Sleep state consumption estimation for vehicle

Abstract

Sleep state power consumption estimation for a vehicle is provided. The electric vehicle may include an electronic control unit (ECU). The ECU may enter a sleep state in response to the electric vehicle entering the sleep mode, and enter an operating state from the sleep state in response to the electric vehicle exiting the sleep mode. The ECU may determine a remaining range estimate for the vehicle based on a range loss estimate corresponding to the estimated power consumption during the sleep mode. In some implementations, the electric vehicle includes a sensor, and the ECU may obtain sensor data indicative of a power consumption measurement during a sleep mode of the electric vehicle from which a remaining range estimate may be determined. In other implementations, the ECU may determine a remaining range estimate based on a predetermined sleep state power consumption rate and a duration of sleep mode without active hardware monitoring.

Description

[0001] Cross-references to related applications

[0002] This application claims the benefit of U.S. Provisional Application Serial No. 63 / 717,751, filed November 7, 2024, entitled “SLEEP STATE CONSUMPTIONESTIMATION FOR VEHICLES”, the disclosure of which is expressly incorporated herein by reference in its entirety. Background Technology

[0003] Vehicles are typically equipped with sensors to sense various aspects of the vehicle's operation and / or condition during operation. When the vehicle is not in operation, these sensors are usually disabled or turned off. Attached Figure Description

[0004] Certain features of the present subject matter are set forth in the appended claims. However, for purposes of explanation, several embodiments of the present subject matter are illustrated in the following figures.

[0005] Figure 1A and Figure 1B A schematic perspective side view illustrating an example embodiment of a vehicle based on one or more specific embodiments is shown.

[0006] Figure 2 Describing what can be included according to one or more specific implementations Figure 1A and Figure 1B A view of an example power system architecture in a vehicle.

[0007] Figure 3 It is a flowchart of an exemplary operation that can be performed based on one or more specific implementations to estimate the sleep state consumption of a vehicle using sensor-based methods.

[0008] Figure 4 It is a flowchart of an exemplary operation based on one or more specific implementations that can be executed to estimate the self-learning sleep state consumption of a vehicle.

[0009] Figure 5 An example is an electronic system in which one or more specific implementations of the techniques of this subject matter can be implemented. Detailed Implementation

[0010] The detailed description set forth below is intended to describe various configurations of the subject matter, and not to represent only the configurations in which the subject matter can be practiced. The accompanying drawings are incorporated herein and form part of the detailed description. The detailed description includes specific details in order to provide a thorough understanding of the subject matter. However, the subject matter is not limited to the specific details set forth herein, and can be practiced using one or more other specific embodiments. Structures and components are shown in block diagram form to avoid confusion with the concepts of the subject matter.

[0011] Vehicles are typically parked in parking lots, garages, and along streets, sometimes for hours or even days at a time. Parked vehicles are usually in a switched-off or sleep mode, in which most (if not all) of their mechanical or electrical systems are also switched off or in standby mode. This reduces the power consumed when the vehicle is in sleep or switched-off mode, which can be particularly beneficial for electrically propelled vehicles. However, even when a vehicle is switched-off or sleep, it can be advantageous to be able to monitor various systems and / or characteristics of the vehicle. As an illustrative example, it could be beneficial to continuously monitor the vehicle's battery to detect power consumption while in sleep mode, because without monitoring, inaccurate estimates of remaining range may be obtained upon waking, potentially causing inconvenience and range anxiety for the user.

[0012] This subject matter addresses the challenge of lacking active systems for monitoring power consumption and estimating mileage loss during vehicle sleep mode. Implementations of this subject matter allow for the integration of hardware sensors (e.g., power sensor integrated circuits) into the vehicle's architecture to continuously monitor power consumption during sleep mode. This sensor can measure power consumption during the vehicle's sleep mode, thereby improving the accuracy of mileage estimation when the vehicle is awake. Aspects of this subject matter can provide monitoring (e.g., for power consumption) within the vehicle (e.g., within the vehicle's battery or battery pack) while the vehicle is in sleep mode. This monitoring can be performed by switching one or more processing cores of the vehicle's electronic control unit (ECU) to an operating or awake state to acquire and analyze sensor data.

[0013] Implementations of this subject matter also allow for setting a predetermined sleep state power consumption rate (e.g., 10 watts per hour) for the high-voltage (HV) battery pack at the start of a vehicle's sleep state without active hardware monitoring. During this sleep state period, all processing cores (including all electronic control units (ECUs)) may become inactive, causing the hardware to fail to track mileage loss. One or more processing cores of an ECU in an active (or awake) state can calculate the mileage loss over the entire sleep duration based on the predetermined sleep state power consumption rate. Upon transitioning to an awake state, the estimated mileage loss can be applied, providing the vehicle's user with a worst-case (or conservative) estimate of remaining mileage. Once the vehicle resumes driving (or active driving is detected), one or more processing cores in an active state can recalibrate the HV battery pack's state of charge to refine the actual sleep state power consumption rate. Whenever the vehicle enters sleep mode, this recalibrated sleep state power consumption rate, updated from the predetermined rate to a more accurate value, can be applied, enabling self-calibration of the mileage loss estimate during sleep mode without active hardware monitoring.

[0014] Figure 1A These are illustrations of a specific embodiment of the apparatus described herein. Figure 1A In the example, the device is a mobile device implemented as a vehicle 100. The vehicle 100 can be implemented as an electric vehicle and can include one or more batteries 110 for powering the vehicle and / or one or more systems and / or components of the vehicle. As shown, the battery pack 110 can include one or more battery cells 120. As shown in Figure 1, the battery pack 110 can also or alternatively include one or more battery cells 120 directly mounted in the battery pack 110 (e.g., in a battery cell-battery pack configuration). The provided battery pack 110 may not have any battery modules 115, but instead has battery cells 120 directly mounted in the battery pack 110 (e.g., in a battery cell-battery pack configuration) and / or in other battery devices installed in the battery pack 110. The vehicle battery pack can include multiple energy storage devices that can be arranged as battery modules or battery devices. The battery devices or modules can include unit components capable of being combined with other elements (e.g., structural frames, thermal management devices) that can protect the unit components from heat, shock, and / or vibration. For example, battery cell 120 may be included in a battery, battery device, battery module, and / or battery pack to power components of vehicle 100. For example, the battery cell housing of battery cell 120 may be disposed in battery module 115, battery pack 110, battery array, or other battery device installed in vehicle 100.

