Research on eco-driving optimization of hybrid electric vehicle queue considering the driving style

The driving range of electric vehicles (EVs) is still an important factor restricting their development. Although the rising battery energy density has reached a bottleneck, which is a key constraint, the drive energy management strategy also has a significant effect and can improve the driving range of EVs, since wheel traction torque control can directly optimize the driving energy consumption of EVs. In order to comprehensively analyze the current research status of driving energy management and clarify its development direction, this review focuses on the driving energy management strategy of EVs and systematically summarizes the configurations and power distribution strategies of the dual-motor coupling drive system (DCDS), as well as torque vectoring control strategies of the decentralized drive system. Firstly, driving energy losses are analyzed in detail, which mainly include electric loss, tire slip energy dissipation, and the power of cornering resistance. Secondly, typical configurations of the DCDS are introduced, and the power distribution strategies of the DCDS are comprehensively reviewed. Finally, as an interesting energy-saving technology, energy-saving torque vectoring, generally applied to decentralized drive systems, is reviewed in detail in terms of its energy-saving pathways and control strategies, which are classified as front-and-rear torque vectoring and left-and-right torque vectoring. Research findings indicate that the driving range of EVs can be effectively increased by applying a driving energy management strategy based on several novel multi-power source drive systems. The development of a driving energy management strategy and the required novel drive systems will be a valuable and crucial direction for further energy conservation in EVs.

https://www.mdpi.com/2071-1050/15/9/7617/pdf?version=1683296406

Review of Energy-Saving Technologies for Electric Vehicles, from the Perspective of Driving Energy Management

Design guidelines for self-similar Mamyshev oscillators

This paper uses machine learning to predict either the voltage, current, or state of charge (SOC) during a charging/discharging of a Li Ion cell. Due to the non-linear characteristics of a Li Ion cells, machine learning is used to create a model for prediction. Predicting the Li Ion cell characteristics is useful for products in determining the end of discharge (EOD) levels under different loading conditions. The model used is a decision tree regressor which is implemented using Python. This model was also compared against a random forest regressor model. The decision tree regressor model has a faster response time and 100 times smaller model size when compared to the random forest regressor model. To train the model approximately 2.8 million data points of charging and discharging data of a Sanyo 18650 3.7V 2.6Ah Li-Ion cell is used. It was found that the accuracy of the model is limited due to the limited ranges of the training data. This model can be used to predict the behavior of Lithium-Ion cells/batteries under different loading conditions.

Use of Machine Learning to Predict Lithium-Ion Cell Voltages During Charging and Discharging

This paper uses machine learning to predict either the voltage, current, or state of charge (SOC) during a charging/discharging of a Li Ion cell. Due to the non-linear characteristics of a Li Ion cells, machine learning is used to create a model for prediction. Predicting the Li Ion cell characteristics is useful for products in determining the end of discharge (EOD) levels under different loading conditions. The model used is a decision tree regressor which is implemented using Python. This model was also compared against a random forest regressor model. The decision tree regressor model has a faster response time and 100 times smaller model size when compared to the random forest regressor model. To train the model approximately 2.8 million data points of charging and discharging data of a Sanyo 18650 3.7V 2.6Ah Li-Ion cell is used. It was found that the accuracy of the model is limited due to the limited ranges of the training data. This model can be used to predict the behavior of Lithium-Ion cells/batteries under different loading conditions. 

A comparative study of machine learning based modeling methods for Lithium-ion battery

Optimization design of mode-locked laser and compression system based on similaritons

Two axles four wheel drive electric vehicle is an important development tendency of electric vehicle. However, how to coordinate the torque distribution between front and rear axles thus ensuring vehicle stability while maximizing motor's efficiency still confronts great challenges. In this paper, we propose a three-layer hierarchical control method, which not only incorporates prior engineering knowledge into the control policy so as to relieve computational burden for real-time usage, but also properly deals with the priority between safety and system efficiency. Simulations are conducted to verify the effectiveness of the proposed method. Results show that the derived control policy can ensure high system efficiency when safety is guarenteed. Simultaneously, it can actively decrease the motor torque to improve vehicle stability when wheel slip occurs.

Torque allocation strategy for two axles four wheel drive electric vehicle with improvement of economy and stability

In intelligent manufacturing technology, machine with speech separation ability can effectively improve the efficiency of human-computer interaction, which is conducive to the rapid development of intelligent manufacturing industry. In single-channel speech separation based on deep learning, the performance of time domain features is better than that of frequency domain features. However, the current methods based on time domain feature have poor robustness in real noise environment, and time domain feature has limitations on the performance of the separation model. Therefore, we propose a Time-and Frequency fusion based on multi-scale convolution model(Tff-MscNet), which integrates time domain features and frequency domain features to improve multidimensional information of data. In order to further improve the performance of separation network, we introduced multiscale convolution block to improve the feature extraction ability of the network. We compare with the Conv-TasNet baseline model and the latest time-frequency fusion speech separation baseline model in GRID speech dataset. Experiments show that the performance and robustness of the proposed method are improved greatly in the experimental environment with real noise.

Zihao Gao

Papers

A comparative study of machine learning based modeling methods for Lithium-ion battery

Optimization design of mode-locked laser and compression system based on similaritons

Torque allocation strategy for two axles four wheel drive electric vehicle with improvement of economy and stability

Time-and-Frequency Fusion based on Multi-scale convolution for Speech separation in intelligent manufacturing