A charging method and system based on power battery charging urgency quantification

Abstract

The application provides a charging method and system based on a power battery charging urgency quantification, which comprises the following steps: determining a travel urgency; collecting real-time state data in a power battery charging process, and solving a battery core temperature according to a battery model, wherein an upper limit of the battery core temperature will change with the travel urgency, that is, when the travel urgency increases, the upper limit of the battery core temperature will also increase accordingly; establishing and training a battery fast charging model considering the user travel urgency; the battery fast charging model considering the user travel urgency solves an optimal charging current of the battery at the current time according to the travel urgency, the collected real-time state data in the battery charging process and the core temperature data, and the power battery is charged according to the optimal charging current.

Description

Technical Field

[0001] This invention belongs to the field of electric vehicle charging technology, and in particular relates to a charging method and system based on the quantification of the urgency of power battery charging. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] To address climate change and align with the trend of green and low-carbon development, electric vehicles (EVs) have gradually emerged and secured a place in today's automotive market. Compared to traditional internal combustion engine vehicles, EVs offer advantages such as lower carbon dioxide emissions and lower operating costs, leading to a continuous increase in their usage over the past two years. However, as an emerging industry, EVs still face numerous technological challenges: in battery charging, they suffer from slow charging speeds, poor safety, and significant damage. As the heart of an EV, the safe and fast charging of the battery has become a major technological bottleneck restricting its widespread application. Solving the problem of safe and fast battery charging is of great significance to the development of EVs.

[0004] There is a contradiction between charging speed and battery damage and energy loss during battery charging. That is, as the charging speed increases, battery damage and energy loss will inevitably increase.

[0005] Currently used charging methods include constant current and constant voltage charging, pulse charging, variable voltage charging, and variable current intermittent charging. These methods can improve charging speed and reduce battery damage to some extent, but their actual effects are limited and they cannot automatically adjust the charging mode according to the user's travel urgency.

[0006] In the current technology, patent 202211635957.4 proposes a battery fast charging method based on an electrochemical-thermal aging model. However, the electrochemical model used in this method is too complex, requiring a large amount of calculation, and is difficult to apply in practical scenarios. Patent 202210372284.1 proposes a variable weight multi-segment constant current charging method, which uses 10% SOC as a control interval. However, it cannot dynamically adjust the charging current based on information such as voltage and temperature, and the single charging time is as long as 86 minutes. Patent 202011087624.3 proposes a fast charging method based on multi-physics field constraints, but this method does not consider the energy loss during the charging process.

[0007] None of the aforementioned charging-related patent technologies take into account factors such as the urgency of users' travel, and can only charge the battery in a constant mode, which is difficult to meet the needs of users in actual application. Summary of the Invention

[0008] To overcome the shortcomings of the prior art, this invention provides a charging method based on the quantification of the urgency of power battery charging. This invention can provide the optimal charging mode according to different urgency levels, and achieve a balance between charging speed and battery damage and power loss. It not only greatly improves the charging speed, but also effectively extends the battery life.

[0009] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions:

[0010] Firstly, a charging method based on quantifying the urgency of power battery charging is disclosed, including:

[0011] Determine the urgency of the trip;

[0012] Real-time status data of the power battery during charging is collected, and the battery core temperature is calculated based on the battery model. The upper limit of the battery core temperature will change with the urgency of travel. That is, as the urgency of travel increases, the upper limit of the battery core temperature will also increase accordingly.

[0013] Establish and train a battery fast charging model that takes into account the urgency of users' travel.

[0014] The battery fast charging model that takes into account the urgency of the user's travel calculates the optimal charging current of the battery at the current moment based on the urgency of the travel, the real-time status data collected during the battery charging process, and the core temperature data. The power battery is then charged according to this optimal charging current.

[0015] As a further technical solution, the battery fast charging model that takes into account the user's travel urgency includes a reward function, wherein the reward function R(k) is:

[0016] R(k) = w1r s (k)+w2r v (k)+w3r t (k)+w4r l (k)+w5r d (k)+w6r i (k)+w7r tar (k)

[0017] Among them, w i These are weighting coefficients under different constraints, 1≤i≤6, r s (k) is the current smoothing reward function, r v (k) is the voltage reward function, r t (k) is the temperature reward function, r d (k) is the battery damage reward function, r l (k) is the energy loss reward function, r i (k) is the current reward function, rtar (k) is the reward for achieving the charging goal.

