Dynamic reservation allocation for electric vehicles

Abstract

This disclosure provides "Dynamic Reserved Allocation for Electric Vehicles." Systems and methods for managing reserved battery capacity in a vehicle are disclosed. The method includes monitoring battery usage patterns to calculate a battery life score; and determining a reserved battery capacity based on the calculated battery life score. When the vehicle is at a predefined distance from a charging facility, the method calculates an extended driving range achievable using the reserved battery capacity. A reserved battery capacity release option is displayed to a user, including the associated extended driving range and the battery life score impact of the released reserved battery capacity. Upon user confirmation, the reserved battery capacity is released, and the battery life score and the reserved battery capacity are dynamically adjusted based on the impact of the released reserved battery capacity.

Description

Technical Field

[0001] This disclosure relates to a battery management system for electric vehicles. Background Technology

[0002] Adventures on unpaved surfaces, such as cement, asphalt, or paved bricks, as well as specialized tasks in remote or difficult conditions, often require vehicles with sufficient energy reserves to handle unexpected challenges. These activities may involve greater energy consumption than driving on paved roads, thus reducing vehicle range and potentially putting users in a difficult situation. Summary of the Invention

[0003] In one aspect of this disclosure, a method for managing an electric vehicle battery is proposed. The method includes: monitoring the battery usage patterns of the electric vehicle to calculate a battery life score; and determining a reserved battery capacity based on the battery life score. The method further includes calculating an extended driving range based on the reserved battery capacity when the electric vehicle is at a predefined distance from a charging facility. The method also includes displaying a reserved battery capacity release option to a user, which has an associated impact on the extended driving range and battery life score, and releasing the reserved battery capacity upon user confirmation. The battery life score and the reserved battery capacity are adjusted based on the impact of the reserved battery capacity release.

[0004] The method may include monitoring battery usage patterns, which includes tracking battery depth of discharge events and charge levels.

[0005] The method may include calculating the battery life score by taking into account heat exposure and charging frequency.

[0006] The method may include determining the reserved battery capacity by ensuring that the release does not exceed a predefined limit to maintain long-term battery health.

[0007] The method may include dynamically adjusting the predefined distance based on terrain data and weather conditions.

[0008] The method may include displaying the reserved battery capacity release options by presenting the user with a graphical interface that shows the expected driving range and the impact on battery life.

[0009] The method may include releasing the reserved battery capacity by applying a phased release process based on the operating requirements of the electric vehicle.

[0010] The method may include adjusting the battery life fraction by updating a threshold for future release of reserved capacity.

[0011] In another aspect of this disclosure, a battery management controller is proposed for managing reserved battery capacity in an electric vehicle. The battery management controller includes a traction battery configured to store electrical energy and supply electrical energy to the electric vehicle. The battery management controller also includes a Global Navigation Satellite System (GNSS) configured to determine the location of the electric vehicle and its distance from a charging facility. The battery management controller further includes a processor configured to monitor usage patterns of the traction battery to calculate a battery lifetime score and determine a reserved battery capacity based on the battery lifetime score. The processor is also configured to receive information from the GNSS indicating that the electric vehicle is farther from a charging facility than could be achieved using the remaining capacity of the traction battery, and to release the reserved battery capacity from the traction battery upon receiving user confirmation. The processor is further configured to adjust the battery lifetime score and the reserved battery capacity based on the calculated impact of the release of the reserved battery capacity.

[0012] The battery management controller may include the processor and is also configured to monitor battery temperature during periods of increased charge levels.

[0013] The battery management controller may include the processor using historical charge and discharge patterns to calculate the battery life score.

[0014] The battery management controller may include the global navigation satellite system providing real-time updates to the processor to determine a predefined distance from the charging facility.

[0015] The battery management controller may include the processor and is also configured to calculate the extended driving range achievable using the reserved battery capacity.

[0016] The battery management controller may include the processor configured to prioritize the release of reserved battery capacity during tracking conditions.

[0017] The battery management controller may include the processor configured to prevent reserved battery capacity from being released when the battery life score indicates excessive degradation.

[0018] The battery management controller may include the processor configured to store and analyze data related to the release of previously reserved battery capacity to optimize future reservation management.

[0019] The battery management controller may include automatically triggering the release of reserved battery capacity when the electric vehicle enters a predefined tracking mode.

[0020] The battery management controller may include a reserved battery capacity release option, which includes multiple phases based on the user's driving destination and remaining driving range.

[0021] The battery management controller may include a system that communicates with cloud-based services to update battery performance metrics and charging facility data.

[0022] In another aspect of this disclosure, a non-transitory computer-readable medium comprising instructions is provided. When executed by one or more computing devices, the instructions cause the devices to perform operations for managing reserved battery capacity. The operations include monitoring battery usage patterns of an electric vehicle to calculate a battery life score; and determining a reserved battery capacity based on the battery life score. The operations also include detecting when the electric vehicle is at a predefined distance from a charging facility, and calculating an extended driving range based on the reserved battery capacity. The operations further include displaying a reserved battery capacity release option to a user, including the extended driving range and the battery life score impact of the reserved battery capacity release. The operations also include releasing the reserved battery capacity upon user confirmation, and adjusting the battery life score and the reserved battery capacity based on the impact of the reserved battery capacity release. Attached Figure Description

[0023] Figure 1 A system architecture for managing reserved battery capacity in electric vehicles is shown.

[0024] Figure 2 The tracking mode decision-making process for managing reserved battery capacity in electric vehicles during critical scenarios is illustrated.

[0025] Figure 3 The process for monitoring and adjusting reserved battery capacity and generating maintenance schedules to optimize battery health and performance is illustrated.

[0026] Figure 4 The process for assessing the impact on battery life and calculating the release of reserved capacity in electric vehicles is illustrated.

[0027] Figure 5 The process for dynamically managing reserved battery capacity in response to battery life assessment and real-time driving conditions is illustrated.

