Capacity configuration multi-objective optimization method for wide-temperature-range passive hybrid energy storage system

Abstract

The application discloses a capacity configuration multi-objective optimization method of a wide-temperature-range passive hybrid energy storage system, first, the dynamic response characteristic models of the lithium battery and the super capacitor of the alternative model and the capacity attenuation model of the lithium battery are calibrated through test data. Then, the capacity configuration of the hybrid energy storage system considering the two optimization objectives of the weight and the service life of the hybrid energy storage system is optimized by using an intelligent optimization algorithm. The application is applied to the field of hybrid energy storage, can greatly reduce the output current fluctuation and the average discharge rate of the lithium battery monomer in the energy storage system, and under the premise that the service life and the single use cost are close, reduces the weight of the hybrid energy storage system, improves the maximum power capability at normal temperature, and can obviously improve the performance of the hybrid energy storage system.

Description

Technical Field

[0001] This invention relates to the field of hybrid energy storage technology, specifically a multi-objective optimization method for capacity configuration of a wide-temperature-range passive hybrid energy storage system. Background Technology

[0002] With the development of technology, many high-tech loads, such as high-power microwave sources and electromagnetic emission systems, exhibit pulse characteristics. In current practical applications, lithium battery energy storage systems are often used to power various high-power pulse loads in vehicles. However, under the current energy storage technology, it is difficult to simultaneously meet the high energy density and high power density requirements of high-power pulse loads in wide temperature range applications using only single-type energy storage devices such as lithium batteries. Therefore, using hybrid energy storage systems that integrate supercapacitors and lithium batteries to replace energy storage systems composed of single-type devices has become one of the main ways to solve this problem.

[0003] Pulsed loads are characterized by high peak power but low average power. For the widely used pure lithium-ion battery energy storage, the frequent high-rate discharge caused by pulsed conditions will rapidly reduce its lifespan, affecting the safety of the energy storage system and significantly increasing operating costs. However, for the mainstream hybrid energy storage system composed of lithium-ion batteries and supercapacitors, when powering pulsed loads, the peak power is mainly provided by the supercapacitor, while the output power of the lithium-ion battery can remain near the average power, effectively extending the overall lifespan of the energy storage system.

[0004] Structurally, hybrid energy storage systems can be divided into two main categories: active hybrid energy storage systems and passive hybrid energy storage systems, such as... Figure 1 As shown in the diagram, active hybrid energy storage systems, through the use of DC / DC converters, can achieve controlled power distribution between batteries and supercapacitors, making them suitable for scenarios with large fluctuations and strong randomness in load power demand, such as providing energy for electric vehicles and ships. Passive hybrid energy storage systems, on the other hand, connect supercapacitors and batteries directly in parallel. The power distribution ratio between them is fixed, determined by their internal resistance, and only achieves optimal performance when the power distribution ratio matches a relatively regular operating condition.

[0005] Although the active hybrid energy storage system has strong versatility and controllable power distribution, the use of DC / DC converter greatly increases the cost, volume and weight of the system, and brings certain energy loss. In addition, it is easy to be disturbed and damaged in complex electromagnetic environment. Although the passive hybrid energy storage system is difficult to cope with random working conditions, it has outstanding advantages and potential in system miniaturization, high energy utilization efficiency and fast response speed. In addition, due to its simple system structure and no need for power distribution control, it has good electromagnetic compatibility. However, since the passive hybrid energy storage system cannot perform controlled power distribution, whether the capacity configuration optimization of the passive hybrid energy storage system for specific working conditions is good or not becomes the key to determine its performance.

