Hybrid Deep Learning Mechanism for Charging Control and Management of Electric Vehicles

Abstract

In perspective of their environmental friendliness and energy efficiency, Electric Vehicles (EVs) are posing a threat to traditional gasoline automobiles. Identifying the future charging needs of EV users may be aided by the forecasting of states linked to EV charging. It might deliver customized charge capacity statistics based on users' real-time locations as well as direct the operation and management of charging infrastructure. Consequently, an emergent problem is the effective model of EV charging state predictions. In this study, a hybrid deep learning approach is suggested to assure safe and dependable charging operations that prevent the battery from being overcharged or discharged. A Recursive Neural Networks (RNNs) for feature extraction process is suggested to acquire adequate feature information on the battery. The bidirectional gated recurrent unit framework (GRU) was then established by the study to predict the state of the EV. The GRU receives its input from the RNNs' output, which substantially enhances the effectiveness of the model. Because of its much simpler structure, the RNN-GRU has a lower computational performance. The experimental findings demonstrate the GRU method's ability to accurately track mileage of the electric vehicle. A hybrid deep learning-based prediction approach could give quick convergence speed less error rate in comparison to the appropriate method for obtaining state of charge estimate over conventional models, as demonstrated by the extensive real-world tests.

Review of Self-Driving Cars – Technologies, Standards and Safety

“…Figures 3a and b show the installation placements, maximum range, and coverage of these common sensors utilised for environment perception in autonomous cars. The combination of these sensors helps the car outperform human drivers in perceiving and responding to the surrounding environment [7]. This technology is critical for achieving self-driving and assuring safe and dependable operation.…”

Electric vehicle charging state predictions through hybrid deep learning: A review

“…For example, in [8], the authors propose a hybrid deep learning model that combines an RNN with an SVM for EV charging state prediction. The proposed model uses the RNN to learn the temporal relationships between the charging data and the charging states, and the SVM to capture the non-linear relationships between the input features and the output variable.…”

Examining Effective Battery Management Systems for Electric Vehicles-a Review Analysis

“…Overcharging or undercharging the battery can reduce its efficiency and lifespan. Therefore, accurate state of charge estimation is critical in ensuring that the battery is charged to the optimal level and that energy is not wasted in the charging process [16][17][18][19][20][21][22].…”

“…For example, if a battery is charged with 10 kWh of energy and provides 9 kWh of energy to the EV, the efficiency of the BMS would be calculated as: In summary, BMS efficiency is a critical factor in ensuring the optimal performance and longevity of electric vehicle batteries. Effective charging algorithms, thermal management systems, and state of charge estimation are key factors in improving BMS efficiency, which can be calculated by comparing the energy output the battery to the energy input provided by the charging source [27][28][29][30][31][32].…”