Edge computing, as an emerging paradigm in distributed computing, introduces a novel data access framework for latency-sensitive applications, enabling data retrieval from edge servers situated closer to users, rather than the remote cloud. This significantly reduces data retrieval latency, thereby enhancing the quality of experience. However, the resources of edge servers are highly constrained. Recent studies in erasure coding-based edge data storage have effectively sealed the gap between storage cost and data retrieval latency. Despite these advantages, the highly dynamic edge computing environments and frequent data updates introduce significant challenges in cache replacement for erasure coding-based edge storage systems. Specifically, direct data replacement, similar to traditional replica-based storage methods, may result in insufficient encoded blocks for reconstructing the original data during retrieval, which can increase data retrieval latency and even compromise data availability. In this paper, we identify and address, for the first time, the cache replacement problem in erasure coding-based edge storage systems. We propose a novel cache replacement algorithm, named ECCR, based on Lyapunov optimization, which effectively solves the cache replacement problem in dynamic edge computing environments. Theoretical analysis and extensive experiments on real-world datasets demonstrate the effectiveness and efficiency of the proposed method, which outperforms two state-of-the-art approaches and achieves an average system cost reduction of 49.78%.