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A real-time, metadata-driven electric vehicle routing optimization to reduce on-road energy requirements is proposed in this work. The proposed strategy employs the state–action–reward–state–action (SARSA) algorithm to learn the EV's maximum travel policy as an agent. As a function of the received reward signal, the policy model evaluates the optimal behavior of the agent. Markov chain models (MCMs) are used to estimate the agent's energy requirements on the road, in which a single Markov step represents the average energy consumption based on practical driving conditions, including driving patterns, road conditions, and restrictions that may apply. A real-time simulation in Python with TensorFlow, NumPy, and Pandas library requirements was run, considering real-life driving data for two EVs trips retrieved from Google's API. The two trips started at 4.30 p.m. on 11 October 2021, in Los Angeles, California, and Miami, Florida, to reach EV charging stations six miles away from the starting locations. According to simulation results, the proposed AI-based energy minimization framework reduces the energy requirement by 11.04% and 5.72%, respectively. The results yield lower energy consumption compared with Google's suggested routes and previous work reported in the literature using the DDQN algorithm. [ABSTRACT FROM AUTHOR]

Information on the 12th Biennial IEEE Conference on Electromagnetic Field Computation (CEFC) 2006 in Miami, Florida is presented. The author discusses that the Institute of Electrical and Electronics Engineer (IEEE) CEFC is one of the most important forums for the computational electromagnetics community. He added that only 614 paper digests were submitted for review to determine their suitability for inclusion in the technical programs.

• An energy consumption minimization framework for electric vehicles (EVs) routing optimization is proposed. • The concept of DDQN as a learning tool to find the maximum travel policy of the EV as an agent is presented. • Dynamic traffic model based on Markov chain is used, with real-time data import from Google's API platform. • The proposed framework yields lower energy requirement for EVs to reach their destinations compared with Google's map original routes. A real-time, data-driven electric vehicle (EVs) routing optimization to achieve energy consumption minimization is proposed in this work. The proposed framework utilizes the concept of Double Deep Q-learning Network (DDQN) in learning the maximum travel policy of the EV as an agent. The policy model is trained to estimate the agent's optimal action per the obtained reward signals and Q-values, representing the feasible routing options. The agent's energy requirement on the road is assessed following Markov Chain Model (MCM), with Markov's unit step represented as the average energy consumption that takes into consideration the different driving patterns, agent's surrounding environment, road conditions, and applicable restrictions. The framework offers a better exploration strategy, continuous learning ability, and the adoption of individual routing preferences. A real-time simulation in the python environment that considered real-life driving data from Google's API platform is performed. Results obtained for two geographically different drives show that the proposed energy consumption minimization framework reduced the energy utilization of the EVs to reach its intended destination by 5.89% and 11.82%, compared with Google's proposed routes originally. Both drives started at 4.30 PM on April 25th, 2019, in Los Angeles, California, and Miami, Florida, to reach EV's charging stations that are located six miles away from both of the starting locations. [ABSTRACT FROM AUTHOR]