Energy Management Strategy For Hybrid Electric Vehicle Research Articles

Energy management strategies of hybrid electric vehicles: A comparative review

AbstractWith increasing environmental problems, including air pollution, rising greenhouse gases and global warming, the need to use clean energy resources seems essential. Therefore, the usage of clean and renewable energy sources is suggested as a suitable solution to overcome the mentioned concerns. The transportation section contributes to a huge percentage of energy consumption. Hybrid electric vehicles (HEVs), by combining several energy resources, are considered as a crucial solution to decrease fossil fuel consumption and improve the environmental challenges. The existence of an alternative energy resource and the internal combustion engine together provides optimal power distribution among them to maximise power usage and minimise fuel consumption. Energy management strategy (EMS) is an essential challenge in HEV's design procedure to deal with the power distribution in multiple power source systems to improve the performance of the HEVs. A review of various EMSs for HEVs, followed by an analysis of each type, including its benefits and drawbacks, is presented by the authors. In addition to this, the major challenges in EMSs for HEVs are described and a comprehensive review is presented for strategies addressing these issues.

Review on Reinforcement Learning-Based Energy Management Strategies for Hybrid Electric Vehicles

Hybrid Electric Vehicles (HEVs) achieve better fuel economy than conventional vehicles by employing two different power sources: a mechanical engine and an electrical motor. These power sources have conventionally been controlled by a rule-based algorithm or optimization-based control. Besides these conventional approaches, reinforcement learning-based control algorithms have actively been studied recently. Reinforcement learning, which is one of three machine learning paradigms, has the capability of determining optimal control actions to maximize a vehicle’s fuel economy without the vehicle model nor a priori driving route information. To provide a useful reference to researchers interested in this technology, this article reviews reinforcement learning-based energy management strategies for HEVs with their advantages and disadvantages.

Uncertainty-Aware Energy Management Strategy for Hybrid Electric Vehicle Using Hybrid Deep Learning Method

Energy management strategy (EMS) is important to ensure energy-saving performance of hybrid electric vehicle (HEV). However, the power coupling property between different power sources, together with stochastic power demand fluctuation poses great challenges for EMS to achieve desirable performance in real-world scenario. This paper presents an uncertainty-aware energy management strategy for HEV. A speed predictor combining convolutional neural network and long short-term memory neural network is proposed to extract temporal features that could reveal speed change mechanism. Then an online self-adaptive transition probability matrix is constructed to estimate the speed prediction uncertainty. Tube model predictive control (tube-MPC) is finally used to solve the optimization control problem in a receding horizon manner. The robust set introduced in the tube-MPC greatly enhances the optimality and robust-ness of the control sequence under the scenario with speed prediction uncertainty. Simulations are conducted to verify the effectiveness of the proposed method. Results show that the speed prediction accuracy is 47.4% and 23.1% higher than exponential decay rate prediction model and autoregressive integrated moving average model respectively. Compared with traditional rule-based and MPC method, the proposed tube-MPC method could achieve 10.7% and 3.0% energy-saving performance improvement in average.

Self-Adaptive Equivalent Consumption Minimization Strategy for Hybrid Electric Vehicles

In order to achieve nearly optimal fuel economy for hybrid electric vehicles (HEVs) using an 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 charging, 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 improvements 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.

Ecological Adaptive Cruise Control and Energy Management Strategy for Hybrid Electric Vehicles Based on Heuristic Dynamic Programming

In this paper, an ecological adaptive cruise controller (ECO-ACC) for parallel hybrid electric vehicles (HEVs) in a car-following scenario is presented to improve the fuel economy and to maintain a desired inter-vehicle distance from the preceding vehicle. An ACC based on action dependent heuristic dynamic programming (ADHDP) is proposed to obtain an ecological velocity profile and realize an active distance control in normal driving situations. ADHDP is able to adapt internal parameters online and can thus deal with systems with disturbances. Furthermore, an adaptive energy management strategy for HEVs is introduced to control the gear shift and power split for fuel consumption optimization. The gear shift command is designed by enumeration, and the power distribution between the engine and the electric motor is performed by ADHDP. The developed ACC and energy management strategy are finally combined to an ECO-ACC to achieve a multi-objective optimization. Only the current velocity and acceleration of the preceding vehicle are used while knowledge about the future velocity is not needed. The simulations of different driving cycles indicate that the ECO-ACC can lead to near-optimal fuel economy and comfortable driving.

Supercapacitors and Battery Energy Management based on New European Driving Cycle

This paper presents a new strategy of embedded energy management between battery and supercapacitors (SC) for hybrid electric vehicles (HEV) applications. This proposal is due to the present trend in the field, knowing that the major drawback of the HEV is the autonomy problem. Thus, using supercapacitors and battery with a good energy management improves the HEV performances. The main contribution of this paper is focused on DC-bus voltage and currents control strategies based on polynomial controller. These strategies are implemented in PIC18F4431 microcontroller for DC/DC converters control. Due to reasons of cost and available components (no optimized), such as the battery and power semiconductors (IGBT), the experimental tests are carried out in reduced scale (2.7 kW). Through some simulations and experimental results obtained in reduced scale, the authors present an improved energy management strategy for HEV.

