CN112329336A - Method for planning charging-cooling process of battery pack of electric vehicle - Google Patents
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Abstract
The invention relates to a method for planning a charging-cooling process of an electric automobile battery pack, which comprises the following steps: 1) forming an initial charging-cooling process design scheme, testing and determining target parameters, and forming a training data set; 2) respectively constructing three neural network regression sub-models which take three target parameters as outputs based on the training data set; 3) respectively training a neural network regression sub-model; 4) predicting target parameters of the extended charging-cooling process design scheme by using the trained neural network regression submodel; 5) screening an optimal scheme in the expanded charging-cooling process design scheme based on the target parameter requirement and the charging rate; 6) and verifying the optimal scheme and applying the optimal scheme to the actual driving using process. Compared with the prior art, the battery pack heat management system can improve the cooling efficiency of the battery pack heat management system, ensure the temperature distribution uniformity among the battery pack monomers while ensuring the charging speed, and control the power consumption in the cooling process.
Description
Technical Field
The invention relates to the field of new energy automobiles, in particular to a method for planning a charging-cooling process of a battery pack of an electric automobile.
Background
In the current society with large consumption of fossil fuel and high carbon emission, the development of renewable energy sources and the maintenance of natural environment have reached global consensus, and countries in the world push energy transformation and develop new energy vehicles, strive to reduce carbon emission in daily life and industrial production and transportation processes, and the carbon emission is used as a technical vehicle type with the highest market application degree in the new energy vehicle technology, the technical development of the pure electric vehicle is particularly critical at present, and the core technology of the pure electric vehicle comprises a battery, a motor and electric control.
For the requirement of the electric vehicle for long-distance driving, in order to enable the battery pack to be charged as soon as possible to continue to be used under the condition that the electric quantity is almost zero, it is particularly important to adopt a safe, efficient and quick charging mode, and quick charging is a major technical problem which restricts the development and popularization of the pure electric vehicle.
For the problem of thermal safety in the battery technology, the thermal management system has a crucial influence on the battery pack and even the whole electric vehicle, a large amount of heat is generated during the charging and discharging of the battery pack, which leads to the increase of the temperature of the battery pack, and further seriously reduces the performance, capacity and service life of the battery, and brings a great influence on the safety of the battery pack and the electric vehicle, meanwhile, the flow rate/flow rate of the coolant of the thermal management system is scientifically and effectively controlled by methods such as tests, measurements and the like, and the realization of efficient balance between the cooling effect and the power consumption is particularly important,
the charging speed of the charging mode currently existing in the market still has a great promotion space, most of single charging modes are not beneficial to ensuring the thermal safety of the battery pack, the temperature of the battery is rapidly increased only by promoting the charging current multiplying power, the risks of spontaneous combustion, explosion and the like exist, and the service life of the battery is greatly damaged.
Most of thermal management process designs in current practical application adopt a single large-flow/quick cooling mode, and in fact, after the flow/flow rate is increased to a certain degree, the effect of improving the cooling performance cannot be achieved by simply increasing the flow/flow rate of coolant, a small amount of temperature rise of a battery pack can be caused under certain specific working conditions, the consistency of the battery pack is damaged, and the power consumption and the operation cost of a thermal management system of the battery pack are increased.
How to increase the charging speed and reduce the power consumption and cost of the thermal management system on the premise of ensuring the thermal safety of the battery is an urgent technical problem to be solved.
Disclosure of Invention
The present invention is directed to overcoming the above-mentioned drawbacks of the prior art and providing a method for planning a charging-cooling process of a battery pack of an electric vehicle.
The purpose of the invention can be realized by the following technical scheme:
a method for planning a charging-cooling process of an electric vehicle battery pack is used for realizing the optimal design of a quick charging process and a cooling process of a vehicle-mounted lithium ion battery pack, and comprises the following steps:
1) an initial charge-cool process design is developed and tested to determine target parameters, including the maximum temperature T of the stackmaxThe temperature standard deviation TSD and the power consumption W in the cooling process of the thermal management system form a training data set;
2) respectively constructing three neural network regression sub-models which take three target parameters as outputs based on the training data set;
3) setting the number of hidden layers of the neural network regression model, a regression algorithm and a training test verification data distribution proportion parameter, and respectively training the neural network regression sub-model;
4) predicting target parameters of the extended charging-cooling process design scheme by using the trained neural network regression submodel;
5) screening an optimal scheme in the expanded charging-cooling process design scheme based on the target parameter requirement and the charging rate;
6) and verifying the optimal scheme, and applying the optimal scheme to the actual driving and using process.
In the step 1), the specific steps for generating the initial design scheme of the charging-cooling process are as follows:
the charging process is divided into a plurality of charging stages, a plurality of charging multiplying powers and a plurality of coolant flow gears are set for the charging current in each charging stage, and a plurality of sets of process design schemes, namely an initial charging-cooling process design scheme, are formed orthogonally.
