System and method for controlling a battery management system

Abstract

The present disclosure relates to systems and methods for controlling a battery management system. The battery management system includes one or more processors, a battery including a plurality of battery cells, an output device, an input device, and a memory having an input module, a battery characteristic prediction module, and an output module. The input module includes instructions that cause the one or more processors to receive a mode selection from a user via the input device. The battery characteristic prediction module includes instructions that cause the one or more processors to predict characteristics of the battery by using an active machine learning model and to predict characteristics of the battery based on the mode selection. The output module includes instructions that cause the one or more processors to output an estimated cost to the output device based on the characteristics of the battery determined by the active machine learning model.

Description

Technical Field

[0001] The subject matter described herein generally relates to systems and methods for controlling a battery management system (“BMS” or “BMS system”), particularly for a BMS system of a vehicle. Background Art

[0002] The provided background art description is generally for the purpose of introducing the context of the present disclosure. To the extent that the work of the inventors that may be described in this background art section, and aspects that may not constitute prior art at the time of filing, are neither expressly nor implicitly admitted as prior art to the present technology.

[0003] Some current vehicles utilize electric propulsion systems, such as battery electric vehicles (“BEV”) and plug-in hybrid electric vehicles (“PHEV”), etc., which may have some advantages compared to their non-electric vehicle counterparts, such as reduced consumption of hydrocarbon-based fuels, improved performance due to the high torque output of the electric propulsion system, and other advantages.

[0004] However, a concerning aspect of these types of vehicles relates to the degradation of vehicle batteries. For example, in the case of lithium-ion batteries, lithium-ion batteries operate by the movement of ions between the positive and negative electrodes. In theory, this mechanism should be effective forever, but cycling, elevated temperatures, and other factors can degrade performance over time. Summary of the Invention

[0005] This section generally summarizes the present disclosure and is not a comprehensive explanation of the entire scope or all features of the present disclosure.

[0006] In one embodiment, a battery management system includes one or more processors, a battery including a plurality of battery cells, an output device, an input device, and a memory having an input module, a battery characteristic prediction module, and an output module. The input module includes instructions that cause the one or more processors to receive, via the input device, a mode selection instruction from a user. The battery characteristic prediction module includes instructions that cause the one or more processors to predict the characteristics of the battery by using an active machine learning model and predict the characteristics of the battery based on the mode selection. The output module includes instructions that cause the one or more processors to output an estimated cost to the output device based on the characteristics of the battery determined by the active machine learning model.

[0007] In another embodiment, a method for managing a battery management system includes the steps of: receiving, via an input device, a mode selection from a user, predicting, by utilizing an active machine learning model, the characteristics of the battery based on the mode selection, and outputting, based on the characteristics of the battery determined by the active machine learning model, an estimated cost to an output device.

[0008] In another embodiment, a non-transitory computer-readable medium for managing a battery management system includes instructions that, when executed by one or more processors, cause the one or more processors to receive, via an input device, a mode selection from a user, predict characteristics of a battery based on the mode selection by utilizing an active machine learning model, and output an estimated cost to an output device based on the characteristics of the battery determined by the active machine learning model.

[0009] Based on the provided description, further applicable fields and various methods for enhancing the disclosed technology will become apparent. The descriptions and specific examples in this summary section are for illustrative purposes only and are not intended to limit the scope of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The drawings included in and constituting a part of the specification illustrate various systems, methods, and other embodiments of the present disclosure. It should be noted that the illustrated element boundaries in the figures (e.g., boxes, groups of boxes, or other shapes) represent one embodiment of the boundaries. In some embodiments, one element may be designed as multiple elements, or multiple elements may be designed as one element. In some embodiments, an element represented as an internal component of another element may be implemented as an external element, and vice versa. Additionally, the various elements may not be drawn to scale.