[0028] Figure 6 The process for dynamically managing reserved battery capacity based on vehicle location and real-time battery assessment is illustrated; and

[0029] Figure 7 An example of a computing device for managing reserved battery capacity in electric vehicles is shown. Detailed Implementation

[0030] Detailed embodiments of the invention are disclosed herein as needed; however, it should be understood that the disclosed embodiments are merely examples of the invention that can be embodied in various forms and alternative forms. The drawings are not necessarily drawn to scale; some features may be enlarged or minimized to show details of specific components. Therefore, the specific structural and functional details disclosed herein are not to be construed as limiting, but only as representative bases for teaching those skilled in the art to employ the invention in various ways.

[0031] In priority driving scenarios or during high-demand operations, such as professional fieldwork or public responders, additional energy may be required to complete the task or reach a location. While some solutions temporarily unlock reserved battery capacity to extend range, these methods are limited in terms of flexibility and user control. A dynamic and efficient approach remains needed to address range anxiety and ensure vehicles can reliably meet the demands of diverse and challenging situations.

[0032] This disclosure relates to systems and methods for intelligent management of reserved battery capacity in electric vehicles. While the following detailed description focuses on specific implementations, those skilled in the art will recognize that variations and modifications are possible without departing from the spirit and scope of this disclosure.

[0033] The battery management system described herein includes a sophisticated battery management controller that forms the core of an intelligent power management ecosystem within the vehicle. This controller is deeply integrated with multiple vehicle subsystems via a high-speed vehicle data network, maintaining constant communication with battery monitoring systems, thermal management systems, vehicle dynamics controllers, navigation systems, and user interface components. The controller includes a dedicated processor optimized for real-time data processing and predictive analytics, working in conjunction with a dedicated memory system that maintains detailed historical operation logs while enabling rapid access to key parameters during vehicle operation.

[0034] The functionality includes continuous monitoring and analysis of battery cycling patterns. The controller implements advanced data collection algorithms that simultaneously track multiple parameters across different time ranges. These parameters include instantaneous measurements such as cell voltage, current flow, and temperature distribution, as well as derived metrics such as depth of discharge trends and charge acceptance. The system monitors deep discharge events, thus maintaining not only a detailed log of the frequency and depth of such events, but also a detailed log of ambient conditions including ambient temperature, vehicle load requirements, and terrain characteristics that may contribute to the discharge pattern.

[0035] The monitoring system employs pattern recognition algorithms to characterize charging behavior, with a particular emphasis on fast charging events. When a vehicle connects to a high-power charging station, the controller records comprehensive charging session data, including initial state of charge, charging power distribution map, thermal response across the battery pack, and charging efficiency metrics. The system correlates this charging data with environmental conditions and battery state parameters to establish a detailed understanding of how different charging modes affect battery health under various conditions.

[0036] Temperature monitoring is performed via a strategically placed sensor array throughout the battery pack, with additional sensors tracking environmental conditions. The controller maintains a thermal history log relating temperature distribution to state of charge, usage patterns, and environmental conditions. This thermal data proves particularly important during periods of high state of charge, as the combination of elevated temperature and high voltage can exacerbate various degradation mechanisms within the battery cells. The system uses this information to implement protective measures, such as adjusting charging power limits or triggering additional cooling system capacity when necessary to protect long-term battery health.

[0037] Temperature monitoring is performed via a strategically placed sensor array throughout the battery pack, with additional sensors tracking environmental conditions. Data from these sensors is transmitted to the controller via a wired or wireless high-speed communication network, such as a Controller Area Network (CAN) or Ethernet, capable of operating at sufficient speed for efficient data transmission. The controller maintains a thermal history log that correlates temperature distribution with state of charge, usage patterns, and environmental conditions. This thermal data proves particularly relevant during periods of high state of charge, as the combination of elevated temperature and high voltage can increase the rate of various degradation mechanisms within the battery cells. The system uses this information to implement protective measures, such as adjusting charging power limits or triggering additional cooling system capacity when necessary, to protect long-term battery health.

[0038] The controller employs advanced machine learning algorithms to calculate a battery life (typically 10 years) score, processing collected usage data through multiple layers of analysis. The scoring system considers not only obvious factors such as charge / discharge cycles but also more subtle influences such as time spent at various states of charge levels, thermal exposure history, and power demand distribution maps. The algorithm applies scientifically derived weighting factors to each parameter based on an established battery aging model and real-world degradation data. These weighting factors are continuously refined via over-the-air updates as new battery aging data becomes available from the vehicle fleet. The controller maintains an updated database of charging station locations, types, and historical availability patterns. This data is integrated with terrain information and real-time weather data, potentially transmitted via networks operating at sufficient speeds, such as cellular or Wi-Fi, for efficient data transfer. The system calculates a dynamic range threshold that determines when a vehicle is considered to be in a remote location, taking into account altitude variations, the impact of temperature on battery performance, and historical energy consumption patterns for similar routes and conditions.

[0039] The controller can sample data at a predefined frequency (e.g., 100 Hz), tracking individual cell voltages with a 1 mV resolution. Current flow can be measured with 0.1 A accuracy by a Hall effect sensor, and temperature distribution can be mapped via a thermistor matrix, providing 0.1 °C accuracy. This sampling can be achieved via a high-speed communication protocol (such as CAN or a similar network) that operates at a speed sufficient for efficient data transmission, ensuring robust data acquisition. Derived metrics (including average depth of discharge, charge acceptance rate, and internal resistance trends) are continuously updated. The system monitors deep discharge events, maintaining not only a detailed log of the frequency and depth of such events but also a detailed log of surrounding conditions such as ambient temperature, vehicle load requirements, and terrain characteristics that may contribute to the discharge pattern.

