33 results about "Energy estimation" patented technology

The invention provides an anti-interference sound signal data extraction method based on energy data perception, and belongs to the technical field of underwater sound signal processing, and the method comprises the steps: carrying out the A / D sampling and digital orthogonal demodulation of a received sound signal, and obtaining a baseband signal; performing low-pass filtering and downsampling on the baseband signal to obtain a preprocessed signal; performing matched filtering on the pre-processed signal by using a reference signal; the key improvement lies in that a local energy estimation function of a preprocessed signal is calculated, and normalization processing is carried out on a matched filtering result and the energy estimation function to obtain a normalized detection signal. The method effectively suppresses the shielding effect of a strong interference signal on a weak target signal under a non-orthogonal reference signal and a strong near-end and weak far-end scene, remarkably reduces the misrecognition probability, and improves the accuracy and reliability of the extraction of a long-distance and low-signal-to-noise-ratio target beacon signal in a complex underwater acoustic environment. The method is suitable for underwater navigation, positioning and communication systems.

The invention discloses a rolling bearing fault diagnosis method based on time-frequency fusion and a double-branch deep network, and relates to the technical field of mechanical equipment state monitoring and fault diagnosis, and the method comprises the steps: carrying out the time-frequency joint representation of VMD and Multi-taper PSD on a single-channel vibration signal, rearranging the signal into a fixed-size gray matrix according to a time sequence, and carrying out the fault diagnosis of the single-channel vibration signal; a time sequence is reserved, and stable spectrum energy estimation is obtained; parallel double branches are constructed on network modeling; hierarchical window self-attention modeling is carried out on a frequency domain branch, and long-time-history dependence and key moment attention are enhanced by a time domain branch through TCN-CBAM; an XPAF module is introduced, pairwise cross attention is taken as a core, priori bias B and a directional mixed structure are combined, feature alignment and a residual path are matched, and time-frequency information consistency and efficient information flow are ensured. According to the method, the separability of weak faults and the robustness under noise / variable working conditions are remarkably improved, and the method is suitable for online monitoring and intelligent diagnosis of rotating machines such as bearings.

The invention provides a radar wave intensity measurement method and system based on multistage filtering amplification, and the method comprises the steps: carrying out the preprocessing of a radar echo signal, and obtaining a preprocessing signal through time domain wavelet packet decomposition and spatial domain wave beam forming collaborative optimization; performing intelligent dynamic gain reconstruction on the preprocessed signal to obtain a gain adjustment signal; performing nonlinear harmonic tracking compensation on the gain adjustment signal to obtain a compensation calibration signal; and performing dynamic threshold intensity extraction on the compensation calibration signal to obtain a final measurement result. According to the method, an environment factor-attenuation compensation mapping relation is established through a support vector regression model, and a dynamic environment compensation coefficient is generated; in combination with wavelet packet multi-scale energy integration and signal-to-noise ratio dynamic weighting, space attenuation is corrected in nonlinear harmonic tracking compensation, and an adaptive threshold and energy estimation are optimized in dynamic threshold intensity extraction; and finally, through Kalman filtering post-processing and spectrum purity monitoring closed-loop verification, the system can perform high-precision measurement in a dynamic environment.

The application discloses a kind of determination methods of maximum take-off weight and battery weight of electric aircraft, wherein the maximum take-off weight determination method of electric aircraft includes: establishing the maximum take-off weight equation of electric aircraft;Solving the maximum take-off weight equation of electric aircraft, obtain the maximum take-off weight of electric aircraft.The determination method of the battery weight of electric aircraft includes: determining the maximum take-off weight of electric aircraft;Maximum take-off weight is used to calculate the battery weight of electric aircraft.The determination method of the maximum take-off weight and battery weight of electric aircraft, the energy consumed by aircraft mission profile is used to estimate the battery weight, the estimation result is more accurate, the reliability is higher, the calculation process is simpler, and the calculation parameter is more simplified.