[0011] Figure 1 Illustrates a vehicle including an example of a battery management system;

[0012] Figure 2 Illustrates Figure 1 a more detailed view of the battery management system;

[0013] Figure 3 Illustrates an example of updating the active machine learning model of the battery management system with model weights from a cloud-based server;

[0014] Figure 4 Illustrates an example of a user interface for selecting a mode from a user;

[0015] Figure 5 Illustrates an example of a user interface for outputting an estimated cost to a user;

[0016] Figure 6 Illustrates a method for controlling a battery management system;

[0017] Figure 7 Illustrates a method for updating the active learning model of the battery management system with model weights from a cloud-based server. DETAILED DESCRIPTION

[0018] Describes a BMS system that can be within a vehicle, such as a BEV or PHEV. The BMS system includes at least one processor that communicates with a battery, and the battery includes a plurality of battery cells. The BMS system can receive a mode selection from a user of the vehicle as an input. A battery characteristic module causes the at least one processor to predict characteristics of the battery based on the mode selection. The battery characteristic module uses an active learning model with one or more model weights to predict the characteristics of the battery based on the mode selection.

[0019] Once the characteristics of the battery are determined, the one or more processors can output an estimated cost via an output device based on the characteristics of the battery determined by the active learning model. The estimated cost can be an indication to the user of how their mode selection affects the range of the vehicle, the life of the battery, and / or other battery-related characteristics. Once the estimated cost is received, the user can decide to change their mode selection in order to increase the range of the vehicle, increase the life of the battery, or change other battery-related characteristics.

[0020] As previously described, the active learning model uses one or more model weights to predict the characteristics of the battery. The BMS system also has a network access device that allows the BMS system to receive updated model weights from a cloud-based server. Once the updated model weights are received, the BMS system can update the active machine learning model to account for the updated model weights from the central server. The updated model weights can be generated by training the active machine learning model on a cloud-based system. The cloud-based system has the ability to receive data related to the performance and characteristics of the battery from a large number of BMS systems, and thus can update and train the active machine learning model and generate updated model weights for later dissemination to other deployed battery management systems.

[0021] See Figure 1 , the figure illustrates an example of a vehicle 100. The "vehicle" used herein is any form of powered transportation. In one or more implementations, the vehicle 100 is an automobile. Although various scenarios will be described herein with respect to an automobile, it should be understood that the embodiments are not limited to automobiles. In some implementations, the vehicle 100 can be, for example, any robotic device or any form of powered transportation that includes one or more automated or autonomous systems and thus benefits from the functionality discussed herein.

[0022] Vehicle 100 may be a vehicle including a battery 115 formed of a plurality of battery cells 116. The battery 115 may be any type of device having one or more battery cells 116 with external connections for powering one or more systems or subsystems of the vehicle 100. In one example, the vehicle 100 is a BEV vehicle or a PHEV vehicle including a powertrain 180. The powertrain 180 may include one or more electric motors that can provide torque to one or more wheels of the vehicle 100 by receiving power from the battery 115. The management of the battery 115 may be performed by a BMS system 170, which will be described in detail later in this specification.

[0023] Vehicle 100 also includes various elements. It should be understood that in various embodiments, the vehicle 100 does not necessarily have Figure 1 all of the elements shown in Figure 1 . The vehicle 100 may have any combination of the various elements shown in Figure 1 . Additionally, the vehicle 100 may have additional elements other than the elements shown in Figure 1 . In some scenarios, the vehicle 100 may be implemented without one or more of the elements shown in Figure 1 . Although in Figure 1 , each element is shown as being located within the vehicle 100, it should be understood that one or more of these elements may be located outside the vehicle 100. Additionally, the shown elements may be physically separated by a large distance and provided as a remote service (e.g., a cloud computing service).

[0024] Some of the possible elements of the vehicle 100 are shown in Figure 1 and will be described in conjunction with the subsequent appended Figure 1 . However, for the sake of brevity of description, the description of many of the elements in Figures 2 - 7 will be provided after discussing Figure 1 . Additionally, it should be realized that for the sake of simplicity and clarity of illustration, reference numerals are repeated in different figures where appropriate to indicate corresponding or similar elements. Additionally, the description outlines numerous specific details to thoroughly understand the various embodiments described herein. The various embodiments described herein may be practiced using various combinations of these elements.

[0025] In any case, the vehicle 100 includes a BMS system 170 for managing the battery 115 of the vehicle 100. Refer to Figure 2, an embodiment of the BMS system 170 is further illustrated in the figure. As shown, the BMS system 170 includes one or more processors 110. Thus, the processor 110 can be a part of the BMS system 170, or the BMS system 170 can access the processor 110 via a data bus or another communication path. In one or more embodiments, the processor 110 is an application-specific integrated circuit configured to implement functions associated with the input module 252, the battery characteristic prediction module 254, the output module 256, the communication module 258, and / or the active learning module 260.

