Xiang Tian

This article presents a robust energy management strategy (EMS) framework combining design for six sigma (DFSS) and long short-term memory (LSTM). Various driving uncertainties affecting plug-in hybrid electric buses (PHEBs) are explicitly addressed. First, the segmented equivalent fuel consumption minimization strategy (ECMS) is developed as the foundational energy management framework. In the offline phase, a robust equivalent factor (EF) sequence for a single driving cycle is designed using the DFSS methodology to account for uncertainties in resistance coefficients and passenger loads. Then, the LSTM is trained with road segment speed clustering labels alongside the robust EF sequence, enabling the online generation of these robust parameters for new driving cycles. By referencing the actual historical speed data, the EF sequence for the entire driving cycle can be proactively designed to mitigate the impact of speed uncertainties. The simulation results show that the robust EMS achieves lower fuel consumption expectations and standard deviation compared to the deterministic design. The results of the hardware-in-the-loop (HIL) test substantiate the validity and effectiveness of the proposed robust design method.

The adaptability of the supervisory control strategy of plug-in hybrid electric buses (PHEBs) to different driving styles determines the energy-saving performance. This paper proposes a driver-oriented adaptive equivalent consumption minimization strategy (ECMS) for PHEBs. The strategy aims to improve the fuel economy of PHEBs as much as possible by adapting to different driving styles while satisfying the physical constraints of the hybrid power system. Firstly, an online driving style recognition algorithm based on the Fuzzy K-means (FKM) algorithm and the random forest (RF) method is devised, in which the FKM algorithm is used to preprocess the feature parameters related to driving styles and the RF method is utilized to identify the driver’s driving style. Secondly, the driving style recognition results are introduced into the ECMS framework to form a driver-oriented energy management strategy. Finally, the proposed control strategy is verified using both Matlab/Simulink and Hardware-in-the-Loop. The verification results demonstrate that the proposed control strategy improves the fuel economy of PHEBs.

The driving style has a significant influence on vehicle energy management, especially in conditions where the driving cycle is indeterminate. To achieve the optimal fuel consumption in the face of different styles of drivers, this article proposed a new energy management strategy (EMS) considering both the driving style and equivalent factor (EF) bounds in equivalent consumption minimization strategy (ECMS). First, based on the influence of characteristic parameters on style determination, a trustworthy three-parameter driving style recognition system is constructed by combining K-means clustering and fuzzy logic. Then, a large number of driving cycles are simulated and counted, and the gray wolf optimization (GWO) algorithm is introduced to get the optimal EF value of each driving cycle. On that basis, an adaptive ECMS (A-ECMS) is designed. The A-ECMS method uses EF boundaries to limit fuel consumption for different driving rounds, with driving styles adapted to different driving cycles. Finally, online simulation proves the feasibility of the proposed EMS. Compared with another A-ECMS and the optimal EF obtained by the optimization algorithm, the EMS has a significant improvement in fuel economy. The comparison results of hardware-in-the-loop (HIL) testing also show that the fuel efficiency of the proposed strategy is consistent with the original design intent.

In order to break the bottleneck of the integer-order transfer function in vehicle ISD (inerter-spring-damper) suspension design, a positive real synthesis design method of vehicle mechatronic ISD suspension based on the fractional-order biquadratic transfer function is proposed. The emergence of the fractional-order components disrupts the equivalence relationship between the passivity of components and the positive realness of integer-order transfer functions in traditional networks. In this paper, the positive real condition of the fractional-order biquadratic transfer function is given. Then, a quarter dynamic model of the vehicle mechatronic ISD suspension is established, and the parameters of the fractional-order biquadratic transfer function and vehicle suspension are obtained by an NSGA-II multi-objective genetic algorithm. Moreover, the structure of the external circuit and the parameters of the electrical components are obtained by the fractional-order passive network synthesis theory. The simulation results show that under the condition of random road input and vehicle speed of 20 m/s, the root-mean-square (RMS) value of the vehicle body acceleration and the dynamic tire load of the fractional-order ISD suspension are reduced by 7.98% and 18.75% compared with the traditional passive suspension, while under the same condition, the integer-order ISD suspension can only reduce by 5.34% and 16.07%, respectively. The results show that employing a fractional-order biquadratic electrical network in the vehicle mechatronic ISD suspension enhances vibration isolation performance compared with the suspension using an integer-order biquadratic electrical network.