[0018] As a further technical solution, the current smoothing reward function is used to suppress sudden changes in current during charging and obtain a smoother charging process. The current smoothing reward function is represented by the absolute value of the difference between the current at the current time and the previous time.

[0019] As a further technical solution, the voltage reward function is used to limit the voltage range during the charging process. When the voltage is lower than the lower cutoff voltage or higher than the upper cutoff voltage, a penalty will be imposed to ensure that the voltage is within the constrained range during the charging process.

[0020] As a further technical solution, the temperature reward function is limited by the battery core temperature. When the battery core temperature exceeds the set upper limit threshold, it will be penalized to ensure that the battery core temperature is within the set range.

[0021] As a further technical solution, the battery damage reward function is used to minimize battery damage during the charging process. By adjusting the proportion of battery damage reward, the battery charging rate under different urgency levels can be controlled.

[0022] As a further technical solution, the energy loss reward function is used to minimize energy consumption during the charging process. The travel urgency coefficient is substituted into the reward function to ensure that the charging mode emphasis can be adjusted according to the charging urgency during the training process.

[0023] As a further technical solution, the current reward function is used to ensure that the battery charging current is within the safety boundary and as close as possible to the safety charging boundary. It is expressed as the absolute value of the difference between the battery charging current and the maximum allowable charging current. As the urgency increases, the maximum battery charging current will also gradually increase.

[0024] As a further technical solution, the reward for achieving the charging target is to give a relatively large reward when the battery SOC is greater than a set value. This reward guides the charging strategy to achieve the set charging target as soon as possible.

[0025] Secondly, a charging system based on the urgency of power battery charging is disclosed, comprising:

[0026] The module for determining the urgency of travel is configured to: determine the urgency of travel;

[0027] The battery core temperature calculation module is configured to: collect real-time status data during the charging process of the power battery, and calculate the battery core temperature according to the battery model. The upper limit of the battery core temperature will change with the degree of travel urgency, that is, as the degree of travel urgency increases, the upper limit of the battery core temperature will also increase accordingly.

[0028] The charging model building module is configured to: build and train a battery fast charging model that takes into account the urgency of users' travel.

[0029] The charging current calculation module is configured to: the battery fast charging model that takes into account the user's travel urgency calculates the optimal charging current of the battery at the current moment based on the travel urgency, the real-time status data collected during the battery charging process, and the core temperature data, and the power battery charges according to the optimal charging current.

[0030] The above one or more technical solutions have the following beneficial effects:

[0031] The overall technical solution of this invention constructs a reward function based on reinforcement learning. By setting factors such as current limit, voltage limit, temperature limit, power loss, and battery damage as the reward function, the charging current is adaptively adjusted according to the battery state, achieving both fast and efficient charging.

[0032] The overall technical solution of this invention defines an urgency coefficient λ (0~100%) and incorporates it into the reward function of current, temperature, battery damage, and energy loss. This is the first time that the urgency of charging for users has been quantified, providing users with a variety of reasonable charging modes.

[0033] The overall technical solution of this invention can autonomously select the appropriate charging mode according to the urgency, which not only greatly improves the charging speed, but also extends the battery life and improves the battery safety, thus resolving the contradiction between charging speed and battery damage and power loss.

[0034] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0035] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0036] Figure 1 This is a graph showing the input-output relationship of the algorithm during the training process in an embodiment of the present invention.

[0037] Figure 2 This is a diagram showing the input-output relationship of the algorithm during the charging process in an embodiment of the present invention.

[0038] Figure 3 This is a schematic diagram of the three-ring charging control system according to an embodiment of the present invention;

[0039] Figure 4 This is a flowchart illustrating the online charging process of a battery according to an embodiment of the present invention.

[0040] Figure 5 This is a schematic diagram of online battery charging control according to an embodiment of the present invention;

[0041] Figure 6 This is a schematic diagram illustrating the change of charging rate over time under different levels of urgency in application examples of the present invention;

[0042] Figure 7 This is a schematic diagram illustrating the voltage change over time under different levels of urgency in application examples of the present invention;

[0043] Figure 8 This is a schematic diagram illustrating the change of nuclear temperature over time under different levels of urgency in application examples of the present invention;

[0044] Figure 9 This is a schematic diagram illustrating the change of SOC over time under different levels of urgency in application examples of the present invention. Detailed Implementation

[0045] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0046] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations of the present invention.

