CN113158461B - Multi-objective optimization design method for vehicle-mounted lithium ion power battery pack thermal management system - Google Patents

Abstract

The invention relates to a multi-objective optimization design method for a vehicle-mounted lithium-ion power battery pack thermal management system, comprising the following steps: step S1, determining design parameters of the battery pack thermal management system; step S2, obtaining the design parameters of each design parameter according to actual vehicle-mounted working conditions Initial value and actual value range; step S3, extract representative samples from the value range space formed by each design parameter, and obtain multiple preliminary design schemes; step S4, define optimization goals; step S5, obtain design parameters and optimization goals Mathematical model of parameters; step S6, constructing an optimal design mathematical model according to the design parameters, optimization goals, and mathematical models of design parameters and optimization goal parameters, and obtaining the optimization goal parameter values of each preliminary design scheme of the sample based on simulation and numerical calculation; step S7 , Based on the trade-offs among the optimization objectives, the optimal design scheme is obtained by screening. Compared with the prior art, the invention has the advantages of short research and development period, good design effect and low research and development cost.

Description

Multi-objective optimization design method for thermal management system of vehicle lithium-ion power battery pack

technical field

The invention relates to the technical field of thermal management of lithium-ion power batteries, in particular to a multi-objective optimization design method for a thermal management system of a vehicle-mounted lithium-ion power battery pack.

Background technique

Lithium-ion power batteries have been widely used in electronic products, pure electric and hybrid vehicles due to their high energy density. However, as market demand continues to increase in terms of cruising range and charging speed, the application scenarios of fast charging and other high-rate working conditions account for an increasing proportion. Lithium-ion power batteries under high current conditions will quickly generate heat, and at the same time, the temperature inconsistency between battery cells and even battery modules in the battery pack will become more obvious. In order to control the temperature and temperature difference of the lithium-ion power battery pack within a suitable working temperature range, the design of the thermal management system of the vehicle battery is becoming more and more important. However, simply increasing the coolant flow/speed cannot effectively alleviate overheating and uneven temperature distribution. However, it will also add unnecessary costs to the thermal management system in terms of power consumption, mass, and volume. Therefore, it is particularly important to optimize the design of the lithium-ion power battery pack equipped with the thermal management system.

At present, there are two main methods for optimizing the design of battery packs based on the thermal management system. excellent.

For the improvement of the temperature control effect, most of the methods proposed so far are by adjusting the flow rate/flow rate, and the research results show that when the flow rate/flow rate increases to a certain extent, the temperature control effect has reached the peak, and continuing to increase the flow rate/flow rate cannot be further improved. Improve temperature control effect.

In fact, the degree of optimization of the thermal management effect that can be achieved by adjusting the arrangement of cells in the battery pack is very limited; and improving the cooling effect only by increasing the distance between the cells of each battery will also increase the volume of the battery pack and reduce the energy density. It is not conducive to the market demand for long cruising range of pure electric vehicles in the future.

The optimization design considering a single design parameter and target parameter can improve the thermal management effect in this target dimension, but it is not conducive to the overall performance optimization of the battery pack, and may even bring further deterioration to the target parameter with the opposite relationship. Therefore, how to comprehensively consider the overall performance and economical multi-objective optimization design of a lithium-ion power battery pack equipped with a thermal management system has become an urgent technical problem in this field.

Contents of the invention

The object of the present invention is to provide a multi-objective optimization design method for a vehicle-mounted lithium-ion power battery pack thermal management system in order to overcome the above-mentioned defects in the prior art.

The purpose of the present invention can be achieved through the following technical solutions:

A multi-objective optimization design method for a thermal management system of a vehicle-mounted lithium-ion power battery pack, which is used to obtain an optimal design scheme for a thermal management system of a vehicle-mounted lithium-ion power battery pack, comprising the following steps:

Step S1, determining the design parameters of the thermal management system of the battery pack;

Step S2, obtaining the initial value and actual value range of each design parameter according to the actual vehicle working conditions;

Step S3, extracting representative samples from the value domain space formed by each design parameter, and obtaining multiple preliminary design schemes;

