Equivalent Fuel Consumption Minimization Strategy Research Articles

Predictive Energy Management Strategy Including Traffic Flow Data for Hybrid Electric Vehicles

Within hybrid electric vehicles (HEVs) predictive energy management strategies (EMSs) have the potential to reduce the fuel consumption compared to conventional EMSs, where the drive cycle is unknown. Typically, predictive EMSs require a future vehicle speed profile prediction. However, when prediction is inaccurate, the systems fuel reduction performance and robustness may be compromised. Among many influential factors, inaccurate prediction is mainly caused by uncertain dynamic traffic conditions, e.g. traffic and traffic lights. This paper develops a predictive EMS, which enhances the equivalent fuel consumption minimization strategy (ECMS) with real-time traffic flow data and traffic light position to maximize fuel reduction performance of HEVs. Moreover, a Monte Carlo approach is exploited to handle the traffic light uncertainty. Simulation results demonstrate the benefits of Monte Carlo approach in predictive EMS to enhance the robustness and fuel reduction performance up to 2-11[%] compared to conventional strategies for various battery capacities.

Adaptive Power Management Strategy for a Four-Mode Hybrid Electric Vehicle

A conventional vehicle propelled with only the internal combustion engine (ICE) is not easy to enhance the fuel economy due to the wide range operation requirement of the powertrain. However, a hybrid electric vehicle (HEV) which consists of an ICE and one or multiple electric motors can effectively improve the efficiency of the powertrain. In this paper, we propose an adaptive power management strategy (PMS) based on the equivalent fuel consumption minimization strategy (ECMS) for a four-mode HEV. The four-mode HEV which consists of an ICE and two motors provides four modes of operation, including electric vehicle (EV) mode, range extended (RE) mode, hybrid mode, and engine mode. The adaptive PMS is designed for charge sustaining such that the state of charge (SOC) can be maintained at a certain value. A self-organizing fuzzy controller (SOFC) is employed to adaptively adjust the equivalence factor of electric energy consumption based on the SOC deviation and the change of SOC deviation. An instantaneous cost function which consists of the fuel consumption of ICE and the equivalent fuel consumption of the battery is minimized to obtain the optimum power distributions of the ICE and two motors. Simulation results show that the adaptive PMS can effectively improve the fuel economy for different driving cycles.

Development of a multi-scheme energy management strategy for a hybrid fuel cell driven passenger ship

Hybrid fuel cell propulsion systems for marine applications are attracting widespread interest due to the need to reduce ship emissions. In order to increase the potential of these systems, the design of an efficient energy management strategy (EMS) is essential to distribute the required power properly between different components of the hybrid system. For a hybrid fuel cell/battery passenger ship, a multi-scheme energy managements strategy is proposed. This strategy is developed using four schemes which are: state-based EMS, equivalent fuel consumption minimization strategy (ECMS), charge-depleting charge-sustaining (CDCS) EMS, the classical proportional-integral (PI) controller based EMS, in addition to a code that chooses the suitable scheme according to the simulation inputs. The main objective of the proposed multi-scheme EMS is to minimize the total consumed energy of the hybrid system in order to increase the energy efficiency of the ship.The world's first fuel cell passenger ship FCS Alsterwasser is considered and its hybrid propulsion system is modelled in MATLAB/Simulink environment. The performance of the developed multi-scheme EMS is compared to the four studied strategies in terms of total consumed energy, hydrogen consumption, total cost and the stresses seen by the hybrid fuel cell/battery system components considering a daily ship operation of 8 h. Results indicate that a maximum energy and hydrogen consumption savings of 8% and 16.7% respectively can be achieved using the proposed multi-scheme strategy.

An improved energy management strategy for a hybrid fuel cell/battery passenger vessel

The combination of a fuel cell and an energy storage system for the reduction of fuel consumption and improving the dynamics of hybrid power systems has successfully been used in transportation applications. In order to realise the benefits of hybrid fuel cell power systems, an energy management strategy is essential for distributing the required power properly between the fuel cell and the energy storage system. For a hybrid fuel cell/battery passenger vessel, an improvement to the classical proportional-integral (PI) controller based energy management strategy is presented. This takes fuel cell efficiency into consideration as an input to maintain higher efficiency of fuel cell and reduce stresses on it and hence reduce its fuel consumption.A 25.5 m long passenger vessel is used and its propulsion system is modelled in MATLAB/Simulink environment using the SimPowerSystems toolbox. The performance of the proposed PI energy management strategy is compared to original PI, equivalent fuel consumption minimization strategy (ECMS), and state-based energy management strategies in terms of consumed energy, battery state of charge, fuel cell efficiency, hydrogen consumption, and the stresses seen by each power source of the hybrid system taking into consideration a daily operation of 8 h. Results indicate that a daily hydrogen saving of 3.5%, 1.7%, and 1.4% compared to the ECMS, state-based, and the original PI strategies respectively can be achieved by adopting the proposed PI strategy in addition to lower stress on the fuel cell.

