System and method for forecasting battery state with imperfect data

An approach to forecasting battery health as a dynamic time-series problem as opposed to a static prediction problem is presented. Systems and methods disclosed herein forecast a trajectory to failure by predicting a path to failure as opposed to only predicting when the battery may fail. A machine-learning model is implemented that extracts unique features taken from time-series data, such as time snippets of charging data. The raw time-series data may include current voltage and temperature with complex transformations and without capturing a full cycle, which permits wider applicability to instances of varying depth of discharge (DoD).

FIELD OF TECHNOLOGY

The present disclosure relates to vehicle battery health, and more particularly, to machine learning time-series models and data-driven features to forecast battery state of health with limited/imperfect data.

BACKGROUND

Battery health is of vital importance to monitoring and maintaining efficient and reliable operation of a vehicle, particularly in electric, hybrid, and/or autonomous or semi-autonomous vehicles. Commercial and consumer vehicles rely more and more on batteries as a dominant power source, the ability to monitor, maintain, and forecast battery capacity and other health metrics is vital to the operation of the vehicle.

Battery degradation is a path-dependent problem. Therefore capturing temporal elements of predictors and indicators of degradation is necessary to forecast future behavior of batteries. Traditional battery monitoring and prediction systems rely on static prediction. These systems are less efficient and accurate as battery health is a dynamic, time-series problem.

Further, machine learning provides a basis for the design, programming and operation of many aspects of vehicles. Autonomous and semi-autonomous vehicles may be trained according to environmental and situational data allowing the vehicle to operate. Sensors installed and configured on a vehicle, such as an autonomous or semi-autonomous vehicle, provide data to a machine learning system. Systems and models may then be trained and redeployed onto a vehicle for improved performance.

SUMMARY

Aspects of the present disclosure provide an approach to forecasting battery health as a dynamic time-series problem as opposed to a static prediction problem. Aspects of the systems and methods disclosed herein forecast a trajectory to failure by predicting a path to failure as opposed to only predicting when the battery may fail. A machine-learning model is implemented that extracts unique features taken from time-series data, such as 10-20 s snippets of charging data. The raw time-series data may include current voltage and temperature without complex transformations and without capturing a full cycle, which permits wider applicability to instances of varying depth of discharge (DoD).

The noted features may involve physical and technological features in addition to the evolution of such features over time to capture the hysteristic nature of battery performance degradation. In particular, the features can include change in current during constant-voltage hold of a charging cycle to capture degradation of kinetic parameters, time duration over 38 C, cell temperature at peak voltages, and temperature at peak currents to capture co-variance of fundamental system variables.

Thus, the disclosed aspects capture voltage and temperature at a cell level from which the algorithm extracts time-based features from small segments of the charging and discharging portions. Accordingly, the system generates features for each cell and further tracks evolution of the features. Gaussian Process regressors trained on the features then generate state-of-health (SOH) estimates as a function of future time to provide predictions about battery health.

According to one aspect of the present disclosure, a method of forecasting a state-of-health for a battery is provided. According to the method, a first dataset may be obtained and an arbitrary time window may be selected from a subset of the first dataset. At least one feature may be selected from the arbitrary time-window. The subset may be structured for feature extraction by a convolutional filter and an evolution of the at least one feature may be captured using the convolutional filter. A state-of-health model may be generated and the state-of-health model may be trained by inputting the at least one feature, the evolution of the at least one feature, and a future state-of-health as variables. A trajectory to failure for the battery may be predicted based on a trained state-of-health-model.

According to another aspect, a system for forecasting a state-of-health for a battery is provided. The system may include one or more processors and a memory communicably coupled to the one or more processors. The memory may store a behavioral forecast system including instructions that when executed by the one or more processors cause the one or more processors, in response, to generate trajectory to failure. A first dataset may be obtained and an arbitrary time window may be selected from a subset of the first dataset. At least one feature may be selected from the arbitrary time-window. The subset may be structured for feature extraction by a convolutional filter and an evolution of the at least one feature may be captured using the convolutional filter. A state-of-health model may be generated and the state-of-health model may be trained by inputting the at least one feature, the evolution of the at least one feature, and a future state-of-health as variables. A trajectory to failure for the battery may be predicted based on a trained state-of-health-model.