Step S4, defining the optimization target for the design of the vehicle-mounted lithium-ion power battery pack thermal management system;

Step S5, obtaining a mathematical model of design parameters and optimization target parameters;

Step S6, constructing an optimization design mathematical model according to the design parameters, optimization objectives, and mathematical models of the design parameters and optimization objective parameters, and obtaining the optimization objective parameter values of each preliminary design scheme of the sample based on simulation and numerical calculation;

Step S7, based on the trade-off between optimization objectives, screening to obtain the optimal design scheme;

Step S8, experimentally verify whether the error between the numerical calculation and the experimental measurement value of the selected optimal design scheme is within the set allowable range, if yes, the selected design scheme is valid, if the error is too large, return to step S3.

In the step S1, the parameters affecting the thermal management efficiency and system power consumption in the lithium-ion power battery pack are selected as the design parameters of the thermal management system of the battery pack.

The design parameters of the battery pack thermal management system are heat exchange medium parameters and structural parameters.

The heat exchange medium parameters include the flow rate in the liquid-cooled plate and the wind speed of the fan in the management system, and the structural parameters include the distance between the cells, the edge distance between the cells and the cold plate, the depth of the flow channel, the lateral width of the flow channel of the liquid-cooled plate, and The longitudinal width of the flow channel of the liquid cold plate.

In the step S4, for the parameters of the heat exchange medium, the optimization objectives of the thermal management system design of the vehicle-mounted lithium-ion power battery pack are the temperature control effect, the uniformity of temperature distribution and the power consumption of the thermal management system under the 3C high-rate discharge condition.

In the described step S6, the expression of the optimized design mathematical model is:

Among them, Q ₁ , Q ₂ , ..., Q _n are the flow rates in n liquid cooling plates respectively, Q _1max , Q _2max ..., Q _nmax and Q _1min , Q _2min ..., Q _nmin are the flow rates in the corresponding n liquid cooling plates respectively. The upper and lower limits of the flow rate, T _max , TSD, P _max are the maximum temperature of the battery pack, the standard deviation of the battery pack temperature, and the maximum pressure of the thermal management system. The setting threshold of each target, V _amax and V _amin are the upper and lower limits of the wind speed V _a of the fan in the management system, f _Tmax , f _TSD , f _Pmax respectively represent the mathematical model of the design parameters and the optimization target parameters.

In the step S4, for the structural parameters, the optimization objectives of the thermal management system design of the on-board lithium-ion power battery pack are the temperature control effect, temperature distribution uniformity, power consumption, volume and weight of the thermal management system under the 2.5C fast charging condition. .

In the described step S6, the expression of the optimized design mathematical model is:

Among them, d ₁ , d ₂ ,...d _n are the spacing between the monomers, sd ₁ , sd ₂ ,...sd _n are the side distances between the monomer and the liquid cooling plate, W _h1 , W _h2 ,...W _hn are the liquid cooling The transverse width of the plate channel, W _v1 , W _v2 ,...W _vn are the longitudinal width of the liquid-cooled plate channel, T _h is the channel depth, T, ΔT, P are the set thresholds corresponding to each target, d _max , d _min are the upper and lower limits of the cell spacing, sd _max , sd _min are the upper and lower limits of the edge distance between the cell and the liquid cooling plate, W _hmax , W _hmin are the upper and lower limits of the lateral width of the liquid cooling plate, and W _vmax and W _vmin are the upper and lower limits of the longitudinal width of the liquid-cooled plate flow channel respectively, T _hmax and T _hmin are the upper and lower limits of the flow channel depth respectively, f _Tmax , f _TSD , and f _Pmax represent the mathematical models of design parameters and optimization target parameters respectively .

In the step S7, the design parameter values corresponding to each preliminary design scheme are substituted into the optimal design mathematical model, and the values of each objective function are calculated, and compared with the set objective function range, if the requirements are met, it is used as a candidate design plan.

In the step S7, if the number of candidate design solutions exceeds the set upper limit, the number of candidate design solutions is reduced by further narrowing the scope of the objective function until an optimal design solution is obtained.