The invention relates to the technical field of energy consumption management, and discloses a method and a system for determining environmental economy coordination degree based on a coupling model, which comprises the following steps of: constructing a de-jitter filtering link around a working condition set for boundary diffusion and sample mismatching which are easily caused by global smoothing and single-scale alignment, generating a window table by using an energy estimation vector and a comparison vector, and determining the environmental economy coordination degree according to the window table. Edge-preserving filtering is performed on a coding amplitude sequence to obtain a smooth set consistent with a working condition set, so that start-stop and load switching neighborhood oscillation convergence, boundary transition maintenance, fragment variance reduction, low-noise input of a short window table and a sample matrix and coordination vector fluctuation reduction are realized, and an alignment table is generated by joint modeling of an alignment scale and a hierarchical index; the minimum cost is executed under hierarchical recording, abnormal index rejection is combined to obtain a delay vector, window-by-window release is performed according to a stable Boolean sequence, and only a coordination vector with controlled delay and stable coupling is output, so that three-channel sampling delay is inhibited, and coordination degree short-window stable output is realized.

The present invention provides a system for energy estimation in batteries, comprising a battery set-up module configured to define a configuration of one or more batteries. The system has a state-of-charge (SOC) estimation module that comprises a calculation module configured to obtain battery specifications and compute system parameters, a graph plotting module configured to generate voltage-energy graph at pre-defined load conditions, and an estimation module configured to determine SOC and runtime values based on discharge voltage and voltage-energy graphs. It also has an analysis module configured to uninterruptedly monitor and analyze battery voltage, SOC, and runtime, a notification module configured to initiate warnings when the monitored voltage SOC and runtime approach predetermined thresholds, and a shut-down module to enable a safe shutdown and backup operation.

The invention relates to the technical field of detectors, and discloses a muon momentum reconstruction method based on particle tracks, which comprises the following steps of: 1, researching and developing an efficient energy detector, and processing position information of muons by adopting Kalman filtering and a REST framework in a gas time projection chamber so as to improve the energy measurement accuracy; 2, introducing a dynamic energy measurement algorithm, developing a self-adaptive algorithm, adjusting energy measurement parameters according to real-time feedback, optimizing muon energy estimation by using Kalman filtering, and reducing background noise influence at the same time; 3, realizing multi-detector array integration, designing a multi-detector array with reasonable layout, ensuring synchronous data acquisition, and enhancing the reliability of a measurement result by using cross validation; 4, data analysis is conducted through the machine learning technology, and a machine learning model is constructed and trained. According to the method, the energy of incident muons is estimated through Kalman filtering, the energy of the muons is screened when the muon scattering imaging technology is applied, and the imaging precision is improved.

The invention discloses a far-field accidental sound source positioning method, system, medium and equipment, and the method comprises the steps: collecting an audio signal through a sensor, and carrying out the preprocessing of the audio signal, and obtaining a preprocessing signal; performing multi-band energy analysis to calculate short-time energy of each band, updating background energy estimation, and dynamically adjusting a detection threshold value; meanwhile, the processed audio features and real-time environment features are fused into multi-modal features, the multi-modal features are input into an accidental event detector for detection statistic calculation, and when the detection statistic reaches a self-adaptive threshold value, an accidental event detection stage is entered: a time window containing an accidental event in the preprocessed signal is determined, extended interception is carried out, and a time window containing the accidental event is obtained; the signals of the sensors in the corresponding time periods are extracted; fast TDOA estimation is carried out, abnormal estimation values are eliminated, and an estimation result is obtained; calculating the three-dimensional coordinates of the sound source by combining the position of the sensor; and calculating positioning confidence evaluation to obtain an effective positioning result. According to the invention, adaptive sound source localization of a complex urban environment is realized.

The application provides an anti-interference acoustic signal data extraction method based on energy data sensing, and belongs to the technical field of underwater acoustic signal processing. The method comprises the following steps: A / D sampling and digital quadrature demodulation are performed on a received acoustic signal to obtain a baseband signal; low-pass filtering and down-sampling are performed on the baseband signal to obtain a pretreated signal; the pretreated signal is matched filtered by using a reference signal; a key improvement lies in that a local energy estimation function of the pretreated signal is calculated, and the matched filtering result is normalized with the energy estimation function to obtain a normalized detection signal. Through energy sensing normalization, the application effectively suppresses the shielding effect of strong interference signals on weak target signals due to non-quadrature reference signals and the "near-end strong and far-end weak" scene, significantly reduces the false recognition probability, and improves the accuracy and reliability of extracting a long-distance and low signal-to-noise ratio target beacon signal in a complex underwater acoustic environment. The application is suitable for underwater navigation, positioning and communication systems.