[0015] exist Figure 1AIn the example, the vehicle 100 is implemented as a truck (e.g., a pickup truck) having one or more batteries 110 (e.g., a battery pack having multiple (such as hundreds or thousands) battery cells) and processing circuitry 108 (e.g., including one or more electronic control units (ECUs) (including one or more processors), memory and / or communication circuitry). Figure 1A The example of vehicle 100, implemented as a pickup truck with a cargo box, is merely illustrative. For example, Figure 1B Another embodiment is illustrated in which a vehicle 100, including a battery pack 110, a sensor 104, and a processing circuit 108, is implemented as a sport utility vehicle (SUV), such as an electric sport utility vehicle.

[0016] As shown in the figure, the vehicle 100 may include one or more sensors 104. Figure 1A In the example, sensor 104 is disposed within battery pack 110 (e.g., within a battery pack housing in which battery cells are also disposed). In various examples, sensor 104 may include a pressure sensor, a temperature sensor, and / or other sensors or combinations thereof. In one exemplary example discussed herein, sensor 104 is a power sensor. Power sensors in electric vehicles can assess the state of charge (SoC) and overall health of battery pack 110, thereby providing useful data for range estimation and energy management. These power sensors can monitor the current flowing into and out of battery pack 110, and measure voltage, current, and temperature to estimate energy usage and available capacity. Sensor 104 may employ coulomb counting, which can track the charge entering and leaving battery pack 110, and may employ advanced algorithms to account for the effects of temperature, aging, and varying load conditions on battery capacity. By measuring this power consumption data, sensor 104 can allow processing circuitry 108 to accurately report remaining range, power usage trends, and expected charging levels, thereby facilitating power management and optimizing the efficiency of vehicle 100. Sensor 104 can be used as a power sensor with a single-channel or multi-channel power monitor, and has a battery and a 32V full-scale range.

[0017] Implementations of this technology allow for addressing the challenges faced by electric vehicles entering sleep mode after parking. Vehicle 100 can enter a low-power state instead of transitioning to a completely de-energized state. In this low-power state, high-voltage systems can be deactivated, while some low-voltage systems (such as alarm functions) can still draw power from battery pack 110. Therefore, some power consumption still occurs in the high-voltage battery pack. The main challenge in this regard is determining the mileage consumed while the vehicle remains in sleep mode. When vehicle 100 enters this sleep mode, all main monitoring electronics are set to inactive, thereby ceasing active power consumption tracking during sleep mode.

[0018] To detect such mileage loss during the sleep mode of vehicle 100, power consumption during the sleep mode can be sensed using processing circuitry 108 and sensor 104. For example, continuous monitoring can be achieved using sensor 104 via coulomb counting, thereby capturing and storing an estimate of mileage loss during the sleep mode of vehicle 100. When processing circuitry 108 determines the mileage loss estimate, it can send an alarm (e.g., via a wired or wireless connection) to another device 109 outside the vehicle (e.g., a portable device), such as the user's smartphone or other electronic device.

[0019] This technology addresses the challenges of mileage estimation by modeling mileage loss during vehicle sleep using a predetermined sleep-state power consumption rate through software (without active hardware monitoring), where both remaining mileage estimation and mileage loss estimation can occur after vehicle 100 exits sleep mode. Any updates to the remaining mileage estimate can be delayed to assess the state of charge of battery pack 110 and the actual remaining mileage of vehicle 100 after resuming driving. For example, the remaining mileage estimate can be updated after vehicle 100 begins driving rather than immediately after transitioning to a wake-up (or active) state.

[0020] Providing end users with an accurate estimate of mileage loss while their vehicles are parked is beneficial, enabling them to effectively plan upcoming trips. This information can support customer-facing features such as planning the next charging interval, determining the remaining mileage estimate for a planned trip, and deciding whether charging before the trip is worthwhile.

[0021] Figure 2 Depicting what can be included Figure 1A and Figure 1BA view of an example power system architecture 200 in a vehicle 100. In the power system architecture 200, a high-voltage battery pack 220 is used as a primary energy source, where power flows through two distinct paths, one designated as a first power signal path 202 and the other designated as a second power signal path 204.

[0022] On the first power signal path 202, the high-voltage battery pack 220 is connected to a set of contactors 230, which can be used as electrically controlled switches for routing power to downstream components. The contactors 230 can enable or disable the power flow to a primary power converter 240, which can be implemented as a main DC-DC converter. The primary power converter 240 can receive power from the high-voltage battery pack 220 via the contactors 230 and progressively reduce the voltage to a level suitable for the vehicle's power load 270. The power delivered through this path can primarily maintain the operational requirements of the vehicle 100 during active driving or high-demand modes.

[0023] In parallel, a second power signal path 204 provides an alternative energy route. In this path, the high-voltage battery pack 220 is directly connected to the secondary power converter 260, which acts as an auxiliary DC-DC converter. When the vehicle 100 enters sleep mode, contactor 230 and primary power converter 240 are de-energized. In this state, power flows from the secondary power converter 260, supplemented by periodic bursts from the low-voltage battery pack 250. During low-power states, such as when the vehicle 100 is in sleep mode, the secondary power converter 260 supplies energy to the vehicle's power load 270 to maintain certain functions with minimal power consumption. The low-voltage battery pack 250 is able to supplement the energy supplied by the secondary power converter 260 in a periodic burst manner, thereby extending the operating time in sleep mode. The low-voltage battery pack 250 includes a 12V battery.

