A method and system for solving a predictive voltage sequence based on a mixture model

Abstract

The application discloses a method and system for solving a predicted voltage sequence based on a hybrid model, wherein the method comprises the following steps: collecting an actual current sequence and an actual voltage sequence in a preset identification time period, and collecting a verification current sequence and a verification voltage sequence in a preset verification time period; substituting the actual current sequence into an electrochemical model to obtain a first simulated voltage sequence; obtaining a real electrochemical model according to the first simulated voltage sequence and the actual voltage sequence; substituting the actual current sequence into the real electrochemical model to obtain a second simulated voltage sequence; identifying to-be-identified parameters in an equivalent capacitor model to obtain a real equivalent capacitor model; and inputting the verification current sequence into a hybrid model composed of the real equivalent capacitor model and the real electrochemical model to calculate a predicted voltage sequence. The application models a double-layer capacitor, and mixes the model with a traditional electrochemical model, thereby improving the accuracy of simulating the real working condition of a battery by the model.

Description

Technical Field

[0001] This invention relates to the field of lithium battery modeling, and more specifically to a method for solving predicted voltage sequences based on a hybrid model. Background Technology

[0002] Against the backdrop of global carbon neutrality, the search for clean energy sources to replace petroleum continues to heat up. Solar, tidal, wind, and hydropower are clean and sustainable energy sources, but the controllability of the energy-generating medium is relatively weak. Lithium-ion batteries are currently the next generation of rechargeable batteries, possessing high energy density and cycle life, and are widely used in mobile communications, digital technology, electric vehicles, and energy storage. Establishing physicochemical models for lithium batteries and obtaining simulated values ​​of the physicochemical state quantities within the battery over time and space can provide a clearer understanding and monitoring of the real-time operating status of lithium batteries, thereby better ensuring their economic efficiency, reliability, and safety.

[0003] Current electrochemical models still simplify some physicochemical processes, resulting in discrepancies between simulated and actual battery operating conditions. However, including all physicochemical processes would inevitably lead to excessive computational costs.

[0004] Current mainstream electrochemical models generally lack modeling of the physicochemical processes involved in double-layer capacitance. During battery relaxation, the simulated operating conditions obtained through electrochemical modeling quickly reach a plateau, while the actual operating conditions of a battery include a slow discharge process. This is because the solid-liquid interface of a real battery contains double-layer capacitance, and its physical behavior conforms to the process described by an ideal capacitor. This often leads to a gradual increase in the discrepancy between the simulated operating conditions output by the electrochemical model and the actual battery operating conditions as the number of charge-discharge cycles increases. Summary of the Invention

[0005] To address the technical problem of large simulation errors caused by the lack of double-layer capacitance in traditional electrochemical models, this invention provides a method for solving predicted voltage sequences based on a hybrid model.

[0006] Specifically, the technical solution of the present invention is as follows:

[0007] In a first aspect, the present invention provides a method for solving predicted voltage sequences based on a hybrid model, comprising:

[0008] Collect the actual current sequence and actual voltage sequence of the battery within a preset identification time period, and collect the verification current sequence and verification voltage sequence of the battery within a preset verification time period;

[0009] Substituting the actual current sequence into the electrochemical model, the first simulated voltage sequence is obtained;

[0010] Based on the first simulated voltage sequence and the actual voltage sequence, a real electrochemical model is obtained;

[0011] Substituting the actual current sequence into the real electrochemical model, a second simulated voltage sequence is obtained;

[0012] The difference sequence calculated based on the second simulated voltage sequence and the actual voltage sequence;

[0013] Using the difference sequence and the actual current sequence, the parameters to be identified in the preset equivalent capacitance model are identified to obtain the identified real equivalent capacitance model.

[0014] The verification current sequence is substituted into a hybrid model obtained by mixing the true equivalent capacitance model and the true electrochemical model to calculate the predicted voltage sequence of the battery within the preset verification time period.

[0015] This implementation method establishes an equivalent capacitance model based on the difference sequence and the actual current sequence, and finally performs lithium battery simulation and prediction based on the hybrid model. This improves the accuracy of the electrochemical model in predicting battery operating conditions, and makes the predicted battery sequence more accurately fit the actual battery operating condition sequence. It avoids the distortion of the simulation results of the electrochemical model in the battery charge and discharge relaxation process and the horsetail process. The horsetail process refers to the phenomenon that the voltage drops rapidly during the discharge process of the battery in the low SOC range.