[0006] In the early research of hybrid energy storage system, passive structure is a common system structure. In the paper“R. A. Dougal, Shengyi Liu, Ralph E. White, Power and life extension of battery-ultracapacitor hybrids[J]. IEEE Transactions on Components and Packaging Technologies, 2002, 25(1): 120”, a large number of experimental tests were carried out on passive lithium battery-ultracapacitor hybrid energy storage system. The experimental results show that passive hybrid energy storage system is superior to pure lithium battery energy storage system in power performance and service life. In the paper“Hou J, Song Z, Hyeongjun P, et al. Implementation and evaluation of real-time model predictive control for load fluctuations mitigation in all-electric ship propulsion systems[J]. Applied Energy, 2018, 230: 62-77”, simulation experiments were carried out to verify the excellent performance of passive hybrid energy storage system in reducing battery discharge rate, prolonging the service life of the whole system and improving power performance under pulse working condition. In the paper“Liu H, Wang Z, Jie C, et al. Improvement on the Cold Cranking Capacity of Commercial Vehicle by Using Supercapacitor and Lead-Acid Battery Hybrid[J]. IEEE Transactions on Vehicular Technology, 2009, 58(3): 1097-1105”, passive hybrid energy storage system was used to replace the vehicle battery pack for cold start of the vehicle. Through performance test of all combinations of battery and ultracapacitor of the alternative model, the passive hybrid energy storage system configuration scheme was obtained, which is significantly superior to the original vehicle battery pack in weight, cold start performance and economy. The passive hybrid energy storage system cannot actively control the power distribution, which limits the research and application of passive hybrid energy storage system. In recent years, the research on hybrid energy storage system is mainly based on active hybrid energy storage system.The document "Song Ziyu. Optimization and control of lithium battery / supercapacitor hybrid energy storage system for passenger cars. Tsinghua University, 2016" aims at the driving cycle of electric vehicles, takes the economic efficiency of the system throughout the life cycle as the target, and realizes the joint optimization of capacity configuration and energy management based on dynamic programming method from several active hybrid energy storage system capacity configuration options. The document "Yuan Jiaxin, Qu Kai, Zheng Xianfeng, et al. Optimization of hybrid energy storage system capacity for high-speed railway [J]. Transactions of Electrical Engineering Technology, 2021(036-019)" aims at the hybrid energy storage system of high-speed railway, also takes the economic efficiency of the system as the optimization target, and optimizes the energy storage configuration scheme by using mixed integer linear programming method. The document "Liu Chang. Optimal configuration and energy management of lithium battery and supercapacitor hybrid energy storage system [D]. University of Science and Technology of China" optimizes the capacity configuration parameters and power distribution parameters of the vehicle active hybrid energy storage system based on multi-objective evolutionary algorithm, taking the vehicle driving mileage throughout the life cycle and the cost per 100 kilometers as the target.

[0007] Overall, the early research on passive hybrid energy storage systems focuses on the performance comparison of energy storage systems composed of a single type of device, and there are more comparative tests on actual systems. In recent years, research has mainly focused on active hybrid energy storage systems. Due to the difference in power distribution principle between active and passive structures, the optimization model and method of active hybrid storage system cannot be directly applied to the capacity configuration optimization of passive hybrid storage system. In addition, most of the existing research takes the economic efficiency of the system as the optimization target, and rarely considers the weight of the hybrid storage system, and also rarely optimizes the hybrid energy storage system under multiple targets. At the same time, most of the existing hybrid energy storage system optimization research only considers the performance of the energy storage system at room temperature 25℃, ignoring the temperature environment that may be as low as-20℃ and as high as 45℃ in actual application. SUMMARY

[0008] In view of the above problems in the prior art that the energy storage system composed of a single type of device such as lithium battery cannot simultaneously meet the requirements of miniaturization and long service life of the vehicle-mounted system under the premise of meeting the power and energy requirements of the vehicle-mounted high-power microwave source in a wide temperature range, the present application provides a capacity configuration multi-objective optimization method for a passive hybrid energy storage system in a wide temperature range, which is used for optimizing the hybrid energy storage primary power supply system of the vehicle-mounted high-power microwave transmitter used in a wide temperature range (-20℃-45℃).

[0009] To achieve the above purpose, the present application provides a capacity configuration multi-objective optimization method for a passive hybrid energy storage system in a wide temperature range, wherein the hybrid energy storage system comprises lithium batteries and supercapacitors in parallel.

[0010] The capacity configuration multi-objective optimization method comprises the following steps:

[0011] Step 1, establish a temperature-dependent dynamic response characteristic model of lithium battery and super capacitor;

[0012] Step 2, construct a temperature-dependent capacity attenuation model of lithium battery, and calibrate the capacity attenuation model;

[0013] Step 3, based on the dynamic response characteristic model and the capacity attenuation model, optimize the capacity configuration of the hybrid energy storage system by using an intelligent optimization algorithm, taking the weight and life of the hybrid energy storage system as the optimization objective, to obtain the Pareto frontier of the hybrid energy storage system.