[0026] Generally, the processor 110 is an electronic processor capable of performing various functions described herein, such as a microprocessor. In one embodiment, the BMS system 170 includes a memory 210 that stores the input module 252, the battery characteristic prediction module 254, the output module 256, the communication module 258, and / or the active learning module 260. The memory 210 is a random access memory (RAM), a read-only memory (ROM), a hard disk drive, a flash memory, or other suitable memory for storing the modules 252, 254, 256, 258, and / or 260. The modules 252, 254, 256, 258, and / or 260 are, for example, computer-readable instructions that, when executed by the processor 110, cause the processor 110 to perform various functions disclosed herein.

[0027] In addition, in one embodiment, the BMS system 170 includes a data warehouse 240. In one embodiment, the data warehouse 240 is an electronic data structure such as a database stored in the memory 210 or another memory, and the electronic data structure is configured with routines executable by the processor 110 to analyze the stored data, provide the stored data, organize the stored data, and so on. Thus, in one embodiment, the data warehouse 240 stores the data used by the modules 252, 254, 256, 258, and / or 260 when performing various functions. In one embodiment, the data warehouse 240 includes an active machine learning model 242 that uses one or more model weights to determine one or more characteristics of the battery 115. The active machine learning model 242 is a trained model. Any of several different machine learning models can be used, such as artificial neural networks, decision trees, support vector machines, regression analysis, Bayesian networks, etc.

[0028] Vehicle 100 may include an input device 135. An "input device" includes any device, component, system, element, or apparatus, or a group of such devices, components, systems, elements, or apparatuses, that enables information / data to be input into a machine. Input device 135 may receive input from a vehicle passenger (e.g., a driver or a passenger). Vehicle 100 may include an output device 130. An "output device" includes any device, component, or apparatus, or a group of such devices, components, or apparatuses, that enables information / data to be presented to a vehicle passenger (e.g., a person, a vehicle passenger, etc.).

[0029] The BMS system 170 may also include a network access device 250 communicatively coupled to the processor 110. The network access device 250 is to be understood as including any one of several different hardware and / or software components that allow the processor 110, and thus the BMS system 170, to communicate with an external system. Communication with the external system may be achieved via a distributed network, such as the Internet, by wired or wireless communication methods. However, it should be understood that any one of several different methods for connecting the processor 110 to an external device may be utilized.

[0030] Thus, the input module 252 includes instructions that, when executed by the processor 110, cause the processor 110 to receive a mode selection from a user via the input device 135. In one example, referring to Figure 4 , the input device 135 may be a touch screen 400 that allows a user to select a mode selection. Figure 4 The example given in is merely an example. Any one of several different methods may be used to receive a mode selection from a user. In this example, the mode selection is made by utilizing a slider bar 406 that includes a slider 408. The slider bar 406 extends between a maximum range mode selection 402 and a maximum battery life mode selection 404.

[0031] The maximum range mode selection 402 represents a mode selection indicating that the user desires to have the maximum range that the battery 115 can provide. Thus, in this situation, the BMS system 170 will allow the battery 115 to be charged to its maximum capacity. In contrast, for the maximum battery life mode selection 404, this represents a mode selection indicating that the user desires to extend the battery life of the battery 115 as much as possible. In this situation, the BMS system 170 may impose restrictions on the battery 115 regarding how often the battery 115 is charged, the maximum charge amount of the battery 115, etc.

[0032] The battery characteristic prediction module 254 includes instructions that, when executed by the processor 110, cause the processor 110 to predict the characteristics of the battery 115 based on the mode selection. The battery characteristic prediction module utilizes the active machine learning model 242 to predict the characteristics of the battery. As previously described, the active machine learning model 242 can utilize one or more model weights obtained by training the active machine learning model on an external system, such as a cloud-based system, which will be described later in this specification.

[0033] Additionally or alternatively, it should be understood that the battery characteristics determined by the battery characteristic prediction module 254 may depend on factors other than the mode selection. For example, the battery characteristic prediction module 254 may include instructions that cause the processor 110 to predict the characteristics of the battery 115 based on the user's driving style. The user's driving style can indicate one or more driving characteristics of the user when operating the vehicle 100. The user's driving characteristics may include the historical distance that the vehicle 100 generally travels between destinations, the historical speed of the vehicle 100, and the charging history of the vehicle's battery 115. This information can be used by the active machine learning model 242 to determine one or more characteristics of the battery 115 and, thus, to determine the estimated cost.