[0016] Implementation methods for some hybrid model-based methods for solving predicted voltage sequences,

[0017] The preset equivalent capacitance model is based on the formula:

[0018]

[0019] Establish;

[0020] Wherein, ΔU is the response voltage of the equivalent capacitance of the battery, I is the actual current sequence or the verification current sequence, C is the capacitive reactance of the equivalent capacitance of the battery, U(t) is the voltage value at time t in the difference sequence, U(t+Δt) is the voltage value at time t+Δt in the difference sequence, t and t+Δt are the times within the preset identification time period, and τ is the relaxation time parameter of the equivalent capacitance.

[0021] This implementation method provides a formula for establishing a preset equivalent capacitance model.

[0022] Implementation methods for some hybrid model-based methods for solving predicted voltage sequences,

[0023] The parameters to be identified include: the equivalent capacitance of the battery and the relaxation time parameter of the equivalent capacitance.

[0024] This implementation method provides the parameters that need to be identified in the equivalent capacitance model.

[0025] In some implementations of methods for solving predicted voltage sequences based on hybrid models, after calculating the predicted voltage sequence of the battery within the preset verification time period, the method further includes:

[0026] If the verification difference between the predicted voltage sequence and the verified voltage sequence exceeds a preset value, the equivalent capacitance of the battery, the relaxation time parameter of the equivalent capacitance, and the iterated electrochemical parameters are re-identified.

[0027] This implementation method recalculates and updates the electrochemical parameters and the parameters to be identified in the equivalent capacitance model when the predicted operating conditions of the battery obtained from the hybrid model differ significantly from the actual battery performance, in order to characterize the aging process of the real battery.

[0028] In some implementation methods for solving predicted voltage sequences based on hybrid models, the step of obtaining a true electrochemical model based on the first simulated voltage sequence and the actual voltage sequence includes:

[0029] Using a heuristic algorithm or Kalman filtering, the electrochemical parameters in the electrochemical model are iterated based on the difference between the first simulated voltage sequence and the actual voltage sequence to obtain a real electrochemical model established by the iterated electrochemical parameters.

[0030] This implementation method provides a way to establish a realistic electrochemical model.

[0031] Secondly, the present invention provides a system for solving predicted voltage sequences based on a hybrid model, comprising:

[0032] The acquisition unit is used to acquire the actual current sequence and the actual voltage sequence of the battery within a preset identification time period, and the verification current sequence and verification voltage sequence of the battery within a preset verification time period; the actual current sequence is sent to the electrochemical model building module and the equivalent capacitance model building module, the actual voltage sequence is sent to the electrochemical model building module, and the verification current sequence and the verification voltage sequence are sent to the hybrid model building module.

[0033] An electrochemical model building module, connected to the acquisition unit, is used to substitute the actual current sequence into the electrochemical model to obtain the first simulated voltage sequence.

[0034] The electrochemical model building module is further configured to obtain a real electrochemical model based on the first simulated voltage sequence and the actual voltage sequence; and send the real electrochemical model to the hybrid model building module.

[0035] The electrochemical model building module is also used to substitute the actual current sequence into the real electrochemical model to obtain a second simulated voltage sequence; and to send the difference sequence calculated based on the second simulated voltage sequence and the actual voltage sequence to the equivalent capacitance model building module.

[0036] The equivalent capacitance model establishment module is used to identify the parameters to be identified in the preset equivalent capacitance model using the difference sequence and the actual current sequence, so as to obtain the identified true equivalent capacitance model; and send the true equivalent capacitance model to the hybrid model establishment module.

[0037] The hybrid model building module is connected to the electrochemical model building module and the equivalent capacitance model building module. It is used to substitute the verification current sequence into the hybrid model obtained by mixing the real equivalent capacitance model and the real electrochemical model, and calculate the predicted voltage sequence of the battery within the preset verification time period.

[0038] This implementation method establishes a true equivalent capacitance module by calculating the difference sequence and the actual current sequence of the battery; and establishes a hybrid model module based on the identified electrochemical model and the true equivalent capacitance module to perform lithium battery simulation and prediction. This improves the accuracy of the electrochemical model in identifying and predicting battery operating conditions and avoids distortion of the simulation results of the electrochemical model in the battery charging and discharging relaxation process and the horsetail process.

