阅读说明：本技术 一种电动自行车的里程计算方法和系统 (Mileage calculation method and system for electric bicycle ) 是由 陈学清 梁日基 李远军 于 2021-08-28 设计创作，主要内容包括：本发明涉及电动自行车技术领域,公开了一种电动自行车的里程计算方法和系统,本发明技术方案包括：构建电池性能分析模型,通过电池性能分析模型采集不同使用状态下的a个电池性能系数；获取a个电池性能系数下对应的a组剩余电量与剩余里程的数据点集；将每组剩余电量与剩余里程的数据点集分别进行拟合,生成a个关系函数；将a个电池性能系数与a个关系函数构建映射关系,从而形成里程计算模型,其中a>0。通过对电池性能影响因素的全面分析,从而对电动自行车的里程进行准确的计算。(The invention relates to the technical field of electric bicycles, and discloses a method and a system for calculating mileage of an electric bicycle, wherein the technical scheme of the invention comprises the following steps: constructing a battery performance analysis model, and acquiring a battery performance coefficients in different use states through the battery performance analysis model; acquiring a data point set of a group of residual electric quantity and residual mileage corresponding to a battery performance coefficients; respectively fitting each group of residual electric quantity and data point sets of residual mileage to generate a relation functions; and constructing a mapping relation between the a battery performance coefficients and the a relation functions so as to form a mileage calculation model, wherein a > 0. Through the comprehensive analysis of the battery performance influence factors, the mileage of the electric bicycle is accurately calculated.)

1. A mileage calculation method of an electric bicycle, comprising:

constructing a battery performance analysis model, and acquiring a battery performance coefficients in different use states through the battery performance analysis model;

acquiring a data point set of a group of residual electric quantity and residual mileage corresponding to a battery performance coefficients;

respectively fitting each group of residual electric quantity and data point sets of residual mileage to generate a relation functions;

and constructing a mapping relation between the a battery performance coefficients and the a relation functions so as to form a mileage calculation model, wherein a > 0.

2. The method for calculating the mileage of an electric bicycle according to claim 1, wherein the battery performance analysis model is constructed by:

classifying negative influence factors of the battery health;

b first factors and second factors corresponding to each first factor are determined;

and performing fusion calculation on the first factor and the second factor to form a battery performance analysis model.

3. The method for calculating the mileage of an electric bicycle according to claim 2, wherein the b first factors are determined, and the second factor corresponding to each first factor is specifically:

constructing a mapping relation between the first factor and the second factor;

and storing the influence degree of the second factor corresponding to the first factor in the mapping relation.

4. The method for calculating the mileage of an electric bicycle according to claim 3, wherein the mapping relationship between the first factor and the second factor is constructed by:

creating a first factor node and a second factor node;

constructing a data packet between the first factor node and the second factor node;

the mapping relationship is stored in a data packet.

5. The method for calculating the mileage of the electric bicycle according to claim 4, wherein the fusion calculation is specifically:

respectively constructing N +1 judgment matrixes according to the influence degree of the first factor on the performance of the battery and the influence degree of the second factor corresponding to the first factor, and performing consistency check on each judgment matrix;

after each judgment matrix passes consistency check, obtaining the influence weight of each second factor on the battery according to the N +1 maximum eigenvectors of the N +1 judgment matrices;

and calculating the battery performance coefficient according to the influence weight of each second factor.

6. The method for calculating the mileage of the electric bicycle according to claim 5, wherein the calculating the battery performance coefficient according to the influence weight of each of the second factors is specifically: obtaining individual scores for all second factors; the battery performance coefficient is the sum of the individual scores of all the second factors multiplied by the corresponding impact weights.

7. The method for calculating the mileage of the electric bicycle according to claim 1, wherein the fitting of each set of the remaining capacity and the remaining mileage data point set is specifically: and performing curve fitting on the data point set of the residual electric quantity and the residual mileage, and outputting a functional relation between the residual electric quantity and the residual mileage.

