Cross-scale modeling-driven multi-state estimation framework for lithium-ion batteries with integrated distributed thermal sensing

Accurate monitoring of the internal operational statuses is crucial for lithium-ion battery (LIB) management. Traditional battery management techniques are fundamentally constrained by the limited availability of measurable parameters (i.e., voltage, load current and surface temperature) and rely on simplistic cell modeling for online estimation of macroscopic states, which not only compromises their accuracy and reliability but also limit detailed insights into the internal states. This study focuses on addressing the scarcity of measurable signals in LIBs by utilizing emerging fiber optic technology for embedded temperature distribution sensing. Leveraging both internal and external thermal distribution profiles during operation, a hierarchical cross-scale modeling and state estimation framework is proposed, where overall electro-thermal behavior is modeled in the macroscale layer and localized non-uniform heat generation and dissipation in the mesoscale layer, accompanied by active integration through bidirectional parameter interactions. On this basis, state parameter estimators are employed at both layers to enable joint estimation of the macroscale heat generation rate, state of charge and maximum usage capacity as well as the mesoscale axial heat generation distribution. Experimental results demonstrate that empowered by distributed thermal perception, the proposed hierarchical framework not only achieves superior accuracy and reliability in macroscale state estimation compared to conventional methods but also achieves first-reported real-time monitoring of axial heat generation profiles at the mesoscale, effectively bridging the gap between global LIB performance assessment and localized thermal management requirements.