Since lithium-ion batteries are the core components and main sources of failures in electric vehicles and energy storage systems, fault diagnosis plays a crucial role in the stable operation of lithium-ion batteries. ...
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With the upcoming next generation wireless network, vehicles are expected to be empowered by artificial intelligence (AI). By connecting vehicles and cloud server via wireless communication, federated learning (FL) al...
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With the upcoming next generation wireless network, vehicles are expected to be empowered by artificial intelligence (AI). By connecting vehicles and cloud server via wireless communication, federated learning (FL) allows vehicles to collaboratively train deep learning models to support intelligent services, such as autonomous driving. However, the large number of vehicles and increasing size of model parameters bring challenges to FL-empowered connected vehicles. Since communication bandwidth is insufficient to upload full-precision local models from numerous vehicles, model compression is usually conducted to reduce transmitted data size. Nevertheless, conventional model compression methods may not be practical for resource-constrained vehicles due to the increasing computational overhead for FL training. The overhead for downloading global model can also be omitted by existing methods since they are originally designed for centralized learning instead of FL. In this paper, we propose a ternary quantization based model compression method on communication-efficient FL for resource-constrained connected vehicles. Specifically, we firstly propose a ternary quantization based local model training algorithm that optimizes quantization factors and parameters simultaneously. Then, we design a communication-efficient FL approach that reduces overhead for both upstream and downstream communications. Finally, simulation results validate that the proposed method demands the lowest communication and computational overheads for FL training, while maintaining desired model accuracy compared to existing model compression methods.
Interconnected powersystem is becoming more and more complex, in order to improve the performance of powersystem, the number and type of controllers are increasing. The interaction among controllers may be detriment...
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Interconnected powersystem is becoming more and more complex, in order to improve the performance of powersystem, the number and type of controllers are increasing. The interaction among controllers may be detrimental to the performance of powersystem, which would weak the performance of controllers. The purpose of this paper is to solve the problem of coordination control among controllers, firstly, a multi-controller parameter coordination model is given. Then, a spherical artificial immune algorithm is proposed to coordinate and optimize parameters among multi-damping controllers. This algorithm can reduce the computational complexity and optimize parameters globally. Finally, this algorithm is verified by the simulation of the four-machine two-area system. Compared with one similar method, simulation results show that this algorithm can effectively realize the coordination control among multi-damping controllers.
Energy Internet (EI) is an inevitable development trend of energy systems under the background of technology development, environmental pressure and energy transition. Multi-energy flow coupling is one of the key char...
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