According to another aspect, a battery monitoring system of a vehicle is provided. The battery monitoring system may include one or more processors and a memory communicably coupled to the one or more processors. The memory may store a behavioral forecast system including instructions that when executed by the one or more processors cause the one or more processors, in response, to generate trajectory to failure of the battery. A first dataset may be obtained and an arbitrary time window may be selected from a subset of the first dataset. At least one feature may be selected from the arbitrary time-window. The at least one feature may include at least one of current data, voltage data, capacity data, temperature data or an engineered metric. The subset may be structured for feature extraction by a convolutional filter and an evolution of the at least one feature may be captured using the convolutional filter. A state-of-health model may be generated and the state-of-health model may be trained by inputting the at least one feature, the evolution of the at least one feature, and a future state-of-health as variables. A trajectory to failure for the battery may be predicted based on a trained state-of-health-model.

DETAILED DESCRIPTION

Aspects of the present disclosure provide an approach to forecasting battery health as a dynamic time-series problem as opposed to a static prediction problem. Aspects of the systems and methods disclosed herein forecast a trajectory to failure by predicting a path to failure as opposed to only predicting when the battery may fail. A machine-learning model is implemented that extracts unique features taken from time-series data, such as 10-20 s snippets of charging data. The raw time-series data may include current voltage and temperature without complex transformations and without capturing a full cycle, which permits wider applicability to instances of varying depth of discharge (DoD).FIG.1is a diagram illustrating an example of a hardware implementation for a Battery Management System (BMS)100, according to aspects of the present disclosure. The BMS100may be part of a passenger vehicle, a carrier vehicle, or other device. For example, as shown inFIG.1, the BMS100may be a component of a component of an autonomous or semi-autonomous car128. Aspects of the present disclosure are not limited to the BMS100being a component of the car128, as other devices, including other battery-driven devices are also contemplated for using the BMS100.

The BMS100may be implemented with a bus architecture, represented generally by a bus130. The bus130may include any number of interconnecting buses and bridges depending on the specific application of the BMS100and the overall design constraints. The bus130may link together various circuits including one or more processors and/or hardware modules, represented by a processor120, a communication module122, a location module118, a sensor module102, a locomotion module126, a planning module124, and a computer-readable medium114. The bus130may also link various other circuits such as timing sources, peripherals, voltage regulators, and power management circuits, which are well known in the art, and therefore, will not be described any further.

The BMS100may include a transceiver116coupled to the processor120, the sensor module102, a behavioral forecast system108, the communication module122, the location module118, the locomotion module126, the planning module124, and the computer-readable medium114. The transceiver116is coupled to an antenna134. The transceiver116communicates with various other devices over a transmission medium. For example, the transceiver116may send and receive commands via transmissions to and from a server or a remote device, such as remote device or server (not shown).

The behavioral forecast system108may include the processor120coupled to the computer-readable medium114. The processor120may perform processing, including the execution of software stored on the computer-readable medium114providing functionality according to the disclosure. The software, when executed by the processor120, causes the BMS100to perform the various functions described for a particular device, such as car128, or any of the modules102,108,114,116,118,120,122,124,126. The computer-readable medium114may also be used for storing data that is manipulated by the processor120when executing the software.

The sensor module102may be used to obtain battery measurements via different sensors, such as a first sensor104, a second sensor106. The first sensor104may be a voltage, current or other electrical sensor. The second sensor106may include a temperature sensor, thermometer, or the like. Of course, aspects of the present disclosure are not limited to the aforementioned sensors as other types of sensors, such as, for example, any sensors related to operation of a battery are also contemplated for either of the sensors104,106. The measurements of the sensors104,106may be processed by one or more of the processor120, the sensor module102, the behavioral forecast system108, the communication module122, the location module118, the locomotion module126, the planning module124, in conjunction with the computer-readable medium114to implement the functionality described herein. In one configuration, the data captured by the first sensor104and the second sensor106, may be transmitted to an external device via the transceiver116. The sensors104,106may be coupled to the car128or may be in communication with the car128.

The location module118may be used to determine a location of the car128. For example, the location module118may use a global positioning system (GPS) to determine the location of the car128. For example, the BMS100may be able to communicate with a remote monitoring service, such as mapping/navigation service, a weather service, or other environmental information provider.

