Battery temperature prediction method and device, medium and battery system

Abstract

The invention relates to the technical field of battery management, in particular to a battery temperature prediction method and device, a medium and a battery system. Comprises: obtaining prediction information of a future driving condition of a vehicle; calculating a future preliminary predicted temperature of the battery; calculating a final battery thermal model error compensation amount; and calculating the future final predicted temperature of the battery. According to the invention, the error compensation amount of the battery temperature prediction thermal model is learned by using the estimated value of the battery temperature and the actual temperature value of the battery, and the future temperature change of the battery is predicted based on the battery temperature prediction thermal model with the error compensation amount, so that the battery temperature prediction precision can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of battery management, and in particular to a battery temperature prediction method, device, medium and battery system. Background Art

[0002] As a complex electrochemical component, the performance of lithium-ion batteries is greatly affected by temperature. At present, the vehicle's future discharge power curve is usually predicted based on the vehicle's navigation information and road condition information from big data statistics, and then the battery's future current changes are predicted based on the discharge power curve, and finally the battery temperature prediction result is obtained based on the predicted current curve. Therefore, the battery temperature is predicted online based on the battery thermal model, and the battery temperature is pre-adjusted in advance through battery thermal management according to the temperature prediction value to ensure that the battery operates within a reasonable temperature range to improve the range of electric vehicles.

[0003] At present, for real-time computing occasions, a lumped parameter model is usually used to predict the future temperature changes of the battery, but due to its low complexity, the prediction results have large deviations. Summary of the invention

[0004] The invention discloses a battery temperature prediction method, device, medium and battery system, which can improve the battery temperature prediction accuracy.

[0005] In order to achieve the above object, on the one hand, a battery temperature prediction method is provided, and the specific method is as follows:

[0006] Obtain prediction information about the vehicle's future driving conditions;

[0007] Substitute the vehicle's future driving conditions into the lumped parameter model to obtain the preliminary predicted future temperature of the battery;

[0008] Substitute the current driving condition of the vehicle into the lumped parameter model to obtain the current estimated temperature of the battery;

[0009] The final battery thermal model error compensation amount is calculated based on the difference between the current estimated battery temperature and the current actual battery temperature;

[0010] The battery's future preliminary predicted temperature is corrected using the final battery thermal model error compensation amount, and the battery's future final predicted temperature is calculated.

[0011] Furthermore, the prediction information of the future driving condition of the vehicle is obtained as follows:

[0012] Predict the vehicle's future discharge power curve through vehicle navigation information and road condition information;

[0013] The future driving conditions of the vehicle are predicted based on the vehicle's future discharge power curve.

[0014] Furthermore, the driving conditions include: battery current, battery open circuit voltage, battery terminal voltage, battery starting temperature, convection heat transfer coefficient, battery surface area, ambient temperature, vehicle speed, battery mass and battery specific heat capacity.

[0015] Furthermore, the lumped parameter model calculates the future preliminary predicted temperature of the battery or the current estimated temperature of the battery, and the specific method is as follows:

[0016] Calculate the battery heat generation rate based on the battery current, battery open circuit voltage, battery terminal voltage and battery initial temperature;

[0017] Calculate the battery heat dissipation rate based on the convection heat transfer coefficient, battery surface area and ambient temperature;

[0018] The battery's future preliminary predicted temperature or the battery's current estimated temperature is calculated based on the battery's initial temperature, the battery's heat generation rate, and the battery's heat dissipation rate.

[0019] Furthermore, the final battery thermal model error compensation amount is calculated, and the specific method is as follows:

[0020] The PI closed-loop control algorithm is used to calculate the current compensation value as the final battery thermal model error compensation amount.

[0021] Furthermore, in the PI closed-loop control algorithm, the proportional coefficient and the integral coefficient are both manually set coefficients.

[0022] Furthermore, the final battery thermal model error compensation amount is calculated, and the specific method is as follows:

[0023] The current compensation value is calculated by the PI closed-loop control algorithm, and the calculated current compensation value is saved as historical data;

[0024] The weighted current compensation value and historical compensation value are used as the final battery thermal model error compensation amount.

[0025] To achieve the above object, on the other hand, a battery temperature prediction device is provided, comprising: an operating condition prediction module, a temperature prediction module and a prediction correction module;

[0026] The working condition prediction module obtains the prediction information of the vehicle's future driving conditions;

[0027] The temperature prediction module substitutes the vehicle's future driving conditions into the lumped parameter model to obtain the preliminary predicted future temperature of the battery;

[0028] The prediction correction module substitutes the current driving condition of the vehicle into the lumped parameter model to obtain the current estimated temperature of the battery; calculates the current compensation value and the final battery thermal model error compensation amount based on the difference between the current estimated temperature of the battery and the current actual temperature of the battery; corrects the battery's preliminary predicted future temperature with the final battery thermal model error compensation amount, and calculates the battery's final predicted future temperature.