[0034] As previously described, the active machine learning model 242 uses one or more model weights to determine the characteristics of the battery 115. By training the active machine learning model 242 on an external system and then providing these updated model weights to the BMS system 170 of the vehicle 100, the active machine learning model 242 and one or more model weights for determining the characteristics of the battery 115 can be generated. With respect to the active machine learning model 242, any one of several different active machine learning models for predicting one or more characteristics of the battery 115 can be utilized. For example, one machine learning model that can be used to determine one or more characteristics of the battery 115 can include the predictive modeling described in U.S. Patent Publication No. 2019 / 0113577 to Severson et al., “Data-driven Model for Lithium-ion Battery Capacity Fade and Lifetime Prediction,” which is incorporated herein by reference in its entirety.

[0035] The output module 256 may include instructions that, when executed by the processor 110, cause the processor 110 to output an estimated cost to the output device 130 based on the characteristics of the battery 115 determined by the active machine learning model 242. The estimated cost can be a representation of how the mode selection chosen by the user affects the cycle life of the battery 115. The cycle life of the battery 115 can be the number of cycles until 80% of the nominal capacity of the remaining battery 115.

[0036] For example, refer to Figure 5 , which shows an example of the estimated cost 500 output by the output device 130. In this example, as shown in Figure 4 , the user moves the slider 408 towards the maximum range mode selection 402. Therefore, the battery characteristic prediction module 254 of the active machine learning model 242 determines that if this mode selection is maintained, the battery life will be reduced by 10% within 12 months. The example of the estimated cost 500 output by the output device 130 is just an example, and any of several different methods for outputting the estimated cost can be used.

[0037] As previously described, the active machine learning model uses one or more model weights to determine one or more characteristics of the battery 115. The active machine learning model 242 can be updated from time to time with updated model weights generated by training additional active machine learning models on an external system, such as a cloud-based server. For example, refer to Figure 3 , which illustrates three BMS systems 170A, 170B, and 170C. Any of many BMS systems can be used, and this is just an example to illustrate how to train an active machine learning model through an external device.

[0038] Here, the external device 310, which can be a cloud-based server, includes one or more processors 312. The processor 312 communicates with a network access device 314, which allows communication with the BMS systems 170A, 170B, and / or 170C via a network 300, which can be a distributed network such as the Internet. The network access device 314 can be any of many different hardware and / or software that allows the external device 310 to communicate with the network 300 and thus with the BMS systems 170A, 170B, and / or 170C.

[0039] One or more processors 312 can communicate with a memory 316, which can be any type of memory capable of storing electronic information. The memory 316 can be similar to the memory 210 described earlier in this specification. The memory 316 includes a training module 318 that enables the processor 312 to train an active machine learning model 342 that can be stored on a data warehouse 320. The data warehouse 320 communicates with the processor 312 and is similar to the data warehouse 240 described earlier.

[0040] The training module 318 causes the processor 312 to train the active machine learning model 342, thereby generating updated model weights. The training set used to train the active machine learning model 342 may come from several different sources. These sources may include observations made by currently deployed BMS systems, such as BMS systems 170A, 170B, and / or 170C. When the active machine learning model 342 is trained and updated model weights are generated, these model weights can be propagated to one or more BMS systems, such as BMS systems 170A, 170B, and / or 170C.

[0041] Return to Figure 2 , the communication module 258 may include instructions that, when executed by the processor 110, cause the processor 110 to receive updated model weights from an external device such as the external device 310. Additionally or alternatively, the communication module 258 may further include instructions that, when executed by the processor 110, cause the processor 110 to transmit battery-related information to the external device 310 by using the network access device 250. The external device 310 may utilize the battery-related information to train the active machine learning model 342.

[0042] The active learning module 260 includes instructions that, when executed by the processor 110, cause the processor 110 to update the active machine learning model 242 of the BMS system 170 with any updated model weights received from the external device 310. In this way, the active machine learning model 242 of the BMS system 170 can be periodically updated with improved model weights to improve the active machine learning model 242 over time. By leveraging an external device (such as the external device 310) to remotely generate model weights by training the active machine learning model 342, the active machine learning model 242 of the BMS system 170 can be continuously updated and improved over time.

