Method and system for verifying health condition of battery of electric vehicle

Abstract

Methods and systems for managing operation of a vehicle traction battery pack are described herein. These methods and systems may be used to improve estimation of battery health, battery state of charge, and battery DC resistance. The method may be used when charging a traction battery.

Description

Technical Field

[0001] The present disclosure relates to a method and system for providing a closed-loop estimation of battery health and DC resistance. Background and Overview

[0002] The vehicle's traction battery may include a battery pack consisting of battery cells connected in series and in parallel. The battery pack may be charged and discharged while the vehicle is in operation. In addition, the battery pack may be charged when the vehicle is not in operation. The vehicle may report the battery state of charge (SOC) and battery health (SOH). The battery state of charge may be described as the ratio of the amount of charge stored in the battery to the total charge of the battery multiplied by 100. The battery health may be described as the ratio of the maximum charge capacity of the battery to the rated charge capacity of the battery (e.g., the maximum charge that can be stored in a new battery) multiplied by one hundred.

[0003] Many battery manufacturers include DC resistance in their State of Health (SOH) calculations. Some battery manufacturers will indicate the State of Power (SOP). This is a measurement of the maximum pulse or continuous discharge current allowed. The State of Power is usually expressed as a percentage of the peak rated discharge power that will not cause the battery to exceed its voltage limit. The voltage limit generally follows Ohm's Law: Vmax = V(battery at rest) + (Imax / DC resistance). As DC resistance increases, the allowed current is also reduced to avoid an overvoltage condition. For example, the rated current allowed for a battery pack is 100A. Due to the increase in DC resistance, the peak current may be reduced to 90A. In this case, the SOP will be displayed as (90A / 100A =) 90% SOP.

[0004] Battery manufacturers can estimate the expected life of a battery by analyzing the battery chemistry. In the laboratory, the battery manufacturer may estimate the battery life by subjecting the battery to charge / discharge, temperature changes, and other factors that may affect the battery life. The battery manufacturer can measure the change in battery cell capacity and the change in the internal resistance of the battery. Based on these measurements, the manufacturer can generate one or more formulas for estimating the battery life, which can be applied to the battery used outside the laboratory. In particular, one or more formulas can be incorporated into a battery management system (BMS) so that the BMS can predict the aging of the battery throughout its life. The BMS can output the estimated battery SOH value to other vehicle systems. The estimated battery SOH value can be based on multiple control parameters, such as but not limited to ambient temperature, battery age, etc.

[0005] The accuracy of the battery SOH value estimated by the BMS may be limited because the estimate is a feed-forward or open-loop estimate and lacks feedback. The battery SOH estimate may be further limited by original test factors that may not compensate for variations in the manufacturing process, changes in the battery after installation, and battery temperature that may not be monitored when the vehicle is turned off. In addition, the battery SOH value estimated under laboratory conditions in use may not be as accurate as the battery SOH value estimated under laboratory conditions using a laboratory-generated formula. For at least the reasons stated above, it would be desirable to provide an improved method for estimating battery SOH.

[0006] The present inventors have recognized the above problems and developed a method for operating a vehicle battery, including: estimating a health condition of the battery based on a first resistance value and a second resistance value; and adjusting a maximum output of the battery based on the health condition.

[0007] By correcting the DC resistance component of the estimated value of the battery health, a technical result of generating a battery health status including feedback compensation can be provided. The feedback compensation can provide a more accurate estimate of the battery health status. The corrected battery health status can be used as a basis for releasing or limiting the output of the battery pack.

[0008] The present disclosure may provide several advantages. In particular, the method may provide a more accurate estimate of the health of a battery pack by incorporating feedback correction. In addition, the method may provide an accurate estimate of the battery DC resistance and the battery health. In addition, the method may be implemented in a vehicle under practical conditions.

[0009] It should be understood that the above summary is intended to introduce in simplified form the concepts that are further described in the detailed description. It is not meant to identify key or essential features of the claimed subject matter, the scope of which is solely determined by the claims that follow the detailed description. Furthermore, the claimed subject matter is not limited to implementations that solve any disadvantages noted above or in any part of this disclosure. Drawing Description

[0010] Figure 1 is a schematic diagram of an example vehicle including an electric energy storage system.

[0011] Figure 2 A method for determining the DC resistance of an electrical energy storage system is shown.

[0012] Figure 3 The method for correcting the SOH and DCR estimates is shown.

[0013] Figure 4 An example of the relationship between DCR and SOH is shown.

[0014] Figure 5 shows a block diagram for generating an open-loop estimate of DCR.

