Nowadays, smartphone-based localization, which is the most common way for car drivers to navigate, has attracted considerable attention. In environments where global positioning system (GPS) signals are obstructed, such as indoor parking lots and large tunnels, smartphone-based localization methods still face challenges in accuracy and update frequency. Current methods utilize inertial measurement units (IMUs) to achieve short-term, high-accuracy position estimation. However, most smartphones are equipped with low-precision IMU due to cost constraints, leading to significant cumulative errors over time caused by sensor noise and external interference. Moreover, indoor positioning methods based on Bluetooth low energy (BLE) can achieve long-term and higher precision positioning estimation. However, due to the limitation of smartphone power consumption, real-time scanning and response of BLE cannot be achieved. BLE scans can only be returned after the scanning cycle is completed. Therefore, when the vehicle is moving at high speed, the update frequency of the BLE-based positioning method is lower. Furthermore, due to the asynchronous reception of BLE, motion distortion will occur, seriously affecting positioning accuracy. To address the above problems, we propose a tightly coupled indoor vehicle positioning and navigation algorithm based on smartphone IMU and BLE, named the CFBS (Synthesis of Coordinate transformation and Forward-Backward propagation) algorithm. The coordinate transformation algorithm converts the inertial dynamics from the smartphone to the vehicle, and the backward propagation algorithm compensates for BLE motion distortion and enhances positioning frequency. Finally, IMU bias and position estimation are achieved using a tightly coupled extended Kalman filter (EKF). Our algorithm is tested in real business scenarios to verify its effect, which effectively improves the positioning frequency and accuracy of indoor positioning services (ILBS).