CN-121995237-A - Online prediction and evaluation method for residual life of power battery

Abstract

The invention relates to the technical field of power battery management and discloses a power battery residual life online prediction and assessment method which comprises the steps of S1, obtaining operation data of a power battery pack and monitoring local maintenance or local replacement events, S2, determining a replacement object and an unrechanged object, S3, constructing a history inheritance mark, S4, constructing a mixed age life state, S5, determining constraint relation of all objects to the whole pack life, S6, performing residual life online prediction to obtain a residual life prediction result, and S7, assessing the residual life prediction result to obtain a residual life assessment result. When the battery is maintained and replaced, the replaced object and the unrechanged object are distinguished, a history inheritance mark is constructed, and life history information or a life initial state is inherited according to the history inheritance mark, so that a mixed life state after local maintenance or local replacement is formed, and the subsequent residual life on-line prediction and evaluation has a definite object boundary and state basis.

Inventors

• NI XIAOYU
• CHENG FANGDONG

Assignees

• 苏州晟建元科技有限公司

Dates

Publication Date

20260508

Application Date

20260321

Claims (10)

1. 1. The method for predicting and evaluating the residual life of the power battery on line is characterized by comprising the following steps: S1, acquiring operation data of a power battery pack, and monitoring whether the power battery pack is subjected to local maintenance or local replacement; s2, under the condition that the local maintenance or the local replacement event is monitored, determining a replacement object and an unrechanged object in the power battery pack; The replacement object is a battery cell, a module or a battery unit which is replaced, and the non-replacement object is a battery cell, a module or a battery unit which is not replaced; s3, constructing a history inheritance mark based on the replaced object and the unrechanged object, wherein the history inheritance mark is used for representing whether the corresponding object inherits service life history information before maintenance or replacement; S4, inheriting service life history information before maintenance or replacement on the non-replaced object according to the history inheritance mark, and resetting a service life initial state or a reconstructed service life initial state on the replaced object to obtain a mixed age service life state after local maintenance or local replacement; S5, determining a constraint relation of all objects in the power battery pack to the whole pack life based on the mixed age life state and the operation data; s6, performing online prediction of the residual life of the power battery pack according to the constraint relation to obtain a residual life prediction result of the power battery pack; and S7, based on the residual life prediction result and the mixed age life state, evaluating the residual life prediction result of the power battery pack to obtain a residual life evaluation result.
2. 2. The method for online predicting and evaluating the remaining life of a power battery according to claim 1, wherein the step S1 specifically comprises: Acquiring operation data of each battery cell, module or battery unit in a power battery pack, wherein the operation data comprises voltage data, current data, temperature data and state of charge data; acquiring maintenance records, configuration records, object coding information or connection relation information corresponding to the power battery pack; and monitoring whether the power battery pack is subjected to local maintenance or local replacement according to the operation data and the maintenance record, the configuration record, the object coding information or the connection relation information.
3. 3. The method for online predicting and evaluating the remaining life of a power battery according to claim 1, wherein the step S2 specifically comprises: after the power battery pack is monitored to have a local maintenance or local replacement event, identifying a battery cell, a module or a battery unit in the power battery pack, wherein the battery cell, the module or the battery unit has object coding change, connection relation change, maintenance mark change or operation parameter mutation; Determining the identified battery cell, module or battery unit as a replacement object; And determining the battery cells, modules or battery units except the replacement object in the power battery pack as the non-replacement object.
4. 4. The method for online predicting and evaluating the remaining life of a power battery according to claim 3, wherein the step S3 specifically comprises: Constructing a first history inheritance marker for the replacement object; constructing a second history inheritance marker for the unrechanged object; The first history inheritance mark is used for representing that the corresponding object does not inherit the service life history information before local maintenance or local replacement, and the second history inheritance mark is used for representing that the corresponding object inherits the service life history information before local maintenance or local replacement; The history inheritance mark comprises an object identification field and an inheritance state field, wherein the object identification field is used for representing a corresponding battery cell, module or battery unit, and the inheritance state field is used for representing a life history information inheritance mode of a corresponding object; and generating the historical inheritance mark according to the object coding information, the connection relation information or the maintenance record, and updating the inheritance state field according to the operation data in the subsequent operation process.
5. 5. The method for online predicting and evaluating the remaining life of a power battery according to claim 4, wherein the step S4 specifically comprises: according to the second history inheritance mark, inheriting the service life history information before local maintenance or local replacement to the unrechanged object; Resetting the life initial state of the replacement object according to the first history inheritance mark, or reconstructing the life initial state according to the initial operation data of the replacement object after local maintenance or local replacement; combining the life history information inherited by the unrechanged object with the life initial state of the changed object to construct a mixed age life state after local maintenance or local change; the history inheritance mark is used as a control parameter for constructing the mixed age life state and used for controlling the introduction mode of the life history information of the corresponding object.
6. 6. The method for online predicting and evaluating the residual life of a power battery according to claim 5, wherein reconstructing the life initial state according to the initial operation data of the replacement object after the local maintenance or the local replacement specifically comprises: Acquiring first wheel operation data of the replacement object after local maintenance or local replacement; Extracting voltage change rate, current response characteristics and temperature response characteristics according to the first-round operation data; according to a mapping relation between a preset life state parameter and an operation characteristic, reversely solving the voltage change rate, the current response characteristic and the temperature response characteristic, and determining a life initial state parameter of the replacement object; and constructing the life initial state of the replacement object according to the life initial state parameter.
7. 7. The method for online predicting and evaluating the remaining life of a power battery according to claim 1, wherein the step S5 specifically comprises: determining life state parameters corresponding to all objects in the power battery pack based on the mixed age life state; constructing a constraint function based on life state parameters corresponding to each object, or constructing a fuzzy membership function based on life state parameters corresponding to each object; And comparing the life state parameters corresponding to the objects with preset life constraint conditions, and determining the constraint relation of the objects in the power battery pack to the whole pack life according to the constraint function or the fuzzy membership function.
8. 8. The method for online predicting and evaluating the remaining life of a power battery according to claim 1, wherein the step S6 specifically comprises: determining a constraint object which plays a role in constraining the residual life of the power battery pack based on the constraint relation; Acquiring life state parameters and operation data corresponding to the constraint objects; Determining the residual life corresponding to the constraint object according to the life state parameter and the operation data corresponding to the constraint object; obtaining a residual life prediction result of the power battery pack according to the residual life corresponding to the constraint object; The history inheritance mark and the mixed age life state are used as inputs of online prediction of the residual life and used for controlling participation modes of life history information of different objects.
9. 9. The method for online predicting and evaluating the remaining life of a power battery according to claim 1, wherein the step S7 specifically comprises: Determining an evaluation parameter corresponding to the residual life prediction result based on the mixed age life state; comparing the evaluation parameters with preset evaluation conditions; Obtaining an evaluation result corresponding to the residual life prediction result according to the comparison result; The evaluation parameters are calculated based on life state parameters and change trends of the objects in the mixed age life state.
10. 10. The method for online predicting and evaluating the remaining life of a power battery according to claim 9, wherein the evaluation result comprises an available mark, an early warning mark or a trusted grade mark.