[0039] Implementation methods for some systems based on hybrid models to solve for predicted voltage sequences,

[0040] The preset equivalent capacitance model is based on the formula:

[0041]

[0042] Establish;

[0043] Wherein, ΔU is the response voltage of the equivalent capacitance of the battery, I is the actual current sequence or the verification current sequence, C is the capacitive reactance of the equivalent capacitance of the battery, U(t) is the voltage value at time t in the difference sequence, U(t+Δt) is the voltage value at time t+Δt in the difference sequence, t and t+Δt are the times within the preset identification time period, and τ is the relaxation time parameter of the equivalent capacitance.

[0044] This implementation method provides a formula for establishing a preset equivalent capacitance model.

[0045] Implementation methods for some systems based on hybrid models to solve for predicted voltage sequences,

[0046] The parameters to be identified include: the equivalent capacitance of the battery and the relaxation time parameter of the equivalent capacitance.

[0047] This implementation method provides the parameters that need to be identified in the equivalent capacitance model.

[0048] Implementation methods for some systems based on hybrid models to solve for predicted voltage sequences,

[0049] The hybrid model building module is also used to re-identify the equivalent capacitance of the battery, the relaxation time parameter of the equivalent capacitance, and the iterated electrochemical parameters when the verification difference between the predicted voltage sequence and the verification voltage sequence exceeds a preset value.

[0050] When the verification voltage sequence of the battery obtained by the hybrid model building module differs significantly from the actual battery performance, the electrochemical parameters and the parameters to be identified in the equivalent capacitance model are recalculated and updated to characterize the aging process of the real battery.

[0051] Implementation methods for some systems based on hybrid models to solve for predicted voltage sequences,

[0052] The electrochemical models include: single-particle model, single-particle model with electrolyte, and pseudo-two-dimensional model.

[0053] This implementation method provides specific models that can be used for electrochemical models.

[0054] Implementation methods for some systems based on hybrid models to solve for predicted voltage sequences,

[0055] The electrochemical model building module is used to iterate the electrochemical parameters in the electrochemical model based on the difference between the first simulated voltage sequence and the actual voltage sequence using a heuristic algorithm or Kalman filtering method, so as to obtain a real electrochemical model built from the iterated electrochemical parameters.

[0056] Compared with the prior art, the present invention has at least one of the following beneficial effects:

[0057] After identifying the electrochemical parameters in the electrochemical model, this invention establishes an equivalent capacitance model, identifies the parameters to be identified in the equivalent capacitance model, and then combines the electrochemical model with the equivalent capacitance model after parameter identification to generate a hybrid model. The hybrid model can fit the slow charge and discharge process of a real battery due to the existence of double-layer capacitance at the solid-liquid interface, reducing the error between the predicted battery conditions obtained by the model and the actual verified battery conditions, improving the accuracy of the electrochemical model in predicting battery conditions, and avoiding the distortion of the simulation results of the electrochemical model in the battery charge and discharge relaxation process and the horsetail process. Attached Figure Description

[0058] The preferred embodiments will now be described in a clear and easy-to-understand manner, in conjunction with the accompanying drawings, to further explain the above-mentioned characteristics, technical features, advantages, and implementation methods of the present invention.

[0059] Figure 1 This is a flowchart of an embodiment of a method for solving predicted voltage sequences based on a hybrid model provided by the present invention;

[0060] Figure 2 This is a system block diagram of an embodiment of a system for solving predicted voltage sequences based on a hybrid model provided by the present invention.

[0061] The following are the definitions of the reference numerals: 10 – Acquisition unit; 20 – Electrochemical model establishment module; 30 – Equivalent capacitance model establishment module; 40 – Hybrid model establishment module. Detailed Implementation

[0062] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the specific implementation methods of the present invention will be described below with reference to the accompanying drawings. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings and other implementation methods can be obtained based on these drawings without any creative effort.

[0063] To keep the drawings concise, each figure only schematically shows the parts relevant to the invention, and these do not represent the actual structure of the product. Furthermore, to facilitate understanding, in some figures, only one of components with the same structure or function is schematically depicted, or only one is labeled. In this document, "one" not only means "only one," but can also mean "more than one."