[0024] As shown in the figure, ECU 210 may include one or more processing cores. One or more processing cores in ECU 210 may transition between multiple available power states or modes. For example, the multiple available power states may include: a running state, in which all aspects of the processing core are available and active; an idle state, in which, for example, the CPU clock is disabled, the direct memory interface and / or program memory interface (DMI / PMI) memory is accessible, and peripheral devices remain active; a sleep state, in which the peripheral clock is gated and the CPU is disabled; and a standby (e.g., minimum power) state, in which the main domain is powered down (e.g., and the spare RAM may be active).

[0025] This sleep state monitoring can be achieved by adding sensors to vehicle 100 that remain active in low-power mode. Sensor 104 is integrated at specific monitoring points to measure power draw and track current levels. ECU 210 can host software to acquire and process the sensor data (e.g., power consumption data) and estimate mileage loss during sleep state based on the sensor data. ECU 210 can switch between multiple power states to allow monitoring of vehicle 100 while it is in sleep mode (e.g., using sensor 104).

[0026] Traditional OEM configurations use low-voltage modules and controllers manufactured by various third parties, which limits the ability to fully aggregate all consumption data across both high-voltage and low-voltage sources. Figure 2 The illustrated embodiment provides an integrated hardware solution that includes a sensor 104 for accurately tracking power draw (e.g., current) and estimating mileage loss during sleep mode, rather than relying on a secondary power converter 260, which, in one or more other embodiments, may not perform active mileage loss monitoring within the power system architecture 200. Therefore, this embodiment addresses these limitations by providing continuous, integrated monitoring of the total consumption of the vehicle 100 during sleep.

[0027] According to various aspects of this disclosure, innovative operations of existing vehicle hardware can be used to achieve monitoring during vehicle sleep (e.g., mileage loss detection). The sleep state monitoring disclosed herein may be independent of the hardware and / or sensors performing the monitoring (e.g., sensing mileage loss estimates) and can be scaled to multiple programs and / or product lines without additional hardware costs. The sleep state monitoring disclosed herein can be applied to existing vehicles via over-the-air (OTA) updates without requiring physical maintenance of the vehicle.

[0028] Sensor 104 can be connected in series with existing vehicle circuitry to accurately capture load. Sensor 104 can be positioned at the outputs of the secondary power converter 260 and the low-voltage battery pack 250 to provide continuous current monitoring during sleep mode. One set of sensors monitors the output of the secondary power converter 260 to capture real-time data on energy flow through the second power signal path 204 during sleep mode. For example, a first sensor 104 can be connected in series along the second power signal path 204 between the output of the secondary power converter 260 and the vehicle power load 270 to monitor power draw from the secondary voltage source during sleep mode. Another set of sensors is positioned to monitor the output of the low-voltage battery pack 250 to provide information on supplemental energy contribution during sleep mode. For example, a second sensor 104 can be connected in series between the output of the low-voltage battery pack 250 and the vehicle power load 270 to monitor power draw from the low-voltage source during sleep mode.

[0029] Sensor 104 can also be positioned at the input of secondary power converter 260 rather than at its output, allowing coulomb counting to account for efficiency variations observable at the output of secondary power converter 260. This placement enables enhanced characterization of battery pack 110 and provides higher resolution for mileage loss and mileage estimation based on real-time sensor data collection.

[0030] ECU 210 can receive, store, and / or process total consumption data of vehicle 100 during sleep mode. In one or more embodiments, sensor data collection, particularly sensor data collection from sensor 104, can be performed when vehicle 100 is in sleep mode. Sensor data collection from sensor 104 can also be performed when vehicle 100 is operating in active driving mode. The data collected from sensor 104 can be consistently processed by ECU 210 for specific applications such as calculating sleep current estimates and monitoring sleep charge dissipation.

[0031] The use of sensor 104 allows for initial estimation based on power draw activity observed at measurement points within the power system architecture 200 during sleep mode. The use of sensor 104 can improve the resolution of mileage loss estimates by serving as a finer-grained baseline built upon predetermined mileage loss estimates used as a coarse baseline, thus contributing to a calibration process that improves the overall accuracy of mileage loss estimates. ECU 210 can allow sensor data, or at least a portion thereof, to be sent to a cloud-based system (not shown) that can integrate geographic location data for dynamic mileage calculation. For example, when vehicle 100 moves from location A to location B, sensor data collected at location B can be used to inform the user of vehicle 100 of expected mileage and charging needs. The collected data can be sent to the cloud-based system, contributing to a comprehensive data pool that facilitates more robust estimations of vehicle performance and mileage calculations.

[0032] Sleep mode power consumption can be sampled by sensor 104 at defined intervals. ECU 210 can use sensor 104 to first establish an average current over longer intervals (such as 30 or 50 minutes) to provide a baseline mileage estimate. In a configuration using coulomb counting hardware, measurements occur at higher resolution, capturing current changes at intervals as short as one second or 30 seconds. This allows for finer-grained monitoring, whereby each 30-second increment can be calculated and accumulated to track real-time current fluctuations, such as those caused by temperature changes. For example, if the vehicle temperature rises by 1 PM compared to 10 PM, coulomb counting can continuously capture these effects, sampling every 30 seconds to dynamically adjust the current calculation. Utilizing this higher sampling frequency, the overall error in the mileage estimate can be minimized.

[0033] Vehicle 100 can incorporate a safety feature that enables the activation of a camera system, allowing access to video feeds via mobile devices. Users of vehicle 100 can request video feeds at random intervals, introducing variability into data collection. The use of sensor 104 allows for continuous monitoring of energy consumption. For example, if a user of vehicle 100 watches a video feed for several minutes while vehicle 100 is in sleep mode, the system can capture data points indicating fluctuations in current draw during the vehicle's sleep period. This ability to obtain fine-grained data using sensor 104 reduces the error in overall mileage loss estimation.