[0014] In one embodiment, in step 1, the dynamic response characteristic model includes but is not limited to an equivalent circuit model, an electrochemical model or a neural network model;

[0015] After establishing the dynamic response characteristic model, the model parameters are calibrated based on the measured data, so that the dynamic response characteristics of lithium battery and super capacitor at different temperatures can be accurately reflected.

[0016] In one embodiment, in step 2, the capacity attenuation model includes but is not limited to using Arrhenius semi-empirical model, attenuation mechanism model or neural network model to model the temperature-dependent capacity attenuation of lithium battery.

[0017] In one embodiment, in step 2, the calibration process of the capacity attenuation model is as follows:

[0018] Carry out accelerated aging test of lithium battery at-20℃, -10℃, 0℃, 15℃, 25℃, 45℃ respectively, and fit the parameters to be calibrated according to the results of accelerated aging test.

[0019] In one embodiment, in step 3, the intelligent optimization algorithm includes but is not limited to using genetic algorithm, evolutionary algorithm, particle swarm optimization algorithm to optimize the capacity configuration of the hybrid energy storage system.

[0020] In one embodiment, in step 3, the weight and life of the hybrid energy storage system are taken as the optimization objective, which is:

[0021] The optimization objective is to minimize the weight and maximize the life of the hybrid energy storage system, that is:

[0022]

[0023] In the formula, F obj is the joint optimization objective, N duty is the number of tasks completed by the hybrid energy storage system before failure, M HESS is the mass of the hybrid energy storage system.

[0024] In one embodiment, the mass M of the hybrid energy storage system HESS Specifically,

[0025] M HESS = (M SC · SN SC PN SC + M BAT SN BAT PN BAT ) / EFF itg

[0026] In the formula, M SC , M BAT are the weights of the supercapacitors and the batteries used in the configuration scheme, EFF itg is the integration efficiency of the hybrid energy storage system, SN SC is the number of supercapacitors in series, PN SC is the number of supercapacitors in parallel, SN BAT is the number of lithium batteries in series, and PN BAT is the number of lithium batteries in parallel.

[0027] In one embodiment, in step 3, during the optimization of the capacity configuration of the hybrid energy storage system, the voltage constraint of the hybrid energy storage system, and the current constraint and capacity constraint of the supercapacitors and lithium batteries are used as constraint conditions.

[0028] The constraint conditions are based on the principles of meeting the load power and voltage demand, meeting the task energy demand, and not violating the device current and voltage limit.

[0029] The wide-temperature-range passive hybrid energy storage system capacity configuration multi-objective optimization method provided by the application first calibrates the model parameters of the dynamic response characteristic model and the capacity attenuation model of the lithium batteries and supercapacitors of the alternative models through test data, and then optimizes the capacity configuration of the hybrid energy storage system considering the two optimization objectives of the weight and service life of the hybrid energy storage system by using an intelligent optimization algorithm. Compared with the pure lithium battery scheme, the output current fluctuation and average discharge rate of the lithium battery cells in the energy storage system can be greatly reduced, and the weight of the hybrid energy storage system can be greatly reduced under the premise of similar service life and single-use cost. BRIEF DESCRIPTION OF DRAWINGS

[0030] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description only constitute some embodiments of the application, and for those skilled in the art, other drawings can also be obtained from the structures shown in the drawings without creative labor.

[0031] Figure 1 (a) is a schematic diagram of a hybrid energy storage system structure in the prior art, and (b) is a schematic diagram of a hybrid energy storage system structure in the prior art;

[0032] Figure 2 (a) is a schematic diagram of a hybrid energy storage system structure in the prior art, and (b) is a schematic diagram of a hybrid energy storage system structure in the prior art;

[0033] Figure 3 (a) is a schematic diagram of a hybrid energy storage system structure in the prior art, and (b) is a schematic diagram of a hybrid energy storage system structure in the prior art;

[0034] Figure 4 (a) is a schematic diagram of a hybrid energy storage system structure in the prior art, and (b) is a schematic diagram of a hybrid energy storage system structure in the prior art;

[0035] Figure 5 (a) is a schematic diagram of a hybrid energy storage system structure in the prior art, and (b) is a schematic diagram of a hybrid energy storage system structure in the prior art;

[0036] Figure 6 (a) is a schematic diagram of a hybrid energy storage system structure in the prior art, and (b) is a schematic diagram of a hybrid energy storage system structure in the prior art;

