A data-driven battery health status evaluation method based on electrochemical models, big data, and mathematical statistics is proposed in this paper to address the issues of long cycle, low efficiency, and high cost in current battery health status detection. This detection method in this study first calculates the Soc-power parameters of the vehicle series based on the log data of valid orders in the history of the vehicle series. Then, combined with the data of each order, the initial evaluation capacity of the order is calculated. Next, an anomaly detection algorithm is used to exclude abnormal orders from the vehicle within the past 60 days. The average of the initial evaluation capacity of the order is used to obtain the final evaluation capacity of the order. Finally, a sliding average is used to obtain the evaluation capacity of the battery, divide the evaluated capacity by the nominal capacity to obtain the battery health status. Based on this technology, the current health status of the battery can be quickly and accurately obtained, while reducing the detection cost and cycle, allowing for real-time detection of the battery's health status.