Hybrid Electric Vehicles (HEVs) have been proven to be a promising solution to environmental pollution and fuel savings. The benefit of the solution is generally realized as the amount of fuel consumption saved, which by itself represents a challenge to develop the right energy management strategies (EMSs) for HEVs. Moreover, meeting the design requirements are essential for optimal power distribution at the price of conflicting objectives. To this end, a significant number of EMSs have been proposed in the literature, which require a categorization method to better classify the design and control contributions, with an emphasis on fuel economy, providing power demand, and real-time applicability. The presented review targets two main headlines: (a) offline EMSs wherein global optimization-based EMSs and rule-based EMSs are presented; and (b) online EMSs, under which instantaneous optimization-based EMSs, predictive EMSs, and learning-based EMSs are put forward. Numerous methods are introduced, given the main focus on the presented scheme, and the basic principle of each approach is elaborated and compared along with its advantages and disadvantages in all aspects. In this sequel, a comprehensive literature review is provided. Finally, research gaps requiring more attention are identified and future important trends are discussed from different perspectives. The main contributions of this work are twofold. Firstly, state-of-the-art methods are introduced under a unified framework for the first time, with an extensive overview of existing EMSs for HEVs. Secondly, this paper aims to guide researchers and scholars to better choose the right EMS method to fill in the gaps for the development of future-generation HEVs.

Accurately predicting changes in speed has a significant impact on the quality of energy management in hybrid vehicles. Many methods for predicting speed have been proposed in the literature, but few fully consider vehicle dynamics to predict speed changes. To this end, a new method is introduced to predict vehicle speed and to perform energy management for hybrid vehicles in situations where lateral dynamics plays a significant role. Based on the tire-road friction coefficient and the GPS signal, the maximum cornering speed of the vehicle, in which each tire force does not saturate, is evaluated. Then the principle of using less friction braking and using more regenerative braking, the vehicle speed prediction controller is designed. In the end, an optimal control method with a new Equivalent Factor (EF) adaptive algorithm is designed to distribute the torque of the engine and the motor, as well as the shift schedule of the gearbox. A driver-in-the-loop experiment is used to prove that the vehicle installed with the proposed speed prediction controller has an average 29.1% increase in energy efficiency compared to vehicle that do not have speed prediction controller. And, the EF adaptive algorithm keeps the battery SoC at a reasonable interval.

The performance of energy management systems in hybrid electric vehicles (HEVs) is highly related to drivers' driving style. This paper proposes a driving style-oriented adaptive equivalent consumption minimization strategy (AECMS-style) in order to improve fuel economy for HEVs. For this purpose, firstly, a statistical pattern recognition approach is proposed to classify drivers into six groups from moderate to aggressive using kernel density estimation and entropy theory. Then, the effects of driving style on EMS are discussed by analyzing the performance of equivalent consumption minimization strategy (ECMS). Based on the comprehensive analysis, we design a new optimal equivalent factor adjustment rule for the AECMS-style and also redesign the braking strategy of motors at driving charging mode for different driving styles. Finally, five drivers with typical driving styles participate in experiments to show the effectiveness of our proposed method. Experimental results demonstrate that the AECMS-style can improve the fuel economy and charging sustainability of HEVs, compared with ECMS.

Rapidly-evolving connected vehicle technologies offer growing opportunities to improve the performance of energy management for hybrid electric vehicles (HEVs). In this context, a computationally efficient energy management approach based on a model predictive control (MPC) framework is proposed to obtain the optimal torque split and gearshift for a parallel HEV. The velocity is predicted by exploiting the information through V2V (Vehicle to Vehicle) communication. The energy management problem is then reformulated by introducing equivalent consumption minimization strategy (ECMS) into MPC framework. A penalty index is directly incorporated into the objective function to avoid frequent gearshift activities considering both fuel economy and drivability. To derive an analytical optimal solution, the energy management is further simplified by assuming that the gearshift command keeps constant over each prediction horizon. As a result, the torque split and gearshift are jointly optimized in the same framework. A sensitivity study for different parameters is conducted, and an equivalence factor (EF) adaptation law is also devised for the ECMS-based MPC. Lastly, simulations are performed in standard cycles and three predicted cycles over different prediction horizons, verifying that the ECMS-based MPC produces a promising computational efficiency, relative to traditional dynamic programming (DP)-MPC, while fuel economy globally converges to that produced by DP.

In order to achieve near-optimal fuel economy for hybrid electric vehicles (HEVs) using the equivalent consumption minimum strategy (ECMS), it is necessary to dynamically tune the equivalent factor (EF). Unlike widely studied ECMSs that adapt EF to driving conditions, the proposed self-adaptive equivalent consumption minimization strategy (SECMS) applies a new idea of determining the EF from the historical driving conditions. To this end, a dynamic EF self-determining algorithm is designed according to the assumption that the electrical energy used at the current moment comes from past recuperation, and an adaptive charge-sustaining algorithm is developed to balance the electrical self-sustainability and optimization performance. To show its effectiveness, the SECMS is implemented to a single-shaft parallel HEV over four standard driving cycles and benchmarked against two conventional ECMS-type control strategies: standard ECMS and adaptive ECMS (AECMS). The results show that the SECMS can achieve significant improvement regarding both the fuel economy and battery charge-sustainability compared to ECMS and AECMS. The proposed SECMS could facilitate a new development of energy management strategies for HEVs based on historical driving information.

This paper presents a new energy management system based on equivalent consumption minimization strategy (ECMS) for hybrid electric vehicles. The aim is to enhance fuel economy and impose state of charge (SoC) charge-sustainability. First, the relationship between the equivalent factor (EF) of ECMS and the co-state of pontryagin’s minimum principle (PMP) is derived. Second, a new method of implementing the adaptation law using fuzzy proportional plus integral (PI) controller is developed to adjust EF for ECMS in real-time. This adaptation law is more robust than one with constant EF due to the variation of EF as well as driving cycle. Finally, simulations for two driving cycles using ECMS are conducted as opposed to the commonly used rule-based (RB) control strategy, indicating that the proposed adaptation law can provide a promising blend in terms of fuel economy and charge-sustainability. The results confirm that ECMS with Fuzzy PI adaptation law is more robust than ECMS with constant EF as well as PI adaptation law and it achieves significant improvements compared with RB in terms of fuel economy, which is enhanced by 4.44% and 14.7% for china city bus cycle and economic commission of Europe (ECE) cycle, respectively.