Abstract:
A device for operating a rechargeable storage for electrical energy, comprising a charging module for providing electric power to charge the storage; a data acquisition module for continually acquiring instantaneous operating parameters of the storage during operation; a prediction module having implemented an adaptive model for deriving from data describing a state of the storage before start-up and data acquired in operation and optionally acquired from other expert knowledge predictions about future states of the storage, the model being operable to be automatically optimized continuously using data acquired in operation and optionally using further expert knowledge; a data memory, and a control module for controlling the charging module, the control module being operable to choose an instantaneous charging strategy for the storage depending on predictions derived from the model and on currently acquired operating parameters of the storage. Furthermore the invention relates to a process for operating a rechargeable storage for electrical energy.

Description:
BACKGROUND OF THE INVENTION  
         [0001]    1. Field of the Invention  
           [0002]    This invention relates to a device and a process for operating a rechargeable storage for electrical energy.  
           [0003]    2. Description of Related Art  
           [0004]    A survey of currently used charging strategies and charging devices for rechargeable electrochemical batteries can be found in Halaczek/Radecke  Batteries and Charging Concepts,  2nd edition, 1998, Franzis&#39; Verlag GmbH, pages 154 to 236. Conventionally, the approach is to choose a certain charging strategy depending on the battery type, wherein, during charging, the voltage and/or the current of the charging device is controlled depending on the time which has elapsed since the start of the charging process, the measured instantaneous battery voltage, the measured instantaneous current flowing in the charging circuit, and/or the measured instantaneous battery temperature, i.e., for control of the charging process, the instantaneous measured values relating to the battery or quantities derived therefrom (for example, the variation as a function of time) are used. These known processes and devices are disadvantageous in that, generally, the instantaneous charging state and the instantaneous qualitative state of the battery cannot be determined from the instantaneous measured values for voltage, current and temperature so accurately that a charging strategy which is optimized with respect, for example, to charging time or service life of the battery can be found.  
           [0005]    On pages 246 to 248 of the aforementioned reference, digital charging devices are described in which the charging state of the battery is determined by measuring the discharge current and the temperature of the battery at regular intervals during discharging, the results being stored in an internal interrogated E 2 PROM.  
           [0006]    U.S. Pat. No. 5,256,957 discloses a charging process in which a characteristic parameter which is derived from the equivalent network of the battery and which is determined continuously from the currently measured charging current and the currently measured battery voltage is used to determine the shut-off time for the charging process. Here, the charging process is terminated as soon as the variation over time of the characteristic parameter approaches zero. In this case too, the charging strategy is determined from the instantaneous measured values of the battery parameters.  
           [0007]    “Determination Of State-Of-Charge And State-Of-Health Of Batteries By Fuzzy Logic Methodology” by Salkind et al.,  Journal of Power Sources  80, 1999, pages 293 to 300, discloses using fuzzy logic to predict the charging state of an electrochemical battery based on frequency-dependent resistance measurements (electrochemical impedance spectroscopy) of the battery. The membership function and rule set of the fuzzy logic are found using a neural network.  
           [0008]    In “Predicting Failure Of Secondary Batteries” by M. Urquidi-Macdonald et al.,  Journal of Power Sources  74, 1998, pages 87 to 98, it is proposed that the future discharge behavior of an electrochemical battery, i.e., the voltage behavior over time for a stipulated charging current behavior at a certain temperature, be predicted by means of a neural network, the neural network being trained with already conducted measurements of the charging and discharging cycles (current, voltage, temperature). Here, it is recommended that the amount of data used for training be reduced, for example, by a wavelet transform.  
         SUMMARY OF THE INVENTION  
         [0009]    The primary object of this invention is to devise a device and a process for operating a rechargeable storage for electrical energy, using an optimized charging strategy, which, for example, enables a maximized efficient storage service life.  
           [0010]    This and other objects are achieved by a device and process for operating a rechargeable storage for electrical energy and a process in accordance with the invention by implementation of an adaptive model of the storage which is automatically optimized continuously using the data acquired in operation. In this way, it always is possible to describe, know and predict the storage state as precisely as possible, and as a result of accurate and continually updated knowledge of the storage state, the charging strategy which is most favorable at the instant can be chosen.  
           [0011]    Preferably the adaptive model is implemented by means of neural networks and/or fuzzy logic.  
           [0012]    In the following, advantageous embodiments of the invention are explained in detail with reference to the accompanying drawings. 
       
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0013]    [0013]FIG. 1 schematically shows the concept of a charging system of the invention;  
         [0014]    [0014]FIG. 2 schematically shows the structure of the charging module of a charging system of the invention;  
         [0015]    [0015]FIG. 3 schematically shows the structure of a control and prediction module of a charging system of the invention; and  
         [0016]    [0016]FIG. 4 schematically shows one example for the data flow in an adaptive model as is used in the charging system of the invention. 
