With the world’s energy reserves under strain and the requirements of national carbon emission regulations, the fuel efficiency and environmental friendliness of automobiles are becoming increasingly important. Due to the combination of long cruising range and energy efficiency, hybrid electric vehicles (HEVs) have been adopted as a reliable option for improving fuel economy and reducing emissions. In order to fully exploit the advantages of hybrid electric vehicles, energy management and torque distribution have become the focus of control strategies for HEVs, while also ensuring battery life and meeting requirements for fuel consumption, emissions and driving performance. Therefore, a great deal of research has been carried out on energy management strategies and many approaches have been offered in the literature. This review provides a comprehensive assessment of the literature, highlighting its contributions and making a complete reference for scholars interested in hybrid vehicle development, control, and optimization.

Nowadays, a growing interest is in improving the efficiency of a parallel hybrid electric bus. In this article, we propose an adaptive energy management strategy (EMS) based on an equivalent consumption minimization strategy (ECMS) by using real-time traffic information described by the average speed, average acceleration, and standard deviation of speed for different road sections. The whole adaptive energy management system consists of offline and online components. Three velocity characteristic parameters are derived by utilizing the data from the vehicle. First, the road segments are classified through the K -means. Then, three Markov transition matrices on different types of roads are constructed with the velocity feature parameters, which can be used to predict the speed in the following time. On the other hand, the online component implements the EMS involved. The EMS incorporates predicted vehicle speed and road information into the ECMS. Based on this, the predicted vehicle speed and traffic information are applied to make adaptive changes to the equivalent factors in the ECMS. Finally, the performance of the proposed strategy is evaluated through a comparative simulation study and the results show the superiority of the approach in improving fuel economy. Comparative outcomes from hardware-in-the-loop tests additionally illustrate that the proposed strategy's fuel efficiency is compatible with the original design intent.

This article presents a nonlinear modeling method for the flux linkage and torque of a switched reluctance motor. The method is based on universal weighted least squares support vector machine regression (WLSSVR), combined with entropy method (EM) and improved coyote optimization algorithm (COA) to optimize the kernel parameters. Among them, EM can well improve the error of the general WLSSVR in the selection of sample weights, and the improved COA can prevent the system from falling into local optimum and improve the iteration speed. This SRM modeling method, which combines WLSSVR based on entropy and improved COA, can improve the modeling speed on the premise of ensuring the modeling accuracy mode speed. Finally, the effectiveness of the algorithm is verified by experiments.

Accurate battery peak power capability prediction plays an essential role in improving the safety and efficiency of battery operations. The end of battery charge or discharge is caused by depleted or saturated surface lithium-ion concentrations of electrode solid particles to avoid damaging side reactions. Precise battery peak power capability prediction necessitates a high-fidelity electrochemical model (EM) that accurately depicts dynamic changes of lithium-ion concentrations inside a battery. One of the critical challenges to apply battery EMs for peak power prediction is how to accurately solve the peak charge and discharge currents from a set of complex model equations. To address the issue, this paper mainly investigates four different peak current solution algorithms, including bisection method, genetic algorithm method, particle swarm optimization method, and grey wolf optimizer (GWO) method for battery EM-based peak power prediction. The dependences of the prediction results using different current solution methods on the predictive time horizon and ambient temperature are validated. Their performance in terms of prediction accuracy, computational speed, complexity, convergence rate, and random-access memory are also evaluated. Among these peak current solution methods, the GWO method is proven to have a better performance in convergence rate and computing time while maintaining high prediction accuracy.