[0037] Figure 7 (a) is a schematic diagram of a hybrid energy storage system structure in the prior art, and (b) is a schematic diagram of a hybrid energy storage system structure in the prior art;

[0038] Figure 8 (a) is a schematic diagram of a hybrid energy storage system structure in the prior art, and (b) is a schematic diagram of a hybrid energy storage system structure in the prior art;

[0039] Figure 9 (a) is a schematic diagram of a hybrid energy storage system structure in the prior art, and (b) is a schematic diagram of a hybrid energy storage system structure in the prior art;

[0040] Figure 10 (a) is a schematic diagram of a hybrid energy storage system structure in the prior art, and (b) is a schematic diagram of a hybrid energy storage system structure in the prior art;

[0041] Figure 11 (a) is a schematic diagram of a hybrid energy storage system structure in the prior art, and (b) is a schematic diagram of a hybrid energy storage system structure in the prior art.

[0042] The implementation, functional features and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0043] With reference to the accompanying drawings, the technical solutions in the embodiments of the present application will be clearly and completely described in order to make the technical solutions in the embodiments of the present application apparent to those skilled in the art. Obviously, the described embodiments are only a part rather than all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts should fall into the protection scope of the present application.

[0044] In addition, the technical solutions in the embodiments of the present application can be combined with each other, but the combination should be based on the fact that the combination can be realized by those skilled in the art. When the combination of technical solutions appears to be contradictory or unachievable, it should be considered that the combination of technical solutions does not exist and is not within the protection scope of the present application.

[0045] Under the premise of guaranteeing the power and energy requirements of the wide-temperature-range application of the vehicle-mounted high-power microwave source, it is difficult for an energy storage system composed of single-type devices such as lithium batteries to simultaneously meet the requirements of the vehicle-mounted system in terms of miniaturization and long service life of the energy storage system. To solve this problem, the present embodiment proposes a capacity configuration multi-objective optimization method for a wide-temperature-range passive hybrid energy storage system, wherein the hybrid energy storage system includes lithium batteries and supercapacitors in parallel, i.e., a passive hybrid energy storage system. Figure 2 The capacity configuration multi-objective optimization method in the present embodiment specifically includes the following steps:

[0046] Step 1: Establishing a temperature-considered equivalent circuit model of lithium batteries and supercapacitors;

[0047] Step 2: Constructing a capacity attenuation model of lithium batteries and calibrating the capacity attenuation model;

[0048] Step 3: On the basis of the equivalent circuit model and the capacity attenuation model, optimizing the capacity configuration of the hybrid energy storage system with the weight and service life of the hybrid energy storage system as the optimization objectives to obtain the Pareto frontier of the hybrid energy storage system.

[0049] In the process of optimizing the capacity configuration of the hybrid energy storage system, the equivalent circuit models of the lithium batteries and supercapacitors of the alternative types in the hybrid energy storage system need to be calibrated first to calculate their dynamic performance; and then the aging model of the lithium batteries needs to be calibrated to rely on the calculation of the service life of the hybrid energy storage system. The types and main parameters of the lithium batteries and supercapacitors selected in the optimization are shown in Table 1.

[0050] Table 1: Types and main parameters of alternative lithium batteries and supercapacitors

[0051]

[0052] In the implementation process, the lithium battery and the super capacitor can adopt a model in the form of an equivalent circuit model, an electrochemical model, a neural network model, etc. considering the dynamic response characteristic model of temperature. Since the Rint model of the lithium battery and the RC model of the super capacitor can achieve good accuracy in describing the short-time discharge process under the pulse working condition, in order to simplify the calculation, the model calibration in the embodiment is based on the Rint model and the RC model.

[0053] In the basic Rint model as shown in Figure 3 , the battery internal resistance R BAT , the capacitor internal resistance R SC , the capacitor capacitance C SC and other parameters are fixed. However, in fact, the internal resistance and other parameters of the battery and the super capacitor are related to temperature and current. Therefore, the basic Rint model is improved in the embodiment, and R BAT and R SC in the basic Rint model are changed into functions of temperature and current, as shown in Figure 4 , wherein T is the Celsius temperature. Since the internal resistance of the super capacitor changes very little when the discharge current exceeds one fourth of the maximum current, and the working condition of the embodiment is almost all large-current discharge, R SC in the improved RC model is only related to temperature. Taking the lithium battery ANR26650M1-B and the super capacitor BCAP0100P270S07 as examples, the R BAT (I BAT , T) graph based on the fitted measured data is shown in Figure 5 , the R SC (T) and C SC (T) graphs are shown in Figure 6 . Thus, the fitting function of the function of the battery internal resistance in the Rint model about temperature and current, and the fitting function of the function of the capacitor internal resistance in the RC model about temperature and current are obtained.