     
    
     DETAILED DESCRIPTION OF THE INVENTION  
       [0017]    [0017]FIG. 1 schematically shows the overall concept of a charging system, wherein a rechargeable electrical energy storage  10  supplies a consumer  12  with electrical energy. There is a charging module  14  for providing electrical power for charging of the energy storage  10 . A combined control and prediction module  16  is used to prepare predictions regarding the instantaneous state and future states of the energy storage  10  using available data with respect to the history and the instantaneous state of the energy storage  10 ; they are used to choose the instantaneous charging strategy for the energy storage  10  and to control the charging module  14  accordingly.  
         [0018]    When the energy storage  10  is started up, the control and prediction module  16  is dependent on data which, for example, were determined in the production of the energy storage  10  and which describe the state of the energy storage  10  before start-up. In operation, the module  16  then continually collects instantaneous measurement data, i.e., the current drained from the energy storage  10  or supplied to it, the voltage of the energy storage  10  and quantities derived therefrom, especially the variation of current and voltage over time, these acquired data being incorporated into the predictions to update and optimize them. For data acquisition, there is a data acquisition module  22  (not shown in FIG. 1).  
         [0019]    One important aspect is that the predictions of the prediction module  16  are prepared by means of an adaptive model, the model being made such that it is continuously optimized using the measurement data acquired in the operation of the data acquisition module. The adaptive model is based preferably on so-called neuro-fuzzy logic as is explained in detail below. The information regarding the operating history of the energy storage  10  thus plays a part in the continuing improvement of the model for the respective energy storage  10  by continuous adaptation via the instantaneous measured values so that the operating history of the energy storage  10  is automatically considered in the preparation of state predictions.  
         [0020]    Furthermore, there is an control interface  18  in FIG. 1 so that, on the one hand, the predictions determined by the prediction module  16  with regard to the energy storage  10  can be output, and on the other hand, the user can access the charging system in order to implement, for example, an emergency-off function or to input special updated data with respect to the energy storage  10  so that they can be considered by the adaptive model.  
         [0021]    This invention is not limited to certain types of energy storages. Thus, electrochemical batteries, for example, NiCd, NiMH, Li ion, Li, Pb, AgMH, Zn/MNO 2  cells, fuel cells, such as, for example, AFC (alkali fuel cell), PEM (polymer electrolyte fuel cell), DMFC (direct methanol fuel cell), PAFC (phosphoric acid fuel cell), MCFC (liquid carbonate fuel cell), SOFC (solid oxide fuel cell), passive components, such as, for example, special capacitors and combinations thereof, can be used.  
         [0022]    [0022]FIG. 2 schematically shows an example of the structure of the charging module  14 . A control logic  40  controls the output of the charging module  14  to the energy storage  10  by controlling, via a driver/limiter  42  which can be turned off, the flow of electric power which is made available via an energy interface  44  which may be connected by wire or in a wireless manner. To produce the control signals for the driver/limiter, there are a DC generator  46 , an AC generator  48 , a frequency synthesizer  50 , a modulator  52 , an adder  54  and a switch  56 . The switch  56  is provided, for example, for pulsed signals or for shut off. The DC generator  46 , the AC generator  48 , the frequency synthesizer  50  and the switch  56  are controlled directly by the control logic  40 . The charging module is designed for great flexibility and can deliver DC and AC signals at a constant current or constant voltage which can be pulsed and/or modulated.  
         [0023]    [0023]FIG. 3 schematically shows an example of the structure of the control and prediction module  16 . The central element thereof is a neuro-fuzzy logic  20 , which can be made such that the membership function and the rule set are found by means of one or more neural networks. Examples of use of neuro-fuzzy logic can be found in the initially mentioned article of Salkind et al. There are numerous software toolboxes for neural networks and fuzzy logic, for example, in the programming language Matlab, and many of these toolboxes can be obtained via the Internet. Among them are a number of toolboxes which allow combined use of fuzzy logic and neural networks in the sense of neuro-fuzzy logic. In particular, also hybrid neuro-fuzzy logic can be used; it can be made, for example, in two stages, the input data being supplied, for example, to two different neuro-fuzzy logics and the output values thereof being supplied to a third neuro-fuzzy logic as input values, the output values of the latter ultimately providing the desired result. An example of hybrid systems can be found in K. M. Bossley,  Neurofuzzy Modeling Approaches in System Identification , Ph.D. thesis, University of Southhampton, Department of Electronics and Computer Science, 1997, pages 94 to 156.  