[0040] The monitoring system employs pattern recognition algorithms to characterize charging behavior, with a particular emphasis on fast charging events. In response to a vehicle connecting to a high-power charging station, the controller records comprehensive charging session data, including initial state of charge, charging power distribution maps, thermal response across the battery pack, and charging efficiency metrics. These data points can be collected via a network such as Controller Area Network Flexible Data Rate (CAN-FD), which can operate at sufficient speed for high-resolution tracking. The system correlates this charging data with environmental conditions and battery state parameters to build a detailed understanding of how different charging modes affect battery health under various conditions.

[0041] The system's location awareness extends beyond simple Global Navigation Satellite System (GNSS) to include a detailed mapping of charging infrastructure availability. The controller maintains an updated database of charging station locations, types, and historical availability patterns. This data is integrated with terrain information and real-time weather data to calculate a dynamic range threshold that determines when a vehicle is considered to be in a remote location. This threshold calculation takes into account factors such as altitude variations, the impact of temperature on battery performance, and historical energy consumption patterns for similar routes and conditions.

[0042] When operating in remote locations, the system implements sophisticated algorithms to determine the acceptable amount of reserved capacity to be released. These calculations consider multiple factors simultaneously, including the current battery health score, precise distances to various charging options, real-time power consumption trends, and the projected energy requirements based on route characteristics and the activated vehicle system. The controller uses predictive modeling to estimate how different amounts of reserved capacity release might affect both immediate operational needs and long-term battery durability.

[0043] The user interface provides comprehensive information through a display system that conveys complex battery management decisions in an easy-to-understand format. For standard consumer vehicles, the interface presents a clear visualization of the current estimated driving range, available reserved capacity, and the meaning of different reserved capacity release options. The display is integrated with the navigation system to show a dynamic range circle that updates in real time based on terrain, weather, and driving style. When reserved capacity release becomes available, the interface presents the user with detailed information about potential range extensions and any associated long-term impacts using clear graphics and simple language to ensure informed decision-making.

[0044] For priority vehicles, the system implements a more sophisticated control scheme that automatically releases reserved capacity based on the vehicle's operating status. In response to the activation of priority indication systems (such as lights and sirens), the controller immediately begins releasing reserved capacity in stages, carefully monitoring power demand and adjusting the release rate to maintain optimal performance while preserving long-term battery health as much as possible. Additionally, the system supports a tracking mode that can include scenarios involving rescue, policing, or medical service operations. Tracking modes can cover extended driving at high speeds, rapid rate increases in speed to reach a location, or maintaining the activity of power-intensive auxiliary systems (such as sirens, radios, or lighting). In tracking mode, the controller dynamically adjusts the reserved capacity release rate by considering factors such as distance to known charging infrastructure, predicted power usage, and battery temperature thresholds to prioritize continuous energy availability. The system can also integrate real-time updates from the navigation system to provide route optimization, ensuring the vehicle completes its mission while conserving sufficient energy for the return trip or immediate charging access. The system continuously calculates the return distance and maintains awareness of the availability of charging infrastructure, thereby ensuring that the tracking operation can be completed while maintaining sufficient energy reserves for the return.

[0045] Trail operation features incorporate advanced terrain analysis capabilities that work in conjunction with specialized trail mapping applications. When a vehicle enters an unpaved area, the system accesses detailed trail data from other vehicles that have already traversed the same trail, including surface type, difficulty level, elevation distribution, and historical energy consumption patterns. This information is fed into a sophisticated energy consumption model that predicts power requirements for different segments of the trail, allowing the system to make intelligent decisions regarding reserved capacity allocation.

[0046] The navigation integration extends beyond simple route planning to include a comprehensive energy management strategy. The system simultaneously considers multiple route options, evaluating each potential path based on factors such as altitude changes, traffic conditions, weather effects, and the availability of charging infrastructure. For each route option, the controller calculates detailed energy consumption projections and determines the optimal use of available battery capacity, including reserving energy for when needed. The navigation system maintains constant awareness of backup options, such as tow truck service access points and areas with reliable cellular coverage, ensuring assistance is available if energy consumption exceeds projections.

[0047] The process of returning to a protected state involves more than simply restricting access to reserved capacity. When conditions allow for a return to a protected state, the system implements a process that includes a detailed analysis of how the reserved capacity has been used. This analysis includes an assessment of any observed changes in discharge rate, temperature distribution during use, and battery performance metrics. The system uses this information to update its battery health model and adjust future reserved capacity release thresholds accordingly.

[0048] The system employs sophisticated predictive analytics to optimize battery durability in its maintenance management. The controller maintains separate usage logs for different operating modes, allowing it to progressively gain a detailed understanding of how various usage modes affect battery health. This information is fed into an adaptive maintenance scheduling algorithm that recommends specific actions to optimize battery life based on observed usage patterns and anticipated future needs.

[0049] The system architecture allows for implementation across various electric vehicle platforms through configurable software parameters and modular hardware interfaces. The core battery management strategy can adapt to different battery chemistry compositions and vehicle architectures while maintaining essential protection features and intelligent reserved capacity management capabilities. This flexibility enables the technology to be widely applied across different vehicle types and use cases, while maintaining optimal performance for each specific implementation.

[0050] Figure 1 A system architecture 100 for managing reserved battery capacity in vehicle 101 is illustrated, including a battery management controller 102 for traction battery 103, a battery management subsystem 104, a thermal management subsystem 106, a vehicle dynamics controller 108, a navigation subsystem 110, and a user interface 112. These subsystems, connected via a high-speed communication network (such as CAN-FD), together enable the battery management controller 102 to make intelligent decisions regarding reserved capacity management, thereby protecting long-term battery health while addressing real-time operational needs. It should be noted that system architecture 100 is merely an example, and more, fewer, and / or differently arranged components can be used to perform the disclosed operations.