[0064] It should also be further understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0065] In this document, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0066] Furthermore, in the description of this application, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0067] In one embodiment, refer to the appendix to the specification. Figure 1 The present invention provides a method for solving predicted voltage sequences based on a hybrid model, comprising:

[0068] S110: Collect the actual current sequence and actual voltage sequence of the battery within a preset identification time period, and collect the verification current sequence and verification voltage sequence of the battery within a preset verification time period.

[0069] In step S110, the actual current sequence and the actual voltage sequence of the battery collected during the preset identification time period are used to test the electrochemical parameters of the electrochemical model and the parameters to be identified in the equivalent capacitance model. The preset verification time period can be used to verify whether the operating condition sequence output by the hybrid model established by the current electrochemical parameters and the parameters to be identified in the equivalent capacitance model is accurate. Preferably, the preset verification time period can be set to collect a verification current sequence and a verification voltage sequence at a fixed period.

[0070] S120, substitute the actual current sequence into the electrochemical model to obtain the first simulated voltage sequence.

[0071] S130, based on the first simulated voltage sequence and the actual voltage sequence, obtain the true electrochemical model.

[0072] Steps S120 and S130 establish a real electrochemical model, and the electrochemical parameters of the real electrochemical model can usually be obtained using the Kalman filter method or heuristic algorithm.

[0073] S140, substitute the actual current sequence into the real electrochemical model to obtain the second simulated voltage sequence.

[0074] S150, calculate the difference sequence based on the second simulated voltage sequence and the actual voltage sequence.

[0075] In step S150, a difference sequence is obtained by subtracting the voltage values ​​at each time point in the second simulated voltage sequence from the actual voltage sequence.

[0076] S160 uses the difference sequence and the actual current sequence to identify the parameters to be identified in the preset equivalent capacitance model, and obtains the identified real equivalent capacitance model.

[0077] In step S160, a preset equivalent capacitance model is identified based on the difference sequence and the actual current sequence. This model compensates for the lack of double-layer capacitance modeling at the solid-liquid interface in the electrochemical model, resulting in a faster charging and discharging speed for the simulated battery operating conditions compared to the actual conditions of a faster battery.

[0078] S170, substitute the verification current sequence into the hybrid model obtained by mixing the real equivalent capacitance model and the real electrochemical model, and calculate the predicted voltage sequence of the battery within the preset verification time period.

[0079] In step S170, during the verification process, the verification current sequence is first input into the real electrochemical model in the hybrid model to obtain the second simulated operating condition sequence during verification; the verification difference sequence of the second simulated voltage sequence and the verification voltage sequence, and the verification current sequence are substituted into the real equivalent capacitance model to obtain the historical accumulation of the equivalent capacitance response voltage; the historical accumulation of the equivalent capacitance response voltage is added to the second simulated operating condition sequence to finally obtain the predicted voltage sequence, which can accurately fit the verification voltage sequence.

[0080] This embodiment establishes a realistic electrochemical model based on the actual voltage / current sequence of the battery. Then, it establishes an equivalent capacitance model based on the difference between the simulated voltage sequence and the actual voltage sequence, and the actual current sequence. Finally, it performs lithium battery simulation and prediction based on the hybrid model. This improves the accuracy of the electrochemical model in predicting battery operating conditions, enabling the predicted battery sequence to more accurately fit the actual operating condition sequence. It avoids distortion in the simulation results of the electrochemical model during the battery's charge / discharge relaxation process and the "horsetail" process. The horsetail process refers to the phenomenon where the voltage drops rapidly during battery discharge in the low SOC range.

[0081] This embodiment provides a method for solving the predicted voltage sequence based on a hybrid model, building upon the previous embodiments.

[0082] The preset equivalent capacitance model is based on the formula:

[0083]

[0084] Establish;

[0085] Where ΔU is the response voltage of the battery's equivalent capacitance caused by each current segment, I is the actual current sequence or verification current sequence, C is the capacitive reactance of the battery's equivalent capacitance, U(t) is the voltage value at time t in the difference sequence, U(t+Δt) is the voltage value at time t+Δt in the difference sequence, t and t+Δt are the times within the preset identification time period, and τ is the relaxation time parameter of the equivalent capacitance.

