Battery pack life prediction method and device, storage medium and computer device

By randomly generating the initial capacity and temperature values ​​of individual battery cells in the battery pack, and combining this with a capacity decay model, the performance changes of the battery pack under different conditions are simulated. This solves the problem of inaccurate battery pack life prediction in existing technologies and achieves more accurate life assessment.

CN121208650BActive Publication Date: 2026-07-21JIANGSU ZENIO NEW ENERGY BATTERY TECH CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
JIANGSU ZENIO NEW ENERGY BATTERY TECH CO LTD
Filing Date
2025-11-20
Publication Date
2026-07-21

AI Technical Summary

Technical Problem

Existing technologies struggle to accurately predict the lifespan of battery packs because they rely on extreme scenarios for individual cells within the pack as a lower limit. However, extreme scenarios are rare in real-world applications, leading to inaccurate predictions.

Method used

By randomly generating the initial capacity and operating temperature values ​​of individual cells in the battery pack, and combining them with a capacity decay prediction model, the performance of the battery pack under different initial conditions and temperature rise conditions is simulated. The capacity retention rate of the battery pack is determined by randomly combining the simulated cell data.

Benefits of technology

It improves the accuracy and comprehensiveness of battery pack life prediction, better reflects the performance changes of battery packs in actual use, and provides reliable guidance for the design, optimization, maintenance and management of battery packs.

✦ Generated by Eureka AI based on patent content.

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Abstract

The application relates to the technical field of batteries, and particularly provides a battery pack life prediction method and device, a storage medium and computer equipment. According to the initial capacity probability distribution, n*m initial capacity values can be randomly generated to simulate the situation that the initial capacity of a single battery cell in an actual battery pack has a certain randomness. Moreover, n*m working temperature values can be randomly generated to simulate the working conditions of the battery pack under different temperature rise conditions. The application can randomly combine the plurality of initial capacity values and the plurality of working temperature values to obtain cell data of a plurality of simulation battery cells, and determine the capacity retention rate of n battery packs. In this way, the application can ensure that the initial capacity and temperature rise of each simulation battery cell have randomness, so that the cell data of the simulation battery cell is closer to the actual working condition, thereby comprehensively evaluating the performance of the battery pack under different initial states and different working conditions, and more accurately predicting the performance and life of the battery pack.
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