[0051] Vehicle 101 can be any of various types of automobiles, crossover multi-purpose vehicles (CUVs), sports multi-purpose vehicles (SUVs), trucks, recreational vehicles, boats, aircraft, or other mobile machinery used for transporting people or goods. Such vehicle 101 can be human-driven or autonomous. In many cases, vehicle 101 can be a battery-electric vehicle powered by one or more electric motors that receive electricity from traction battery 103. As a further possibility, vehicle 101 can be a hybrid electric vehicle powered by both an engine and one or more electric motors. Because the type and configuration of vehicle 101 can vary, the capabilities of vehicle 101 can vary accordingly. As some other possibilities, vehicle 101 can have different capabilities in terms of passenger capacity, towing capacity and storage capacity. For ownership, inventory, and other purposes, vehicle 101 can be associated with a unique identifier such as a vehicle identification number (VIN).

[0052] Traction battery 103 refers to a type of rechargeable battery specifically designed to provide power to the electric motor of an electric or hybrid vehicle 101. Traction battery 103 can be constructed using various battery chemistry compositions (such as lithium-ion) designed for high power-to-weight ratios and good energy density. To ensure the lifespan of traction battery 103, thermal management features and sensors can be integrated into traction battery 103 to measure its operating state.

[0053] The battery management controller 102 can be configured to integrate and coordinate the various subsystems and controllers of the system architecture 100 to ensure optimal performance and battery health of the traction battery 103. Therefore, the battery management controller 102 can be configured to manage the execution of the various processes and procedures discussed in detail herein.

[0054] The battery management subsystem 104 can monitor key battery parameters of the traction battery 103 based on sensors on the traction battery 103. As some examples, these parameters may include cell voltage (e.g., with a resolution up to 1 mV), current flow (e.g., which can be monitored with a sensitivity of 0.1 A using precision sensors), and temperature distribution (which can be monitored with an accuracy of 0.1°C using a thermistor). The battery management subsystem 104 can be used to provide data on depth of discharge, charge / discharge mode, and thermal condition to the battery management controller 102 for real-time analysis.

[0055] The thermal management subsystem 106 can be configured to use thermal management features of the traction battery 103 to ensure the thermal stability of the traction battery 103. In examples, the thermal management subsystem 106 can perform operations such as dynamically adjusting the cooling mechanism and analyzing temperature distribution to prevent overheating. In some examples, these cooling adjustments can be invoked in response to operations involving large-scale battery charging or discharging (such as reserved capacity release or fast charging events).

[0056] The vehicle dynamics controller 108 can be configured to aid in intelligent energy management by analyzing power demand based on the operation of the vehicle 101. For example, the vehicle dynamics controller 108 can monitor the vehicle bus or other data sources to capture information about driving conditions such as terrain, load, and demand on the traction battery 103, providing capabilities such as terrain analysis and estimated energy consumption.

[0057] The navigation subsystem 110 integrates a charging infrastructure database with route optimization tools to calculate efficient routes using real-time weather, elevation, and terrain data, and to ensure accessibility to charging facilities or exit points during unpaved road operations, which may involve cement, asphalt, or paving brick work. In this example, the navigation subsystem 110 may utilize the GNSS functionality of vehicle 101 to determine the current location of vehicle 101 and / or the distance of vehicle 101 to charging stations or other points of interest. The navigation subsystem 110 may also be configured to estimate the required energy usage from the traction battery 103 to guide vehicle 101 through the route.

[0058] User interface 112 is configured to provide a human-machine interface (HMI) that allows vehicle occupants to understand the current driving range, projected reserve capacity, and the potential impact of reserved usage on battery health, thereby enabling informed decision-making. To this end, user interface 112 may utilize a display screen to provide information visually, a speaker to provide information audibly, buttons or a touch panel display to receive touch input, and / or a microphone to receive voice input.

[0059] Figure 2 A tracking mode decision-making process 200 for managing reserved battery capacity in vehicle 101 during critical scenarios is illustrated. In this example, the tracking mode decision-making process 200 can be executed by components of the system architecture 100 discussed in detail herein.

[0060] The tracking mode decision-making process 200 begins in normal operation 202, where vehicle 101 operates without any reserved capacity release. Upon activation of a priority indication system (such as lights or alarms 204), system architecture 100 transitions vehicle 101 to tracking mode 208. This may include initiating a phased release of reserved battery capacity. In response to tracking being marked as complete 206, vehicle 101 can exit tracking mode 208 and return to normal operation 202. If tracking mode 208 is not activated, vehicle 101 may remain in normal operation 202.

[0061] Tracking mode 208 may include one or more phases of reserved capacity release 210. In the first phase of these phases, an immediate 25% reserved capacity release 210 is triggered to ensure sufficient power availability for tracking operations. If additional power is required, system architecture 100 switches vehicle 101 to phase 1 release 212, where battery power output is assessed. If system architecture 100 detects that the power exceeds an upper limit threshold (e.g., 80%) for a sustained duration (e.g., two seconds) 214, system architecture 100 switches vehicle 101 to phase 2 release 216, providing further release of reserved energy. If energy demand continues to rise, additional checks determine if further reserved capacity is needed 218, resulting in phase 3 release 220, which allocates the maximum available reserved capacity.

[0062] In parallel with the reserved release process, system architecture 100 calculates the tracking route via route calculation 222. This includes prioritizing paths based on factors via prioritized route selection 224, such as locating the nearest charging station 226, directing to a rally area 228, or ensuring the route maintains cellular coverage 230 for uninterrupted communication with the tracking service. These route selection decisions are updated dynamically in real time to reflect changes in the condition or performance of vehicle 101.

[0063] Figure 3 A process 300 for monitoring and adjusting reserved battery capacity and generating a maintenance schedule to optimize battery health and performance is shown. Similar to the tracking mode decision-making process 200, process 300 can also be executed by components of the system architecture 100 that controls the vehicle 101.

