关键词：
置信规则库
机器学习
电池
健康预测
摘要：
电池以其卓越的储能能力,成为现代科技与生活不可或缺的能源核心,因此对电池老化状态精准诊断和预测是非常重要的。对于一般预测方法而言,电化学模型依赖电池内部机制进行预测,但高度敏感于材料、结构及工作条件变化。机器学习模型依赖高质量大数据与先进算法估算电池健康,但受限于数据质量和算法选择。鉴于上述模型的不足,本文提出了一种创新的电池健康预测模型——基于置信规则库的预测模型。通过构建一系列基于不确定性和模糊性处理的规则,有效应对电池内部状态的复杂性和外部环境的多变性。经实验验证该模型能够提高预测的准确性和可靠性,为锂离子电池健康状态估计及寿命预测领域提供了新的思路和方法,有望在未来能源管理和电池维护中发挥重要作用。Batteries, with their exceptional energy storage capabilities, have emerged as an indispensable energy core in modern technology and daily life. Consequently, accurate diagnosis and prediction of battery aging status are of paramount importance. Traditional prediction methods, such as electrochemical models, rely on the internal mechanisms of batteries for prediction but are highly sensitive to changes in materials, structures, and operating conditions. On the other hand, machine learning models estimate battery health based on high-quality big data and advanced algorithms, yet they are constrained by data quality and algorithm selection. Given the limitations of these models, this paper introduces an innovative battery health prediction model—a prediction model based on a Belief Rule Base. By constructing a series of rules that address uncertainty and fuzziness, it effectively tackles the complexity of the battery’s internal state and the variability of the external environment. Experimental validation demonstrates that this model enhances prediction accuracy and reliability, offering new insights and methodologies for lithium-ion battery health state estimation and lifetime prediction. It is anticipated to play a pivotal role in future energy management and battery maintenance.
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