CN109987000A - A kind of temperature of powered cell forecasting system and method - Google Patents
A kind of temperature of powered cell forecasting system and method Download PDFInfo
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- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 claims abstract description 23
- 229910001416 lithium ion Inorganic materials 0.000 claims abstract description 23
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- 238000007599 discharging Methods 0.000 claims description 31
- 238000006243 chemical reaction Methods 0.000 claims description 30
- 238000009831 deintercalation Methods 0.000 claims description 10
- 238000009830 intercalation Methods 0.000 claims description 10
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- 238000006900 dealkylation reaction Methods 0.000 abstract 1
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- 230000008859 change Effects 0.000 description 4
- 230000003044 adaptive effect Effects 0.000 description 3
- 241001481789 Rupicapra Species 0.000 description 2
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Abstract
The invention belongs to power battery fields, and in particular to a kind of temperature of powered cell forecasting system and method.Temperature of powered cell forecasting system of the invention, including acquisition module and temperature prediction module, the multiple objective functions such as the time charged by the flux of the embedding dealkylation reaction of lithium ion in charge and discharge process, battery capacity attenuation and low-temperature heat determine that key temperatures parameter carries out temperature prediction.Method of the invention improves battery temperature precision of prediction, reduces temperature to the adverse effect of battery performance.
Description
Technical Field
The invention belongs to the field of power batteries, and particularly relates to a power battery temperature prediction system and a power battery temperature prediction method.
Background
The performance of a power battery of an electric vehicle is greatly influenced by temperature, and battery parameters, voltage output and discharge efficiency are different at different temperatures, so that the maximum allowable power and the residual available energy of the battery at different temperatures are different, and the power output and the driving range of the vehicle in actual use are influenced. The heat generated during charging and discharging of the battery during the current and future use also causes the temperature to change, thereby causing the performance of the battery to change. Therefore, in order to improve the prediction accuracy of the performance of the power battery, it is necessary to predict the temperature change of the battery in real time, improve the estimation accuracy of the driving range or the charging time, and improve the energy utilization rate of the battery system and the mileage utilization rate of the vehicle.
A patent with publication number CN106842050A discloses a battery temperature prediction method and device, a patent with publication number CN104881550A discloses a power battery operation temperature adaptive prediction method, a patent with publication number CN204791125U discloses a power battery temperature prediction and heat dissipation device for an electric vehicle, a patent with publication number KR101449276B1 discloses a system and method for predicting the temperature of a battery, a patent with publication number CN103415957A discloses a battery temperature control device, a patent with publication number CN105048021A discloses a battery temperature estimation system, a patent with publication number CN107392352A discloses a battery future temperature prediction method and system based on a fusion limit learning machine, a vehicle power battery temperature prediction model research based on pso _ FSVM (modern electronic technology, 2018, 41) and the like introduce a vehicle power battery temperature prediction model research based on pso _ FSVM, a vehicle power battery temperature prediction model research based on thermal effect and a classification research (the chamois university of the chamois engineering university of the battery temperature prediction and classification (the chamois university of the chamois field of the product) 2013), temperature characteristics of the battery are explored according to historical data of charge and discharge cycles, a neural network is utilized to establish a prediction model of the battery temperature, temperature prediction is carried out on the battery under different environmental temperatures and working currents, different tab heat dissipation schemes are provided for a high-capacity soft package power lithium ion battery module in the power lithium ion battery system heat management research (doctor graduation thesis of Beijing university of science and technology, 2017), and a battery temperature dynamic matrix prediction control algorithm comprising a battery module working current feedforward link is deduced by using the thermal model; the document, "study on thermal model and temperature control of electric vehicle battery pack based on air cooling heat dissipation" (master paper of Jilin university, 2017) introduces a thermal model of a single battery, which analyzes a heat generation mechanism and heat transfer characteristics between the battery and outside air in the charging and discharging processes of a lithium ion battery, and establishes a time-varying internal resistance and heat transfer coefficient of the battery according to a heat generation rate model and a Newton cooling law.
The analysis shows that the temperature as an important influence factor of the power battery is widely concerned and deeply researched, and the research mainly focuses on establishing a prediction model by the model and/or the neural network, so that the temperature can be predicted more quickly and accurately, the temperature of the battery can be controlled more effectively, and the performance attenuation caused by the temperature can be reduced. However, the existing related research also has the problem to be improved, for example, the temperature amplitude assumption can not truly reflect the battery temperature change in the actual running process of the vehicle; the model calculation amount is large, so that the load rate of a microprocessor of the battery management system in the prior practical application is overlarge, and the predicted temperature value is difficult to give in real time; the deep learning workload is large, the difference of actual driving habits is obvious, the heat generation of the battery is different due to different vehicle power outputs, and the adaptive capacity is adaptive to the actual operation and needs to be further improved.
Disclosure of Invention
The invention provides a power battery temperature prediction system capable of improving the problems of battery temperature estimation precision, thermal management pre-control and the like, and solves the defects of the conventional power battery temperature prediction system and temperature prediction method.
The technical scheme of the invention is described as follows by combining the attached drawings:
a power battery temperature prediction system comprises an acquisition module and a temperature prediction module in communication connection with the acquisition module, wherein a sampling chip in the acquisition module is used for acquiring the highest temperature T of a battery acquired by a voltage detection circuit, a current sensor and a temperature sensormaxMinimum temperature T of batteryminThe sum current value I is input to the temperature prediction module; an operation chip of the temperature prediction module calculates internal resistance R and reaction thermal coefficient k according to the real-time temperature and SOC, and predicts the battery temperature T at the k moment as the input quantity of the temperature prediction equationkAnd the obtained predicted temperature value T is usedkInputting the temperature data into a battery thermal management system, and predicting the temperature of the whole vehicle controller according to the temperature input by the battery thermal management systemTkAnd carrying out heating and heat dissipation management on the battery.