[0064] Process 300 begins with usage mode monitoring 302, where the system collects real-time and historical data on battery usage patterns, including charge / discharge cycles, temperature variations, and reserved capacity utilization. This information is used to determine operating mode 304, which is categorized into three different modes: normal operation 306, tracking operation 308, or unpaved road operation 310.

[0065] Unpaved road operation 310 includes operations in which vehicle 101 operates outside of a paved road surface such as cement, asphalt, or paving bricks. Unpaved road surfaces may include terrain such as gravel, mud or soil, sand, grass, mulch or sawdust, or unpaved compacted paths.

[0066] For each operating mode 304, the system executes a mode-specific impact score 312 to assess how the battery is affected under the corresponding conditions. This score incorporates factors such as energy consumption trends, depth of discharge, and thermal effects. The results of the impact score are fed into a maintenance schedule generation 314, which organizes the required actions based on three main factors: reserved usage frequency 316, operating conditions 318, and cumulative impact 320. This ensures proactive battery maintenance based on battery usage and performance data.

[0067] After the maintenance schedule is generated (314), the system calculates adjustments (322) to the future allowance for reservations by analyzing the frequency of reserved use, the severity of operating condition (318), and the cumulative impact (320) of reserved discharges on battery durability. Using this analysis, the system continues to update protection parameters (324), thereby modifying thresholds and limits to maintain the long-term battery health of the traction battery (103). These updates may include recalibrating metrics such as minimum reservation levels and depth of discharge to ensure the system dynamically adapts to the evolving state of the battery.

[0068] Process 300 can also perform operations to adjust the release threshold 326, where it fine-tunes the criteria used for reserved capacity release 210. These adjustments ensure that reserved energy is intelligently allocated across different operating modes, thereby balancing immediate needs with maintaining long-term battery performance. Therefore, process 300 demonstrates a holistic approach for system architecture 100 to guide vehicle 101 in integrating real-time monitoring, impact analysis, and adaptive maintenance to enhance the durability and operational reliability of traction battery 103.

[0069] Figure 4 A process 400 for assessing the impact of battery life and calculating the release of reserved capacity 210 in vehicle 101 is shown. Like the tracking mode decision-making process 200 and process 300, process 400 can also be performed by components of the system architecture 100 that controls vehicle 101.

[0070] Process 400 begins with historical data collection 402, in which system architecture 100 continuously collects detailed usage data, including charging patterns, discharge events, and environmental factors.

[0071] This data is fed into parameter analysis 404, where specific factors affecting battery health are assessed. As some non-limiting examples, these parameters may include discharge depth mode 406, frequency of fast charging 408, high charge periods 410 above a defined threshold for state of charge (SOC) (e.g., 80%), low charge periods 412 below a defined threshold (e.g., 20%) SOC, and temperature 414 during high charge periods.

[0072] Insights from this analysis are used to calculate a battery life impact score 416, which quantifies the cumulative impact of these factors on battery degradation 320. This score is then graded 418 to determine its implications for reserved capacity management. In parallel, system architecture 100 integrates location data 420 (e.g., using the GMSS functionality of navigation subsystem 110) to identify whether vehicle 101 is operating in a remote location 422, a useful factor for calculating energy requirements when access to charging stations is limited.

[0073] Additionally, system architecture 100 takes into account current power requirements 424 based on real-time vehicle 101 needs. This information can be captured, for example, using vehicle dynamics controller 108.

[0074] Using this combined data, system architecture 100 continues to perform reserved capacity calculation 426, in which process 400 determines the optimal amount of reserved energy that can be released without compromising long-term battery health.

[0075] Next, system architecture 100 performs release determination 428. This includes system architecture 100 specifying the exact reserved capacity available for immediate use based on operational needs, environmental conditions, and a calculated battery impact score.

[0076] Therefore, process 400 provides an integrated approach for reserved battery capacity management, combining real-time monitoring, predictive analytics, and adaptive control strategies to optimize battery utilization while maintaining long-term reliability. As executed by system architecture 100, process 400 adapts to changing operational needs while maintaining the battery health of traction battery 103 through protection mechanisms and smart release protocols.

[0077] Figure 5 A process 500 for dynamically managing reserved battery capacity in response to battery life assessment and real-time driving conditions is shown. Process 500 can be executed by components of the system architecture 100 that controls the vehicle 101.

[0078] Process 500 begins with monitoring battery usage mode 502, where system architecture 100 continuously collects real-time and historical battery usage data to calculate a battery lifetime score. The battery lifetime score serves as an indicator of overall battery health and durability, incorporating factors such as depth of discharge cycling, thermal stress events, and charge / discharge efficiency. The system monitors instantaneous parameters such as individual cell voltage, temperature distribution, and current flow trends, while also tracking long-term operating characteristics. Battery management controller 102 uses predictive analytics and machine learning algorithms to process this data to identify usage trends and degradation patterns.

[0079] Based on battery life score, the system determines the reserved battery capacity 504 of the traction battery 103. Reserved battery capacity represents a portion of stored energy that remains inaccessible under normal driving conditions but can be selectively released under specific circumstances. The system dynamically adjusts the reserved battery capacity based on real-time assessments of battery health, prioritizing long-term durability while ensuring tracking energy availability when needed. The battery management subsystem 104 continuously refines these determinations by evaluating charge retention performance, internal resistance trends, and historical energy requirements.

[0080] Using available reserved battery capacity, the system calculates an extended driving range 506 when it detects that vehicle 101 is at a predefined distance from a charging facility. This calculation integrates multiple factors, including vehicle efficiency metrics, road conditions, elevation maps, and the expected energy consumption rate along the route. The navigation subsystem 110 plays a crucial role in these calculations by cross-referencing known charging station locations, estimated power requirements, and anticipated weather conditions that may affect battery efficiency. The extended driving range estimate ensures that vehicle 101 can reach the charging facility before its primary energy reserves are depleted.