[0086] This embodiment provides a formula for establishing a preset equivalent capacitance model, where U(t) is the voltage value of the equivalent capacitance at time t, that is, the voltage value at time t in the difference sequence.

[0087] This embodiment provides a method for solving the predicted voltage sequence based on a hybrid model, building upon the previous embodiments.

[0088] The parameters to be identified include: the battery's equivalent capacitance and the relaxation time parameter of the equivalent capacitance.

[0089] This embodiment provides the parameters that need to be identified in the equivalent capacitance model, including the equivalent capacitance and relaxation time parameters. The equivalent capacitance and relaxation time parameters of batteries in the same batch are usually not much different, but as the battery ages after use, the equivalent capacitance and relaxation time parameters will change, and the equivalent capacitance and relaxation time parameters of different batteries will also be different.

[0090] This embodiment provides a method for solving the predicted voltage sequence based on a hybrid model, building upon the previous embodiments.

[0091] After calculating the predicted voltage sequence of the battery within the preset verification time period, the method further includes:

[0092] If the verification difference between the predicted voltage sequence and the verified voltage sequence exceeds the preset value, the capacitive reactance of the battery's equivalent capacitance, the relaxation time parameter of the equivalent capacitance, and the iterated electrochemical parameters are re-identified.

[0093] In this embodiment, when the predicted operating conditions of the battery obtained from the hybrid model differ significantly from the actual battery performance, the electrochemical parameters and the parameters to be identified in the equivalent capacitance model are recalculated and updated to characterize the aging process of the real battery.

[0094] This embodiment provides a method for solving predicted voltage sequences based on a hybrid model, building upon the aforementioned embodiments. The method for obtaining a true electrochemical model based on a first simulated voltage sequence and the actual voltage sequence includes:

[0095] Using heuristic algorithms or Kalman filtering, the electrochemical parameters in the electrochemical model are iterated based on the difference between the first simulated voltage sequence and the actual voltage sequence, resulting in a true electrochemical model established by the iterated electrochemical parameters.

[0096] This embodiment iterates the electrochemical parameters continuously using a heuristic algorithm or Kalman filtering to obtain the difference value; preferably, a threshold value for the difference value can be preset. When the difference value is less than the threshold value, the electrochemical parameters of the current cycle are output, and a real electrochemical model is established based on the electrochemical parameters.

[0097] The difference value can be calculated using the difference calculation formula. Calculate, where MSE is the difference value, n is the number of sampling time points, i is a positive integer, usim,i is the voltage value of the first analog voltage sequence at the i-th time point, and u real,i a is the voltage value of the actual voltage sequence at time point i. i It is the preset voltage weight at time point i.

[0098] In one embodiment, refer to the appendix to the specification. Figure 2 This invention provides a system for solving predicted voltage sequences based on a hybrid model, comprising:

[0099] The acquisition unit 10 is used to acquire the actual current sequence and actual voltage sequence of the battery within a preset identification time period, and the verification current sequence and verification voltage sequence of the battery within a preset verification time period; the actual current sequence is sent to the electrochemical model establishment module 20 and the equivalent capacitance model establishment module 30, the actual voltage sequence is sent to the electrochemical model establishment module 20, and the verification current sequence and verification voltage sequence are sent to the hybrid model establishment module 40.

[0100] The electrochemical model building module 20 is connected to the acquisition unit 10 and is used to substitute the actual current sequence into the electrochemical model to obtain the first simulated voltage sequence.

[0101] The electrochemical model building module 20 is also used to iterate the electrochemical parameters in the electrochemical model based on the difference between the first simulated voltage sequence and the actual voltage sequence using a heuristic algorithm or Kalman filtering method, so as to obtain a real electrochemical model built from the iterated electrochemical parameters; and send the real electrochemical model to the hybrid model building module 40; the electrochemical model includes: single-particle model, single-particle model with electrolyte, and pseudo two-dimensional model.

[0102] The electrochemical model building module 20 is also used to substitute the actual current sequence into the real electrochemical model to obtain the second simulated voltage sequence; and to send the difference sequence calculated based on the second simulated voltage sequence and the actual voltage sequence to the equivalent capacitance model building module 30.