The invention discloses an electro-hydrogen system optimization regulation and control method and system based on system energy efficiency characteristic modeling, a medium, equipment and a product in the technical field of power system operation and intelligent control. The method comprises the following steps: according to energy efficiency characteristics of a target electricity hydrogen system, constructing an electricity hydrogen system nonlinear physical energy efficiency model, and according to the electricity hydrogen system nonlinear physical energy efficiency model, constructing an electricity hydrogen system optimization scheduling model taking the minimum daily operation total net cost of the electricity hydrogen system as a target function; and according to the real-time operation data of the electro-hydrogen system, solving the electro-hydrogen system optimization scheduling model by adopting a nonlinear programming algorithm to obtain an optimal power distribution instruction. According to the method, the energy efficiency characteristics of the electrolytic cell in the medium-low load interval can be accurately identified, the energy estimation deviation caused by a traditional fixed efficiency model is effectively avoided, and the economic benefit of a wind-hydrogen system is remarkably improved.

The invention discloses a method for determining the maximum takeoff weight and the battery weight of an electric aircraft, and the method comprises the steps: building a maximum takeoff weight equation of the electric aircraft; and solving the maximum takeoff weight equation of the electric aircraft to obtain the maximum takeoff weight of the electric aircraft. The method for determining the weight of the battery of the electric aircraft comprises the following steps: determining the maximum takeoff weight of the electric aircraft; and calculating the battery weight of the electric aircraft by using the maximum takeoff weight of the electric aircraft. According to the method for determining the maximum takeoff weight and the battery weight of the electric aircraft, the battery weight is estimated by utilizing the energy consumed by the mission profile of the aircraft, the estimation result is more accurate, the credibility is higher, the calculation process is simpler, and calculation parameters are more simplified.

The invention relates to an energy estimation system and method for an openBMC-based firmware stack. Embodiments of the present disclosure provide an energy estimation system and method for an openBMC-based firmware stack that empirically determines a power load offset value that can be applied to a BMC executing the openBMC-based firmware stack to enable an IHS to run more efficiently. According to one embodiment, an information handling system (IHS) includes a baseboard management controller (BMC) including firmware to apply a power load offset value to adjust a speed of one or more fans configured on a target IHS when an openBMC-based firmware stack is executed on the BMC, and to apply a power load offset value to adjust a speed of one or more fans configured on the target IHS when a standard firmware stack is executed on the BMC. The power load offset value is applied to adjust the speed of the fan without applying the power load offset value.

The application discloses a kind of sunny long endurance unmanned aerial vehicle hyperspectral image reflectivity correction method based on solar radiation energy estimation model, comprising the following steps: S1.select different types of multiple unmanned aerial vehicles, carry hyperspectral camera, so as to obtain high-quality hyperspectral image data;S2.through in different time, different area is carried out multiple different types of unmanned aerial vehicle aerial experiment, the actual operation process of long time unmanned aerial vehicle is simulated comprehensively, and the performance of unmanned aerial vehicle under long time running condition is evaluated systematically, verify the effectiveness of model in image radiation correction, while combining ground spectrometer data, ensure the accuracy and applicability of experimental results;S3.in different test area, collect multiple types of data for unmanned aerial vehicle reflectivity precision verification and evaluation analysis.The application is aimed at hyperspectral image obtained by long time unmanned aerial vehicle under sunny condition, and the applicability and stability of verification model under different ground surface type conditions are evaluated through reflectivity precision evaluation.

This invention provides a distributed radar noncoherent direct localization method, device, terminal, and medium for data anomaly scenarios, relating to the field of radar technology. The method includes: acquiring echo signal data received by each observation array; constructing a signal model characterizing the mapping relationship between the echo signal data and potential source locations, where the common detection area of ​​each observation array is a two-dimensional grid, the grid point coordinates correspond to the potential source locations, the signal model does not contain phase synchronization parameters between observation arrays, and is adapted to the noncoherent characteristics of the signal data; and constructing a system based on the signal model... 2,1 This invention addresses the group sparse optimization problem of norms. A pre-defined optimization algorithm is used to solve the problem, yielding a row sparse matrix. Based on this matrix, the true source localization result is determined, and the column vector elements of the row sparse matrix correspond to the potential source reflection energy estimation results at the grid points. This invention utilizes echo signal data from various observation arrays to achieve primary localization, eliminating secondary localization errors and reducing abnormal noise interference.