[0034] ECU 210 can set a predetermined sleep state power consumption rate (e.g., 10 watts per hour) for the high-voltage battery pack 220 when the vehicle begins sleep mode. Upon entering sleep mode, ECU 210 can apply the predetermined sleep state power consumption rate and calculate the total mileage loss during the sleep mode based on the sleep mode duration, which is calculated as t. wake -t0, where t0 is the time when vehicle 100 enters sleep mode, and t wake This is the time when vehicle 100 exits sleep mode and enters wake-up mode. A mileage loss estimate can be calculated by selecting a conservatively estimated sleep state power consumption rate (e.g., in the range of 10 watt-hours (wph) to 30 wph). When vehicle 100 transitions from sleep mode to wake-up mode, ECU 210 can calculate the actual mileage loss based on the sleep mode duration and the predetermined sleep state power consumption rate to provide the user of vehicle 100 with a remaining mileage estimate. The remaining mileage estimate may be lower than the actual estimate, resulting in a surplus of mileage for the user.

[0035] Upon resumption of driving, ECU 210 can recalibrate the state of charge of the high-voltage battery pack 220 by updating the remaining range estimate based on real-time conditions and generating an updated range loss estimate. Utilizing continuous monitoring capabilities integrated into the vehicle architecture, including software-defined functionality, a self-learning implementation can iteratively update the sleep-state power consumption estimate. These iterative updates allow the vehicle 100 to improve its own understanding of its power consumption while in sleep mode, reducing the complexity and cost associated with additional hardware while achieving more accurate remaining range prediction.

[0036] Feedback mechanisms can facilitate the calculation of updated range loss estimates that take into account various factors, such as battery degradation, environmental conditions, and other external variables that affect battery performance over time. As batteries age, the rate of range loss may vary, with temperature acting as an additional influencing factor. For example, in high-temperature geographic regions, electronics may exhibit higher leakage during sleep states compared to geographic regions with temperate climates, leading to regional variations in energy consumption.

[0037] The feedback mechanism continuously updates the range loss estimate by collecting data on battery cell voltage, voltage sag, and battery module balance during each driving cycle. Battery data can be integrated with samples aggregated from various system sensors. When vehicle 100 begins driving, state-of-charge estimation begins by analyzing voltage sag and the battery's ability to support current loads. Each sample acquired helps refine the range loss estimate during subsequent sleep intervals.

[0038] Environmental conditions can be represented by more conservative estimates of predetermined sleep-state power consumption rates, pre-configured as worst-case estimates across different climates. This subject matter technique allows for adaptation to local environmental conditions, thus providing region-specific adjustments to improve prediction accuracy for users in different environments. This adaptability to different climates may be beneficial in reducing the discrepancy between estimated and actual mileage loss by considering both vehicle-specific configurations and user input (if available).

[0039] This technology can also allow for dynamic mileage loss estimates based on vehicle configuration. For example, fleet management data can enable curve-specific estimates, such as higher power consumption curves for vehicles equipped with additional devices (e.g., cameras, safety systems) that draw power during sleep mode. Each vehicle configuration can be optimized for individual mileage loss, resulting in mileage loss estimates consistent with specific operating characteristics.

[0040] Vehicle 100 includes connectivity capabilities that allow it to store collected data and transmit it via a network to a central repository (not shown). This centralized system can aggregate fleet-wide data, enabling further refinement of predictive models. Other vehicles including vehicle 100 can access fleet-based insights, facilitating a common dataset that enhances mileage loss estimation for vehicles within the same geographic area.

[0041] The initial mileage loss estimate can be derived from cloud feed data. This cloud feed data can provide an updated mileage loss estimate, which can be sent to both vehicle 100 and the cloud system (not shown). The cloud system can then generate an updated remaining mileage estimate based on the updated mileage loss estimate, thereby facilitating continuous synchronization between the local mileage loss estimate and the cloud-based mileage loss estimate.

[0042] Figure 3 A flowchart illustrating an example process for performing sensor-based sleep state consumption estimation of a vehicle, according to a specific implementation of the art of this subject, is provided. For illustrative purposes, this document primarily refers to... Figures 1A to 1B The vehicle 100, sensor 104, processing circuit 108 and Figure 2 The process 300 is described using ECU 210. However, the process 300 is not limited to... Figures 1A to 1BThe vehicle 100, sensor 104, and processing circuitry 108, and one or more blocks (or operations) of process 300 may be performed by one or more other components of other suitable mobile devices, equipment, or systems. Also for illustrative purposes, some blocks of process 300 are described herein as occurring sequentially or linearly. However, multiple blocks of process 300 may occur in parallel. Furthermore, the blocks of process 300 do not need to be performed in the order shown, and / or one or more blocks of process 300 need not be performed and / or may be replaced by other operations.

[0043] like Figure 3 As illustrated, at block 302, in response to a vehicle (e.g., vehicle 100) entering a sleep mode, one or more processing cores of the vehicle's electronic control unit (e.g., ECU 210) can be set to a sleep state.

[0044] At block 304, state-of-charge loss during the vehicle's sleep mode is monitored by dynamically performing coulomb counting using one or more sensors (e.g., sensor 104). The duration of the sleep state may not affect data collection while in sleep mode, as one or more processing cores read directly from one or more sensors 104. Process 300 may allow continuous monitoring during the sleep mode of the vehicle 100. The one or more sensors 104 may operate using a local power source supplied by a secondary power converter 260, which also powers auxiliary systems such as a vehicle alarm during sleep mode. A first coulomb count measurement may be obtained at a first location between the high-voltage battery pack 220 and the vehicle power load 270 using a first sensor of a plurality of sensors. For example, the first sensor 104 may be located at the output of the secondary power converter 260. In another example, the first sensor 104 may be located at the input of the secondary power converter 260. A second sensor from a plurality of sensors can be used to obtain a second coulomb count measurement at a second location between the low-voltage battery pack 250 and the vehicle power load 270. For example, the second sensor 104 can be located at the output of the low-voltage battery pack 250.