[0015] Figure 6 An example of a baseline battery resistance table is shown. Detailed description

[0016] A method and system for operating an automotive battery pack is described herein. The method and system are applicable to electric vehicles and hybrid vehicles. In one example, the method and system generates a battery health status based on the DC resistance of the battery pack. The battery health status can be broadcasted to other vehicle systems, and the output of the battery pack can be limited based on the DC resistance value. The method and system can be applied to Figure 1 Vehicle of the type shown. Figure 2 and Figure 3 A method may be included in a vehicle to generate a health status value. Figure 4 The relationship between battery DC resistance and battery health is shown. Figure 5 shows a block diagram for generating a feed-forward or open-loop battery DC resistance estimate, Figure 6 An example of a baseline battery resistance table is shown.

[0017] Figure 1 An example of a vehicle propulsion system 199 for a vehicle 10 is shown. A front end 110 of the vehicle 10 is labeled, and a rear end 111 of the vehicle 10 is also labeled. When the front of the vehicle moves the vehicle 10, the vehicle 10 moves in a forward direction. When the rear of the vehicle moves the vehicle 10, the vehicle 10 moves in a reverse direction. The vehicle propulsion system 199 includes a propulsion source 105 (e.g., a motor), but two or more propulsion sources may be provided in other examples. In one example, the propulsion source 105 may be a motor that operates as a motor or a generator. The propulsion source 105 is fixed to an electrified axle 190. Figure 1 In the figure, the mechanical connections between the components are represented by solid lines, while the electrical connections between the components are represented by dotted lines.

[0018] The vehicle propulsion system 199 includes an electrified axle 190 (e.g., an axle including an integrated electric machine that provides propulsion for the vehicle). The electrified axle 190 may include two half-axles, including a first or right half-axle 190a and a second or left half-axle 190b. The vehicle 10 also includes front wheels 102 and rear wheels 103.

[0019] The electrified axle 190 may be an integrated axle including a differential gear 106, a gear set 107, and a propulsion source 105. The electrified axle 190 may include a first speed sensor 119 for sensing the speed of the propulsion source 105, a second speed sensor 122 for sensing the speed of the output shaft (not shown), a first clutch actuator 112, and a clutch position sensor 113. The power inverter 115 is electrically coupled to the propulsion source 105. The axle control unit 116 is electrically connected to the sensors and actuators of the electrified axle 190.

[0020] The propulsion source 105 can transmit mechanical power to or receive mechanical power from the gear set 107. Therefore, the gear set 107 can be a multi-speed gear set that can shift between gears when a command is issued through the axle control unit 116. The axle control unit 116 includes a processor 116a and a memory 116b. The memory 116b may include a read-only memory, a random access memory, and a keep-alive memory. The gear set 107 can transmit mechanical power to or receive mechanical power from the differential gear 106. The differential gear 106 can transmit mechanical power to or receive mechanical power from the rear wheel 103 through the right half shaft 190a and the left half shaft 190b. The propulsion source 105 can consume alternating current (AC) power provided by the power inverter 115. Alternatively, the propulsion source 105 can provide AC power to the power inverter 115. The power inverter 115 can obtain high-voltage direct current (DC) power from the battery 160 (e.g., a traction battery, which can also be referred to as a battery pack). The power inverter 115 can convert the DC power of the battery 160 into AC power for use by the propulsion source 105. In addition, the power inverter 115 can also provide AC power from the propulsion source 105. The power inverter 115 can convert the AC power of the propulsion source 105 into DC power and store it in the battery 160.

[0021] The battery 160 may periodically receive electrical energy from a power source, such as a stationary power grid (not shown) located external to the vehicle (e.g., not part of the vehicle). As a non-limiting example, the vehicle propulsion system 199 may be configured as a plug-in electric vehicle (EV), wherein electrical energy may be supplied to the battery 160 via a power grid (not shown).

[0022] The battery 160 may include a BMS controller 139 (e.g., a battery management system controller) and a distribution box 162. The battery controller 139 may provide charge balancing between energy storage elements (e.g., battery cells) and communicate with other vehicle controllers (e.g., vehicle control unit 152). The BMS controller 139 also includes a memory 139a (e.g., a read-only memory and a random access memory) and a central processing unit 139b.

[0023] The vehicle 10 may include a vehicle control unit (VCU) controller 152 that can communicate with the power inverter 115, the axle control unit 116, the friction or foundation brake controller 170, the global positioning system (GPS) 188, the BMS controller 139, and the instrument panel 130 and the components therein via a controller area network (CAN) 120. The VCU 152 includes a memory 114, which may include a read-only memory (ROM or non-transitory memory) and a random access memory (RAM). The VCU also includes a digital processor or central processing unit (CPU) 161, and inputs and outputs (I / O) 118 (e.g., digital inputs, digital outputs, analog inputs, and analog outputs including counters, timers, and discrete inputs). The VCU can receive signals from sensors 154 and provide control signal outputs to actuators 156. The sensors 154 may include, but are not limited to, lateral acceleration sensors, longitudinal acceleration sensors, yaw rate sensors, tilt sensors, temperature sensors, battery voltage and current sensors, and other sensors described herein. In addition, the sensors 154 may also include a steering angle sensor 197, a driver demand pedal position sensor 141, a vehicle ranging sensor (including radio detection and ranging (RADAR), light detection and ranging (LIDAR), sound navigation and ranging (SONAR), and a brake pedal position sensor 151). The actuators may include, but are not limited to, the inverter, transmission controller, display device, human-machine interface, friction brake system, and battery controller described herein.

