A fast charging method for improving energy efficiency of a charging system

Abstract

The application relates to the technical field of power batteries, in particular to a fast charging method for improving the energy efficiency of a charging system, which comprises the following steps: acquiring a load rate-efficiency characteristic curve of a charging device in the charging system; performing a mixed power pulse characteristic experiment, and constructing a power battery modeling parameter table; measuring the specific heat capacity and the convection heat transfer coefficient of the power battery by using the heat flux density; establishing a power battery equivalent circuit-thermal coupling model according to the power battery modeling parameter table and the specific heat capacity of the power battery; constructing a multi-objective optimization model of the charging system, and solving the charging sequence by using an intelligent algorithm. The application proposes a multi-objective optimization charging strategy for the charging system, and realizes the improvement of the energy efficiency under the premise of fast charging.

Description

Technical Field

[0001] This invention relates to the field of power battery technology, specifically to a fast charging method for improving the energy efficiency of a charging system. Background Technology

[0002] Currently, new energy electric vehicles are developing on a large scale, and as the core energy storage device for electric vehicles, optimizing and improving fast charging is one of the hot topics in the field of power battery research.

[0003] Existing technologies employ various charging strategies to optimize the fast charging process. Constant current constant voltage (CCCV) is a commonly used fast charging strategy for power batteries. This strategy charges the battery through two stages: constant current and constant voltage. It is highly operable and simple to implement. However, this strategy leads to high charging temperature rise and significant capacity loss in the battery. Multi-stage constant current charging strategies gradually reduce the charging current when the battery voltage reaches the cutoff voltage. This method increases the battery's cycle life while reducing charging time, but it does not consider the impact of temperature rise on battery safety.

[0004] On the other hand, the energy flow path during charging is from the charging device to the power battery. Therefore, the power battery and charging device can be considered as a whole as a charging system, currently with a charge-discharge energy efficiency of about 85%. However, the room for improvement in the energy efficiency of charging devices is very limited; even with measures such as adopting new switching transistors, optimizing topology, and improving control strategies, the improvement is generally no more than 1%. Furthermore, the energy efficiency of charging devices is positively correlated with the load rate, typically requiring a load rate of at least 50% of the charging device's rated power. Current charging strategies usually only consider battery characteristics, and the system cannot achieve optimal energy conversion efficiency at low load rates, thus reducing the overall energy efficiency of the charging system.

[0005] Therefore, developing a charging method that balances charging speed, charging temperature rise, and system energy efficiency is of great significance for energy conservation, emission reduction, and improving economic benefits. Summary of the Invention

[0006] This invention provides a fast charging method to improve the energy efficiency of a charging system. It proposes a multi-objective optimization charging strategy for the charging system to improve energy efficiency while achieving fast charging.

[0007] This application provides the following technical solution:

[0008] A fast charging method for improving the energy efficiency of a charging system, the charging system comprising a power battery and a charging device, the fast charging method comprising the following steps:

[0009] S1. Obtain the load rate-efficiency characteristic curve of the charging device;

[0010] S2. Conduct hybrid power pulse characteristic experiments and construct a battery modeling parameter table with wide temperature range and multiple rates;

[0011] S3. The specific heat capacity and convective heat transfer coefficient of the power battery are calculated using heat flux density.

[0012] S4. Based on the power battery modeling parameter table, establish the power battery equivalent circuit-thermal coupling model;

[0013] S5. Construct a multi-objective optimization model for the charging system and solve the charging current sequence using intelligent algorithms.

[0014] Furthermore, the load rate-characteristic curve mentioned in step S1 includes characteristic curves for different rated power levels.

[0015] Furthermore, step S2 is detailed as follows:

[0016] S21. Select at least three target temperatures according to the operating temperature range of the power battery specifications, including low temperature, room temperature and high temperature;

[0017] S21. Select at least three target temperatures according to the operating temperature range of the power battery specifications, including low temperature, room temperature and high temperature;

[0018] S22. Discharge the power battery to the cutoff voltage according to the standard discharge conditions at room temperature, and let the power battery stand for 4 hours.