8. A mileage calculating system of an electric bicycle, comprising:

the battery performance coefficient acquisition module: the battery performance coefficient acquisition module is used for constructing a battery performance analysis model and acquiring a battery performance coefficients in different use states through the battery performance analysis model;

a data point set acquisition module: the data point set acquisition module is used for acquiring a data point set of a group of residual electric quantity and residual mileage corresponding to a battery performance coefficients;

a fitting module: the fitting module is used for respectively fitting each group of residual electric quantity and data point sets of residual mileage to generate a relation functions;

the mileage calculation model generation module: the mileage calculation model generation module is used for constructing a mapping relation between the a battery performance coefficients and the a relation functions so as to form a mileage calculation model, wherein a > 0.

The battery performance coefficient acquisition module is connected with the data point set acquisition module; the data point set acquisition module is connected with the fitting module; the fitting module is connected with the mileage calculation model generation module.

9. A computer-readable storage medium, characterized in that it stores computer program instructions adapted to be loaded by a processor and to execute the method of any of claims 1 to 7

10. A mobile terminal comprising a processor and a memory, the processor being configured to execute a program stored in the memory to implement the method of any one of claims 1 to 7.

Technical Field

The invention relates to the technical field of electric bicycles, in particular to a mileage calculation method and system of an electric bicycle.

Background

The mileage of the electric bicycle is estimated according to the endurance thereof. However, the cruising ability of the battery gradually decreases with the aging of the battery, so that the mileage is not accurately estimated, and a great influence is generated on a user.

The prior art has a single consideration on the endurance, but the factors actually influencing the endurance of the battery include high or low temperature, the state of charge of the battery, and the like. Single factor considerations tend to result in inaccuracies in the estimates.

Disclosure of Invention

In view of this, the present application provides a method and a system for calculating a mileage of an electric bicycle, which accurately calculate the mileage of the electric bicycle by comprehensively analyzing battery performance influencing factors. The scheme is as follows:

a mileage calculation method of an electric bicycle, comprising:

constructing a battery performance analysis model, and acquiring a battery performance coefficients in different use states through the battery performance analysis model;

acquiring a data point set of a group of residual electric quantity and residual mileage corresponding to a battery performance coefficients;

respectively fitting each group of residual electric quantity and data point sets of residual mileage to generate a relation functions;

and constructing a mapping relation between the a battery performance coefficients and the a relation functions so as to form a mileage calculation model, wherein a > 0.

Preferably, the method for constructing the battery performance analysis model comprises the following steps:

classifying negative influence factors of the battery health;

b first factors and second factors corresponding to each first factor are determined;

and performing fusion calculation on the first factor and the second factor to form a battery performance analysis model. There are many factors affecting the battery performance, and the factors need to be classified, and the factors can be connected to perform fusion analysis after the first factor and the corresponding second factor are determined. The method can comprehensively analyze the battery performance from multiple dimensions through the fusion calculation of the influence factors.

Preferably, the b first factors are determined, and the second factor corresponding to each first factor is specifically:

constructing a mapping relation between the first factor and the second factor;

and storing the influence degree of the second factor corresponding to the first factor in the mapping relation. The influence degrees are stored in the mapping relations, and the influence degrees correspond to the mapping relations one to one, so that the confusion of data can be prevented when the data are called, and data calling errors are caused.

Preferably, the method for constructing the mapping relationship between the first factor and the second factor comprises:

creating a first factor node and a second factor node;

constructing a data packet between the first factor node and the second factor node;

the mapping relationship is stored in a data packet. The data packet can represent the relationship between the flow direction of the data and the data group, and the relationship between the first factor and the second factor can be determined through the data packet, so that the mapping relationship is formed.

Preferably, the fusion calculation is specifically:

respectively constructing N +1 judgment matrixes according to the influence degree of the first factor on the performance of the battery and the influence degree of the second factor corresponding to the first factor, and performing consistency check on each judgment matrix;

after each judgment matrix passes consistency check, obtaining the influence weight of each second factor on the battery according to the N +1 maximum eigenvectors of the N +1 judgment matrices;

and calculating the battery performance coefficient according to the influence weight of each second factor. The fusion calculation can accurately determine the influence weight of each second factor on the battery performance, so that data support is provided for subsequent analysis, and the subsequent analysis is more accurate.

