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基于凸优化的锂离子电池SOC与SOH解耦联合估计

Reduced-Coupling Coestimation of SOC and SOH for Lithium-Ion Batteries Based on Convex Optimization

语言:

中文摘要

本文针对锂离子电池SOC与SOH估计中存在的强耦合与非线性问题,提出了一种新型的解耦联合估计算法。通过引入凸优化方法,简化了观测器网络设计并降低了稳定性分析的复杂性,有效提升了电池状态估计的精度与鲁棒性。

English Abstract

Model-based state-of-charge (SOC) and state-of-health (SOH) estimation for lithium-ion batteries has been widely applied in electrified vehicles, while the SOC and SOH estimators are highly coupled and nonlinear in conventional techniques. This leads to a bulky design of observer network and complicates the stability analyses. In this article, a new reduced-decoupling SOC and SOH coestimation algo...
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SunView 深度解读

该研究直接服务于阳光电源的储能业务核心——电池管理系统(BMS)。在PowerTitan和PowerStack等大规模储能系统中,高精度的SOC/SOH估计是实现电池簇均衡、延长系统寿命及保障安全运行的关键。传统的非线性观测器计算量大且难以收敛,而本文提出的凸优化解耦算法能显著降低BMS计算负载,提升系统在复杂工况下的状态感知能力。建议研发团队将其引入iSolarCloud智能运维平台,通过云端大数据与本地BMS算法协同,优化储能电站的精细化运维与全生命周期管理。