[0029] In order to achieve the above object, on the other hand, a storage medium is provided, which stores a plurality of instructions, and a processor loads the instructions to execute the above battery temperature prediction method.

[0030] In order to achieve the above object, on the other hand, a battery system is provided, comprising the above battery temperature prediction device and / or the above storage medium.

[0031] Due to the adoption of the above technical solution, the present invention has the following beneficial effects:

[0032] 1. The present invention uses the estimated value of the battery temperature and the actual temperature value of the battery to learn the error compensation amount of the battery temperature prediction thermal model and predicts the future temperature change of the battery based on the battery temperature prediction thermal model with error compensation amount, which can effectively improve the accuracy of battery temperature prediction.

[0033] 2. The present invention is based on high-precision online prediction results of battery temperature and pre-adjusts the battery temperature through advance battery thermal management to ensure that the battery operates within a reasonable temperature range, so as to give full play to the performance of the battery, improve the cruising range of the electric vehicle, and thus improve the user's travel experience.

[0034] It should be noted that the terms "first", "second" and similar terms used in this article are only for describing the various components in the technical solution, and do not constitute a limitation of the technical solution, nor can they be understood as an indication or suggestion of the importance of the corresponding elements; elements with "first", "second" and similar terms indicate that the corresponding technical solution contains at least one of the element. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solution of the present invention and facilitate further understanding of the technical effects, technical features and purposes of the present invention, the present invention is described in detail below in conjunction with the accompanying drawings. The accompanying drawings constitute an essential part of the specification and are used together with the embodiments of the present invention to illustrate the technical solution of the present invention, but do not constitute a limitation to the present invention.

[0036] The same reference numerals in the accompanying drawings represent the same components, specifically: Figure 1 It is a schematic diagram of the overall process of the present invention. DETAILED DESCRIPTION

[0037] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. Of course, the specific embodiments described below are only for explaining the technical solutions of the present invention, rather than for limiting the present invention. In addition, the parts described in the embodiments or the accompanying drawings are only examples of the relevant parts of the present invention, rather than the whole of the present invention.

[0038] Embodiment 1:

[0039] Battery temperature prediction generally uses a lumped parameter model to predict future battery temperature changes. The lumped parameter model includes a heat generation model and a heat transfer model.

[0040] The battery heat generation model mainly includes Joule heat and reaction heat, which is used to calculate the heat generated by the battery during normal charging and discharging. The specific formula is as follows:

[0041]

[0042] in, is the battery heat generation rate, I is the current, U OCV is the current open circuit voltage of the battery, U t is the battery terminal voltage, T bat is the battery temperature.

[0043] In addition, the heat transfer model of the battery is mainly used to reflect the heat transfer inside the battery and the heat exchange between the battery and the outside world. Considering the needs of actual vehicle calculations, a concentrated mass model is used as the heat transfer model of the battery. It assumes that the temperature inside the battery is equal, ignoring the heat transfer inside the battery, and only considering the heat exchange between the battery and the outside environment. The heat exchange between the battery and the outside world is carried out by convection heat transfer. The specific formula is as follows:

[0044]

[0045] in, is the ambient heat dissipation rate, h bat is the convective heat transfer coefficient, A bat is the battery surface area, T env is the ambient temperature.

[0046] Thus, the battery temperature prediction model of lumped parameters can be obtained. The specific formula is as follows:

[0047]

[0048] Where m bat is the battery mass, c p is the specific heat capacity of the battery.

[0049] Divide both sides of the equation by m bat c p , the following battery temperature prediction formula can be obtained:

[0050]

[0051] in, Represents the heat generation coefficient, which is related to the battery temperature. Represents the heat transfer coefficient, which is related to vehicle speed and ambient temperature.

[0052] After discretization, the following battery temperature prediction formula can be obtained:

[0053]

[0054] However, the current battery temperature prediction thermal model based on lumped parameters has made certain simplifications to the battery heat generation and heat transfer. For example, it ignores the heat transfer inside the battery and treats the battery as a lumped mass model; it ignores the reaction heat generated by the battery side reactions and the mixing heat caused by the concentration difference of materials in various parts of the battery; the heat generation coefficient and the heat transfer coefficient are often identified offline, without considering the influence of factors such as battery aging and dispersion differences between different batteries. All of the above will cause certain modeling deviations in the thermal model used for battery temperature prediction, thereby affecting the accuracy of online battery temperature prediction.

[0055] Therefore, Example 1 proposes a battery temperature prediction method, the specific steps are as follows:

[0056] S1. Obtain prediction information of the vehicle's future driving conditions.