[0045] At box 306, in response to the vehicle exiting sleep mode, one or more processing cores can wake from sleep to running. In each wake-up cycle, the one or more processing cores can collect sensor data regarding energy consumption. This dynamic reading of one or more sensors 104 can reflect mileage loss during sleep mode without requiring updates to the baseline mileage loss value. The one or more processing cores can operate by directly sampling one or more sensors 104 each time, thus eliminating the need to recalibrate the baseline mileage loss value.

[0046] At box 308, one or more processing cores can acquire sensor data indicating power consumption during the vehicle's sleep mode from one or more sensors. Whenever the vehicle 100 transitions from sleep mode to active state, one or more processing cores can read measurements from one or more sensors 104. For example, the process can begin with an initial estimate after the vehicle 100 enters sleep mode. Upon waking, one or more processing cores can acquire values ​​from one or more sensors 104 to update the mileage loss estimate. The updated mileage loss can then be calculated using a fine-grained and accurate dataset from one or more sensors 104. When the vehicle 100 exits sleep mode, and when the user enters the vehicle and the vehicle 100 subsequently travels, consumed mileage loss data can be extracted from one or more sensors 104.

[0047] At box 310, one or more processing cores can determine an estimate of mileage loss corresponding to power consumption during the vehicle's sleep mode based on sensor data from one or more sensors. Once the vehicle 100 is awake (or active), a new estimate of mileage loss can be calculated based on the latest sensor data acquired from one or more sensors 104.

[0048] At box 312, one or more processing cores can use the mileage loss estimate to determine the estimated remaining range of the vehicle's battery (e.g., battery pack 110). Before entering sleep mode, vehicle 100 has available range, and during sleep mode, one or more sensors 104 can measure power consumption values. Upon exiting sleep mode, vehicle 100 can recalculate the actual remaining range based on the readings from one or more sensors 104.

[0049] At box 314, one or more processing cores may provide an estimate of remaining mileage for display when the vehicle's active driving status is detected. Additionally, at box 314, one or more processing cores may return from the running state to the sleep state when the vehicle is detected to have entered sleep mode (e.g., at box 302).

[0050] Figure 4 A flowchart illustrating an example process for performing self-learning sleep state consumption estimation of a vehicle, according to a specific implementation of the art of this subject, is provided. For illustrative purposes, this document primarily refers to... Figures 1A to 1B The vehicle 100, sensor 104, processing circuit 108 and Figure 2 The process 400 is described using ECU 210. However, the process 400 is not limited to... Figures 1A to 1BThe vehicle 100, sensor 104, and processing circuitry 108, and one or more blocks (or operations) of process 400 may be performed by one or more other components of other suitable mobile devices, equipment, or systems. Also for illustrative purposes, some blocks of process 400 are described herein as occurring sequentially or linearly. However, multiple blocks of process 400 may occur in parallel. Furthermore, the blocks of process 400 do not need to be performed in the order shown, and / or one or more blocks of process 400 need not be performed and / or may be replaced by other operations.

[0051] like Figure 4 As illustrated, at box 402, a predetermined sleep state power consumption rate value can be set. The predetermined sleep state power consumption rate value can be in the range of 10 watt-hours to 30 watt-hours.

[0052] At block 404, in response to a vehicle (e.g., vehicle 100) entering a sleep mode, one or more processing cores of the electronic control unit (e.g., ECU 210) of vehicle 100 can be set to a sleep state. Upon entering sleep mode, ECU 210 can apply a predetermined sleep state power consumption rate. At block 406, in response to vehicle 100 exiting sleep mode, one or more processing cores can be woken from the sleep state to an operating state.

[0053] At box 408, one or more processing cores can determine an estimated remaining range of the vehicle 100's battery (e.g., battery pack 110) based on a predetermined sleep state power consumption rate value and the duration the vehicle 100 is in sleep mode. Upon exiting sleep mode, ECU 210 can calculate the total range loss during sleep mode based on the sleep mode duration, which is calculated as t. wake -t0, where t0 is the time when vehicle 100 enters sleep mode, and t wake This refers to the time when vehicle 100 exits sleep mode and enters wake-up state. For example, when vehicle 100 transitions from sleep mode to wake-up state, ECU 210 can calculate a mileage loss estimate based on the sleep mode duration and a predetermined sleep state power consumption rate to provide the user of vehicle 100 with a remaining mileage estimate.

[0054] At box 410, one or more processing cores may determine the state of charge (SOC) of the vehicle's battery pack 110 in response to the vehicle 100 entering active driving mode after exiting sleep mode. When driving resumes, the ECU 210 may recalibrate the SOC of the high-voltage battery pack 220 by updating the remaining range estimate based on real-time conditions and generating an updated range loss estimate.

[0055] At box 412, one or more processing cores can modify the remaining range estimate to an updated remaining range estimate based on the state of charge of battery pack 110. At box 414, one or more processing cores can determine an updated range loss estimate based on the updated remaining range estimate.

[0056] At box 416, one or more processing cores can update the predetermined sleep state power consumption rate value using the updated mileage loss estimate via a feedback mechanism. The feedback mechanism allows vehicle 100 to track the duration of the sleep mode, calculate the corresponding mileage loss during that duration, and iteratively update the baseline mileage loss estimate. During each critical cycle, vehicle 100 can recalculate its remaining mileage estimate based on newly acquired data, thereby establishing an updated remaining mileage estimate for subsequent intervals. For each critical cycle, the feedback mechanism can be recalibrated to dynamically adjust the mileage loss estimate across variable usage modes.