Compared with the prior art, the present invention has the following advantages:

(1) Stronger practicability: the design parameters defined in the present invention are all combined with simulation and numerical calculation to judge whether they are key influencing parameters, and the value range and value accuracy of the parameters have taken into account the actual battery pack production, manufacturing, assembly and application. selection criteria;

(2) The sample selection is representative: the sample taken covers each value interval segment of the full set sample space formed by the orthogonal design of the design parameters;

(3) Effectively reduce the amount of calculation: under the premise of ensuring that the sampled samples are representative, there is no interval overlap between the sample points to avoid unnecessary waste of computing resources;

(4) The optimization goal is more comprehensive: the optimization design proposed by the present invention needs to comprehensively consider various extreme working conditions (such as low temperature, fast charging, overheating environment, thermal runaway), and the temperature control effect, temperature distribution uniformity, and thermal management system performance. The energy consumption, thermal management system volume and thermal management system weight are defined as optimization goals;

(5) Combined with mathematical model support theory analysis: the mathematical model of the relationship between the design parameters and target parameters obtained in the optimal design proposed by the present invention can be used to analyze the influence trend and degree of each design parameter on each target parameter.

(6) in conjunction with the reliability of the experimental verification optimal scheme: the optimal scheme selected by the present invention to balance each optimization target needs to be verified by experiments under each working condition, so as to ensure that the actual measured parameter value and the theoretical calculation value error are within a certain range, To prove the reliability of this method.

Description of drawings

Fig. 1 is a flowchart of the present invention.

Fig. 2 is a cloud diagram of the temperature distribution of the 18650 lithium iron phosphate battery pack under the action of the initial thermal management system in Example 1 of the present invention.

Fig. 3 is a cloud diagram of the pressure distribution of the coolant in the flow channel of the initial design scheme of the liquid cold plate in Example 1 of the present invention.

Fig. 4 is a cloud diagram of the temperature distribution of the 18650 lithium iron phosphate battery pack under the action of the optimal thermal management system in Example 1 of the present invention.

Fig. 5 is a cloud diagram of the pressure distribution of the coolant in the flow channel of the optimal liquid-cooled plate in Example 1 of the present invention.

FIG. 6 is a schematic diagram of the response surface of the relationship between design parameters and target parameters of the thermal management system in Embodiment 1 of the present invention.

FIG. 7 is a schematic diagram of a sensitivity analysis of the relationship between design parameters and target parameters of the thermal management system in Embodiment 1 of the present invention.

Fig. 8 is a schematic diagram of the channel width parameters of the liquid cooling plate in Example 2 of the present invention.

FIG. 9 is a schematic diagram of a sensitivity analysis of the relationship between design parameters and target parameters of the thermal management system in Embodiment 2 of the present invention.

Fig. 10 is a cloud diagram of the temperature distribution of the square lithium-ion power battery pack under the action of the initial thermal management system in Example 2 of the present invention.

Fig. 11 is a cloud diagram of the pressure distribution of the initial design scheme of the liquid-cooled plate in Example 2 of the present invention.

Fig. 12 is a cloud diagram of the temperature distribution of the square lithium-ion power battery pack under the action of the optimal thermal management system in Example 2 of the present invention.

Fig. 13 is a cloud diagram of the pressure distribution of the optimal design scheme of the liquid-cooled plate in Example 2 of the present invention.

14 is a schematic diagram of the response surface of the relationship between design parameters and target parameters of the thermal management system in Embodiment 2 of the present invention.

Detailed ways

The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments. Note that the description of the following embodiments is merely an illustration in nature, and the present invention is not intended to limit the applicable objects or uses thereof, and the present invention is not limited to the following embodiments.

The present invention starts from two aspects of the fluid medium and the battery structure, carries out parameter design on the thermal management system of the vehicle-mounted lithium-ion power battery pack, and integrates various factors to provide an efficient and comprehensive solution for the lithium-ion power battery pack equipped with the thermal management system The multi-objective optimization design technical solution can improve the performance problems of the thermal management system that are not involved in the single optimization objective design method, and effectively design a high-efficiency thermal management system that is more suitable for actual vehicle applications. Low cost and other advantages.