[0103] The equivalent capacitance model establishment module 30 is used to identify the parameters to be identified in the preset equivalent capacitance model using the difference sequence and the actual current sequence, and obtain the identified real equivalent capacitance model; and send the real equivalent capacitance model to the hybrid model establishment module 40.

[0104] The hybrid model building module 40 is connected to the electrochemical model building module 20 and the equivalent capacitance model building module 30. It is used to substitute the verification current sequence into the hybrid model obtained by mixing the real equivalent capacitance model and the real electrochemical model, and calculate the predicted voltage sequence of the battery within the preset verification time period.

[0105] In this embodiment, an electrochemical model building module 20 is used to establish a realistic electrochemical model based on the actual voltage / current sequence of the battery. Next, an equivalent capacitance model building module 30 is used to establish an equivalent capacitance model based on the difference sequence and the actual current sequence. Finally, lithium battery simulation and prediction are performed using a hybrid model building module 40. This improves the accuracy of the electrochemical model in identifying and predicting battery operating conditions, making the predicted battery sequence more accurately fit the actual operating condition sequence, and avoiding distortion of the simulation results of the electrochemical model in the battery charging and discharging relaxation process and the horsetail process.

[0106] This embodiment provides a system for solving predicted voltage sequences based on a hybrid model, building upon the previous embodiment.

[0107] The preset equivalent capacitance model is based on the formula:

[0108]

[0109] Establish;

[0110] Where ΔU is the response voltage of the battery's equivalent capacitance caused by each current segment, I is the actual current sequence or verification current sequence, C is the capacitive reactance of the battery's equivalent capacitance, U(t) is the voltage value at time t in the difference sequence, U(t+Δt) is the voltage value at time t+Δt in the difference sequence, t and t+Δt are the times within the preset identification time period, and τ is the relaxation time parameter of the equivalent capacitance.

[0111] This embodiment provides a formula for establishing a preset equivalent capacitance model, where U(t) is the voltage value of the equivalent capacitance at time t, that is, the voltage value at time t in the difference sequence.

[0112] This embodiment provides a system for solving predicted voltage sequences based on a hybrid model, building upon the previous embodiment.

[0113] The parameters to be identified include: the battery's equivalent capacitance and the relaxation time parameter of the equivalent capacitance.

[0114] This embodiment provides that the parameters to be identified in the equivalent capacitance model establishment module 30 include equivalent capacitance and relaxation time parameters. The equivalent capacitance and relaxation time parameters of batteries in the same batch are usually not much different, but as the battery ages after use, the equivalent capacitance and relaxation time parameters will change, and the equivalent capacitance and relaxation time parameters of different batteries will also be different.

[0115] This embodiment provides a system for solving predicted voltage sequences based on a hybrid model, building upon the previous embodiment.

[0116] The hybrid model building module 40 is also used to re-identify the battery's equivalent capacitance, the relaxation time parameter of the equivalent capacitance, and the iterated electrochemical parameters when the verification difference between the predicted voltage sequence and the verification voltage sequence exceeds a preset value.

[0117] In this embodiment, when the predicted operating conditions of the battery obtained by the hybrid model building module 40 differ significantly from the actual battery performance, the electrochemical parameters and the parameters to be identified in the equivalent capacitance model are recalculated and updated to characterize the aging process of the real battery.

[0118] It should be noted that the above embodiments can be freely combined as needed. The above are merely preferred embodiments of the present invention. It should be pointed out that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for solving predicted voltage sequences based on a hybrid model, characterized in that, include: Collect the actual current sequence and actual voltage sequence of the battery within a preset identification time period, and collect the verification current sequence and verification voltage sequence of the battery within a preset verification time period; Substituting the actual current sequence into the electrochemical model, the first simulated voltage sequence is obtained; Based on the first simulated voltage sequence and the actual voltage sequence, a real electrochemical model is obtained; Substituting the actual current sequence into the real electrochemical model, a second simulated voltage sequence is obtained; The difference sequence is calculated based on the second simulated voltage sequence and the actual voltage sequence; Using the difference sequence and the actual current sequence, the parameters to be identified in the preset equivalent capacitance model are identified to obtain the identified real equivalent capacitance model. Substitute the verification current sequence into the hybrid model obtained by mixing the real equivalent capacitance model and the real electrochemical model, and calculate the predicted voltage sequence of the battery within the preset verification time period; The parameters to be identified include: the equivalent capacitance of the battery and the relaxation time parameter of the equivalent capacitance; The preset equivalent capacitance model is established based on the following formula: ， ； in, The response voltage is the equivalent capacitance of the battery, I is the actual current sequence or the verification current sequence, and C is the capacitance value of the equivalent capacitance of the battery. Let be the voltage value at time t in the difference sequence. For the difference sequence Voltage value at time, , τ represents the time within the preset identification time period, and τ is the relaxation time parameter of the equivalent capacitance.