The charging process is divided into three charging stages, each charging stage is provided with three charging multiplying powers and three coolant flux gears for charging current, and 81 sets of process design schemes are formed in an orthogonal mode.
The three charging multiplying powers are respectively 0.5C, 1.5C and 2.5C, and the three coolant flow gears are respectively 36ml/min,72ml/min and 108 ml/min.
The step 2) is specifically as follows:
respectively according to the highest temperature T of the battery packmaxTemperature standard deviation TSD and power consumption W of the cooling process of the thermal management system are used as output/target parameters, and three-stage charging current I is constructed1、I2、I3And coolant flow Q as input/design parameter neural network regression submodel NN1、NN2、NN3。
The step 3) is specifically as follows:
and respectively inputting the training data set into the three neural network regression submodels for training, checking the regression performance of each neural network regression submodel, namely the prediction accuracy, and if the prediction accuracy does not meet the expected requirement, adjusting the number of hidden layers of the neural network regression submodel and the data proportion of training, testing and verifying until the prediction accuracy meets the expected requirement.
In the step 4), the specific steps for generating the extended charging-cooling process design scheme are as follows:
the charging process is divided into three charging stages, each charging stage is provided with five charging multiplying powers and three coolant flux gears for charging current, and 375 sets of process design schemes, namely an expanded charging-cooling process design scheme, are formed in an orthogonal mode.
The five charging multiplying powers are respectively 0.5C, 1C, 1.5C, 2C and 2.5C, and the three coolant flow gears are respectively 36ml/min,72ml/min and 108 ml/min.
The extended charge-cool process design is further extended by the increase in the charge phase, charge rate and number of coolant flow steps.
In the step 6), the verification content of the optimal scheme includes the accuracy in practical application and the error between the target parameter and the regression prediction value.
Compared with the prior art, the invention has the following advantages:
firstly, the thermal safety index of the lithium ion battery pack and the power consumption of the thermal management system are obtained through measurement, training and calculation before the lithium ion battery pack is put into practical application, so that the research and development cost is greatly reduced, and the risks of out-of-control and accidents in the research and development process are avoided.
And secondly, after the neural network submodel with better regression performance is obtained through training, the neural network submodel can be used for predicting a charging-cooling process planning scheme with a wider prediction range, and the prediction accuracy is ensured.
And thirdly, the charging-cooling planning sample data set used for model training can be used for comparing and checking the accuracy of the model prediction result.
And for the battery pack in the low-temperature environment, the method can also be used for planning the charging-heating process in the low-temperature environment, and has better universality.
And fifthly, the method can further plan the flow of the cooling process, and can further optimize the target parameters.
Sixthly, a heat dissipation design combining multiple cooling methods can also be used in the method, so that the heat generation temperature gradient of the battery pack is further improved, and the temperature distribution uniformity is improved.
The method simultaneously ensures the control of the temperature rise of the battery pack, the temperature distribution uniformity and the power consumption of the thermal management system, greatly ensures the high consistency of the lithium ion battery monomer, prolongs the service life of the battery pack, and also gives better consideration to the power consumption and the cost in the thermal management process.
Drawings
FIG. 1 is a technical scheme of the method of the present invention.
Fig. 2 is a schematic diagram of the charging process of the battery pack according to the present invention.
FIG. 3 is a schematic diagram of a sample experimental determination of a process planning scheme.
FIG. 4 is a diagram illustrating a neural network regression sub-model structure.
Fig. 5 is a schematic diagram of a neural network regression model prediction process planning scheme.
Detailed Description
The following detailed description of the preferred embodiments of the present invention, taken in conjunction with the accompanying drawings, will make the advantages and features of the invention easier to understand by those skilled in the art, and thus will clearly and clearly define the scope of the invention.
