Method for predicting a quantity of energy recoverable by an electronic device, and associated device

Abstract

Method for predicting a quantity of energy recoverable by an electronic device, and associated device The invention relates to a method for predicting a quantity of energy recoverable by an electronic device, the method comprising: determining a type of a kinetic energy source by an energy source classification model from a signal representative of an acceleration of said electronic device (10A); and predicting, by an energy prediction model, a quantity of kinetic energy recoverable by the electronic device (10A) as a function of the type of the kinetic energy source and a history of quantities of kinetic energy actually obtained with said kinetic energy source. Figure for abstract: Fig. 1A.

Description

Title of the invention: Method for predicting a quantity of energy recoverable by an electronic device, and associated device Technical field

[0001] The present invention belongs to the general field of energy recovery systems. It relates more particularly to a method for predicting a quantity of energy recoverable by an electronic device. It also relates to an electronic device configured to implement such a method.

[0002] The invention may relate for example (but not only) to any autonomous electronic device, i.e. which is not powered by a constant energy source, such as an electrical network, and / or to any electronic device implementing ambient energy collection technology, such as photovoltaic, kinetic, thermal, radiofrequency and / or osmotic energy.

[0003] The invention finds an application in particular for applications of the "Internet of Things" type ("Internet of Things" or loT in English literature). Prior art

[0004] Connected objects (sometimes also called "smart objects") are hardware and / or software devices characterized by their ability to interact with their immediate environment, generally through a microcontroller for controlling a sensor and / or an actuator, as well as by their connectivity. These objects are connected to a communication network, such as for example the public Internet network within the framework of the Internet of Things (IoT), and can thereby communicate with other systems to obtain and / or provide information.

[0005] Thus, connected objects make it possible to capture and send back to the network the current value of information specific to their environment and / or their operation, and / or to receive from the network a command whose execution can have an effect on this environment and / or this operation.

[0006] The fields in which these connected objects are used are very varied, and include in particular:

[0007] - industry - sometimes then called "industry 4.0" -, for example with the use connected robots to enable more detailed monitoring of the different stages of production or with the use of predictive maintenance systems;

[0008] - the smart city, for example in order to monitor and manage a cir culture and transport;

[0009] - security, for example with the use of cameras and presence sensors connected;

[0010] - health, for example with the reuse of connected medical devices or dis fall detection positives to combat loss of autonomy;

[0011] - energy, for example with the use of communicating electricity meters with an electricity network manager;

[0012] - or household appliances, for example with the use of food processors or washing machines connected dishes.

[0013] The massive deployment of these new technologies in such diverse fields is, however, hampered by energy constraints, and it is now accepted that the successful deployment of the next generations of connected objects will be conditioned by their ability to acquire energy autonomy. Even if the use of electrical energy generation units - such as electric batteries, accumulators or batteries - at least partially resolves this problem by ensuring relative autonomy for the connected object, it still requires regular maintenance of the connected object so that these generation units are recharged and / or replaced. In addition, their manufacture requires the use of rare resources - for example nickel, zinc, lithium or cobalt - the extraction of which is particularly polluting and the recycling of which is relatively complex and costly, particularly in terms of energy.

[0014] Several solutions have been considered which aim to use so-called "alternative" or "renewable" energy sources to operate connected objects. Among these solutions, photovoltaic technology has proven its effectiveness when integrated into connected objects, but it still remains conditioned by the presence of light and therefore cannot constitute a universal answer to the power supply of connected objects.

[0015] There is therefore a need for a new energy recovery solution to power electronic devices, which does not have the drawbacks of the prior art. Statement of the invention

[0016] The present invention aims to remedy all or part of the drawbacks of the prior art, in particular those set out above, by proposing a solution which makes it possible to predict a quantity of energy likely to be recovered by an electronic device from a source of kinetic energy setting this electronic device in motion.

[0017] To this end, and according to a first aspect, the invention relates to a method for predicting a quantity of energy recoverable by an electronic device, the method comprising:

[0018] - a determination of at least one type of a first source of kinetic energy by an energy source classification model and from a signal representative of an acceleration of said electronic device; and

[0019] - a prediction, by an energy prediction model, of a quantity of energy kinetic energy recoverable by the electronic device depending on the type of the first source of kinetic energy and a history of quantities of kinetic energy actually obtained with said first source of kinetic energy.

[0020] By "kinetic energy source" is meant here a physical phenomenon generating kinetic energy due to a movement (for example due to this fact alone). As mentioned below, a motor vehicle, a road, a human activity (and corresponding for example to an action of daily life) are different examples of types of kinetic energy sources. It is important to note that no limitation is attached to the types of kinetic energy sources considered (i.e., the types of kinetic energy sources considered are not a limiting factor of the invention).

[0021] Acceleration is a physical quantity representative of a change in the speed of a movement as a function of time. This quantity can be positive or negative, and is also called "deceleration" when it is negative. In certain embodiments, the acceleration corresponds to a vibratory movement of the electronic device which then oscillates around a first position (such as a reference position).

