DC charging pile energy measurement method based on wavelet threshold denoising and smoothing optimization

In the context of DC charging for electric vehicles, noise interference in DC grid signals significantly compromises the accuracy of energy measurement during charging, leading to potential billing inaccuracies and operational inefficiencies. To address this challenge, this paper proposes a high-precision energy measurement method for DC charging piles, integrating wavelet threshold denoising and signal smoothing techniques. First, an improved wavelet threshold denoising algorithm is applied to process the noisy raw signals. Unlike conventional approaches, this method employs particle swarm optimization (PSO) to dynamically determine the optimal parameters of a soft-hard threshold compromise function, ensuring near-optimal noise suppression while preserving critical signal features. Following denoising, a moving average smoothing filter is applied to further refine the signal, reducing residual fluctuations. The final energy measurement is computed using gradient integration, enhancing precision in cumulative energy calculations. Addressing the noise and precision challenges in DC charging pile metering requires a dedicated signal processing strategy. This work presents an integrated solution combining PSO-based adaptive wavelet denoising with post-processing smoothing to meet these requirements. Simulation experiments demonstrate that the PSO-optimized wavelet denoising algorithm significantly outperforms traditional methods in terms of signal-to-noise ratio and mean squared error, confirming its effectiveness in noise reduction. Additionally, the smoothing process is shown to improve energy measurement accuracy by minimizing transient distortions. A comparative analysis reveals that while cubic spline interpolation offers minor improvements, its impact on overall measurement precision is limited compared to the proposed de-noising-smoothing framework. The proposed method not only enhances the reliability of DC charging energy metering but also provides a robust solution for noise-laden industrial signal processing. Future work will explore real-time implementation and hardware optimization for large-scale charging infrastructure.