CN112818594B - Multi-objective battery pack structure optimizing method based on neural network - Google Patents
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Abstract
The invention discloses a multi-objective battery pack structure optimizing method based on a neural network, which comprises the following steps: s1: converting the three-dimensional model of the battery pack into a two-dimensional model, and then simulating in COMSOL to generate a plurality of groups of structural parameters affecting the performance value of the battery pack; s2, taking a plurality of groups of structural parameters as the input of the neural network, taking the performance value of the battery pack as the output of the neural network, and training the neural network by adopting a Bayesian regularization algorithm; and S3, optimizing structural parameters of the neural network trained in the step S2 by adopting an NSGA2 algorithm to obtain a plurality of groups of pareto optimal solutions, and selecting the group with the lowest highest temperature in the pareto optimal solutions as a final scheme of the battery pack. The invention greatly shortens the period of optimizing the structure of the battery pack and improves the performance of the battery pack.
Description
Technical Field
The invention relates to the technical field of battery systems, in particular to a multi-objective battery pack structure optimizing method based on a neural network.
Background
With the increasing problems of air pollution and resource shortage, more and more countries are beginning to push the development of electric automobiles. The core of the electric automobile is a power battery, and the performance of the power battery influences the quality and user experience of the electric automobile. The temperature has remarkable influence on the performance of the power battery, and when the high-current discharge is carried out, if the heat generated by the battery is not dissipated in time, the battery can have the problems of service life attenuation, combustion, explosion and the like. Therefore, in order to maintain the temperature in an optimal range and improve the endurance mileage of the electric automobile, the battery pack with reasonable design has important significance for improving the performance of the power battery.
At present, three-dimensional battery packs are widely adopted for simulating the performance of the battery packs, but each time the three-dimensional battery packs are simulated, a lot of time is consumed. When the neural network is trained by using big data, a large amount of simulation is needed to obtain the performance data of the battery pack, so that the three-dimensional simulation of the battery pack consumes a large amount of time, and even a few months are needed to obtain the trained neural network. Moreover, previous battery pack optimizations were targeted at maximum temperature alone, without taking into account the power consumption required for battery pack heat dissipation. Therefore, the current battery pack structure simulation has the problems of long process time and factor consideration, so that the structure setting of the battery pack can be unreasonable.
Disclosure of Invention
The invention aims to provide a multi-objective optimization battery pack structure method based on a neural network. The invention greatly shortens the period of optimizing the structure of the battery pack and improves the performance of the battery pack.
The technical scheme of the invention is as follows: a multi-objective optimization battery pack structure method based on a neural network comprises the following steps:
s1: converting the three-dimensional model of the battery pack into a two-dimensional model, and then simulating in COMSOL to generate a plurality of groups of structural parameters affecting the performance value of the battery pack;
s2, taking a plurality of groups of structural parameters as the input of the neural network, taking the performance value of the battery pack as the output of the neural network, and training the neural network by adopting a Bayesian regularization algorithm;
and S3, optimizing structural parameters of the neural network trained in the step S2 by adopting an NSGA2 algorithm to obtain a plurality of groups of pareto optimal solutions, and selecting the group with the lowest highest temperature in the pareto optimal solutions as a final scheme of the battery pack.
The above multi-objective optimization battery pack structure method based on the neural network, wherein the structure parameters comprise an inlet position, an outlet position, an inlet width, an outlet width and a battery interval.
The method for optimizing the battery pack structure based on the neural network comprises the steps that the inlet position and the outlet position are adjusted and increased once every 20mm, and the range is 20100 mm; the inlet width and the outlet width are adjusted and increased once every 5mm, and the range is 1530 mm; the cell spacing is adjusted and increased every 0.4mm, so that the range is 23.6 mm; there are a total of 2000 battery pack structural parameter combinations.
The aforementioned neural network-based multi-objective optimized battery pack structure method uses 70% of 2000 battery pack structure parameters for training the neural network, 15% for prediction, and 15% for verification.