[0022] Furthermore, no limitation is attached to the "energy source classification model" considered. Any model implementing a learning algorithm ("machine learning") and providing, as output, a designation of a kinetic energy source having generated this acceleration (for example the most probable energy source) can be considered in the context of the invention. Furthermore, the energy source classification model can be, at least in certain embodiments, independent of the training method considered for training this model, and any training criterion can be considered during the training phase of the energy source classification model, such as the least squares method or the minimization of cross entropy.

[0023] No limitation is otherwise attached to the "energy prediction model" considered. Thus, any model analyzing quantities of energy previously obtained by the electronic device to predict a quantity of recoverable energy can be considered in the context of the invention.

[0024] Finally, the "quantity of recoverable energy" is expressed for example in Watt-hours (or Joules). However, it is also possible to represent it by other quantities, such as a power expressed in Watts, or an electrical voltage expressed in Volts. Thus, in the remainder of the description, the "quantity of energy "recoverable kinetics" considered as an example is represented by a generated electrical voltage usable by the electronic device.

[0025] Generally speaking, it is considered that the steps of a method should not be interpreted as being linked to a notion of temporal succession.

[0026] In certain embodiments, the method for predicting a quantity of recoverable energy may further comprise one or more of the following characteristics, taken in isolation or in all technically possible combinations.

[0027] In certain embodiments, the energy source classification model comprises a support vector machine (SVM) or a multilayer perceptron (MLP) type neural network.

[0028] In some embodiments, the energy prediction model comprises a statistical model, such as an exponential moving average (EMA) or an autoregressive integrated moving average (ARIMA).

[0029] In the present application, a "moving average", also called a "sliding average", is used. This type of statistical average can be suitable, for example, for the analysis of time series, because it removes transient fluctuations so as to highlight longer-term trends. This average is called a moving average because it is recalculated continuously, using a subset of elements in which a new element replaces the oldest or is added to the subset at each calculation.

[0030] A moving average is said to be "exponential" when it uses an exponentially decreasing weighting of terms. The weight of each value participating in the average is greater than the value preceding it in the series, which gives more importance to the most recent observations, without however completely eliminating the effect of the oldest values.

[0031] In some embodiments, an autoregressive integrated moving average may be used, as it is a statistical model that may be particularly suitable for analyzing time series in order to make predictions. Since this model is autoregressive, the values integrated into the time series are then determined based on previous observed values. Furthermore, the use of this statistical model may help to track and anticipate the evolution of a phenomenon.

[0032] In some embodiments, the method further comprises training the energy source classification model from labeled input data, the data being derived from a plurality of signals representative of an access leration of said electronic device due to a plurality of different kinetic energy sources, a label identifying the kinetic energy source among the plurality of kinetic energy sources.

[0033] In certain embodiments, the method further comprises adapting the functionalities and / or performances of the electronic device, as a function of the predicted quantity of recoverable kinetic energy.

[0034] Such implementations can help to adapt the electrical consumption of the electronic device based on a prediction of a quantity of electrical energy likely to be obtained.

[0035] In certain embodiments, the method further comprises recording a value representative of a quantity of energy actually obtained by the electronic device during a first time interval (prior to the current instant) in the history of quantities of kinetic energy.

[0036] In certain embodiments, the predicted quantity of kinetic energy corresponds to a quantity of kinetic energy recoverable during a second time interval (after the current time).

[0037] The use of time intervals to determine values of amounts of kinetic energy actually obtained and to predict an amount of recoverable kinetic energy is based on the assumption that a source of kinetic energy is potentially volatile, but generates, on average, approximately the same amount of kinetic energy during a given time interval.

[0038] In certain embodiments, the second time interval has a duration similar (denoted T p hereinafter) to that of the first time interval.

[0039] In certain embodiments, the value representative of a quantity of energy actually obtained by the electronic device during a first time interval corresponds to an average of values of quantity of energy obtained by the electronic device and with said first source of kinetic energy during the first time interval.

[0040] In some embodiments, the determination of the type of the first source of kinetic energy is implemented regularly at a frequency / 1, and the prediction of a quantity of recoverable kinetic energy is implemented regularly at a frequency / 2, with for example / 1 > / 2.

[0041] Such implementation methods can help to adapt the prediction in real time, and, if necessary, to take into account a change in the source of kinetic energy.

[0042] In certain embodiments, the first and second time intervals may correspond to sliding time intervals, i.e. having a certain duration, and of which the upper limit (eg, the end date) of the first time interval and the lower limit (eg, the start date) of the second time interval correspond to the same instant during which the quantity of recoverable kinetic energy is predicted.

[0043] In certain embodiments, the method further comprises:

[0044] - a detection of a change in the source of kinetic energy, the acceleration of said electronic device then being generated by a second source of kinetic energy; and

[0045] - a correction of at least one prediction of the quantity of kinetic energy re recoverable after the detection time, considering a history of quantities of kinetic energy actually obtained by said electronic device with the second source of kinetic energy.

[0046] In certain embodiments, the method further comprises, prior to a correction of a prediction of quantity of recoverable kinetic energy, a synchronization of the lower limit of a time interval (of duration Tp) between two predictions of quantity of kinetic energy with the instant of detection of a change of source of kinetic energy.

