Patent Number: 
Section: description

An exemplary embodiment of the present invention may include state sensing for energy stores, such as automobile batteries, but is not limited to this application. FIG. 1 shows the components for state sensing according to an exemplary embodiment of the present invention of an energy store 1, such as an automobile battery. A sensor and measurement unit 2 perform measurements of performance quantities x, such as current, voltage, and/or temperature, on battery 1. The measured performance quantities are supplied by lines 7 to a state estimator 3, which, for example, may determine state variables that characterize the current system state using a Kalman filter. Such state variables may include the available charge or the age of battery 1. State estimator utilizes a model, in which measured performance quantities x are entered to determine the state variables a. The model operates using model parameters p, which are also dependent on the aging processes of energy store 1. To avoid the model losing its validity due to changed parameters p, model parameters p are updated using a parameter estimator 4. For this purpose, a parameter estimation routine is used, which uses measured performance quantities x and may also use additionally estimated state variables a as input quantities. Updated parameters p are then delivered to state estimator 3. For this purpose, state estimator 3 and parameter estimator 4 are connected to one another. State variables a, determined by state estimator 3, are processed further to perform favorable measures (for example, charge state displays, modification of the energy supply). FIG. 1B shows a suitable state estimator 3 and parameter estimator 4, in which the individual components for state sensing, according to an exemplary embodiment of the present invention, are each present and assembled into one unit. Measured performance quantities x are supplied by lines 7 to state estimator 3 and/or parameter estimator 4. Subtractors or differentiators, which produce gradients of one measurement quantity x at a time, are used as a detection arrangement 8 for detecting the dynamic response of measured performance quantities x. A selection unit 9, which selects state variable a and/or parameters p subsequently estimated depending on the detected dynamic response of the performance quantities x, is connected downstream. Selected performance quantities x are supplied at state estimator 3, together with updated parameters p, to a computation unit 10, which computes specific state variables a using a model. Most estimation models operate with covariance matrices, the values assigned to the individual state variables of which converge toward zero, if the estimated value approximates the real value over time. These matrix values (covariances) may therefore be used for rating the quality of the estimation. To rate the quality of the estimation, threshold values associated with the respective covariances may be, for example, fixed in a unit 11, and the quality of the estimation may be determined by subtracting the estimated value from the fixed threshold value. If, for example, an estimated state variable does not fall below the threshold value after a predetermined number of cycles, the estimated value may be rejected and the previously estimated value may be maintained instead. In this way, increasing deterioration of the estimation may be prevented. FIG. 2A shows an example of a rapidly converging estimated state variable a(3), which is not subject to any fluctuations after convergence. Such state variables, such as the concentration overvoltage, have large time constants. The associated matrix element of the covariance matrix shown in FIG. 2B, in this example K(3,3) to a(3), i.e., the covariance to this state variable, converges rapidly toward zero. To check the quality of the estimation, a threshold that may be reached after a certain number of cycles may be fixed, i.e., a number of iterative estimations. If not, the estimation for the state variable may be rejected. An example of a divergence of a current state variable xc3xa3(1) and associated estimated value a(1) is shown in FIG. 3. The fluctuating time curve of current state variable xc3xa3(1) and estimated state value a(1), which moves away from the zero line over time, is shown in FIG. 3A below the zero line. Associated covariance K(1,1) to state variable a(1) indicates that the estimation may not be suitable. The covariance does not converge, but increases continuously over time, as shown in FIG. 3B. Cases, such as that of FIG. 3, may be avoided by an exemplary embodiment of the present invention, which utilizes xe2x80x9cbackupxe2x80x9d methods, if the quality of the estimation is not sufficient. FIG. 4 shows a flow diagram of an exemplary method according to the present invention. At the beginning of the estimation method, a specific time Tmin1 passes, before the system assumes a state suitable for state estimation, which occurs before the actual estimation method begins. Subsequently, the dynamic response of the excitation, i.e., the dynamic response of measured performance quantities x, is scanned (S1). These may be, for example, time-dependent quantities current, temperature, and voltage. If, for example, the discharge current of the battery remains nearly zero over a relatively long period of time, since, for example, the consumer may be completely supplied by the generator, specific state variables a or parameters p dependent on the current may not be subject to change. Further measurement values are then awaited, until a further time interval Tmin2 passes (S2). If a dynamic response of the measured performance quantities begins, the quantity of the dynamic response is scanned (S3). For a low dynamic response of the measurement values, it is first determined whether the system is in a limit state or boundary region (in batteries, for example, the fully charged or drained state). The same scanning also occurs if there is a large dynamic response of the measured performance quantities (S4 and/or S5). If the system is not in a boundary region, the actual estimation of the state variables may be started. According to an exemplary embodiment of the present invention, at a low dynamic response of the measurement values, state variables having small time constants are maintained (S6), while state variables having large time constants are estimated (S7). In contrast, for measurement values having a large dynamic response, the state variables having large time constants are maintained (S8), while the state variables having small time constants are estimated (S9). In the battery application described above, the ohmic values and the charge-transfer overvoltage represent state variables/parameters having small time constants, while, for example, the concentration overvoltage may represent a state variable having a large time constant. According to an exemplary embodiment of the present invention, the parameters and state variables that are not expected to cause changes in the dynamic response of the system are not redetermined by estimation. In this way, enlarging inaccuracies during the estimation due to unnecessarily frequent estimations, which may invalidate the model or provide incorrect state results, may be avoided. If, while checking (S4, S5) whether the system is in a limit state (boundary region), it is determined that the system is in a limit state (boundary region), the state variables/parameters may be, for example, maintained or evaluated using xe2x80x9cbackupxe2x80x9d methods (S10, S11), to avoid incorrect estimations (boundary regions of the model accuracy). These methods are based on stable models, in which no divergence is expected. After the routine shown in FIG. 4 is finished, one cycle of the state estimation, according to an exemplary embodiment of the present invention, ends, and further cycles may follow immediately or with delays, which may be fixed. FIG. 5 shows a flow diagram for determining the quality of the estimation described above. For this purpose, a measurable quantity, which is calculated from estimated quantities, is compared with the quantity actually measured (T1). If there is good correspondence (for example of the estimated and measured battery voltages), the convergence of the covariances associated with the state variables/parameters is checked (T2). Specifically, individual covariances may not yet have sufficiently converged (see also FIG. 2B), so that a specific typical convergence time Tmin still should be awaited (T3) until sufficiently good convergence results. When this occurs, the estimated state variables/parameters are evaluated (T4) and from this, for example, the charge state or the age of the battery may be determined. In contrast, if time Tmin has already passed without the associated covariances having sufficiently converged, i.e., for example, having passed below a specific threshold value, the estimated quantities are rejected and the parameter estimation routine and/or the state estimation routine (Kalman filter) is restarted (T5). Until the reestimated quantities are received, simple xe2x80x9cbackupxe2x80x9d methods may be utilized (T6). If there is no sufficient correspondence between an easily measurable and estimable reference quantity (for example battery voltage) from the start, the covariance matrix may not be sufficiently converged. The result may be rechecked after a time period Tmin* (T7). If the result remains unchanged, the parameter and/or state estimation is restarted (see FIG. 4) and xe2x80x9cbackupxe2x80x9d methods may be utilized (T8, T9).