Example 1

As shown in Figures 1-7, this embodiment provides a multi-objective optimization design method for a vehicle-mounted lithium-ion power battery pack thermal management system, which includes the following steps:

Step S1, defining the design parameters of the thermal management system of the battery pack (the flow rates Q ₁ , Q ₂ , Q ₃ , and Q ₄ in the thermal management systems of the liquid cooling plates, the fan speed V _a );

Step S2, defining the initial value and actual value range of each parameter according to the actual vehicle working conditions;

Step S3, performing representative sample extraction on the value range space formed by each design parameter;

Step S4, defining optimization goals (temperature control effect, temperature distribution uniformity, thermal management system power consumption under 3C high-rate discharge conditions);

Step S5, constructing a mathematical model of design parameters and optimization target parameters;

Step S6, solving target parameters of each design scheme of the sample based on simulation and numerical calculation;

Step S7, iteratively screening the optimal thermal management system design scheme based on the trade-off between optimization objectives;

Step S8, experimentally verifying whether the error between the numerical calculation of the selected optimal scheme and the experimental measurement value is within the allowable range, if yes, the selected design scheme is valid. If the error is too large, return to step S3.

The specific introduction of each step is as follows:

In the process of steps S1-S2, key design parameters such as the thermal management effect and system power consumption in the lithium-ion power battery pack are selected, and the initial selection of each parameter is defined according to factors such as the actual electric vehicle battery pack energy density and available space. value and the actual achievable value range.

In the process of step S3, the value range of each design parameter is considered to conduct orthogonal design to obtain a multi-dimensional sample space, and a general sampling method is used to extract design plan samples to ensure that the selected samples are representative and can effectively reduce the workload of computer numerical calculations.

In the process of step S4, it is emphasized that the optimization design needs to consider extreme working conditions (such as 3C discharge), and the temperature control effect, temperature distribution uniformity, and power consumption of the thermal management system are defined as optimization goals.

In the process of step S5, it is necessary to construct a mathematical model of the optimal design function between each optimization target parameter and the design parameter.

In the process of step S7, all optimization objectives (temperature control effect, uniformity of temperature distribution, power consumption of the thermal management system) need to be considered comprehensively, instead of achieving a certain objective while ignoring practical factors such as system weight, volume and economy.

In the process of step S8, it is necessary to verify the selected optimized scheme to build a test battery pack and system. If the error between the repeatedly measured value and the expected value of the target parameters of the prototype under the specified working conditions is within the allowable range, it proves the reliability of the selected optimization scheme.

The case takes the flow rate Q ₁ , Q ₂ , Q ₃ , and Q ₄ in the four liquid cooling plates as the design parameters of the fan speed _{V a} in the thermal management system, and takes the maximum temperature T _max of the battery pack, the standard deviation TSD of the battery pack temperature, and the thermal management system The maximum pressure P _max is the optimization target, and the mathematical model for the optimal design is constructed as follows:

By using this multi-objective optimization design method, the comprehensive optimization of the battery pack temperature control effect, temperature distribution uniformity and thermal management system power consumption can be achieved, which greatly improves the limitations and limitations of the single-objective thermal management system optimization design method. Improved thermal management. Through the optimization of the coolant flow and fan speed, the power consumption can be effectively controlled under the premise of ensuring the thermal management effect.

Example 2

As shown in Figures 8-14, this embodiment provides a multi-objective optimization design method for a vehicle-mounted lithium-ion power battery pack thermal management system, which includes the following steps:

Step S1, define the design parameters of the thermal management system of the battery pack (distance between cells d ₁ , d ₂ ... d ₇ , distance between cells and liquid cooling plate sd ₁ , sd ₂ , depth of flow channel T _h , lateral direction of flow channel of liquid cooling plate Width W _h1 , W _h2 ... W _h6 , longitudinal width of the channel of the liquid cold plate W _v1 , W _v2 );

Step S2, defining the initial value and actual value range of each parameter according to the actual vehicle working conditions;

Step S3, performing representative sample extraction on the value range space formed by each design parameter;

Step S4, defining optimization goals (temperature control effect, temperature distribution uniformity, thermal management system power consumption under 2.5C fast charging conditions);

Step S5, constructing a mathematical model of design parameters and optimization target parameters;

Step S6, solving target parameters of each design scheme of the sample based on simulation and numerical calculation;

Step S7, iteratively screening the optimal thermal management system design scheme based on the trade-off between optimization objectives;

Step S8, experimentally verify whether the error between the numerical calculation of the selected optimal scheme and the experimental measurement value is within the allowable range, if yes, the selected design scheme is valid, if the error is too large, return to step S3.