As shown in fig. 1, the present invention provides a method for planning a charging-cooling process of a battery pack of an electric vehicle, which comprises the following steps:
first, a representative charge-cooling process scheme, i.e., an initial charge-cooling process scheme for training, is designed, tests are performed to determine target parameters (maximum temperature, temperature standard deviation, and power consumption of the thermal management system),
forming a training data set;
because the general working temperature range of the lithium ion battery is 0-40 ℃, and the reference test temperature is more within the range of 23 +/-5 ℃, the over-high temperature can cause the thermal runaway of the lithium ion battery monomer so as to affect other battery monomers of the battery pack, thereby causing safety accidents such as combustion, explosion and the like, and the control on the highest temperature becomes an important index for evaluating the thermal safety no matter in the research or production and manufacturing test of the lithium ion battery, therefore, the highest temperature is selected as a first target parameter in the embodiment;
in addition, in order to ensure that the lithium ion battery pack has better performance, actual use capacity and service life, the consistency among the lithium ion battery monomers is required to be ensured as much as possible, and the difference of thermal and electrical properties can cause the actual capacity waste of partial battery monomers and the incomplete charging of the whole battery pack/battery pack, so the temperature standard deviation selected in the embodiment is used as a second target parameter for measuring important indexes of the thermal consistency and the temperature distribution uniformity of the lithium ion battery pack;
finally, the thermal management system becomes an essential part of the electric vehicle, and from the aspect of the use economy and the system efficiency of the electric vehicle, on the premise of ensuring the thermal safety, reducing the power consumption of the thermal management system in the cooling process is a necessary way for improving the use economy of the electric vehicle, so that the power consumption of the thermal management system is selected as one of the target parameters in the example;
then, constructing a neural network regression sub-model taking three target parameters (the highest temperature, the standard deviation of the temperature and the power consumption of the thermal management system) as output parameters based on the training data set;
debugging and researching parameters such as the number of hidden layers of a neural network regression model, a regression algorithm, a training test verification data distribution proportion and the like to obtain a neural network regression sub-model with higher fitting accuracy;
predicting target parameters of a wider range of charge-cooling process design schemes (extended charge-cooling process design schemes) using the trained regression model;
screening out an optimal charging-cooling process design scheme based on target parameter (highest temperature, temperature standard deviation and heat management system power consumption) requirements and charging rate measurement (increase of SOC of the battery pack);
finally, the selected optimal design scheme is verified through tests and is combined and applied to the actual driving and using process.
The detailed steps of the above steps are as follows:
as shown in FIG. 2, the 15 minute charging process was divided into three charging stages, each stage was set with a corresponding charging current, and the peristaltic pump body, whose rapid charging process controlled coolant flow, was set with three different flow steps, three charging process charging currents (I)1,I2,I3) Three charging rates (0.5C, 1.5C, 2.5C) are selectable respectively, and are orthogonal to three gears (36ml/min,72ml/min,108ml/min) of the coolant flow (Q), and the design scheme of the process is totally 81 groups, as shown in figure 3.
The 81 groups of design schemes are tested to obtain 81 groups of target parameter values (the highest temperature T of the battery pack) of process designmaxTSD, power consumption W) of the cooling process of the thermal management system according to the maximum temperature of the battery pack, the standard deviation of the temperature and the work of the cooling process of the thermal management system respectivelyThree-stage charging current (I) constructed for output/target parameters1,I2,I3) Neural network regression submodel (NN) with coolant flow (Q) as input/design parameter1,NN2,NN3) As shown in fig. 4.
81 sets of training data were respectively substituted into three neural network regression sub-models (NN)1,NN2,NN3) And (5) training, and checking the regression performance (prediction accuracy) of each sub-model. If the accuracy rate does not meet the expected requirement, the number of hidden layers of the neural network regression sub-model and the data proportion of training, testing and verifying are adjusted until higher regression performance is obtained.
Regression sub-model (NN) using trained neural networks1,NN2,NN3) Predicting target parameter values for the expanded 375 set charge-cooling process planning scheme, this data set consisting of three-phase charging current (I)1,I2,I3) The corresponding five charging rates (0.5C, 1C, 1.5C, 2C, 2.5C) and three steps (36ml/min,72ml/min,108ml/min) of coolant flow (Q) are constructed orthogonally as shown in fig. 5.
For the 6C quick charging technology which is verified for the measurement of the battery cell in the laboratory at present, the quick charging multiplying power which is close to or reaches 6C can be applied to the method so as to obtain larger selection of the charging-cooling process planning sample.
Screening the maximum temperature T of the battery pack from the predicted wider range of charge-cooling process planning schemesmaxThe temperature standard deviation TSD and the cooling process power consumption W of the thermal management system all accord with a charging-cooling process scheme of a screening standard, and the optimal charging-cooling process scheme obtained by screening is subjected to experimental verification to verify the accuracy of the optimal charging-cooling process scheme in practical application and the error between a target parameter and a regression prediction value.
The invention can greatly reduce the design cost of the charging-cooling process and improve the design efficiency, has better universality and can simultaneously ensure the control of the charging rate of the lithium ion battery pack of the electric automobile, the temperature rise of the battery pack and the uniformity of the temperature distribution.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes performed by the present specification and drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A method for planning a charging-cooling process of a battery pack of an electric vehicle is used for realizing the optimal design of a quick charging process and a cooling process of a vehicle-mounted lithium ion battery pack, and is characterized by comprising the following steps:
1) an initial charge-cool process design is developed and tested to determine target parameters, including the maximum temperature T of the stackmaxThe temperature standard deviation TSD and the power consumption W in the cooling process of the thermal management system form a training data set;
2) respectively constructing three neural network regression sub-models which take three target parameters as outputs based on the training data set;
3) setting the number of hidden layers of the neural network regression model, a regression algorithm and a training test verification data distribution proportion parameter, and respectively training the neural network regression sub-model;
4) predicting target parameters of the extended charging-cooling process design scheme by using the trained neural network regression submodel;
5) screening an optimal scheme in the expanded charging-cooling process design scheme based on the target parameter requirement and the charging rate;
6) and verifying the optimal scheme, and applying the optimal scheme to the actual driving and using process.