The communication module122may be used to facilitate communications via the transceiver116. For example, the communication module122may be configured to provide communication capabilities via different wireless protocols, such as Bluetooth, Wi-Fi, long term evolution (LTE), 3G, 5G, or the like. The communications module may also be configured to establish a communication channel between the car128and an information provider. The communication module122may also be used to communicate with other components of the car128that are not modules of the behavioral forecast system108.

The planning module124, as well as other modules described herein, may be software modules running in the processor120, resident/stored in the computer-readable medium114, one or more hardware modules coupled to the processor120, or some combination thereof.

The behavioral forecast system108may be in communication with the sensor module102, the transceiver116, the processor120, the communication module122, the location module118, the locomotion module126, the planning module124, and the computer-readable medium114. In one configuration, the behavioral forecast system108may receive sensor data from the sensor module102. The sensor module102may receive the sensor data from the sensors104,106, including battery data such as voltage, current and/or temperature. According to aspects of the disclosure, the sensor module102may filter the data to remove noise, encode the data, decode the data, merge the data, or perform other functions. In an alternate configuration, the behavioral forecast system108may receive sensor data directly from the sensors104,106.

As shown inFIG.1, the behavioral forecast system108may receive battery data from the sensor module102including, for example, current, voltage, temperature, capacity, load, state-of-health (SOH) data, remaining useful life (RUL) data or the like. The sensor module102may be in communication with the battery110and the sensors104,106. According to one aspect, the sensors104,106(and/or sensor module102) may be included in the battery110or may be auxiliary sensors in communication with the battery110and sensor module102. According to one aspect, the behavioral forecast system108may function to process data from the battery110to process and forecast a degradation or trajectory to failure of the battery. Moreover, while depicted as a standalone component, in one or more embodiments, the behavioral forecast system108may be integrated with the locomotion module126, the sensor module102, or another module of the vehicle128. The noted functions and methods will become more apparent with a further discussion of the figures.

As described herein, aspects of the behavioral forecast system108provide a model framework and a class of machine learning algorithms that may use time-series data of fundamental battery measurements such as current, voltage, temperature, or the like, to forecast battery behavior. The behavioral forecast system108may include a convolutional neural network or a Gaussian-process convolutional neural network (CNN/GP-CNN112, as described herein. The behavioral forecast system108may, using machine learning, forecast battery state-of-health (SOH) based on data measured over a finite window without need for costly, explicit diagnostic cycles. Additionally, the behavioral forecast system108may forecast a trajectory to failure or end of life under current and/or modified user behavior, for example, in second life applications.

According to one aspect, the behavioral forecast system108may include data collection and model training capabilities. For example, sensor data, such as current, voltage and temperature at a cell level, may be recorded by the BMS or an attached sensor module102or other module during the course of the life of a batch of electric vehicles, driven by different users, in different geographical locations over extended periods of time, for example between zero and three years. Data may be augmented with cell cycling data from a lab or other computational environment, mimicking driving conditions or loading profiles.

According to one aspect, time-based features from small segments of the charging and discharging portions may be extracted at different stages of life of the cells. These features may be directly recorded and reported from a vehicle, without complex data transformations that are lossy or lead to artifacts, or those that require perfect or complete data. According to one aspect, a vector of features may be generated for each cell at a point in time. For each element in the vector, evolution over time also may be recorded and used as “raw” data in the training dataset. (FIG.3(d)).

According to another aspect, Gaussian Process regressors may be trained using the CNN/GP-CNN112on the features to generate SOH estimates as a function of future time, or predict the time taken to reach predefined health points.

According to one aspect in which the behavioral forecast system108is implemented in the car128, once the Gaussian Processor (or another regressor) is trained, the model may be embedded in a BMS100or other onboard computing device. In the car128, the BMS100and behavioral forecast system108may forecast, and continuously refine, the future trajectory of the vehicle as the sensor module108continually collects new data. Further, since training data will capture similar present and past states as the car128, but different future use-cases, the model may offer the driver suggestions on how different use-patterns will improve or maximize certain performance metrics of one or more of the cells of a battery pack or module, for example, max power vs max capacity, per charge).

According to another aspect, behavioral forecast system108may use time-based features for the machine learning algorithms that do not require complete charge/discharge cycles to be predictive. Sample features include a change in current during a constant-voltage hold portion of charging, such as for example in the first 10 s, 30 s or other time interval. The delta current may capture the degradation of kinetic parameters in the system and may correlated with battery life.