The application discloses a ship vibration test evaluation system with adaptive order filtering, and relates to the technical fields of ship vibration detection and signal processing. In view of the problems of low-frequency numerical instability, waste of calculation resources and uneven precision distribution caused by the fact that the existing 1 / 3 octave filtering method uniformly adopts fixed order filters, the application obtains vibration signals of a specified position of a ship through an acceleration signal acquisition module, obtains energy estimation values of each frequency band through spectrum estimation, solves the optimal order of each frequency band filter through a constraint optimization model according to a weighted sensitivity index, numerical stability constraints and sideband attenuation requirements by an adaptive order determination module, supports a partition fixed order simplification mode, and outputs a habitability evaluation result after frequency band filtering and weighted calculation. The application improves the low-frequency filtering stability, reduces the overall system calculation amount, realizes the optimal balance between filtering precision and calculation efficiency, and is suitable for a ship-mounted embedded real-time vibration monitoring system.

To provide a distribution device, distribution method, and program capable of distributing a video obtained by imaging a competition in a manner that makes it easy for beginners to understand the competition.SOLUTION: A distribution device comprises: a tracking unit that tracks, for each player, the position of the player on the basis of coordinates of the player in time-series frames of a video obtained by imaging players competing in an athletic field; a measurement unit that measures, for each player, the movement speed of the player on the basis of differences in the tracked positions of the players; an energy estimation unit that estimates, for each player, the energy exerted by the player on the basis of the movement speed of the player; a physical strength estimation unit that estimates, for each player, the remaining physical strength of the player on the basis of a cumulative value of the energy exerted by the player; an image processing unit that superimposes, on the time-series frames, a physical strength gauge image representing the remaining physical strength of each player in association with the player; and a communication unit that distributes the time-series frames with the physical strength gauge image superimposed thereon to a user terminal.SELECTED DRAWING: Figure 1

This application discloses an intermittent deep neural network inference method for energy harvesting IoT, specifically relating to the field of neural network inference methods. It includes: retrieving input data, model parameters, and progress tracking variables for each layer of a deep neural network model from non-volatile memory; processing the input data through multiple sub-operations in each layer, updating the progress tracking variables upon completion of each sub-operation, and writing the output of the sub-operation and the corresponding progress tracking variables into non-volatile memory; if a processing interruption occurs during the processing of input data through multiple sub-operations in each layer, determining the completed sub-operations based on the progress tracking variables in non-volatile memory, retrieving the input data and model parameters required for the remaining sub-operations, and completing the remaining sub-operations. This eliminates the need for application partitioning inference, thus avoiding energy estimation.

The application discloses a kind of considering the retirement of power battery sorting method of battery aging state distribution imbalance, comprising;1. using voltage measuring equipment to measure and record the end voltage of retired battery;2. current batch of retired power battery data is randomly extracted as test battery;3 based on design step, test battery is executed test experiment and data is collected and stored;4. data cleaning and pretreatment are carried out to test data, and then the adaptive feature engineering designed is executed;5. using the output of last step, the pre-training of remaining energy estimation model embedded with feature distribution smoothing module is carried out.6. repeat steps 3-4 to the remaining battery, and deploy the pre-training model to execute remaining energy estimation.The application can overcome the error caused by aging distribution imbalance, so as to reduce the time cost and energy consumption cost of retired battery sorting work under the premise of ensuring sorting accuracy.

The invention relates to an energy management device (100) for an energy store (106), comprising an energy store (106), an energy storage management apparatus (103) and an energy estimation apparatus (104), wherein the energy storage management apparatus (103) is designed to detect and provide a plurality of energy storage parameters present in the energy store (106), wherein the energy estimation apparatus (104) is designed to receive the plurality of energy storage parameters and to provide an estimated energy value from the plurality of energy storage parameters, characterised in that the energy estimation apparatus (104) selects substantially only electric current parameters from the provided plurality of energy storage parameters and wherein an electric current parameter is a parameter which has an influence on the electric current flow into and / or out of the energy store (106).

The application aims to provide a weak signal detection method based on motion energy estimation, comprising the following steps: A, constructing a motion model of 12 target signals in an image, and the motion model has a size of 5*5; B, multiplying the sequence image with the motion model to obtain an image to be detected; C, scanning all pixels in the current frame image one by one, taking the neighborhood of the currently scanned pixel as the center to construct the center neighborhood block and the corresponding neighborhood blocks in the four directions above, below, left and right, calculating the gradient, and taking the position of the pixel point as a candidate target position if the gradient in more than three of the four directions is greater than a predetermined gradient threshold; D, performing high-order energy correlation enhancement on the candidate target point, obtaining the energy maximum value of the target, and outputting a target image; E, performing the same processing on the sequence image, superimposing all target images, and obtaining a target motion trajectory image. The application can effectively eliminate noise and extract a real target, and greatly improve the detection rate of the algorithm.