[0054] In the implementation process, the capacity attenuation model can adopt an Arrhenius semi-empirical model, an attenuation mechanism model, a neural network model, etc. to model the capacity attenuation of the lithium battery considering the temperature influence. Since the cycle life of the alternative super capacitor is 50 to 100 million times, which is much higher than the cycle life of 1000 times of the selected M1-B lithium battery, only the aging of the lithium battery is considered in the optimization of the hybrid energy storage system. In the implementation process, the capacity attenuation modeling is as follows:

[0055]

[0056] In the formula, Q loss is the battery capacity attenuation percentage, and E ais the activation energy (J / mol), R is the ideal gas constant (8.314 J / (mol·K), C rate is the battery charge-discharge current rate, A h is the ampere-hour flux during the battery charge-discharge process; A0, B and z are parameters to be calibrated in the model, T bat is the Kelvin temperature of the battery.

[0057] Since the lithium battery in the research of the present embodiment is in a cycle working condition rather than a charge-discharge test working condition, the derivation and difference of formula (1) are performed, so that the application range can be extended to the cycle working condition. The incremental expression of the cycle capacity attenuation is obtained as formula (2):

[0058]

[0059]

[0060] In addition, considering the characteristics of accelerated battery attenuation at low temperature, T b As the minimum battery working temperature of attenuation, formula (2) is transformed, and the result is shown in formula (4):

[0061]

[0062] In the formula, Q loss,p+1 is the battery capacity attenuation percentage at p+1, Q loss,p is the battery capacity attenuation percentage at p, ΔA h is the ampere-hour flux from p to p+1, T b is the minimum battery working temperature of attenuation, T c is the temperature compensation coefficient, t p+1 is at p+1, t p is at p, I bat is the battery current, and t is time (dt means integration with respect to time).

[0063] Finally, the accelerated aging test of the ANR26650M1-B battery is carried out at-20℃, -10℃, 0℃, 15℃, 25℃ and 45℃, respectively. According to the results of the accelerated aging test, the parameters A0, B and z to be calibrated are fitted, and the fitting results are as follows:

[0064]

[0065] In the specific implementation process, the present embodiment considers a certain type of vehicle-mounted microwave transmitter with a peak power of 200kW and an average power of 50kW per unit, and uses a hybrid energy storage system as its primary power system. The power demand of the transmitter during operation can be approximated as a square wave pulse with an amplitude of 200kW, a period of 200ms, and a duty cycle of 0.25, as shown in Figure 7As shown, the input voltage range needs to be in the range of 450V-510V.

[0066] To investigate the performance of the hybrid energy storage system in a wide temperature range, this embodiment is based on the monthly average minimum temperature data of the Tibet Ali-China border area in 2021, and the monthly average maximum temperature data of the national heat pole of Xinjiang Turpan in 2021, and designs the optimized load operation scene temperature distribution. In order to ensure the representativeness of the wide temperature range application scene and reduce the calculation amount, this embodiment discretizes the monthly average temperature data of Ali area and Turpan area to-20℃, -10℃, 0℃, 15℃, 25℃, 45℃ six temperature points, and then obtains the weight of each temperature by statistical average, as shown in Table 2.

[0067] Table 2 Weight of each temperature point in application scene

[0068]

[0069]

[0070] The alternative devices in the capacity configuration optimization of this embodiment include the ANR26650M1-B lithium battery in Table 1, and five models of supercapacitors with capacitance values of 100F, 350F, 500F, 650F, and 1200F. The optimal result including device selection and lithium battery, supercapacitor series-parallel scheme needs to be obtained through optimization solution. Therefore, the decision variables in optimization can be determined as the type of supercapacitor TYPE SC , the number of supercapacitor series SN SC , the number of supercapacitor parallel PN SC , the number of lithium battery series SN BAT , and the number of lithium battery parallel PN BAT .