The above-mentioned multi-objective optimization battery pack structure method based on the neural network, wherein the battery pack performance values include the highest temperature, the maximum temperature difference and the power consumed by the battery pack for cooling the battery; wherein, for cooling the power W consumed by the battery p The expression is as follows:
W p ＝(P in -P out )×Q 0 ；
wherein: p (P) in Is the pressure at the inlet location; p (P) out Is the pressure at the outlet location; q (Q) 0 Is the inlet air flow.
In the method for optimizing the battery pack structure based on the multiple targets of the neural network, the number of hidden layers and output layers of the neural network is respectively set to 20 and 3.
In the above-mentioned multi-objective battery pack structure optimizing method based on the neural network, in step S3, when the structural parameters are optimized, the genetic algebra is set to 500, and the population number is set to 200.
Compared with the prior art, the invention considers that each simulation of the three-dimensional battery pack needs to consume a lot of time, when the neural network is trained by adopting big data, a lot of simulation is needed to obtain the performance data of the battery pack, so the three-dimensional simulation of the battery pack can consume a lot of time, even a plurality of months can be needed to obtain the trained neural network, therefore, the invention converts the battery model into the two-dimensional model to conduct COMSOL simulation, reduces the simulation period, improves the design efficiency, then takes a plurality of groups of structural parameters affecting the performance value of the battery pack obtained by the two-dimensional model simulation as the input of the neural network, takes the performance value as the output, aims at a strong nonlinear system with complex battery pack, trains the neural network by utilizing a Bayesian regularization algorithm, effectively solves the problem of excessive fitting, and optimizes the structural parameters by utilizing an NSGA2 algorithm to obtain a plurality of groups of Patuo optimal solutions, and the final temperature difference of the obtained battery pack is reduced by 67.4% through the test, and the power consumption is reduced by 26%. The performance of the battery pack is greatly improved. In addition, the invention further optimizes the training, input and output step parameters of the neural network, and the genetic algebra and population quantity of the structural parameter optimization, thereby further improving the prediction precision of the neural network.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a schematic view of a three-dimensional model of a battery pack;
FIG. 3 is a schematic diagram of a two-dimensional model of a battery pack;
FIG. 4 is a graph showing the maximum temperature performance parameter profiles obtained for different combinations of battery packs;
FIG. 5 is a graph showing the distribution of maximum temperature difference performance parameters obtained under different battery pack combinations;
FIG. 6 is a graph showing power consumption performance parameter profiles obtained under different battery pack combinations;
FIG. 7 is a training set regression diagram;
FIG. 8 is a regression diagram of a test set;
FIG. 9 is a neural network regression graph;
FIG. 10 is a graph of pareto optimal solution sets;
FIG. 11 is a schematic diagram of the temperature of the cells in the battery pack prior to optimization;
fig. 12 is a schematic view of the temperature of the battery in the battery pack after optimization.
Detailed Description
The invention is further illustrated by the following figures and examples, which are not intended to be limiting.
Examples: a multi-objective optimization battery pack structure method based on a neural network, as shown in fig. 1, comprises the following steps:
s1: converting the three-dimensional model of the battery pack into a two-dimensional model, and then simulating in COMSOL to generate a plurality of groups of structural parameters affecting the performance value of the battery pack; since the calculation of the three-dimensional battery pack requires several hours to obtain a numerical result, and the calculation of the two-dimensional battery pack requires only several minutes, the two-dimensional battery pack can be replaced by the three-dimensional battery pack to simulate so as to simplify the structure of the battery pack, the three-dimensional model of the battery pack is axisymmetric, small details in the battery pack are omitted, and the schematic diagram of the air-cooled three-dimensional battery pack is shown in fig. 2. The left lower side of the battery pack is provided with an air inlet for cooling air, the right upper side of the battery pack is provided with an air outlet for air, and the air enters the battery pack from the inlet and cools the battery and then flows out of the battery pack from the outlet; the two-dimensional model of the battery pack is shown in fig. 3. Therefore, after the three-dimensional model of the battery pack is converted into a two-dimensional model, simulation is carried out in COMSOL, and a plurality of groups of structural parameters affecting the performance value of the battery pack are generated, wherein the structural parameters comprise an inlet position I po Outlet position J po Width of inlet I wi Width J of outlet wi And a cell pitch d; the battery pack performance values include a maximum temperature, a maximum temperature difference, and power consumed in the battery pack for cooling the battery; to ensure safe operation of the battery thermal management system and to improve the performance output of the battery pack, the maximum temperature within the battery packThe smaller the difference, the highest temperature and the power consumption, wherein the power W consumed for cooling the battery p The expression is as follows:
W p ＝(P in -P out )×Q 0 ；
wherein: p (P) in Is the pressure at the inlet location; p (P) out Is the pressure at the outlet location; q (Q) 0 Is the inlet air flow.
