Soc and soh cooperative estimation system and method for electric vehicles

Abstract

The present invention relates to a SOC and SOH cooperative estimation system and method for an electric vehicle. A system for performing an electrical estimation process for an electric vehicle using a controller is provided. The controller estimates an internal state of an energy storage power source of the electric vehicle. The internal state represents a state of charge (SOC) and / or a state of health (SOH) of the energy storage power source. The controller estimates a SOC value and / or a SOH value of the energy storage power source based on at least one of: a present current level, a present voltage level, a present temperature, and time-based information. The controller further estimates a bounded SOC value based on the SOC value, a first upper bound, and a first lower bound, and / or a bounded SOH value based on the SOH value, a second upper bound, and a second lower bound. The controller then controls an electrification process of the electric vehicle based on the bounded SOC value and / or the bounded SOH value.

Description

[0001] Cross-reference to related applications

[0002] This application claims the benefit of U.S. Provisional Application No. 63 / 036,198, filed on June 8, 2020. Technical Field

[0003] This disclosure generally relates to methods and systems for diagnosing power management systems used in electric vehicles, and more particularly to estimating the internal state of the energy storage power source of the power management system. Background Technology

[0004] Electric management systems can be used for pure electric vehicles (EVs) and / or hybrid electric vehicles (HEVs) with an electric motor and an internal combustion engine (ICE). As used herein, the term "electric vehicle" refers to hybrid electric vehicles and / or pure electric vehicles that provide an alternative to conventional fuel engine systems to supplement or completely replace engine systems such as ICEs. In one example, the electric vehicle is a range-extended electric vehicle (EREV). In an EREV, primary electric drive is achieved using a battery or associated rechargeable energy storage system (RESS) that acts as a direct current (DC) voltage source for an electric motor, generator, or transmission, which in turn can be used to provide the energy needed to rotate one or more of the vehicle's wheels. When the RSS is depleted, backup power can be drawn from the ICE to provide auxiliary onboard power generation.

[0005] During operation, the power management system estimates the internal state of energy storage sources (such as batteries) in electric vehicles to maintain the vehicle at an appropriate level within its operating range. Typically, the internal state of the energy storage source is its state of charge (SOC) and / or state of health (SOH). For example, SOC information can be used as a fuel gauge for a battery, and SOH information can be used as an indication of the battery's current total capacity and / or internal resistance. In another example, SOC information represents the available energy or power remaining in the energy storage source, and SOH information represents the degree of degradation of the energy storage source.

[0006] Since SOC and SOH information cannot be directly measured, estimation algorithms are used to estimate the SOC and SOH information of energy storage power sources. Existing estimation algorithms, known as Kalman filters, can be used to estimate SOC and SOH information. Exemplary Kalman filters include: dual nonlinear Kalman filters (DNKF), extended Kalman filters, unscented Kalman filters, capacitive Kalman filters, etc. Kalman filters estimate the SOC and SOH information of energy storage power sources by calculating the estimated SOC and SOH values ​​and the corresponding error bounds.

[0007] However, this dual estimation method of the Kalman filter is prone to bias after a predetermined period. For example, for a newly assembled and validated battery pack, the SOC and SOH estimates may initially be accurate, but after a certain period, biases in the SOC and SOH estimates may occur due to increased sensor bias and noise, hardware and / or software failures in the power management system, or battery degradation caused by aging components in the electric vehicle. Furthermore, large biases in SOC and SOH estimates can cause unnecessary damage to the power management system and other components of the electric vehicle.

[0008] Thus, it is desirable to reduce or eliminate the estimation bias of SOC and SOH and limit the corresponding error bounds. Therefore, there is an opportunity to develop enhanced power management systems and methods that can more efficiently estimate the SOC and SOH information of energy storage sources. Summary of the Invention

[0009] In one embodiment of this disclosure, a controller is provided that performs power estimation processing for an electric vehicle. The controller includes a processor and a memory. The memory includes instructions that, when executed by the processor, cause the controller to: perform power estimation processing by estimating the internal state of an energy storage power source of the electric vehicle using the processor. The internal state represents at least one of the following: the state of charge (SOC) and state of health (SOH) of the energy storage power source. The processor further causes the controller to estimate at least one of the SOC and SOH values ​​of the energy storage power source based on at least one of the following: a current level associated with the energy storage power source, a current voltage level, and a current temperature, and time-based information. The processor further causes the controller to: calculate a first upper bound and a first lower bound associated with the SOC value, and estimate a bounded SOC value of the energy storage power source based on the SOC value, the first upper bound, and the first lower bound; and calculate a second upper bound and a second lower bound associated with the SOH value, and estimate a bounded SOH value of the energy storage power source based on the SOH value, the second upper bound, and the second lower bound. The controller then controls the electrification process of the electric vehicle based on at least one of the bounded SOC value and the bounded SOH value.

[0010] In one aspect, the processor enables the controller to calculate the amp-hour SOC and voltage SOC. The amp-hour SOC is based on the current current level and current temperature associated with the energy storage source; and the voltage SOC is based on the current voltage level and current temperature associated with the energy storage source. The processor then also enables the controller to calculate a first upper bound and a first lower bound associated with the SOC values ​​based on the amp-hour SOC and voltage SOC. The maximum value of the amp-hour SOC and voltage SOC can be used for the first upper bound, while the minimum value can be used for the first lower bound. The processor also enables the controller to filter the voltage SOC to remove noise.

[0011] On the other hand, the processor enables the controller to calculate the full-cycle SOH and the partial-cycle SOH. The full-cycle SOH is based on the start and end times associated with a full charge cycle of the energy storage power source, and the partial-cycle SOH is based on the start and end times associated with a partial charge cycle of the energy storage power source. The processor then also enables the controller to calculate a second upper bound and a second lower bound associated with the SOH values ​​based on the full-cycle SOH and the partial-cycle SOH. The maximum value of the full-cycle SOH and the partial-cycle SOH can be used as the second upper bound, while the minimum value of the full-cycle SOH and the partial-cycle SOH can be used as the second lower bound.

[0012] In another aspect, the time-based information includes one or more historical estimates of the SOC and SOH values. In yet another aspect, the processor also enables the controller to estimate bounded SOC and bounded SOH values ​​based on whether a predetermined time period has elapsed. In yet another aspect, the controller controls the electrification process by at least one of the following: modifying the cooling of the energy storage power source, modifying the charge / discharge limits of the energy storage power source, reducing the number of charge / discharge cycles of the energy storage power source, and modifying the minimum SOC threshold. The controller may include dual nonlinear Kalman filters.

[0013] In another embodiment of this disclosure, a method for performing power estimation processing of an electric vehicle using a controller is provided. The method includes the steps of: performing the power estimation processing by estimating the internal state of an energy storage power source of the electric vehicle, wherein the internal state represents at least one of the following: the state of charge (SOC) and state of health (SOH) of the energy storage power source. The method further includes the steps of: estimating at least one of the SOC and SOH values ​​of the energy storage power source based on at least one of the following: a current level associated with the energy storage power source, a current voltage level, a current temperature, and time-based information. The method further includes the steps of: calculating a first upper bound and a first lower bound associated with the SOC value; estimating a bounded SOC value of the energy storage power source based on the SOC value, the first upper bound, and the first lower bound; calculating a second upper bound and a second lower bound associated with the SOH value; and estimating a bounded SOH value of the energy storage power source based on the SOH value, the second upper bound, and the second lower bound. Furthermore, the method includes the step of: controlling the electrification process of the electric vehicle based on at least one of the bounded SOC and bounded SOH values.

[0014] In one aspect, the method includes the steps of: calculating the ampere-hour SOC and the voltage SOC. The ampere-hour SOC is based on the current current level and current temperature associated with the energy storage power source; and the voltage SOC is based on the current voltage level and current temperature associated with the energy storage power source. The method further includes the steps of: calculating a first upper bound and a first lower bound associated with the SOC values ​​based on the ampere-hour SOC and the voltage SOC. The maximum value of the ampere-hour SOC and the voltage SOC can be used as the first upper bound, while the minimum value of the ampere-hour SOC and the voltage SOC can be used as the first lower bound. The method further includes the step of: filtering the voltage SOC to remove noise.

[0015] In another aspect, the method includes the steps of: calculating the full-cycle SOH and the partial-cycle SOH. The full-cycle SOH is based on the start and end times associated with a full charge cycle of the energy storage power source, and the partial-cycle SOH is based on the start and end times associated with a partial charge cycle of the energy storage power source. The method further includes the steps of: calculating a second upper bound and a second lower bound associated with the SOH values ​​based on the full-cycle SOH and the partial-cycle SOH. The maximum value of the full-cycle SOH and the partial-cycle SOH can be used as the second upper bound, while the minimum value of the full-cycle SOH and the partial-cycle SOH can be used as the second lower bound.

[0016] In another aspect, the time-based information includes one or more historical estimates of the SOC and SOH values. In yet another aspect, the method includes the step of estimating bounded SOC and bounded SOH values ​​based on whether a predetermined time period has elapsed. In yet another aspect, the method includes the step of controlling the electrification process by at least one of the following: modifying the cooling of the energy storage power source, modifying the charge / discharge limit of the energy storage power source, reducing the number of charge / discharge cycles of the energy storage power source, and modifying the minimum SOC threshold. The method for performing the power estimation process can be implemented using a dual nonlinear Kalman filter.

[0017] Although several embodiments have been disclosed, other embodiments of the disclosed subject matter will become apparent to those skilled in the art from the following detailed description of exemplary embodiments shown and described. Therefore, the drawings and detailed description are to be regarded as illustrative rather than limiting in nature. Attached Figure Description

[0018] The above and other features and objects of this disclosure, as well as the ways in which they are obtained, will become clearer and the disclosure itself will be better understood by referring to the following description of embodiments of this disclosure in conjunction with the accompanying drawings, in which:

[0019] Figure 1 This is a schematic diagram of an engine and electric motor system of an electric vehicle characterized by a power estimator according to an embodiment of the present disclosure.

[0020] Figure 2A and Figure 2B An exemplary configuration of an energy storage power source used in an electric vehicle according to an embodiment of the present disclosure is illustrated;

[0021] Figure 3 It is based on the embodiments of this disclosure. Figure 1 A schematic diagram of a power estimator;

[0022] Figure 4This illustrates the use of embodiments according to this disclosure. Figure 1 A flowchart illustrating the exemplary SOC bounding process of a power estimator;

[0023] Figure 5 This illustrates the use of embodiments according to this disclosure. Figure 1 A flowchart of another exemplary SOC delimitation process for a power estimator;

[0024] Figure 6 This illustrates the use of embodiments according to this disclosure. Figure 1 A flowchart illustrating the exemplary SOH delimitation process of a power estimator;

[0025] Figure 7 This illustrates the use of embodiments according to this disclosure. Figure 1 A flowchart of another exemplary SOH delimitation process for a power estimator;

[0026] Figure 8 It is based on the embodiments of this disclosure. Figure 1 Another schematic diagram of a power estimator;

[0027] Figure 9 It is based on the embodiments of this disclosure. Figure 1 A schematic diagram of the SOC delimiting unit of the power estimator;

[0028] Figure 10A This is a flowchart illustrating an exemplary method for estimating SOC using upper and lower bounds of a battery according to an embodiment of the present disclosure;

[0029] Figure 10B This is a flowchart illustrating an exemplary method for estimating SOC using the upper and lower bounds of a battery pack having multiple battery cells or cell groups, according to an embodiment of the present disclosure.

[0030] Figure 11A This is a flowchart illustrating an exemplary method for estimating the bounded SOC of a generated battery (e.g., a battery cell) according to embodiments of the present disclosure;

[0031] Figure 11B This is a flowchart illustrating an exemplary method for generating a bounded SOC estimate of a battery pack having multiple battery cells or cell packs according to embodiments of the present disclosure.

[0032] Figure 12A This is a schematic diagram of a battery management device / chip used according to an embodiment of the present disclosure;

[0033] Figure 12B This is another schematic diagram of a battery management device / chip used according to an embodiment of the present disclosure;

[0034] Figure 13 This is a schematic diagram of a battery management system / device / chip according to an embodiment of the present disclosure;

[0035] Figure 14 Examples of SOC-OCV models and / or functions according to embodiments of this disclosure are illustrated; and

[0036] Figure 15A and Figure 15B An example of bounded SOC estimation according to an embodiment of the present disclosure is illustrated.

[0037] Throughout these views, corresponding reference numerals indicate corresponding parts. Although the accompanying drawings illustrate embodiments of the present disclosure, the drawings are not necessarily drawn to scale, and certain features may be exaggerated in order to better illustrate and explain the present disclosure. The examples set forth herein illustrate embodiments of the present disclosure in one form, and such examples should not be construed as limiting the scope of the present disclosure in any way. Detailed Implementation

[0038] The embodiments disclosed below are not intended to be exclusive or to limit this disclosure to the precise forms disclosed in the following detailed description. Rather, these embodiments have been chosen and described to enable others skilled in the art to utilize the teachings of these embodiments. Those skilled in the art will recognize that the provided embodiments can be implemented in hardware, software, firmware, and / or combinations thereof. The programming code according to the embodiments can be implemented in any feasible programming language, such as C, C++, HTML, XTML, JAVA, or any other feasible high-level programming language, or a combination of high-level and low-level programming languages.