[0047] In certain embodiments, the method further comprises:

[0048] - obtaining the signal representative of an acceleration of said electronic device

[0049] - a determination of a power spectral density (or "Power Spectral Density", PSD, according to Anglo-Saxon terminology) of the signal obtained;

[0050] - a decomposition of the power spectral density of the signal into bands of frequencies; and

[0051] - a determination, for each frequency band, of a quadratic mean, so as to obtain a plurality of characteristics representative of the acceleration signal.

[0052] Thus, the acceleration signal at the input of the module for determining at least one type of kinetic energy source can be processed so as to generate frequency characteristics representative of the acceleration signal, and this, for a given source. In certain embodiments, these steps of processing the input signal can be implemented during the training phase, to identify a set of frequency characteristics (called “reference”) specific to a given kinetic energy source. In certain embodiments, these steps of processing the input signal can be implemented during the inference phase to determine the characteristics of an acceleration signal whose source must be determined.

[0053] In some embodiments, the first and / or second determined source of kinetic energy corresponds to a motor vehicle - for example, an automobile, a bus, a four-wheeler, a tram, a subway, a train - selected from among a plurality of motor vehicles, a road of a certain type - for example a motorway, a national road, a departmental road, a metropolitan road, a municipal or rural road - selected from among a plurality of road types, or to a human activity - for example ironing, vacuuming, going up and / or down stairs, walking, Nordic walking, cycling, running, etc. rural - selected from among a plurality of human activities.

[0054] According to a second aspect, the invention relates to a computer program comprising instructions for implementing a method for predicting a quantity of recoverable energy, when said program is executed by a processor.

[0055] This program may use any programming language, and be in the form of source code, object code, or intermediate code between source code and object code, such as in a partially compiled form, or in any other desirable form.

[0056] According to a third aspect, the invention relates to a computer-readable recording medium on which the computer program according to the invention is recorded.

[0057] The information carrier may be any entity or device capable of storing the program. For example, the carrier may comprise a storage means, such as a rewritable non-volatile memory or ROM, for example a CD ROM or a microelectronic circuit ROM, or a magnetic recording means, for example a hard disk.

[0058] On the other hand, the information medium may be a transmissible medium such as an electrical or optical signal, which may be conveyed via an electrical or optical cable, by radio or by other means. The program according to the invention may in particular be downloaded from an Internet-type network.

[0059] Alternatively, the information carrier may be an integrated circuit in which the program is incorporated, the circuit being adapted to execute or to be used in the execution of the method in question.

[0060] According to a fourth aspect, the invention relates to an electronic device comprising at least one processor configured (or configured together) to implement the method for predicting a quantity of recoverable energy previously mentioned in any of its modes of implementation.

[0061] According to a fifth aspect, the invention relates to an electronic device configured to predict a quantity of recoverable energy comprising:

[0062] - a module for determining at least one type of a kinetic energy source by an energy source classification model from a signal representative of an acceleration of said electronic device; and

[0063] - a prediction module, by an energy prediction model, of a quantity of kinetic energy recoverable by the electronic device depending on the type of the source of kinetic energy and a history of quantities of kinetic energy actually obtained with said source of kinetic energy.

[0064] For each step of the method for predicting a quantity of recoverable energy, in any of its embodiments, the electronic device of the present application may comprise a corresponding module configured to carry out said step.

[0065] According to a sixth aspect, the invention relates to a system for predicting a quantity of recoverable energy including a transport vehicle (land, sea, air and / or space) in which the electronic device mentioned above is embedded, in any of its modes of implementation. Brief description of the drawings

[0066] Other characteristics and advantages of the present invention will emerge from the description given below, with reference to the appended drawings which illustrate an exemplary embodiment thereof without any limiting character. In the figures:

[0067] [Fig. 1 A] [Fig. 1 A] is a first example of an environment in which a process prediction of a quantity of recoverable energy can be implemented;

[0068] [Fig.lB] [Fig.lB] is a second example of an environment in which a method for predicting a quantity of recoverable energy can be implemented;

[0069] [Fig.2] [Fig.2] represents modules embedded in an electronic device of predicting a quantity of recoverable energy, according to an exemplary implementation of the invention;

[0070] [Fig.3] [Fig.3] represents an example of hardware architecture of an electronic device electronics for predicting a quantity of recoverable energy;

[0071] [Fig.4] [Fig.4] illustrates, in the form of a flowchart, the main stages of a method for predicting a quantity of recoverable energy of the present application, according to an exemplary implementation;

[0072] [Fig.5A] [Fig.5A] illustrates the temporal evolution of the relative standard deviation of several human activities;

[0073] [Fig.5B] [Fig.5B] illustrates the temporal evolution of the relative standard error of several human activities;

[0074] [Fig.6A] [Fig.6A] illustrates the experimental results of actual human activity changes as detected by the electronic device of the present application; and

[0075] [Fig.6B] [Fig.6B] illustrates the experimental results of actual and predicted energy quantities by the electronic device of the present application. Description of Embodiments

[0076] [Fig. 1 A] is a first example of an environment in which a method of prediction of a quantity of recoverable energy can be implemented.

[0077] As illustrated by [Fig.lA], an electronic device 10A for predicting a quantity of recoverable energy according to the invention is located inside a train. This mode of transport can generate elastic waves. These waves, commonly called "vibrations", are generated by the interaction between the rail and the train, and can in particular be induced by the irregularities of the surfaces of the wheels of the train and the rail with which they are in contact. The condition of the rails, the type of train - for example a high-speed train, TGV, or a regional train -, the type of trains - for example cars dedicated to the transport of passengers or wagons dedicated to the transport of animals or goods -, the speed of travel of the train, the composition of the underlying land and subsoil are all factors that can influence these vibrations.