[0043] See Figure 6 , a method 600 for controlling a BMS system is illustrated in the figure. The method 600 will be described from the perspective of the vehicle 100 and Figure 1 the BMS system 170 of Figure 2 . However, this is just an example of implementing the method 600. Although the method 600 is discussed in conjunction with the BMS system 170, it should be realized that the method 600 is not limited to being implemented within the BMS system 170. Instead, this is merely an example of a system in which the method 600 can be implemented.

[0044] The method 600 begins at step 602, where the input module 252 causes the processor 110 to receive a mode selection from the user. The mode selection from the user can be input into the input device 135 by using the user interface. The user interface can be associated with Figure 4Similar to the user interface shown in the illustration and described previously in the above paragraphs. The mode selection from the user may include a maximum driving range mode in which the driving range of vehicle 100 is maximized, or a maximum battery life mode in which the battery life of the vehicle's battery 115 is maximized.

[0045] In step 604, the battery characteristic prediction module 254 causes the processor 110 to predict the characteristics of the battery 115 based on the mode selection previously received in step 602. The prediction of the characteristics of the battery 115 is performed by the active machine learning model 242, which utilizes one or more model weights to determine one or more characteristics of the battery 115 based on the mode selection.

[0046] In step 606, the output module 256 causes the processor 110 to output an estimated cost to the output device 130 based on the characteristics of the battery 115 previously determined in step 604. In one example, the estimated cost may provide the user with information regarding the impact that the mode selection will have on the driving range of vehicle 100 and / or the battery life of the vehicle's battery 115. An example of the estimated cost output by the output device 130 is shown in Figure 5 and described previously in the above paragraphs.

[0047] As previously mentioned, the ability to predict the characteristics of the battery 115 is at least partially based on the active machine learning model 242 that utilizes one or more model weights. These model weights may have been generated by an external device, such as a cloud-based server, that was trained using one or more training data sets.

[0048] See Figure 7 , which depicts a method 700 for updating an active machine learning model, such as the active machine learning model 242 of the BMS system 170. Method 700 will be described from the perspective of the BMS system 170, although this is just one example of performing method 700.

[0049] In step 702, the communication module 258 causes the processor 110 to receive updated model weights from an external system. The external system may be Figure 3 external device 310. The updated model weights may be generated by training the active machine learning model 342 on the external device 310. When training the active machine learning model 342 on the external device 310, the updated model weights are generated. These updated model weights may then be transferred from the external device 310 to the BMS system 170 and routed to the processor 110.

[0050] In step 704, the active learning module 260 causes the processor 110 to update the active machine learning model 242 of the BMS system 170 with the updated model weights. In this way, the active machine learning model 242 can be continuously updated and improved, so as to be able to better predict one or more characteristics of the battery 115 of the vehicle 100.

[0051] Now discuss in great detail Figure 1 , as an illustrative environment in which the systems and methods disclosed herein may operate. In one or more embodiments, the vehicle 100 may be an autonomous vehicle, a semi-autonomous vehicle, or a non-autonomous vehicle. The vehicle 100 may include one or more processors 110. In one or more scenarios, the processor 110 may be the main processor of the vehicle 100. For example, the processor 110 may be an electronic control unit (ECU). The terms "operatively connected" and / or "communicate with" used in this specification may include direct or indirect connections, including connections without direct physical contact.

[0052] The vehicle 100 may include a sensor system 120. The sensor system 120 may include one or more sensors. "Sensor" means any device, component, and / or system capable of detecting and / or sensing something. The one or more sensors may be configured to detect and / or sense in real time. The term "real time" used herein means immediate enough for a user or system with respect to a particular process or determination to be made, or enabling a processor to keep up with the processing response level of some external process.

[0053] In a scenario where the sensor system 120 includes multiple sensors, the sensors may work independently of each other. Alternatively, two or more of the sensors may work in combination with each other. In this case, the two or more sensors may form a sensor network. The sensor system 120 and / or one or more sensors may be operatively connected to the processor 110 and / or other elements of the vehicle 100 (including Figure 1 any of the elements shown in). The sensor system 120 may acquire data of at least a part of the external environment of the vehicle 100 (e.g., nearby vehicles).