[0071] Considering the actual needs of vehicle-mounted microwave transmitter power supply in terms of mobility and service life, this embodiment carries out capacity configuration optimization with the minimum weight and maximum life of the hybrid energy storage system as the goal, which is:

[0072]

[0073] In the formula, N duty is the number of tasks that the microwave transmitter can complete before the failure of the hybrid energy storage system, which is used to quantify the service life of the hybrid energy storage system in the application scene of this embodiment (the microwave transmitter needs to continuously emit 2000 pulses to complete a task, and the hybrid energy storage system will be charged to the rated voltage before executing the task; the capacity of the lithium battery pack in the hybrid energy storage system is considered to be failed when it accumulates 20% attenuation); M HESSThe calculation method of the mixed energy storage system mass is shown in equation (7). Since the design of the wide-temperature-range microwave transmitter power supply is mainly focused on the performance of system weight and life, the system cost is not sensitive, and the minimization of system weight has directly limited the purchase cost, the economic cost of the system is not taken as one of the optimization objectives.

[0074]

[0075] In the formula, M SC , M BAT are the weights of the supercapacitor and the battery cell respectively, EFF itg is the integration efficiency of the mixed energy storage system, and the integration efficiency of the mixed energy storage system in the embodiment is set to 0.65.

[0076] In the optimization problem of the embodiment, the constraint conditions are composed of three parts, i.e., the voltage constraint of the mixed energy storage system, the current constraint and the capacity constraint of the supercapacitor and the lithium battery. The constraint conditions are set to meet the load power and voltage demand, meet the task energy demand, and not violate the device current and voltage limit. The specific constraint set is not described here.

[0077] For the foregoing optimization problem, intelligent optimization algorithms such as genetic algorithm, evolutionary algorithm, and particle swarm algorithm can be used to solve the optimization problem. In the embodiment, the non-dominated sorting genetic algorithm with elitist strategy (NSGA-II) is used to solve it. In the solving process, the calculation of the target value relies on the previously calibrated RC model, Rint model and improved capacity attenuation modeling, and the mixed energy storage system simulation circuit is built based on the RC model and the Rint model. Based on this, the constraint violation in the response process of the mixed energy storage system is calculated.

[0078] The solution result of the multi-objective optimization problem of the wide-temperature-range passive mixed energy storage system capacity configuration in the embodiment is shown in Figure 8 . The star point is the optimization result of the mixed energy storage system; the optimization result of the pure lithium battery system under the same constraint and parameter setting is represented by a circle point. It can be seen that the Pareto front formed by the mixed energy storage system scheme is significantly better than the pure lithium battery system as a whole. In the optimal scheme, when the energy storage system weight is more than 350 kg, the performance of the mixed energy storage system and the pure lithium battery system in life and weight tends to be consistent; when the system weight is less than 350 kg, the smaller the system weight, the greater the system life improvement of the mixed energy storage system compared with the pure lithium battery system.

[0079] The single-use cost corresponding to the solution on the optimal Pareto front of the mixed energy storage system and the pure lithium battery is shown in Figure 9 . The square is the single-use cost of the pure lithium battery scheme, and the diamond is the single-use cost of the mixed energy storage scheme. For easy observation,Figure 9 is to sort the originally scattered points and then draw them. It can be seen that the single-use cost of the optimal hybrid energy storage system and the optimal pure lithium battery system is close, and there is no solution with too high cost, so the solution set is effective for the application scenarios of the embodiment which are not sensitive to economy.

[0080] The pure lithium battery solution with a system weight of 200.34 kg on the Pareto frontier is taken as a reference, and the hybrid storage solutions with similar life indicators are selected for further comparative analysis. The selected configuration solutions and related indicators are shown in Table 3.

[0081] Table 3 Selected capacity configuration solutions and related indicators

[0082]

[0083] It can be seen from the comparison that under the premise of ensuring the service life, the single-use cost of the hybrid energy storage system is relatively high compared to the pure lithium battery, but the system weight is reduced by 10.97%, and the maximum power capability at room temperature is increased by 14.37%, which has obvious improvement in performance.

[0084] In the temperature range of -20℃-45℃ examined in the application scenario of the embodiment, the electrical performance of the device is the worst at -20℃, and the battery ages the fastest. Therefore, subsequent comparative analysis is carried out at -20℃. At -20℃, the current distribution of the hybrid energy storage solution in Table 3 during the completion of a task by the pulse load is shown in Figure 10 Due to the limitation of image size, Figure 10 only the first 8 cycles of 2000 cycles of a task are shown.