[0078] These vibrations propagate in the air, the ground of the railway track, but also in the train, and are detected by the electronic device 10A for predicting a quantity of recoverable energy via the reception, by the electronic device 10A, of a vibration signal.

[0079] The electronic device 10A is for example a user terminal such as a laptop, a personal assistant, a connected watch or a mobile telephone of the "smartphone" type - a router, etc. Generally speaking, no limitation is attached to the structural form which can be taken by this electronic device 10A.

[0080] In the present embodiment, and for the purpose of simplifying the description, it is considered that the environment comprises a single electronic device 10A, as well as a single source of kinetic energy corresponding, in this example, to a train. It should however be noted that no assumption is made as to the number of electronic devices 10A and sources of kinetic energy. The following developments can in fact be generalized without difficulty by the person skilled in the art to the case where more than one electronic device and / or more than one source of kinetic energy are considered.

[0081] The electronic device 10A analyzes the detected vibrations, and identifies a source of kinetic energy likely to have generated these vibrations, from a frequency analysis of the vibration signal. In this regard, the electronic device 10A uses a (local or remote) energy source classification model configured to determine at least one type of kinetic energy source (for example the most probable) from the vibration signal. In certain embodiments, the energy source classification model designates at output at least the most probable type of energy source from among a plurality of types of potential kinetic energy sources. Optionally, it can also generate at output a probability associated with one or more (e.g. each) of the source types of this plurality of potential kinetic energy source types.

[0082] In this example, the electronic device 10A is configured to determine the most likely type of kinetic energy source from a plurality of potential kinetic energy source types including train, car, bicycle, airplane, and running and to deduce therefrom an amount of kinetic energy recoverable by this same electronic device 10A (or by another device subjected to vibrations similar or correlated to those received by the device 10A). For example, after determining that the most likely source of kinetic energy is a train, the electronic device 10A predicts, using an energy prediction model, an amount of kinetic energy recoverable by this same electronic device 10A thanks to the vibrations generated by this train.

[0083] More precisely, the knowledge of the source of kinetic energy producing these vibrations is used by the electronic device 10A to predict, using the energy prediction model, a quantity of kinetic energy that it is likely to recover from the vibrations emitted by this train.

[0084] Based on the predicted amount of kinetic energy, the electronic device 10 may decide to adapt its own functionalities. In this example, the vibrations emitted are significant in terms of amplitude and repetitive nature, and the predicted amount of kinetic energy may prompt the electronic device 10 to activate functionalities that were previously deactivated because they required too much energy.

[0085] [Fig.lB] is a second example of an environment in which a method for predicting a quantity of recoverable energy can be implemented.

[0086] In this second example, an electronic device 10B for predicting a quantity of recoverable energy according to the invention is located inside a car traveling on a deformed road surface comprising bumps and / or potholes.

[0087] As previously, the electronic device 10B analyzes the detected vibrations, and identifies the type(s) of kinetic energy source that likely generated these vibrations from a frequency analysis of the vibration signal. In this example, the electronic device 10B is, however, configured to determine an energy source corresponding to a probable type of road on which this car is traveling from among a plurality of types of roads such as a highway; a national road; a departmental road and a municipal road.

[0088] [Fig.2] represents modules embedded in an electronic device 10 for predicting a quantity of recoverable energy, according to an exemplary implementation of the invention.

[0089] This electronic device 10 corresponds for example to one of the electronic devices 10A and 10B of figures 1A and 1B, and comprises in particular:

[0090] - a MOD_SRC module for determining at least one type of energy source SRC kinetics from a SIG signal representative of an acceleration of this electronic device 10.

[0091] - a MOD_PRED module for predicting PRED a quantity of kinetic energy capable of being recovered from accelerations (or, in a particular case, from vibrations) produced by a kinetic energy source. As illustrated by [Fig.2], this MOD_PRED module takes as input the type of SRC kinetic energy source determined by the SRC module, and can also access an association table (referenced BDD) each entry of which associates an SRC source with a history of quantities of kinetic energy actually obtained with said SRC source. This history can take the form of a list of quantities of kinetic energy, this list being ordered according to the times at which the quantities of kinetic energy were obtained.

[0092] - a TDC transducer configured to convert an acceleration (or a vibration) into an electrical signal. This TDC transducer corresponds, for example, to a piezoelectric pressure transducer, also called a "piezoelectric transducer" or "piezoelectric sensor". Such a piezoelectric transducer is, for example, composed of a piezoelectric material, such as quartz or ceramic, positioned between two electrodes. When the material is subjected to mechanical stress, such as accelerations or vibrations, it deforms and generates an electric field. This electric field is then converted into electrical energy by the piezoelectric transducer.

[0093] - an STK energy storage unit for storing the energy produced by the TDC transducer and taking for example the form of a battery, a battery or a capacitor.

[0094] - and an energy management module M0D_MNG configured to adapt the functions functionalities and / or performances of the electronic device 10, depending on the predicted quantity of recoverable kinetic energy.