[0024] The driver demand pedal position sensor 141 is connected to the driver demand pedal 140 for determining the degree of depression of the driver demand pedal 140 by a human 142. The brake pedal position sensor 151 is connected to the brake pedal 150 for determining the degree of depression of the brake pedal 150 by a human 142. The steering angle sensor 197 is configured to determine the steering angle according to the position of the steering wheel 198.

[0025] The vehicle propulsion system 199 is provided with a global positioning system 188 which receives timing and position data from one or more GPS satellites 189. The global positioning system may also include a geographic map in ROM for determining the location of the vehicle 10 and the characteristics of the roads on which the vehicle 10 may be traveling.

[0026] The vehicle propulsion system 199 may also include a dashboard 130 with which the vehicle operator can interact. The dashboard 130 may include a display system 132 configured to display information to the vehicle operator. As a non-limiting example, the display system 132 may include a touch screen or a human-machine interface (HMI) display that enables the vehicle operator to view graphical information and enter commands. In some examples, the display system 132 may be wirelessly connected to the Internet (not shown) via the VCU 152. Thus, in some examples, the vehicle operator may communicate with an Internet website or software application (App) and the VCU 152 via the display system 132.

[0027] The instrument panel 130 may further include an operator interface 136 through which the vehicle operator can adjust the operating state of the vehicle. Specifically, the operator interface 136 can be configured to activate and / or deactivate the operation of the vehicle drive system (e.g., the propulsion source 105) based on the operator input. In addition, the operator can request the axle mode (e.g., parking, reverse, neutral, drive) through the operator interface. Various examples of the operator interface 136 may include an interface that requires a physical device (e.g., a key), which can be inserted into the operator interface 136 to activate the electric axle 190 and the propulsion source 105 and turn on the vehicle 10, or the key can be removed to turn off the electric axle and the propulsion source 105 to turn off the vehicle 10. The electric axle 190 and the propulsion source 105 can be started by supplying power to the propulsion source 105 and / or the power inverter 115. The electric axle 190 and the motor can be deactivated by stopping the supply of power to the electric axle 190 and the propulsion source 105 and / or the power inverter 115. Still other examples may additionally or alternatively use a start / stop button that is manually pressed by an operator to start or stop the electric axle 190 and the propulsion source 105 to start or shut down the vehicle. In other examples, remote electric axle or electric machine activation may be initiated by a remote computing device (not shown), such as a cell phone or smartphone-based system, where the user's cell phone sends data to a server that communicates with the vehicle controller 152 to activate the electric axle 190 including the inverter and electric machine. The spatial orientation of the vehicle 10 is indicated by the axis 175.

[0028] Also shown is a base or friction brake controller 170 of the vehicle 10. The friction brake controller 170 can selectively apply and release the friction brakes (e.g., 172a and 172b) by allowing hydraulic fluid to flow to the friction brakes. The application and release of the friction brakes can prevent the friction brakes from locking the front wheels 102 and the rear wheels 103. Wheel position or vehicle speed sensors 173 can provide wheel speed data to the friction brake controller 170. The vehicle propulsion system 199 can provide torque to the rear wheels 103 to propel the vehicle 10.

[0029] A human or autonomous driver may request a driver requested wheel torque, or a driver requested wheel power, by depressing a driver requested pedal 140 or issuing a driver requested wheel torque / power request to a vehicle controller 152. The vehicle controller 152 may then request the propulsion source 105 to provide torque or power by commanding the axle control unit 116. The axle control unit 116 may command the power inverter 115 to provide the driver requested wheel torque / power through the electrified axle 190 and the propulsion source 105. The power inverter 115 may convert the direct current of the battery 160 into alternating current and supply the alternating current to the propulsion source 105. The propulsion source 105 rotates and transmits torque / power to the gear set 107. The gear set 107 may transmit the torque of the propulsion source 105 to the differential gear 106, which transmits the torque of the propulsion source 105 to the rear wheels 103 through the half shafts 190a and 190b.

[0030] In the case of a driver-requested full release of the pedal, when the speed of the vehicle 10 is greater than a threshold speed, the vehicle controller 152 may request a small amount of negative braking power or regenerative braking power to gradually slow the speed of the vehicle 10. The amount of regenerative braking force requested may be a function of the driver's requested pedal position, battery state of charge (SOC), vehicle speed, and other conditions. If the driver requests full release of the pedal 140, and the vehicle speed is less than a threshold speed, the vehicle controller 152 may request a small amount of positive torque / positive power (e.g., propulsion torque) from the propulsion source 105, which may be referred to as a climbing torque or climbing power. The climbing torque or power may keep the vehicle 10 stationary when the vehicle 10 is on a positive slope.