Preferably, the calculating the battery performance coefficient according to the influence weight of each second factor specifically includes: obtaining individual scores for all second factors; the battery performance coefficient is the sum of the individual scores of all the second factors multiplied by the corresponding impact weights. The battery coefficient of performance can be specifically calculated by this step.

Preferably, the fitting of each group of remaining capacity and remaining mileage data point sets is specifically: and performing curve fitting on the data point set of the residual electric quantity and the residual mileage, and outputting a functional relation between the residual electric quantity and the residual mileage.

A mileage calculating system of an electric bicycle, comprising:

the battery performance coefficient acquisition module: the battery performance coefficient acquisition module is used for constructing a battery performance analysis model and acquiring a battery performance coefficients in different use states through the battery performance analysis model;

a data point set acquisition module: the data point set acquisition module is used for acquiring a data point set of a group of residual electric quantity and residual mileage corresponding to a battery performance coefficients;

a fitting module: the fitting module is used for respectively fitting each group of residual electric quantity and data point sets of residual mileage to generate a relation functions;

the mileage calculation model generation module: the mileage calculation model generation module is used for constructing a mapping relation between the a battery performance coefficients and the a relation functions so as to form a mileage calculation model, wherein a > 0.

The battery performance coefficient acquisition module is connected with the data point set acquisition module; the data point set acquisition module is connected with the fitting module; the fitting module is connected with the mileage calculation model generation module.

A computer readable storage medium having stored thereon computer program instructions adapted to be loaded by a processor and to execute said method for calculating a mileage of an electric bicycle.

A mobile terminal comprises a processor and a memory, wherein the processor is used for executing a program stored in the memory so as to realize the mileage calculation method of an electric bicycle.

According to the method and the system for calculating the mileage of the electric bicycle, provided by the technical scheme of the invention, the mileage of the electric bicycle is accurately calculated by combining the analysis of the battery performance influence factors. Specifically, a battery performance analysis model is constructed by combining various battery performance influence factors, the battery performance analysis model is fitted to a relation function of the remaining electric quantity and the remaining mileage to obtain a mileage calculation model, and the remaining mileage corresponding to the current electric quantity is calculated through the mileage calculation model. The calculation result is more comprehensive and accurate.

Drawings

In order to more clearly illustrate the embodiments of the present application or technical solutions in related arts, the drawings used in the description of the embodiments or prior arts will be briefly introduced below, it is obvious that the drawings in the following description are only embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.

The structure, proportion, size and the like shown in the drawings are only used for matching with the content disclosed in the specification, so that the person skilled in the art can understand and read the description, and the description is not used for limiting the limit condition of the implementation of the invention, so the method has no technical essence, and any structural modification, proportion relation change or size adjustment still falls within the scope of the technical content disclosed by the invention without affecting the effect and the achievable purpose of the invention.

Fig. 1 is a schematic flowchart of a method for calculating a mileage of an electric bicycle according to an embodiment of the present invention;

fig. 2 is a schematic structural diagram of another electric bicycle mileage calculation system according to an embodiment of the present invention.

Detailed Description

The embodiments of the present application will be described more fully hereinafter with reference to the accompanying drawings, in which some, but not all embodiments of the invention are shown. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

The invention aims to comprehensively analyze and recommend the remaining mileage of the battery by combining a plurality of influence factors of the battery endurance capacity, and provide good use experience for users. However, the inventor finds that the remaining capacity and the remaining mileage of the battery are not a regular function curve under different aging degrees, so that the calculation difficulty of the mileage is greatly increased, and therefore, the inventor can analyze the relationship function by the aid of the following curve fitting method, and can analyze a plurality of influence factors of the battery endurance, and the specific scheme is as follows:

as shown in fig. 1, fig. 1 is a method for calculating a mileage of an electric bicycle, including:

s1, constructing a battery performance analysis model, and acquiring a battery performance coefficients in different use states through the battery performance analysis model;

s2, acquiring a data point set of a group of residual electric quantity and residual mileage corresponding to a battery performance coefficients;

s3, fitting each group of residual electric quantity and data point sets of residual mileage respectively to generate a relation functions;

and S4, constructing a mapping relation between the a battery performance coefficients and the a relation functions to form a mileage calculation model, wherein a is greater than 0.