The invention relates to the technical field of data processing, and discloses a method, a system, equipment and a medium for estimating a hidden area of a pilot of a black-flight unmanned aerial vehicle, and the method comprises the steps: measuring an electromagnetic signal of remote control equipment of the black-flight unmanned aerial vehicle through multiple mobile wireless direction finding equipment, so as to carry out the angle difference and energy estimation of the remote control equipment of the black-flight unmanned aerial vehicle; therefore, an estimation result of each mobile wireless direction finding device is obtained; carrying out incoherent imaging on the plurality of estimation results to obtain an imaging result obtained by carrying out incoherent superposition on the plurality of estimation results; and the moving direction and the moving distance of the mobile wireless direction finding equipment are controlled according to the imaging result and the estimation result, so that the mobile wireless direction finding equipment moves to a preset position, and then the first step is returned until the area where the black flight unmanned aerial vehicle remote control equipment is located is locked. According to the invention, only two antennas are utilized to carry out orientation estimation, at least two devices are needed to realize target signal area search, the method has the advantages of low cost and accurate result, and powerful support can be provided for subsequent photographing and evidence obtaining.

The application discloses a kind of audio signal processing methods of vehicle cabin, comprising: obtaining the first audio signal in vehicle cabin and second audio signal;According to first audio signal and second audio signal, determine residual signal;According to residual signal, determine residual signal function;According to second audio signal and residual signal function, determine residual echo function;According to residual echo function and residual signal function, determine the audio signal state in vehicle cabin, to output processed audio signal for speech recognition use.The application adopts new method outside correlation determination and energy determination, avoids the damage of low correlation determination result caused by too large remote signal when correlation determination is used, also avoids the signal confusion caused by energy difference of different loudspeaker devices when energy estimation is used and further causes the damage of energy estimation result.

The invention discloses an audio separation model training method, an audio separation method, electric equipment and a computer readable storage medium. The method comprises the following steps: determining an estimated output audio signal according to an obtained mixed audio signal sample based on an audio separation model; performing energy estimation processing on the estimated output audio signal and the background audio signal sample, and determining a target energy weighted loss function; performing frequency division processing on the estimated output audio signal and the background audio signal sample, and determining a time domain frequency division loss function; determining a fusion loss function of the audio separation model according to the time domain frequency division loss function and the target energy weighted loss function; and training an audio separation model according to the fusion loss function. Therefore, the audio separation model is trained based on the loss function fused by the target energy weighted loss function and the time domain frequency division loss function, the convergence speed can be increased, and the accuracy of the audio separation model for separating the mixed audio is improved.

The application discloses a kind of DNN efficient inference methods based on size capacitance and mixed quantization in batteryless power supply system.The method comprises: constructing the energy monitoring model of batteryless power supply system based on size capacitance architecture, the accurate estimation of available energy in three working states is realized by double-channel voltage synchronization mechanism;Establish a mixed quantization multi-outlet deep neural network inference model, support multi-level quantization accuracy and dynamic selection of multiple early exits;Design a fine-grained-coarse-grained inference strategy based on double-agent reinforcement learning, through the collaborative decision of module agent and outlet agent, dynamically adjust the inference path and quantization level according to real-time energy availability and time constraints.The method can solve the problems of inaccurate energy estimation, low inference reliability and limited computing resources in batteryless power supply system, maximize inference-aware efficiency (IAE), and improve the DNN inference performance of batteryless energy harvesting devices.

The invention discloses a molecular ground state energy estimation method and a variable component sub-feature solver. The method comprises the following steps: determining a quantum bit Hamiltonian according to a target molecule to be simulated; then, based on a preset graph isomorphism algorithm, according to the quantum bit Hamiltonian and acquired topological structure configuration information, determining a target quantum circuit and a target mapping area for operating the target quantum circuit, the topological structure configuration information is hardware topological structure information of computer equipment running the target quantum circuit. And finally, estimating the ground state energy of the target molecule according to the target mapping region and the target quantum circuit. Thus, according to the quantum bit Hamiltonian and the topological structure configuration information, the target quantum circuit and the target mapping area for running the target quantum circuit are determined, the hardware adaptability of the target quantum circuit and the computer equipment of the target quantum circuit can be optimized, and therefore the running efficiency of the target quantum circuit is improved.