Global Optimal Discrete Equivalent Factor of Equivalent Fuel Consumption Minimization Strategy Based Energy Management Strategy for a Series-parallel Plug-in Hybrid Electric Vehicle

Global Optimal Discrete Equivalent Factor of Equivalent Fuel Consumption Minimization Strategy Based Energy Management Strategy for a Series-parallel Plug-in Hybrid Electric Vehicle

Research of Ant Colony Optimized Adaptive Control Strategy for Hybrid Electric Vehicle

Energy management control strategy of hybrid electric vehicle has a great influence on the vehicle fuel consumption with electric motors adding to the traditional vehicle power system. As vehicle real driving cycles seem to be uncertain, the dynamic driving cycles will have an impact on control strategy’s energy-saving effect. In order to better adapt the dynamic driving cycles, control strategy should have the ability to recognize the real-time driving cycle and adaptively adjust to the corresponding off-line optimal control parameters. In this paper, four types of representative driving cycles are constructed based on the actual vehicle operating data, and a fuzzy driving cycle recognition algorithm is proposed for online recognizing the type of actual driving cycle. Then, based on the equivalent fuel consumption minimization strategy, an ant colony optimization algorithm is utilized to search the optimal control parameters “charge and discharge equivalent factors” for each type of representative driving cycle. At last, the simulation experiments are conducted to verify the accuracy of the proposed fuzzy recognition algorithm and the validity of the designed control strategy optimization method.

Development of equivalent fuel consumption minimization strategy for hybrid electric vehicles

Power distribution between an internal combustion engine and electric motors is one of main features of hybrid electric vehicles that improves their fuel economy. An equivalent fuel consumption minimization strategy can instantaneously identify the optimal power distribution by converting the battery power into the equivalent fuel power and minimizing the overall fuel consumption. To guarantee the effectiveness of the strategy, it is essential to find the proper value of the conversion factor used to obtain the equivalent fuel power. However, finding the proper value is not a straightforward process because it is necessary to consider the overall power conversion efficiencies and battery charge sustaining strategy for the target driving cycle in advance. In this study, a model-based parameter optimization method is introduced to find the optimal conversion factor. A hybrid electric vehicle simulation model capable of estimating fuel consumption was developed, and the optimal conversion factor was discovered using a genetic algorithm that evaluates its population members using the simulation model. A series of simulations and vehicle tests was conducted to verify the effectiveness of the optimized strategy, and the results show a distinct improvement in fuel economy.

Optimal energy management for a complex hybrid electric vehicle: Tolerating power-loss of motor

The energy management may perform well under normal conditions, but may lead to poor behavior under abnormal situations. To tackle this problem, an optimal control strategy called rule-based equivalent fuel consumption minimization strategy (RECMS) is developed for a new complex hybrid electric vehicle (CHEV). It optimizes the energy efficiency and drive performance to cater for normal and power-loss operations of the tractive motor. Firstly, the strategy formulates a novel objective function based on the equivalent fuel concept. By accounting for the actual fuel cost, the equivalent fuel cost for the electric machines and virtual fuel cost for the drivability, the cost function is obtained. Furthermore, some penalty factors are presented to optimize the performance target. Finally, experiments for a practical CHEV are performed to validate a simulation model. Then simulations are carried out for both rule-based and RECMS. The results show that the optimal energy management is working well.

A Comparative Study Of Supervisory Control Strategies for Hybrid Electric Vehicles

Hybrid electric vehicles (HEVs) improvements in fuel economy and emissions strongly depend on the energy management strategy. The parallel HEV control problem involves the determination of the time profiles of the power flows from the engine and the electric motor. This is also referred to as the power split between the conventional and the electric sources. The objective of HEV control is in fact to find out the sequence of optimal power splits at each instant of time that minimizes the fuel consumption over given driving schedule. Big obstacles to the control design are the model complexity and the necessity of a knowledge of torque and velocity profiles. This paper presents three different energy management approaches for the control of parallel hybrid electric sport-utility-vehicle that do not require priori knowledge of the driving cycle. The considered approaches are: rule-based control, an adaptive equivalent fuel consumption minimization strategy (A-ECMS), and the Hinfin control. Results, compared with the optimal solution given by the dynamic programming, show that the A-ECMS strategy is the best performing strategy