The battery pack structure requires multiple changes in optimization and the neural network needs to be rebuilt once the structure changes. Thus, to increase simulation efficiency, the model was parameterized in COMSOL, with the inlet and outlet positions adjusted to increase every 20mm, in the range of [20100]]mm; the inlet width and the outlet width are adjusted and increased once every 5mm, and the range is [1530]]mm; the cell pitch was adjusted to be increased every 0.4mm in the range of [23.6]]mm; a total of 2000 battery pack structural parameter combinations; the inlet flow of COMSOL was 0.014m 3 And/s, the outlet adopts the boundary condition of free flow, so that the highest temperature, the maximum temperature difference and the power consumption performance parameters of the 2000 groups of battery packs are obtained, and are respectively shown in fig. 4, 5 and 6.
S2, taking a plurality of groups of structural parameters as the input of a neural network, taking the performance value of a battery pack as the output of the neural network, adopting the BP neural network in the embodiment, establishing an approximate function relation between the structural parameters of the battery pack and the performance values of the battery pack by using the BP neural network based on the advantages of strong mapping capability, strong self-adaption, fault tolerance and the like of the BP neural network, wherein 70% of the structural parameters of 2000 battery packs are used for training the neural network, 15% are used for prediction, and 15% are used for verification; the number of hidden layers and output layers of the neural network are respectively set to 20 and 3; in the neural network, aiming at a strong nonlinear system with complex battery packs, a Bayesian regularization algorithm is adopted to train the neural network in order to effectively solve the problem of excessive fitting; after training, a BP neural network regression chart as shown in fig. 7-9 is obtained, fig. 7 is a diagram for describing regression capability of a training set, fig. 8 is a diagram for describing regression capability of a test set, it can be seen from fig. 7 and 8 that a target value and an output result are basically on the same straight line, the closer R is to 1, the higher the prediction accuracy is, fig. 9 is a neural network regression chart of total data, and the higher the prediction accuracy of a lifting network after training is seen from fig. 9.
And S3, carrying out structural parameter optimization on the neural network trained in the step S2 by adopting an NSGA2 algorithm, wherein when the structural parameter is optimized, the genetic algebra is set to be 500, and the population number is set to be 200, so that an ideal result can be better obtained. Since multiple targets do not exist where multiple targets are optimal at the same time, there is a set of optimal solution sets, called Pareto solution sets. The elements in the pareto solution set are called non-dominant solutions. The Pareto solution set distribution of power consumption, maximum temperature and maximum temperature difference after optimizing through NSGA2 algorithm (namely a fast non-dominant multi-objective optimization algorithm with elite retention strategy is a multi-objective optimization algorithm based on Pareto optimal solution) is shown in fig. 10, the Pareto solution with the minimum maximum temperature is taken as the final solution after optimizing the algorithm among a plurality of solutions meeting the requirements, and the battery pack design scheme corresponding to the solution in the final embodiment is as follows: the inlet position is 86.2mm, the outlet position is 76.6mm, the inlet width is 26.4mm, the outlet width is 30.0mm, and the cell spacing is 2.6mm.