Hybrid electric vehicles, as an important means of vehicle development for new energy, have received a lot of research in structure and control in the last decades. However, the coupling between the parameters of structure level and control level is seldom considered leading to only local optimal solutions. To obtain the global driving performance, in this article, a simultaneous-level optimization method is proposed. The study mainly focuses on the optimization design of transmission and energy management control for a plug-in hybrid electric bus (PHEB), with the energy management control utilizing the fuzzy logic control (FLC) strategy. First, the driving data of the local bus routes is collected, processed, clustered, and synthesized into local driving cycles. Then, a hierarchical optimization framework is presented which considers the sizing and control parameters simultaneously. In each layer, the Kriging model and NSGA II algorithm are used to improve optimization efficiency. The results show that the proposed method can reduce emissions and fuel consumption. Compared to the initial value, the optimized fuel consumption decreased by 10.09% and carbon monoxide (CO) emissions decreased.

In this paper, a robust optimization design method is presented to improve the energy management effect of plug-in hybrid electric buses (PHEB). Various uncertain factors are taken into account, including passenger load, resistance, and efficiency. First, the deterministic design of the energy management strategy is conducted under a city bus route, which is divided into 20 segments according to bus stations. The segmented equivalent consumption minimization strategy (ECMS) is established wherein the equivalent factors undergo optimization by the dynamic programming (DP) algorithm. Then, the sequential Taguchi method is utilized to optimize the equivalent factors based on deterministic results. Uncertain factors are designated as noise factors, while equivalent factors serve as control factors. The total fuel consumption is chosen as the optimization objective, with consideration given to the final state of charge (SOC) limit. The simulation results demonstrate that the energy management system obtained by robust optimization achieves a 1.9% reduction in fuel consumption expectation compared to the deterministic optimization. The result proves the validity of the proposed robust optimization method.

This article provides a comprehensive overview of on-board wireless motors based on WPT system. Based on the mathematical models of common wireless motors, the coupling strength of on-board wireless motors is divided, and their characteristics and advantages are comprehensively summarized and classified. In order to provide power to the three-phase wireless motors, the rotary transformer is used to replace the common coupling coil in the WPT system. This paper conducts electromagnetic model on the rotary transformer, as well as a comprehensive summary of structures and materials of the rotary transformer. The resonant compensation topology of WPT system is crucial. Comparing the transmission characteristics and advantages and disadvantages of various compensation topologies is beneficial for selecting appropriate compensation topologies in different application scenarios. For the control strategy of WPT system, this paper mainly focuses on the issues of output power and transmission efficiency. Finally, the research difficulties and directions of on-board wireless motors are proposed, such as electromagnetic interference and resonance, and this technology is extended to other fields, including wireless charging, robotics, etc.

With the increasingly widespread application of permanent magnet synchronous motors (PMSMs), it is required that PMSMs can still maintain high efficiency and high reliability in complex environments. Due to the nonlinearity of PMSMs and the influence of external disturbances, its precise control is still a challenge. As a modern control scheme, sliding mode control can improve the control PMSM drive system. It has been successfully applied in the control of PMSMs and has become a hot spot in the motor drive system. This paper provides an overview of the current research status of sliding mode control strategies based on PMSMs and introduces the design of sliding mode controllers, as well as the development and application of high-order sliding mode control. The method of suppressing the chattering problem is summarized, and the deficiencies of it are put forward. Finally, the development trend of sliding mode control technology is discussed, and the future development direction of SMC for PMSM is prospected.