[0057] Figure 5 An example electronic system 500 is illustrated, which can be used to implement various aspects of this disclosure. The electronic system 500 can be used for providing reference. Figures 1A to 4 The features described and the processes performed with reference to these accompanying drawings may be part of any electronic device and / or include, but are not limited to, vehicles, computers, servers, smartphones, and wearable devices. Electronic system 500 may include various types of computer-readable media and interfaces for various other types of computer-readable media. Electronic system 500 includes persistent storage device 502, system memory 504 (and / or buffers), input device interface 506, output device interface 508, sensor 510, ROM 512, processing unit 514, network interface 516, bus 518, and / or subsets and variations thereof.

[0058] Bus 518 collectively represents the numerous internal devices and / or components of electronic system 500 that are communicatively connected (such as those mentioned above). Figure 5 The bus 518 connects all systems, peripherals, and chipsets of any component in the vehicle 100 under discussion. Bus 518 communicatively connects one or more processing units 514 to ROM 512, system memory 504, and persistent storage device 502. From these various memory units, one or more processing units 514 retrieve instructions to be executed and data to be processed in order to perform the processes disclosed herein. In various embodiments, one or more processing units 514 may be a single processor or a multi-core processor. In one or more embodiments, one or more processing units within processing unit 514 may include processing circuitry 108.

[0059] ROM 512 stores static data and instructions required by one or more processing units 514 and other modules of the electronic system 500. On the other hand, persistent storage device 502 can be a read-write memory device. Persistent storage device 502 can be a non-volatile memory cell that stores instructions and data even when the electronic system 500 is turned off. In one or more embodiments, a mass storage device (such as a magnetic disk or optical disk and its corresponding disk drive) can be used as persistent storage device 502.

[0060] In one or more embodiments, a removable storage device (such as a flash drive and its corresponding solid-state device) may be used as persistent storage device 502. Similar to persistent storage device 502, system memory 504 may be a read-write memory device. However, unlike persistent storage device 502, system memory 504 may be volatile read-write memory, such as RAM. System memory 504 may store any of the instructions and data that one or more processing units 514 may need during operation. In one or more embodiments, the processes disclosed in this subject matter are stored in system memory 504, persistent storage device 502, and / or ROM 512. From these various memory units, one or more processing units 514 retrieve instructions to be executed and data to be processed in order to execute the processes of one or more embodiments.

[0061] Persistent storage device 502 and / or system memory 504 may include one or more machine learning models. Machine learning models (such as those described herein) are typically used to form predictions, solve problems, identify objects in image data, and so on. For example, the machine learning model described herein can be used to predict mileage loss during sleep patterns in vehicles. Various specific implementations of machine learning models are possible. For example, a machine learning model can be a deep learning network, a transformer-based model (or other attention-based model), a multilayer perceptron or other feedforward network, a neural network, etc. In various examples, machine learning models can be more adaptive because they can be improved over time by retraining the machine learning model as additional data becomes available.

[0062] Bus 518 is also connected to input device interface 506 and output device interface 508. Input device interface 506 enables a user to communicate information to electronic system 500 and select commands. Input devices that can be used with input device interface 506 may include, for example, an alphanumeric keypad, a touchscreen, and pointing devices. Output device interface 508 enables electronic system 500 to communicate information to the user. For example, output device interface 508 can provide a display of an image generated by electronic system 500. Output devices that can be used with output device interface 508 may include, for example, printers and display devices such as liquid crystal displays (LCDs), light-emitting diode (LED) displays, organic light-emitting diode (OLED) displays, flexible displays, flat panel displays, solid-state displays, projectors, or any other device for outputting information.

[0063] One or more embodiments may include a device that acts as both an input device and an output device, such as a touchscreen. In these embodiments, the feedback provided to the user can be any form of sensory feedback, such as visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including sound input, voice input, or tactile input.

[0064] Bus 518 is also connected to sensor 510. In one or more embodiments, sensor 510 includes sensor 104. Sensor 510 may include a position sensor that can be used to determine the location of a device based on positioning techniques. For example, the position sensor may provide one or more of GNSS positioning, wireless access point positioning, cellular signal positioning, Bluetooth signal positioning, image recognition positioning, and / or inertial navigation systems (e.g., via motion sensors such as accelerometers and / or gyroscopes). Sensor 510 may be used to detect movement, travel, and orientation of electronic system 500. For example, the sensor may include an accelerometer, a rate gyroscope, and / or other motion-based sensors.

[0065] Bus 518 also couples electronic system 500 to one or more networks and / or one or more network nodes via one or more network interfaces 516. In this way, electronic system 500 can be part of a computer network, such as a local area network or a wide area network. Any or all components of electronic system 500 may be used in conjunction with the disclosure of this subject matter.

[0066] Specific embodiments within the scope of this disclosure may be implemented in part or in whole using tangible computer-readable storage media (or multiple tangible computer-readable storage media of one or more types) that encode one or more instructions. The tangible computer-readable storage media may also be non-transitory in nature.

[0067] Computer-readable storage media can be any storage medium that can be read, written, or otherwise accessed by general-purpose or special-purpose computing devices, including any processing electronics and / or processing circuitry capable of executing instructions. For example, but not limited to, computer-readable media can include any volatile semiconductor memory, such as RAM, DRAM, SRAM, T-RAM, Z-RAM, and TTRAM. Computer-readable media can also include any non-volatile semiconductor memory, such as ROM, PROM, EPROM, EEPROM, NVRAM, flash memory, nvSRAM, FeRAM, FeTRAM, MRAM, PRAM, CBRAM, SONOS, RRAM, NRAM, track memory, FJG, and Millipede memory.

[0068] Furthermore, computer-readable storage media can include any non-semiconductor memory, such as optical disc storage devices, magnetic disk storage devices, magnetic tape, other magnetic storage devices, or any other medium capable of storing one or more instructions. Tangible computer-readable storage media can be directly coupled to a computing device, while in other specific implementations, tangible computer-readable storage media can be indirectly coupled to a computing device, for example, via one or more wired connections, one or more wireless connections, or any combination thereof.