2. The method for solving the predicted voltage sequence based on a hybrid model according to claim 1, characterized in that, After calculating the predicted voltage sequence of the battery within the preset verification time period, the method further includes: If the verification difference between the predicted voltage sequence and the verified voltage sequence exceeds a preset value, the capacitance value of the battery's equivalent capacitance, the relaxation time parameter of the equivalent capacitance, and the iterated electrochemical parameters are re-identified.

3. A method for solving predicted voltage sequences based on a hybrid model according to claim 1 or 2, characterized in that, The process of obtaining a true electrochemical model based on the first simulated voltage sequence and the actual voltage sequence includes: Using a heuristic algorithm or Kalman filtering, the electrochemical parameters in the electrochemical model are iterated based on the difference between the first simulated voltage sequence and the actual voltage sequence to obtain a real electrochemical model established by the iterated electrochemical parameters.

4. A system for solving predicted voltage sequences based on a hybrid model, characterized in that, include: The acquisition unit is used to acquire the actual current sequence and the actual voltage sequence of the battery within a preset identification time period, and the verification current sequence and verification voltage sequence of the battery within a preset verification time period; the actual current sequence is sent to the electrochemical model building module and the equivalent capacitance model building module, the actual voltage sequence is sent to the electrochemical model building module, and the verification current sequence and the verification voltage sequence are sent to the hybrid model building module. An electrochemical model building module, connected to the acquisition unit, is used to substitute the actual current sequence into the electrochemical model to obtain the first simulated voltage sequence. The electrochemical model building module is further configured to obtain a real electrochemical model based on the first simulated voltage sequence and the actual voltage sequence; and send the real electrochemical model to the hybrid model building module. The electrochemical model building module is also used to substitute the actual current sequence into the real electrochemical model to obtain a second simulated voltage sequence; and to send the difference sequence calculated based on the second simulated voltage sequence and the actual voltage sequence to the equivalent capacitance model building module. The equivalent capacitance model establishment module is used to identify the parameters to be identified in the preset equivalent capacitance model using the difference sequence and the actual current sequence, so as to obtain the identified true equivalent capacitance model; and send the true equivalent capacitance model to the hybrid model establishment module. The hybrid model building module is connected to the electrochemical model building module and the equivalent capacitance model building module, and is used to substitute the verification current sequence into the hybrid model obtained by mixing the real equivalent capacitance model and the real electrochemical model, and calculate the predicted voltage sequence of the battery within the preset verification time period. The parameters to be identified include: the equivalent capacitance of the battery and the relaxation time parameter of the equivalent capacitance; The preset equivalent capacitance model is based on the formula: ， Establish; in, The response voltage is the equivalent capacitance of the battery, I is the actual current sequence or the verification current sequence, and C is the capacitance value of the equivalent capacitance of the battery. Let be the voltage value at time t in the difference sequence. For the difference sequence Voltage value at time, , τ represents the time within the preset identification time period, and τ is the relaxation time parameter of the equivalent capacitance.

5. The system for solving predicted voltage sequences based on a hybrid model according to claim 4, characterized in that, The hybrid model building module is also used to re-identify the equivalent capacitance value of the battery, the relaxation time parameter of the equivalent capacitance, and the iterated electrochemical parameters when the verification difference value between the predicted voltage sequence and the verification voltage sequence exceeds a preset value.

6. A system for solving predicted voltage sequences based on a hybrid model according to claim 4 or 5, characterized in that, The electrochemical model building module is used to iterate the electrochemical parameters in the electrochemical model based on the difference between the first simulated voltage sequence and the actual voltage sequence using a heuristic algorithm or Kalman filtering method, so as to obtain a real electrochemical model built from the iterated electrochemical parameters.

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