The battery performance is divided into a plurality of stages, and a is an integer and represents a plurality of states, and the battery performance has a plurality of service conditions, namely a plurality of different service states, so that the battery performance coefficients in the a service states need to be obtained, a plurality of data point sets of the residual electric quantity and the residual mileage under the battery performance coefficients are collected, and the data point sets are fitted to generate a relation function, so the battery performance coefficients in each state correspond to one relation function, and the mileage calculation model is generated by corresponding the battery performance coefficients and the relation functions one by one. During calculation, the battery performance coefficient in the state needs to be analyzed, then the battery performance coefficient is input into the mileage calculation model, a corresponding relation function of the remaining electric quantity and the remaining mileage can be found out, and the remaining mileage is calculated according to the remaining electric quantity.

In specific implementation, the invention adopts an analytic hierarchy process to arrange the factors influencing the battery performance according to the sequence of the total scores, and obtains a battery performance analysis model through the importance analysis and calculation between every two factors, and the analytic hierarchy process specifically comprises the following steps:

step S1: and (3) constructing a battery performance analysis model, and acquiring a battery performance coefficients in different use states through the battery performance analysis model. The construction method of the battery performance analysis model comprises the following steps: classifying negative influence factors of the battery health; b first factors and second factors corresponding to each first factor are determined; and performing fusion calculation on the first factor and the second factor to form a battery performance analysis model. There are many factors affecting the battery performance, and the factors need to be classified, and the factors can be connected to perform fusion analysis after the first factor and the corresponding second factor are determined. The method can comprehensively analyze the battery performance from multiple dimensions through the fusion calculation of the influence factors. The first factor is a main factor, the second factor is a specific factor, and the combination of the first factor and the second factor can evaluate the performance of the battery more systematically and accurately.

The determining b first factors and the second factor corresponding to each first factor are specifically as follows: constructing a mapping relation between the first factor and the second factor; and storing the influence degree of the second factor corresponding to the first factor in the mapping relation. The influence degrees are stored in the mapping relations, and the influence degrees correspond to the mapping relations one to one, so that the confusion of data can be prevented when the data are called, and data calling errors are caused.

The construction method of the mapping relation between the first factor and the second factor comprises the following steps: creating a first factor node and a second factor node; constructing a data packet between the first factor node and the second factor node; the mapping relationship is stored in a data packet. The data packet can represent the relationship between the flow direction of the data and the data group, and the relationship between the first factor and the second factor can be determined through the data packet, so that the mapping relationship is formed.

After the battery performance analysis model is constructed, the second factor influencing the battery is graded,

the fusion calculation specifically comprises: respectively constructing N +1 judgment matrixes according to the influence degree of the first factor on the performance of the battery and the influence degree of the second factor corresponding to the first factor, and performing consistency check on each judgment matrix; after each judgment matrix passes consistency check, obtaining the influence weight of each second factor on the battery according to the N +1 maximum eigenvectors of the N +1 judgment matrices; and calculating the battery performance coefficient according to the influence weight of each second factor.

The calculating the battery performance coefficient according to the influence weight of each second factor specifically comprises: obtaining individual scores for all second factors; the battery performance coefficient is the sum of the individual scores of all the second factors multiplied by the corresponding impact weights.

The influence weight is one embodiment of influence degree, when the weight among the factors of each layer is determined, two factors are compared with each other, the difficulty of comparing the factors with different properties with each other is reduced as much as possible, and the accuracy is improved.

After a large amount of research is carried out according to historical data, the relative weight value of the first factors of the middle layer, which affects the performance of the highest-layer battery, is determined, and a judgment matrix of a criterion layer is constructed. Similarly, according to the relative weight values of the second factors of the bottommost layer to the first factors of the corresponding middle layer, N judgment matrixes of the bottommost layer are respectively constructed, and N +1 judgment matrixes are obtained in total. For each judgment matrix, the random consistency ratio needs to be calculated, and if the random consistency ratio is less than 0.1, the judgment matrix is reasonable in structure and can be used for calculating the weight.

