This paper delves into the realm of electric vehicle (EV) battery data trading markets, focusing on data valuation and revenue allocation. In the face of fast-developing electric mobility, the safety of EV batteries becomes more and more important, driving the need for robust anomaly detection models. For newly found EV companies lacking extensive data, data markets offer a solution, facilitated by trading platforms. We shape this landscape, outline a transaction process involving data buyers, data sellers, and platforms. Our exploration extends to data valuation methodologies, encompassing the classic Shapley value and the least core algorithm. Considering the complicated mechanisms in EV battery, we unveil a deep learning framework for anomaly detection, treating EV batteries as dynamic systems. To explain data value from an economic perspective, we utilize a utility function considering the direct economic costs saved for the EV company to refine the evaluation process. Based on data value, we further propose revenue allocation schemes to allocate part of EV company's revenue to data sellers, offering diverse perspectives on fair and equitable profit distribution. A case study is conducted based on real world EV battery dataset to illustrate how the different revenue allocation schemes allocate payoffs to data sellers.