The development of electric vehicles (EVs) has been considered one of the most efficient ways to reduce the carbon footprint of the transportation system. Among battery EV designs, a dual-motor configuration is introduced as a promising solution to improve dynamic performance and energy efficiency. In this study, a novel energy management strategy framework based on an Adaptive Network-based Fuzzy Inference System (ANFIS) is proposed for torque distribution optimization between two different motors. At first, Dynamic Programming (DP) is employed to find global optimization of the torque distribution. After training with the DP-obtained data set, the ANFIS model can execute a torque distribution online. By minimizing the battery energy consumption, the motor torque-speed solution pair is found under representative driving cycles (only three cycles). In addition, the best ANFIS model has been selected using a clustering technique, goodness-of-fit metrics, and sensitivity analysis. This distinguishes the optimization problem in this study from previously published literatures. The simulation results show that over an unknown Urban Dynamometer Driving Schedule (UDDS) cycle, the torque prediction using this ANFIS model achieves 98.3% of the benchmark DP result. As a result, the overall efficiency of the proposed strategy is increased to 73.3%, which is 3.4% higher than that of the rule-based method. Furthermore, the signal hardware-in-the-loop (S-HIL) experiments validate the real-time prediction of the ANFIS-based approach.

Recently, demand for electric vehicles (EVs) has increased significantly as people are becoming more conscious of the environment and the need to reduce carbon emissions. The introduction of multi-motor systems in EVs has brought new challenges in terms of energy efficiency and performance. This paper presents a Multi-Ensemble Learning (MEL)-based approach to design an Energy Management Strategy (EMS) for a Dual Motor Electric Vehicle (DMEV) where MEL is a new powerful Machine Learning approach implemented using Python programming language. To make our study concrete, we studied a real DMEV that is modeled using Energetic Macroscopic Representation and whose control is simulated using Matlab/Simulin ^TM . The designed EMS aims to distribute the instant torque between the two electric motors in an efficient manner, with the objective of minimizing energy consumption as much as possible. Contrary to existing EMSs, an important advantage of our designed EMS is that it determines the instant torque distribution in real-time (while the vehicle is running), without knowing in advance how physical parameters (such as the speed and traction force) will evolve during the current trip. The real-time simulation is carried out under unknown driving cycles based on a validated numerical EV model with a significantly lower computational cost while achieving a high degree of accuracy in predicting and allocating torque, and a high degree of performance in terms of energy consumption.

This paper introduces a novel approach in electric vehicle technology by combining dual-motor coupling with a hybrid energy storage system (HESS) using batteries and supercapacitors. This innovation enhances vehicle performance and prolongs battery life. An energy management strategy (EMS) based on Pontryagin's minimum principle (PMP) is used to optimize power distribution within the HESS. To improve PMP performance, the proposal integrates driving pattern recognition (DPR) and co-state variable ( λ ) control. DPR employs an adaptive network-based fuzzy inference system (ANFIS) for real-time pattern recognition. The process involves creating a sample driving cycle, employing subtractive clustering to establish the original fuzzy inference system (FIS), and fine-tuning FIS parameters through neural network training. λ values are updated based on recognition results to adapt control actions for various driving styles. Real-time simulations on Opal-RT reveal significant improvements compared to EMS without DPR. Battery current root mean square and standard deviation decrease by 11.4% and 29.4%, respectively, during the unknown in advance Federal Test Procedure (FTP) cycle. This adaptable DPR method offers versatility for various EMSs and clarifies the impact of disturbances like supercapacitor size, state of charge variations, and off-road conditions on HESS performance, aiding researchers in designing more efficient systems.

Electric vehicle (EV) traction drives should be associated with flux‐weakening (FW) techniques wide‐range speed demands. In this study, the EV performance with an optimal FW strategy is studied in relation to the battery voltage variation caused by cell state‐of‐charge and temperature changes. The results show that the battery suffers from a voltage reduction by larger internal resistance as the temperature decreases. Moreover, the higher current is required for activating the FW process. However, the inner resistance growth produces more heat inside the cell that affects the battery electrical parameters as well as the system. To assess this effect by simulation, an improved electro‐thermal model of lithium‐ion battery ls dynamically coupled to the optimal FW strategy. In this model, all the electrical parameters are temperature‐dependent deduced from experimental measurements of an off‐road EV. The simulation results confirm the effect of the cell self‐heating on the battery voltage at sub‐zero temperatures. The higher battery voltage can support the FW operation at −10°C for more 1200 s under the modified NEDC driving cycle, whereas the motor drive voltage is saturated after 1118 s by using the simple battery model without thermal effects.

Electric vehicles (EVs) have been regarded as one of the promising alternatives to zero-emission transportation. New EV registrations have increased as a result of government policies and consumers’ awareness of climate change. Moreover, EV technology is being improved through ongoing research and development efforts. Among these, a powertrain with a combination of two electric motors has been proposed for high driving and efficiency performance. The study presents a comprehensive state-of-the-art review of architectures and energy distribution for a dual-motor equipped with dual-source EV system. In detail, various dual-motor configurations, and energy management strategies (EMSs) used in the literature are investigated and categorized. A comparison of the benefits and drawbacks of existing topologies and the EMSs of hybrid energy storage systems (HESSs) is also discussed. Following that, research gaps have been considered. This study can be used as a reference for researchers who are interested in the design and optimal control of the dual-motor dual-source EVs.

The increasing demand for Electric Vehicles (EV s) has made energy efficiency and performance crucial. This paper proposes a Multi-Ensemble Learning-based Energy Management Strategy (EMS) approach for a Dual-Motor Electric Vehicle (DM-EV) to address these challenges. Energetic Macroscopic Representation is used to model the DM-EV, and Matlab/Simulink™ is used to simulate the control. The proposed model is designed with Python programming language and aims to distribute the instant torque between the two electric motors efficiently, minimizing energy consumption in real-time, without prior knowledge of physical parameters. A real-time simulation under an unknown driving cycle was validated using a numerical EV model and achieved promising results while having a significantly lower computational cost compared to existing EMSs. The proposed model shows a high degree of efficiency in predicting and allocating torque, making it a promising solution for efficient energy management in multi-motor EVs.