A power battery temperature prediction method comprises the following steps:
s1: the temperature sensors are respectively arranged at the position of the highest temperature point and the lowest temperature point of the battery module corresponding to the temperature distribution cloud chart, and the collected highest temperature TmaxAnd a minimum temperature TminTransmitting to a battery management system; the battery management system collects the highest temperature T of all batteries by the battery sensormaxAnd the lowest temperature T of the batteryminOr predicted maximum battery temperature Tk，maxAnd the lowest temperature T of the batteryk，minTransmitting to the acquisition module all TminMinimum value of (d) and temperature input set point TmBy comparison, the process of the first and second steps,
when T ismin＜TmWhen, TminOr Tk，minThe minimum value of the three parameters is used as the input variable of the current value I, the internal resistance R at different temperatures and different SOC and the reaction thermal coefficient k to obtain I, R, k, TminThe minimum value of the sum is input to a temperature prediction module;
when T ismin≥TmWhen, TmaxOr Tk，maxThe maximum value of the current value I, the internal resistance R at different temperatures and different SOC and the reaction thermal coefficient k are used as input variables for obtaining I, R, k, TmaxInputting the temperature data to a temperature prediction module;
wherein the temperature is input to a set value TmFor temperature control purposes, T is calculated through an objective functionmA value; flux J and battery capacity attenuation Q generated by lithium ion intercalation and deintercalation reaction in battery charging and discharging processesdeAnd the time t of low-temperature heating charging is a temperature-related function so as to increase the flux J generated by the lithium ion intercalation and deintercalation reaction and reduce the capacity decrement Q in the charging and discharging process of the batterydeAnd the time t of low-temperature heating charging establishes an objective function,
constraint condition T is less than or equal to Thde，ThdeCritical temperature for heating and exiting the battery while charging;
the lithium ion intercalation and deintercalation reaction generates a flux J ═ f (i) in the charging and discharging process of the battery0，αn，αp，η，T)，αpAnd αnPositive and negative transfer coefficients, η overpotential, i0To exchange the current density, i0＝f(κn，κp，αn，αp，cs，max，cs，surf，cl)，κn，κpIs the rate constant of electrochemical reaction of positive and negative electrodes, cs，maxIs the maximum concentration of solid-phase lithium ions, cs，surfSolid phase surface lithium ion concentration, clLiquid-phase lithium ion concentration;
the capacity decrement Q in the charging and discharging process of the batteryde＝f(T，t，I，Ahthr，Ea)；
The time T ═ f (T, I) of the low-temperature heating charging;
the objective functions (1), (2) and (3) are normalized, and different weight coefficients omega are added1，ω2，ω3Obtaining the corresponding temperature when the total objective function takes the minimum value, wherein the temperature is the temperature set value Tm(ii) a Wherein the weight ω is1+ω2+ω3＝1；
S2: when the lowest temperature value T of the batterymin＜TmJudging the charge-discharge state of the battery according to the running state of the vehicle; the specific method comprises the following steps:
2a) during charging, the charging mode is judged according to the communication interaction between the charging device and the battery management system, and when the charging mode is in the direct-current charging mode, the lowest temperature value T of the batteryminAnd critical temperature T for heating batteryhd0Comparison when T ismin＜Thd0Heating on, charging off, input to temperature predictionThe current value of the module is 0; lowest temperature value T of batteryminAnd critical temperature T for heating batteryhd0And critical temperature T for battery heating exithdeComparison when T ishd0≤Tmin＜ThdeHeating on, charging on, Tmin＝ThdeWhen heating is switched off, the current I input to the temperature prediction module k at the moment is estimated according to a charging strategyk(ii) a Lowest temperature value T of batteryminCritical temperature T of charging without heating batteryhdmAnd critical temperature T for heating and exiting while charging the batteryhdeComparison when T ishdm≤Tmin≤ThdeHeating is not started, charging is started, and the current I input to the temperature prediction module k at the moment is estimated according to a charging strategyk；
2b) When in AC charging mode, the lowest temperature value T of the batteryminAnd critical temperature T for heating batteryha0Comparison when T ismin＜Tha0Heating is started, charging is not started, and the current value input to the temperature prediction module is 0; when T ismin≥Tha0Heating is turned off, charging is turned on, and the current I input to the temperature prediction module at the moment k is estimated according to a charging strategyk；
2c) During discharge, the lowest temperature value T of the batteryminAnd critical temperature T of battery discharge heatingh0Comparison when T ismin＜Th0Heating on, predicting current I at time k input to temperature prediction module based on operating conditionskThe current value collected by the initial time sensor is used as the initial value I of the current input to the temperature prediction module0(ii) a When T ismin≥Th0Heating off, predicting the current I at time k input to the temperature prediction module based on operating conditionskThe current value collected by the initial time sensor is used as the initial value I of the current input to the temperature prediction module0；
When the temperature input module inputs the minimum temperature value T of the battery to the current input modulemin≥TmAt the same time, according to the running state of the vehicle, the charging and discharging of the battery are judgedA state; the method comprises the following specific steps:
2d) during charging, the highest temperature value TmaxAnd critical temperature T of battery charging and heat dissipationcc0Comparison when T ismax＜Tcc0The heat dissipation is not started, and the current I input to the temperature prediction module k moment is predicted based on the operation conditionkThe current value collected by the initial time sensor is used as the initial value I of the current input to the temperature prediction module0(ii) a Maximum temperature value TmaxAnd critical temperature T of battery charging and heat dissipationcc0Comparison when T ismax≥Tcc0The heat dissipation is started, and the current I input to the temperature prediction module k moment is predicted based on the operation conditionkThe current value collected by the initial time sensor is used as the initial value I of the current input to the temperature prediction module0(ii) a Maximum temperature value TmaxAnd the battery charging heat dissipation exits the critical temperature TcceComparison when T ismax＜TcceAnd when the heat dissipation is closed, predicting the current I input to the temperature prediction module k moment based on the operation conditionkThe current value collected by the initial time sensor is used as the initial value I of the current input to the temperature prediction module0；
2e) At discharge, the maximum temperature value TmaxCritical temperature T for discharging and heat dissipating of batterycd0Comparison when T ismax＜Tcd0The heat dissipation is not started, and the current I input to the temperature prediction module k moment is predicted based on the operation conditionkThe current value collected by the initial time sensor is used as the initial value I of the current input to the temperature prediction module0(ii) a When T ismax≥Tcd0The heat dissipation is started, and the current I at the time k input to the temperature prediction module is predicted based on the operation conditionkThe current value collected by the initial time sensor is used as the initial value I of the current input to the temperature prediction module0(ii) a Maximum temperature value TmaxAnd the battery discharge heat dissipation exit critical temperature TcdeComparison when T ismax＜TcdeHeat dissipation is turned off, and the current I at the time k input to the temperature prediction module is predicted based on the operating conditionkThe current value collected by the sensor at the initial moment is used as the input to the temperature prediction modelInitial value of current I of block0；