[0054] The sensor system 120 may include any suitable type of sensor. Various examples of different types of sensors will be described herein. However, it should be understood that the embodiments are not limited to the specific sensors described. The sensor system 120 may include one or more vehicle sensors 121. The vehicle sensors 121 may detect, determine, and / or sense information about the vehicle 100 itself. In one or more scenarios, the vehicle sensors 121 may be configured to detect and / or sense changes in the position and orientation of the vehicle 100, such as based on inertial acceleration. In one or more scenarios, the vehicle sensors 121 may include one or more accelerometers, one or more gyroscopes, an inertial measurement unit (IMU), a dead reckoning system, a global navigation satellite system (GNSS), a global positioning system (GPS), a navigation system 147, and / or other suitable sensors. The vehicle sensors 121 may be configured to detect and / or sense one or more characteristics of the vehicle 100. In one or more scenarios, the vehicle sensors 121 may include a speedometer that determines the current speed of the vehicle 100.

[0055] Alternatively, or additionally, the sensor system 120 may include one or more environmental sensors 122 configured to acquire and / or sense driving environment data. "Driving environment data" includes data or information about the external environment in which the autonomous vehicle is located or one or more parts thereof. For example, the one or more environmental sensors 122 may be configured to detect, quantify, and / or sense obstacles in at least a portion of the external environment of the vehicle 100, and / or information / data about such obstacles. Such obstacles may be stationary objects and / or dynamic objects. The one or more environmental sensors 122 may be configured to detect, measure, quantify, and / or sense other things in the external environment of the vehicle 100, such as lane markings, signs, traffic lights, traffic signs, lane lines, crosswalks, curbs adjacent to the vehicle 100, objects outside the road, and the like.

[0056] Various examples of the sensors of the sensor system 120 will be described herein. The exemplary sensors may be part of one or more environmental sensors 122 and / or vehicle sensors 121. However, it should be understood that the embodiments are not limited to the specific sensors described. For example, in one or more scenarios, the sensor system 120 may include one or more radar sensors 123, one or more light detection and ranging (LIDAR) sensors 124, one or more sonar sensors 125, and / or one or more cameras 126. In one or more scenarios, the one or more cameras 126 may be high dynamic range (HDR) cameras or infrared (IR) cameras.

[0057] The vehicle 100 may include one or more vehicle systems 140. Figure 1Various examples of the one or more vehicle systems 140 are shown. However, vehicle 100 may include more, fewer, or different vehicle systems. It should be appreciated that although specific vehicle systems are defined separately, each or any one of the individual systems or portions of the systems may be otherwise combined or separated within vehicle 100 via hardware and / or software. Vehicle 100 may include a propulsion system 141, a braking system 142, a steering system 143, a throttle system 144, a driveline system 145, a signaling system 146, and / or a navigation system 147. Each of these systems may include one or more devices, components, and / or combinations thereof that are currently known or developed in the future.

[0058] The navigation system 147 may include one or more devices, applications, and / or combinations thereof that are currently known or developed in the future and are configured to determine the geographic location of vehicle 100 and / or determine the driving route of vehicle 100. The navigation system 147 may include one or more map applications that determine the driving route of vehicle 100. The navigation system 147 may include a global positioning system, a local positioning system, or a geolocation system.

[0059] Vehicle 100 may include one or more actuators 150. The actuator 150 may be any element or combination of elements that is operable to modify, adjust, and / or change one or more of the vehicle systems 140 or its components in response to a signal or other input received from the processor 110. Any suitable actuator may be used. For example, the one or more actuators 150 may include an electric motor, a pneumatic actuator, a hydraulic piston, a relay, a solenoid, and / or a piezoelectric actuator, to name just a few examples.

[0060] Vehicle 100 may include one or more modules, at least some of which are described herein. The modules may be implemented as computer-readable program code that, when executed by the processor 110, implements one or more of the various processes described herein. One or more of the modules may be components of the processor 110, or one or more of the modules may be executed on other processing systems to which the processor 110 is operatively connected, and / or distributed among the other processing systems. The modules may include instructions (e.g., program logic) executable by one or more processors 110.

[0061] In one or more scenarios, one or more of the modules described herein may include artificial or computational intelligence elements, such as neural networks, fuzzy logic, or other machine learning algorithms. Additionally, in one or more scenarios, one or more of the modules may be distributed among multiple modules described herein. In one or more scenarios, two or more of the modules described herein may be combined into a single module.