[0095] The invention has so far been described in the case where the energy source classification model and the energy prediction model are embedded in the electronic device 10, respectively in the MOD_SRC and MOD_PRED modules. These provisions are however not limiting of the invention, and nothing excludes the possibility of the energy source classification model and / or the energy prediction model being executed by one or more electrical devices distinct from the electronic device 10. In this case, the MOD_SRC module and / or the MOD_PRED module are for example configured to interact with these electrical devices distinct from the electronic device 10 through a telecommunications network.

[0096] Furthermore, it is also important to note that the TDC transducer, the BDD association table, the STK energy storage unit and / or the M0D_MNG module of energy management may also be embedded on one or more electronic devices separate from the electronic device 10.

[0097] Thus, in the case where the energy management module M0D_MNG is embedded on an electronic device separate from the electronic device 10, the module M0D_PRED for predicting a quantity of recoverable kinetic energy is for example configured to interact with this remote module M0D_MNG via a telecommunications network.

[0098] [Fig.3] represents an example of hardware architecture of an electronic device 10 for predicting a quantity of recoverable energy.

[0099] As illustrated by [Fig. 3], the electronic device 10 for predicting a quantity of recoverable energy has the hardware architecture of a computer. Thus, the electronic device 10 for predicting a quantity of recoverable energy comprises in particular a processor 1, a random access memory 2, a read-only memory 3 and a non-volatile memory 4. It further comprises a communication module 5.

[0100] The read-only memory 3 of the electronic device 10 for predicting a quantity of recoverable energy constitutes a recording medium as proposed, readable by the processor 1 and on which is recorded a computer program PROG in accordance with the invention, comprising instructions for executing steps of the method for predicting a quantity of recoverable energy as proposed in the present application. The program PROG defines one or more functional modules of the electronic device 10 for predicting a quantity of recoverable energy, which rely on or control the hardware elements 1 to 5 cited above, and which include in particular:

[0101] - a MOD_SRC module for determining at least one type of energy source SRC kinetics from a SIG signal representative of an acceleration of this electronic device 10; and

[0102] - a MOD_PRED module for predicting a quantity of kinetic energy above capable of being recovered from vibrations or more generally from accelerations produced by a source of kinetic energy.

[0103] Furthermore, the electronic device 10 for predicting a quantity of recoverable energy may also comprise other modules, in particular for implementing certain embodiments of the method for predicting a quantity of recoverable energy, as described in more detail later.

[0104] [Fig. 4] illustrates, in the form of a flowchart, the main steps of a method for predicting a quantity of recoverable energy of the present application, according to an exemplary implementation. This method is for example implemented by the electronic device 10 of FIGS. 2 and 3.

[0105] As illustrated by [Fig.4], the prediction method comprises a step S100 at during which at least one type of kinetic energy source SRC causing accelerations or vibrations of the electronic device 10 is determined using an energy source classification model from a signal representative of these accelerations. This step can be implemented for example by the module MOD_SRC for determining at least one type of kinetic energy source described with reference to FIGS. 2 and 3. In certain implementations, the energy source classification model can comprise a support vector machine or a neural network of the "multilayer perceptron" type.

[0106] This source SRC of kinetic energy is then used during a step SI 10 to predict, using an energy prediction model, a quantity of recoverable energy likely to be obtained from an acceleration generated by this source SRC of kinetic energy. This step is for example implemented by the module MOD_PRED for predicting a quantity of kinetic energy described with reference to FIGS. 2 and 3.

[0107] In some implementations, the energy prediction model may be a statistical model, such as an exponential moving average or an autoregressive integrated moving average.

[0108] During this step SI 10, a first "timer" elapsing during a time interval of duration TR between at least two determinations of a source of kinetic energy and a second "timer" elapsing during a time interval of duration TP between at least two predictions of a quantity of kinetic energy likely to be recovered from accelerations are triggered. In certain embodiments, the values of these "timers" may for example be such that TR < TP.

[0109] As mentioned previously, this feature is advantageous in that it allows the prediction to be adapted in real time, in particular in the case of a change in kinetic energy source, with more reactivity than when TR > T / >, which can help to potentially obtain a more reliable prediction.

[0110] A "timer" here corresponds to a counter register which increments or decrements according to the pulses of a clock which may be that of processor 1 (for example at each pulse of the clock).

[0111] The prediction method further comprises a step S120 during which the electronic device 10 checks whether the duration TR between at least two determinations (for example between two determinations) of a type of kinetic energy source has elapsed or not. If this is the case (choice "Y"), a step S130 is implemented during which the electronic device 10 determines the current source of kinetic energy causing accelerations of the electronic device 10. This step is for example implemented by the module MOD_SRC. During this step 130, the electronic device 10 also resets the first "timer" elapsing during of the time interval of duration TK between at least two determinations of a type of kinetic energy source.

[0112] Returning to step S120, if on the other hand the duration TR between at least two determinations minations of a type of kinetic energy source has not elapsed (choice "N"), the electronic device implements a step S160 during which it checks whether the duration TP between two predictions of a quantity of kinetic energy likely to be recovered from accelerations has elapsed or not. If this duration TP has not yet elapsed (choice "N"), the prediction method loops back to step S120. If, on the other hand, this duration has elapsed (choice "Y"), the electronic device 10 implements a step S170 of determining a value representative of a quantity of energy actually obtained by the electronic device during a time interval, called the first interval, of duration TP and ending at the current instant (i.e., the time interval of duration TP immediately prior to the current instant.

