AI-powered EV Energy Harvesting and Management[AI を活用した EV エネルギーの収集と管理]

Z. Wang, A. Ben Abdallah, ''A Robust Multi-stage Power Consumption Prediction Method in a Semi-decentralized Network of Electric Vehicles,'' IEEE Access, 2022. DOI: 10.1109/ACCESS.2022.3163455 
A Virtual Power Plant (VPP) balances the load on a power grid by allocating power generated by various interconnected units during periods of peak demand. In addition, demand-side energy devices such as Electric Vehicles (EVs) and mobile robots can also balance energy supply and demand when effectively deployed. However, the fluctuation of energy generated by renewable resources makes balancing energy supply a challenging goal. This paper proposes a semi-decentralized robust network of electric vehicles (NoEV) integration system for power management in a smart grid platform. The proposed approach integrates an aggregator with EV fleets into a blockchain framework. The EVs execute a multi-stage algorithm to predict the power consumption based on a novel federated learning algorithm named Federated Learning for Qualified Local Model Selection (FL-QLMS). From the evaluation results, the proposed system requires 35% fewer transactions in short intervals and propagation delays than the previous approaches and achieves better network efficiency while maintaining a high level of security. Moreover, NoEV achieves a 5.7% lower root mean square error (RMSE) than the conventional approach for power consumption prediction, which is a significant improvement. In addition, the FL-QLMS approach outperforms state-of-the-art methods in terms of robustness to client-side attacks. The evaluation results also show that the performance of FL-QLMS is not affected when 10% to 40% percent of the models are manipulated.

Z. Wang, M. Ogbodo, H. Huang, C. Qiu,  M. Hisada, A. Ben Abdallah, "AEBIS: AI-Enabled Blockchain-based Electric Vehicle Integration System for Power Management in Smart Grid Platform," IEEE Access, vol. 8, pp. 226409-226421, 2020, doi:10.1109/ACCESS.2020.3044612.
A Virtual Power Plant (VPP) is a network of distributed power generating units, flexible power consumers, and storage systems. A VPP balances the load on the grid by allocating the power generated by different linked units during periods of peak load. Demand-side energy equipment, such as Electric Vehicles (EVs) and mobile robots, can also balance the energy supply-demand when effectively deployed. However, fluctuation of the power generated by the various power units makes the supply power balance a challenging goal. Moreover, the communication security between a VPP aggregator and end facilities is critical and has not been carefully investigated. This paper proposes an AI-enabled, blockchain-based electric vehicle integration system, named AEBIS for power management in a smart grid platform. The system is based on an artificial neural-network and federated learning approaches for EV charge prediction, in which the EV fleet is employed as a consumer and as a supplier of electrical energy within a VPP platform. The evaluation results show that the proposed approach achieved high power consumption forecast with R 2 score of 0.938 in the conventional training scenario. When applying a federated learning approach, the accuracy decreased by only 1.7%. Therefore, with the accurate prediction of power consumption, the proposed system produces reliable and timely service to supply extra electricity from the vehicular network, decreasing the power fluctuation level. Also, the employment of AI-chip ensures a cost-efficient performance. Moreover, introducing blockchain technology in the system further achieves a secure and transparent service at the expense of an acceptable memory and latency cost.