The pre-and post-optimization battery pack designs were then simulated in COMSOL to obtain pre-and post-optimization battery pack performance outputs as shown in fig. 11 and fig. 12. As can be seen from fig. 11 and 12, the maximum temperature of the battery pack before optimization was 320.4K, the maximum temperature difference was 14.1K, the power consumption was 0.9W, the maximum temperature of the battery pack after optimization according to the neural network and NSGA2 algorithm was 312.9K, the maximum temperature difference was 4.6K, and the power consumption was 0.67W. According to the invention, the highest temperature of the battery pack optimized by using the neural network and the NSGA2 algorithm is reduced by 7.5K, the maximum temperature difference is reduced by 67.4%, the power consumption is reduced by 26%, and the design efficiency can be greatly improved by converting the three-dimensional model into the two-dimensional model, and a large amount of time is saved.
In conclusion, aiming at the defect that the traditional neural network prediction result established by a small amount of data obtained through a three-dimensional model is not ideal, the neural network established by obtaining a large amount of battery pack performance data through two-dimensional battery pack simulation can more accurately predict the performance of the battery pack, and the design period of the battery pack is improved. The neural network established according to the two-dimensional battery pack model is combined with an NSGA2 algorithm, so that the neural network has important guiding significance on the structural design of the battery pack. The invention solves the contradiction between the highest temperature, the maximum temperature difference and the power consumption of the battery pack, and can obtain the battery pack structure with low highest temperature, small maximum temperature difference and low power consumption. Therefore, the temperature in the battery pack is ensured to be in a proper range, and meanwhile, the endurance mileage of the electric automobile is improved.
Claims (7)
1. A multi-objective optimization battery pack structure method based on a neural network is characterized by comprising the following steps of: the method comprises the following steps:
s1: converting the three-dimensional model of the battery pack into a two-dimensional model, and then simulating in COMSOL to generate a plurality of groups of structural parameters affecting the performance value of the battery pack;
s2, taking a plurality of groups of structural parameters as the input of the neural network, taking the performance value of the battery pack as the output of the neural network, and training the neural network by adopting a Bayesian regularization algorithm;
and S3, optimizing structural parameters of the neural network trained in the step S2 by adopting an NSGA2 algorithm to obtain a plurality of groups of pareto optimal solutions, and selecting the group with the lowest highest temperature in the pareto optimal solutions as a final scheme of the battery pack.
2. The neural network-based multi-objective optimized battery pack structure method of claim 1, wherein: the structural parameters include inlet position, outlet position, inlet width, outlet width, and cell spacing.
3. The neural network-based multi-objective optimized battery pack structure method of claim 2, wherein: wherein the inlet position and the outlet position are adjusted and increased once every 20mm, and the range is [20100] mm; the inlet width and the outlet width are adjusted and increased once every 5mm, and the range is 1530 mm; the cell spacing is adjusted and increased every 0.4mm, so that the range is 23.6 mm; there are a total of 2000 battery pack structural parameter combinations.
4. The neural network-based multi-objective optimized battery pack structure method of claim 3, wherein: 70% of the 2000 battery pack structural parameters were used to train the neural network, 15% for prediction, and 15% for validation.
5. The neural network-based multi-objective optimized battery pack structure method of claim 2, wherein: the battery pack performance values include a maximum temperature, a maximum temperature difference, and power consumed in the battery pack for cooling the battery; wherein, for cooling the power W consumed by the battery p The expression is as follows:
W p ＝(P in -P out )×Q 0 ；
wherein: p (P) in Is the pressure at the inlet location; p (P) out Is the pressure at the outlet location; q (Q) 0 Is the inlet air flow.
6. The neural network-based multi-objective optimized battery pack structure method of claim 1, wherein: the number of hidden layers and output layers of the neural network are set to 20 and 3, respectively.
7. The neural network-based multi-objective optimized battery pack structure method of claim 1, wherein: in step S3, the number of genetics is set to 500 and the population number is set to 200 at the time of optimizing the structural parameters.
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