[0039] Now refer to Figure 1 The illustration shows a hybrid power system 100 for an electric vehicle 102. The electric vehicle 102 can be plugged into a power outlet to be connected to a power grid system (not shown) to perform electrification processing of the electric vehicle 102. In various embodiments, electrification processing can refer to various operations related to power generation and power distribution and management associated with the electric vehicle 102. Exemplary electrification processing includes: modifying battery cooling, modifying charge and / or discharge limits, reducing the number of charge and / or discharge cycles, modifying minimum state of charge thresholds, etc. The electric vehicle 102 can be a commercial vehicle, such as a bus that can be connected to a power grid system.

[0040] In one implementation, the power grid system may be a power grid system implemented in a specific commercial facility, such as a bus stop. In another implementation, the power grid system may be a power grid implemented in conjunction with multiple power plants (such as power plants and other power generation facilities). Figure 1Although the electric vehicle 102 is described as a parallel hybrid system, this disclosure can also be applied to range-extended vehicles or series hybrid vehicles to suit different applications. Thus, the electric vehicle 102 can be any electric vehicle (e.g., a hybrid vehicle, a pure electric vehicle, and / or a range-extended vehicle) with an electric propulsion system.

[0041] Although an electric vehicle 102 with an internal combustion engine (ICE) 104 is shown, this disclosure can be applied to purely electric vehicles powered solely by batteries without the ICE 104. The ICE 104 can be powered by any type of fuel, such as gasoline, diesel, natural gas, liquefied petroleum gas, biofuels, etc. In this embodiment, the hybrid power system 100 may include the ICE 104 having a crankshaft 106 and a crankshaft sprocket (not shown) coupled to the crankshaft. The ICE 104 is not particularly limited and can be on-board (e.g., a range-extended vehicle) or off-board (e.g., a generator set located at a bus stop).

[0042] The hybrid power system 100 may also include an electric motor 108 mechanically connected to the crankshaft sprocket. For example, the electric motor 108 may be a traction motor used to propel the electric vehicle 102. In various embodiments, the electric motor 108 may be connected via the crankshaft 106 to a speed sensor 110, a torque sensor 112, an ICE 104, a clutch or torque converter 114, and a transmission 116. In various embodiments, the speed sensor 110 and the electric motor 108 are mechanically connected to the crankshaft 106. Furthermore, the electric motor 108 is not particularly limited and may, for example, be a motor / generator, a synchronous motor, or an induction motor.

[0043] In one embodiment, the hybrid power system 100 also includes a controller 118 that is in electrical communication with a speed sensor 110 and a torque sensor 112. The controller 118 may include a non-transitory memory 120 with instructions executed by a processor 122, which causes the processor 122 to determine the speed or torque value of the electric motor 108. The electric motor 108 receives power from a rechargeable energy storage power source 124, such as a battery pack or battery assembly, and the energy storage power source 124 may provide data representing state of charge (SOC) and / or state of health (SOH) information to the controller 118. The processor 122, the non-transitory memory 120, and the controller 118 are not particularly limited and may, for example, be physically separate. Additionally, a vehicle monitoring unit 128 may be included in the controller 118 or may be a separate, independent unit adapted to different applications.

[0044] In some implementations, controller 118 may form part of a processing subsystem that includes one or more computing devices having memory, processing, and communication hardware. Controller 118 may be a single device or a distributed device, and the functions of controller 118 may be executed by hardware and / or as computer instructions on a non-transitory computer-readable storage medium (such as non-transitory memory 120).

[0045] In some embodiments, controller 118 includes one or more interpreters, determiners, evaluators, regulators, and / or processors 122 that functionally perform the operations of controller 118. The description including interpreters, determiners, evaluators, regulators, and / or processors emphasizes the structural independence of certain aspects of controller 118 and exemplifies a set of operations and responsibilities of controller 118. Other groups performing similar overall operations are understood to be within the scope of this disclosure. Interpreters, determiners, evaluators, regulators, and processors may implement computer instructions in hardware and / or as non-transitory computer-readable storage media and may be distributed across various hardware or computer-based components.

[0046] Examples and non-limiting implementation elements that functionally perform the operation of controller 118 include: sensors that provide any value as defined herein (such as speed sensor 110 and torque sensor 112), sensors that provide any value as a precursor to the value defined herein, data link and / or network hardware (including communication chips, oscillating crystals, communication links, cables, twisted pairs, coaxial cables, shielded cables, transmitters, receivers and / or transceivers), logic circuits, hardwired logic circuits, reconfigurable logic circuits in a specific non-transient state configured according to the module specification, any actuators (including at least electric actuators, hydraulic actuators or pneumatic actuators), solenoids, operational amplifiers, analog control elements (springs, filters, integrators, adders, dividers, gain elements) and / or digital control elements.

[0047] Some of the operations described herein include operations for interpreting and / or determining one or more parameters or data structures. The interpretation or determination utilized herein includes receiving values ​​by any method known in the art, including at least: receiving values ​​from data link or network communication; receiving electronic signals indicating values ​​(e.g., voltage, frequency, current, or PWM signals); receiving computer-generated parameters indicating values; reading values ​​from memory locations on non-transitory computer-readable storage media; receiving values ​​as runtime parameters by any means known in the art; and / or by receiving values ​​from which parameters can be calculated and interpreted; and / or by referencing default values ​​interpreted as parameter values.

[0048] In the illustrated embodiment, the controller 118 includes a power estimator 126 configured to estimate the internal state of the energy storage power source 124 of the electric vehicle 102. The internal state of the energy storage power source 124 represents its State of Charge (SOC) and / or State of Hypothesis (SOH). The power estimator 126 can be configured to set at least one of an upper bound and a lower bound for estimating the SOC and / or SOH of the energy storage power source 124. During the power estimation processing of the SOC and / or SOH of the energy storage power source 124, the power estimator 126 automatically applies at least one of the upper and lower bounds to filter out or cut off high or low values ​​associated with the SOC and / or SOH of the energy storage power source 124, thereby preventing any potentially large estimation biases that could cause unnecessary damage to the electric vehicle 102. The power estimator 126 can perform power estimation processing on the SOC and SOH information of the energy storage power source 124 using independent and separate bounding algorithms. The following section discusses... Figures 3 to 9 The relevant paragraphs provide a detailed description of the bounding algorithm.

[0049] In one embodiment, the power estimator 126 is configured to measure the current level and / or current voltage level of the energy storage power source 124 using the vehicle monitoring unit 128. For example, the power estimator 126 is configured to automatically communicate with the vehicle monitoring unit 128 to determine the current current level and current voltage level of the energy storage power source 124 of the electric vehicle 102. In one embodiment, the vehicle monitoring unit 128 may be a remote communication system associated with the electric vehicle 102. In another embodiment, the vehicle monitoring unit 128 is configured to monitor one or more vehicle characteristics associated with the electric vehicle 102.

[0050] For example, vehicle characteristics may include: information about one or more components of the electric vehicle 102 (such as ICE 104 or electric motor 108), navigation information based on a navigation system (e.g., Global Positioning System (GPS)), thermal information (e.g., temperature) of one or more components of the electric vehicle 102 (such as the current temperature of electric motor 108), and environmental information (e.g., time, weather, road or load conditions, etc.) related to the specific route of the electric vehicle 102's mission. Other exemplary components of the electric vehicle 102 may include electrification, powertrain, and various vehicle components such as energy storage power source 124 (e.g., battery), electric motor 108, ICE 104, charging system, cooling system, separate generator (not shown), powertrain or powertrain (e.g., crankshaft), driveshaft assembly (not shown), etc.

[0051] In one implementation, the power estimator 126 automatically communicates with the vehicle monitoring unit 128 to obtain thermal information of at least one electrical device of the electric vehicle 102 (such as the energy storage power supply 124), which is provided to the vehicle monitoring unit 128 by the temperature sensor 132. For example, the power estimator 126 communicates with the vehicle monitoring unit 128 to detect the temperature of the battery pack. In another example, the power estimator 126 communicates with the vehicle monitoring unit 128 to detect the temperature of the electric motor 108. Other suitable uses of the temperature sensor 132 are also contemplated for this application.

[0052] In one embodiment, the power estimator 126 interfaces with a network 130, such as a wireless communication facility (e.g., a Wi-Fi access point). In another embodiment, network 130 may be an onboard controller area network (e.g., a CAN bus) of the electric vehicle 102. In yet another embodiment, network 130 may be a cloud computing network outside the electric vehicle 102. Other similar networks known in the art are also conceivable. For example, network 130 may be a cloud network or vehicle-to-grid (V2G) network between the electric vehicle 102 and the power grid system, or a vehicle-to-vehicle (V2V) network between the electric vehicles 102. In embodiments, any type of computer network having a collection of computers, servers, and other hardware interconnected via communication channels is conceivable, such as the Internet, intranets, Ethernet, LANs, cloud networks, etc.

[0053] Now refer to Figure 2A and Figure 2B An exemplary configuration of the energy storage power supply 124 is shown. Figure 2A In this embodiment, the energy storage power supply 124 includes a single battery. In one implementation, the vehicle monitoring unit 128 can measure the current voltage level V and the current current level I of the energy storage power supply 124, and send the current voltage level V and the current current level I to the power estimator 126 for further processing as needed. Figure 2B In this embodiment, the energy storage power supply 124 includes a battery pack having multiple battery cells 124a, 124b, ..., 124n. In this example, the battery pack includes: a first array (e.g., i=1) having battery cells 124a, a second array (e.g., i=2) having battery cells 124b, and an nth array (e.g., i=n) having battery cells 124n. In one embodiment, the vehicle monitoring unit 128 can measure the current voltage levels V1, V2, ... V of each array of the energy storage power supply 124. n And the current level I, and the current voltage levels V1, V2, ... V nThe current level I is sent to the power estimator 126 for further processing as needed. Other suitable layouts can also be envisioned to suit different applications.

[0054] Reference Figures 3 to 9 Various implementations of the power estimator and SOC delimitation process are described. (See reference...) Figures 10A to 15B Additional embodiments of the method for estimating the SOC boundary are described. The foregoing embodiments can be found in reference to... Figure 1 , Figure 2A as well as Figure 2B The following describes the implementation in the vehicle.

[0055] Now refer to Figure 3 An exemplary schematic diagram of a power estimator 126 is shown. In the illustrated embodiment, the power estimator 126 includes a SOC / SOH estimator 200 and a boundary estimator 202. The SOC / SOH estimator 200 is configured to estimate the SOC value SOC based on the current current level I and / or the current voltage level V of the energy storage power source 124. Est (For example, 60%). For example, the SOC / SOH estimator 200 could be a DNKF. Furthermore, the SOC / SOH estimator 200 is configured to estimate the SOH value SOH based on the current current level I and / or current voltage level V of the energy storage power supply 124. Est (For example, 80%).

[0056] Despite Figure 3 The power estimator 126 is shown as integrating the SOC / SOH estimator 200 and the boundary estimator 202; however, in some embodiments, the SOC / SOH estimator 200 and the boundary estimator 202 can be installed separately or independently in any suitable system associated with the electric vehicle 102. Return to Figure 1 In one embodiment, the battery management system (BMS) 134 can be installed separately from the controller 118. The BMS 134 may include another non-transitory memory 136 and a processor 138. In this example, the BMS 134 may include a boundary estimator 202 along with other control algorithms in the processor 138. In another example, the BMS 134 may include a SOC / SOH estimator 200 in the processor 138 to suit different applications. In various embodiments, the BMS 134 can perform power estimation processing on the SOC and SOH information of the energy storage power source 124. Furthermore, the BMS 134 can provide an estimate of the available power of the energy storage power source 124.

[0057] Return to Figure 3In one embodiment, the SOC / SOH estimator 200 includes: an SOC estimator 204, an SOC regulator 206, an SOH estimator 208, and an SOH regulator 210. The SOC estimator 204 is configured to estimate the SOC based on the current current level I of the energy storage power supply 124, a general embedded battery model, and / or time-based information. Est For example, it can be based on the SOC measured for electric vehicle 102. Est Estimating SOC using time-based information from one or more historical inputs Est The SOC regulator 206 is configured to receive the SOC from the SOC estimator 204. Est And adjust the SOC based on the current voltage level V of the energy storage power supply 124. Est For example, the State of Charge (SOC) can be corrected or tuned based on the current voltage level V of the energy storage power supply 124 as measured by the vehicle monitoring unit 128. Est .

[0058] The SOH estimator 208 is configured to estimate SOH based on a general embedded battery model and time-based information. Est For example, the SOH measured for electric vehicle 102 can be used as a basis. Est Historical inputs to estimate SOH Est The SOH regulator 210 is configured to receive SOH from the SOH estimator 208. Est And adjust SOH based on the current voltage level V of the energy storage power supply 124. Est For example, the SOH can be corrected or tuned based on the current voltage level V of the energy storage power supply 124 as measured by the vehicle monitoring unit 128. Est In some embodiments, other suitable parameters (e.g., battery resistance, impedance, or conductance) that change as the energy storage power supply 124 ages may also be used to estimate SOH. Est .