[0047] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0048] Regarding the charging of power batteries, in emergency situations, the primary consideration should be to increase the charging speed, with less or no consideration given to the impact of battery damage and energy loss. In non-emergency situations, the charging speed can be appropriately reduced, with more consideration given to energy loss and battery damage during the charging process, in order to achieve a balance between charging speed and battery damage and energy loss.

[0049] Example 1

[0050] This embodiment discloses a charging method based on the urgency of power battery charging, including:

[0051] Step 1: Using a second-order RC model of lithium-ion batteries that considers thermal effects, construct a training environment for a battery fast charging strategy based on reinforcement learning, and build a battery fast charging model that takes into account the urgency of users' travel.

[0052] Step 2: Use the TD3 reinforcement learning algorithm to train a battery fast charging model that takes into account the urgency of users' travel.

[0053] Step 3: After iterative training using reinforcement learning to obtain the charging model, the online charging control of the battery is as follows: Figure 2 As shown. Its charging process is as follows:

[0054] 3-1) First, the user determines the urgency of the trip and transmits the command to the host computer.

[0055] 3-2) The sensor collects data such as voltage, current and temperature during the battery charging process, and solves the core temperature of the battery according to the second-order RC model of lithium-ion battery considering thermal effects, and transmits the data to the host computer in real time.

[0056] The second-order RC model of lithium-ion batteries is existing technology. In this example, by inputting the charging current into the second-order RC model of lithium-ion batteries, the core temperature during the charging process can be obtained.

[0057] 3-3) The host computer stores a trained algorithm model. The algorithm model calculates the optimal charging current at the current moment based on the user's travel urgency and real-time data such as voltage, current and core temperature collected by the sensor, and sends the charging current to the charging device.

[0058] In this step, when solving for the optimal charging current at the current moment, reinforcement learning is used to output the optimal charging current based on the current state of the battery.

[0059] 3-4) The charging equipment charges according to the charging current sent by the host computer and provides real-time feedback on current, voltage and other information.

[0060] 3-5) Detect the battery SOC in real time. If the SOC is ≥ 80%, stop charging; otherwise, return to step 3-2.

[0061] In step one above, a second-order RC model of a lithium-ion battery considering thermal effects is used to construct a training environment for a battery fast-charging strategy based on reinforcement learning. Specifically, a battery fast-charging strategy that takes into account the user's travel urgency needs to control the charging speed according to the user's urgency, reducing battery damage and energy loss. To achieve this goal, the state variable s selected in this invention... k and action variable a k The input and output relationships during the training and charging processes are as follows: Figure 1 and Figure 2 As shown:

[0062] s k ={I(k),V(k),SOC(k),T c (k),λ} (1)

[0063] a k =I(k) (2)

[0064] Where I(k) is the charging current at time k, V(k) is the terminal voltage at time k, SOC(k) is the state of charge at time k, and T c (k) represents the battery's internal temperature at time k, and λ is the user's charging urgency coefficient, ranging from 0 to 100%, which the user can choose according to their needs. This example considers the user's charging urgency coefficient to improve the user's charging experience. In actual training, the user's urgency coefficient is transformed into constraints such as voltage, current, and temperature during the charging process, and its three-loop control principle diagram is shown below. Figure 3 As shown, by comparing the voltage, current, and temperature limits of the three-loop control with the observed parameters and limit values ​​at the current moment and inputting them into the reward function, the action at the previous moment can be evaluated, thereby training the reinforcement learning algorithm to achieve optimal charging.

[0065] In step two, this example uses the TD3 reinforcement learning algorithm to train a fast-charging battery model that takes into account the user's travel urgency. The TD3 algorithm employs an Actor-Critic network framework, where the Actor network selects actions based on probability, and the Critic network evaluates the quality of the Actor network's actions based on a Q-value function. The TD3 algorithm integrates the dual Q-learning idea from DQN on top of DDPG, employing a dual network, namely two Critic networks. This effectively suppresses Q-value overestimation during training, improving training performance.

[0066] The TD3 reinforcement learning algorithm was used to train a fast-charging battery model that takes into account the urgency of user travel. Specifically, the process included: first, constructing a second-order RC model of a lithium-ion battery considering thermal effects; then, building a reinforcement learning network and a reward function. During training, the reinforcement learning algorithm provided a corresponding charging current based on the real-time battery state, and the reward function calculated the reward for that action. Subsequently, the reinforcement learning algorithm updated the network based on the action and reward value to find the action that yields the maximum reward. The reward function was designed to satisfy the constraints during the charging process. The trained model was then deployed to a real-world charging environment, providing the optimal charging current based on the real-time battery state.