[0019] S23. Perform charging pulse tests on the power battery at different rates in sequence: each charging pulse lasts for 10 seconds, rest for 5 minutes, discharge at 1C to make the discharge capacity the same as the charging capacity, and then perform the charging pulse test at the next rate.

[0020] S24. Charge the battery with a 1C current to 5% of its rated capacity, let it stand for 1 hour, and read the open-circuit voltage of the battery.

[0021] S25. Repeat steps S23-S24 until 100% SOC is reached, and collect temperature, voltage and current data at different SOCs and different magnification rates.

[0022] S26. Change the target temperature setting and repeat steps S22-S25;

[0023] S27. Construct a battery modeling parameter table with wide temperature range and multiple rates based on the data obtained in S24-S26.

[0024] Furthermore, step S3 is detailed as follows:

[0025] S31. Place the heat flux density sensor in close contact with the center of the battery's maximum heat dissipation surface;

[0026] S32, The formula for calculating the specific heat capacity of a power battery is: ,

[0027] The formula for calculating the convective heat transfer coefficient is as follows: ;

[0028] in, Represents any time i heat flux density, A Indicates the battery surface area. Indicates the heat flux density sampling interval. m Indicates battery quality. and These represent the highest and lowest temperatures on the battery surface during a charging process, respectively. Indicates ambient temperature.

[0029] Furthermore, the equivalent circuit-thermal coupling model of the power battery includes the battery equivalent circuit model and the lumped thermal model, wherein the battery equivalent circuit model is expressed as:

[0030]

[0031] in, U Indicates the battery terminal voltage. OCV Indicates the battery open-circuit voltage. I Represents current. R 0 represents ohmic internal resistance. R p1 Indicates the electrochemical polarization resistance. R p2 surface Indicates concentration polarization internal resistance, C p1 Indicates electrochemical polarization capacitance. C p2 Indicates concentration polarization capacitance. This represents the time constant corresponding to electrochemical polarization. This represents the time constant corresponding to concentration polarization. U p1 Indicates electrochemical polarization voltage. U p2 Indicates concentration polarization voltage. t Indicates time;

[0032] The lumped thermal model is expressed as:

[0033]

[0034] in, Indicates the battery thermal resistance. Indicates the surface temperature of the battery; This indicates the surface temperature of the battery at the initial moment of charging.

[0035] Furthermore, the S5 steps are as follows:

[0036] S51. Construct a multi-objective optimization model for the charging system. The evaluation objectives of the multi-objective optimization model include charging time. System average charging energy efficiency and average temperature rise rate ,in

[0037]

[0038]

[0039]

[0040] in, Indicates the battery's rated capacity; describes the charging process. Divided into N stages, Indicates the battery is in the first k The change in SOC during a single current charging process; Indicates the battery number k One current value; Indicates open-circuit voltage; Indicates the voltage and current sampling interval; Indicates the battery charging terminal voltage; This indicates the surface temperature of a single battery cell; This indicates the surface temperature of a single battery cell at the initial moment of charging.

[0041] S52. Solve the multi-objective optimization model of the charging system using a genetic algorithm.

[0042] Furthermore, the multi-objective optimization model is specifically expressed as:

[0043]

[0044]

[0045]

[0046] The constraints to be satisfied include:

[0047]

[0048] in, This indicates the current of a single battery cell. This indicates the voltage of a single battery cell. Indicates the current of the power battery. Indicates the power of the battery. min and max These represent the minimum and maximum values, respectively. limit This indicates that the corresponding parameter value is taken according to the specification or user-defined settings.

[0049] Furthermore, step S52 adopts NSGA. III. The multi-objective genetic algorithm solves the multi-objective optimization model of the charging system and outputs the final charging current sequence in the form of a Pareto optimal solution set, specifically including:

[0050] First, an initial population is generated, consisting of current values ​​for N SOC intervals. The initial population is then input into the multi-objective optimization model of the charging system to obtain the charging time, average charging energy efficiency, and average temperature rise rate corresponding to each initial population.

[0051] Then, based on the charging time, average system charging energy efficiency, and average temperature rise rate corresponding to each initial population, the initial populations are compared, mutated, and crossoverd to form new populations, and the process is iterated.