[0069] Instructions can be directly executable or can be used to develop executable instructions. For example, instructions can be implemented as executable or non-executable machine code, or as instructions in a high-level language that can be compiled to produce executable or non-executable machine code. Furthermore, instructions can be implemented as data or may include data. Computer executable instructions can also be organized in any format, including routines, subroutines, programs, data structures, objects, modules, applications, applets, functions, etc. As will be recognized by those skilled in the art, details including, but not limited to, the number, structure, sequence, and organization of instructions can vary significantly without altering the underlying logic, functionality, processing, and output.

[0070] While the above discussion primarily concerns microprocessors or multi-core processors that execute software, one or more specific implementations are executed by one or more integrated circuits such as ASICs or FPGAs. In one or more implementations, such integrated circuits execute instructions stored on the circuit itself.

[0071] Unless otherwise specified, elements mentioned in the singular are not intended to mean one and only one, but rather one or more. For example, a “one” module can refer to one or more modules. Without further constraints, elements beginning with “a,” “an,” “the,” or “the” do not exclude the presence of additional identical elements.

[0072] Titles and subtitles (if any) are used for convenience only and do not limit the invention. The use of the word "exemplary" is intended to mean as an example or illustration. With regard to the scope of use of terms such as "comprising" or "having," such terms are intended to be inclusive in a manner similar to the term "including," as understood when "comprising" is used as a transitional word in the claims. Relational terms such as "first" and "second" may be used to distinguish one entity or action from another without necessarily requiring or implying any actual such relationship or order between these entities or actions.

[0073] Phrases such as one aspect, aspect, on the other hand, some aspects, one or more aspects, one implementation, implementation, another implementation, some implementations, one or more implementations, one implementation scheme, implementation scheme, another implementation scheme, some implementation schemes, one or more implementation schemes, one configuration, configuration, another configuration, some configurations, one or more configurations, subject matter technology, disclosure, this disclosure, other variations thereof, and similar phrases are for convenience and do not imply that the disclosure associated with such phrases is necessary for the subject matter technology, or that such disclosure applies to all configurations of the subject matter technology. The disclosure associated with such phrases may apply to all configurations or one or more configurations. One or more examples of the disclosure associated with such phrases may be provided. Phrases such as one aspect or some aspects may refer to one or more aspects, and vice versa, and this similarly applies to other foregoing phrases.

[0074] The phrase “at least one of” following a series of items, along with the terms “and” or “or” used to separate any of these items, modifies the entire list, not each of its constituent items. The phrase “at least one of” does not require the selection of at least one item; rather, it allows for the inclusion of the meaning of: at least one of any of these items, and / or at least one of any combination of these items, and / or at least one of each of these items. As an example, each of the phrases “at least one of A, B, and C” or “at least one of A, B, or C” refers to only A, only B, or only C; any combination of A, B, and C; and / or at least one of each of A, B, and C.

[0075] It should be understood that the specific order or hierarchy of the disclosed steps, operations, or processes is an example of an exemplary method. Unless otherwise expressly stated, it should be understood that the specific order or hierarchy of steps, operations, or processes can be performed in a different order. Some steps, operations, or processes can be performed simultaneously. The appended method claims (if any) present elements of various steps, operations, or processes in a sample order and are not intended to limit them to the specific order or hierarchy presented. These can be performed sequentially, linearly, in parallel, or in different orders. It should be understood that the described instructions, operations, and systems can generally be integrated together in a single software / hardware product or packaged into multiple software / hardware products.

[0076] In one respect, the term "coupled" can refer to direct coupling. In another respect, the term "coupled" can refer to indirect coupling.

[0077] Terms such as top, bottom, front, back, side, horizontal, and vertical refer to arbitrary frames of reference, not ordinary gravitational frames of reference. Therefore, such terms can extend upward, downward, diagonally, or horizontally within a gravitational frame of reference.

[0078] This disclosure is provided to enable any person skilled in the art to practice the various aspects described herein. In some instances, well-known structures and components are shown in block diagram form to avoid confusion with the concepts of this subject matter. This disclosure provides various examples of this subject matter, and this subject matter is not limited to these examples. Various modifications to these aspects will be apparent to those skilled in the art, and the principles described herein can be applied to other aspects.

[0079] All structural and functional equivalents of the elements of the various aspects described throughout this disclosure are known or will later become apparent to those skilled in the art, and are expressly incorporated herein by reference and intended to be covered by the claims. Furthermore, nothing disclosed herein is intended to serve the public, whether or not such disclosure is expressly stated in the claims. It should not be applied under 35 USC. The provisions of 112(f) shall apply to any claim element unless the element is expressly stated using the phrase “component for…” or, in the case of a method claim, the element is stated using the phrase “step for…”.

[0080] Those skilled in the art will understand that the various exemplary blocks, modules, elements, components, methods, and algorithms described herein can be implemented as hardware, electronic hardware, computer software, or combinations thereof. To illustrate this hardware-software interchangeability, various exemplary blocks, modules, elements, components, methods, and algorithms have been described above in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the system as a whole. Those skilled in the art can implement the described functionality in different ways for each specific application. Various components and blocks can be arranged differently (e.g., in different orders or divided in different ways), all without departing from the scope of the subject matter.

[0081] The title of the invention, background art, description of the drawings, abstract of the specification, and drawings are hereby incorporated in this disclosure and are provided as illustrative examples rather than as limiting descriptions. It is understood at the time of filing this document that they are not intended to limit the scope or meaning of the claims. Furthermore, in the detailed description, it will be apparent that, for the purpose of simplifying this disclosure, the description provides illustrative examples, and various features are grouped together in various specific embodiments. The methods of this disclosure should not be construed as reflecting an intention to require more features than are expressly stated in each claim. Rather, as reflected in the claims, the subject matter of the invention does not lie in all features of a single disclosed configuration or operation. The claims are hereby incorporated in the detailed description, wherein each claim is independently claimed as a separate subject matter.