The process of calculating the random consistency ratio is as follows:

and calculating the maximum eigenvector of each judgment matrix by using a sum-product method, obtaining the maximum characteristic root from the maximum eigenvector, and calculating the consistency index C.I of the matrix through the maximum characteristic root, wherein the larger the value of C.I, the larger the deviation degree of the judgment matrix from the complete consistency is, and otherwise, the closer the judgment matrix is to the complete consistency.

Step S2: and acquiring a data point set of the corresponding a group of residual electric quantity and residual mileage under the a battery performance coefficients. And testing the batteries under different use conditions, and acquiring a group of data point sets of the residual electric quantity and the residual mileage under each battery performance coefficient so as to acquire a group a.

Step S3: and respectively fitting the data point sets of each group of residual electric quantity and residual mileage to generate a relation functions. The step of respectively fitting the data point sets of each group of residual electric quantity and residual mileage specifically comprises the following steps: and performing curve fitting on the data point set of the residual electric quantity and the residual mileage, and outputting a functional relation between the residual electric quantity and the residual mileage. Curve fitting is a method of approximating discrete data by analytical expressions, in which a continuous curve is used to approximately describe or compare the functional relationship between coordinates represented by a set of discrete points on a plane. In the invention, a suitable curve fitting method can be selected, and the specific method is not limited.

Step S4: and constructing a mapping relation between the a battery performance coefficients and the a relation functions so as to form a mileage calculation model, wherein a > 0.

The fitting of the battery performance coefficient to the relation function is specifically as follows: establishing a relation function of the remaining electric quantity and the remaining mileage; acquiring a battery performance coefficient; fitting the battery performance coefficient with the relation function to obtain a fitted function equation; and obtaining the current residual electric quantity, inputting the current residual electric quantity into a fitting equation, and obtaining the residual mileage.

As shown in fig. 2, fig. 2 is a mileage calculating system of an electric bicycle, including:

battery coefficient of performance collection module 1: the battery performance coefficient acquisition module 1 is used for constructing a battery performance analysis model and acquiring a battery performance coefficients in different use states through the battery performance analysis model;

the data point set acquisition module 2: the data point set acquisition module 2 is used for acquiring a data point set of a group of residual electric quantity and residual mileage corresponding to a battery performance coefficients;

and a fitting module 3: the fitting module 3 is used for respectively fitting each group of residual electric quantity and data point sets of residual mileage to generate a relation functions;

the mileage calculation model generation module 4: the mileage calculation model generation module 4 is configured to construct a mapping relationship between the a battery performance coefficients and the a relationship functions, so as to form a mileage calculation model, where a > 0.

The battery performance coefficient acquisition module 1 is connected with the data point set acquisition module 2; the data point set acquisition module 2 is connected with the fitting module 3; the fitting module 3 is connected with a mileage calculation model generation module 4.

The battery performance coefficient acquisition module 1 is used for constructing a battery performance analysis model and acquiring a battery performance coefficients in different use states through the battery performance analysis model; the construction method of the battery performance analysis model comprises the following steps: classifying negative influence factors of the battery health; b first factors and second factors corresponding to each first factor are determined; and performing fusion calculation on the first factor and the second factor to form a battery performance analysis model. There are many factors affecting the battery performance, and the factors need to be classified, and the factors can be connected to perform fusion analysis after the first factor and the corresponding second factor are determined. The method can comprehensively analyze the battery performance from multiple dimensions through the fusion calculation of the influence factors. The first factor is a main factor, the second factor is a specific factor, and the combination of the first factor and the second factor can evaluate the performance of the battery more systematically and accurately.

The determining b first factors and the second factor corresponding to each first factor are specifically as follows: constructing a mapping relation between the first factor and the second factor; and storing the influence degree of the second factor corresponding to the first factor in the mapping relation. The influence degrees are stored in the mapping relations, and the influence degrees correspond to the mapping relations one to one, so that the confusion of data can be prevented when the data are called, and data calling errors are caused.

The construction method of the mapping relation between the first factor and the second factor comprises the following steps: creating a first factor node and a second factor node; constructing a data packet between the first factor node and the second factor node; the mapping relationship is stored in a data packet. The data packet can represent the relationship between the flow direction of the data and the data group, and the relationship between the first factor and the second factor can be determined through the data packet, so that the mapping relationship is formed.