2. The method for planning the charging-cooling process of the battery pack of the electric vehicle according to claim 1, wherein the step 1) of generating the initial design scheme of the charging-cooling process comprises the following specific steps:
the charging process is divided into a plurality of charging stages, a plurality of charging multiplying powers and a plurality of coolant flow gears are set for the charging current in each charging stage, and a plurality of sets of process design schemes, namely an initial charging-cooling process design scheme, are formed orthogonally.
3. The method of claim 2, wherein the charging process is divided into three charging stages, each charging stage is configured with three charging rates and three coolant flow gears for charging current, and the three charging rates and the three coolant flow gears are orthogonal to each other to form 81 sets of process design schemes.
4. The method as claimed in claim 3, wherein the three charging rates are 0.5C, 1.5C and 2.5C, and the three coolant flow steps are 36ml/min,72ml/min and 108 ml/min.
5. The method for planning the charging-cooling process of the battery pack of the electric vehicle according to claim 1, wherein the step 2) is specifically as follows:
respectively according to the highest temperature T of the battery packmaxTemperature standard deviation TSD and power consumption W of the cooling process of the thermal management system are used as output/target parameters, and three-stage charging current I is constructed1、I2、I3And coolant flow Q as input/design parameter neural network regression submodel NN1、NN2、NN3。
6. The method for planning the charging-cooling process of the battery pack of the electric vehicle according to claim 1, wherein the step 3) is specifically as follows:
and respectively inputting the training data set into the three neural network regression submodels for training, checking the regression performance of each neural network regression submodel, namely the prediction accuracy, and if the prediction accuracy does not meet the expected requirement, adjusting the number of hidden layers of the neural network regression submodel and the data proportion of training, testing and verifying until the prediction accuracy meets the expected requirement.
7. The method for planning the charging-cooling process of the battery pack of the electric vehicle according to claim 1, wherein the step 4) of generating the extended charging-cooling process design scheme comprises the following specific steps:
the charging process is divided into three charging stages, each charging stage is provided with five charging multiplying powers and three coolant flux gears for charging current, and 375 sets of process design schemes, namely an expanded charging-cooling process design scheme, are formed in an orthogonal mode.
8. The method as claimed in claim 7, wherein the five charging rates are 0.5C, 1C, 1.5C, 2C and 2.5C, and the three coolant flow steps are 36ml/min,72ml/min and 108 ml/min.
9. The method of claim 7, wherein the extended charge-cooling process is further extended by increasing the number of charging stages, charging rates, and coolant flow steps.
10. The method for planning the charging-cooling process of the battery pack of the electric vehicle according to claim 1, wherein in the step 6), the verification content of the optimal solution comprises the accuracy in practical application and the error between the target parameter and the regression prediction value.
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Cited By (3)
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| CN113158461A (en) * | 2021-04-20 | 2021-07-23 | 同济大学 | Multi-objective optimization design method for vehicle-mounted lithium ion power battery pack thermal management system |
| CN113435016A (en) * | 2021-06-10 | 2021-09-24 | 同济大学 | Multi-objective optimization design method of hybrid thermal management system based on regression model algorithm |
| DE102022106806A1 (en) | 2022-03-23 | 2023-09-28 | Dr. Ing. H.C. F. Porsche Aktiengesellschaft | Method, system and computer program product for thermal management of a traction battery |
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| 陈思琦 等: "基于液冷的电池热管理系统快充-冷却耦合规划方法", 《ENGINEERING》 * |
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| CN113158461A (en) * | 2021-04-20 | 2021-07-23 | 同济大学 | Multi-objective optimization design method for vehicle-mounted lithium ion power battery pack thermal management system |
| CN113435016A (en) * | 2021-06-10 | 2021-09-24 | 同济大学 | Multi-objective optimization design method of hybrid thermal management system based on regression model algorithm |
| DE102022106806A1 (en) | 2022-03-23 | 2023-09-28 | Dr. Ing. H.C. F. Porsche Aktiengesellschaft | Method, system and computer program product for thermal management of a traction battery |
| DE102022106806B4 (en) | 2022-03-23 | 2023-11-23 | Dr. Ing. H.C. F. Porsche Aktiengesellschaft | Method, system and computer program product for thermal management of a traction battery |
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