The invention relates to the technical field of automobile batteries, and discloses a battery service life prediction method, which comprises the following steps: S1, collecting use data of a battery; s2, cleaning and preprocessing the collected battery data, removing abnormal data, filling missing values, and performing standardization or normalization processing; s3, a nonlinear dynamic model is constructed, the change relation of the service life of the battery along with time is described, and the model considers multiple influence factors of the battery; and S4, based on battery historical data. According to the method, the prediction strategy is dynamically adjusted by combining Gaussian process regression and reinforcement learning, and the problem that a static model is poor in adaptability to battery state change in the prior art is solved; bayesian optimization is introduced to optimize hyper-parameters, so that prediction precision and stability are improved; and through a real-time data monitoring and dynamic adjustment mechanism, the real-time performance and the accuracy of the prediction model during battery state change are ensured.
本申请涉及汽车电池技术领域,公开了一种电池使用寿命预测方法,包括以下步骤:S1、采集电池的使用数据;S2、对所采集的电池数据进行清洗和预处理,去除异常数据、填补缺失值,并进行标准化或归一化处理;S3、构建非线性动力学模型,描述电池寿命随时间的变化关系,所述模型考虑电池的多个影响因素;S4、基于电池历史数据。本发明通过结合高斯过程回归与强化学习,实现了动态调整预测策略,解决了现有技术中静态模型对电池状态变化适应性差的问题;引入贝叶斯优化优化超参数,提升了预测精度和稳定性;并通过实时数据监控与动态调整机制,确保了预测模型在电池状态变化时的实时性和准确性。
Method for predicting service life of battery
一种电池使用寿命预测方法
2025-03-11
Patent
Electronic Resource
Chinese
IPC: | G01R Messen elektrischer Größen , MEASURING ELECTRIC VARIABLES / B60L PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES , Antrieb von elektrisch angetriebenen Fahrzeugen / G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen |
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