[0059] In one implementation, the boundary estimator 202 is configured to estimate the bounded SOC value SOC. Bounded and / or bounded SOH value SOH Bounded For example, boundary estimator 202 calculates SOC. Bounded This enables the SOC Bounded Set in SOC Est The upper bound of SOC Est Between the lower bounds. In another example, boundary estimator 202 calculates SOH. Bounded This makes SOH Bounded Set at SOH Est The upper bound of SOH Est Between the lower and upper bounds.

[0060] In the illustrated embodiment, the boundary estimator 202 includes a SOC bounding unit 212 and a SOH bounding unit 214. In one embodiment, the SOC bounding unit 212 is configured to use an SOC value based on ampere-hours (Ah). Ah and voltage-based SOC value V Based on this, calculate SOC Bounded The upper and lower bounds. In one embodiment, the SOH delimiting unit 214 is configured to define the upper and lower bounds based on the SOH value of a fully cycled system. F and SOH value based on partial cycle P Based on this, calculate SOH Bounded The upper and lower bounds. For example, when a full charge cycle is available for energy storage power supply 124, the SOH can be calculated. F Furthermore, when a portion of the charging cycle is available for energy storage power supply 124, the SOH can be calculated. P .

[0061] In some implementations, the boundary estimator 202 can determine the self-updating SOC at block 216. Bounded Whether a predetermined period has elapsed (e.g., a macroscopic timeframe of approximately 1 to 2 months) has been determined. The predetermined period can be adjusted as needed. When the boundary estimator 202 determines the updated SOC based on the predetermined period... Bounded The time, the SOC delimiting unit 212 outputs the SOC. Bounded This is used to power other systems of the electric vehicle 102 for subsequent processing.

[0062] For example, SOC Bounded The data is sent to the SOH regulator 210 or display device for review by a technician. In another example, when the boundary estimator 202 determines that the SOC update has not been completed based on a predetermined time period... Bounded The time can be used to upgrade the SOC. Bounded The feedback value is sent to the SOC estimator 204. Although for SOC... Bounded Box 216 is shown, but it can be used for SOH. Bounded Implement box 216 to suit this application. Furthermore, SOH can be... Bounded The feedback value is sent as feedback to at least one of the SOC estimator 204 and the SOH estimator 208 or to a display device for subsequent viewing.

[0063] Now refer to Figure 4 An exemplary SOC delimitation process is shown according to an embodiment of the subject matter disclosed herein. As disclosed herein, the hybrid power system 100 is not particularly limited and can perform any of the methods described within the scope of this disclosure. Figure 4 The image illustrates a method 400 for performing SOC delimitation processing using SOC delimitation unit 212. More specifically, when a single battery is used in energy storage power supply 124 (e.g., Figure 2A When the predetermined time period (e.g., a microscopic time of approximately 1 to 2 seconds) is reached, the SOC delimiting unit 212 executes... Figure 4 One or more steps are shown.

[0064] At box 402, the SOC estimator 204 estimates the SOC based on the current current level I of the energy storage power supply 124 and time-based information. Est For example, a general SOC estimator (such as DNKF) can be used to estimate SOC. Est .

[0065] At box 404, SOC delimiting unit 212 calculates SOC based on the current current level I and current temperature T of energy storage power supply 124. Ah In one example, the Coulomb counting technique can be used to calculate the State of Charge (SOC). Ah An exemplary SOC can be defined as shown in expression (1) below. Ah .

[0066]

[0067] Where I(t) is the input current during time t, SOC0 represents the initial SOC at the initial time t0, and Capacity represents the current total capacity generated by the energy storage power supply 124.

[0068] At box 406, SOC delimiting unit 212 calculates SOC based on the current voltage level V and current temperature T of energy storage power supply 124. V An exemplary SOC can be defined as shown in expression (2) below. V In one implementation, the SOC can be... V Filtering is performed to remove noise caused by dynamic voltage response.

[0069] SOC V (t)=[OCV -1 (V(t)+I(t·R0(T))] filtered (2)

[0070] Where OCV is the battery open-circuit voltage, a function of SOC; R0 represents the battery internal resistance, which depends on temperature T. In one example, a single-pole low-pass filter can be used to perform filtering. In one implementation, a single-pole low-pass filter can be used to remove SOC. VThe amplified noise in the image may be due to measurements from the OCV approximation and imperfect fidelity. Other suitable filters can also be envisioned for different applications. In another example, a linear-phase low-pass filter could also be used to remove amplified noise.

[0071] At box 408, SOC delimiting unit 212 is based on the SOC calculated in boxes 404 and 406, respectively. Ah and SOC V To calculate SIC Est The upper and lower bounds. An exemplary upper bound can be defined as shown in expressions (3) and (5), and an exemplary lower bound can be defined as shown in expressions (4) and (6).

[0072] SOC u，bnd (t)=max{SOC Ah (t), SOC V (t)}+|SOC Ah (t)-SOC V (t)|+E desg (3)

[0073] SOC l，bnd (t)=min{SOC Ah (t), SOC V (t)}-|SOC Ah (t)-SOC V (t)|-E desg (4)

[0074] SOC u，bnd (t)∈[0,1] (5)

[0075] SOC l，bnd (t)∈[0,1] (6)

[0076] Among them, E desg This indicates the predetermined or designed error tolerance selected by the SOC delimiting unit 212.

[0077] As shown in expressions (3) and (4) above, SOC can be... Ah and SOC V The maximum value can be used as the baseline for the upper bound, but additional tolerances can be applied. Furthermore, the SOC can be... Ah and SOC V The minimum value is used as the baseline for the lower bound, and then additional tolerances can be applied. For example, the SOC can be modeled by taking into account the errors / noise in the current sensor and / or voltage sensor and modeling other errors according to the measured voltage as shown in expression (2) (e.g., OCV is calculated as a function of SOC). Ah With SOCV The abstract value between these values ​​is used as an additional tolerance. Thus, the abstract value |SOC Ah -SOC V | can represent the uncertainty in measurements and / or models used for SOC delimitation. In some implementations, additional accuracy tolerances (such as E) can be applied. desg (For example, apply an accuracy of ±3%).

[0078] At box 410, SOC delimiting unit 212 generates a value set at the upper SOC. u，bnd (t) and the lower bound SOC l，bnd SOC between (t) Bounded An exemplary SOC can be defined as shown in the following expression (7). Bounded .

[0079] SOC l，bnd (t)≤SOC Bounded (t)≤SOC u，bnd (t) (7) At box 412, controller 118 is based on SOC Bounded The controller 118 controls the electrification process of the electric vehicle 102. For example, the controller 118 can modify battery cooling or charging and / or discharging limits, reduce the number of charging and / or discharging cycles, or based on the state of charge (SOC). Bounded To modify the minimum state of charge threshold.

[0080] Now refer to Figure 5 According to an embodiment of the subject matter disclosed herein, another exemplary SOC delimitation process is shown. As disclosed herein, the hybrid power system 100 is not particularly limited and can perform any of the methods described within the scope of this disclosure. Figure 5 The image shows a method 500 for performing SOC delimitation processing using SOC delimitation unit 212. More specifically, when the battery pack is used as an energy storage power source 124 (e.g., Figure 2B When the predetermined time period (e.g., a microscopic time of approximately 1 to 2 seconds) is reached, the SOC delimiting unit 212 executes... Figure 5 One or more steps are shown.

[0081] At box 502, the SOC estimator 204 estimates the SOC of all cells 124a, 124b, ..., 124n in each array i based on the current current level I of the energy storage power supply 124 and time-based information. Est，i For example, a general SOC estimator (such as DNKF) can be used to estimate SOC. Est，i In one implementation, for i = 1, 2, ..., n, n represents multiple cells or groups of cells connected in series.

[0082] At box 504, SOC delimiting unit 212 calculates SOC based on the current current level I and current temperature T of energy storage power supply 124. Ah In one example, the Coulomb counting technique can be used to calculate the State of Charge (SOC). Ah An exemplary SOC can be defined as shown in the following expression (8). Ah .

[0083]

[0084] Where I(t) is the input current during time t, SOC0 represents the initial SOC at the initial time t0, and Capacity represents the current total capacity generated by the energy storage power supply 124.

[0085] At box 506, SOC delimiting unit 212 calculates the SOC of all cells 124a, 124b, ..., 124n in the battery pack based on the current voltage level V and current temperature T of the energy storage power supply 124. V，i An exemplary SOC can be defined as shown in the following expression (9). V，i In one implementation, the SOC can be... V，i Filtering is performed to remove noise caused by dynamic voltage response.

[0086] SOC V，i (t)=[OCV -1 (V i (t)+I(t)R 0，i ] filtered (9)

[0087] Where OCV is the battery open-circuit voltage, which is a function of SOC; R 0，i This represents the battery internal resistance of each array i, which depends on the temperature T. In one example, a single-pole low-pass filter can be used to perform filtering. In one implementation, a single-pole low-pass filter can be used to remove state of charge (SOC). V，i The amplified noise in the image may be due to measurements from the OCV approximation and imperfect fidelity. Other suitable filters can also be envisioned for different applications. In another example, a linear-phase low-pass filter could also be used to remove amplified noise.

[0088] At block 508, SOC delimiting unit 212 is based on the SOC calculated in blocks 504 and 506, respectively. Ah and SOC V，i To calculate SOC EstThe upper and lower bounds. An exemplary upper bound can be defined as shown in expressions (10) and (12), and an exemplary lower bound can be defined as shown in expressions (11) and (13).

[0089] SOC u，bnd (t)=max(SOC Ah (t), SOC V，i (t)}+max{|SOC Ah (t)-SOC V，i (t)|}+E desg (10)

[0090] SOC l，bnd (t)=min{SOC Ah (t)SOC V，i (t)}-max{|SOC Ah (t)-SOC V，i (t)|}-E desg (11)

[0091] SOC u，bnd (t)∈[0,1] (12)

[0092] SOC l，bnd (t)∈[0,1] (13)

[0093] Among them, E desg This indicates the predetermined or designed error tolerance selected by the SOC delimiting unit 212.

[0094] As shown in expressions (10) and (11) above, SOC can be... Ah and SOC V，i The maximum value can be used as the baseline for the upper bound, but additional tolerances can be applied. Furthermore, the SOC can be... Ah and SOC V，i The minimum value is used as the baseline for the lower bound, and then additional tolerances can be applied. For example, the SOC can be modeled by taking into account the errors / noise in the current sensor and / or voltage sensor and modeling other errors according to the measured voltage as shown in expression (9) (e.g., OCV is calculated as a function of SOC). Ah With SOC V，i The abstract values ​​between these are used as additional tolerances. Thus, the maximum value max{|SOC Ah -SOC V，i |} can represent the uncertainty in measurements and / or models used for SOC delimitation. In some implementations, additional accuracy tolerances (such as E) can be applied. desg (For example, apply an accuracy of ±3%).

[0095] At box 510, SOC delimiting unit 212 generates a value set at the upper SOC. u，bnd (t) and the lower bound SOC l，bnd SOC between (t) Bounded The SOC can be defined as shown in the following expression (14). Bounded .

[0096] SOC l，bnd (t)≤SOC Bounded，i (t)≤SOC u，bnd (t) (14)

[0097] At box 512, controller 118 is based on SOC. Bounded，i The controller 118 controls the electrification process of the electric vehicle 102. For example, the controller 118 can modify battery cooling or charging and / or discharging limits, reduce the number of charging and / or discharging cycles, or based on the state of charge (SOC). Bounded，i To modify the minimum state of charge threshold.

[0098] Now refer to Figure 6 An exemplary SOH delimitation process is shown according to an embodiment of the subject matter disclosed herein. As disclosed herein, the hybrid power system 100 is not particularly limited and can perform any of the methods described within the scope of this disclosure. Figure 6 The image shows a method 600 for performing SOH delimitation processing using SOH delimitation unit 214. More specifically, when a single battery is used in energy storage power supply 124 (e.g., Figure 2A When the predetermined period (e.g., a macroscopic time of approximately one to two months) is reached, the SOH delimitation unit 214 executes. Figure 6 One or more steps are shown.

[0099] At box 602, the SOH estimator 208 estimates the SOH based on time-based information. Est For example, a general SOC estimator (such as DNKF) can be used to estimate SOH. Est .

[0100] At box 604, SOH delimiting unit 214 calculates SOH based on the start and end times associated with energy storage power source 124. F SOH can be F The SOH value is stored in memory 120 for later retrieval and processing. For example, the full-cycle SOH value represents the estimated SOH value at the time of the last capacity check of the energy storage power source 124. F(L). It is generally recommended to perform a full charge and discharge operation on the energy storage power supply 124 every n charge cycles or m months. When such a full charge and discharge operation is possible, an exemplary SOH at the time of the last capacity check during the full charge cycle can be defined as shown in the following expression (15). F (L).