According to another aspect, other physically-informed features may include, for example, time duration over 38 C, cell temperature at peak voltages, and temperature at peak currents to capture co-variance of fundamental system variables (as opposed to treating them independently). By explicitly including time as a variable, the behavioral forecast system108may be flexible to handle as much or as little of the data available with which to generate predictions. Thus, uncertainty estimates on predictions may be conditioned on amount of data available.

According to another aspect, implementation of Gaussian Process regression as a probabilistic technique may inherently capture a relation between similar points through covariance functions, and generate forecasts with error estimates. The behavioral forecast system's108use of a CNN architecture for capturing the temporal evolution of physically informed features may add to the model's predictive capability. According to one aspect, the CNN architecture may include a number of layers, for example:Layer 1: a 1D convolutional layer (with strides of 2 and kernel size of 3, but may be tunable hyperparameters) and a Rectified Linear Unit (ReLU) activationLayer 2: Maxpool layer (reduces the dimensionality by 2×)Layer 3: 1D convolutional layer (with strides of 2 and kernel size of 3, but may be tunable hyperparameters) and a ReLU activationLayer 3: Maxpool layer (reduces the dimensionality by 2×)Layer 5: Fully connected layer with ReLU activationLayer 6: Fully connected layer

According to one aspect, the convolution layers may capture how the signals evolve between time-steps, while the maxpool layers are meant to achieve compression of the data and prevent over-fitting. The convolutional layers are critical in capturing the temporal dependence of features. Depending on the dataset and number of points, one of skill in the art will recognize that the number of layers and the stride sizes may be varied to optimize the model.

According to another aspect, training data may be assembled from batteries or cells which have been used until their end of life, so that ground-truth may be available (i.e. the time taken to reach set values of SOH).

According to another aspect, the behavioral forecast system108may accommodate imperfect or limited data and also variable usage profile over time. Such a functionality may be particularly useful for real-world implementation where user behavior may evolve over time. The behavioral forecast system108may also forecast future behavior based on small time-window of measurements taken at any state of the lifecycle of the cell, without having knowledge of its prior history until that point. Further, the behavioral forecast system108may implement a novel feature extraction technique that uses both ‘physically’ important yet easily measurable features (in a car) and their evolution over time to capture the hysteristic nature of battery performance degradation over a wide range of possibilities.

FIG.2depicts a flow diagram200of forecasting battery state-of-health according to one aspect of the present disclosure.FIGS.3(a)-3(d)depict representative and corresponding plots and tables of the flow200. As shown in block202the system may obtain a battery training dataset. The training data set may include a variety of cells cycled to end-of-life and stressed under varying load profiles. According to one aspect, electrochemical measurements collected during the cycling/operation of the battery cell, including time-series of current, voltage, and temperature may be processed through a data-cleaning pipeline into standard data-structures.

The data-structures may contain data in a format more conducive for machine learning. For example, the data structures may ensure that the time-series are of the same length across cycles, or contain the same window of time, or same resolution for example. The data structures may also use some domain knowledge to interpolate derived metrics such as capacity (current integrated over time) over voltage or time to obtain some summary statistics. For instance, one metric may be the amount of time the battery takes to charge from 3.5 to 4.1 V at constant current, or amount of time the battery takes to reach either zero current or a certain value of current at a constant voltage hold, as two examples.

As shown in block204, a sliding window (commensurate with the duration of data desired to train a model) is selected from the dataset to define a blob of training dataset302, with one of more outcomes. Training data sets302are depicted as the selected SOH data between two time intervals, t1and t2, shown in the plot of percentage SOH vs. time inFIG.3(a). The time-windows may be chosen arbitrarily to obtain features for model training. Further, the available data may be from any window of lifetime and may not require complete historical data.