[0062] Detailed embodiments are disclosed herein. However, it is to be understood that the disclosed embodiments are merely examples. Thus, the specific structural and functional details disclosed herein should not be construed as limiting, but merely as a basis for the claims and as a representative basis for teaching one skilled in the art to variously employ the various aspects herein in substantially any appropriate detailed structure. Additionally, the terminology and phrases used herein are not limiting, but rather provide an understandable description of the various possible implementations. Figures 1 - 7 Various embodiments are illustrated, however, the embodiments are not limited to the illustrated structures or applications.

[0063] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments. In this regard, each block in the flowchart or block diagram may represent a module, segment, or portion of code that contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions recited in the blocks may occur out of the order shown in the figures. For example, two blocks shown in succession may in fact be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending on the functions involved.

[0064] The systems, components, and / or processes described above can be implemented in hardware, or a combination of hardware and software, and can be implemented centrally in one processing system or distributedly. In the case of distributed implementation, the different elements are distributed among several interconnected processing systems. Any type of processing system or other device suitable for executing the various methods described herein is suitable. A typical combination of hardware and software can be a processing system with computer-usable program code that, when loaded and executed, controls the processing system such that the processing system executes the methods described herein. The systems, components, and / or processes can also be embedded in a computer-readable memory, such as a computer program product or other data program storage device, which can be read by a machine and tangibly contains an instruction program executable by the machine to perform the methods and processes described herein. These elements can also be embedded in an application product that contains all the features enabling the implementation of the methods described herein and, when loaded into a processing system, can execute these methods.

[0065] In addition, the various solutions described herein may take the form of a computer program product embodied in one or more computer-readable media having computer-readable program code embodied thereon, e.g., stored thereon. Any combination of one or more computer-readable media may be utilized. The computer-readable media may be a computer-readable signal medium or a computer-readable storage medium. The phrase "computer-readable storage medium" means a non-transitory storage medium. A computer-readable storage medium may be, for example (but not limited to), an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination thereof. More specific examples (a non-exhaustive list) of the computer-readable storage medium include: a portable computer disk, a hard disk drive (HDD), a solid state drive (SSD), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a portable compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device.

[0066] Generally, the modules used herein include routines, programs, objects, components, data structures, etc. that perform a particular task or implement a particular data type. In an additional aspect, the memory generally stores the modules mentioned. The memory associated with the module may be a buffer or cache embedded within the processor, RAM, ROM, Flash memory, or other suitable electronic storage medium. In another additional aspect, the modules contemplated by the present disclosure are implemented as application specific integrated circuits (ASICs), hardware components of a system on a chip (SoC), programmable logic arrays (PLAs), or other suitable hardware components embedded with a defined set of configurations (e.g., instructions) for performing the disclosed functions.

[0067] The program code embodied on the computer-readable medium may be transmitted using any suitable medium, including (but not limited to) wireless, wired, fiber optic, cable, RF, etc., or any suitable combination thereof. The computer program code for performing the operations of the various aspects of the present invention may be written in one or more programming languages, including such as Java TMwritten in any combination of object-oriented programming languages such as Smalltalk, C++, and conventional procedural programming languages such as the "C" programming language or similar programming languages. The program code can run entirely on the user's computer, partially on the user's computer, run as a stand-alone software package, partially on the user's computer and partially on a remote computer, or run entirely on a remote computer or server. In the latter case, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0068] As used herein, the singular forms are defined to mean one or more than one. The term "plural" as used herein is defined to mean two or more than two. The term "another" as used herein is defined to mean at least a second or more. The terms "comprising" and / or "having" as used herein are defined to include (i.e., open language). The phrase "at least one of... and..." as used herein refers to and includes any possible combination of one or more of the associated listed items. For example, the phrase "at least one of A, B, and C" includes only A, only B, only C, or any combination thereof (e.g., AB, AC, BC, or ABC).

[0069] The various aspects herein can be embodied in other forms without departing from their spirit or essential attributes. Thus, when indicating the scope of the present disclosure, reference should be made to the following claims rather than the above description.