[0113] In certain embodiments, the value representative of a quantity of energy actually obtained by the electronic device during this first time interval corresponds to an average of values of quantity of energy actually obtained by the electronic device 10 and with this source SRC of kinetic energy during this first time interval.

[0114] The prediction method further comprises a step S180 during which the value determined during step S170 is recorded in an association table, each entry of which associates a source SRC with a history of quantities of kinetic energy actually obtained with said source of kinetic energy SRC. In certain embodiments, this history corresponds to a list ordered according to the instant during which a quantity of kinetic energy was actually determined (i.e., the instant during which the interval considered for the entry ended).

[0115] During a step S190, the electronic device 10 predicts, using an energy prediction model, a quantity of kinetic energy recoverable by the electronic device 10 as a function of said kinetic energy source and the history of quantities of kinetic energy actually obtained with said kinetic energy source SRC. This step S190 is for example implemented by the MOD_PRED module of the electronic device 10.

[0116] In certain implementations, this prediction is stored in a list accessible by the M0D_MNG energy management module associating predictions and times to which the predictions refer. This may be, for example, a list ordered according to the times associated with the predictions (for example chronologically).

[0117] In some embodiments, step S190 further comprises a transmission, to the MOD_MNG energy management module of said prediction, so as to allow the MOD_MNG management module to adapt the functionalities of the electronic device 10 accordingly. This adaptation includes, for example, the activation of functionalities previously deactivated because they are deemed to be too energy-intensive or, on the contrary, the deactivation of functionalities previously activated, as well as the modification of parameters (eg, the brightness of the screen, the location of the electronic device 10, etc.)

[0118] In certain embodiments, the energy management module M0D_MNG is embedded in an electronic device separate from the electronic device 10, for example an electronic device for supervising the energy management of the electronic device 10 (or of a plurality of electronic devices including the electronic device 10). In this case, step S190 may comprise a transmission, to the electronic supervision device, of data representative of the prediction.

[0119] In certain embodiments, the quantity of predicted kinetic energy corresponds to a quantity of kinetic energy recoverable by this electronic device during a time interval, called the second interval, subsequent to the instant associated with a prediction and of duration for example equal to the prediction duration Tp.

[0120] During this same step, the second "timer" elapsing during a time interval of duration TP is reset. Then the prediction method loops back to step S120.

[0121] During step S130, once the source of kinetic energy has been determined, the electronic device 10 determines, during a step S140, whether the source determined in step S130 is different from that determined during the previous determination. If this is not the case (eg, if the source determined in step S130 is identical to that determined during the previous determination, choice "N"), then the prediction method implements step S160 previously described.

[0122] If, on the other hand, it is determined in step S140 that the source of kinetic energy has changed (choice "Y"), the electronic device 10 implements a step S150 during which at least the last calculated prediction (i.e., the prediction subsequent to the instant during which a change of source is detected) is deleted from the list of predictions. Furthermore, during this same step, the first and second "timers" are reset. Then the prediction method loops back to step S190 previously described.

[0123] Experimental results

[0124] The experimental results set out below are for example implemented by the electronic device 10 previously described, in one of its embodiments. It is important to note that these experimental results are only given as examples, and are not limiting of the invention. In particular, the devices used in the context of this experiment and the implementation choices do not in any way limit the scope of this application.

[0125] These experimental results were obtained by considering different human activities as sources of kinetic energy. This field of study is sometimes called "human activity recognition" (or "Human Activity Recognition", HAR, according to Anglo-Saxon terminology), and in the following, the "energy source classification model" is therefore also called "human activity recognition model".

[0126] Determination of human activities

[0127] Input data

[0128] The raw data considered for this experiment come from the "PAMAP2" physical activity monitoring dataset. These data were processed and missing and / or erroneous data were deleted. Table 1 summarizes the dataset considered as input to the model (corresponding to the "energy source classification model" previously mentioned).

[0129] [Tables 1] Activity Duration of Number of Percentage of acceleration samples ge ns (%) (min) Static position 94.6 568 29.37 Ironing 39.6 238 12.34 Vacuuming 29.1 175 9.07 Stairs 37 222 11.5 Walking 38.6 232 12.03 Nordic walking 31 186 9.63 Cycling 27.5 165 8.52 Running 16.16 97 5 Skipping rope 8.1 49 2.53 TOTAL 321.8 1931 100

[0130] Feature Extraction

[0131] The raw data were divided into labeled samples with a duration of 10 seconds. The final dataset considered contained 1931 samples.

[0132] The samples were then sorted by activity and the power spectral density (PSD) of each sample was calculated. The spectrum was then decomposed into frequency bands, (0-0.5), (0.5-1), (1-4), (4-7), (7-10), (10-13), (13-16), (16-19), (19-22), (22-25), (25-30) and (30-50).