[0081] Once the extended driving range is determined, the system displays option 508 for the driver to release the reserved battery capacity. This user interface notification 112 provides clear information about the impact of releasing the reserved battery capacity, including the possible additional driving range and the potential impact on the battery life score. The system can use navigation overlays to present a visual representation of the extended driving range, indicating available charging stations and estimated energy levels to be reached. Additionally, the interface can provide notification if the battery life score indicates that reserved use may contribute to long-term degradation exceeding an acceptable threshold.

[0082] Upon user confirmation, the system performs a release of reserved battery capacity 510. This process involves modifying the internal discharge limits within the traction battery 103 to allow temporary access to the stored energy. The battery management controller 102 carefully controls the release operation to ensure power delivery in a manner that minimizes thermal stress and maintains overall battery stability. During this process, the vehicle dynamics controller 108 continuously monitors the real-time energy consumption rate and adjusts the power delivery parameters accordingly to optimize efficiency and extend driving range.

[0083] After the reserved battery capacity is released, the system performs adjustments 512 to the battery lifetime score and the reserved battery capacity. These adjustments are based on the observed impact of reserved usage events, where factors such as discharge rate, temperature fluctuations, and total energy expenditure are analyzed to improve future battery management decisions. System architecture 100 updates its predictive model accordingly, ensuring that subsequent reserved battery capacity determination reflects the latest battery performance data. The revised battery lifetime score is then displayed to the driver, allowing for informed decisions regarding future energy management.

[0084] Figure 6 A process 600 for dynamically managing reserved battery capacity based on vehicle location and real-time battery assessment is illustrated. Process 600 is executed by a processor (such as a battery management controller 102) that integrates battery usage monitoring, navigation data, and user input to optimize battery performance and vehicle range.

[0085] Process 600 begins by monitoring battery usage patterns to calculate a battery life score 602. The processor continuously collects data on charge and discharge cycles, thermal condition, depth of discharge trends, and charging behavior. This information is processed using machine learning algorithms and predictive models to generate a comprehensive battery life score that reflects the overall health of the traction battery 103. The scoring system considers real-time operational data, including energy consumption rates, temperature variations, and past reserved capacity usage. The battery management subsystem 104 provides the processor with continuous updates to battery health and degradation indicators.

[0086] The processor then receives information from a Global Navigation Satellite System (GNSS) to determine the vehicle's position 604 relative to known charging infrastructure. The navigation subsystem 110 provides real-time positioning data, which is cross-referenced with a database of charging stations, terrain information, and estimated driving energy consumption. The processor analyzes this data to determine if the vehicle 101 is beyond a threshold distance where the remaining charge in the traction battery 103 will be insufficient to reach the charging facility. This determination incorporates variables such as altitude changes, traffic conditions, weather effects, and historical energy consumption patterns. If the system identifies that the vehicle 101 may deplete its available energy before reaching the charging station, the processor prepares to release reserved battery capacity.

[0087] Once it is determined that reserved battery capacity may be necessary, the processor initiates a user prompt and releases the reserved battery capacity 606 upon confirmation. The user interface 112 presents the driver with the option to extend the vehicle's range by accessing the reserved energy, providing clear data on the estimated additional driving distance and the potential impact on battery life score. Upon user confirmation, the processor instructs the battery management controller 102 to modify its internal discharge limits, thereby allowing access to the previously restricted energy reservation.

[0088] After reserved battery capacity is released, the processor adjusts the battery lifetime score and reserved battery capacity allocation based on the observed effects of the release. This adjustment takes into account factors such as discharge rate, thermal effects, and reserved utilization depth. The updated battery lifetime score is stored in the system and used for future calculations regarding battery health and reservation management. The processor can also update the thresholds used for subsequent reserved releases, ensuring that reserved energy remains available when necessary while optimizing long-term battery performance.

[0089] Figure 7 This is an example 700 of a computing device 702 for managing reserved battery capacity in vehicle 101. As shown, the computing device 702 includes a processor 704 operatively connected to a storage device 706, a network device 708, an output device 710, and an input device 712. It should be noted that this is merely an example, and computing devices 702 with more, fewer, or different components may be used.

[0090] Processor 704 may include one or more integrated circuits that implement the functionality of a central processing unit (CPU) and / or a graphics processing unit (GPU). In some examples, processor 704 is a system-on-a-chip that integrates the functionality of both the CPU and GPU. The SoC may also optionally include other components, such as storage device 706 and networking device 708, into a single integrated device. Alternatively, the CPU and GPU may be interconnected via peripheral connectivity devices, such as high-speed peripheral component interconnect (PCI) or another suitable data connection. The CPU may be a commercially available device that implements instruction sets such as x86, ARM, Power, or the MIPS microprocessor instruction set family without interlocking pipelines.

[0091] During operation, processor 704 executes program instructions stored in storage device 706. These instructions enable processor 704 to perform tasks such as monitoring battery usage patterns, calculating battery life fraction, determining reserved battery capacity, and coordinating the release of reserved capacity based on the vehicle's distance from charging facilities, terrain, and other factors. Processor 704 can also integrate data from subsystems such as navigation, thermal management, and vehicle dynamics to enhance battery management strategies.

[0092] Storage device 706 includes non-volatile memory and volatile memory. Non-volatile memory may include solid-state storage devices, such as NAND flash memory, magnetic storage devices, or optical media, which retain data even when power is removed. Volatile memory may include random access memory (RAM) that stores data and program instructions during operation of computing device 702. Storage device 706 may contain historical battery usage data, environmental factors, and charging infrastructure information.

[0093] Network device 708 allows communication with external systems, such as cloud-based services that update battery health metrics and charging facility locations. Network device 708 can also transmit system-generated data, such as reserved battery usage history or performance metrics, to a remote server for fleet-wide analysis and optimization. Examples of suitable network devices include Ethernet interfaces, Wi-Fi transceivers, cellular transceivers, Bluetooth adapters, or ultra-wideband (UWB) transceivers.

