电讯技术2025，Vol.65Issue(7)：1033-1041,9.DOI:10.20079/j.issn.1001-893x.250202001
隐私保护的电动汽车充电行为安全预测方法
A Secure Charging Behaviour Forecasting Method with Privacy Protection
摘要
Abstract
With the rapid development of electric vehicles(EVs),charging infrastructure has become the policy priority of new infrastructure.The location and capacity planning of charging plies is the core issue,where the prediction of charging load is crucial.However,due to the privacy of the user̓s charging behavior data,the prediction models constructed in the current works lack consideration of this important factor,resulting in the leakage of sensitive information.To address this problem,a privacy-preserving method for EV charging load and charging pile number forecasting is proposed.First,the probability density function of charging load characteristics is fit by charging behavior.Second,Monte Carlo algorithm is used for predicting charging load with privacy protection,which combines the algorithm with the Paillier encryption scheme.Finally,according to the load prediction result,particle swarm optimization is used to optimize traversal in the regional charging pile layout and then the charging pile number forecasting result is obtained.The simulation results show that the proposed method can achieve good charging load prediction accuracy while protecting charging data privacy.The maximum error is 11.9%,and the minimum error is 2.8%.Compared with the existing schemes,the proposed solution enhances the security and meanwhile reduces the prediction error by about 32.98%.关键词
车桩网/数据隐私安全/充电负荷预测/同态加密/充电桩布局优化/粒子群优化Key words
car pile net/data privacy and security/charging load forecasting/homomorphic encryption/optimization of charging load layout/particle swarm optimization分类
信息技术与安全科学引用本文复制引用
郭静,顾智敏,朱道华,梁伟..隐私保护的电动汽车充电行为安全预测方法[J].电讯技术,2025,65(7):1033-1041,9.基金项目
国家电网有限公司总部管理科技项目(5700-202441247A-1-1-ZN) （5700-202441247A-1-1-ZN）