S3: inputting a set value T according to the temperaturemCritical temperature T for heating batteryhd0Critical temperature T for heating and withdrawing while charging batteryhdeThe battery is not heated and the charging critical temperature T ishdmCritical temperature T for heating batteryha0Critical temperature T for battery discharge heatingh0Critical temperature T for battery charging and heat dissipationcc0And the battery charging heat dissipation exits the critical temperature TcceCritical temperature T for discharging and heat dissipation of batterycd0The battery discharge heat dissipation exits the critical temperature TcdeDetermining the temperature interval to be-20-45 ℃, calculating the internal resistance value R at different SOC, and according to the temperature input value TmaxOr TminOr Tk，maxOr Tk，minAnd inputting the internal resistance value R to the temperature prediction module together with the SOC input value of the battery management system;
s4: inputting a set value T according to the temperaturemCritical temperature T for heating batteryhd0Critical temperature T for heating and withdrawing while charging batteryhdeThe battery is not heated and the charging critical temperature T ishdmCritical temperature T for heating batteryha0Critical temperature T for battery discharge heatingh0Critical temperature T for battery charging and heat dissipationcc0And the battery charging heat dissipation exits the critical temperature TcceCritical temperature T for discharging and heat dissipation of batterycd0The battery discharge heat dissipation exits the critical temperature TcdeDetermining the temperature interval to be-20-45 ℃, calculating the reaction thermal coefficient k at different SOC, and inputting the value T according to the temperaturemaxOr TminOr Tk，maxOr Tk，minAnd the SOC input value of the battery management system, and the reaction heat coefficient k is input to the temperature prediction module;
s5: temperature T input to temperature prediction modulemaxOr TminCurrent at time kkThe internal resistance value R, the reaction thermal coefficient k and the SOC value input to the temperature prediction module by the battery management system are used as input quantity of the temperature prediction module,predicting battery temperature T at time kk；
The equation of state for the prediction of the battery temperature,
an observed equation for the prediction of the battery temperature,
Tmk＝DTk-1+vk-1
wherein,
D＝[0 0 1]
in the formula, ωk-1Is state noise, vk-1To observe noise, h is the convective heat transfer coefficient, A is the heat exchange area, m is the battery mass, Tk-1Predicted temperature of battery at time k-1, q is heat generation amount of battery per unit time, TcTo cool the temperature of the medium, CpThe specific heat capacity of the battery.
S6: predicted temperature T output by temperature prediction modulekThe temperature is input into a thermal management system for temperature control, and the controller with the temperature as an input variable is called.
Predicting current I at time k input to the temperature prediction module based on operating conditions as set forth in S2kComprising predicting the current I at time k based on historical operating conditionskAnd predicting the current I at time k based on future operating conditionskPredicting the current I at the k moment based on the near moment of the operation conditionk。
Critical for heating the battery described in S2Temperature Thd0Is-20 to 10 ℃; the critical temperature T for heating and exiting the battery while heating and charginghde10-30 ℃; t is the critical temperature of the battery not heatedhdmIs-20 to 20 ℃; the critical temperature T of battery heatingha0At-20 to 10 ℃, and the discharge heating critical temperature T of the batteryh0At-10 to 20 ℃, and the critical temperature T of charging and heat dissipation of the batterycc0At 30-45 deg.C, the battery charging and heat dissipating exit critical temperature TcceAt 32-45 deg.C, and critical temperature T of battery discharge and heat dissipationcd0At 20-45 deg.C, and the critical temperature T for discharging and heat dissipating of the batterycdeIs 15 to 45 ℃.
The invention has the beneficial effects that: the power battery temperature prediction system comprises an acquisition module and a temperature prediction module, and the temperature prediction is carried out by determining key temperature parameters through multi-target functions such as the flux of lithium ion intercalation and deintercalation reaction, the battery capacity attenuation, the time of low-temperature heating and charging and the like in the charging and discharging processes. The method improves the prediction accuracy of the battery temperature and reduces the adverse effect of the temperature on the battery performance.
Drawings
FIG. 1 is a cloud graph of a temperature profile of an embodiment of the invention.
FIG. 2 is a schematic diagram of a temperature prediction method strategy according to an embodiment of the present invention.
FIG. 3 is a graph of temperature prediction for an embodiment of the present invention.
Detailed Description
The invention aims to provide a power battery temperature prediction system and a power battery temperature prediction method, which are used for predicting temperature changes caused by changes of heat energy and electric energy in the charging and discharging processes of a power battery module.
Power battery temperature prediction systemThe system comprises an acquisition module and a temperature prediction module in communication connection with the acquisition module, wherein a sampling chip in the acquisition module acquires the highest temperature T of the battery acquired by a voltage detection circuit, a current sensor and a temperature sensormaxMinimum temperature T of batteryminThe sum current value I is input to the temperature prediction module; an operation chip of the temperature prediction module calculates internal resistance R and reaction thermal coefficient k according to the real-time temperature and SOC, and predicts the battery temperature T at the k moment as the input quantity of the temperature prediction equationkAnd the obtained predicted temperature value T is usedkInputting the temperature into a battery thermal management system, and predicting a temperature predicted value T by the vehicle controller according to the temperature input by the battery thermal management systemkAnd carrying out heating and heat dissipation management on the battery.
A power battery temperature prediction method comprises the following steps:
the temperature sensors are respectively arranged at the position of the highest temperature point and the lowest temperature point of the battery module corresponding to the temperature distribution cloud chart, and the collected highest temperature TmaxAnd a minimum temperature TminTransmitting to a battery management system; FIG. 1 is a temperature field distribution cloud chart of a power battery module consisting of four battery cells, which is used for designing the highest temperature point T of the battery module in the embodiment of the inventionmaxAnd a minimum temperature point TminThe sensor location of (2). The highest temperature point T of the battery module in FIG. 1maxThe sensor arrangement position is (244, 485), the lowest point of temperature TminThe sensor arrangement position is (56, 289).