[0031] A human or autonomous driver may also request a negative or regenerative driver requested braking torque, or a driver requested braking force, by depressing the brake pedal 150 or issuing a driver requested braking force request to the vehicle control unit 152. The vehicle controller 152 may request that a first portion of the driver requested braking force be generated by the electric axle 190 and the propulsion source 105 by instructing the axle control unit 116. Additionally, the vehicle controller 152 may request that a portion of the driver requested braking force be provided by the friction brake 172 through the friction brake controller 170 to provide a second portion of the driver requested braking force.

[0032] After the vehicle controller 152 determines the braking power request, the vehicle controller 152 may instruct the axle control unit 116 to distribute the portion of the braking power requested by the driver to the electrified axle 190. The power inverter 115 may convert the AC power generated by the propulsion source 105 into DC power for storage in the battery 160. The propulsion source 105 may convert the vehicle kinetic energy into AC power.

[0033] The axle control unit 116 includes a predetermined transmission shift schedule according to which the fixed ratio gears of the gear set 107 are selectively engaged and disengaged. The shift schedule stored in the axle control unit 116 selects shift points or shift conditions based on the wheel torque requested by the driver and the vehicle speed.

[0034] therefore, Figure 1 A system for a vehicle is provided, the system comprising: a battery pack comprising a plurality of battery cells; a controller comprising executable instructions stored in a non-transitory memory, the instructions causing the controller to generate a corrected health of the battery pack, the corrected health being based on a health error of the battery pack. In a first example, the system comprises wherein the health error state is based on a baseline state of battery health. In a second example that may include the first example, the system further comprises additional executable instructions causing the controller to adjust vehicle operation in response to the corrected health of the battery pack. In a third example that may include one or both of the first and second examples, the system comprises wherein the corrected health of the battery pack is based on data collected when charging the battery pack. In a fourth example that may include one or more of the first to third examples, the system comprises wherein the corrected health of the battery pack is a health reported by a battery management system plus a health error. In a fifth example that may include one or more of the first to fourth examples, the system comprises wherein the health reported by the battery management system is a feed-forward estimate generated by the battery management system. In a sixth example that may include one or more of the first to fifth examples, the system further comprises additional executable instructions for correcting a DC resistance value of the battery pack.

[0035] Now watch Figure 2 , a method for determining the direct current (DC) resistance of an electrical energy storage system (ESS), such as a battery, is shown. Figure 2 The method can be used as a storage controller (for example Figure 1 The executable instructions in the non-volatile memory (e.g., read-only memory) of the BMS controller 139 in the BMS controller 139 are incorporated into Figure 1 in the system. Figure 2 The method can be used with Figure 1 systems and Figure 3 and Figure 4 In addition, the method may also include changing the physical world by a controller and / or a person Figure 1 Operations on system operating status.

[0036] At 202, method 200 couples an electric vehicle (EV) with an electric vehicle supply equipment (EVSE) (e.g., a charging station), and the vehicle's electrical energy storage device management system (EESDMS) or battery management system (BMS) communicates with the EVSE to determine whether the electric vehicle is electrically coupled to the EVSE in the required manner and completes a system check (e.g., the battery is in a state that can receive charging). If the electric vehicle system check passes and the EVSE is correctly connected to the electric vehicle, method 200 will enter 204. Otherwise, method 200 may stay at 202 or exit.

[0037] At 204, method 200 measures the ESS output voltage before delivering the charging current to the ESS. In one example, the EVSE can determine the ESS output voltage (e.g., the output voltage of the entire battery). Alternatively, method 200 can measure the output voltage of each battery cell in the battery. The measured ESS output voltage (V0) and current (I0) are stored in the controller memory. If the voltage and current of a single battery cell are determined, they can be identified by the battery cell digital indicators Vxxx=(V0) and Ixxx(I0), where Vxxx is the voltage of the battery cell, xxx is the battery cell digital identifier, each x represents an integer, V0 is the measured battery cell voltage, Ixxx is the current of the battery cell, and I0 is the measured battery cell current. Method 200 enters 206.

[0038] At 206, method 200 begins applying the maximum current that the ESS can withstand under the current ESS operating conditions for at least 10 seconds. For example, if the ESS is capable of receiving a current of 100 amperes, the EVSE begins to provide a current of 100 amperes to the ESS for 15 seconds. In addition, within the first two seconds of applying the maximum current to the ESS, the BMS measures the output voltage and current of the ESS. For example, the BMS may determine the ESS output voltage 2 seconds after the EVSE begins to provide the maximum current that the ESS can receive. The voltage of the entire battery pack may be V1, and the current of the entire battery pack may be I1. If the voltage of individual battery cells is to be measured, they can be identified similar to the method mentioned above. Method 200 enters 208.

