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This article 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 ($\lambda $) 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. $\lambda $ 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.

The primary objective of an energy management strategy (EMS) in fuel cell (FC) hybrid electric vehicles (HEVs) is twofold: to minimize hydrogen consumption and to extend the lifetime of the power sources. However, these power sources are susceptible to degradation under various operational and ambient conditions, be it from cycling or calendar aging. To achieve optimal performance, the EMS must consider variations in the power sources’ characteristics due to degradation. This article succinctly discusses the necessity of employing a health-wise EMS and the indispensable tools it requires, such as health-monitoring techniques. Subsequently, the study investigates the impact of a health-wise EMS on the total operational cost of a low-speed urban FC-HEV truck through simulations. The simulation results demonstrate that health-wise EMSs can significantly reduce fuel consumption and mitigate FC and battery degradations, resulting in a noteworthy reduction in the total operational cost.

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/Simulink™. 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.

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.

To lower the circuit complexity and required space, and to prolong the lifetime of the hybrid energy storage system, a Z-source inverter (ZSI) topology is a suitable substitute for the traditional two-stage ones and has drawn attention in fuel cell (FC) hybrid electric vehicles (HEVs). This replacement requires a suitable selection of passive components for the Z-source network (ZSN) and a strict control over the FC current. This article puts forward a novel high-performance active switched quasi-ZSI (HP-AS-qZSI) for the application of an FC-HEV. First, a lithium-ion capacitor (LiC) bank replaces one regular capacitor in the ZSN to decrease the passive components size and volume. By inserting an antiparallel power switching device, the proposed inverter is enabled to tackle distorted/uncontrollable dc-link voltages in a wide range of loads compared to the original AS-qZSI. Second, an improved-differential-evolution-based linear–quadratic regulator controller is also proposed to enhance the dynamic performance of the FC current control. Finally, signal hardware-in-the-loop tests are implemented to verify the performance of the proposed topology and controller. The proposed topology demonstrates an improved average efficiency (2.71%) and a decreased size and volume of passive elements over a traditional two-stage inverter.

This paper proposes a double-layer energy management system (EMS) for electric vehicles driven by multiple permanent magnet synchronous motors. The system minimizes energy consumption and ensures safe longitudinal motion. The inner-layer distributes torques and flux currents by minimizing the motor input power. The outer-layer generates the total torque command by controlling the aggregated motor speed via a disturbance observer-based controller. A design condition that sufficiently guarantees the system's L2 stability is presented. The condition is independent of the torque distribution ratios and can be checked conveniently via passivity notation without linearizing the total system. Various validation tests were performed using a three-wheel recreational electric vehicle (EV) platform. The advantage of the double-layer EMS has been compared with several EMSs proposed recently in the literature. Critical testing scenarios were employed, including sharp change in road friction during high acceleration. Test results reveal that, regardless of such condition, the double-layer EMS can prevent the wheel slip, thereby significantly reducing energy consumption. The New European Driving Cycle test was also conducted to demonstrate the merit of simultaneously optimizing torque distribution ratios and motor flux-currents.