The temperature cloud chart is drawn for simulation and/or actual test.
The battery management system collects the highest temperature T of all batteries by the sensormaxAnd the lowest temperature T of the batteryminOr predicted maximum battery temperature Tk，maxAnd the lowest temperature T of the batteryk，minTransmitting to the acquisition module all TminMinimum value of (d) and temperature input set point TmBy comparison, the process of the first and second steps,
when T ismin＜TmWhen, TminOr Tk，minThe minimum value of the three parameters is used as the input variable of the current value I, the internal resistance R at different temperatures and different SOC and the reaction thermal coefficient k to obtain I, R, k, TminIs input to the temperature prediction module.
When T ismin≥TmWhen, TmaxOr Tk，maxThe maximum value of the current value I, the internal resistance R at different temperatures and different SOC and the reaction thermal coefficient k are used as input variables for obtaining I, R, k, TmaxInput to the temperature prediction module.
Wherein the temperature is input to a set value TmFor temperature control purposes, T is calculated through an objective functionmA value; flux J and battery capacity attenuation Q generated by lithium ion intercalation and deintercalation reaction in battery charging and discharging processesdeAnd the time t of low-temperature heating charging is a temperature-related function so as to increase the flux J generated by the lithium ion intercalation and deintercalation reaction and reduce the capacity decrement Q in the charging and discharging process of the batterydeAnd the time t of low-temperature heating charging establishes an objective function,
constraint condition T is less than or equal to Thde，ThdeA critical temperature at which the battery is heated and withdrawn while being charged.
The lithium ion intercalation and deintercalation reaction generates a flux J ═ f (i) in the charging and discharging process of the battery0，αn，αp，η，T)，αpAnd αnPositive and negative transfer coefficients, η overpotential, i0To exchange the current density, i0＝f(κn，κp，αn，αp，cs，max，cs，surf，cl)，κn，κpIs the rate constant of electrochemical reaction of positive and negative electrodes, cs，maxIs the maximum concentration of solid-phase lithium ions, cs，surfSolid phase surface lithium ion concentration, clLiquid phase lithium ion concentration.
The capacity decrement Q in the charging and discharging process of the batteryde＝f(T，t，I，Ahthr，Ea)。
The time T of the low-temperature heating charging is f (T, I).
The objective functions (1), (2) and (3) are normalized, and different weight coefficients omega are added1，ω2，ω3Obtaining the corresponding temperature when the total objective function takes the minimum value, wherein the temperature is the temperature set value Tm(ii) a Wherein the weight ω is1+ω2+ω3＝1。
Referring to fig. 2, when the temperature input module inputs the minimum temperature T of the battery to the current input modulemin＜TmAnd judging the charge and discharge state of the battery according to the running state of the vehicle.
During charging, the charging mode is judged according to the communication interaction between the charging device and the battery management system, and when the charging mode is in the direct-current charging mode, the lowest temperature value T of the batteryminAnd critical temperature T for heating batteryhd0Comparison when T ismin＜Thd0Heating is started, charging is not started, and the current value input to the temperature prediction module is 0; lowest temperature value T of batteryminAnd critical temperature T for heating batteryhd0And critical temperature T for battery heating exithdeComparison when T ishd0≤Tmin＜ThdeHeating on, charging on, Tmin＝ThdeWhen heating is switched off, the current I input to the temperature prediction module k at the moment is estimated according to a charging strategyk(ii) a Lowest temperature value T of batteryminCritical temperature T of charging without heating batteryhdmAnd critical temperature T for heating and exiting while charging the batteryhdeComparison when T ishdm≤Tmin≤ThdeHeating is not started, charging is started, and the current I input to the temperature prediction module k at the moment is estimated according to a charging strategyk；
When in AC charging mode, the lowest temperature value T of the batteryminAnd critical temperature T for heating batteryha0Comparison when T ismin＜Tha0Heating is started, charging is not started, and the current value input to the temperature prediction module is 0; when T ismin≥Tha0Heating is turned off, charging is turned on, and the current I input to the temperature prediction module at the moment k is estimated according to a charging strategyk；
During discharge, the lowest temperature value T of the batteryminAnd critical temperature T of battery discharge heatingh0Comparison when T ismin＜Th0Heating on, predicting current I at time k input to temperature prediction module based on operating conditionskThe current value collected by the initial time sensor is used as the initial value I of the current input to the temperature prediction module0(ii) a When T ismin≥Th0Heating off, predicting the current I at time k input to the temperature prediction module based on operating conditionskThe current value collected by the initial time sensor is used as the initial value I of the current input to the temperature prediction module0；
When the temperature input module inputs the minimum temperature value T of the battery to the current input modulemin≥TmJudging the charge-discharge state of the battery according to the running state of the vehicle; the method comprises the following specific steps:
during charging, the highest temperature value TmaxAnd critical temperature T of battery charging and heat dissipationcc0Comparison when T ismax＜Tcc0The heat dissipation is not started, and the current I input to the temperature prediction module k moment is predicted based on the operation conditionkThe current value collected by the initial time sensor is used as the initial value I of the current input to the temperature prediction module0(ii) a Maximum temperature value TmaxAnd critical temperature T of battery charging and heat dissipationcc0Comparison when T ismax≥Tcc0The heat dissipation is started, and the current I input to the temperature prediction module k moment is predicted based on the operation conditionkThe current value collected by the initial time sensor is used as the initial value I of the current input to the temperature prediction module0(ii) a Maximum temperature value TmaxAnd the battery charging heat dissipation exits the critical temperature TcceComparisonWhen T ismax＜TcceAnd when the heat dissipation is closed, predicting the current I input to the temperature prediction module k moment based on the operation conditionkThe current value collected by the initial time sensor is used as the initial value I of the current input to the temperature prediction module0；
