Existing Energy Management Strategies Research Articles

Integrated Energy and Catalyst Thermal Management for Plug-In Hybrid Electric Vehicles

With plug-in hybrid electric vehicles (PHEVs), the catalyst temperature is below the light-off temperature due to reduced engine load, extended engine off period, and frequent engine on/off shifting. The conversion efficiency of a three-way catalyst (TWC) and tailpipe emissions were proven to depend heavily on the temperature of the catalyst. The existing energy management strategy (EMS) of the PHEVs focuses on the improvement of fuel efficiency and emissions based on hot engine characteristics, but neglects the effect of catalyst temperature on tailpipe emissions. This paper presents a new EMS that incorporates a catalyst thermal management method. First, an additional cost is established to implement additional constraints on catalyst temperature, and then the global cost function is created using this additional cost and the fuel consumption. Second, we find the global optimal solution using Pontryagin’s minimum principle method, which provides an optimal control policy and state trajectories. Then, based on the analysis of the optimal control policy, an engine on/off filter (eng on/off filter) is introduced to command the engine on/off shifting. This filter plays an important role in adjusting both the energy and catalyst thermal management strategy for PHEVs. Finally, a practical approach based on the eng on/off filter is developed, and a genetic algorithm is applied to optimize the time constants of this filter. Simulation results demonstrate that the proposed approach‘s fuel consumption increased slightly, but the tailpipe emissions of HC (hydrocarbons), CO (carbon monoxide) and NOx (nitrogen oxide) significantly decreased compared with the standard approach.

Energy Management Strategy for Rural Communities’ DC Micro Grid Power System Structure with Maximum Penetration of Renewable Energy Sources

The AC and DC power system structures need to be modernized to meet consumer demands. DC microgrids are suitably admired due to their high efficiency, consistency, reliability, and load sharing performance, when interconnected to DC renewable and storage sources. The main control objective for any DC microgrid is providing proper load–power balancing based on the Distributed Generator (DG) sources. Due to the intermittent nature of renewable energy sources, batteries play an important role in load–power balancing in a DC microgrid. The existing energy management strategy may be able to meet the load demand. However, that technique is not suitable forrural communities’ power system structure. This research offers an energy management strategy (EMS) for a DC microgrid to supply power to rural communities with solar, wind, fuel cell, and batteries as input sources. The proposed EMS performs the load–power balancing between each source (renewable and storage) in a DC microgrid for dynamic load variation. Here, the EMS handles two battery sources (one is used to deliver power to the priority load, and the other is utilized in the common DC bus) to meet the required demand. The proposed EMS is capable of handling load–power balancing using renewable energy sources with less consumption of non- conventional energy sources (such as a diesel generator). The performance of the system is analyzed based on different operating conditions of the input sources. The MATLAB/Simulink simulation model for the proposed DC microgrid with their EMS control system is developed and investigated, and their results are tabulated under different input and load conditions. The proposed EMS is verified through a laboratory real-time DC microgrid experimental setup, and the results are discussed.

Hybrid-Trip-Model-Based Energy Management of a PHEV With Computation-Optimized Dynamic Programming

Plug-in hybrid electric vehicles (PHEVs) with fuel and electricity have demonstrated the capability to reduce fuel consumption and emissions by adopting appropriate energy management strategies. In the existing energy management strategies, the dynamic programming (DP)-based energy management strategy (EMS) can realize the global optimization of the fuel consumption if the global vehicle-speed trajectory is known in advance. The global vehicle-speed trajectory can be obtained by applying GPS data of vehicles when the trip path is determined. However, for a trip path without GPS data, the global vehicle-speed trajectory is difficult to be gained. In this case, the DP-based EMS cannot be utilized to achieve the globally optimal fuel consumption, which is the issue discussed in this paper. This paper makes the following two contributions to solve this issue. First of all, the cell transmission model of the road traffic flow and the vehicle kinematics are introduced to obtain the traffic speeds of road segments and the accelerations of the PHEV. On this basis, a hybrid trip model is presented to obtain the vehicle-speed trajectory for the trip path without GPS data. Next, a DP-based EMS with prediction horizon is proposed, and moreover, in order to improve its real-time implementation, a search range optimization algorithm of the state of charge (SOC) is designed to reduce the computational load of DP. In summary, we propose a computation-optimized DP-based EMS through applying the hybrid trip model. Finally, a simulation study is conducted for applying the proposed EMS to a practical trip path in Beijing road network. The results show that the hybrid trip model can effectively construct the vehicle-speed trajectory online, and the average accuracy of the vehicle-speed trajectory is more than 78%. In addition, compared with the existing optimization algorithm for DP calculation, the SOC search range optimization algorithm can further reduce the calculation load of DP. More importantly, compared to the globally optimal DP-based EMS, although the proposed EMS makes the fuel consumption grow less than 5.36%, it can be implemented in real time. Moreover, compared with the existing real-time strategies, it can further reduce the fuel consumption and emissions. Thus, the proposed EMS can offer an effective solution for the PHEV applying it online in the trip path without GPS data.

A comprehensive overview of hybrid construction machinery

With the increasing attention of energy saving and emission reduction technology, the recent application of hybrid powertrain technology affects the development of construction machinery industry. This article reviews these publications and provides comprehensive references. This article reviews the state-of-art for the hybrid wheel loader and excavator, which focuses on powertrain configuration, energy storage devices, and energy management strategies. The basis of classification and characteristic of each powertrain configuration are described. Advantages and disadvantages of batteries, supercapacitors, hydraulic accumulators, and flywheel used in hybrid construction machinery are summarized. The existing energy management strategies for hybrid construction machinery are also elaborated. The technological challenges and developing trends in the near future for hybrid construction machinery are discussed.

