Vehicle charging control method and system and vehicle

By acquiring multi-source heterogeneous data and using predictive models to optimize the charging power sequence, the problem of the inability to integrate user future scenario predictions in existing technologies is solved. This achieves a combination of optimal battery life and user convenience, extending battery life and avoiding the risk of undercharging.

CN121848978APending Publication Date: 2026-04-14CAMEL GRP WUHAN NEW ENERGY TECH CO LTD
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Patent Information

Application Number
CN202610168236.9
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-02-05
Publication Date
2026-04-14

AI Technical Summary

Technical Problem

Existing charging control solutions cannot proactively incorporate predictions of future user scenarios, resulting in shorter battery life and failing to achieve optimal battery life while satisfying user convenience.

Method used

By acquiring multi-source heterogeneous data and utilizing pre-trained prediction models of parking duration and travel demand, constraints are constructed to minimize cumulative aging costs as the optimization objective. The charging power sequence is then obtained to control the vehicle's charging process.

Benefits of technology

It significantly reduces battery stress and aging under high charge conditions, extends the entire lifespan of the battery system, ensures that the charging process can meet the power demand within the user-defined time window, and avoids the risk of trip interruption.

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Abstract

The invention provides a vehicle charging control method and system and a vehicle, and the method comprises the steps: obtaining multi-source heterogeneous data of the vehicle, the multi-source heterogeneous data comprising user scene data, vehicle travel data and battery reference state data; respectively inputting the multi-source heterogeneous data into a pre-trained parking time length prediction model and a pre-trained travel demand prediction model, and correspondingly obtaining an expected available charging time length and an estimated minimum required electric quantity; constructing a constraint condition based on the expected available charging duration and the estimated minimum required electric quantity, and solving to obtain a charging power sequence by taking the minimum cumulative aging cost as an optimization target; and executing the charging power sequence to control a charging process of the vehicle. According to the method, the minimum estimated cumulative aging cost is taken as an optimization target, and the stress and aging of the battery in the high charge state can be remarkably reduced, so that the full life cycle of the battery system is prolonged.
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