At discharge, the maximum temperature value TmaxCritical temperature T for discharging and heat dissipating of batterycd0Comparison when T ismax＜Tcd0The heat dissipation is not started, and the current I input to the temperature prediction module k moment is predicted based on the operation conditionkThe current value collected by the initial time sensor is used as the initial value I of the current input to the temperature prediction module0(ii) a When T ismax≥Tcd0The heat dissipation is started, and the current I at the time k input to the temperature prediction module is predicted based on the operation conditionkThe current value collected by the initial time sensor is used as the initial value I of the current input to the temperature prediction module0(ii) a Maximum temperature value TmaxAnd the battery discharge heat dissipation exit critical temperature TcdeComparison when T ismax＜TcdeHeat dissipation is turned off, and the current I at the time k input to the temperature prediction module is predicted based on the operating conditionkThe current value collected by the initial time sensor is used as the initial value I of the current input to the temperature prediction module0；
Inputting a set value T according to the temperaturemCritical temperature T for heating batteryhd0Critical temperature T for heating and withdrawing while charging batteryhdeThe battery is not heated and the charging critical temperature T ishdmCritical temperature T for heating batteryha0Critical temperature T for battery discharge heatingh0Critical temperature T for battery charging and heat dissipationcc0And the battery charging heat dissipation exits the critical temperature TcceCritical temperature T for discharging and heat dissipation of batterycd0The battery discharge heat dissipation exits the critical temperature TcdeDetermining the temperature interval to be-20-45 ℃, calculating the internal resistance value R at different SOC, and according to the temperature input value TmaxOr TminOr Tk，maxOr Tk，minAnd inputting the internal resistance value R to the temperature prediction module together with the SOC input value of the battery management system；
Inputting a set value T according to the temperaturemCritical temperature T for heating batteryhd0Critical temperature T for heating and withdrawing while charging batteryhdeThe battery is not heated and the charging critical temperature T ishdmCritical temperature T for heating batteryha0Critical temperature T for battery discharge heatingh0Critical temperature T for battery charging and heat dissipationcc0And the battery charging heat dissipation exits the critical temperature TcceCritical temperature T for discharging and heat dissipation of batterycd0The battery discharge heat dissipation exits the critical temperature TcdeDetermining the temperature interval to be-20-45 ℃, calculating the reaction thermal coefficient k at different SOC, and inputting the value T according to the temperaturemaxOr TminOr Tk，maxOr Tk，minAnd the SOC input value of the battery management system, and the reaction heat coefficient k is input to the temperature prediction module;
temperature T input to temperature prediction modulemaxOr TminCurrent at time kkThe internal resistance value R, the reaction thermal coefficient k and the SOC value input to the temperature prediction module by the battery management system are used as input quantity of the temperature prediction module, and the battery temperature T at the moment k is predicted through a state equation and an observation equation of battery temperature predictionk；
The equation of state for the prediction of the battery temperature,
an observed equation for the prediction of the battery temperature,
Tmk＝DTk-1+vk-1
wherein,
D＝[0 0 1]
in the formula, ωk-1Is state noise, vk-1To observe noise, h is the convective heat transfer coefficient, A is the heat exchange area, m is the battery mass, Tk-1Predicted temperature of battery at time k-1, q is heat generation amount of battery per unit time, TcTo cool the temperature of the medium, CpThe specific heat capacity of the battery.
Predicted temperature T output by temperature prediction modulekInput to a thermal management system for temperature control and other controller calls.
Predicting the current I at the time k input into the temperature prediction module based on the operation conditionkComprising predicting the current I at time k based on historical operating conditionskAnd predicting the current I at time k based on future operating conditionskPredicting the current I at the k moment based on the near moment of the operation conditionk。
The critical temperature T of battery heatinghd0Is-20 to 10 ℃, and the critical temperature T for heating and exiting when the battery is heated and chargedhdeAt 10-30 deg.C, and the critical temperature of battery not heating and charginghdmAt-20 to 20 ℃, and the critical temperature T of heating the batteryha0At-20 to 10 ℃, and the discharge heating critical temperature T of the batteryh0At-10 to 20 ℃, and the critical temperature T of charging and heat dissipation of the batterycc0At 30-45 deg.C, the battery charging and heat dissipating exit critical temperature TcceAt 32-45 deg.C, and critical temperature T of battery discharge and heat dissipationcd0At 20-45 deg.C, and the critical temperature T for discharging and heat dissipating of the batterycdeIs 15 to 45 ℃.
The power battery includes a battery for a weak hybrid vehicle, a plug-in hybrid vehicle, an extended range electric vehicle, a pure electric vehicle, and a fuel cell vehicle. FIG. 3 is a predicted value and an actual measured value curve of the temperature of the power battery of the pure electric vehicle under the working condition of US 06.
The minimum temperature value T input into the current input module by the temperature input modulemin＜TmWhen the hybrid electric vehicle is used, the battery is heated by the heat circulation of the exhaust gas of the engine of the hybrid electric vehicle, and the battery is heated by the heating component and/or the motor waste heat circulation of the pure electric vehicle. During charging, the air conditioner can provide a part of heat dissipation capacity.
Claims (4)
1. A power battery temperature prediction system is characterized by comprising an acquisition module and a temperature prediction module in communication connection with the acquisition module; the sampling chip in the acquisition module acquires the highest temperature T of the battery acquired by the voltage detection circuit, the current sensor and the temperature sensormaxMinimum temperature T of batteryminThe sum current value I is input to the temperature prediction module; the operation chip of the temperature prediction module calculates the internal resistance R and the reaction thermal coefficient k according to the real-time temperature and the SOC, and predicts the battery temperature T at the k moment as the input quantity of the temperature prediction equationkAnd the obtained predicted temperature value T is usedkInputting the temperature into a battery thermal management system, and predicting a temperature predicted value T by the vehicle controller according to the temperature input by the battery thermal management systemkAnd carrying out heating and heat dissipation management on the battery.