[0057] The future driving conditions of the vehicle are obtained in the following steps:

[0058] Predict the vehicle's future discharge power curve through vehicle navigation information and road condition information;

[0059] The future driving conditions of the vehicle are predicted based on the vehicle's future discharge power curve.

[0060] The future driving conditions include: battery current I, battery open circuit voltage U OCV , battery terminal voltage U t , Battery starting temperature T bat , Convective heat transfer coefficient h bat 、Battery surface area A bat 、Ambient temperature T env , vehicle speed, battery mass m bat and the battery specific heat capacity c p .

[0061] S2. Substitute the vehicle's future driving conditions into the lumped parameter model to obtain the preliminary predicted future temperature of the battery.

[0062] The lumped parameter model calculates the battery's future preliminary predicted temperature or the battery's current estimated temperature. The specific steps are as follows:

[0063] S21. Calculate the battery heat generation rate based on the battery current, battery open circuit voltage, battery terminal voltage and battery initial temperature. The specific formula is:

[0064]

[0065] S22. Calculate the battery heat dissipation rate based on the convection heat transfer coefficient, battery surface area and ambient temperature. The specific formula is:

[0066]

[0067] S23. Calculate the battery's future preliminary predicted temperature or the battery's current estimated temperature based on the battery's initial temperature, the battery's heat generation rate, and the battery's heat dissipation rate. The specific formula is as follows:

[0068]

[0069] Divide both sides of the equation by m bat c p , the following battery temperature prediction formula can be obtained:

[0070]

[0071] in, Represents the heat generation coefficient, which is related to the battery temperature. Represents the heat transfer coefficient, which is related to vehicle speed and ambient temperature.

[0072] After discretization, the following battery temperature prediction formula can be obtained:

[0073]

[0074] Among them, Δt represents the calculation period, Δf old is the historical error compensation amount.

[0075] S3. Substitute the current driving condition of the vehicle into the lumped parameter model to obtain the current estimated temperature of the battery.

[0076] In step S3, when the battery is just started to be used, the battery temperature estimation value T is initialized. bat,est [0] = T bat,act [0], where T bat,act [0] is the actual battery temperature at the initial moment, which is used to initialize the error compensation value Δf of the battery thermal model. new =0, and obtain the historical error compensation value Δf of the battery thermal model from the non-volatile memory old .

[0077] According to the current measured battery remaining capacity SOC act [k] and the actual battery temperature Tbat,act [k], calculate the partial derivative of the battery temperature Then according to the current measured current value I act [k] and terminal voltage value U t,act [k] Calculate the current heat generation rate of the battery

[0078]

[0079] According to the actual battery temperature T bat,act [k] Calculate the current heat generation coefficient of the battery: k bat [k].

[0080] According to the actual vehicle speed v act [k] and ambient temperature T env,act [k] Calculate the current heat dissipation coefficient of the battery: k env [k].

[0081] According to the actual measured ambient temperature T env,act [k], calculate the estimated value of the battery temperature at the next moment: T bat,est [k+1]:

[0082]

[0083] Among them, Δt represents the calculation period, Δf old Compensation for historical misjudgments.

[0084] S4. Calculate the final battery thermal model error compensation amount based on the difference between the current estimated battery temperature and the current actual battery temperature.

[0085] In this embodiment, the final battery thermal model error compensation amount is calculated, and the specific method is as follows:

[0086] S41, using the PI closed-loop control algorithm, calculating the current compensation value, and saving the calculated current compensation value as historical data;

[0087] S42: weighting the current compensation value and the historical compensation value to obtain a final battery thermal model error compensation amount.

[0088] In step S4, the estimated current battery temperature T calculated in step S3 is bat,estp [k] and the actual battery temperature T bat,act The difference ε[k] of [k] is:

[0089] ε[k]=T bat,est [k]-T bat,act [k]

[0090] Using ε[k], the error compensation Δf[k] of the battery temperature prediction model is calculated according to the following formula:

[0091] Δf[k]=k p ·ε[k]+k i ·∫ε[k]dt

[0092] Among them, k p and k i It is a calibrable parameter.

[0093] When the above Δf[k makes the battery temperature estimation value T bat,est [k] and the actual battery temperature T bat,act When the difference ε[k] of [k] gradually converges to near 0, it is considered that the Δf[k] obtained by self-learning at this time is the error compensation amount of the battery temperature prediction model.

[0094] The error compensation value Δf obtained by this self-learning new and historical error compensation Δf old , weighted to obtain the final error compensation Δf of the battery temperature prediction model wgh :

[0095] Δf wgh =w*Δf new +(1-w)*Δf old

[0096] Among them, w is the weight value, and finally Δf wgh Stored in non-volatile memory.

[0097] S5. Correct the preliminary predicted future temperature of the battery using the final battery thermal model error compensation amount, and calculate the final predicted future temperature of the battery.