[0094] Output device 710 provides the driver with visual, auditory, or tactile feedback to display key information such as the reserved capacity release 210 option, battery health metrics, and estimated range extension. Examples of output devices include electronic displays, speakers, and devices for tactile feedback, such as Braille displays. For example, output device 710 may graphically display the vehicle's current range, the additional range achievable using reserved capacity, and the impact of reserved release on long-term battery health.

[0095] Input device 712 enables users to interact with computing device 702. It allows users to configure the system, confirm reserved capacity release 210, and adjust system parameters such as range thresholds. Suitable input devices include touchscreens, keyboards, voice input systems, and other human-machine interface devices.

[0096] The processor 704, working in conjunction with the storage device 706 and the network device 708, also supports real-time location-based calculations. For example, when the vehicle is further from a charging facility than its current battery capacity allows, the processor 704 calculates whether reserved capacity can make up for the gap. If so, the system notifies the driver via output device 710 and requests confirmation via input device 712 to release the reserved capacity.

[0097] The computing device 702 is seamlessly integrated with the vehicle's battery management subsystem 104, thermal management subsystem 106, and navigation subsystem 110 to provide intelligent reserved capacity management. This integration ensures that the reserved capacity release process dynamically adapts to real-time conditions, including terrain, weather, and vehicle usage patterns, while protecting long-term battery health.

[0098] System Architecture 100 supports implementations across various vehicle platforms. It combines hardware components, such as application-specific integrated circuits (ASICs) or field-programmable gate arrays (FPGAs), with software and firmware to provide a robust and scalable solution. This enables a flexible and reliable approach to managing battery reserve capacity under various operating scenarios, including high-demand, tracking, and off-road conditions.

[0099] The processes, methods, and algorithms disclosed in this application for managing reserved battery capacity in vehicle 101 can be implemented using processing devices, controllers, or computing systems. These implementations may involve programmable electronic control units (ECUs), dedicated battery management controllers 102, or other suitable control devices within the vehicle's power management system. The disclosed processes and methods can be stored as data and executable instructions on various storage media, such as read-only memory (ROM), random access memory (RAM), solid-state storage devices (e.g., NAND flash memory), or magnetic storage devices. Alternatively, the processes, methods, and algorithms can be embedded in software applications or implemented partially or entirely in hardware (such as ASICs, FPGAs, or state machines). In some cases, these implementations may be a combination of hardware, software, and firmware components integrated into the vehicle architecture.

[0100] In this application, unless otherwise expressly stated, the first instance of an acronym or abbreviation defines its meaning for all subsequent uses, including grammatical variations. Unless otherwise expressly described, the same techniques referenced elsewhere in this specification are used to determine measurements of properties such as battery capacity, thermal condition, or driving range.

[0101] As used herein, unless the context clearly indicates otherwise, the singular terms “a,” “an,” and “the” also include the plural forms. For example, unless otherwise clearly stated, a reference to “battery” includes one or more batteries.

[0102] As used herein, the term "comprising" means "including," "having," "containing," or "characterized by," and is open-ended, allowing for the inclusion of additional, unstated elements or steps. In contrast, "consisting of" excludes any element or step not expressly specified. When "consisting of" appears in a claim clause, it limits only that clause and not the entire claim. The phrase "substantially constitutes" allows for the inclusion of additional elements or steps, provided they do not materially affect the essential or novel characteristics of the claimed invention. The terms "one or more" and "at least one" mean "one or more" and include the concept of "a plurality of."

[0103] The specific embodiments described herein are intended to provide examples of the disclosed systems and methods for managing reserved battery capacity, but they do not limit the full scope of the invention. Modifications and adaptations can be made without departing from the spirit of this disclosure. For example, the system can be implemented using different combinations of hardware and software, and features from various embodiments can be combined to create additional embodiments that may not be explicitly described.

[0104] Certain embodiments of this disclosure are described as advantageous or preferred for particular applications, such as improving range in remote conditions, maintaining long-term battery health, or dynamically adapting reservation management during priority operation. However, these properties can vary depending on desired trade-offs for a particular implementation, such as design requirements, durability, manufacturability, or user experience. Even embodiments described as having less preferred characteristics remain within the scope of this disclosure and may be desirable for specific use cases.

[0105] According to the present invention, a method for managing reserved battery capacity includes: monitoring battery usage patterns of a vehicle's traction battery to calculate a battery life score; determining a reserved battery capacity of the traction battery based on the battery life score; calculating an extended driving range based on the reserved battery capacity when the vehicle is at a predefined distance from a charging facility; displaying options for performing reserved battery capacity release, the options indicating the associated extended driving range and the impact of performing the reserved battery capacity release on the battery life score of the traction battery; releasing the reserved battery capacity upon user confirmation; and adjusting the battery life score and the reserved battery capacity of the traction battery based on performing the reserved battery capacity release.

[0106] In one aspect of the invention, monitoring the battery usage pattern includes tracking the depth of discharge events and charge levels of the traction battery.

[0107] In one aspect of the invention, calculating the battery life fraction includes taking into account thermal exposure and charging frequency.

[0108] In one aspect of the invention, determining the reserved battery capacity includes ensuring that the release does not exceed a predefined limit to maintain long-term battery health.

[0109] In one aspect of the invention, the predefined distance is dynamically adjusted based on terrain data and weather conditions.

[0110] In one aspect of the invention, the options for performing the release of the reserved battery capacity include presenting a graphical interface that illustrates the expected driving range and the impact on battery life.

[0111] In one aspect of the invention, releasing the reserved battery capacity includes applying a phased release process based on the vehicle's operational requirements.

[0112] In one aspect of the invention, adjusting the battery life score includes updating the threshold for future release of reserved capacity.