After the battery performance analysis model is constructed, the second factor influencing the battery is graded,

the fusion calculation specifically comprises: respectively constructing N +1 judgment matrixes according to the influence degree of the first factor on the performance of the battery and the influence degree of the second factor corresponding to the first factor, and performing consistency check on each judgment matrix; after each judgment matrix passes consistency check, obtaining the influence weight of each second factor on the battery according to the N +1 maximum eigenvectors of the N +1 judgment matrices; and calculating the battery performance coefficient according to the influence weight of each second factor.

The calculating the battery performance coefficient according to the influence weight of each second factor specifically comprises: obtaining individual scores for all second factors; the battery performance coefficient is the sum of the individual scores of all the second factors multiplied by the corresponding impact weights.

The influence weight is one embodiment of influence degree, when the weight among the factors of each layer is determined, two factors are compared with each other, the difficulty of comparing the factors with different properties with each other is reduced as much as possible, and the accuracy is improved.

After a large amount of research is carried out according to historical data, the relative weight value of the first factors of the middle layer, which affects the performance of the highest-layer battery, is determined, and a judgment matrix of a criterion layer is constructed. Similarly, according to the relative weight values of the second factors of the bottommost layer to the first factors of the corresponding middle layer, N judgment matrixes of the bottommost layer are respectively constructed, and N +1 judgment matrixes are obtained in total. For each judgment matrix, the random consistency ratio needs to be calculated, and if the random consistency ratio is less than 0.1, the judgment matrix is reasonable in structure and can be used for calculating the weight.

The process of calculating the random consistency ratio is as follows:

and calculating the maximum eigenvector of each judgment matrix by using a sum-product method, obtaining the maximum characteristic root from the maximum eigenvector, and calculating the consistency index C.I of the matrix through the maximum characteristic root, wherein the larger the value of C.I, the larger the deviation degree of the judgment matrix from the complete consistency is, and otherwise, the closer the judgment matrix is to the complete consistency.

The data point set acquisition module 2 is used for acquiring a data point set of a group of residual electric quantity and residual mileage corresponding to a battery performance coefficients; and testing the batteries under different use conditions, and acquiring a group of data point sets of the residual electric quantity and the residual mileage under each battery performance coefficient so as to acquire a group a.

The fitting module 3 is used for respectively fitting each group of residual electric quantity and data point sets of residual mileage to generate a relation functions; the step of respectively fitting the data point sets of each group of residual electric quantity and residual mileage specifically comprises the following steps: and performing curve fitting on the data point set of the residual electric quantity and the residual mileage, and outputting a functional relation between the residual electric quantity and the residual mileage. Curve fitting is a method of approximating discrete data by analytical expressions, in which a continuous curve is used to approximately describe or compare the functional relationship between coordinates represented by a set of discrete points on a plane. In the invention, a suitable curve fitting method can be selected, and the specific method is not limited.

The mileage calculation model generation module 4 is configured to construct a mapping relationship between the a battery performance coefficients and the a relationship functions, so as to form a mileage calculation model, where a > 0. The fitting of the battery performance coefficient to the relation function is specifically as follows: establishing a relation function of the remaining electric quantity and the remaining mileage; acquiring a battery performance coefficient; fitting the battery performance coefficient with the relation function to obtain a fitted function equation; and obtaining the current residual electric quantity, inputting the current residual electric quantity into a fitting equation, and obtaining the residual mileage.

In addition to the above, the present invention also provides a computer-readable storage medium storing computer program instructions adapted to be loaded by a processor and to perform the mileage calculating method of an electric bicycle.

In addition to the above, the present invention further provides a mobile terminal, which includes a processor and a memory, wherein the processor is configured to execute a program stored in the memory to implement the mileage calculating method for an electric bicycle.

The embodiments in the present description are described in a progressive manner, or in a parallel manner, or in a combination of a progressive manner and a parallel manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments can be referred to each other.

It should be noted that in the description of the present invention, it is to be understood that the terms "upper", "lower", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, are only used for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention. When a component is referred to as being "connected" to another component, it can be directly connected to the other component or intervening components may also be present.

It is further noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that an article or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such article or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in an article or device that comprises the element.

The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.