[0067] To address the goal of fast battery charging that takes into account the urgency of users' travel, a reward function is constructed as follows. This reward function is then used to train the reinforcement learning algorithm. During training, the reinforcement learning algorithm provides the charging current based on the current battery state, and the reward function evaluates the quality of the action. The reward function at time k mainly consists of the following parts:

[0068] R(k) = w1rs (k)+w2r v (k)+w3r t (k)+w4r l (k)+w5r d (k)+w6r i (k)+w7r tar (k) (3)

[0069] Among them, w i (1≤i≤6) are the weighting coefficients under different constraints, r s (k) is the current smoothing reward function. This reward function suppresses sudden current changes during charging, resulting in a smoother charging process. It can be obtained by adjusting the current I at the current moment. k Compared with the current I at the previous moment k-1 The absolute value of the difference is expressed as:

[0070] r s (k)=-|I(k)-I(k-1)| (4)

[0071]

[0072] r v (k) is the voltage reward function, which limits the voltage range during charging. V upp It is the upper limit of battery voltage, V low This refers to the upper limit of the battery voltage. Based on the battery type, the upper and lower limits for this experiment are set at 3.65V and 2.5V respectively. Penalties will be imposed if the voltage is below the lower cutoff voltage or above the upper cutoff voltage to ensure the voltage remains within the constrained range during charging.

[0073]

[0074] r t (k) is the temperature reward function, ensuring that the temperature during charging is within a safe range, T tar This represents the maximum permissible core temperature of the battery under current urgency. Battery temperature is directly related to charging safety and battery damage; the higher the temperature, the worse the battery safety and the greater the battery damage during charging. Here, core temperature is primarily used for limitation. When the core temperature exceeds a set upper threshold, a penalty is imposed to ensure the core temperature remains within the set range. Furthermore, the upper limit of core temperature T... tar This will change with the level of urgency; that is, as the urgency increases, the upper limit of battery temperature T increases. tar It will also rise accordingly. That is...

[0075] T tar =T max -α·λ (7)

[0076] Among them, T max It is the maximum core temperature that the battery can withstand. In this experiment, it was set to 50 degrees. α is the temperature-stress coefficient. As the stress coefficient gradually decreases, the maximum core temperature of the battery also gradually decreases.

[0077] r d (k)=(1-λ)|ΔSoH(k)| (8)

[0078] r d (k) is the battery damage reward function, which minimizes battery damage during the charging process. Where ΔSoH k The decrease in battery SOH is due to charging. λ is the user's travel urgency coefficient. The proportion of battery damage varies under different levels of urgency: in high urgency, the proportion of damage reward to total reward is small; in low urgency, the proportion of damage reward is large. By adjusting the proportion of battery damage reward, and by adjusting the coefficient before the battery damage reward function under different levels of urgency, the proportion of battery damage reward function can be adjusted, thereby controlling the battery charging rate under different levels of urgency.

[0079] r l (k)=(1-λ)P loss (9)

[0080] r l (k) is the energy loss reward function, which minimizes the energy consumption during the charging process. loss It represents the power loss at the current moment, similar to battery damage. The user's travel urgency factor is substituted into the reward function to ensure that the charging mode emphasis can be adjusted according to the user's charging urgency during the training process.

[0081] r i (k)=|I(k)-I tar | (10)

[0082] r i (k) is the current reward function, I tar This represents the maximum charging current of the battery under the current urgency level. This reward function ensures that the battery charging current remains within the safe boundary and approaches it as closely as possible. It can be expressed as the absolute value of the difference between the battery charging current and the maximum allowable charging current. Furthermore, as the urgency level increases, the maximum battery charging current I... tar It will also gradually increase, as shown in Formula 11:

[0083] I tar =I0+ρ·λ (11)

[0084]

[0085] r tar (k) is the reward for achieving the charging target. When the battery SOC is greater than the set value, a relatively large reward is given at once. This reward can guide the charging strategy to achieve the set charging target as soon as possible.