[0133] Results of human activity recognition

[0134] The dataset was divided into 80% training data and 20% test or inference data. Only low-resource-intensive techniques that could be embedded in microcontrollers were studied. In particular, two classifiers were preselected:

[0135] - support vector machines (SVM);

[0136] - a multi-layer perceptron (MLP) comprising 2 hidden layers of 24 and 19 nodes. This architecture was determined by a "grid search" type optimization.

[0137] Tables 2 and 3 illustrate the confusion matrices using the SVM and MLP models respectively. It can be concluded that, overall, both models have almost similar results, with an average Fl score of 90.41% and 91.45%, respectively. These results are superior to those of other studies working on the same dataset, which obtain an Fl score varying between 82% for SVM and 95% for convolutional neural network (CNN) models.

[0138] [Tables2] Activities Planned activity Static position Ironing Vacuum cleaner Stairs Walking Nordic walking Cycling Running Jumping rope Recall % A Position ue 109 0 1 1 1 ue 0 0 1 0 96.46 and iv static Ironing 3 39 2 3 0 0 1 0 0 81.25 it c Vacuum cleaner 2 2 31 0 0 0 0 0 0 88.57 real Stairs 4 0 0 37 2 1 0 0 0 84.09 Walking 1 1 1 1 43 0 0 0 0 91.49 Walking 0 0 0 0 0 37 0 0 0 100 Nordic walking Cycling 3 0 3 0 1 0 26 0 0 78.79 Run at 0 0 0 0 0 0 1 17 1 89.47 foot Rope at 0 0 1 1 0 0 0 0 8 80 jump Accuracy 89.34 92.86 79.49 86.0 91.4 97.37 92.8 94.4 88.89 Fl = % 5 9 6 4 90.41

[0139]

[0140] [Tables 3] Activities Planned activity Position Meal Vacuum Esca Mar Walk Cycling Course Cor Rappe static sage ateur liers che nordiqu isme e to of to 1 e foot jump % er A Position 110 0 1 2 0 0 0 0 0 97.35 and static iv Ironing 3 42 1 1 0 0 1 0 0 87.5 it 0 Vacuum cleaner 3 1 31 0 0 0 0 0 0 88.57 Stairs 4 1 1 35 2 1 0 0 0 79.55 real Walk 0 1 1 1 44 0 0 0 0 93.62 the Walk 0 0 0 0 0 37 0 0 0 100 Nordic Cycling 2 2 0 0 1 1 27 0 0 81.82 Run at 1 0 0 0 0 0 0 18 0 94.74 foot Rope at 0 0 1 0 0 0 0 0 9 90 jump Accuracy 89.43 89.36 86.11 89.7 93.6 94.87 96.4 100 100 Fl = % 4 2 3 91.45

[0141]

[0142]

[0143]

[0144] Energy prediction model To test the hypothesis that the same activity generates approximately the same amount of energy over a given period of time, a solution was developed to generate a data set based on the PSDs of the samples available for each activity. More precisely, several PSDs of the same activity were randomly mixed with random phases. Thus, random time series were generated for each activity with different durations. The generated signals were then used to control a vibration source (in the experiment, the PM20 permanent magnet shaker from DynaLab ©) in order to approximately reproduce the vibrations generated by a human activity. A piezoelectric cantilever "S452-J1FR-1808XB", with dimensions 71 mm x 25.4 mm x 1.32 mm, was fixed on the vibration source. Since its resonant frequency was 246 Hz, a weight was added to reduce its resonant frequency to about 20 Hz, which is a suitable frequency for simulating vibrations from human activities.

[0145] Twenty time signals were created having durations of 30, 60, 90, 120 and 300 seconds. [Fig.5A] illustrates the time evolution of the relative standard deviation for several human activities, and [Fig.5B] illustrates the time evolution of the relative standard error for these human activities.

[0146] It can be seen that the relative standard deviation (also called "coefficient of variation") decreases as the vibration duration increases. For a duration of 300 seconds, it can be seen that the relative standard deviation fell to 11.49, 8.78, 9.52 and then 6.76%. In a relatively similar manner, the standard error decreased significantly. These results therefore validate the hypothesis that an activity allows approximately the same amount of energy to be generated during a certain time interval, here 300s. Therefore, it can be deduced that it may be possible to estimate the harvestable energy of each activity using a moving average and statistical models.

[0147] Prediction models

[0148] To be able to apply energy prediction models, signals of a duration much longer than that of the samples were required for each activity.

[0149] As before, three 2-hour signals were generated for each activity. Then the results were divided into 300-second windows. Therefore, for each activity, 72 samples were available. The effective voltage of each sample was then calculated, and 72 observations were thus obtained.

[0150] The time series were divided into two datasets, one for training and one for validation. Five models were studied: the exponential moving average (EMA) model, the autoregressive (AR) model, the moving average (MA) model, the autoregressive integrated moving average (ARIMA) model, and the vector error correction (VEC) model.

[0151] The last 10 values of energy quantities actually obtained were used to predict the next recoverable energy quantity. The results in terms of relative error are summarized in Table 4.