[0101]

[0102] Where I(t) is the input current during time t. This indicates the start time of the upper voltage limit of the energy storage power supply 124 during full discharge. This indicates the end time when the voltage reaches the lower limit of the cutoff voltage of the energy storage power supply 124 during full discharge, and the nominal capacity indicates the total capacity generated by the energy storage power supply 124 when it was first installed with 100% SOH.

[0103] However, when certain applications cannot use or do not allow this full charge and discharge operation, another exemplary full cycle SOH value can be defined as shown in the following expression (16). F (L).

[0104]

[0105] Where I(t) is the input current during time t. This indicates the start time when the SOC value of the energy storage power supply 124 is less than approximately 20% before it is fully charged. This indicates the end time when the voltage reaches the upper voltage limit during full charging, and the nominal capacity indicates the total capacity generated by the energy storage power supply 124 when it was last installed with 100% SOH. In the embodiment, the exemplary full-cycle SOH value SOH shown in expression (16) can be calculated in the following cases (1) to (3). F (L): (1) After the last operation of the electric vehicle 102, the SOC value of the energy storage power supply 124 is less than 20%; (2) a subsequent charging event fully charges the energy storage power supply 124 to the upper voltage limit (e.g., SOC = 100%); and (3) the temperature of the energy storage power supply 124 is approximately between 25 degrees Celsius and 35 degrees Celsius (25°C to 35°C).

[0106] At box 606, SOH delimiting unit 214 calculates the partial cycle SOH value based on the start and end times associated with energy storage power source 124. P SOH can also be used. PThe SOH value is stored in memory 120 for subsequent retrieval and processing. For example, the partial cycle SOH value represents the estimated SOH value at the time of the last capacity check of the energy storage power supply 124. P (L). An exemplary SOH during the final capacity check of a partial charge cycle can be defined as shown in the expression (17) below. P (L).

[0107]

[0108] Where t1 represents the start time of a partial loop, and t2 represents the end time of a partial loop.

[0109] At block 608, the SOH delimiting element 214 is based on the SOH calculated in blocks 604 and 606, respectively. F (L) and SOH P (L) to calculate SOH Est The upper and lower bounds. An exemplary upper bound can be defined as shown in expressions (18) and (20), and an exemplary lower bound can be defined as shown in expressions (19) and (21).

[0110] SOH u，bnd (L)=max{SOH F (L), SOH P (L)}+|SOH F (L)-SOH P (L)|+E desg (18)

[0111] SOH l，bnd (L)=min{SOH F (L), SOH P (L)}-|SOH F (L)-SOH P (L)|-E desg (19)

[0112] SOH u，bnd (L)∈[0,1] (20)

[0113] SOH l，bnd (L)∈[0,1] (21)

[0114] Among them, E desg This indicates the predetermined or design error tolerance selected by the SOH delimiting unit 214.

[0115] As shown in expressions (18) and (19) above, SOH can be... F and SOH PThe maximum value can be used as the baseline for the upper bound, but additional tolerances can be applied. Furthermore, the SOH can be... F and SOH P The minimum value is used as the baseline for the lower bound, and then additional tolerances can be applied. For example, by considering SOH F and SOH P The uncertainty of the estimate can be used to estimate SOH. F and SOH P The abstract values ​​between these are used as additional tolerances. In some cases, SOH F It may not be accurate enough due to unexpected changes during capacity checks performed every few months. As another example, SOH P It may be inaccurate due to undesirable sensor errors, battery hysteresis, and unknown coulombic efficiency (e.g., charge loss due to the passage of time). Thus, the abstract value |SOH F (L)-SOH P (L)| can represent the uncertainty in measurements and / or models used for SOH delimitation. In some implementations, additional accuracy tolerances (such as E) can be applied. desg (For example, apply an accuracy of ±3%).

[0116] At box 610, SOH delimiting unit 214 generates the SOH set at the upper boundary. u，bnd (L) and lower bound SOH l，bnd SOH between (L) Bounded An exemplary SOH can be defined as shown in the following expression (22). Bounded .

[0117] SOH l，bnd (L)≤SOH Bounded (L)≤SOH u，bnd (L) (22)

[0118] At box 612, controller 118 is based on SOH. Bounded The controller 118 controls the electrification process of the electric vehicle 102. For example, the controller 118 can modify battery cooling or charging and / or discharging limits, reduce the number of charging and / or discharging cycles, or based on SOH (State of Health). Bounded To modify the minimum state of charge threshold.

[0119] Now refer to Figure 7 According to embodiments of the subject matter disclosed herein, another exemplary SOH delimitation process is shown. As disclosed herein, the hybrid power system 100 is not particularly limited and can perform any of the methods described within the scope of this disclosure. Figure 7The image shows a method 700 for performing SOH delimitation processing using SOH delimitation unit 214. More specifically, when the battery pack is used as an energy storage power source 124 (e.g., Figure 2B When the predetermined period (e.g., a macroscopic time of approximately one to two months) is reached, the SOH delimitation unit 214 executes. Figure 7 One or more steps are shown.

[0120] At box 702, the SOH estimator 208 estimates the SOH based on time-based information. Est，i For example, a general SOC / SOH estimator (such as DNKF) can be used to estimate SOH. Est，i .

[0121] At box 704, SOH delimiting unit 214 calculates the full-cycle SOH value of the battery pack based on the start and end times associated with the energy storage power source 124. F SOH can be F The SOH value is stored in memory 120 for later retrieval and processing. For example, the full-cycle SOH value represents the estimated SOH value at the time of the last capacity check of the energy storage power source 124. F (L). It is generally recommended to perform a full charge and discharge operation on the energy storage power supply 124 every n charge cycles or m months. When such a full charge and discharge operation is possible, an exemplary SOH at the time of the last capacity check during the full charge cycle can be defined as shown in the following expression (23). F (L).

[0122]

[0123] Where I(t) is the input current during time t. This indicates the start time of the upper voltage limit of the energy storage power supply 124 during full discharge. This indicates the end time when the voltage reaches the lower limit of the cutoff voltage of the energy storage power supply 124 during full discharge, and the nominal capacity indicates the total capacity generated by the energy storage power supply 124 when it was first installed with 100% SOH.

[0124] However, when certain applications cannot use or do not allow this full charge and discharge operation, another exemplary full cycle SOH value can be defined as shown in the following expression (24). F (L).

[0125]

[0126] Where I(t) is the input current during time t. This indicates the start time when the SOC value of the energy storage power source 124 is less than 20% during full charging. This indicates the end time when the voltage reaches the upper voltage limit during full charging, and the nominal capacity indicates the total capacity generated by the energy storage power supply 124 when it was last installed with 100% SOH. In the embodiment, the exemplary SOH shown in expression (24) can be calculated in the following cases (1) to (3). F (L): (1) After the last operation of the electric vehicle 102, the SOC value of the energy storage power supply 124 is less than 20%; (2) a subsequent charging event fully charges the energy storage power supply 124 to the upper voltage limit (e.g., SOC = 100%); and (3) the temperature of the energy storage power supply 124 is approximately between 25 degrees Celsius and 35 degrees Celsius (25°C to 35°C).

[0127] At box 706, SOH delimiting unit 214 calculates the partial cycle SOH value based on the start and end times associated with energy storage power source 124. P，i SOH can also be used. P，i The SOH value is stored in memory 120 for subsequent retrieval and processing. For example, the partial cycle SOH value represents the estimated SOH value at the time of the last capacity check of the energy storage power supply 124. P，i (L). An exemplary SOH during the final capacity check of a partial charge cycle can be defined as shown in the expression (25) below. P，i (L).

[0128]

[0129] Where t1 represents the start time of a partial loop, and t2 represents the end time of a partial loop.

[0130] At block 708, the SOH delimiting element 214 is based on the SOH calculated in blocks 704 and 706, respectively. F (L) and SOH P，i (L) to calculate SOH Est The upper and lower bounds. An exemplary upper bound can be defined as shown in expressions (26) and (28), and an exemplary lower bound can be defined as shown in expressions (27) and (29).

[0131] SOH u，bnd (L)=max{SOH F (L), SOH P，i (L)}+max{|SOH F (L)-SOH P，i (L)|}+Edesg (26)

[0132] SOH l，bnd (L)=min{SOH F (L), SOH P，i (L)}-max{|SOH F (L)-SOH P，i (L)|}-E desg (27)

[0133] SOH u，bnd (L)∈[0,1] (28)

[0134] SOH l，bnd (L)∈[0,1] (29)

[0135] Among them, E desg This indicates the predetermined or design error tolerance selected by the SOH delimiting unit 214.

[0136] As shown in expressions (26) and (27) above, SOH can be... F and SOH P，i The maximum value can be used as the baseline for the upper bound, but additional tolerances can be applied. Furthermore, the SOH can be... F and SOH P，i The minimum value is used as the baseline for the lower bound, and then additional tolerances can be applied. For example, by considering SOH F and SOH P，i The uncertainty of the estimate can be used to estimate SOH. F and SOH P，i The abstract values ​​between these are used as additional tolerances. In some cases, SOH F It may not be accurate enough due to unexpected changes during capacity checks performed every few months. As another example, SOH P，i It may be inaccurate due to undesirable sensor errors, battery hysteresis, and unknown coulombic efficiency (e.g., charge loss due to the passage of time). Thus, the maximum value max{|SOH F (L)-SOH P，i (L)|} can represent the uncertainty in measurements and / or models used for SOH delimitation. In some implementations, additional accuracy tolerances (such as E) can be applied. desg (For example, apply an accuracy of ±3%).

[0137] At box 710, SOH delimiting unit 214 generates a value set at the upper limit of SOH. u，bnd (L) and lower bound SOH l，bnd SOH between (L) Bounded，iAn exemplary SOH can be defined as shown in the following expression (30). Bounded，i .

[0138] SOH l，bnd (L)≤SOH Bounded，i (L)≤SOH u，bnd (L) (30)

[0139] At box 712, controller 118 is based on SOH. Bounded，i The controller 118 controls the electrification process of the electric vehicle 102. For example, the controller 118 can modify battery cooling or charging and / or discharging limits, reduce the number of charging and / or discharging cycles, or based on SOH (State of Health). Bounded，i To modify the minimum state of charge threshold.

[0140] Now refer to Figure 8 Another exemplary schematic diagram of the power estimator 126 is shown. In the illustrated embodiment, the power estimator 126 includes a SOC / SOH estimator 200 and a SOC delimiting unit 212. The illustrated embodiment can be used for both individual battery cells and battery packs to suit different applications. Figure 8 In this configuration, the SOC / SOH estimator 200 is configured to receive the current current level I and current voltage level V of the energy storage power supply 124 from the vehicle monitoring unit 128. Furthermore, the SOC / SOH estimator 200 is configured to receive the current temperature T of the energy storage power supply 124 from the vehicle monitoring unit 128. The SOC / SOH estimator 200 is configured to estimate the SOC based on the current current level I, current voltage level V, and current temperature T of the energy storage power supply 124. Est (For example, 60%). SOC Est Send to SOC delimiting unit 212.

[0141] Configure the SOC delimiting unit 212 to receive SOC from the SOC / SOH estimator 200. Est It also receives the current current level I, current voltage level V, and current temperature T of the energy storage power supply 124 from the vehicle monitoring unit 128. The SOC delimiting unit 212 is configured based on the current current level I, current voltage level V, current temperature T, and SOC. Est To calculate SOC Est Applicable upper and lower bounds. The SOC delimiting unit 212 is configured to generate an SOC set between the upper and lower bounds. Bounded Configure the SOC delimiting unit 212 to output SOC. Bounded The upper and lower bounds are defined to allow for further processing as needed. For example, controller 118 can be based on a SOC. Bounded To control the electrification of electric vehicle 102.

[0142] Now refer to Figure 9 , showed Figure 8 An exemplary schematic diagram of the SOC delimiting unit 212 is shown. In the illustrated embodiment, the SOC delimiting unit 212 includes an Ah-based SOC calculation unit 900 and a voltage-based SOC calculation unit 902. The Ah-based SOC calculation unit 900 is configured to calculate the SOC based on the current current level I of the energy storage power supply 124. Ah The voltage-based SOC calculation unit 902 is configured to calculate the SOC based on the current voltage level V, current temperature T, and current level I of the energy storage power source 124. V Filters such as single-pole low-pass filters can be used for SOCs. V Filtering is performed to remove noise.

[0143] An exemplary calculation of the upper bound using the MinMax cell, Add1 cell, Abs cell, constant, and Add cell is shown in the expression (31) below:

[0144] SOC u，bnd =max{SOC Ah SOC V}+|SOC Ah -SOC V |+E desg (31)

[0145] An exemplary calculation of the lower bound using the MinMax1, Add3, Abs1, Constant1, and Add2 cells is shown in expression (32) below:

[0146] SOC l，bnd =min{SOC Ah SOC V}-|SOC Ah -SOC V |-E desg (32)

[0147] SOC delimiting unit 212 also includes a filtering unit 904, which is configured to receive SOC. Est Upper and lower bounds. Configure filter unit 904 to use upper and lower bounds on the SOC. Est Perform filtering and generate SOC. Bounded This makes SOC Bounded It is set between the upper and lower bounds. The SOC delimiting unit 212 can output the SOC. Bounded The upper and lower bounds are defined to allow for subsequent processing as desired.