[0052] If the required number of iterations is reached, the final charging current sequence is output in the form of a Pareto optimal solution set.

[0053] Furthermore, the power battery is a lithium manganese oxide power battery, a lithium iron phosphate power battery, or a ternary material power battery.

[0054] The principle and advantages of this invention are as follows: the energy flow path during the charging process is from the charging device to the power battery. Therefore, the charging system composed of the power battery and the charging device can be considered as a whole. Currently, the charging energy efficiency of this system is about 85%. However, the room for improvement in the energy efficiency of the charging device is very limited; the energy efficiency improvement of existing measures is generally no more than 1%. At the same time, the energy efficiency of the charging device is positively correlated with the load rate, typically requiring the load rate to reach more than 50% of the rated power of the charging device. Furthermore, existing charging methods have not systematically considered the coordinated control of the charging device and the power battery, resulting in greater energy consumption.

[0055] This invention considers the load rate-efficiency characteristics of charging equipment and establishes a multi-objective optimization charging algorithm framework based on the equivalent circuit-thermal coupling model, which includes charging time, charging system energy efficiency, and temperature rise rate. It proposes a charging method that takes into account both charging speed and energy efficiency, thereby optimizing and improving both charging speed and energy efficiency. At the same time, it effectively controls the charging temperature rise to avoid battery capacity loss caused by charging temperature rise. Attached Figure Description

[0056] Figure 1 This is a flowchart of a fast charging method for improving the energy efficiency of a charging system according to the present invention;

[0057] Figure 2 A load-efficiency characteristic curve for charging equipment;

[0058] Figure 3 A diagram showing the comparison of charging power before and after optimization;

[0059] Figure 4 This is a diagram showing the comparison of charging time before and after optimization. Detailed Implementation

[0060] Existing technologies employ various charging strategies to optimize the fast charging process, including constant current constant voltage (CCCV) and multi-stage constant current charging strategies. However, traditional optimization strategies cannot adequately assess charging temperature rise, failing to avoid capacity loss and safety impacts caused by temperature increases. Furthermore, current charging strategies typically only consider battery characteristics, resulting in the system failing to achieve optimal energy conversion efficiency at low load rates, thus reducing the overall energy efficiency of the charging system.

[0061] This invention provides a fast charging method to improve the energy efficiency of a charging system. It can balance the charging speed and energy efficiency of the charging system, improve the energy efficiency of the charging system under the premise of fast charging, and effectively control the charging temperature rise. Compared with traditional charging optimization strategies, it can further save energy and reduce emissions, and improve the durability of the battery.

[0062] The following detailed description illustrates the specific implementation method:

[0063] Example 1

[0064] A fast charging method to improve the energy efficiency of a charging system, such as... Figure 1 As shown, it includes the following steps:

[0065] Step 1: Obtain the load rate-efficiency characteristic curve of the charging device;

[0066] The charging equipment described in this invention includes DC charging piles and AC charging piles, and can adjust the current and voltage to charge the power battery; the power battery is a lithium manganese oxide power battery, a lithium iron phosphate power battery, or a ternary lithium battery. The load rate-efficiency characteristic curve of the charging equipment can be obtained from the equipment manufacturer, such as... Figure 2 As shown in the figure. Here, load factor refers to the ratio between the actual load (i.e., the power being used) and the device's rated maximum power. The efficiency of a charging device is the ratio of the power transferred from the charging pile to the battery to the power obtained by the charging pile from the grid.

[0067] DC charging piles with fixed power output suffer from poor compatibility when facing the charging power demands of different vehicles. When the load power exceeds 50% of the rated power, the energy efficiency of the charging equipment remains relatively stable at a high level. However, if the allowable load rate of the charging equipment is too low, it cannot meet the user's charging needs. In the intermediate load rate range, the energy efficiency of the charging equipment varies significantly, and the energy efficiency of the charging system can be optimized by adjusting the charging strategy.

[0068] Depend on Figure 2 It can be seen that the energy change efficiency of the charging device is more obvious in the range of 30% to 60% SOC. By adjusting the charging strategy, the energy efficiency of the charging system can be optimized. Therefore, this embodiment selects the range of 30% to 60% SOC for optimization.