[0113] According to the present invention, a battery management controller for managing reserved battery capacity is provided, the battery management controller comprising: a traction battery configured to store electrical energy and supply electrical energy to a vehicle; a global navigation satellite system configured to determine the location of the vehicle; and a processor configured to: monitor the usage patterns of the traction battery to calculate a battery life score and determine a reserved battery capacity based on the battery life score; receive information from the global navigation satellite system indicating that the vehicle is farther from a charging facility than can be achieved using the remaining capacity of the traction battery; and release the reserved battery capacity from the traction battery upon receiving user confirmation, and adjust the battery life score and the reserved battery capacity based on the calculated effect of the release of the reserved battery capacity on the traction battery.

[0114] According to an embodiment, the processor is also configured to monitor battery temperature during periods of increased charge levels.

[0115] According to an embodiment, the processor uses historical charging and discharging patterns to calculate the battery life score.

[0116] According to an embodiment, the global navigation satellite system provides the processor with real-time updates to determine a predefined distance from the charging facility.

[0117] According to an embodiment, the processor is also configured to calculate the extended driving range achievable using the reserved battery capacity.

[0118] According to an embodiment, the processor is configured to prioritize the release of reserved battery capacity during tracking conditions.

[0119] According to an embodiment, the processor is configured to prevent reserved battery capacity from being released when the battery life score indicates excessive degradation.

[0120] According to an embodiment, the processor is configured to store and analyze data related to the release of previously reserved battery capacity to optimize future reservation management.

[0121] According to an embodiment, when the vehicle enters a predefined tracking mode, the reserved battery capacity is automatically released.

[0122] According to an embodiment, the reserved battery capacity release option includes multiple phases based on the vehicle's destination and remaining driving range.

[0123] According to an embodiment, the system communicates with cloud-based services to update battery performance metrics and charging facility data.

[0124] According to the present invention, a non-transitory computer-readable medium is provided having instructions that, when executed by one or more computing devices, cause the devices to perform operations for managing reserved battery capacity, the operations including: monitoring battery usage patterns of a vehicle's traction battery to calculate a battery life score; determining a reserved battery capacity based on the battery life score; in response to detecting when the vehicle is at a predefined distance from a charging facility: calculating an extended driving range based on the reserved battery capacity; displaying options for performing reserved battery capacity release, the options indicating the extended driving range and the impact of the reserved battery capacity release on the battery life score; releasing the reserved battery capacity upon user confirmation; and adjusting the battery life score and the reserved battery capacity based on the impact of the reserved battery capacity release.

Claims

1. A method for managing battery reserved capacity, comprising: Monitor the battery usage patterns of the vehicle's traction battery to calculate the battery life score; Based on the battery lifespan fraction, the reserved battery capacity of the traction battery is determined; When the vehicle is at a predefined distance from the charging facility, the extended driving range is calculated based on the reserved battery capacity. Displays options for performing reserved battery capacity release, indicating the associated extended driving range and the impact of performing the reserved battery capacity release on the battery life fraction of the traction battery; Release the reserved battery capacity upon user confirmation; as well as Based on the release of the reserved battery capacity, the battery life fraction and the reserved battery capacity of the traction battery are adjusted.

2. The method of claim 1, wherein monitoring the battery usage pattern includes tracking the depth of discharge events and charge levels of the traction battery.

3. The method of claim 1, wherein calculating the battery life fraction includes taking into account thermal exposure and charging frequency.

4. The method of claim 1, wherein determining the reserved battery capacity includes ensuring that the release does not exceed a predefined limit to maintain long-term battery health.

5. The method of claim 1, wherein the predefined distance is dynamically adjusted based on terrain data and weather conditions.

6. The method of claim 1, wherein displaying the options for performing the release of the reserved battery capacity includes presenting a graphical interface showing the expected driving range and the impact on battery life.

7. The method of claim 1, wherein releasing the reserved battery capacity includes applying a phased release process based on the vehicle's operational requirements.

8. The method of claim 1, wherein adjusting the battery lifetime fraction includes updating the threshold for future reserved capacity release.

9. A battery management controller for managing reserved battery capacity, comprising: A traction battery, configured to store electrical energy and supply the electrical energy to the vehicle; A global navigation satellite system, configured to determine the location of the vehicle; as well as Processor, the processor being configured to: The usage pattern of the traction battery is monitored to calculate the battery life score and determine the reserved battery capacity based on the battery life score. Receive information from the global navigation satellite system indicating that the vehicle is farther from the charging facility than can be achieved using the remaining capacity of the traction battery; as well as Upon receiving user confirmation, the reserved battery capacity is released from the traction battery, and the battery life fraction and the reserved battery capacity are adjusted based on the calculated impact of the release of the reserved battery capacity on the traction battery.

10. The battery management controller of claim 9, wherein the processor is further configured to monitor battery temperature during periods of elevated charge levels.

11. The battery management controller of claim 9, wherein the processor uses historical charge and discharge patterns to calculate the battery life score.

12. The battery management controller of claim 9, wherein the global navigation satellite system provides real-time updates to the processor to determine a predefined distance from the charging facility.

13. The battery management controller of claim 9, wherein the processor is further configured to calculate the extended driving range achievable using the reserved battery capacity.

14. The battery management controller of claim 9, wherein the processor is configured to prioritize the release of reserved battery capacity during tracking conditions.

15. A non-transitory computer-readable medium including instructions that, when executed by one or more computing devices, cause the devices to perform operations for managing battery reserved capacity, the operations including: Monitor the battery usage patterns of the vehicle's traction battery to calculate the battery life score; Based on the battery lifespan fraction, determine the reserved battery capacity; In response to detecting when the vehicle is at a predefined distance from the charging facility: The extended driving range is calculated based on the reserved battery capacity; Displays options for performing reserved battery capacity release, the options indicating the impact of the extended driving range and the reserved battery capacity release on the battery life fraction; Release the reserved battery capacity upon user confirmation; as well as The battery life score and the reserved battery capacity are adjusted based on the impact of the release of the reserved battery capacity.

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