[0086] By incorporating the user's travel urgency coefficient λ as a hyperparameter into the reward functions for temperature, current, battery damage, and energy loss, and considering that the reward functions include penalties for current, voltage, and core temperature (which change with current), the reward functions can adaptively adjust according to the charging urgency. This achieves the following: when the user's urgency is high, the proportion of speed reward increases, and the algorithm prioritizes charging speed; when the user's urgency is low, the proportion of energy loss and battery damage reward functions increases, and the algorithm prioritizes energy loss and battery damage. This invention can provide the optimal charging mode based on the urgency of travel, achieving a balance between charging speed and battery damage / energy loss. After training, it can charge to 80% capacity in as little as 7.33 minutes, with voltage and temperature remaining within thresholds, ensuring battery safety.

[0087] Application Cases

[0088] In the experiment, this invention used an ITECH IT-M3900C bidirectional DC power supply to charge the battery, with the battery model A123 26650ANR26650M1B selected. The host computer communicated with the power supply via LabVIEW software.

[0089] Figures 5-8 This demonstrates how various battery variables change during charging under different levels of urgency. Figure 5 The demonstration showed the change in charging rate during the charging process; as the urgency decreased, the maximum charging rate of the battery gradually decreased. The maximum voltage set in the experiment was 3.6V. Figure 6 It can be seen that none of the voltages exceeded the maximum limiting voltage during the charging process; the experiment was set with a core temperature threshold of 50 degrees Celsius at 100% stress level and 45 degrees Celsius at 90% stress level. Thereafter, for every 10% decrease in stress level, the battery core temperature limit decreased by 1 degree Celsius. Figure 7 It can be seen that the maximum battery core temperature decreases linearly with the decrease in urgency, and never exceeds the threshold for the current urgency level, only reaching the set threshold at the very last moment. Based on the principle that the shorter the time the battery is exposed to high temperatures, the less damage it suffers, this charging method minimizes battery damage while ensuring charging speed. Figure 8 The demonstration shows the SOC change process during charging, with all batteries reaching the set target of 80% SOC.

[0090] Table 1 compares battery charging time with energy loss and battery damage under different levels of urgency, where battery damage is represented by the remaining number of uses. As the user's travel urgency decreases, battery charging time gradually increases, while charging rate, energy loss, and battery damage gradually decrease, with energy loss showing a gradual decline. Under the most urgent conditions, an ultra-fast charge of 80% was achieved in 7.33 minutes, with over 1500 remaining battery uses and minimal damage, demonstrating the rationality of the reward function setting. Assuming the same usage frequency for each charging urgency level, the battery can be used approximately 3127 times, with an average energy loss of approximately 7.03 Wh. Based on a charging frequency of once every three days, the battery can be used for approximately 25.7 years.

[0091] Table 1. Energy loss and battery damage under different levels of urgency.

[0092]

[0093] This embodiment discloses a method for quantifying the charging urgency of electric vehicle power batteries and an intelligent charging method. It defines a charging urgency coefficient λ (0–100%) for the first time and incorporates it into a reward function that considers current, temperature, battery damage, and energy loss, enabling adaptive adjustment of the charging safety boundary based on the urgency level. This invention can provide an optimal charging mode according to different urgency levels, achieving a balance between charging speed and battery damage and energy loss. It not only significantly improves charging speed, achieving 80% charge in as little as 7.33 minutes, but also effectively extends battery life, with an average lifespan of 3127 cycles and a usable lifespan of 25.7 years.

[0094] Example 2

[0095] The purpose of this embodiment is to provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the above-described method.

[0096] Example 3

[0097] The purpose of this embodiment is to provide a computer-readable storage medium.

[0098] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the steps of the above method.

[0099] Example 4

[0100] The purpose of this embodiment is to provide a charging system based on the urgency of power battery charging, including:

[0101] The module for determining the urgency of travel is configured to: determine the urgency of travel;

[0102] The battery core temperature calculation module is configured to: collect real-time status data during the charging process of the power battery, and calculate the battery core temperature according to the battery model. The upper limit of the battery core temperature will change with the degree of travel urgency, that is, as the degree of travel urgency increases, the upper limit of the battery core temperature will also increase accordingly.

[0103] The charging model building module is configured to: build and train a battery fast charging model that takes into account the urgency of users' travel.

[0104] The charging current calculation module is configured to: the battery fast charging model that takes into account the user's travel urgency calculates the optimal charging current of the battery at the current moment based on the travel urgency, the real-time status data collected during the battery charging process, and the core temperature data, and the power battery charges according to the optimal charging current.