In the process of steps S1-S2, key design parameters such as the thermal management effect and system power consumption in the lithium-ion power battery pack are selected, and the initial selection of each parameter is defined according to factors such as the actual electric vehicle battery pack energy density and available space. value and the actual achievable value range.

In the process of step S3, the value range of each design parameter is considered to conduct orthogonal design to obtain a multi-dimensional sample space, and a general sampling method is used to extract design plan samples to ensure that the selected samples are representative and can effectively reduce the workload of computer numerical calculations.

In the process of step S4, it is emphasized that the optimization design needs to consider the 2.5C fast charging condition, and the temperature control effect, temperature distribution uniformity, and thermal management system power consumption are defined as optimization goals.

In the process of step S5, it is necessary to construct a mathematical model of the optimal design function between each optimization target parameter and the design parameter.

In the process of step S7, all optimization objectives (temperature control effect, uniformity of temperature distribution, power consumption of the thermal management system) need to be considered comprehensively, instead of achieving a certain objective while ignoring practical factors such as system weight, volume and economy.

In the process of step S8, it is necessary to verify the selected optimized scheme to build a test battery pack and system. If the error between the repeatedly measured value and the expected value of the target parameters of the prototype under the specified working conditions is within the allowable range, it proves the reliability of the selected optimization scheme.

In this case, the spacing of seven cells d ₁ , d ₂ ... d ₇ , the distance between cells and the liquid cooling plate sd ₁ , sd ₂ , the depth of the flow channel T _h , the lateral width of the flow channel of the liquid cooling plate W _h1 , W _h2 ... W _h6 , the longitudinal widths W _v1 and W _v2 of the flow channel of the liquid-cooled plate are design parameters, and the maximum temperature of the battery pack, the standard deviation of the battery pack temperature and the maximum pressure of the thermal management system are the optimization objectives. The mathematical model for the optimal design is constructed as follows:

By using this multi-objective optimization design method, the comprehensive optimization of the battery pack temperature control effect, temperature distribution uniformity and thermal management system power consumption can be achieved, which greatly improves the limitations and limitations of the single-objective thermal management system optimization design method. Improved thermal management. Through the optimization of the coolant flow, the power consumption can be effectively controlled under the premise of ensuring the thermal management effect. In addition, the optimization of the battery cell spacing can also effectively control the volume of the battery module.

The above-mentioned embodiments are merely examples, and do not limit the scope of the present invention. These embodiments can also be implemented in various other forms, and various omissions, substitutions, and changes can be made without departing from the scope of the technical idea of the present invention.

Claims (6)

1. A multi-objective optimization design method for a vehicle-mounted lithium-ion power battery pack thermal management system, used to obtain an optimal vehicle-mounted lithium-ion power battery pack thermal management system design scheme, is characterized in that, comprising the following steps: Step S1, determine the design parameters of the thermal management system of the battery pack, select the parameters that affect the thermal management efficiency and system power consumption in the lithium-ion power battery pack as the design parameters of the thermal management system of the battery pack, the thermal management system of the battery pack The design parameters are heat exchange medium parameters and structural parameters; Step S2, obtaining the initial value and actual value range of each design parameter according to the actual vehicle working conditions; Step S3, extracting representative samples from the value domain space formed by each design parameter, and obtaining multiple preliminary design schemes; Step S4, define the optimization goal of the thermal management system design of the vehicle-mounted lithium-ion power battery pack. For the structural parameters, the optimization goals of the design of the vehicle-mounted lithium-ion power battery pack thermal management system are temperature control effect, temperature distribution uniformity, Thermal management system power consumption; Step S5, obtaining a mathematical model of design parameters and optimization target parameters; Step S6. Construct an optimal design mathematical model according to the design parameters, optimization objectives, and mathematical models of the design parameters and optimization objective parameters, and obtain the optimization objective parameter values of each preliminary design scheme of the sample based on simulation and numerical calculation, and optimize the expression of the design mathematical model for:

Among them, d ₁ , d ₂ ,...d _n are the spacing between the monomers, sd ₁ , sd ₂ ,...sd _n are the side distances between the monomer and the liquid cooling plate, W _h1 , W _h2 ,...W _hn are the liquid cooling The transverse width of the plate channel, W _v1 , W _v2 ,...W _vn are the longitudinal width of the liquid-cooled plate channel, T _h is the channel depth, T, ΔT, P are the set thresholds corresponding to each target, d _max , d _min are the upper and lower limits of the cell spacing, sd _max , sd _min are the upper and lower limits of the edge distance between the cell and the liquid cooling plate, W _hmax , W _hmin are the upper and lower limits of the lateral width of the liquid cooling plate, and W _vmax and W _vmin are the upper and lower limits of the longitudinal width of the liquid-cooled plate flow channel respectively, T _hmax and T _hmin are the upper and lower limits of the flow channel depth respectively, f _Tmax , f _TSD , and f _Pmax represent the mathematical models of design parameters and optimization target parameters respectively , T _max , TSD, P _max are the maximum temperature of the battery pack, the standard deviation of the battery pack temperature and the maximum pressure of the thermal management system, respectively; Step S7, based on the trade-off between optimization objectives, screening to obtain the optimal design scheme; Step S8, experimentally verify whether the error between the numerical calculation and the experimental measurement value of the selected optimal design scheme is within the set allowable range, if yes, the selected design scheme is valid, if the error is too large, return to step S3. 2. The multi-objective optimization design method for a thermal management system of a vehicle-mounted lithium-ion power battery pack according to claim 1, wherein the parameters of the heat exchange medium include the flow rate in the liquid cooling plate and the wind speed of the fan in the management system, The structural parameters include the distance between the cells, the edge distance between the cells and the cold plate, the depth of the flow channel, the transverse width of the flow channel of the liquid cooling plate and the longitudinal width of the flow channel of the liquid cooling plate. 3. The multi-objective optimization design method for a thermal management system of a vehicle-mounted lithium-ion power battery pack according to claim 2, characterized in that, in the step S4, for the parameters of the heat exchange medium, the heat transfer rate of the vehicle-mounted lithium-ion power battery pack is The optimization goals of the management system design are the temperature control effect, the uniformity of temperature distribution and the power consumption of the thermal management system under the condition of high-rate discharge. 4. A kind of vehicle-mounted lithium-ion power battery pack thermal management system multi-objective optimal design method according to claim 3, is characterized in that, in described step S6, the expression of optimal design mathematical model is:

Among them, Q ₁ , Q ₂ , ..., Q _n are the flow rates in n liquid cooling plates respectively, Q _1max , Q _2max ..., Q _nmax and Q _1min , Q _2min ..., Q _nmin are the flow rates in the corresponding n liquid cooling plates respectively. The upper and lower limits of the flow rate, T _max , TSD, P _max are the maximum temperature of the battery pack, the standard deviation of the battery pack temperature, and the maximum pressure of the thermal management system. The setting threshold of each target, V _amax and V _amin are the upper and lower limits of the wind speed V _a of the fan in the management system, f _Tmax , f _TSD , f _Pmax respectively represent the mathematical model of the design parameters and the optimization target parameters. 5. The multi-objective optimization design method for a vehicle-mounted lithium-ion power battery pack thermal management system according to claim 1, wherein in the step S7, the design parameter values corresponding to each preliminary design scheme are substituted into the optimal design In the mathematical model, the value of each objective function is calculated and compared with the set objective function range, and if it meets the requirements, it is used as a candidate design scheme. 6. The multi-objective optimization design method for a thermal management system of a vehicle-mounted lithium-ion power battery pack according to claim 5, characterized in that, in the step S7, if the candidate design scheme exceeds the set upper limit, then pass Further narrow the scope of the objective function to reduce the number of candidate design schemes until the optimal design scheme is obtained.

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