Evaluating performances of real-time strategies for hybrid energy storage systems (HESSs) of electric vehicles (EVs) always requires optimal energy management strategies (EMSs) as offline benchmarks. Dynamic programming (DP) is well-known due to its ability to obtain global optimal solutions based on the numerical searching technique. Nevertheless, DP accuracy depends on the numericalization fineness. Analytical optimal control methods, typically Pontryagin’s minimum principle (PMP), are also frequently used as effective counterparts. However, solving optimal control problems based on these methods often depends on the complexity and the characteristic of the system model; basically, it is sophisticated since there is no general way to solve the issue. This article proposes an alternative approach of using PMP to develop an optimal EMS for battery/supercapacitor HESSs. The novel strategy is based on formulating the problem in terms of power and energy, which forms a state-constrained optimal control problem. PMP is then applied with a penalty function, in which the inequality state constraints are reformulated to deduce a new state-unconstrained problem. The proposed optimal EMS is hundreds of times faster than DP with better results. Moreover, the optimal solution is piecewise constant that could give significant insights to develop real-time strategies in future studies.

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 L ₂ 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.

The necessity of rejecting the external disturbance and model uncertainty is vital for control design to achieve high performance and robustness of the system. Disturbance cancelation techniques are, therefore, of interest for both academic and industrial research and development. Among them, disturbance observer based control (DOBC) and active disturbance rejection control (ADRC) are widely applied, thanks to their high performance and effectiveness in synthesis and realization. However, two knowledge gaps vis-à-vis the dynamical delay and the unification of these techniques exist in literature. This article proposes a study on the unification of DOBC and ADRC for the application of speed control of permanent magnet synchronous motor. The explicit formulas of the estimated disturbance are carried out and the dynamical delays of the observations are, therefore, straightforwardly deduced. By addressing these transparent delay formulas, the unification of DOBC and ADRC is verified. The identical performance of two methods in rejecting both external disturbance and model uncertainty is demonstrated by simulation and experimental validations.

Power electronics play a fundamental role for electric transportation, renewable energy conversion and many other industrial applications. They have the ability to help achieve high efficiency and performance in power systems. However, traditional inverters such as voltage source and current source inverters present some limitations. Consequently, many research efforts have been focused on developing new power electronics converters suitable for many applications. Compared with the conventional two-stage inverter, Z-source inverter (ZSI) is a single-stage converter with lower design cost and high efficiency. It is a power electronics circuit of which the function is to convert DC input voltage to a symmetrical AC output voltage of desired magnitude and frequency. Recently, ZSIs have been widely used as a replacement for conventional two-stage inverters in the distributed generation systems. Several modifications have been carried out on ZSI to improve its performance and efficiency. This paper reviews the-state-of-art impedance source inverter main topologies and points out their applications for multisource electric vehicles. A concise review of main existing topologies is presented. The basic structural differences, advantages and limitations of each topology are illustrated. From this state-of-the-art review of impedance source inverters, the embedded quasi-Z-source inverter presents one of the promising architectures which can be used in multisource electric vehicles, with better performance and reliability. The utilization of this new topology will open the door to several development axes, with great impact on electric vehicles (EVs).

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.

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.