[0105] The steps and methods involved in the apparatuses of Embodiments 2, 3, and 4 above correspond to those in Embodiment 1. For specific implementation details, please refer to the relevant description section of Embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood as including any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and enabling the processor to perform any of the methods in this invention.

[0106] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computer devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computer device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. The present invention is not limited to any particular combination of hardware and software.

[0107] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A charging method based on the quantification of the urgency of power battery charging, characterized in that, include: Determine the urgency of the trip; Real-time status data of the power battery during charging is collected, and the battery core temperature is calculated based on the battery model. The upper limit of the battery core temperature will change with the urgency of travel. That is, as the urgency of travel increases, the upper limit of the battery core temperature will also increase accordingly. Establish and train a battery fast charging model that takes into account the urgency of users' travel. The battery fast charging model that takes into account the user's travel urgency calculates the optimal charging current of the battery at the current moment based on the travel urgency, the real-time status data collected during the battery charging process, and the core temperature data. The power battery is then charged according to this optimal charging current. The battery fast charging model that takes into account the urgency of user travel includes a reward function. for: in, ( ) are the weighting coefficients under different constraints. It is a current smoothing reward function. It is a voltage reward function. It is a temperature reward function. It is the battery damage reward function. It is the energy loss reward function. It is the current reward function. It is a reward for achieving the charging goal; The current smoothing reward function It can be expressed as the absolute value of the difference between the current at the current moment and the current at the previous moment: The voltage reward function Represented as: in, It is the upper limit of battery voltage. This is the upper limit of battery voltage; for k Terminal voltage at time; The temperature reward function Represented as: in, T tar This is the maximum allowable core temperature of the battery under the current level of urgency; for k Monitor the internal temperature of the battery at all times; The battery damage reward function Represented as: in, It is the user's travel urgency level; the proportion of battery damage varies depending on the level of urgency. The power loss reward function Represented as: in, It represents the power loss at the current moment; It is the user's travel urgency factor; The current reward function Represented as: in, This is the maximum charging current of the battery under the current urgency. for k Constant charging current; The reward for completing the charging goal Represented as: in, for k The state of charge at any given moment.

2. The charging method based on the quantification of the urgency of power battery charging as described in claim 1, characterized in that, The current smoothing reward function is used to suppress sudden current changes during charging, resulting in a smoother charging process.

3. The charging method based on the urgency of power battery charging as described in claim 1, characterized in that, The voltage reward function is used to limit the voltage range during the charging process. When the voltage is lower than the lower cutoff voltage or higher than the upper cutoff voltage, a penalty will be imposed to ensure that the voltage is within the constrained range during the charging process.

4. The charging method based on the quantification of the urgency of power battery charging as described in claim 1, characterized in that, The temperature reward function is limited by the battery core temperature. When the battery core temperature exceeds the set upper limit threshold, it will be penalized to ensure that the battery core temperature is within the set range.

5. A charging method based on the quantification of the urgency of power battery charging as described in claim 1, characterized in that, The battery damage reward function is used to minimize battery damage during the charging process. By adjusting the proportion of battery damage reward, the battery charging rate under different urgency levels can be controlled.

6. A charging method based on the quantification of the urgency of power battery charging as described in claim 1, characterized in that, The energy loss reward function is used to minimize energy consumption during the charging process. The travel urgency coefficient is substituted into the reward function to ensure that the charging mode can be adjusted according to the charging urgency during the training process.

7. A charging system based on the quantification of the urgency of power battery charging, employing the charging system based on the quantification of the urgency of power battery charging as described in any one of claims 1-6, characterized in that, include: The module for determining the urgency of travel is configured to: determine the urgency of travel; The battery core temperature calculation module is configured to: collect real-time status data during the charging process of the power battery, and calculate the battery core temperature according to the battery model. The upper limit of the battery core temperature will change with the degree of travel urgency, that is, as the degree of travel urgency increases, the upper limit of the battery core temperature will also increase accordingly. The charging model building module is configured to: build and train a battery fast charging model that takes into account the urgency of users' travel. The charging current calculation module is configured to: the battery fast charging model that takes into account the user's travel urgency calculates the optimal charging current of the battery at the current moment based on the travel urgency, the real-time status data collected during the battery charging process, and the core temperature data, and the power battery charges according to the optimal charging current.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it performs the steps of the method described in any one of claims 1-6 above.

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