[0069] Step 2: Conduct hybrid power pulse characteristic experiments and construct a wide-temperature-range, multi-rate power battery modeling parameter table, which includes the following steps:

[0070] S21. Select at least three target temperatures according to the operating temperature range of the power battery specifications, including low temperature, room temperature and high temperature;

[0071] S21. Select at least three target temperatures according to the operating temperature range of the power battery specifications, including low temperature, room temperature and high temperature;

[0072] S22. Discharge the power battery to the cutoff voltage according to the standard discharge conditions at room temperature, and let the power battery stand for 4 hours.

[0073] S23. Perform charging pulse tests on the power battery at different rates in sequence: each charging pulse lasts for 10 seconds, rest for 5 minutes, discharge at 1C to make the discharge capacity the same as the charging capacity, and then perform the charging pulse test at the next rate.

[0074] S24. Charge the battery with a 1C current to 5% of its rated capacity, let it stand for 1 hour, and read the open-circuit voltage of the battery.

[0075] S25. Repeat steps S23-S24 until 100% SOC is reached, and collect temperature, voltage and current data at different SOCs and different magnification rates.

[0076] S26. Change the target temperature setting and repeat steps S22-S25;

[0077] S27. Construct a battery modeling parameter table with wide temperature range and multiple rates based on the data obtained in S24-S26.

[0078] The battery modeling parameter table includes the battery open-circuit voltage associated with temperature and SOC, as well as multiple sets of battery impedance parameters associated with temperature, SOC, and charging rate.

[0079] Step 3: Calculate the specific heat capacity and convective heat transfer coefficient of the power battery using heat flux density;

[0080] Specifically, the steps include the following:

[0081] S31. Assuming that the heat flux distribution on the maximum surface of the power battery is uniform, place the heat flux density sensor close to the center of the maximum heat dissipation surface of the battery.

[0082] S32, The formula for calculating the specific heat capacity of a power battery is: ,

[0083] The formula for calculating the convective heat transfer coefficient is as follows: ;

[0084] in, Represents any time i heat flux density, A Indicates the battery surface area. Indicates the heat flux density sampling interval. m Indicates battery quality. and These represent the highest and lowest temperatures on the battery surface during a charging process, respectively. Indicates ambient temperature.

[0085] Step 4: Using the power battery modeling parameter table and the measured specific heat capacity of the power battery, establish the power battery equivalent circuit-thermal coupling model. The power battery equivalent circuit-thermal coupling model includes the battery equivalent circuit model and the lumped thermal model.

[0086] The battery equivalent circuit model is represented as follows:

[0087]

[0088] In the formula, U Indicates the battery terminal voltage. OCV Indicates the battery open-circuit voltage. I Represents current. R 0 represents ohmic internal resistance. R p1 Indicates the electrochemical polarization resistance. R p2 surface Indicates concentration polarization internal resistance, C p1 Indicates electrochemical polarization capacitance. C p2 Indicates concentration polarization capacitance. This represents the time constant corresponding to electrochemical polarization. This represents the time constant corresponding to concentration polarization. U p1Indicates electrochemical polarization voltage. U p2 Indicates concentration polarization voltage. t Indicates time.

[0089] The lumped thermal model is expressed as:

[0090]

[0091] In the formula, Indicates the battery thermal resistance. Indicates the surface temperature of the battery; This indicates the surface temperature of the battery at the initial moment of charging.

[0092] Step 5: Construct a multi-objective optimization model for the charging system and solve the charging current sequence using intelligent algorithms.

[0093] Specifically, the steps include the following:

[0094] S51. Construct a multi-objective optimization model for the charging system. The evaluation objectives of the multi-objective optimization model include charging time. System average charging energy efficiency and average temperature rise rate ,in

[0095]

[0096]

[0097]

[0098] In the formula, Indicates the battery's rated capacity; describes the charging process. Divided into N stages, Indicates the battery is in the first k The change in SOC during a single current charging process; Indicates the battery number k One current value; Indicates open-circuit voltage; Indicates the voltage and current sampling interval; Indicates the battery charging terminal voltage; This indicates the surface temperature of a single battery cell; This represents the surface temperature of a single battery cell at the initial moment of charging. In this invention, the system's average charging energy efficiency is defined as the energy efficiency of the power battery. Charging energy efficiency of charging devices The product of the power battery and the power battery is used to find the energy efficiency of the charging equipment based on the load rate-efficiency characteristic curve of the charging equipment.

