The proliferation of electric vehicles (EVs) has exacerbated the shortage of charging stations. Vehicle-to-vehicle (V2V) energy sharing schemes, known for their cost-effectiveness and flexibility, give rise to privacy concerns, making the protection of user privacy a pressing issue requiring attention. In this article, we propose a solution for V2V energy sharing(VES) scheme based on the local differential privacy(LDP) method, which does not rely on a trusted third party and can effectively resist trajectory inference attacks, uniform attack, accidental attack, traffic analysis-based attack, auxiliary information attack and data mining-based attack. This solution makes a significant contribution by effectively addressing the balance between privacy protection and data analysis utility through the use of privacy budget adaptive allocation technology. It introduces a novel trajectory perturbation algorithm to safeguard privacy while retaining the trajectory’s usability. Additionally, the privacy-weighted averaging model is innovatively employed to mitigate distortions caused by the LDP method, enhancing the trajectory’s utility. The application of these methods in evaluating the VES scheme using real Beijing taxi traffic data demonstrates their effectiveness in simultaneously safeguarding participant privacy and preserving the usability of the perturbed trajectory.