Description

Online prediction and evaluation method for residual life of power battery Technical Field The invention relates to the technical field of power battery management, in particular to an online prediction and evaluation method for the residual life of a power battery. Background The power battery is a core energy component in the fields of new energy automobiles, electric energy storage systems and the like, the running state and the residual life of the power battery directly influence the safety and the use reliability of the system, the residual life of the power battery is predicted and evaluated on line, the time or the cycle number of the battery which can be continuously used in the current state is predicted by analyzing the running data of the battery, the prediction result is evaluated to assist in operation and maintenance decision and safety management, in practical application, the power battery usually runs in a battery pack formed by battery monomers, modules or battery units, the service life state of the power battery has the characteristic of evolving along with time, and the existing power battery residual life on-line prediction method models the whole battery or each formed object on the basis of the historical running data and continuously updates the prediction result in the running process. However, in the current technology, when the power battery pack is locally maintained or locally replaced, a replaced object and an unremoved object exist in the battery pack at the same time, the existing method generally still processes according to a unified object, uniformly inherits historical life information or uniformly resets life states of all objects, and is difficult to accurately distinguish the life state boundaries of different objects, so that the accuracy of the online prediction and evaluation results of the residual life of the power battery is affected. Disclosure of Invention Aiming at the defects of the prior art, the invention provides an online prediction and evaluation method for the residual life of a power battery, which solves the problem that the life state of the power battery is difficult to accurately distinguish after local maintenance or replacement. In order to achieve the purpose, the invention is realized by the following technical scheme that the method for predicting and evaluating the residual life of the power battery on line comprises the following steps: S1, acquiring operation data of a power battery pack, and monitoring whether the power battery pack is subjected to local maintenance or local replacement; s2, under the condition that the local maintenance or the local replacement event is monitored, determining a replacement object and an unrechanged object in the power battery pack; The replacement object is a battery cell, a module or a battery unit which is replaced, and the non-replacement object is a battery cell, a module or a battery unit which is not replaced; s3, constructing a history inheritance mark based on the replaced object and the unrechanged object, wherein the history inheritance mark is used for representing whether the corresponding object inherits service life history information before maintenance or replacement; S4, inheriting service life history information before maintenance or replacement on the non-replaced object according to the history inheritance mark, and resetting a service life initial state or a reconstructed service life initial state on the replaced object to obtain a mixed age service life state after local maintenance or local replacement; S5, determining a constraint relation of all objects in the power battery pack to the whole pack life based on the mixed age life state and the operation data; s6, performing online prediction of the residual life of the power battery pack according to the constraint relation to obtain a residual life prediction result of the power battery pack; and S7, based on the residual life prediction result and the mixed age life state, evaluating the residual life prediction result of the power battery pack to obtain a residual life evaluation result. Preferably, the step S1 specifically includes: Acquiring operation data of each battery cell, module or battery unit in a power battery pack, wherein the operation data comprises voltage data, current data, temperature data and state of charge data; acquiring maintenance records, configuration records, object coding information or connection relation information corresponding to the power battery pack; and monitoring whether the power battery pack is subjected to local maintenance or local replacement according to the operation data and the maintenance record, the configuration record, the object coding information or the connection relation information. Preferably, the step S2 specifically includes: after the power battery pack is monitored to have a local maintenance or local replacement event, identifying a battery cell, a module or a bat