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基于多指标的储能技术健康诊断与预测:模糊综合评价与改进多变量灰色模型

Multiple Indicators-Based Health Diagnostics and Prognostics for Energy Storage Technologies Using Fuzzy Comprehensive Evaluation and Improved Multivariate Grey Model

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中文摘要

针对电池内部电化学反应复杂且不可观测导致的健康状态(SOH)预测难题,本文提出了一种基于模糊综合评价与改进多变量灰色模型的电池健康评估框架。该方法通过多指标融合,实现了对储能电池健康状态的精准诊断与寿命预测,有效提升了储能系统的可靠性与运行效率。

English Abstract

Precise health diagnostics and prognostics for batteries, which can improve the reliability and efficiency of energy storage technologies are significant. It is still a challenge to predict and diagnose state-of-health (SOH) of batteries due to the complicated and unobservable electrochemical reaction inside the batteries. In this article, a novel battery health estimation framework based on an op...
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SunView 深度解读

该研究直接服务于阳光电源PowerTitan和PowerStack等大型储能系统。精准的SOH预测是提升BMS核心竞争力的关键,能够显著优化电池簇的均衡管理,延长系统循环寿命。建议将该算法集成至iSolarCloud智能运维平台,通过多指标融合的健康诊断模型,实现对电站侧储能资产的精细化运维,提前识别电池性能衰减风险,从而降低运维成本并提升电站调峰调频的可靠性。该技术对于提升阳光电源储能产品的全生命周期价值具有重要参考意义。