[0039] At 208, method 200 measures the ESS output voltage and current by the BMS 10 seconds after the maximum current is applied to the ESS. For example, the BMS may determine the ESS output voltage 10 seconds after the EVSE begins to provide the maximum current that the ESS can receive. The voltage may be V2 and the current may be I2.

[0040] It should be noted that the values ​​of V0-V2 and current I0-I2 can be values ​​for the entire battery or values ​​for a single battery cell. If the voltage and current of a single battery cell are available, the V0-V2 and I0-I2 values ​​of each battery cell and the corresponding short DC resistance value and long DC resistance value can be determined. If the voltage of a single battery cell is to be measured, it can be identified similarly to the method mentioned above. Short DC resistance measurements may be more indicative of the pure resistance behavior of the battery cell, while long DC resistance may be more indicative of the resistance behavior of the battery cell and the chemical effects of the battery cell. Both short DC resistance and long DC resistance may indicate aging of the battery cell. Method 200 proceeds to 210.

[0041] At 210, method 200 determines the actual short DC resistance and the actual long DC resistance of the ESS and records them in memory. In one example, the actual short DC resistance of the entire battery can be determined by the following formula: short_DC = (V1-V0) / (I1-I0). If the voltage of individual battery cells is measured, their respective DC short circuit resistance values ​​can be determined in a similar manner and can be identified using the methods mentioned above. The short DC resistance value describes the pure resistance value without the "stress voltage" generated by the ESS chemical effect.

[0042] The method 200 can also determine the actual long DC resistance of the entire ESS or battery by the following formula: long_DC = (V2-V0) / (I2-I0). If the voltage of individual battery cells is measured, their respective long DC resistance values ​​can be determined in a similar manner and they can be identified using the methods mentioned above. This long DC resistance value describes the resistance value, including the "stress voltage" generated by the ESS chemical effect. The actual short DC resistance value and the long DC resistance value are stored in the memory. The method 200 proceeds to 212.

[0043] At 212, method 200 selectively evaluates the actual short DC resistance value and long DC resistance value (DCR) of a single battery cell based on the short DC resistance values ​​and long DC resistance values ​​of other single battery cells. The maximum DC resistance value of the actual short DC resistance value and the long DC resistance value can be used to evaluate the BMS SOP value. The actual short DC resistance value of each battery cell can be used together with the actual short DC resistance values ​​of all other battery cells in the ESS to determine the actual average short DC resistance value and the actual short DC resistance standard deviation. Similarly, the actual long DC resistance value of each battery cell can be used together with the actual long DC resistance values ​​of all other battery cells in the ESS to determine the actual average long DC resistance value and the actual long DC resistance standard deviation.

[0044] There are a number of methods that can be used to identify non-standard data, which can provide an indication of cell degradation. These methods include, but are not limited to: cell DCR that is more than 4 standard deviations from the actual mean DCR (short or long) value; cell DCR that exceeds 1.5*interquartile range (IQR); cells that exceed a predetermined resistance threshold or limit; the actual DCR of the cell exceeds the maximum increase in DCR expected at the end of the cell's life by more than 20%; and other similar methods of evaluating outliers or defective cells.

[0045] Indications of battery cell degradation may be displayed via a human-machine interface, and vehicle systems may be adjusted to compensate for battery cell degradation. For example, the output of one or more electric machines providing propulsion power to the electric vehicle may be limited to less than a threshold amount (e.g., limited to less than 50% of the rated output capacity of the electric machine) based on indications of one or more battery cells. In another example, if the DC resistance exceeds a threshold resistance, the maximum power output of the battery may be reduced. Method 200 proceeds to 214.

[0046] At 214 , method 200 compares the actual long and short DC resistance values ​​of the entire ESS (eg, battery) or a single cell to a predetermined baseline or nominal short DC resistance value of the entire battery pack or a single cell stored in the controller memory. Figure 6 As shown, a predetermined baseline or open-loop short DC resistance value for the entire battery pack (e.g., all battery cells arranged in series and parallel) or a baseline resistance value for each individual battery cell may be determined by a battery manufacturer's data sheet and a function or table stored in a controller memory. In one example, the comparison may generate one or more error values. For example, the DC resistance error amount of an ESS (e.g., a battery) may be determined based on the short DC resistance by the following formula: DCerrshort=DCbase(time,accur)-shortDC Wherein, DCerrshort is the DC resistance error of the entire ESS (e.g., battery) based on shortDC; DCbase is a function or table in the controller memory that returns the expected DC resistance of the entire ESS or a single battery cell; time is the age of the battery; accur is the cumulative current flowing into and out of the entire ESS or a single battery cell; shortDC is the DC resistance of the entire ESS or a single battery cell. The baseline DC resistance DCbase can be referred to as the open-loop or theoretical DC resistance of the entire battery or a single battery cell. In addition, the method 200 can also determine the DC resistance error of the ESS based on the long DC resistance by the following formula: DCerrlong=DCbase(time,accur)-longDC Where DCerrlong is the DC resistance error of the entire ESS based on longDC. After determining the DC resistance error of the entire battery (one DC resistance error value) or the DC resistance error of each battery cell (multiple DC resistance error values), method 200 enters 216. In addition, method 200 can also report the DCerr value in the equation: DCerr = max(DCerrlong, DCerrshort), where max is a function that returns the larger value of DCerrlong and DCerrshort. In this way, the DCerr value can be selected as the highest DC resistance value, so that the estimated values ​​of other variables generated based on the DCerr value will be biased towards a lower battery life estimate, so that the owner can receive a notification of low battery life within a desired time frame. Method 200 enters 216.