[0099] The specific expression of the multi-objective optimization model is as follows:

[0100]

[0101]

[0102]

[0103] The constraints to be satisfied include:

[0104]

[0105] in, This indicates the current of a single battery cell. This indicates the voltage of a single battery cell. Indicates the current of the power battery. Indicates the power of the battery. min and max These represent the minimum and maximum values, respectively. limit This indicates that the corresponding parameter value is taken according to the specification or user-defined settings.

[0106] The above constraints mean that the maximum battery temperature during charging does not exceed the maximum temperature allowed in the specifications, the current, voltage and power values ​​of the battery cells should not exceed the maximum values ​​allowed in the specifications, the battery temperature rise rate does not exceed 1℃ / min, the charging cut-off voltage does not exceed the allowable cut-off voltage, and the charging termination SOC meets the SOC set by the OEM or expected by the user.

[0107] S52. Solve the multi-objective optimization model of the charging system using a genetic algorithm.

[0108] Specifically, this embodiment uses NSGA. III. The multi-objective genetic algorithm solves the multi-objective optimization model of the charging system and outputs the final charging current sequence in the form of a Pareto optimal solution set, specifically including:

[0109] First, an initial population is generated, consisting of current values ​​for N SOC intervals. The initial population is then input into the multi-objective optimization model of the charging system to obtain the charging time, average charging energy efficiency, and average temperature rise rate corresponding to each initial population.

[0110] Then, based on the charging time, average system charging energy efficiency, and average temperature rise rate corresponding to each initial population, the initial populations are compared, mutated, and crossoverd to form new populations, and the process is iterated.

[0111] If the required number of iterations is reached, the final charging current sequence is output in the form of a Pareto optimal solution set.

[0112] Based on the above optimization methods, a 160kW DC charging pile from a certain manufacturer was selected for verification testing of the optimization strategy. The results are as follows: Figure 3 and Figure 4 As shown in the figure, the comparison strategy is a multi-segment constant current charging strategy, using 2C constant current charging in the 30% to 60% range. As can be seen from the figure, the optimization effect on battery charging power and charging time is very significant.

[0113] The above are merely embodiments of the present invention, and the invention is not limited to the fields covered by these embodiments. Commonly known structures and characteristics in the solutions are not described in detail here. It should be noted that those skilled in the art can make various modifications and improvements without departing from the structure of the present invention, and these should also be considered within the scope of protection of the present invention. These modifications and improvements will not affect the effectiveness of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A rapid charging method for improving energy efficiency of a charging system including a power battery and a charging device, characterized in that, Includes the following steps: S1. Obtain the load rate-efficiency characteristic curve of the charging device; S2. Conduct hybrid power pulse characteristic experiments and construct a power battery modeling parameter table with wide temperature range and multiple rates; S3. The specific heat capacity and convective heat transfer coefficient of the power battery are calculated using heat flux density. S4. Based on the power battery modeling parameter table, establish the power battery equivalent circuit-thermal coupling model; The equivalent circuit-thermal coupling model of the power battery includes a battery equivalent circuit model and a lumped thermal model, wherein... The equivalent circuit model of the battery is represented as follows: wherein, U represents the battery terminal voltage, OCV represents the battery open circuit voltage, I represents the current, R 0 represents the ohmic internal resistance, R p1 represents the electrochemical polarization internal resistance, R p2 surface represents the concentration polarization internal resistance, C p1 represents the electrochemical polarization capacitance, C p2 represents the concentration polarization capacitance, represents the time constant corresponding to the electrochemical polarization, represents the time constant corresponding to the concentration polarization, U p1 represents the electrochemical polarization voltage, U p2 represents the concentration polarization voltage, t represents time; The lumped thermal model is expressed as follows: wherein, represents the battery thermal resistance, represents the battery surface temperature; represents the battery surface temperature at the initial time of charging; S5. Construct a multi-objective optimization model for the charging system and solve the charging current sequence using intelligent algorithms; The specific steps for S5 are as follows: S51. Construct a multi-objective optimization model for the charging system. The evaluation objectives of the multi-objective optimization model include charging time. System average charging energy efficiency and average temperature rise rate ,in in, Indicates the battery's rated capacity; describes the charging process. Divided into N stages, Indicates the battery is in the first k The change in SOC during a single current charging process; Indicates the battery number k One current value; Indicates open-circuit voltage; Indicates the voltage and current sampling interval; Indicates the battery charging terminal voltage; This indicates the surface temperature of a single battery cell; This indicates the surface temperature of a single battery cell at the initial moment of charging. S52. Solve the multi-objective optimization model of the charging system using a genetic algorithm.