[0148] Despite Figure 8 and Figure 9 The diagram shows the SOC / SOH estimator 200 and the SOC delimiting unit 212, but other suitable arrangements (such as the SOC / SOH estimator 200 and the SOH delimiting unit 214) can be envisioned to suit different applications.

[0149] As shown above, in embodiments of this disclosure, a controller is provided that performs power estimation processing for an electric vehicle (such as electric vehicle 102), the electric vehicle including a controller 118 having a non-transitory memory 120 and a processor 122. The non-transitory memory 120 includes instructions, in response to execution of the processor 122, causing various methods described below to be performed, including estimating the internal state of an energy storage source (e.g., a battery). Example variables of the internal state include SOC and SOH. In some embodiments of this disclosure, SOC estimation is performed for a battery comprising battery cells and battery packs. Typically, battery cells and battery packs are used in electric or hybrid vehicles. Thus, SOC is an indicator of the remaining charge of the battery system. SOC quantifies the available energy at the current time and reflects the remaining power range. Knowing the SOC allows for the provision of high performance as needed while ensuring the battery operates within safe limits. SOC is typically not a direct measurement but is incorporated into the estimation in one or another manner.

[0150] State of Charge (SOC) indicates the equivalent value of a battery's fuel gauge. SOC estimation of a battery system is a crucial input for balance calculations, energy calculations, and power calculations. Accurate SOC estimation provides benefits such as lifespan, performance, and reliability. Estimations typically carry some error due to sensing and / or modeling errors. For an SOC estimator, if it is poorly calibrated and / or fed with bad data, the estimated SOC can be misinterpreted as the true SOC. Therefore, it is crucial to define reliable estimation error boundaries in real time (e.g., an upper and lower bound on the estimated SOC) so that the estimated SOC is reliable for its intended use. A BMS based on such reliable SOC estimation ensures safe battery operation while actively utilizing the entire battery pack capacity. Existing methods tend to estimate battery SOC without providing estimation error boundaries. Without real-time SOC error boundaries, the use of a battery system, especially in very high and low SOC ranges, can be overly conservative, as overcharging and over-discharging often occur under these conditions when using inaccurate SOC information.

[0151] Some embodiments of this disclosure focus on State of Charge (SOC) estimation, which has upper and lower bounds, and in some cases, these upper and lower bounds are relatively narrow but reliable. Furthermore, some embodiments of this disclosure focus on systems and methods for independently determining upper and lower bound estimates in real time by combining current, voltage, and temperature measurements. In some cases, by limiting the estimated SOC with upper and lower bounds, the BMS reduces possible discrepancies in the SOC estimate, ensuring safe and aggressive battery operation and maximizing full battery capacity.

[0152] Additionally, the SOC upper and lower bounds in this disclosure can be used for on-board diagnostics of sensors and conventional embedded SOC estimators. Once the upper and / or lower bounds widen, the BMS can determine that uncertainty in the measurement results is increasing and that it is time to recalibrate the sensor and / or SOC estimator. Some embodiments of this disclosure provide robust battery SOC estimation under various operating conditions throughout the battery's lifespan by including SOC upper and lower bounds.

[0153] At least some embodiments of this disclosure relate to a method for estimating the State of Charge (SOC) of a battery. The method includes the steps of: receiving a series of current data indicating measurements of current flowing through the battery; receiving a series of voltage data indicating measurements of battery voltage; using the series of current data to calculate an ampere-hour-based SOC estimate (Ah-SOC); using the series of voltage data and the series of current data to calculate a voltage-based SOC estimate (V-SOC); and generating a bounded SOC estimate including an upper SOC bound and a lower SOC bound. The upper SOC bound is determined based on the larger of Ah-SOC and V-SOC. The lower SOC bound is determined based on the smaller of Ah-SOC and V-SOC.

[0154] At least some embodiments of this disclosure relate to a method for estimating the State of Charge (SOC) of a battery pack having multiple battery cells. The method includes the steps of: receiving a series of current data for each of the multiple battery cells, the series of current data indicating measurements of current flowing through the battery cells; receiving a series of voltage data for each of the multiple battery cells, the series of voltage data indicating measurements of voltage in the battery cells; using the series of current data for the respective battery cells to calculate an ampere-hour-based SOC estimate (Ah-SOC) for each of the multiple battery cells; using the series of voltage data and the series of current data for the respective battery cells to calculate a voltage-based SOC estimate (V-SOC) for each of the multiple battery cells; and generating a bounded SOC estimate including an upper SOC bound and a lower SOC bound. The upper SOC bound is determined based on the Ah-SOC and V-SOC values ​​of the multiple battery cells. The lower limit of SOC is determined based on the Ah-SOC and V-SOC values ​​of the plurality of battery cells.

[0155] Now, referring to Figure 10A According to embodiments of the subject matter disclosed herein, an exemplary method 1000A is shown for estimating the State of Charge (SOC) using upper and lower bounds of a battery (e.g., a battery cell). Aspects of embodiments of method 1000A may be performed, for example, by a battery management system, battery management device, controller 118, and / or integrated circuit chip. As used herein, BMS refers to a system, apparatus, and / or integrated circuit chip for measuring, estimating, and / or managing the use of a battery (e.g., a battery cell, battery pack, cell assembly, etc.). One or more steps of method 1000A are optional and / or can be modified by one or more steps of other embodiments described herein. Additionally, one or more steps of other embodiments described herein may be added to method 1000A. In some embodiments, the BMS may measure or receive SOC measurement results (1050A). The BMS receives a series of current data (1100A) and a series of voltage data (1150A) of the battery. In embodiments, this series of current and voltage data is collected in real time. In embodiments, this series of current and voltage data is collected during battery operation. In some cases, this series of current and voltage data is associated with time information (e.g., timestamps).

[0156] Next, the BMS calculates the State of Charge (SOC) based on ampere-hours (Ah-SOC) (1200A) using the current data. In some implementations, the Ah-SOC is determined based on at least a portion of the series of current data. In one implementation, the Ah-SOC is calculated based on the integral of at least a portion of the series of current data. In some cases, the Ah-SOC is determined based on the battery capacity. As used herein, battery capacity refers to a measure of the charge stored in the battery (e.g., in ampere-hours).

[0157] Furthermore, the BMS can calculate the voltage-based SOC (V-SOC) (1250A). In some embodiments, V-SOC is determined based on at least a portion of the series of voltage data. In some embodiments, V-SOC is determined based on at least a portion of the series of current data. In some cases, the battery has a known SOC-OCV relationship, where OCV refers to the battery's open-circuit voltage. In embodiments, the BMS estimates OCV and determines V-SOC based on the reciprocal of the SOC-VOC relationship. In some cases, OCV is estimated using the current measurement and the voltage measurement at the time. In some cases, OCV is estimated using the battery's internal resistance as a parameter, where the battery's internal resistance changes with temperature. In embodiments, V-SOC is further determined based on the inverse open-circuit voltage function. In embodiments, V-SOC is further determined by applying a filter. In some cases, this filter is a low-pass filter.

[0158] In one implementation, the BMS determines the upper bound of the State of Charge (SOC) (1300A) based on the larger of Ah-SOC and V-SOC. In some cases, the upper bound of the SOC is determined at least in part based on the difference between Ah-SOC and V-SOC. The difference between Ah-SOC and V-SOC can indicate sensing error and / or model error. In some cases, the upper bound of the SOC is determined, for example, at least in part based on the design accuracy of the battery, to fine-tune the estimate. In one implementation, the upper bound of the SOC is determined at least in part based on the absolute value of the difference between Ah-SOC and V-SOC plus the larger of Ah-SOC and V-SOC.

[0159] In some implementations, the BMS determines the lower bound of SOC (1350A) based on the smaller of Ah-SOC and V-SOC. In some cases, the lower bound of SOC is determined at least partially based on the difference between Ah-SOC and V-SOC. In some cases, the lower bound of SOC is determined, for example, at least partially based on the design accuracy of the battery, to fine-tune the estimate. In one implementation, the lower bound of SOC is determined at least partially based on the smaller of Ah-SOC and V-SOC minus the absolute value of the difference between Ah-SOC and V-SOC.

[0160] In some implementations, the BMS can generate a bounded SOC estimate with an upper and lower SOC bound (1400A). As used herein, a bounded SOC estimate includes an upper SOC estimate and / or a lower SOC estimate. In some cases, the BMS can, for example, compare the SOC measurement result with the upper and lower SOC bounds (1450A) to determine a bounded SOC value. In one example, if the SOC measurement result is greater than the upper SOC bound estimate, the BMS sets the bounded SOC value as the upper SOC bound. In one example, if the SOC measurement result is less than the lower SOC bound, the BMS sets the bounded SOC value as the lower SOC bound. In some cases, if the SOC measurement result is less than or equal to the upper SOC bound and the SOC measurement result is greater than or equal to the lower SOC bound, the BMS sets the bounded SOC value as the SOC measurement result. In some cases, the bounded SOC estimate also includes a bounded SOC value. In some designs, the SOC measurement result is received from a conventional SOC measurement device.

[0161] Now refer to Figure 10BAccording to embodiments of the subject matter disclosed herein, an exemplary method 1000B is shown for estimating the State of Charge (SOC) using upper and lower bounds of a battery pack having multiple battery cells or cell groups. Aspects of the implementation of method 1000B can be performed, for example, by a battery management system, a battery management device, and / or an integrated circuit chip. One or more steps of method 1000B are optional and / or can be modified by one or more steps of other embodiments described herein. Additionally, one or more steps of other embodiments described herein can be added to method 1000B. As used herein, a battery cell refers to a single battery cell or a group of battery cells whose electrodes (including anode and cathode) are connected together. In some embodiments, the BMS can measure or receive SOC measurement results (1050B) for each of the multiple battery cells. The BMS receives a series of current data (1100B) and a series of voltage data (1150B) for each battery cell. In embodiments, this series of current and voltage data is collected in real time. In this implementation, the series of current and voltage data is collected during battery pack operation. In some cases, this series of current and voltage data is associated with time information (e.g., timestamps).

[0162] Next, the BMS calculates the ampere-hour-based State of Charge (SOC) estimate (Ah-SOC) for each battery cell based on the current data of the respective battery cell (1200B). In some embodiments, the Ah-SOC is determined based on at least a portion of the series of current data for the respective battery cell. In one embodiment, the Ah-SOC of the battery cell is calculated based on the integration of at least a portion of the series of current data for the respective battery cell. In some cases, the Ah-SOC of the battery cell is determined based on the capacity of the respective battery cell. In some cases, the capacity of the battery cell is represented by the integration of the current from SOC 0 to SOC 1.

[0163] Furthermore, the BMS can calculate the voltage-based State of Charge (V-SOC) (1250B) of each battery cell. In some embodiments, the V-SOC of the battery cell is determined based on at least a portion of the series of voltage data for the corresponding battery cell. In some embodiments, the V-SOC is determined based on at least a portion of the series of current data. In some cases, the battery cell has a known SOC-OCV relationship, where OCV refers to the open-circuit voltage of the battery. In embodiments, the BMS estimates the OCV of the battery cell and determines the V-SOC based on the reciprocal of the SOC-VOC relationship. In some cases, the OCV is estimated using the current measurement results and the current voltage measurement results at the time. In some cases, the OCV is estimated using the battery's internal resistance as a parameter, where the battery's internal resistance changes with temperature. In some cases, temperature data is received by the BMS and used to calculate the battery's internal resistance. In embodiments, the V-SOC is further determined based on the inverse open-circuit voltage function. In embodiments, the V-SOC is further determined by applying a filter. In some cases, this filter is a low-pass filter.

[0164] In some implementations, the BMS determines the upper bound of the State of Charge (SOC) based on the Ah-SOC and V-SOC values ​​of the battery cells in the battery pack (1300B). In some cases, the upper bound of the SOC is determined based on the maximum value of the Ah-SOC and V-SOC of the battery cells in the battery pack. In some cases, the upper bound of the SOC is determined at least partially based on the difference between the Ah-SOC and V-SOC of the battery cells. In one implementation, the upper bound of the SOC is determined at least partially based on the maximum difference between Ah-SOC and V-SOC among the absolute differences between Ah-SOC and V-SOC of all battery cells. In some cases, the upper bound of the SOC is determined, for example, at least partially based on the design accuracy of the battery, to fine-tune the estimate. In one implementation, the upper bound of the SOC is determined at least partially based on the maximum value of the Ah-SOC and V-SOC of the battery cells in the battery pack plus the maximum absolute value of the difference between the Ah-SOC and V-SOC of the battery cells.