2. The prediction method of the power battery temperature prediction system according to claim 1, characterized by comprising the following steps:
step one, arranging temperature sensors at the highest temperature point and the lowest temperature point of a battery module corresponding to a temperature distribution cloud chart respectively, and collecting the highest temperature T by the sensorsmaxAnd a minimum temperature TminTransmitting to a battery management system BMS; battery management system BMS (battery management system) collects the highest temperature T of the battery by all sensorsmaxAnd a minimum temperature TminTransmitting the temperature to a temperature input module which outputs the lowest temperature TminMinimum value of, and temperature input set point TmComparison when T ismin＜TmIn time, the temperature input module is used for acquiring the lowest temperature T of the sensorminMinimum value of (1) or predicted lowest temperature Tk，minThe temperature input module, the current input module, the internal resistance input module and the reaction heat coefficient module are input; when T ismin≥TmThe temperature input module inputs the highest temperature TmaxMaximum value of (1) or predicted maximum temperature Tk，maxThe current is input into a temperature prediction module, a current input module, an internal resistance input module and a reaction thermal coefficient module; wherein the temperature is input to a set value TmFor temperature control purposes, T is calculated through an objective functionmA value; flux J and battery capacity attenuation Q generated by lithium ion intercalation and deintercalation reaction in battery charging and discharging processesdeAnd the time t of low-temperature heating charging is a temperature-related function so as to increase the flux J generated by the lithium ion intercalation and deintercalation reaction and reduce the capacity decrement Q in the charging and discharging process of the batterydeAnd the time t of low-temperature heating charging establishes an objective function,
constraint condition T is less than or equal to Thde，ThdeCritical temperature for heating and exiting the battery while charging;
the lithium ion intercalation and deintercalation reaction generates a flux J ═ f (i) in the charging and discharging process of the battery0,αn,αp,η,T)，αpAnd αnPositive and negative transfer coefficients, η overpotential, i0To exchange the current density, i0＝f(κn,κp,αn,αp,cs,max,cs,surf,cl)，κn，κpIs the rate constant of electrochemical reaction of positive and negative electrodes, cs,maxIs the maximum concentration of solid-phase lithium ions, cs,surfSolid phase surface lithium ion concentration, clLiquid-phase lithium ion concentration;
the capacity decrement Q in the charging and discharging process of the batteryde＝f(T,t,I,Ahthr,Ea)；
The time T ═ f (T, I) of the low-temperature heating charging;
the objective functions (1), (2) and (3) are normalized, and different weight coefficients omega are added1，ω2，ω3Obtaining the corresponding temperature when the total objective function takes the minimum value, wherein the temperature is the temperature set value Tm(ii) a Wherein the weight ω is1+ω2+ω3＝1；
Step two, when the temperature input module inputs the lowest temperature value T of the battery of the current input modulemin＜TmJudging the charge-discharge state of the battery according to the running state of the vehicle; the specific method comprises the following steps:
21) during charging, the charging mode is judged according to the communication interaction between the charging device and the battery management system BMS, and when the charging mode is in a direct current charging mode, the temperature input module inputs the lowest temperature value T of the battery to the current input moduleminAnd critical temperature T for heating batteryhd0Comparison when T ismin＜Thd0Heating is started, charging is not started, and the current value input to the temperature prediction module is 0; the battery minimum temperature value input to the current input module by the temperature input moduleTminAnd critical temperature T for heating batteryhd0And critical temperature T for battery heating exithdeComparison when T ishd0≤Tmin＜ThdeHeating on, charging on, Tmin＝ThdeWhen heating is switched off, the current I input to the temperature prediction module k at the moment is estimated according to a charging strategyk(ii) a The lowest temperature value T of the battery input by the temperature input module to the current input moduleminCritical temperature T of charging without heating batteryhdmAnd critical temperature T for heating and exiting while charging the batteryhdeComparison when T ishdm≤Tmin≤ThdeHeating is not started, charging is started, and the current I input to the temperature prediction module k at the moment is estimated according to a charging strategyk；
22) When the battery is in the alternating current charging mode, the temperature input module inputs the lowest temperature value T of the battery to the current input moduleminAnd critical temperature T for heating batteryha0Comparison when T ismin＜Tha0Heating is started, charging is not started, and the current value input to the temperature prediction module is 0; when T ismin≥Tha0Heating is turned off, charging is turned on, and the current I input to the temperature prediction module at the moment k is estimated according to a charging strategyk；
23) When discharging, the temperature input module inputs the lowest temperature value T of the battery to the current input moduleminAnd critical temperature T of battery discharge heatingh0Comparison when T ismin＜Th0Heating on, predicting current I at time k input to temperature prediction module based on operating conditionskThe current value collected by the initial time sensor is used as the initial value I of the current input to the temperature prediction module0(ii) a When T ismin≥Th0Heating off, predicting the current I at time k input to the temperature prediction module based on operating conditionskThe current value collected by the initial time sensor is used as the initial value I of the current input to the temperature prediction module0；
When the temperature input module inputs the minimum temperature value T of the battery to the current input modulemin≥TmIn time, according to the running state of the vehicle,judging the charge and discharge state of the battery; the method comprises the following specific steps:
24) when charging, the temperature input module inputs the maximum temperature value T of the current input modulemaxAnd critical temperature T of battery charging and heat dissipationcc0Comparison when T ismax＜Tcc0The heat dissipation is not started, and the current I input to the temperature prediction module k moment is predicted based on the operation conditionkThe current value collected by the initial time sensor is used as the initial value I of the current input to the temperature prediction module0(ii) a The highest temperature value T input into the current input module by the temperature input modulemaxAnd critical temperature T of battery charging and heat dissipationcc0Comparison when T ismax≥Tcc0The heat dissipation is started, and the current I input to the temperature prediction module k moment is predicted based on the operation conditionkThe current value collected by the initial time sensor is used as the initial value I of the current input to the temperature prediction module0(ii) a The highest temperature value T input into the current input module by the temperature input modulemaxAnd the battery charging heat dissipation exits the critical temperature TcceComparison when T ismax＜TcceAnd when the heat dissipation is closed, predicting the current I input to the temperature prediction module k moment based on the operation conditionkThe current value collected by the initial time sensor is used as the initial value I of the current input to the temperature prediction module0；