An outcome may be a static prediction or dynamic (or continuous or rolling prediction). A static prediction may take some time window of data and predict the time taken for a cell to reach a threshold life, or SOH at a future time. Dynamic/continuous predictions take this a considerable step further by using historical data (allowing the use of any arbitrary window of time) and forecasting the trajectory the cell takes to reach end-of-life. As such, and shown in block206, input vectors may be selected in the form of raw time-series data and/or engineered features within the window of time are used as a training dataset, along with their time-stamps relative to a reference age of the cell. The input vectors may be raw time-series data, such as vector X1depicted in the Voltage (V) vs. Time (t1to t2) plot ofFIG.3(b). Alternatively or additionally, the input vectors may include feature engineering along time domain (i.e., interpolation along time domain to ensure a uniformly space intervals, or interpolation along voltage axis to ensure important electrochemical signatures are captured, without requiring complete cycles or the full range of charge/discharge. The outcomes may be one or more future times at which a predicted SOH is desired.

As shown in block208, during training one or more future SOHs may be selected and input as a variable. Doing so may allow the ability to predict the remaining-useful-life (RUL) corresponding to the multiple input SOHs (i.e., trajectory of degradation), based on the limited window of data. Representative RULs304are depicted as yRULas a function of SOH (XSOH) in the percentage SOH vs time plot ofFIG.3(c).

As shown in block210, a training set (X, y) (FIG.3(d)) may be constructed. While traditional machine learning approaches may take the training dataset as independent points, aspects of the present disclosure may use the convolutional layers in such a manner to capture how the features evolve over time instead of collapsing them into a summary statistic. The CNN weights may be optimized through backpropagation. Alternatively, the output from the CNN may be piped into a Gaussian Process (GP), and the weights may be optimized in a similar manner. GP, in one aspect, may provide uncertainty estimates.

The convolutional layers may also be used to capture how both the inter-cycle, and intra-cycle convolutions may affect future health of a battery, which may change fundamentally depending on the sequence of its loading/usage history. The number, stride and kernel size of the convolution layers may be tuned to capture both short and long-term aging effects within the time window of measurement. As shown in block212, the CNN/GP-CNN may be tuned after its initial build training the model on additional data. Once the model is fit, for a given window of measurement in test dataset and current SOH, the time taken to reach any future SOH can be estimated. That is, the model trained on data from arbitrarily long windows and at different stages of a battery life, inference of a SOH may be faster and more efficient.

Based on the teachings, one skilled in the art should appreciate that the scope of the present disclosure is intended to cover any aspect of the present disclosure, whether implemented independently of or combined with any other aspect of the present disclosure. For example, an apparatus may be implemented or a method may be practiced using any number of the aspects set forth. In addition, the scope of the present disclosure is intended to cover such an apparatus or method practiced using other structure, functionality, or structure and functionality in addition to, or other than the various aspects of the present disclosure set forth. It should be understood that any aspect of the present disclosure may be embodied by one or more elements of a claim.

The various illustrative logical blocks, modules and circuits described in connection with the present disclosure may be implemented or performed with a processor specially configured to perform the functions discussed in the present disclosure. The processor may be a neural network processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array signal (FPGA) or other programmable logic device (PLD), discrete gate or transistor logic, discrete hardware components or any combination thereof designed to perform the functions described herein. Alternatively, the processing system may comprise one or more neuromorphic processors for implementing the neuron models and models of neural systems described herein. The processor may be a microprocessor, controller, microcontroller, or state machine specially configured as described herein. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or such other special configuration, as described herein.

The steps of a method or algorithm described in connection with the present disclosure may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in storage or machine readable medium, including random access memory (RAM), read only memory (ROM), flash memory, erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, a hard disk, a removable disk, a CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. A software module may comprise a single instruction, or many instructions, and may be distributed over several different code segments, among different programs, and across multiple storage media. A storage medium may be coupled to a processor such that the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor.

The processor may be responsible for managing the bus and processing, including the execution of software stored on the machine-readable media. Software shall be construed to mean instructions, data, or any combination thereof, whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise.

If implemented in software, the functions may be stored or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media include both computer storage media and communication media including any storage medium that facilitates transfer of a computer program from one place to another.

Further, it should be appreciated that modules and/or other appropriate means for performing the methods and techniques described herein can be downloaded and/or otherwise obtained by a user terminal and/or base station as applicable. For example, such a device can be coupled to a server to facilitate the transfer of means for performing the methods described herein. Alternatively, various methods described herein can be provided via storage means, such that a user terminal and/or base station can obtain the various methods upon coupling or providing the storage means to the device. Moreover, any other suitable technique for providing the methods and techniques described herein to a device can be utilized.