[0165] In some implementations, the BMS determines the lower bound of SOC (1350B) based on the Ah-SOC and V-SOC values ​​of the battery cells in the battery pack. In some cases, the lower bound of SOC is determined based on the minimum of the Ah-SOC and V-SOC values ​​of the battery cells in the battery pack. In some cases, the lower bound of SOC is determined at least partially based on the difference between the Ah-SOC and V-SOC values ​​of the battery cells. In some cases, the lower bound of SOC is determined, for example, at least partially based on the design accuracy of the battery, to fine-tune the estimate. In one implementation, the lower bound of SOC is determined based on the maximum absolute value of the difference between the Ah-SOC and V-SOC values ​​of the battery cells in the battery pack, respectively.

[0166] In some implementations, the BMS can generate a bounded SOC estimate with an upper and lower SOC limit (1400B). In some cases, the BMS can, for example, compare the SOC measurement result of the battery cell with the upper and lower SOC limits (1450B) to determine the bounded SOC value. In one example, if the corresponding SOC measurement result is greater than the upper SOC estimate, the BMS sets the bounded SOC value as the upper SOC limit. In one example, if the corresponding SOC measurement result is less than the lower SOC limit, the BMS sets the bounded SOC value as the lower SOC limit. In some cases, if the corresponding SOC measurement result is less than or equal to the upper SOC limit and the corresponding SOC measurement result is greater than or equal to the lower SOC limit, the BMS sets the bounded SOC value to the corresponding SOC measurement result. In some cases, the bounded SOC estimate also includes a bounded SOC value. In some designs, the SOC measurement result is received from a conventional SOC measurement device.

[0167] Now refer to Figure 11ABased on embodiments of the subject matter disclosed herein, an exemplary method 2000A for generating bounded SOC estimates of a battery (e.g., a battery cell) is shown. Aspects of embodiments of method 2000A can be performed by a BMS. One or more steps of method 2000A are optional and / or can be modified by one or more steps of other embodiments described herein. Additionally, one or more steps of other embodiments described herein can be added to method 2000A. In some embodiments, the BMS can measure or receive temperature data (2050A). The BMS receives or measures a series of current data (2100A) and a series of voltage data (2150A) of the battery. In some embodiments, the series of current and voltage data is collected in real time. In others, the series of current and voltage data is collected during battery operation. In some cases, the series of current and voltage data is associated with time information (e.g., a timestamp).

[0168] Next, the BMS determines the battery capacity (2170A). In one embodiment, the capacity is determined by integrating the current (I) over time (t). In another embodiment, the capacity of the battery and / or battery cell is calculated using the following expression (33).

[0169]

[0170] Here, Capacity is the integral of the current (I) from time SOC = 0 to time SOC = 1.

[0171] In some implementations, the BMS calculates the SOC (Ah-SOC) based on ampere-hours (2200A). In some cases, the Ah-SOC is determined based on current data. In some implementations, the Ah-SOC is determined based on at least a portion of the series of current data. In one implementation, the Ah-SOC is calculated based on the integral of at least a portion of the series of current data. In some cases, the Ah-SOC is determined based on the battery capacity. In one case, the Ah-SOC of the battery and / or battery cell at time t can be estimated using the following expression (34).

[0172]

[0173] Among them, SOC Ah I(t) is the Ah-SOC at time t, SOC0 is the SOC estimate at t0, I(t) is the current data measured over time, and Capacity is the capacity of the battery and / or battery cell.

[0174] Furthermore, the BMS can determine the battery resistance (2220A). Battery resistance is typically affected by temperature. In some implementations, the BMS can retrieve a SOC-OCV model (2240A), where OCV is the open-circuit voltage. In these implementations, the SOC-OCV model represents a nonlinear relationship. Figure 14 An example of a SOC-OCV model and / or function is shown. In this example, SOC and OCV have a nonlinear relationship. In some cases, the SOC-OCV model is affected by temperature, causing the BMS to retrieve or select the SOC-OCV model based on the temperature at which the SOC estimation is performed.

[0175] In some implementations, the BMS can calculate the voltage-based SOC (V-SOC) (2250A) based on, for example, the many parameters listed above. In some implementations, V-SOC is determined based on at least a portion of the series of voltage data. In some implementations, V-SOC is determined based on at least a portion of the series of current data. In some implementations, the BMS estimates OCV and determines V-SOC based on the reciprocal of the SOC-VOC relationship. In some cases, OCV is estimated using the current measurement and the current voltage measurement. In some cases, OCV is estimated using the battery's internal resistance as a parameter, where the battery's internal resistance changes with temperature. In some implementations, V-SOC is further determined based on the reverse open-circuit voltage function. In some implementations, V-SOC is further determined by applying a filter.

[0176] In one case, the V-SOC of the battery and / or battery cell can be estimated using the following expression (35).

[0177] SOC V (t)=f(OCV -1 (V(t)+I(t)R0(T)) (35)

[0178] Among them, SOC V (t) is the V-SOC at time t, f() is the filter function, and OCV -1 () is the inverse function of SOC-OCV, where V(t) is the voltage data measured at time t, I(t) is the current data measured at time t, and R0(T) is the battery resistance estimated based on the measured temperature T. In one implementation, the filter is a low-pass filter.

[0179] In one implementation, the BMS determines the upper bound of the State of Charge (SOC) (2300A) based on the larger of Ah-SOC and V-SOC. In some cases, the upper bound of the SOC is determined at least in part based on the difference between Ah-SOC and V-SOC. The difference between Ah-SOC and V-SOC can indicate sensing error and / or model error. In some cases, the upper bound of the SOC is determined, for example, at least in part based on the design accuracy of the battery, to fine-tune the estimate. In one implementation, the upper bound of the SOC is determined at least in part based on the larger of Ah-SOC and V-SOC plus the absolute value of the difference between Ah-SOC and V-SOC.

[0180] In one case, the upper bound of the SOC estimate of the battery and / or battery cell at time t can be determined using the following expression (36).

[0181] SOC Ubnd (t)=max{SOC Ah (t), SOC V (t)}+|SOC Ah (t)-SOC V (t)|+E (36)

[0182] Among them, SOC Ubnd (t) is the upper bound of the SOC estimate at time t, where SOC Ah (t) is Ah-SOC at time t, SOC V (t) is the V-SOC at time t, and E is the estimated design accuracy (e.g., 3%). In some cases, |SOC Ah (t)-SOC V (t)| can be an indication of sensing error and / or model error.

[0183] In some implementations, the BMS determines the lower bound of SOC (2350A) based on the smaller of Ah-SOC and V-SOC. In some cases, the lower bound of SOC is determined at least partially based on the difference between Ah-SOC and V-SOC. In some cases, the lower bound of SOC is determined, for example, at least partially based on the design accuracy of the battery, to fine-tune the estimate. In one implementation, the lower bound of SOC is determined at least partially based on the smaller of Ah-SOC and V-SOC minus the absolute value of the difference between Ah-SOC and V-SOC.

[0184] In one case, the lower bound of the SOC estimate of the battery and / or battery cell at time t can be determined using the following expression (37).

[0185] SOC Lbnd (t)=min{SOCAh (t), SOC V (t)}-|SOC Ah (t)-SOC V (t)|-E (37)

[0186] Among them, SOC Lbnd (t) is the lower bound of the SOC estimate at time t, where SOC Ah (t) is Ah-SOC at time t, SOC V (t) is the V-SOC at time t, and E is the estimated design accuracy (e.g., 3%). In some cases, |SOC Ah (t)-SOC V (t)| can be an indication of sensing error and / or model error.

[0187] In some implementations, the BMS can generate a bounded SOC estimate with an upper and lower SOC bound (2400A). In some cases, the BMS can, for example, compare the SOC measurement result with the upper and lower SOC bounds during operation to determine the bounded SOC value. In one example, if the SOC measurement result is greater than the upper SOC bound estimate, the BMS sets the bounded SOC value as the upper SOC bound. In another example, if the SOC measurement result is less than the lower SOC bound, the BMS sets the bounded SOC value as the lower SOC bound. In some cases, if the SOC measurement result is less than or equal to the upper SOC bound and the SOC measurement result is greater than or equal to the lower SOC bound, the BMS sets the bounded SOC value as the SOC measurement result. In some cases, the bounded SOC estimate also includes a bounded SOC value. In some designs, the SOC measurement result is received from a conventional SOC measurement device.

[0188] Now refer to Figure 11BAccording to embodiments of the subject matter disclosed herein, an exemplary method 2000B for generating a bounded SOC estimate of a battery pack having multiple battery cells or cell groups is shown. Aspects of the implementation of method 2000B can be performed, for example, by a battery management system, a battery management device, and / or an integrated circuit chip. One or more steps of method 2000B are optional and / or can be modified by one or more steps of other embodiments described herein. Additionally, one or more steps of other embodiments described herein can be added to method 2000B. In some embodiments, the BMS can measure or receive temperature data of the battery pack (2050B). The BMS receives a series of current data (2100B) and a series of voltage data (2150B) for each battery cell. In some embodiments, this series of current and voltage data is collected in real time. In some embodiments, this series of current and voltage data is collected during battery pack operation. In some cases, this series of current and voltage data is associated with time information (e.g., timestamps).

[0189] Next, the BMS determines the capacity of each battery cell (2170B). In one embodiment, the capacity is determined by integrating the current (I) of the corresponding battery cell over time (t). In one embodiment, the capacity of the battery cell can be determined using the expression (33) above. The BMS calculates the ampere-hour-based SOC (Ah-SOC) of each battery cell based on the current data of the corresponding battery cell (2200B). In some embodiments, the Ah-SOC is determined based on at least a portion of the series of current data for the corresponding battery cell. In one embodiment, the Ah-SOC of the battery cell is calculated based on the integration of at least a portion of the series of current data for the corresponding battery cell. In some cases, the Ah-SOC of the battery cell is determined based on the capacity of the corresponding battery cell. In some cases, the capacity of the battery cell is represented by the integral of the current over the time from SOC 0 to SOC 1.

[0190] In one case, the Ah-SOC of battery cell i in the battery pack at time t can be estimated using the following expression (38).

[0191]

[0192] Among them, SOC Ah，i (t) is the Ah-SOC of battery cell i at time t, and SOC0 is the SOC estimate at t0. i (t) is the current data of battery cell i measured at time t, and Capacity. iThis refers to the capacity of battery cell i. In one implementation, the current measurement result can be the current of the battery pack.

[0193] Furthermore, the BMS can determine the resistance of each battery cell (2220B). The resistance of a battery cell is typically affected by temperature. In some embodiments, the BMS can retrieve a SOC-OCV model (2240B), where OCV is the open-circuit voltage of the battery cell. In some embodiments, the SOC-OCV model represents a nonlinear relationship. Figure 14 An example of a SOC-OCV model and / or function is shown. In the illustrative example, SOC and OCV have a nonlinear relationship. In some cases, the SOC-OCV model is affected by temperature, causing the BMS to retrieve the SOC-OCV model based on the current temperature at the time of SOC estimation.

[0194] Furthermore, the BMS can calculate the voltage-based SOC (V-SOC) (2250B) of each battery cell. In some embodiments, the V-SOC of the battery cell is determined based on at least a portion of the series of voltage data for the corresponding battery cell. In some embodiments, the V-SOC is determined based on at least a portion of the series of current data. In some cases, the battery cell has a known SOC-OCV relationship, where OCV refers to the open-circuit voltage of the battery. In embodiments, the BMS estimates the OCV of the battery cell and determines the V-SOC based on the reciprocal of the SOC-VOC relationship. In some cases, the OCV is estimated using the current measurement results and the current voltage measurement results at the time. In some cases, the OCV is estimated using the internal resistance of the battery as a parameter, where the internal resistance of the battery changes with temperature. In embodiments, the V-SOC is further determined based on the inverse open-circuit voltage function. In embodiments, the V-SOC is further determined by applying a filter.

[0195] In one case, the V-SOC of battery cell i at time t can be estimated using the following expression (39):

[0196] SOC V，i (t)=f(OCV -1 (V i (t)+I i (t)R 0，i (T)) (39)

[0197] Among them, SOC V (t) is the V-SOC at time t, f() is the filter function, and OCV -1 () is the inverse function / model of SOC-OCV, V i (t) is the current data of battery cell i measured at time t, I i(t) represents the current data of battery cell i measured at time t, and R 0，i (T) is the estimated resistance of battery cell i based on the measured temperature T. In some cases, this filter is a low-pass filter.

[0198] In some implementations, the BMS determines the upper bound of the State of Charge (SOC) based on the Ah-SOC and V-SOC values ​​of the battery cells in the battery pack (2300B). In some cases, the upper bound of the SOC is determined based on the maximum value of the Ah-SOC and V-SOC of the battery cells in the battery pack. In some cases, the upper bound of the SOC is determined at least partially based on the difference between the Ah-SOC and V-SOC of the battery cells. In one implementation, the upper bound of the SOC is determined at least partially based on the maximum difference between Ah-SOC and V-SOC among the absolute differences between Ah-SOC and V-SOC of all battery cells. In some cases, the upper bound of the SOC is determined, for example, at least partially based on the design accuracy of the battery, to fine-tune the estimate. In one implementation, the upper bound of the SOC is determined at least partially based on the maximum value of the Ah-SOC and V-SOC of the battery cells in the battery pack plus the maximum value of the difference between the Ah-SOC and V-SOC of the battery cells.