25) When discharging, the temperature input module inputs the maximum temperature value T of the current input modulemaxCritical temperature T for discharging and heat dissipating of batterycd0Comparison when T ismax＜Tcd0The heat dissipation is not started, and the current I input to the temperature prediction module k moment is predicted based on the operation conditionkThe current value collected by the initial time sensor is used as the initial value I of the current input to the temperature prediction module0(ii) a When T ismax≥Tcd0The heat dissipation is started, and the current I at the time k input to the temperature prediction module is predicted based on the operation conditionkThe current value collected by the initial time sensor is used as the initial value I of the current input to the temperature prediction module0(ii) a The highest temperature value T input into the current input module by the temperature input modulemaxAnd the battery discharge heat dissipation exit critical temperature TcdeComparisonWhen T ismax＜TcdeHeat dissipation is turned off, and the current I at the time k input to the temperature prediction module is predicted based on the operating conditionkThe current value collected by the initial time sensor is used as the initial value I of the current input to the temperature prediction module0；
Step three, inputting a set value T according to the temperature of the temperature input modulemCritical temperature T for heating batteryhd0Critical temperature T for heating and withdrawing while charging batteryhdeThe battery is not heated and the charging critical temperature T ishdmCritical temperature T for heating batteryha0Critical temperature T for battery discharge heatingh0Critical temperature T for battery charging and heat dissipationcc0And the battery charging heat dissipation exits the critical temperature TcceCritical temperature T for discharging and heat dissipation of batterycd0The battery discharge heat dissipation exits the critical temperature TcdeDetermining the internal resistance temperature interval of the internal resistance input module to be-20-45 ℃, calculating internal resistance values R at different SOC, and inputting the internal resistance values R to the temperature prediction module according to the temperature input value of the temperature input module and the SOC input value of the battery management system;
step four, inputting a set value T according to the temperature of the temperature input modulemCritical temperature T for heating batteryhd0Critical temperature T for heating and withdrawing while charging batteryhdeThe battery is not heated and the charging critical temperature T ishdmCritical temperature T for heating batteryha0Critical temperature T for battery discharge heatingh0Critical temperature T for battery charging and heat dissipationcc0And the battery charging heat dissipation exits the critical temperature TcceCritical temperature T for discharging and heat dissipation of batterycd0The battery discharge heat dissipation exits the critical temperature TcdeDetermining the temperature interval of the reaction heat coefficient input module to be-20-45 ℃, calculating the reaction heat coefficient k at different SOCs, and inputting the reaction heat coefficient k to the temperature prediction module according to the temperature input value of the temperature input module and the SOC input value of the battery management system;
step five, inputting the temperature T input into the temperature prediction module by the temperature input modulemaxOr TminThe current I at the time k input by the current input module to the temperature prediction modulekThe internal resistance value R input to the temperature prediction module by the internal resistance input module, the reaction heat coefficient k input to the temperature prediction module by the reaction heat coefficient module and the SOC value input to the temperature prediction module by the battery management system BMS are used as input quantity of the temperature prediction module, and the battery temperature T at the moment k is predicted by a state equation and an observation equation predicted by the battery temperaturek；
The equation of state for the prediction of the battery temperature,
an observed equation for the prediction of the battery temperature,
Tmk＝DTk71+vk-1
wherein,
D＝[0 0 1]
in the formula, ω6-1Is state noise, vk-1To observe noise, h is the convective heat transfer coefficient, A is the heat exchange area, m is the battery mass, Tk-1Predicted temperature of battery at time k-1, q is heat generation amount of battery per unit time, TcTo cool the temperature of the medium, CpThe specific heat capacity of the battery.
And step six, the temperature prediction module inputs the predicted temperature Tk into the thermal management module for temperature control, and the controller with the temperature as an input variable is called.
3. The prediction method of the power battery temperature prediction system according to claim 2, wherein the current I at the time k input to the temperature prediction module is predicted based on the operation condition in the second stepkComprising predicting the current I at time k based on historical operating conditionskAnd predicting the current I at time k based on future operating conditionskPredicting the current I at the k moment based on the near moment of the operation conditionk。
4. The prediction method of a power battery temperature prediction system according to claim 2, wherein the battery heating critical temperature T in step twohd0Is-20 to 10 ℃; the critical temperature T for heating and exiting the battery while heating and charginghde10-30 ℃; t is the critical temperature of the battery not heatedhdmIs-20 to 20 ℃; the critical temperature T of battery heatingha0At-20 to 10 ℃, and the discharge heating critical temperature T of the batteryh0At-10 to 20 ℃, and the critical temperature T of charging and heat dissipation of the batterycc0At 30-45 deg.C, the battery charging and heat dissipating exit critical temperature TcceAt 32-45 deg.C, and critical temperature T of battery discharge and heat dissipationcd0At 20-45 deg.C, and the critical temperature T for discharging and heat dissipating of the batterycdeIs 15 to 45 ℃.
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Cited By (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110703115A (en) * | 2019-10-30 | 2020-01-17 | 同济大学 | Online estimation method for average temperature of storage battery of electric vehicle |
| CN111071106A (en) * | 2019-12-25 | 2020-04-28 | 浙江合众新能源汽车有限公司 | Low-temperature charging management system and method for electric automobile |
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| CN112455287A (en) * | 2019-09-06 | 2021-03-09 | 比亚迪股份有限公司 | Vehicle and cooling method and device of power battery of vehicle |