An Improved Energy Management Strategy for HEV Based on Driving Condition Prediction Within a Finite Time Horizon

Energy management based on driving condition information or driving condition prediction within a finite time horizon is a way of further improving the fuel economy of hybrid electric vehicles (HEVs). The future power demand of vehicles is predicted by using support vector regression (SVR) and fuel economy is further optimised on-line to acquire the optimal operating points of both engine and motor. According to the speed characteristics, the driving cycle is classified as a certain speed mode by using the K nearest neighbour (KNN) algorithm. By taking the speed characteristics as training data, the prediction model for vehicle speed based on SVR under a certain speed mode is obtained through training by applying partial swarm optimisation (PSO). The PSO-SVR method can greatly improve speed prediction accuracy within a finite time horizon both accurately and efficiently. The power demand within the finite time horizon is calculated and the assigned values of various control variables at every sample time are acquired by using dynamic programming (DP) within a finite time horizon. The research results indicated that the control strategy based on predicted information shows a significantly positive effect in optimising fuel consumption and battery utilisation.

Energy management strategy for HEV based on KFCM and neural network

SummaryAiming at the deficiency of optimal control energy management strategy, a model of energy management controller for hybrid electric vehicle (HEV) is constructed based on Kernel Fuzzy C‐means Clustering (KFCM) and multi‐neural network. Using energy management control strategy based on PMP, the operational parameters of the four driving modes for HEV is extracted; the data cluster corresponding to the driving mode is generated by clustering through the KFCM method and is used as the training samples for the feedforward neural network. Taking the battery SOC, needed power and speed as the inputs of neural network, and taking engine power as the output of neural network, four sub‐neural network models are established. Taking the vehicle driving needed power at the current moment and the engine output power at the previous moment as characteristic parameters, the corresponding sub‐neural network model is selected for output prediction according to the proportional relationship between the driving demand torque and the engine output power. The simulation results show that, compared with the energy management strategy based on PMP, the calculation time is greatly shortened using the proposed control strategy, and the real‐time performance is better. The fuel economy is a little decreased under the condition of meeting the requirements, but better dynamic performance can be obtained.

Rule-corrected energy management strategy for hybrid electric vehicles based on operation-mode prediction

Abstract The energy crisis and exhaust emissions are serious problems that are largely related to road traffic. One solution to these threats is to switch from traditional gasoline-based vehicles to hybrid electric vehicles (HEVs) or electric vehicles (EVs), which also has the benefit of promoting a more sustainable economy. Energy management strategies (EMS) for HEVs or EVs play an important role in improving fuel economy. As a stochastic prediction method, a Markov chain, has been widely used in the prediction of driving conditions, but the application of a Markov chain in the prediction of HEV-operating modes in a rule-based EMS has rarely been presented in the literature. In addition, the threshold selection of rule-based EMS is usually based on experience and it is difficult to achieve optimal performance. In this paper, the impact of operation-mode prediction on rule-based EMS fuel economy has been explored to achieve real-time on-line corrections to motor and engine torque and to enhance their capacity for on-line optimization. Thus, a new EMS for HEV has been proposed based on operation-mode prediction using a Markov chain, which determines the on-line correction of torque distribution between the engine and the electric motor. The Markov decision processes and transition matrix are introduced first, and then, the transition probability matrix and torque correction model are established using the MATLAB/Simulink platform. The results of the simulation show that the proposed approach provides a 13.1% and 9.6% improvement in real fuel consumption under the New European Driving Cycle and the Urban Dynamometer Driving Schedule respectively, in comparison with the conventional rule-based control strategies without operation-mode prediction. The results also show that the proposed control strategy can significantly enhance the real-time optimization control performance of the EMS while maintaining the state of charge within a reasonable range.

Energy Management Strategy for HEVs Including Battery Life Optimization

This paper presents an optimal control-based energy management strategy for a parallel hybrid electric vehicle (HEV). Not only does this strategy try to minimize fuel consumption while maintaining the state of charge of the battery within reasonable bounds, it also seeks to minimize wear of the battery and extend its life. This paper focuses on understanding the optimal control solution offered by Pontryagin’s minimum principle (PMP) in this context. Simulation-based results are presented and analyzed, which show that the control algorithm is able to reduce battery wear by decreasing battery operating severity factor with minimal fuel economy penalty. The benefit of this strategy is especially evident when ambient and driving conditions are especially severe.

Trends, features and recent research efforts in the field of hybrid electric vehicles

Hybrid Electric Vehicles (HEVs) have attracted tremendous attention as a commercially viable alternative to either traditional vehicles or electric vehicles. Successful HEV design requires optimal sizing of its key mechanical and electrical components. Recently, energy management strategies for HEV have been extensively studied. In addition, for a HEV to be as efficient as possible, proper management of its energy elements is required. Several research studies have been focused on the optimal sizing of HEV components. In this case, a fixed control strategy is employed. On the other hand, there are a number of studies on the optimisation of HEV power management strategy to find the optimal control parameters. In this case, the component sizes are fixed. This paper presents an overview on HEVs including trends, features and recent research efforts in this field.