[0199] In one case, the upper bound of the SOC estimate of the battery pack at time t can be determined using the following expression (40).

[0200] SOC Ubnd (t)=max{SOC Ah，i (t), SOC V，i (t)}+max{|SOC Ah，i (t)-SOC V，i (t)|}+E (40)

[0201] Among them, SOC Ubnd (t) is the upper bound of the SOC estimate at time t, where SOC Ah，i (t) is Ah-SOC at time t, SOC V，i (t) is the V-SOC at time t, and E is the estimated design accuracy (e.g., 3%). In some cases, |SOC Ah，i (t)-SOC V，i (t)| can be an indication of sensing error and / or model error. In this implementation, the maximum value of Ah-SOC and V-SOC is used. In other implementations, different selection criteria for Ah-SOC and V-SOC can be used.

[0202] In some implementations, the BMS determines the lower bound of SOC (2350B) based on the Ah-SOC and V-SOC values ​​of the battery cells in the battery pack. In some cases, the lower bound of SOC is determined based on the minimum of the Ah-SOC and V-SOC values ​​of the battery cells in the battery pack. In some cases, the lower bound of SOC is determined at least partially based on the difference between the Ah-SOC and V-SOC values ​​of the battery cells. In some cases, the lower bound of SOC is determined, for example, at least partially based on the design accuracy of the battery, to fine-tune the estimate. In one implementation, the lower bound of SOC is determined based on the maximum value of the difference between the Ah-SOC and V-SOC values ​​of the battery cells in the battery pack, respectively.

[0203] In one case, the lower bound of the SOC estimate of the battery and / or battery cell at time t can be determined using the following expression (41).

[0204] SOC Lbnd (t)=min{SOC Ah，i (t), SOC V，i (t)}-max{|SOC Ah i (t)-SOC V i (t)|}-E (41)

[0205] Among them, SOC Lbnd (t) is the lower bound of the SOC estimate at time t, where SOC Ah，i (t) is Ah-SOC at time t, SOC V，i (t) is the V-SOC at time t, and E is the estimated design accuracy (e.g., 3%). In some cases, |SOC Ah，i (t)-SOC V，i (t)| can be an indication of sensing error and / or model error. In this implementation, the minimum of Ah-SOC and V-SOC is used. In other implementations, different selection criteria for Ah-SOC and V-SOC can be used.

[0206] In some implementations, the BMS can generate a bounded SOC estimate with an upper and lower SOC limit (2400B). In some cases, the BMS can, for example, compare the SOC measurement of the battery cell with the upper and lower SOC limits during operation to determine the bounded SOC value. In one example, if the corresponding SOC measurement is greater than the upper SOC estimate, the BMS sets the bounded SOC value as the upper SOC limit. In another example, if the corresponding SOC measurement is less than the lower SOC limit, the BMS sets the bounded SOC value as the lower SOC limit. In some cases, if the corresponding SOC measurement is less than or equal to the upper SOC limit and the corresponding SOC measurement is greater than or equal to the lower SOC limit, the BMS sets the bounded SOC value to the corresponding SOC measurement. In some cases, the bounded SOC estimate also includes a bounded SOC value. In some designs, the SOC measurement results are received from a conventional SOC measurement device.

[0207] Now refer to Figure 12A A schematic diagram is shown for use in the BMS 3000A. In some cases, the BMS 3000A is configured to implement any of the embodiments described herein. In this example, the BMS 3000A includes a plurality of input terminals 3100A, which include, for example, a current data terminal 3120A configured to receive current data 3002A, a temperature data terminal 3130A configured to receive temperature data 3003A, a voltage data terminal 3140A configured to receive voltage data 3004A, and / or a SOC measurement terminal 3160A. In this example, the BMS 3000A is configured to be coupled to a SOC measurement device / chip 3300A. The SOC measurement device / chip 3300A is configured to receive current data 3002A at terminal 3320A, temperature data 3003A at terminal 3330A, and voltage data 3004A at terminal 3340A, and output the SOC measurement result via terminal 3420A. The BMS 3000A receives SOC measurement results via terminal 3160A. Based on the input data received from input terminal 3100A, the BMS 3000A is configured to generate multiple output data at the plurality of output terminals 3200A. In one embodiment, the output terminals 3200A include: a bounded SOC terminal 3220A configured to output a bounded SOC estimate 3520A, an upper bound terminal 3240A configured to output an upper SOC limit 3540A, and / or a lower bound terminal 3260A configured to output a lower SOC limit 3560A.

[0208] Now refer to Figure 12BA schematic diagram is shown for use in a BMS 3000B. In some cases, the BMS 3000B is configured to implement any of the embodiments described herein. In this example, the BMS 3000B has multiple input terminals 3100B, including, for example, a current terminal 3120B configured to receive current data, a temperature terminal 3130B configured to receive temperature data, a voltage terminal 3140B configured to receive voltage data, and a SOC terminal 3160B configured to receive SOC measurement results. The current terminal 3120B is connected to an Ah-based SOC estimator 3200B and a V-based SOC estimator 3250B. The temperature terminal 3130B and the voltage terminal 3140B are connected to the V-based SOC estimator 3250B. The Ah-based SOC estimator 3200B is configured to output an Ah-SOC estimate 3220B based on the current data. Configure the V-based SOC estimator 3250B to output V-SOC estimates based on current data, temperature data, and voltage data 3270B.

[0209] The Ah-SOC estimator 3200B and V-SOC estimator 3250B are configured to directly or indirectly provide Ah-SOC estimates 3220B and V-SOC estimates 3270B to a number of operators used for various calculations, including, for example, 3300B, 3320B, 3340B, 3360B, 3420B, 3440B, 3520B, 3550B, and 3580B. In one embodiment, operator 3300B is a maximum value operator configured to select the maximum value based on the input; operator 3360B is a minimum value operator configured to select the minimum value based on the input; and operators 3320B and 3340B are differential operators configured to compute the difference between the inputs. In one embodiment, operators 3420B and 3440B are absolute value operators configured to generate the absolute value of the input. In one implementation, operators 3520B and 3550B are arithmetic operators configured to apply arithmetic calculations to the input. In this example, BMS 3000B includes design accuracy parameters of the battery (e.g., battery cells, battery packs, and / or cell arrays) stored or received at 3460B. Operator 3520B is configured to generate an upper SOC bound 3540B. Operator 3550B is configured to generate a lower SOC bound 3560B.

[0210] Operator 3580B is configured to receive SOC measurement results via terminal 3160B, SOC upper bound 3540B, and SOC lower bound 3560B, and determine a bounded SOC value 3590B, for example, to determine a bounded SOC value. In one embodiment, operator 3580B executes a saturation function. In one example, if the corresponding SOC measurement result is greater than the SOC upper bound estimate, operator 3580B sets the bounded SOC value as the SOC upper bound. In one example, if the corresponding SOC measurement result is less than the SOC lower bound, operator 3580B sets the bounded SOC value as the SOC lower bound. In some cases, if the corresponding SOC measurement result is less than or equal to the SOC upper bound and the corresponding SOC measurement result is greater than or equal to the SOC lower bound, operator 3580B sets the bounded SOC value as the corresponding SOC measurement result. In some cases, the bounded SOC estimate includes: a bounded SOC value, an SOC upper bound, and a SOC lower bound. In this example, the BMS 3000B includes multiple output terminals 3600B for outputting bounded SOC estimation. These output terminals include, for example, a bounded SOC terminal 3620B for outputting a bounded SOC value of 3590B, an upper SOC terminal 3640B for outputting an upper SOC limit of 3540B, and a lower SOC terminal 3660B for outputting a lower SOC limit of 3560B.

[0211] Now refer to Figure 13 A schematic diagram of a BMS 4000 is shown. The BMS 4000 is configured to implement any of the methods 1000A, 1000B, 2000A, and / or 2000B as described herein. In this example, the BMS 4000 includes a sensor group 4005, a SOC estimator 4300, and a memory 4400. In some embodiments, the sensor group 4005 includes a temperature sensor 4100 and / or a current / voltage sensor 4200. In one embodiment, the SOC estimator 4300 is implemented on a computing device (e.g., a processor or processing unit). In one embodiment, the SOC estimator 4300 is used... Figure 12A and / or Figure 12B The schematic diagram shown illustrates this implementation. In some cases, the memory 4400 may include, for example, a data storage library 4500 that stores current data, voltage data, temperature data, SOC measurement results, bounded SOC estimates, etc.

[0212] In some embodiments, a computing device (e.g., a SOC estimator 4300) includes buses that directly and / or indirectly connect to devices such as processors, memory, input / output (I / O) ports, I / O components, and power supplies. The computing device may also include any number of additional components, different components, and / or combinations of components. A bus may represent one or more buses (e.g., address buses, data buses, or combinations thereof). Similarly, in some embodiments, the computing device may include multiple processors, multiple memory components, multiple I / O ports, multiple I / O components, and / or multiple power supplies. Furthermore, any number of these components or combinations of components may be distributed and / or replicated across multiple computing devices.

[0213] In one embodiment, memory 4400 includes a computer-readable medium in the form of volatile and / or non-volatile memory, transient and / or non-transient storage media, and may be removable, non-removable, or a combination thereof. Examples of media include: random access memory (RAM); read-only memory (ROM); electronically erasable programmable read-only memory (EEPROM); flash memory; optical or holographic media; magnetic tape cassettes, magnetic tape, disk storage units, or other magnetic storage devices; data transfer; and / or any other medium that can be used to store information and can be accessed by a computing device (e.g., quantum state memory). In some embodiments, memory 4400 stores computer-executable instructions for causing a processor (e.g., SOC estimator 4300) to implement aspects of embodiments of the system components discussed herein and / or perform aspects of embodiments of the methods and processes described herein.

[0214] Computer-executable instructions may include, for example, computer code, machine-usable instructions, and program components that can be executed by one or more processors associated with a computing device. Any number of different programming environments can be used to program these program components, including various languages, development kits, frameworks, etc. Some or all of the functionalities envisioned herein may also be implemented, or alternatively, in hardware and / or firmware.

[0215] Data store 4500 can be implemented using any of the configurations described below. The data store may include: random access memory, flat files, XML files, and / or one or more database management systems (DBMS) running on one or more database servers or data centers. The database management system may be a relational (RDBMS), hierarchical (HDBMS), multidimensional (MDBMS), object-oriented (ODBMS or OODBMS), or object-relational (ORDBMS) database management system, etc. The data store may, for example, be a single relational database. In some cases, the data store may include multiple databases that can exchange and aggregate data through data integration processing or software applications. In exemplary embodiments, at least a portion of the data store 4500 may be hosted in a cloud data center. In some cases, the data store may be hosted on a single computer, server, storage device, cloud server, etc. In some other cases, the data store may be hosted on a network of networked computers, servers, or devices. In some cases, the data store may be hosted at a tier of data storage devices, including local, regional, and central levels.

[0216] Various components of the BMS 4000 can communicate or be connected to a communication interface (e.g., a wired or wireless interface). Communication interfaces include, but are not limited to, any wired or wireless short-range and long-range communication interfaces. Wired interfaces can use cables, wires, etc. Short-range communication interfaces can be, for example, local area networks (LANs) compliant with known communication standards (such as...). Standards, IEEE 802 standards (e.g., IEEE 802.11), Interfaces can be based on similar standards (such as those based on the IEEE 802.15.4 standard) or other public or proprietary wireless protocols. Long-range communication interfaces can be, for example, wide area network (WAN) interfaces, cellular network interfaces, satellite communication interfaces, etc. Communication interfaces can be used within private computer networks such as intranets, or on public computer networks such as the Internet.

[0217] Now refer to Figure 15A and Figure 15B ,Should Figure 15A and Figure 15B An example of bounded SOC estimation is shown. Figure 15A An example of a bounded SOC estimate with relatively close upper and lower bounds is shown, such that the difference between the upper and lower bounds is relatively small. Figure 15BAn example of bounded SOC estimation with relatively distinct upper and lower bounds is shown, such that the difference between the upper and lower bounds is relatively large. In one example, if the difference between the upper and lower SOC bounds is greater than a predetermined threshold, the BMS can be configured to recalibrate the sensor and / or the SOC estimator.

[0218] Additional exemplary embodiments of the foregoing aspects of this disclosure are described below.

[0219] A method for estimating the state of charge (SOC) of a battery is provided, the method comprising the steps of: receiving a series of current data indicating measurements of current flowing through the battery; receiving a series of voltage data indicating measurements of voltage in the battery; using the series of current data to calculate an ampere-hour-based SOC estimate (Ah-SOC); using the series of voltage data and the series of current data to calculate a voltage-based SOC estimate (V-SOC); and generating a bounded SOC estimate including an upper bound and a lower bound. The upper bound of SOC is determined based on the larger of Ah-SOC and V-SOC; and the lower bound of SOC is determined based on the smaller of Ah-SOC and V-SOC.

