Though 2,5-di-tert-butyl-1,4-bis(2-methoxyethoxy)benzene (DBBB) is a promising active species for non-aqueous redox flow batteries, the development and optimization of suitable electrolytes is still required to realize this potential. Given the breadth and diversity of non-aqueous electrolytes, combinatorial screening using automated platforms offers an efficient means of mapping out the design space. Employing an automated electrolyte synthesis and characterization system, we studied electrolytes consisting of DBBB, various alkali ion salts, and carbonate solvents, with an overarching goal of improving DBBB solubility and ionic conductivity. Via this high-throughput approach, mixtures of linear and cyclic carbonates were found to improve solubility and conductivity including certain mixtures that demonstrated both improved DBBB solubility (>0.5 M) and electrolyte conductivity (>5 mS/cm), over the base case scenario. In general, the addition of LiTFSI as a salt (or co-salt) improved DBBB solubility in electrolytes and the ionic conductivity of solutions. Furthermore, the electrochemical behavior of DBBB was systematically investigated over a range of concentrations (0.005 M to 0.3 M) in an optimized electrolyte using both macroelectrode and ultramicroelectrode techniques. Determining the trends of key electrochemical parameters may help bridge the gap between low concentration screening experiments and higher concentration energy storage applications.

Permanent Magnet Linear Synchronous Motor (PMLSM) servo system was widely used in high precision application and advanced control strategy was necessary for its control system. Cerebellar Model Articulation Controller (CMAC) neural network was introduced into control system with conceptual mapping and physical mapping process analyzed, also the mathematical models of PMLSM was constructed. The compound control system with PID controller as feedback control and CMAC neural network as feed- forward control was constructed. The simulation shows the controller can realize inverse dynamic model and robust control.

This paper proposes an improved thermal modeling approach for fractional-slot concentrated winding (FSCW) permanent magnet (PM) machines, based on the thermal network method (TNM). In the traditional TNM models, the core loss is considered uniformly distributes in the stator core, which actually leads to an unavoidable error in FSCW PM machine thermal prediction. In order to achieve a high accuracy, the discrete loss distribution model is built for machine stator, where the stator core is divided into amounts of blocks. Based on the Bertotti model, the accurate core loss distributions are calculated under different load rates. Then, the calculated core losses are allocated to corresponding blocks, and the temperature rise is predicted by TNM. The predicted results by the proposed model reveals that there exists the local hot spot in the stator tip caused by the local loss concentration in FSCW PM machines, which is not ever reported. In order to validate the predicted results, the experimental platform with a 12-slot 10-pole FSCW PM machine is built and tested. The experimental results reveal that the proposed model features a higher temperature prediction accuracy, while the local hot spot really exists in stator tip. It is believed that the proposed model can give a more reliable support in FSCW PM machine design considering magnetic-thermal coupling.

The steer-by-wire system severs the mechanical link between the steering wheel and the steering gear. This configuration enhances the angular transmission characteristics. Entering the nonlinear region of the tires could result in a reduction in the vehicle’s steering gain. In order to improve the comfort of vehicle steering operation, we have developed a variable transmission ratio controller for the steer-by-wire (SBW) system. This controller utilizes information on the vehicle speed and steering wheel angle to generate a variable transmission ratio coefficient, thereby adjusting the steering ratio. We introduce a multi-objective comprehensive evaluation index that takes into account vehicle lateral deviation, driver steering burden, vehicle stability, and safety. To harmonize the transmission ratio weights of constant steering gain, we employ the coefficient of variation method. Ultimately, a fuzzy neural network is employed to craft a nonlinear controller. We conducted steady-state circular motion tests, double lane-change tests, and step input tests to validate the performance of the variable transmission ratio control. The results suggest that, in comparison to conventional fixed transmission ratio systems, the variable transmission ratio control within the steer-by-wire system significantly alleviates the driver’s operational burden while enhancing the vehicle’s handling stability and safety.

This paper suggests a hierarchical coordination control strategy to enhance the stability of distributed drive electric bus. First, an observer based on sliding mode observer (SMO) and adaptive neural fuzzy inference system (ANFIS) was designed to estimate the vehicle state parameters. Then the upper layer of the strategy primarily focuses on coordinating active front steering (AFS) and direct yaw moment control (DYC). The phase plane method is utilized in this layer to provide an assessment basis for the switching control safety of AFS and DYC. The lower layer of the strategy designs an integral terminal sliding mode controller (ITSMC) and a non-singular fast terminal sliding mode controller (NFTSMC) to obtain the optimal additional front wheel steering angle to improve handling performance. A fuzzy sliding mode controller (FSMC) is also proposed to obtain additional yaw moment to ameliorate yaw stability. Finally, the strategy proposed in this paper is subjected to simulation testing and compared with the performance of AFS and DYC systems. The proposed strategy is also evaluated for tracking errors in sideslip angle and yaw rate under two conditions. The results demonstrate that the proposed strategy can effectively adapt to various extreme environments and improve the maneuvering and yaw stability of the bus.

The prerequisite for intelligent vehicles to achieve autonomous driving and active safety functions is acquiring accurate vehicle state parameters. Traditional Kalman filters often underperform in non-Gaussian noise environments due to their reliance on Gaussian assumptions. This paper presents the IMM-MCCKF filter, which integrates the interacting multiple model theory (IMM) and the maximum correntropy cubature Kalman filter method (MCCKF), for estimating the state parameters of intelligent vehicles. The IMM-MCCKF successfully suppresses non-Gaussian noise by optimizing a nonlinear cost function using the maximum correntropy criteria, allowing it to capture and analyze signal data outliers accurately. The filter designs various state and measurement noise submodels to adapt to the environment dynamically, thus reducing the impact of unknown noise statistical properties. Accurately measuring the velocity of a vehicle and the angle at which its center of mass drifts sideways is of utmost importance for its ability to maneuver, maintain stability, and ensure safety. These parameters are critical for implementing advanced control systems in intelligent vehicles. The study begins by constructing a nonlinear Dugoff tire model and a three-degrees-of-freedom (3DOF) vehicle model. Subsequently, utilizing low-cost vehicle sensor signals, joint simulations are conducted on the CarSim-Simulink platform to estimate vehicle state parameters. The results demonstrate that in terms of estimation accuracy and robustness in non-Gaussian noise scenarios, the proposed IMM-MCCKF filter consistently outperforms the MCCKF and CKF algorithms.