[0085] It can be seen from Figure 10 that the hybrid energy storage solution mainly relies on the super capacitor for high-current discharge, and the discharge current of the lithium battery monomer in the system fluctuates little and has a low discharge rate. Further comparison of the lithium battery output current between the hybrid energy storage solution and the pure lithium battery solution: at -20℃, the lithium battery current output of the pure lithium battery energy storage solution and the hybrid energy storage solution during the completion of a task by the pulse load is shown in Figure 11 , and the first 8 cycles are also shown.

[0086] From the comparison of the monomer discharge current Figure 11 , it can be seen that under similar energy storage system use cost and cycle life, the hybrid energy storage solution can greatly reduce the output current fluctuation and average discharge rate of the lithium battery monomer in the energy storage system.

[0087] The above merely describes the preferred embodiments of the present application, and is not intended to limit the patent scope of the present application. Any equivalent structural changes made according to the content of the present application specification and drawings, or direct / indirect application in other related technical fields, are included in the patent protection scope of the present application.

Claims

1. A method for capacity configuration multi-objective optimization of a wide-temperature-range passive hybrid energy storage system, characterized in that, The mixed energy storage system comprises lithium batteries and supercapacitors in parallel; The capacity configuration multi-objective optimization method comprises the following steps: Step 1, a temperature considering dynamic response characteristic model of lithium batteries and supercapacitors is established; Step 2, a temperature considering capacity attenuation model of lithium batteries is constructed, which includes but is not limited to using Arrhenius semi-empirical model, attenuation mechanism model or neural network model to model the temperature considering capacity attenuation of lithium batteries, and calibrating the capacity attenuation model, specifically: carrying out accelerated aging test of lithium batteries at-20℃, -10℃, 0℃, 15℃, 25℃, 45℃ respectively, and fitting the to-be-calibrated parameters according to the results of the accelerated aging test; Step 3, on the basis of the dynamic response characteristic model and the capacity attenuation model, taking the weight and life of the mixed energy storage system as the optimization objective, using intelligent optimization algorithm to optimize the capacity configuration of the mixed energy storage system, and obtaining the Pareto frontier of the mixed energy storage system.

2. The capacity configuration multi-objective optimization method of the wide-temperature-range passive hybrid energy storage system according to claim 1, characterized in that, In step 1, the dynamic response characteristic model includes but is not limited to equivalent circuit model, electrochemical model or neural network model; After establishing the dynamic response characteristic model, the model parameters are calibrated based on the measured data, so that they can accurately reflect the dynamic response characteristics of lithium batteries and supercapacitors at different temperatures.

3. The capacity configuration multi-objective optimization method of the wide-temperature-range passive hybrid energy storage system according to claim 1 or 2, characterized in that, In step 3, the intelligent optimization algorithm includes but is not limited to using genetic algorithm, evolutionary algorithm, particle swarm algorithm to optimize the capacity configuration of the mixed energy storage system.

4. The capacity configuration multi-objective optimization method of the wide-temperature-range passive hybrid energy storage system according to claim 1 or 2, characterized in that, In step 3, the weight and life of the mixed energy storage system are taken as the optimization objective, specifically: Taking the minimum weight and the maximum life of the mixed energy storage system as the optimization objective, that is: In the formula, F obj is the joint optimization objective, N duty is the number of tasks completed before failure of the hybrid energy storage system, M HESS is the quality of the hybrid energy storage system.

5. The capacity configuration multi-objective optimization method of the wide-temperature-range passive hybrid energy storage system according to claim 4, characterized in that, Mass of the hybrid energy storage system M HESS Specifically: wherein, M SC , M BAT are the weight of the supercapacitor and the battery cell respectively, EFF itg is the hybrid storage system integration efficiency, SN SC is the number of supercapacitors in series, PN SC is the number of supercapacitors in parallel, SN BAT is the number of lithium batteries in series, PN BAT is the number of lithium batteries in parallel.

6. The capacity configuration multi-objective optimization method of the wide-temperature-range passive hybrid energy storage system according to claim 1 or 2, characterized in that, In step 3, in the process of optimizing the capacity configuration of the mixed energy storage system, the voltage constraint of the mixed energy storage system, and the current constraint and capacity constraint of the supercapacitor and lithium battery are taken as the constraint conditions; The constraint conditions are based on the principles of meeting the load power and voltage demand, meeting the task energy demand and not violating the device current and voltage limit.

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