Embodiments of the present disclosure provide an energy estimation system and method for openBMC-based firmware stacks that empirically determines a power loading offset value that may be applied to a BMC executing an openBMC-based firmware stack to make an IHS run more efficiently. According to one embodiment, an Information Handling System (IHS) includes a Baseboard Management Controller (BMC) that includes firmware to when an openBMC-based firmware stack is executed on the BMC, apply a power loading offset value to adjust a speed of one or more fans configured on the target IHS, and when a standard firmware stack is executed on the BMC, apply a power loading offset value to adjust the speed of the fans without applying the power loading offset value.

The invention relates to the technical field of new energy vehicle battery management systems, and particularly discloses an energy state SOE estimation method based on a battery equivalent model, and the method comprises the steps: building the battery equivalent model: employing an equivalent circuit model to estimate the state of a battery; estimating static theoretical residual energy: acquiring open-circuit voltage information of the battery by the battery management system, correcting an OCV-SOE relationship according to the standing time of the battery and the state before standing, and then calculating a corresponding battery SOE value according to a current OCV value; predicting future residual discharge energy based on current state estimation and future state prediction of the battery; the BMS enters a working state after being awakened, and residual energy of the battery at the current moment is obtained through calculation; according to the method, the estimation precision of the residual energy is effectively improved, so that the endurance mileage is further accurately estimated, the driving problem caused by estimation errors is reduced, and the method is of great significance in promoting development and popularization of electric vehicles.

This invention relates to the field of logistics scheduling technology, specifically disclosing a method and system for intelligent scheduling of goods in multi-temperature zones for cold chain logistics. The method involves acquiring multi-source dynamic datasets; constructing a virtual mapping body that includes the coupling relationship between the thermodynamic decay characteristics of each temperature zone and vehicle kinematic constraints; using the virtual mapping body to simulate future road network congestion events in advance, generating cold energy decay curves and remaining cold energy budget values ​​under different congestion scenarios; employing a multi-objective combined optimization algorithm based on the remaining cold energy budget value and the congestion scenario to pre-calculate a set of candidate scheduling strategies, including route detour schemes and temperature zone pre-cooling intervention schemes; when actual congestion occurs, matching and selecting the optimal strategy for execution, and dynamically correcting the virtual mapping body based on real-time feedback data. This invention realizes a shift from passive response after congestion occurs to proactive pre-congestion planning, effectively avoiding the scrapping of goods due to insufficient cold energy estimation, and improving the reliability and economy of cold chain distribution.

The invention relates to the technical field of simulation detection of electronic devices, and discloses a method and a system for estimating fusing energy of a fuze module by simulating a filament. The fusing energy estimation method is applied to electronic simulation equipment and specifically comprises the following steps: S101, based on a physical structure of simulated silk, establishing a three-dimensional heat conduction physical model comprising a metal fuse layer, an insulating layer and a semiconductor substrate layer, and accurately representing that heat is generated from the metal fuse layer; quantitative definition is completed through the whole process of conducting to the constant-temperature heat sink through the silicon dioxide insulating layer and the silicon substrate layer in sequence and through specific material parameters and boundary conditions. According to the multilayer heat conduction physical model, the heat conduction characteristics of the metal fuse layer, the insulating layer and the semiconductor substrate layer are comprehensively considered, accurate characterization of a heat transfer path is achieved by quantitatively defining material parameters and constant-temperature boundary conditions, and the accuracy and reliability of fusing energy estimation are remarkably improved.

A computer system for adaptive state-of-charge (SoC) window control is provided, including processing circuitry configured to: obtain a minimum required usable energy value for an electric energy storage system (ESS); obtain indications of uncertainties for ESS usable energy estimation associated with at least one of i) aging of the ESS, ii) usage of the ESS, and iii) measurement errors of one or more parameters of the ESS; adapt, based on the indicated uncertainties, the minimum required usable energy value to a most likely minimum required usable energy value and / or a range of minimum required usable energy values for the ESS; define a SoC window for the ESS to match the most likely minimum required usable energy value and / or a value within the range of minimum required usable energy values, and control a discharging and / or charging of the ESS in accordance with the SoC window.