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Unknown distributed incipient thermal fault detection and localization are vital to the safe operation of batteries while they have not been given sufficient attention in existing works compared to the studies on estimation of State of Charge (SoC) as well as State of Health (SoH). In order to fill this gap, a backstepping-based fault localization filter (FLF) is presented. Generally, full-state temperature measurement is required to achieve fault localization, which is impossible in applications. However, with the help of interpolation-based approximation, the required number of sensors decreases from infinity to only a few, which guarantees the usability of FLF. A comprehensive methodology framework, including the FLF design, residual evaluation in a distributed manner, and threshold computation, is introduced to guarantee reliable and robust performance in a real-time pattern. Theoretic analysis as well as experiment validations are presented to for validation.