Autonomous driving paper index
AI driven adaptive resonance compensation for high efficiency wireless power transfer in electric vehicle charging
One-line summary
Wireless Power Transfer (WPT) has emerged as a transformative charging technology for electric vehicles (EVs) due to its convenience, safety, and operational efficiency.
Engineering notes
Experimental results demonstrate superior performance, achieving 96.8% power transfer efficiency, an R² value of 0.99, and minimal prediction errors with MAE of 0.012 and RMSE of 0.024.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
Wireless Power Transfer (WPT) has emerged as a transformative charging technology for electric vehicles (EVs) due to its convenience, safety, and operational efficiency. However, existing WPT architectures often suffer from performance degradation caused by coil misalignment, resonance detuning, load fluctuations, and external electromagnetic interference, which limit their adaptability and reliability in dynamic charging environments. To address these challenges, this study proposes a hybrid Feedforward Neural Network (FNN) and Temporal Convolutional Network (TCN) framework optimized using Particle Swarm Optimization (PSO) to enhance WPT system performance. The FNN effectively processes static system parameters such as coil geometry, compensation topology, and operating frequency, while the TCN captures temporal dependencies associated with dynamic alignment variations and load changes. PSO is employed to optimize network hyperparameters, ensuring improved predictive accuracy and computational efficiency. The proposed framework is validated using a real-world EV charging dataset augmented with synthetic perturbations to emulate WPT-specific operating conditions. Experimental results demonstrate superior performance, achieving 96.8% power transfer efficiency, an R² value of 0.99, and minimal prediction errors with MAE of 0.012 and RMSE of 0.024. Furthermore, the system maintains low inference latency of 15 ms and total computation time of 120 s, supporting real-time deployment. Implemented in Python with robust simulation libraries, the proposed model offers a scalable and reliable solution for next-generation intelligent WPT-enabled EV charging systems.
Links and sources
Need this topic turned into a technical roadmap?
Full Self Driving can prepare a custom autonomous driving literature review, code map, dataset map, and B2B technology assessment.
Request B2B research
Comments