[0047] At 216 , method 200 transmits the DC resistance error value of the battery or the DC resistance error value of the battery cell to other routines and / or processes in the BMS (eg, Figure 4 and Figure 5 In addition, the method 200 may communicate the SOH error of the battery or the SOH error of the battery cell to other routines or processes in the BMS. In one example, the SOH may be a function of the DC resistance via an SOH relationship or transfer function, thereby generating the actual or measured SOH based on the actual or measured DC resistance value (e.g., short DC resistance). In one example, the relationship or function may be as follows: Figure 5 The SOH error can be determined by the following formula: SOHerrshort=SOHbase(Qmax,Cr)-SOHDC(shortDC) Where SOHerrshort is the health condition error based on the short DC resistance, SOHbase is the baseline or open-loop theoretical value of the battery health condition, Qmax is the maximum charge that can be stored in the battery, Cr is the rated charge capacity of the battery, and SOHDC is the battery measurement or actual health condition determined based on the short DC resistance shortDC. Similarly, SOHerr can also be generated by longDC. In addition, method 200 can also report the SOHerr value in the equation: SOHerr = max(SOHerrlong, SOHerrshort), where max is a function that returns the larger value of SOHerrlong and SOHerrshort. In this way, the SOHerr value can be selected as the highest DC resistance value, thereby correcting the SOH error and making the estimated value of the battery life lower so that the owner can be notified of the low battery life within the ideal time frame. The baseline state of battery health can be determined based on an empirical data table or function, which is stored in the controller memory and can be referenced by vehicle operating conditions (such as battery age, current source and sedimentation through the battery, etc.). Method 200 enters 218.

[0048] At 218 , the method passes the DC resistance value or DC resistance value to other routines and / or processes in the BMS to correct the SOC or SOH value generated by the BMS based on the actual or measured DC resistance value or DC resistance value. Method 200 continues to exit.

[0049] Thus, method 200 can generate an SOH estimate or SOH estimate based on the DC resistance value and the SOH error, which can be used as a basis for updating or not updating the SOH and reported by the BMS to other vehicle systems. In addition, the DC resistance value can be used to correct the SOH estimate. The DC resistance measurement can also be used as an early indicator of premature battery aging or manufacturing anomalies.

[0050] As Figure 2An example of the method, the DC resistance value of the battery is expected to be 10 milliohms (representing a 100% SOH state), and it is expected to rise by 30% (3 milliohms) within a 6-year service life (representing a 70% SOH). The DC resistance is expected to rise by 0.5 milliohms per year (a 5% decrease in SOH). In this example, due to vehicle exposure, the BMS displays a value of 89% after several years. The measurement results show that the SOP increases to 11.5 milliohms, or 85%. From this, it can be concluded that there is an error in the calculation of the SOP: BMS degradation = (reported) / expected degradation) = (100%-89%) / 30% = 36.6%; measured degradation = (calculated / expected degradation) = (100%-85%) / 30% = 50%; BMS degradation is 36.6%-50% = 13% less than the actual. Alternatively, BMS DCR increase = (10mohms*(100%-SOH)) = (10*(100%-85%)) = 1.1 milliohms; measured DCR increase = (10mohms*(100%-SOP)) = (10*(100%-85%)) = 1.5 milliohms; BMS degradation is 1.1-1.5 / 3 milliohms = 13% less than the actual value.

[0051] Reference now Figure 3 , a method for comparing a BMS ESS or battery SOH estimate and a vehicle-based ESS or battery SOH estimate is shown. Figure 4 The method can be used as a storage controller (for example Figure 1 The executable instructions in the non-volatile memory (e.g., read-only memory) of the BMS controller 139 in the BMS controller 139 are incorporated into Figure 1 in the system. Figure 3 The method can be used with Figure 1 systems and Figure 2 In addition, the method may also include changing the physical world by a controller and / or a person Figure 1 Operations on the system operating status.

[0052] At 302 , the BMS receives a DC resistance (DCR) from the vehicle. In particular, a vehicle controller (e.g., a vehicle control unit or a powertrain control unit) may generate DC resistance values. The BMS may receive these values ​​via a communication network (e.g., CAN). Method 300 proceeds to 304 .

