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.

Constrained by traditional fuel-saving technologies that have almost reached the limit of fuel-saving potential, the difficulty in changing urban congestion, and the low market penetration rate of new energy vehicles, in the short term, eco-driving seems to be an effective way to achieve energy-saving and emissions reduction in the transportation industry. This paper reviews the energy-saving theory and technology of eco-driving, eco-driving capability evaluation, and the practical application of eco-driving, and points out some limitations of previous studies. Specifically, the research on eco-driving theory mostly focuses on a single vehicle in a single scene, and there is a lack of eco-driving research for fleets or regions. In addition, the parameters used to evaluate eco-driving capabilities mainly focus on speed, acceleration, and fuel consumption, but external factors that are not related to the driver will affect these parameters, making the evaluation results unreasonable. Fortunately, vehicle big data and the Internet of Vehicles (V2I) provides an information basis for solving regional eco-driving, and it also provides a data basis for the study of data-driven methods for the fair evaluation of eco-driving. In general, the development of new technologies provides new ideas for solving some problems in the field of eco-driving.

This paper presents a prescient energy management strategy based on model predictive control (MPC) for parallel plug-in hybrid electric vehicles (PHEVs). In this hierarchical strategy, DP, with its improved calculation speed, is chosen as the solution algorithm to calculate the optimal power distribution combinations in the predicted receding horizon and under the given terminal battery SOC (state of charge) terminal constraint. A synthesized velocity profile prediction (SVPP) method is adopted. The macroscopically and microcosmically predicted velocities obtained by the participatory sensing data (PSD)-based method and the Markov chain (MC), respectively, are synthesized by the linear regression method, obtaining the final velocity profile. In the linear regression step, a particle filter (PF) is implemented for parameter estimation. According to the characteristics of the driving conditions and components, the terminal battery SOC in each control horizon is constrained by a novel method. Finally, we demonstrate the capability of the proposed scheme in terms of fuel economy improvement by comparing the value of this metric with those of other strategies through simulation.

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.

In the era of big data, using big data to realize the online estimation of battery SOH has become possible. Traditional solutions based on theoretical models cannot take into account driving behavior and complicated environmental factors. In this paper, an approximate SOH degradation model based on real operating data and environmental temperature data of electric vehicles (EVs) collected with a big data platform is proposed. Firstly, the health indicators are extracted from the historical operating data, and the equivalent capacity at 25 °C is obtained based on the capacity–temperature empirical formula and the capacity offset. Then, the attenuation rate during each charging and discharging process is calculated by combining the operating data and the environmental temperature. Finally, the long short-term memory (LSTM) neural network is used to learn the degradation trend of the battery and predict the future decline trend. The test results show that the proposed method has better performance.