[0152] [Tables4] Activities EM AR MA ARI VE A MA C Cycling 5.35 5.61 5.30 5.80 4.74 Nordic walking 7.50 6.52 6.61 6.61 6.89 Vacuum cleaner 8.04 8.26 6.62 7.25 7.45 Running 6.97 8.25 10.8 9 7.06 8.05 Average 6.97 7.16 7.36 6.68 6.78

[0153]

[0154] It can be seen that all predictions have a relative error of less than 10%. High-frequency activities, such as cycling and Nordic walking, perform better than the other two activities in this experiment. On average, the ARIMA model has the best accuracy. It can also be seen that the EMA model, which requires few resources to implement, performs well for all activities, even outperforming the ARIMA model for cycling and running.

[0155] [Fig.6A] illustrates the experimental results of actual human activity changes and as detected by an electronic device according to the present application and [Fig.6B] illustrates the experimental results of actual energy quantities and as predicted by the electronic device of the present application.

[0156] To obtain these experimental results, an 8-hour signal representative of several human activities having random durations was generated. In addition, a human activity recognition model of the SVM type, an energy prediction model of the EMA type, a time interval of a duration TR of 120 s between two determinations of energy source and a time interval of a duration TP of 300 s between two predictions of quantity of recoverable energy were considered.

[0157] [Fig.6A] illustrates the determination of human activities considering this 8-hour signal. It can be concluded that human activities are well recognized, with a slight delay caused by the 120-s time interval of the human activity recognition model. It can also be observed that, in this experiment, over the entire 8 hours, only one false determination lasting 2 false minutes was made.

[0158] [Fig.6B] illustrates the prediction of the amount of energy and the energy actually obtained by considering this 8-hour signal. The vertical lines represent the instants during which the activity has changed. It can be observed that the curve representing the predicted amount of energy follows that of the energy actually obtained, and that the variations in the amount of energy are anticipated when the activity changes.

[0159] It can also be observed that the energy quantity prediction model achieved a relative error of 11.25%. The increase in the relative error compared to the root mean square deviation when the activities are analyzed separately can be explained by the fact that the mixing of the activities during the generation of the 8-hour signal biased the model previously trained on distinct human activities. Nevertheless, these results prove that the electronic device of the present application effectively predicts the short-term recoverable kinetic energy, even with random and / or variable accelerations.

Claims

Claims

1. Method for predicting an amount of energy recoverable by an electronic device (10, 10A, 10B), the method comprising: • a determination (S 130) of at least one type of a first source (SRC) of kinetic energy by an energy source classification model from a signal representative of an acceleration of said electronic device; and, • a prediction (S 190), by an energy prediction model, of an amount (PRED) of kinetic energy recoverable by the electronic device as a function of the type of the first source of kinetic energy and a history of amounts of kinetic energy actually obtained with said first source of kinetic energy.

2. The method of claim 1, further comprising adapting the functionality and / or performance of the electronic device, based on the predicted amount of recoverable kinetic energy.

3. A method according to claim 1 or 2, further comprising recording (S 180) a value representative of an amount of energy actually obtained by the electronic device during a first time interval, in the history of kinetic energy amounts;

4. The method of claim 3, wherein the predicted amount of kinetic energy corresponds to an amount of kinetic energy recoverable during a second time interval.

5. Method according to claim 3 or 4, the value representative of a quantity of energy actually obtained by the electronic device during a first time interval corresponding to an average of values of quantity of energy obtained by the electronic device and with said first source of kinetic energy during the first time interval.

6. A method according to one of claims 1 to 5, the determination (S 130) of the type of the first kinetic energy source being carried out regularly at a frequency / 1, and the prediction (S 190) of a quantity of recoverable kinetic energy is carried out regularly at a frequency / 2, with / 1 > / 2.

7. Method according to claim 6, further comprising: • a detection (S 140) of a change of source of kinetic energy, the acceleration of said electronic device then being generated by a second source of kinetic energy; and, • a correction (S 150) of at least one prediction of the quantity of recoverable kinetic energy subsequent to the instant of detection, by considering a history of quantities of kinetic energy actually obtained by said electronic device with the second source of kinetic energy.

8. The method of claim 7, further comprising, prior to correcting a prediction of recoverable kinetic energy quantity, synchronizing the lower bound of a time interval between two predictions of kinetic energy quantity with the detection time (S 140) of a change of kinetic energy source.

9. Method according to one of claims 1 to 8, further comprising, - obtaining the signal representative of an acceleration of said electronic device; - determining a power spectral density (or "Power Spectral Density", PSD, according to English terminology) of the signal obtained; - decomposing the power spectral density of the signal into frequency bands; and - determining, for each frequency band, a quadratic mean, so as to obtain a plurality of characteristics representative of the acceleration signal.

10. A method according to one of claims 1 to 9, wherein the first and / or second source of kinetic energy corresponds to a motor vehicle selected from a plurality of motor vehicles, a road of a certain type selected from a plurality of road types, or a human activity selected from a plurality of human activities.

11. Electronic device (10, 10A, 10B) configured to predict a quantity of recoverable energy comprising: • a module (MOD_SRC) for determining at least one type of source (SRC) of kinetic energy by an energy source classification model from a signal representative of an acceleration of said electronic device; and, a module (MOD_PRED) for prediction, by an energy prediction model, of a quantity (PRED) of kinetic energy recoverable by the electronic device as a function of the type of the source of kinetic energy and a history of quantities of kinetic energy actually obtained with said source of kinetic energy.

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