[0053] At 304, method 300 determines whether to correct one or more SOH estimates generated by the BMS. For example, if the SOH error value exceeds a threshold and the vehicle measurement data is within an expected threshold range, the SOH estimate can be corrected. In addition, method 300 may require a threshold time interval between SOH corrections. If method 300 determines that the SOH value needs to be corrected, method 300 may correct the SOH value and pass the corrected SOH value to other vehicle systems (such as a vehicle controller, an electric mechanical controller, etc.). The battery management system may reduce the maximum battery output power based on the corrected battery health status that is less than the health status threshold. For example, when the corrected battery health status is greater than 70%, the maximum output power of the battery pack may be 300 kilowatts. However, when the corrected battery health status is less than 70%, the maximum output power of the battery pack may be reduced to 200 kilowatts. In one example, the corrected SOH can be generated by the following formula: SOHcorr=SOHbase+SOHerr Where SOHcorr is the corrected SOH value, SOHbase is the open-loop SOH value reported by the BMS, and SOHerr is the SOH error value. Method 300 proceeds to 306 .

[0054] At 306, method 300 corrects the DCR and transmits the correction value to other vehicle systems. In one example, the DCR value can be corrected by the following formula: DCRcorr=DCbase+DCerr Wherein, DCRcorr is the corrected DC resistance, DCbase is the open-loop DC resistance value, and DCerr is the DC resistance error. Method 400 communicates the corrected DC resistance value to other vehicle systems. In addition, in some examples, the vehicle controller may adjust vehicle operation based on the corrected battery health and / or the corrected DC resistance. For example, if the corrected battery health decreases to a threshold level less than the corrected battery DC resistance exceeding the threshold resistance, the vehicle controller may reduce the maximum rate of battery charging and / or discharging, thereby extending battery life. In addition, method 300 may also display or indicate the battery health and / or the corrected DC resistance to vehicle users and service personnel through a human-machine interface.

[0055] In this way, the SOH estimate can be corrected through feedback from the DC resistance and the charge can be used to replenish the ESS. The corrected SOH and DC resistance values ​​may be more accurate than baseline values ​​based on an aging model without a feedback mechanism. The improvement in accuracy may be more significant as the battery ages, or in situations where external influences may cause premature battery aging. The corrected estimates can be propagated throughout the vehicle so that the motors and other electrical systems that provide propulsion can be adjusted based on the corrections (e.g., limiting or constraining torque output, limiting or constraining power consumed, etc.).

[0056] therefore, Figure 2 and Figure 3 A method for operating a vehicle battery is provided, including: estimating a health state of the battery based on a first resistance value and a second resistance value; and adjusting a maximum output of the battery in response to the health state. In a first example, the method includes: adjusting the maximum output of the battery includes reducing the maximum output of the battery. In a second example, which may include the first example, the method includes: estimating the health state includes referencing a function by a first resistance value or a second resistance value. In a third example, which may include one or both of the first and second examples, the method further includes generating a resistance value by selecting a larger value of the first resistance value and the second resistance value. In a fourth example, which may include one or more of the first to third examples, the method includes: estimating the health state of the battery includes estimating the health state of the battery based on the resistance value. In a fifth example, which may include one or more of the first to fourth examples, the method includes: the first resistance value is based on a first voltage of the battery divided by a first current of the battery. In a sixth example, which may include one or more of the first to fifth examples, the method includes: the first voltage and the first current are measured within a first predetermined time after the maximum current of the battery is started to be provided from the battery. In a seventh example, which may include one or more of the first to sixth examples, the method includes: the second resistance value is based on a second voltage of the battery divided by a second current of the battery, the second voltage of the battery being different from the first voltage.

[0057] Figure 2 and Figure 3 The method also provides a method for operating a vehicle battery, including: estimating the DC resistance of the battery based on data collected when the battery is charged; and adjusting the maximum output of the battery based on the DC resistance of the battery. In a first example, the method includes estimating the DC resistance through the battery voltage and the battery current. In a second example that may include the first example, the method further includes generating a DC resistance error. In a third example that may include one or both of the first and second examples, the method further includes generating a battery health status through the DC resistance. In a fourth example that may include one or more of the first to third examples, the method further includes generating a battery health status error through the DC resistance.

[0058] Now please see Figure 4 , the figure shows the relationship between the battery DC resistance and the battery SOH. The vertical axis represents SOH, and SOH increases along the direction of the vertical axis arrow. The horizontal axis represents the battery DC resistance, and the battery DC resistance increases along the direction of the horizontal axis arrow. Line 402 describes the relationship between the battery DC resistance and the battery health status. It can be seen that the battery SOH decreases as the battery DC resistance increases. Figure 4 The representation of the curve graph in FIG. 1 can be stored in the controller memory as a function that can be referenced or indexed by the battery DC resistance. The function outputs an estimate of the battery SOH.