         [0024]    A major task of the prediction module  16  consists in obtaining, from known data for the energy storage  10  which reflect the operating history thereof, predictions regarding the instantaneous storage state and the evolution of the storage state in the future. Here, a prediction as accurate as possible is derived from the existing data sets. In doing so, it must be considered that, for example, in batteries, in the course of operation, changes of battery properties occur which develop only slowly and which are of very small, for example, corrosion reactions or slow changes of the mechanical and geometrical properties. The simple analytic battery models ordinarily used are not able to model these changes of battery properties with sufficient accuracy. In particular, they are unable to analytically simulate the changes of the internal battery properties from the easily accessible measurement quantities, i.e., voltage, current and temperature behavior. For this reason, such an analytic description is omitted here, and instead, an attempt is made to reach a prediction into the future with sufficient accuracy by means of a self-learning model directly from as many available data as possible regarding the history of the energy storage in order to predict, for example, on the one hand, the service life of the energy storage, or on the other hand, to choose, for example, the optimum charging strategy which, for example, leads to charging as fast as possible or to charging as carefully as possible or charging of the energy storage as energy-efficiently as possible. With consideration of the long-term lifetime prediction, for example, the charging strategy can be chosen such that the expected lifetime of the energy storage is optimized.  
         [0025]    Before start-up of a specific energy storage  10 , there are still no data which had been measured in the operation of this specific energy storage  10 . For this reason, the neuro-fuzzy logic  20 , at this time, can use only data which describe the state of the energy storage  10  before start-up. They can be, on the one hand, type data, i.e., data which are specific to the type of the energy storage  10 , or data characteristic to the individual energy storage  10 , for example, data which were determined in the production of the energy storage  10 . They are physical or chemical data, for example, the mass, dimensions or geometrical characteristics, the porosity and grain sizes including their three-dimensional distributions and electrical characteristic data, such as the frequency-dependent impedance of the energy storage  10 . In addition, the starting configuration of the neuro-fuzzy logic  20  is preset. As soon as operation of the energy storage  10  has begun, the prediction module  16  can acquire, by means of a measurement module  22 , instantaneous measurement data, for example, with respect to the current from or to the energy storage  10  and to the voltage present on the storage terminals, each as a function of time, which are stored as data sets in a data memory  24  for later use. Using the operating history of the storage  10  which has been recorded in this way, the neuro-fuzzy logic  20  is subjected to regular retraining to continually improve the prediction accuracy for the specific energy storage  10 , i.e., the model of the storage  10  becomes more accurate with increasing operation time.  
         [0026]    It is an important aspect of the invention that, in this way, for predicting the storage state, not only instantaneous measured values are used, but rather the entire operating history of the energy storage  10 , if possible. It is only thereby that long-term predictions into the future are possible, for example, with respect to the service life of the energy storage  10 .  
         [0027]    Preferably, the acquired measurement data sets are preprocessed before being processed by the neuro-fuzzy logic  20 . In doing so, for example, a wavelet transform can be used to eliminate noise and to provide for data compression without significant loss of accuracy. However, statistical processes also can be used.  
         [0028]    The output of the neuro-fuzzy logic  20  is operatively connected to an action predictor module  26  and an information predictor module  28 . The action predictor module  26  is used to convert the predictions of the neuro-fuzzy logic  20  into short-term and long-term control of the charging module  14 , wherein, for example, a charging strategy can be chosen such that the expected lifetime of the energy storage  10  is optimized. The information predictor module  28  is used as the interface  18  for the user to output, for example, the instantaneous prediction for the expected lifetime of the energy storage  10  and assistance for maintenance of the energy storage  10  or the time remaining until the next required charging process at the instantaneous load caused by the consumer  12 . Furthermore, for example, the remaining duration of the charging process can be output. On the other hand, via this interface, the user can also influence the control of the charging module  14 , for example, by inputting an emergency-off signal. Furthermore, the user can also input, for example, updated expert knowledge regarding the type of energy storage used; this is of special interest when the lifetime of the energy storage  10  extends over many years.  
         [0029]    The energy storage  10  is, for example, preferably, the power supply part of an implant in the human body, for example, a hearing system. Here, charging of the energy storage takes place by transcutaneous transmission of electrical energy from the energy transmitting part of the charging module  14  to a corresponding energy receiving part of the energy storage  10 . In such a system, it is especially important, on the one hand, to know the instantaneous charging state of the energy storage, and on the other hand, to also have available a reliable prediction of its entire lifetime.  
         [0030]    [0030]FIG. 4 schematically shows the data flow of the neuro-fuzzy logic  20  of the prediction module  16 . For training of the neuro-fuzzy logic  20  the available data are divided into learning data, validation data and test data. In the first stage the learning data are fed to the neuro-fuzzy logic and the parameters thereof are changed until the pertinent target values which correspond to the input data are reproduced as well as possible. Using the validation data then the success of learning can be checked. Learning and validation can however also take place at the same time. Finally, a final check of the model can be done using the test data.  
         [0031]    Determining the initial parameters of the neuro-fuzzy logic can also be performed externally and then transmitted to the charging control.