Vehicle Energy Management for On/off Controlled Auxiliaries: Fuel Economy vs. Switching Frequency

In this paper, an integrated approach for designing energy management strategies concerning vehicle auxiliaries with on/off control is proposed. This approach provides the possibility of making different trade-offs between fuel economy and switching frequency. In this paper, we demonstrate the validity of this approach by using game theory for obtaining the online implementable energy management strategy while the design philosophy can be generally applied to other existing energy management strategies. A case study is carried out on a model of a heavy-duty truck with an electric power take off (ePTO) connected to a refrigerated semitrailer for which the cooling unit can only be switched on or off. The results show that different trade-offs can be made between fuel savings and switching frequency.

Optimal adaptation of equivalent factor of equivalent consumption minimization strategy for fuel cell hybrid electric vehicles under active state inequality constraints

Among existing energy management strategies (EMSs) for fuel cell hybrid electric vehicles (FCHEV), the equivalent consumption minimization strategy (ECMS) is often considered as a practical approach because it can be implemented in real-time, while achieving near-optimal performance. However, under real-world driving conditions with uncertainties such as hilly roads, both near-optimality and charge-sustenance of ECMS are not guaranteed unless the equivalent factor (EF) is optimally adjusted in real-time. In this paper, a methodology of extracting the globally optimal EF trajectory from dynamic programming (DP) solution is proposed for the design of EF adaptation strategies. In order to illustrate the performance and process of the extraction method, a FCHEV energy management problem under hilly road conditions is investigated as a case study. The main goal is to learn how EF should be adjusted and the impact of EF adaptation on fuel economy under several hilly road cases. Using the extraction method, the DP-based EF is computed, and its performance is compared with those of Pontryagin's minimum principle (PMP) and conventional ECMS. The results show that the optimal EF adaptation significantly improves fuel economy when the battery SoC constraint becomes active, and thus EF must be properly adjusted under severely hilly road conditions.

Energy-Efficient Sensing in Wireless Sensor Networks Using Compressed Sensing

Sensing of the application environment is the main purpose of a wireless sensor network. Most existing energy management strategies and compression techniques assume that the sensing operation consumes significantly less energy than radio transmission and reception. This assumption does not hold in a number of practical applications. Sensing energy consumption in these applications may be comparable to, or even greater than, that of the radio. In this work, we support this claim by a quantitative analysis of the main operational energy costs of popular sensors, radios and sensor motes. In light of the importance of sensing level energy costs, especially for power hungry sensors, we consider compressed sensing and distributed compressed sensing as potential approaches to provide energy efficient sensing in wireless sensor networks. Numerical experiments investigating the effectiveness of compressed sensing and distributed compressed sensing using real datasets show their potential for efficient utilization of sensing and overall energy costs in wireless sensor networks. It is shown that, for some applications, compressed sensing and distributed compressed sensing can provide greater energy efficiency than transform coding and model-based adaptive sensing in wireless sensor networks.

Optimal energy management in a dual-storage fuel-cell hybrid vehicle using multi-dimensional dynamic programming

Hybrid storage systems consisting of battery and ultra-capacitor have recently emerged as an alternative to the conventional single buffer layout in hybrid vehicles. Their high power and energy density could improve the performance indices of the vehicle, provided that an optimal energy management strategy is employed that could handle systems with multiple degrees of freedom (DOF). The majority of existing energy management strategies is limited to a single DOF and the small body of work on multi-DOF systems is mainly heuristic-based.We propose an optimal solution to the energy management problem in fuel-cell hybrid vehicles with dual storage buffer for fuel economy in a standard driving cycle using multi-dimensional dynamic programming (MDDP). An efficient MDDP code is developed using MATLAB™'s vectorization feature that helps reduce the inherently high computational cost of MDDP. Results of multiple simulated experiments are presented to demonstrate the applicability and performance of the proposed strategy. A comparison is also made between a single and a double buffer fuel-cell hybrid vehicle in various driving cycles to determine the maximum reduction in fuel consumption that can be achieved by the addition of an ultra-capacitor.

Integrated thermal and energy management of plug-in hybrid electric vehicles

In plug-in hybrid electric vehicles (PHEVs), the engine temperature declines due to reduced engine load and extended engine off period. It is proven that the engine efficiency and emissions depend on the engine temperature. Also, temperature influences the vehicle air-conditioner and the cabin heater loads. Particularly, while the engine is cold, the power demand of the cabin heater needs to be provided by the batteries instead of the waste heat of engine coolant. The existing energy management strategies (EMS) of PHEVs focus on the improvement of fuel efficiency based on hot engine characteristics neglecting the effect of temperature on the engine performance and the vehicle power demand. This paper presents a new EMS incorporating an engine thermal management method which derives the global optimal battery charge depletion trajectories. A dynamic programming-based algorithm is developed to enforce the charge depletion boundaries, while optimizing a fuel consumption cost function by controlling the engine power. The optimal control problem formulates the cost function based on two state variables: battery charge and engine internal temperature. Simulation results demonstrate that temperature and the cabin heater/air-conditioner power demand can significantly influence the optimal solution for the EMS, and accordingly fuel efficiency and emissions of PHEVs.