[0059] Now please see Figure 5 , a block diagram for generating an open loop or feed forward estimate of the DC resistance of a battery is shown. Block 502 represents a model that outputs the DC resistance of a battery cell based on inputs such as the battery manufacturing time, the total energy flowing into and out of the battery, and the battery temperature. The model can be an empirically determined model that can be generated by monitoring the operation of the battery while charging and discharging. Alternatively, the model can be a mathematical representation of the DC resistance.

[0060] Now watch Figure 6 , an example table for determining a reference or baseline battery resistance is shown. In this example, table 600 references the age of the battery (e.g., the cumulative time since the battery pack was manufactured) via the vertical axis. Table 600 also references the cumulative current value entering and leaving the battery pack via the horizontal axis. The entry in cell 602 of the table can be determined by aging and measuring the DC resistance of the battery pack, as the battery pack ages, the current flowing into and out of the battery pack increases.

[0061] Please note that the control and estimation routine examples included herein can be used in various powertrain and / or vehicle system configurations. The control methods and routines disclosed herein can be stored as executable instructions in non-volatile memory and can be executed by a control system including a controller in combination with various sensors, actuators and other transmission and / or vehicle hardware. In addition, some methods may be physical operations taken in the real world to change the state of the device. Therefore, the actions, operations and / or functions described can graphically represent the code that will be programmed into the non-volatile memory of a computer-readable storage medium in the vehicle and / or transmission control system. The specific routines described herein may represent one or more of any number of processing strategies, such as event-driven, interrupt-driven, multi-tasking, multi-threading, etc. Therefore, the various operations, operations and / or functions illustrated can be performed in the illustrated order, in parallel, or omitted in some cases. Similarly, the processing order is not necessarily required to achieve the features and advantages of the examples described herein, but is only for ease of illustration and description. Depending on the specific strategy used, one or more of the actions, operations and / or functions illustrated can be repeatedly performed. If necessary, one or more method steps described herein can be omitted.

[0062] Although various embodiments are described above, it should be understood that these embodiments are examples only and not limitations. It is obvious to those familiar with the relevant technology that the disclosed subject matter can be embodied in other specific forms without departing from the spirit of the subject matter. Therefore, the embodiments described above should be regarded as illustrative and not restrictive in all respects. Therefore, the configurations and routines disclosed herein are exemplary in nature, and these specific examples should not be regarded as restrictive because there can be many variations. For example, the above-mentioned technology can be applied to power systems containing different types of propulsion sources, including different types of motors, internal combustion engines and / or transmissions. The subject matter of the present disclosure includes all novel and non-obvious combinations and sub-combinations of various systems and configurations, as well as other features, functions and / or characteristics disclosed herein.

[0063] The following claims particularly point out certain combinations and subcombinations regarded as novel and non-obvious. The claims may refer to "an" element or a "first" element or the equivalent. The claims may be construed to include one or more such elements, neither requiring nor excluding two or more such elements. Other combinations and subcombinations of the disclosed features, functions, elements, and / or properties may be claimed by amendment of the present claims or by presentation of new claims in this or a related application. Such claims, whether broader, narrower, equal, or different in scope to the original claims, are also deemed to be included in the subject matter of the present disclosure.

Claims

1. A method of operating a vehicle battery, comprising: estimating a health status of the battery based on the first resistance value and the second resistance value; as well as The maximum output power of the battery is adjusted according to the health condition. 2 . The method according to claim 1 , wherein adjusting the maximum output power of the battery comprises reducing the maximum output power of the battery.

3. The method of claim 1, wherein estimating the health condition comprises referencing a function using the first resistance value or the second resistance value.

4. The method of claim 1, further comprising generating a resistance value by selecting a larger value of the first resistance value and the second resistance value. The method of claim 4 , wherein estimating the health condition comprises estimating the health condition of the battery based on the resistance value. The method of claim 1 , wherein the first resistance value is based on a first voltage of the battery divided by a first current of the battery. 7 . The method of claim 6 , wherein the first voltage and the first current are measured within a first predetermined time after the battery starts to provide a maximum current.

8. The method of claim 7, wherein the second resistance value is based on a second voltage of the battery divided by a second current of the battery, the second voltage being different from the first voltage.

9. A vehicle system comprising: A battery pack including a plurality of battery cells; A controller includes executable instructions stored in a non-transitory memory that cause the controller to generate a corrected health state of a battery pack, the corrected health state being based on a health state error of the battery pack.

10. The system of claim 9, wherein the health condition error is based on a baseline health condition.

11. The system of claim 10, further comprising additional executable instructions that cause the controller to adjust vehicle operation based on the corrected health of the battery pack.

12. The system of claim 9, wherein the corrected health of the battery pack is based on data collected while the battery pack is charging.

13. The system of claim 9, wherein the corrected health of the battery pack is the health reported by the battery management system plus a health error.

14. The system of claim 13, wherein the health status reported by the battery management system is a feed-forward estimate generated by the battery management system.

15. The system of claim 13, further comprising additional executable instructions for correcting a DC resistance value of the battery pack.

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