[0098] In step S5, the error compensation amount Δf of the weighted battery temperature prediction model is used. wgh , the preliminary predicted temperature of the battery in the future is corrected, the specific formula is as follows:

[0099]

[0100] Embodiment 2:

[0101] A battery temperature prediction device, comprising: an operating condition prediction module, a temperature prediction module and a prediction correction module;

[0102] The working condition prediction module obtains the prediction information of the vehicle's future driving conditions;

[0103] The temperature prediction module substitutes the vehicle's future driving conditions into the lumped parameter model to obtain the preliminary predicted future temperature of the battery;

[0104] The prediction correction module substitutes the current driving condition of the vehicle into the lumped parameter model to obtain the current estimated temperature of the battery; calculates the current compensation value and the final battery thermal model error compensation amount based on the difference between the current estimated temperature of the battery and the current actual temperature of the battery; corrects the battery's preliminary predicted future temperature with the final battery thermal model error compensation amount, and calculates the battery's final predicted future temperature.

[0105] It should be noted that the above embodiments are only for more clearly illustrating the technical solutions of the present invention. Those skilled in the art will understand that the implementation methods of the present invention are not limited to the above contents, and obvious changes, replacements or substitutions based on the above contents do not exceed the scope of the technical solutions of the present invention; other implementation methods will also fall within the scope of the present invention without departing from the concept of the present invention.

Claims

1. A battery temperature prediction method, characterized in that: The specific method is as follows: Obtain prediction information about the vehicle's future driving conditions; Substitute the vehicle's future driving conditions into the lumped parameter model to obtain the preliminary predicted future temperature of the battery; Substitute the current driving condition of the vehicle into the lumped parameter model to obtain the current estimated temperature of the battery; The final battery thermal model error compensation amount is calculated based on the difference between the current estimated battery temperature and the current actual battery temperature; The battery's future preliminary predicted temperature is corrected using the final battery thermal model error compensation amount, and the battery's future final predicted temperature is calculated.

2. The battery temperature prediction method according to claim 1, characterized in that: The prediction information of the vehicle's future driving conditions is obtained as follows: Predict the vehicle's future discharge power curve through vehicle navigation information and road condition information; The future driving conditions of the vehicle are predicted based on the vehicle's future discharge power curve.

3. The battery temperature prediction method according to claim 1, characterized in that: The driving conditions include: battery current, battery open circuit voltage, battery terminal voltage, battery initial temperature, convection heat transfer coefficient, battery surface area, ambient temperature, vehicle speed, battery mass and battery specific heat capacity.

4. The battery temperature prediction method according to claim 4, characterized in that: The lumped parameter model calculates the battery's future preliminary predicted temperature or the battery's current estimated temperature in the following manner: Calculate the battery heat generation rate based on the battery current, battery open circuit voltage, battery terminal voltage and battery initial temperature; Calculate the battery heat dissipation rate based on the convection heat transfer coefficient, battery surface area and ambient temperature; The battery's future preliminary predicted temperature or the battery's current estimated temperature is calculated based on the battery's initial temperature, the battery's heat generation rate, and the battery's heat dissipation rate.

5. The battery temperature prediction method according to claim 1, characterized in that: Calculate the final battery thermal model error compensation amount. The specific method is as follows: The PI closed-loop control algorithm is used to calculate the current compensation value as the final battery thermal model error compensation amount.

6. The battery temperature prediction method according to claim 5, characterized in that: In the PI closed-loop control algorithm, the proportional coefficient and the integral coefficient are both manually set coefficients.

7. The battery temperature prediction method according to claim 1, characterized in that: Calculate the final battery thermal model error compensation amount. The specific method is as follows: The current compensation value is calculated by the PI closed-loop control algorithm, and the calculated current compensation value is saved as historical data; The weighted current compensation value and historical compensation value are used as the final battery thermal model error compensation amount.

8. A battery temperature prediction device, characterized in that: include: Working condition prediction module, temperature prediction module and prediction correction module; The working condition prediction module obtains the prediction information of the vehicle's future driving conditions; The temperature prediction module substitutes the vehicle's future driving conditions into the lumped parameter model to obtain the preliminary predicted future temperature of the battery; The prediction and correction module substitutes the current driving condition of the vehicle into the lumped parameter model to obtain the current estimated temperature of the battery; The current compensation value and the final battery thermal model error compensation amount are calculated based on the difference between the current estimated battery temperature and the current actual battery temperature; The battery's future preliminary predicted temperature is corrected using the final battery thermal model error compensation amount, and the battery's future final predicted temperature is calculated.

9. A storage medium, characterized in that: A plurality of instructions are stored, and a processor loads the instructions to execute the battery temperature prediction method according to any one of claims 1 to 7.

10. A battery system, characterized in that: It includes the battery temperature prediction device as described in claim 8, and / or the storage medium as described in claim 9.

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