[0220] Furthermore, the Ah-SOC is determined based on the integration of at least a portion of the series of current data, and the Ah-SOC is also determined based on the battery capacity. Additionally, the V-SOC is determined based on at least one voltage data point from the series of voltage data.

[0221] The method further includes the steps of: receiving temperature data; and determining the battery's internal resistance based at least in part on the temperature data. The V-SOC is determined based on at least one of the battery's current data and internal resistance.

[0222] Furthermore, V-SOC is determined based on the inverse function of the open-circuit voltage. Additionally, V-SOC is further determined by applying a low-pass filter.

[0223] Furthermore, the upper bound of SOC is determined based on the difference between Ah-SOC and V-SOC. Additionally, it is determined at least in part based on the larger of Ah-SOC and V-SOC plus the absolute value of the difference between Ah-SOC and V-SOC. Moreover, the upper bound of SOC is determined at least in part based on the accuracy of the battery design.

[0224] Additionally, the lower bound of SOC is determined at least in part based on the absolute value of the difference between Ah-SOC and V-SOC, which is the larger of Ah-SOC and V-SOC. Furthermore, the lower bound of SOC is determined at least in part based on the design accuracy of the battery.

[0225] The method further includes the following steps: receiving a SOC measurement result; and comparing the SOC measurement result with an upper and lower bound of SOC to determine a bounded SOC value. If the SOC measurement result is greater than the upper bound of SOC, the bounded SOC value is set as the upper bound of SOC. If the SOC measurement result is less than the lower bound of SOC, the bounded SOC value is set as the lower bound of SOC. If the SOC measurement result is less than or equal to the upper bound of SOC and greater than or equal to the lower bound of SOC, the bounded SOC value is set as the SOC measurement result. Bounded SOC estimation also includes a bounded SOC value.

[0226] A method is provided for estimating the state of charge (SOC) of a battery pack having multiple battery cells, the method comprising the steps of: receiving a series of current data for each of the multiple battery cells, the series of current data indicating the measurement results of current flowing through the battery cells; receiving a series of voltage data for each of the multiple battery cells, the series of voltage data indicating the measurement results of voltage of the battery cells; using the series of current data for the respective battery cells to calculate an ampere-hour-based SOC estimate (Ah-SOC) for each of the multiple battery cells; using the series of voltage data and the series of current data for the respective battery cells to calculate a voltage-based SOC estimate (V-SOC) for each of the multiple battery cells; and generating a bounded SOC estimate including an upper bound and a lower bound. The upper limit of SOC is determined based on the Ah-SOC and V-SOC values ​​of the plurality of battery cells, and the lower limit of SOC is determined based on the Ah-SOC and V-SOC values ​​of the plurality of battery cells.

[0227] Furthermore, the Ah-SOC of the battery cell is determined based on the integration of at least a portion of the series of current data for the battery cell. Additionally, the Ah-SOC of the battery cell is determined based on its capacity. Moreover, the V-SOC of the battery cell is determined based on at least a portion of the series of voltage data for the battery cell.

[0228] The method further includes the steps of: receiving temperature data; and determining the internal resistance of the battery cell based at least in part on the temperature data. The V-SOC of the battery cell is determined based on at least a portion of the series of current data and internal resistance of the battery cell.

[0229] Furthermore, the V-SOC of the battery cell is determined based on the inverse function of the open-circuit voltage. Additionally, the V-SOC of the battery cell is determined by applying a low-pass filter.

[0230] Furthermore, the upper bound of SOC is determined at least in part based on the maximum value of the Ah-SOC and V-SOC of the plurality of battery cells. Additionally, the upper bound of SOC is determined based on the difference between the Ah-SOC and V-SOC of the plurality of battery cells.

[0231] Furthermore, the upper bound of the SOC is determined at least in part based on the absolute value of the largest difference between the larger of the Ah-SOC and V-SOC of the plurality of battery cells and the largest difference between the differences between the Ah-SOC and V-SOC of the plurality of battery cells. Additionally, the upper bound of the SOC is determined at least in part based on the design accuracy of the battery.

[0232] Furthermore, the lower bound of SOC is determined at least in part based on the maximum value of the Ah-SOC and V-SOC of the plurality of battery cells. Additionally, the lower bound of SOC is also determined based on the difference between the Ah-SOC and V-SOC of the plurality of battery cells.

[0233] Furthermore, the lower bound of SOC is determined at least in part based on the absolute value of the maximum difference between the minimum of the Ah-SOC and V-SOC of the plurality of battery cells and the maximum difference between the differences between the Ah-SOC and V-SOC of the plurality of battery cells. Additionally, the lower bound of SOC is determined at least in part based on the design accuracy of the battery.

[0234] The method further includes the steps of: receiving one or more SOC measurement results for the plurality of battery cells; and comparing the SOC measurement results with an upper SOC bound and a lower SOC bound to determine a bounded SOC value. If the SOC measurement result is greater than the upper SOC bound, the bounded SOC value is set as the upper SOC bound. If the SOC measurement result is less than the lower SOC bound, the bounded SOC value is set as the lower SOC bound. If the SOC measurement result is less than or equal to the upper SOC bound and greater than or equal to the lower SOC bound, the bounded SOC value is set as the SOC measurement result. Bounded SOC estimation also includes a bounded SOC value.

[0235] A method for performing power estimation processing of an electric vehicle using a controller is provided, the method comprising the steps of: performing power estimation processing by estimating the internal state of an energy storage power source of the electric vehicle; estimating at least one of a state of charge (SOC) value and a state of health (SOH) value of the energy storage power source based on at least one of the following: a current level, a current voltage level, and a current temperature associated with the energy storage power source, and time-based information; calculating a first upper bound and a first lower bound associated with the SOC value; estimating a bounded SOC value of the energy storage power source based on the SOC value, the first upper bound, and the first lower bound; calculating a second upper bound and a second lower bound associated with the SOH value; estimating a bounded SOH value of the energy storage power source based on the SOH value, the second upper bound, and the second lower bound; and controlling the electrification processing of the electric vehicle based on at least one of the bounded SOC value and the bounded SOH value. The internal state represents at least one of the following: the SOC and SOH of the energy storage power source.

[0236] Any step of the methods mentioned above is used to control the electrification of electric vehicles.

[0237] A vehicle is provided that includes a controller, the controller including a processor and a memory having processing instructions that, when executed by the processor, are operable to implement any of the methods mentioned above.

[0238] A battery management system is provided, comprising a controller including a processor and a memory having processing instructions that, when executed by the processor, are operable to implement any of the methods mentioned above. A vehicle is provided that includes the battery management system.

[0239] It should be understood that the connecting lines shown in the various figures contained herein are intended to represent exemplary functional relationships and / or physical connections between various elements. It should be noted that many alternative or additional functional relationships or physical connections may exist in a practical system. However, any benefit, advantage, solution to a problem, or element that makes any benefit, advantage, or solution present or more significant is not considered a critical, essential, or fundamental feature or element. Therefore, the scope is not limited by anything other than the appended claims, wherein, unless expressly stated otherwise, reference to an element in the singular does not mean "one and only one," but rather "one or more." Furthermore, where phrases like "at least one of A, B, or C" are used in the claims, it is intended to be interpreted as meaning that in an embodiment, a single A may exist, in an embodiment, a single B may exist, in an embodiment, a single C may exist, or in a single embodiment, any combination of elements A, B, or C may exist; for example, A and B, A and C, B and C, or A and B and C.

[0240] In the detailed description herein, references to "one embodiment," "an embodiment," "an example embodiment," etc., indicate that the described embodiment may include a particular feature, structure, or characteristic, but each embodiment may not necessarily include that particular feature, structure, or characteristic. Furthermore, such phrases do not necessarily refer to the same embodiment. Moreover, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is proposed that such feature, structure, or characteristic be implemented, within the knowledge of those skilled in the art who benefit from this disclosure, in conjunction with other embodiments, whether explicitly described or not. After reading this description, those skilled in the art should understand how this disclosure can be implemented in alternative embodiments.

[0241] Furthermore, regardless of whether an element, component, or method step is expressly stated in the claims, the elements, components, or method steps in this disclosure are not intended for public use only. None of the elements of the claims herein should be construed in accordance with 35 U.S.SC 112(f) unless the element is expressly stated using the phrase “means for…”. As used herein, the terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements does not necessarily include only those elements, but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0242] Various modifications and additions may be made to the exemplary embodiments discussed without departing from the scope of the subject matter disclosed herein. For example, while the above embodiments relate to specific features, the scope of this disclosure also includes embodiments having different combinations of features and embodiments that do not include all of the features described. Therefore, the scope of the subject matter disclosed herein is intended to cover all such alternatives, modifications, variations, and all equivalents falling within the scope of the claims.

Claims

1. A controller for performing power estimation processing for an electric vehicle, the controller comprising: processor; as well as The memory includes instructions that, when executed by the processor, cause the controller to: The power estimation process is performed by estimating the internal state of the energy storage power source of the electric vehicle, wherein the internal state represents the state of charge (SOC) of the energy storage power source. The SOC value of the energy storage power source is estimated based on at least one of the following: the current level associated with the energy storage power source, the current voltage level, the current temperature, and time-based information. as well as Calculate the upper and lower bounds associated with the SOC value, and estimate the bounded SOC value of the energy storage power source based on the SOC value, the upper bound, and the lower bound; Wherein, the energy storage power source is a battery, and when the instruction is executed by the processor, it also causes the controller to: Receive a series of current data, which indicates the measurement results of the current flowing through the battery; Receive a series of voltage data, the series of voltage data indicating the measurement results of the voltage of the battery; The aforementioned series of current data is used to calculate the Ampere-Hour SOC estimate Ah-SOC; The voltage SOC estimate V-SOC is calculated using the aforementioned series of voltage data and the aforementioned series of current data; and Generate the bounded SOC value of the battery, the bounded SOC value including the upper bound and the lower bound; The upper bound is determined based on the larger of the Ah-SOC and the V-SOC, and the lower bound is determined based on the smaller of the Ah-SOC and the V-SOC. Compare the SOC measurement results with the upper and lower bounds; and In response to determining that the SOC measurement result is greater than the upper bound, the bounded SOC value is set as the upper bound. The controller controls the electrification process of the electric vehicle based on the bounded SOC value.

2. The controller according to claim 1, wherein, The time-based information includes one or more historical estimates of the SOC value.

3. The controller according to claim 1, wherein, When the instruction is executed by the processor, it also causes the controller to estimate the bounded SOC value based on whether a predetermined period of time has elapsed.

4. The controller according to claim 1, wherein, The controller controls the electrification process by at least one of the following: modifying the cooling of the energy storage power source, modifying the charging / discharging limit of the energy storage power source, reducing the number of charging / discharging cycles of the energy storage power source, and modifying the minimum SOC threshold.

5. The controller according to claim 1, wherein, The controller includes dual nonlinear Kalman filters.

6. A method for performing power estimation processing for an electric vehicle using a controller, the method comprising the following steps: The power estimation process is performed by estimating the internal state of the energy storage power source of the electric vehicle, wherein the internal state represents the state of charge (SOC) of the energy storage power source. The SOC value of the energy storage power source is estimated based on at least one of the following: the current level, current voltage level, and current temperature associated with the energy storage power source, and time-based information; Calculate the upper and lower bounds associated with the SOC value; The bounded SOC value of the energy storage power source is estimated based on the SOC value, the upper bound, and the lower bound. Wherein, the energy storage power source is a battery, and the method further includes: Receive a series of current data, which indicates the measurement results of the current flowing through the battery; Receive a series of voltage data, the series of voltage data indicating the measurement results of the voltage of the battery; The aforementioned series of current data is used to calculate the Ampere-Hour SOC estimate Ah-SOC; The voltage SOC estimate V-SOC is calculated using the aforementioned series of voltage data and the aforementioned series of current data; and Generate the bounded SOC value of the battery, the bounded SOC value including the upper bound and the lower bound; The upper bound is determined based on the larger of the Ah-SOC and the V-SOC, and the lower bound is determined based on the smaller of the Ah-SOC and the V-SOC. Compare the SOC measurement results with the upper and lower bounds; and In response to determining that the SOC measurement result is greater than the upper bound, the bounded SOC value is set as the upper bound. The electrification process of the electric vehicle is controlled based on the bounded SOC value.

7. The method according to claim 6, wherein, The time-based information includes one or more historical estimates of the SOC value.

8. The method according to claim 6, wherein, The bounded SOC value is also estimated based on whether a predetermined time period has elapsed.

9. The method according to claim 6, wherein, The power estimation process is performed using a dual nonlinear Kalman filter.

10. The method according to claim 6, wherein, Controlling the electrification process includes at least one of the following: modifying the cooling of the energy storage power source, modifying the charging / discharging limit of the energy storage power source, reducing the number of charging / discharging cycles of the energy storage power source, and modifying the minimum SOC threshold.

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