[0082] The claims are not intended to be limited to the aspects described herein, but should be given the full scope consistent with the language of the claims and to cover all legal equivalents. Nevertheless, none of the claims is intended to include subject matter that fails to meet the requirements of applicable patent law, nor should it be interpreted in this way.

Claims

1. An electronic control unit for a vehicle, the electronic control unit comprising: One or more processing cores; and The one or more processing cores are configured as follows: The vehicle enters a sleep state in response to the vehicle entering sleep mode. In response to the vehicle exiting the sleep mode and entering the operating state from the sleep state; and The remaining mileage estimate of the vehicle is determined based on the mileage loss estimate corresponding to the estimated power consumption during the sleep mode.

2. The electronic control unit according to claim 1, wherein, The one or more processing cores are configured to: Sensor data indicating power consumption measurements during the sleep mode of the vehicle are obtained from one or more sensors of the vehicle; as well as The estimated mileage loss corresponding to the power consumption measurement is determined based on the sensor data.

3. The electronic control unit according to claim 2, wherein, The one or more processing cores are configured to provide the remaining mileage estimate of the vehicle to the user's device in response to determining the mileage loss estimate.

4. The electronic control unit according to claim 2, wherein, The one or more sensors include a power sensor.

5. The electronic control unit according to claim 1, wherein, The one or more processing cores are configured to provide the remaining mileage estimate for display when the active driving status of the vehicle is detected.

6. The electronic control unit according to claim 1, wherein, The one or more processing cores are further configured to: A first coulomb count measurement is obtained at a first location between the high-voltage battery pack and the vehicle's power load using a first sensor among a plurality of sensors; and A second coulomb count measurement is obtained at a second location between the low-voltage battery pack and the vehicle's power load using the second of the plurality of sensors. The estimated mileage loss is determined using the first coulomb count measurement and the second coulomb count measurement.

7. The electronic control unit according to claim 6, wherein, The one or more processing cores configured to obtain the first coulomb count measurement are also configured to obtain sensor data from the first sensor, which is located at the output of a secondary power converter connected to the output of the high-voltage battery pack.

8. The electronic control unit according to claim 6, wherein, The one or more processing cores configured to obtain the first coulomb count measurement are also configured to obtain sensor data from the first sensor, which is located at the input of a secondary power converter connected to the output of the high-voltage battery pack.

9. The electronic control unit according to claim 1, wherein, The remaining mileage estimate is determined based on a predetermined sleep state power consumption rate and the duration of the sleep mode, wherein the one or more processing cores are configured to: The state of charge of the vehicle's battery is determined in response to the vehicle entering an active driving state after exiting the sleep mode; The remaining range estimate is modified to an updated remaining range estimate based on the battery's state of charge; and The updated remaining mileage estimate is provided for display.

10. The electronic control unit according to claim 9, wherein, The one or more processing cores are configured to determine an updated mileage loss estimate based on the updated remaining mileage estimate.

11. The electronic control unit according to claim 10, wherein, The one or more processing cores are configured to update the predetermined sleep state power consumption rate using the updated mileage loss estimate.

12. The electronic control unit according to claim 11, wherein, After each update of the mileage loss estimate, the predetermined sleep state power consumption rate is updated iteratively.

13. A method, the method comprising: In response to the vehicle entering a sleep mode, one or more processing cores of the vehicle's electronic control unit are set to a sleep state; One or more sensors are used to perform coulomb counting to monitor the loss of state of charge during the vehicle's sleep mode; In response to the vehicle exiting the sleep mode, the one or more processing cores are awakened from the sleep state and put into operation. as well as The one or more processing cores determine, based on the coulomb count, an estimated mileage loss corresponding to the state-of-charge loss during the vehicle's sleep mode.

14. The method according to claim 13, further comprising: Sensor data indicating power consumption measurements during the sleep mode of the vehicle are obtained from the one or more sensors, wherein the mileage loss estimate corresponds to the power consumption measurement.

15. The method according to claim 13, further comprising: The remaining mileage estimate is determined by one or more processing cores based on the mileage loss estimate; as well as The remaining mileage estimate is provided for display when the active driving status of the vehicle is detected.

16. The method according to claim 13, wherein, Performing the coulomb counting includes: A first coulomb count measurement is obtained at a first location between the high-voltage battery pack and the vehicle's power load using a first sensor among a plurality of sensors; and A second coulomb count measurement is obtained at a second location between the low-voltage battery pack and the vehicle's power load using the second of the plurality of sensors. The estimated mileage loss is determined using the first coulomb count measurement and the second coulomb count measurement.

17. The method according to claim 16, wherein, Obtaining the first coulomb count measurement includes obtaining sensor data from the first sensor, which is located at the output of a secondary power converter connected to the output of the high-voltage battery pack.

18. The method according to claim 16, wherein, Obtaining the first coulomb count measurement includes obtaining sensor data from the first sensor, which is located at the input of a secondary power converter connected to the output of the high-voltage battery pack.

19. An electric vehicle, the electric vehicle comprising: Electronic control unit, the electronic control unit comprising: One or more processing cores; and The one or more processing cores are configured as follows: The electric vehicle enters a sleep state in response to entering a sleep mode. In response to the electric vehicle exiting the sleep mode, it enters the operating state from the sleep state; The remaining mileage estimate is determined based on the predetermined sleep state power consumption rate and the duration of the sleep pattern; The state of charge of the battery of the electric vehicle is determined in response to the electric vehicle entering an active driving state after exiting the sleep mode; The remaining range estimate is modified to an updated remaining range estimate based on the battery's state of charge; and The updated remaining mileage estimate is provided for display.

20. The electric vehicle according to claim 19, wherein, The one or more processing cores are further configured to: The mileage loss estimate is modified to the updated mileage loss estimate based on the updated remaining mileage estimate; and The predetermined sleep state power consumption rate is updated using the updated mileage loss estimate. Specifically, after each update of the estimated mileage loss, the predetermined sleep state power consumption rate is iteratively updated.

Images