Claims

1. A battery management system, comprising: one or more processors; a battery including a plurality of battery cells, the battery communicating with the one or more processors; an output device communicating with the one or more processors; an input device communicating with the one or more processors; a memory communicating with the one or more processors, the memory having an input module, a battery characteristic prediction module, and an output module; wherein the input module includes instructions that, when executed by the one or more processors, cause the one or more processors to receive a mode selection from a user via the input device, the mode selection including a selection between extending the driving range of a vehicle and increasing the cycle life of the battery; wherein the battery characteristic prediction module includes instructions that, when executed by the one or more processors, cause the one or more processors to predict the characteristics of the battery based on the mode selection, wherein the battery characteristic prediction module uses an active machine learning model to predict the characteristics of the battery, and wherein the characteristics of the battery are the cycle life of the battery; and wherein the output module includes instructions that, when executed by the one or more processors, cause the one or more processors to output an estimated cost to the output device based on the characteristics of the battery determined by the active machine learning model, the estimated cost being a prediction of how the selection of the mode selection affects the driving range of the vehicle and the cycle life of the battery.

2. The battery management system according to claim 1, wherein the cycle life of the battery is the number of cycles until 80% of the nominal capacity of the battery.

3. The battery management system according to claim 1, further comprising: a network access device communicating with the one or more processors: wherein the memory further includes a communication module having instructions that, when executed by the one or more processors, cause the one or more processors to receive updated model weights from an external system via the network access device; and wherein the memory further includes an active learning module having instructions that, when executed by the one or more processors, cause the one or more processors to update the active machine learning model with the model weights obtained by training the active machine learning model on the external system.

4. The battery management system according to claim 1, wherein the battery management system is installed in a vehicle.

5. The battery management system according to claim 4, wherein the battery characteristic prediction module includes instructions that, when executed by the one or more processors, cause the one or more processors to predict the characteristics of the battery based on the mode selection and the driving style of the user, the driving style of the user indicating one or more driving characteristics of the user when operating the vehicle.

6. The battery management system according to claim 5, wherein the one or more driving characteristics include the historical distance traveled by the vehicle between destinations, the historical speed of the vehicle, and the charging history of the vehicle's battery.

7. A method of managing a battery management system, comprising the following steps: One or more processors receive a mode selection from a user via an input device, the mode selection including a choice between extending the vehicle's driving range and increasing the battery's cycle life; The one or more processors predict battery characteristics based on the mode selection by utilizing an active machine learning model, the battery including a plurality of battery cells, wherein the battery characteristic is the battery's cycle life; and The one or more processors output an estimated cost to an output device based on the battery characteristics determined by the active machine learning model, the estimated cost being a prediction of how the choice of mode selection affects the vehicle's driving range and the battery's cycle life.

8. The method of managing a battery management system according to claim 7, wherein the cycle life of the battery is the number of cycles until 80% of the battery's nominal capacity.

9. The method of managing a battery management system according to claim 7, further comprising the steps of: The one or more processors receive updated model weights from an external system via a network access device; and The one or more processors update the active machine learning model with the model weights obtained by training the active machine learning model on the external system.

10. The method of managing a battery management system according to claim 7, wherein the battery management system is installed in a vehicle.

11. The method of managing a battery management system according to claim 10, further comprising the step of predicting battery characteristics based on the mode selection and the user's driving style, the user's driving style indicating one or more driving characteristics of the user when operating the vehicle.

12. The method of managing a battery management system according to claim 11, wherein the one or more driving characteristics include the historical distance traveled by the vehicle between destinations, the historical speed of the vehicle, and the charging history of the vehicle's battery.

13. A non-transitory computer-readable medium for controlling a battery management system, the non-transitory computer-readable medium including instructions that, when executed by one or more processors, cause the one or more processors to perform the following operations: Receive a mode selection from a user via an input device, the mode selection including a choice between extending the vehicle's driving range and increasing the battery's cycle life; Predict battery characteristics based on the mode selection by utilizing an active machine learning model, the battery including a plurality of battery cells, wherein the battery characteristic is the battery's cycle life; and Output an estimated cost to an output device based on the battery characteristics determined by the active machine learning model, the estimated cost being a prediction of how the choice of mode selection affects the vehicle's driving range and the battery's cycle life.

14. The non-transitory computer-readable medium according to claim 13, wherein the cycle life of the battery is the number of cycles until 80% of the battery's nominal capacity.

15. The non-transitory computer-readable medium according to claim 13, further including instructions that, when executed by the one or more processors, cause the one or more processors to perform the following operations: Receiving updated model weights from an external system via a network access device; and Updating the active machine learning model with the model weights obtained by training the active machine learning model on the external system.

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