.1007/s00521-021-05976-x
![]() |
[37] |
C. Wang, N. Lu, S. Wang, Y. Cheng, B. Jiang, Dynamic long short-term memory neural-network-based indirect remaining-useful-life prognosis for satellite lithium-ion battery, Appl. Sci., 8 (2018), 2078. https://doi.org/10.3390/app8112078 doi: 10.3390/app8112078
![]() |
[38] |
P. Li, Z. Zhang, Q. Xiong, B. Ding, S. Li, State-of-health estimation and remaining useful life prediction for the lithium-ion battery based on a variant long short term memory neural network, J. Power Sources, 459 (2020), 228069. https://doi.org/10.1016/j.jpowsour.2020.228069 doi: 10.1016/j.jpowsour.2020.228069
![]() |
[39] |
M. Geraldi, E. Ghisi, Data-driven framework towards realistic bottom-up energy benchmarking using an Artificial Neural Network, Appl. Energy, 306 (2022), 117960. https://doi.org/10.1016/j.apenergy.2021.117960 doi: 10.1016/j.apenergy.2021.117960
![]() |
[40] |
R. Lei, Z. Li, H. Sheng, S. Zhao, W. Hao, Z. Lin, Remaining useful life prediction for lithium-ion battery: A deep learning approach, IEEE Access, 6 (2018), 50587–50598. https://doi.org/10.1109/ACCESS.2018.2858856 doi: 10.1109/ACCESS.2018.2858856
![]() |
[41] |
N. Harting, R. Schenkendorf, N. Wolff, U. Krewer, State-of-health identification of lithium-ion batteries based on nonlinear frequency response analysis: First steps with machine learning, Appl. Sci., 8 (2018), 821. https://doi.org/10.3390/app8050821 doi: 10.3390/app8050821
![]() |
[42] |
B. Zraibi, C. Okar, H. Chaoui, M. Mansouri, Remaining useful life assessment for lithium-ion batteries using CNN-LSTM-DNN hybrid method, IEEE Trans. Veh. Technol., 70 (2021), 4252–4261. https://doi.org/10.1109/TVT.2021.3071622 doi: 10.1109/TVT.2021.3071622
![]() |
[43] |
Y. Anagun, S. Isik, E. Seke, SRLibrary: Comparing different loss functions for super-resolution over various convolutional architectures, J. Visual Commun. Image Represent., 61 (2019), 178–187. https://doi.org/10.1016/j.jvcir.2019.03.027 doi: 10.1016/j.jvcir.2019.03.027
![]() |
[44] |
Y. Zhou, M. Huang, Lithium-ion batteries remaining useful life prediction based on a mixture of empirical mode decomposition and ARIMA model, Microelectron. Reliab., 65 (2016), 265–273. https://doi.org/10.1016/j.microrel.2016.07.151 doi: 10.1016/j.microrel.2016.07.151
![]() |
[45] |
R. Sekhar, P. Shah, S. Panchal, M. Fowler, R. Fraser, Distance to empty soft sensor for ford escape electric vehicle, Results Control Optim., 9 (2022), 100168, https://doi.org/10.1016/j.rico.2022.100168 doi: 10.1016/j.rico.2022.100168
![]() |
[46] |
M. Li, J. Lu, Z. Chen, K. Amine, 30 years of lithium-ion batteries, Adv. Mater., 30 (2018), 1800561. https://doi.org/10.1002/adma.201800561 doi: 10.1002/adma.201800561
![]() |
[47] |
M. M. Kabir, D. E. Demirocak, Degradation mechanisms in Li-ion batteries: a state-of-the-art review, Int. J. Energy Res., 41 (2017), 1963–1986. https://doi.org/10.1002/er.3762 doi: 10.1002/er.3762
![]() |
[48] |
J. Vetter, P. Novák, M. R. Wagner, C. Veit, K. C. Möller, J. O. Besenhard, et al., Ageing mechanisms in lithium-ion batterie, J. Power Sources, 147 (2005), 269–281. https://doi.org/10.1016/j.jpowsour.2005.01.006 doi: 10.1016/j.jpowsour.2005.01.006
![]() |
[49] | B. Saha, K. Goebel, Battery data set, in NASA Ames Prognostics Data Repository, 2007. Available from: http://ti.arc.nasa.gov/project/prognostic-data-repository. |
[50] |
L. Alzubaidi, J. Zhang, A. J. Humaidi, A. Al-Dujaili, Y. Duan, O. Al-Shamma, et al., Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions, J. Big Data, 8 (2021), 1–74. https://doi.org/10.1186/s40537-021-00444-8 doi: 10.1186/s40537-021-00444-8
![]() |
[51] |
D. Yao, B. Li, H. Liu, J. Yang, L. Jia, Remaining useful life prediction of roller bearings based on improved 1D-CNN and simple recurrent unit, Measurement, 175 (2021), 109166. https://doi.org/10.1016/j.measurement.2021.109166 doi: 10.1016/j.measurement.2021.109166
![]() |
[52] |
A. Sherstinsky, Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network, Physica D, 404 (2020), 132306. https://doi.org/10.1016/j.physd.2019.132306 doi: 10.1016/j.physd.2019.132306
![]() |
[53] |
Z. Shi, A. Chehade, A dual-LSTM framework combining change point detection and remaining useful life prediction, Reliab. Eng. Syst. Saf., 205 (2021), 107257. https://doi.org/10.1016/j.ress.2020.107257 doi: 10.1016/j.ress.2020.107257
![]() |
[54] |
Y. Choi, S. Ryu, K. Park, H. Kim, Machine learning-based lithium-ion battery capacity estimation exploiting multi-channel charging profiles, IEEE Access, 7 (2019), 75143–75152. https://doi.org/10.1109/ACCESS.2019.2920932 doi: 10.1109/ACCESS.2019.2920932
![]() |
[55] |
X. Hu, J. Jiang, D. Cao, B. Egardt, Battery health prognosis for electric vehicles using sample entropy and sparse bayesian predictive modeling, IEEE Trans. Ind. Electron., 63 (2016), 2645–2656. https://doi.org/10.1109/TIE.2015.2461523 doi: 10.1109/TIE.2015.2461523
![]() |
1. | Alireza Alihajimohammad, Reza Saadati, Generalized modular fractal spaces and fixed point theorems, 2021, 2021, 1687-1847, 10.1186/s13662-021-03538-y | |
2. | Godwin Amechi Okeke, Daniel Francis, Aviv Gibali, On fixed point theorems for a class of α-ˆv-Meir–Keeler-type contraction mapping in modular extended b-metric spaces, 2022, 30, 0971-3611, 1257, 10.1007/s41478-022-00403-3 | |
3. | Mahpeyker Öztürk, Abdurrahman Büyükkaya, Fixed point results for Suzuki‐type Σ‐contractions via simulation functions in modular b ‐metric spaces , 2022, 45, 0170-4214, 12167, 10.1002/mma.7634 | |
4. | Olivier Olela Otafudu, Katlego Sebogodi, On w-Isbell-convexity, 2022, 23, 1989-4147, 91, 10.4995/agt.2022.15739 | |
5. | Maria del Mar Bibiloni-Femenias, Oscar Valero, Modular Quasi-Pseudo Metrics and the Aggregation Problem, 2024, 12, 2227-7390, 1826, 10.3390/math12121826 | |
6. | Daniel Francis, Godwin Amechi Okeke, Aviv Gibali, Another Meir-Keeler-type nonlinear contractions, 2025, 10, 2473-6988, 7591, 10.3934/math.2025349 |