2. The fast charging method for improving the energy efficiency of a charging system according to claim 1, characterized in that: The load factor-characteristic curves mentioned in step S1 include characteristic curves for different rated power levels.

3. The fast charging method for improving the energy efficiency of a charging system according to claim 2, characterized in that, Step S2 is as follows: S21. Select at least three target temperatures according to the operating temperature range of the power battery specifications, including low temperature, room temperature and high temperature; S22. Discharge the power battery to the cutoff voltage according to the standard discharge conditions at room temperature, and let the power battery stand for 4 hours. S23. Perform charging pulse tests on the power battery at different rates in sequence: each charging pulse lasts for 10 seconds, rest for 5 minutes, discharge at 1C to make the discharge capacity the same as the charging capacity, and then perform the charging pulse test at the next rate. S24. Charge the battery with a 1C current to 5% of its rated capacity, let it stand for 1 hour, and read the open-circuit voltage of the battery. S25. Repeat steps S23-S24 until 100% SOC is reached, and collect temperature, voltage and current data at different SOCs and different magnification rates. S26. Change the target temperature setting and repeat steps S22-S25; S27. Construct a battery modeling parameter table with wide temperature range and multiple rates based on the data obtained in S24-S26.

4. A fast charging method for improving the energy efficiency of a charging system according to claim 3, characterized in that, Step S3 is as follows: S31. Place the heat flux density sensor in close contact with the center of the battery's maximum heat dissipation surface; S32, The formula for calculating the specific heat capacity of a power battery is: The formula for calculating the convective heat transfer coefficient is as follows: in, Represents any time i heat flux density, A Indicates the battery surface area. Indicates the heat flux density sampling interval. m Indicates battery quality. and These represent the highest and lowest temperatures on the battery surface during a charging process, respectively. Indicates ambient temperature.

5. A fast charging method for improving the energy efficiency of a charging system according to claim 4, characterized in that: The multi-objective optimization model is specifically expressed as: Satisfying constraints include: in, This indicates the current of a single battery cell. This indicates the voltage of a single battery cell. Indicates the current of the power battery. Indicates the power of the battery. min and max These represent the minimum and maximum values, respectively. limit This indicates that the corresponding parameter value is taken according to the specification or user-defined settings.

6. A fast charging method for improving the energy efficiency of a charging system according to claim 5, characterized in that, Step S52 uses NSGA III. The multi-objective genetic algorithm solves the multi-objective optimization model of the charging system and outputs the final charging current sequence in the form of a Pareto optimal solution set, specifically including: First, an initial population is generated, consisting of current values ​​for N SOC intervals. The initial population is then input into the multi-objective optimization model of the charging system to obtain the charging time, average charging energy efficiency, and average temperature rise rate corresponding to each initial population. Then, based on the charging time, average system charging energy efficiency, and average temperature rise rate corresponding to each initial population, the initial populations are compared, mutated, and crossoverd to form new populations, and the process is iterated. If the required number of iterations is reached, the final charging current sequence is output in the form of a Pareto optimal solution set.

7. A fast charging method for improving the energy efficiency of a charging system according to claim 1, characterized in that: The power battery is a lithium manganese oxide power battery, a lithium iron phosphate power battery, or a ternary material power battery.

Images