This paper presents a comprehensive review of energy management control strategies utilized in hybrid electric vehicles (HEVs). These can be categorized as rule-based strategies and optimization-based strategies. Rule-based strategies, as the most basic strategy, are widely used due to their simplicity and practical application. The focus of rule-based strategies is to determine and optimize the optimal threshold for mode switching; however, they fall into a local optimal solutions. To have better performance in energy management, optimization-based strategies were developed. The categories of the existing optimization-based strategies are identified from the latest literature, and a brief study of each strategy is discussed, which consists of the main research ideas, the research focus, advantages, disadvantages and improvements to ameliorate optimality and real-time performance. Deterministic dynamic programming strategy is regarded as a benchmark. Based on neural network and the large data processing technology, data-driven strategies are put forward due to their approximate optimality and high computational efficiency. Finally, the comprehensive performance of each control strategy is analyzed with respect to five aspects. This paper not only provides a comprehensive analysis of energy management control strategies for HEVs, but also presents the emphasis in the future.

The Cooperative Adaptive Cruise Control (CACC) is considered to be an effective method to improve traffic flow. However, the comfortability and economy need to be paid more attention to besides stability and safety. In this paper, a CACC strategy is proposed for an electric vehicle platoon to improve the economy, following performance, safety, and comfortability characteristics, based on the model predictive control (MPC) with simulated annealing-particle swarm optimization (SA-PSO) algorithm. Firstly, the braking force distribution strategy of electric vehicles is designed to improve the efficiency of regenerative braking. Secondly, based on the variable vehicle spacing with fixed time headway, a vehicle platoon following strategy is established to meet the following performance and safety. Thirdly, an MPC controller is used to control the states of the vehicles in the platoon to satisfy the performance. A multi-objective function of the MPC controller is established, including the economy, following performance, comfortability, and safety of the vehicles in the platoon. The SA-PSO algorithm effectively solves the problem of the discrete variables in the objective function. Simulations are conducted to validate the sufficient conditions of the economy, following performance, comfortability, and safety. Simulation results demonstrate that the economic efficiency of the CACC strategy with the economic index is 16.5% higher than that of the existing ACC strategy. Meanwhile, the other characteristics can also meet the control requirement.

Benefitting from the advances in sensor nets, wireless communication, and embedded systems, the cyber-physical system (CPS) has been implemented in many practical areas. The cyber world and the physical world in CPS are closely linked and merged, guaranteeing the reliable and efficient performance of the physical world. The service-oriented CPS has been the hot research and development. In this paper, we present a novel velocity-profile prediction method based on the specific CPS architecture. Three main efforts have been made. First, a CPS architecture which is appropriate for velocity-profile prediction is built. Second, the hybrid velocity-profile prediction (HVPP) method is developed based on the exclusive CPS architecture. The HVPP method accomplishes the velocity-profile prediction by cooperatively employing different control units in CPS. Third, a case study in a plug-in hybrid electric vehicle (PHEV) is performed to evaluate the effect of CPS-based service in the real application. Results of the case study demonstrate that the HVPP method can improve the fuel economy of PHEV significantly.

With the rapid development of the intelligent driving technology, achieving accurate path planning for unmanned vehicles has become increasingly crucial. However, path planning algorithms face challenges when dealing with complex and ever-changing road conditions. In this paper, aiming at improving the accuracy and robustness of the generated path, a global programming algorithm based on optimization is proposed, while maintaining the efficiency of the traditional A* algorithm. Firstly, turning penalty function and obstacle raster coefficient are integrated into the search cost function to increase the adaptability and directionality of the search path to the map. Secondly, an efficient search strategy is proposed to solve the problem that trajectories will pass through sparse obstacles while reducing spatial complexity. Thirdly, a redundant node elimination strategy based on discrete smoothing optimization effectively reduces the total length of control points and paths, and greatly reduces the difficulty of subsequent trajectory optimization. Finally, the simulation results, based on real map rasterization, highlight the advanced performance of the path planning and the comparison among the baselines and the proposed strategy showcases that the optimized A* algorithm significantly enhances the security and rationality of the planned path. Notably, it reduces the number of traversed nodes by 84%, the total turning angle by 39%, and shortens the overall path length to a certain extent.

This work presents the control strategies for the configuration involving an open-end winding induction motor (OW-IM) fed by dual two-level inverters, which can realize the controllable power sharing between two isolated power sources without using the DC/DC converter. Based on the mathematical model of the OW-IM and the principles of the voltage vector distribution, two rotor flux calculation algorithms, namely the motor loss minimization (MLM) algorithm and the maximum power sharing capability (MPSC) algorithm are proposed and compared, with their selection strategy to select the suitable algorithm depending on the operating condition. The MLM algorithm has the optimal efficiency performance, while the MPSC algorithm can maximize the power sharing capability of the dual inverter while holding the widest operation range of the motor. Based on the dual space vector pulse-width modulation (SVPWM) control scheme, the golden section (GS) search method is introduced to allocate the stator voltage vector to the dual inverter for the power sharing demand, with a flexible iteration number to obtain a stable and efficient computational performance, which fully utilizes the power sharing capability via the voltage vector distribution. Simulation results prove the validity and the advantages of the proposed strategy. This system also lays a foundation for energy management function of the dual-power electric vehicles.

An open-end winding permanent magnet synchronous motor (PMSM) has a larger range of speed regulation than normal PMSM with the same DC voltage, and the control method is more flexible. It can also manage energy distribution between two power sources without a DC/DC converter. This paper aims at an electric vehicle equipped with OW-PMSM drive system with dual power sources and dual inverters; based on analyzing the external characteristics of each winding mode, we propose a winding mode switching strategy whose torque saturation judgmental algorithm, which is insensitive to motor’s parameters, could automatically realize upswitching of the winding mode. The proposed multi-level current hysteresis modulation algorithm could set the major power source and switch it at any time in independent mode, which accomplishes energy distribution between two power sources; its two control methods, low switching frequency method and high power difference method, could achieve different energy distribution effects. Simulation results confirm the validity and effectiveness of the winding mode switching strategy and current modulation method. They also show that an electric vehicle under the proposed control methods has better efficiency than one equipped with a traditional OW-PMSM drive system under traditional control.