| CN112519635A (en) * | 2019-09-17 | 2021-03-19 | 深圳市英维克科技股份有限公司 | Control method and related device for battery thermal management |
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| CN114184964A (en) * | 2021-12-08 | 2022-03-15 | 蜂巢能源科技(无锡)有限公司 | Method and device for evaluating temperature distribution inside battery cell |
| CN114355997A (en) * | 2021-12-15 | 2022-04-15 | 广东亿嘉和科技有限公司 | Temperature control method and system of infrared camera based on intelligent prediction algorithm |
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| CN116306032A (en) * | 2023-05-17 | 2023-06-23 | 宁德时代新能源科技股份有限公司 | Temperature prediction method, device, equipment and storage medium |
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Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP4630113B2 (en) * | 2005-04-12 | 2011-02-09 | 古河電気工業株式会社 | Secondary battery deterioration state determination method and secondary battery deterioration state determination device |
| JP5061839B2 (en) * | 2007-10-17 | 2012-10-31 | ソニー株式会社 | Battery pack and temperature estimation method |
| US20140324379A1 (en) * | 2013-04-26 | 2014-10-30 | Hyundai Motor Company | System and method for predicting temperature of battery |
| CN104462847A (en) * | 2014-12-23 | 2015-03-25 | 哈尔滨工业大学 | Method for predicting internal temperature of battery in real time |
| CN104865534A (en) * | 2015-04-29 | 2015-08-26 | 同济大学 | Inner temperature estimation method for single cell |
| CN104881550A (en) * | 2015-06-15 | 2015-09-02 | 清华大学 | Self-adaptive prediction method for running temperature of power battery |
| CN204791125U (en) * | 2015-07-21 | 2015-11-18 | 桂林电子科技大学 | Prediction of electric automobile power battery temperature and heat abstractor |
| CN105206888A (en) * | 2015-08-31 | 2015-12-30 | 浙江工业大学之江学院 | Lithium ion battery internal temperature monitoring method |
| CN106154171A (en) * | 2016-06-17 | 2016-11-23 | 清华大学 | Set up the method for damping function in battery DC |
| CN106289566A (en) * | 2016-07-19 | 2017-01-04 | 清华大学 | A kind of method secondary cell internal temperature estimated based on electrochemical impedance |
| CN106842050A (en) * | 2017-01-24 | 2017-06-13 | 中国电力科学研究院 | A kind of battery temperature Forecasting Methodology and device |
| CN107539137A (en) * | 2016-06-27 | 2018-01-05 | 比亚迪股份有限公司 | The temperature control system of power battery pack, method and electric automobile in electric automobile |
-
2019
- 2019-02-19 CN CN201910123752.XA patent/CN109987000B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP4630113B2 (en) * | 2005-04-12 | 2011-02-09 | 古河電気工業株式会社 | Secondary battery deterioration state determination method and secondary battery deterioration state determination device |
| JP5061839B2 (en) * | 2007-10-17 | 2012-10-31 | ソニー株式会社 | Battery pack and temperature estimation method |
| US20140324379A1 (en) * | 2013-04-26 | 2014-10-30 | Hyundai Motor Company | System and method for predicting temperature of battery |
| CN104462847A (en) * | 2014-12-23 | 2015-03-25 | 哈尔滨工业大学 | Method for predicting internal temperature of battery in real time |
| CN104865534A (en) * | 2015-04-29 | 2015-08-26 | 同济大学 | Inner temperature estimation method for single cell |
| CN104881550A (en) * | 2015-06-15 | 2015-09-02 | 清华大学 | Self-adaptive prediction method for running temperature of power battery |
| CN204791125U (en) * | 2015-07-21 | 2015-11-18 | 桂林电子科技大学 | Prediction of electric automobile power battery temperature and heat abstractor |
| CN105206888A (en) * | 2015-08-31 | 2015-12-30 | 浙江工业大学之江学院 | Lithium ion battery internal temperature monitoring method |
| CN106154171A (en) * | 2016-06-17 | 2016-11-23 | 清华大学 | Set up the method for damping function in battery DC |
| CN107539137A (en) * | 2016-06-27 | 2018-01-05 | 比亚迪股份有限公司 | The temperature control system of power battery pack, method and electric automobile in electric automobile |
| CN106289566A (en) * | 2016-07-19 | 2017-01-04 | 清华大学 | A kind of method secondary cell internal temperature estimated based on electrochemical impedance |
| CN106842050A (en) * | 2017-01-24 | 2017-06-13 | 中国电力科学研究院 | A kind of battery temperature Forecasting Methodology and device |
Cited By (30)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2022504862A (en) * | 2019-08-29 | 2022-01-13 | エルジー エナジー ソリューション リミテッド | Temperature estimation model determination method and equipment, battery management system to which the temperature estimation model is applied |
| JP7167415B2 (en) | 2019-08-29 | 2022-11-09 | エルジー エナジー ソリューション リミテッド | Temperature estimation model determination method and device, battery management system to which temperature estimation model is applied |
| US11754630B2 (en) | 2019-08-29 | 2023-09-12 | Lg Energy Solution, Ltd. | Method and device for determining temperature estimating model, and battery management system to which the temperature estimating model is applied |
| CN112455287A (en) * | 2019-09-06 | 2021-03-09 | 比亚迪股份有限公司 | Vehicle and cooling method and device of power battery of vehicle |
| CN112519635B (en) * | 2019-09-17 | 2023-10-20 | 深圳市英维克科技股份有限公司 | Control method and related device for battery thermal management |
| CN112519635A (en) * | 2019-09-17 | 2021-03-19 | 深圳市英维克科技股份有限公司 | Control method and related device for battery thermal management |
| CN110703115B (en) * | 2019-10-30 | 2020-11-27 | 同济大学 | Online estimation method for average temperature of storage battery of electric vehicle |
| CN110703115A (en) * | 2019-10-30 | 2020-01-17 | 同济大学 | Online estimation method for average temperature of storage battery of electric vehicle |
| CN111071106A (en) * | 2019-12-25 | 2020-04-28 | 浙江合众新能源汽车有限公司 | Low-temperature charging management system and method for electric automobile |
| CN111398827A (en) * | 2020-03-18 | 2020-07-10 | 珠海迈巨微电子有限责任公司 | Ambient temperature prediction method, battery temperature prediction method and electric quantity calculation method |
| CN111948540A (en) * | 2020-06-29 | 2020-11-17 | 东风商用车有限公司 | Power battery pack temperature prediction method and system |
| CN112016190A (en) * | 2020-08-04 | 2020-12-01 | 华人运通(上海)新能源驱动技术有限公司 | Power battery temperature correction method and device and computer readable storage medium |
| CN112161712A (en) * | 2020-09-29 | 2021-01-01 | 上海汽车工业(集团)总公司 | All-weather temperature monitoring system for electric automobile |
| CN112436201A (en) * | 2020-11-25 | 2021-03-02 | 中国第一汽车股份有限公司 | Device and method for testing thermal runaway gas temperature of battery |
| CN113022378B (en) * | 2021-03-01 | 2023-03-07 | 中国第一汽车股份有限公司 | Temperature consistency prediction method, temperature consistency prediction device, prediction equipment and storage medium |
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| CN113255205B (en) * | 2021-04-02 | 2023-11-21 | 南京工程学院 | Life cycle cost and battery temperature optimization method based on electric automobile battery |
| CN113255205A (en) * | 2021-04-02 | 2021-08-13 | 南京工程学院 | Life cycle cost and battery temperature optimization method based on electric vehicle battery |
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| CN116494816A (en) * | 2023-06-30 | 2023-07-28 | 江西驴宝宝通卡科技有限公司 | Charging management system and method for charging pile |
| CN116494816B (en) * | 2023-06-30 | 2023-09-15 | 江西驴宝宝通卡科技有限公司 | Charging management system and method for charging pile |
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