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Copyright of DYNA - Ingeniería e Industria is the property of Publicaciones Dyna SL and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The fuel cell hybrid electric vehicle (FCHEV) has earned great interest among the automotive industry in recent years. However, the power allocation strategy between proton exchange membrane fuel cell (PEMFC) and lithium-ion battery remains a technological challenge. To conquer this problem, a multi-objective predictive energy management strategy (EMS) based on model predictive control (MPC) is proposed in this paper, combined with velocity forecast and driving pattern recognition. The comparative study is conducted to reveal the interaction between each optimization objectives. Simulation results illustrate that the proposed EMS could maintain SOC around reference, reduce fuel consumption by 6.67%, and avoid PEMFC degradation which caused by frequent start-off and rapid load change. • Velocity forecast further improves fuel economy. • Three optimization objectives are considered. • The hydrogen consumption is promoted by over 3% compared with traditional non-forecast EMS. • The effectiveness of proposed method is validated by simulations. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents an energy management strategy for a car park with charging stations for electric vehicles. The study focuses on the supervision strategy with a fuzzy logic controller. The power comes from renewable energy sources (wind turbine, photovoltaic panel) and main grid. The algorithm objective is to distribute the grid power and the renewable power according to the electrical load demand of the parking. The main goal of the design is to obtain maximum utilization of the renewable source energy. Simulation results show that the fuzzy logic is a suitable energy management control strategy. The use of renewable energy reduces the impact on the power grid caused by increasingly high number of electric vehicles deployed worldwide. [ABSTRACT FROM AUTHOR]

This paper presents an efficient hybrid approach-based energy management strategy for grid-connected microgrid (MG) system. The proposed hybrid technique is the combination of both random forest (RF) and cuttlefish algorithm (CFA) named as RFCFA. The proposed hybrid technique is utilized to decrease the electricity cost and increase the power flow between the source and load side. The MG system is tracked by the RF technique. The CFA is optimized based on the MG with the predicted load demand. MG employs two energy management strategies to reduce the impact of renewable energy prediction errors. The first strategy seeks at minimizing electricity costs during MG's operation. And the second strategy is aimed at balancing the power flow and reducing forecast error effects. In the grid-connected MG system, the objective function of the proposed technique is characterized with the inclusion of fuel cost, grid power variation, operation and maintenance cost. Battery energy storage systems (BESSs) can stabilize the output power and allow renewable power system units to operate at stable rate of output power. The proposed hybrid technique is executed in the working platform of MATLAB/Simulink, and the execution is evaluated using existing techniques such as GA, CFA and RBFNBBMO. [ABSTRACT FROM AUTHOR]

In this paper, we present a new approach for the optimization of energy management of the hybridization of three sources battery/Fuel Cell/Photovoltaic (B/FC/PV) vehicles configurations in order to reduce hydrogen consumption. An advanced control optimization strategy is proposed using an artificial intelligence (AI) algorithm carried out in a Matlab/Simulink environment. The power control of the fuel cell is obtained by regulating the powers of the two other sources as well as the state of charge (SOC) of the battery with hybridization via a parameter PH (parameter of hybridization). The regulation of the power of both battery and the solar PV system is achieved to the regulation of the DC bus voltage according to the reference current of the fuel cell during the optimization of the output value via a parameter PO (parameter of optimization). The activation outputs of the three sources are generated by the AI algorithm developed while including the dynamics and the profile/condition of the road as well as the demand of the vehicle. An optimization is proposed via the introduction of two parameters PH and PO, during phases of high energy demands. The results show that the proposed strategy will provide a new approach for the advanced energy management system for hybrid vehicles. [ABSTRACT FROM AUTHOR]

Abstract In recent years, the fuel cell electric vehicles have been attracting attentions for their zero emission of greenhouse gases. In the vehicle applications the most promising fuel cell is the polymer electrolyte membrane (PEM) fuel cell because of its relatively small size, lightweight nature, and low reaction temperature. However, a pure fuel cell vehicle has several disadvantages. Therefore the fuel cell systems are always combined with other energy storage systems such as batteries and supercapacitors. This paper proposes a rule-based energy management strategy based on power prediction of the lithium-ion battery and supercapacitor. The case conditions of different operation modes have been discussed. The hydrogen consumptions of 10%, 30%, and 50% state-of-charge (SOC) thresholds are 1.154 kg, 1.273 kg and 1.382 kg. However, the fuel cell has relatively small power fluctuation when the expected SOC threshold is set to 30%. [ABSTRACT FROM AUTHOR]

Abstract Energy Management strategy is indispensable for the power allocation control and energy-saving performance of a dual-motor coupling propulsion electric bus (DMCEB). Considering the constraints and instantaneous optimization, a strategy based on model predictive control is proposed. Firstly, the stationary feature of the driving cycle trend is analyzed and a novel acceleration sign prediction process is explored based on auto-regressive moving average (ARMA) method. Secondly, two specific state transition probability matrices are established according to the acceleration sign and piecewise Markov chain model is utilized to predict velocity sequences in the 5-second horizon followed by prior process. The fluctuations are significantly moderated under the effect of acceleration sign prediction and the RMSE can be controlled within around 1.4119 km/h. At last, dynamic programming (DP) is adopted as the online rolling optimization part of the model predictive control (MPC) based strategy and DP-based results are used as the benchmark to evaluate the control effect. The simulation results show that, the energy economy based on the proposed strategy decreased by 21.4% comparing with the preliminary rule-based strategy and is only 6.8% worse than that based on DP. With the 85.75 kWh/100 km energy consumption performance and low mode switch frequency, the strategy is reasonable and suitable for electric bus. [ABSTRACT FROM AUTHOR]

Hybrid electric vehicle (HEV) provides drivers with uncompromised drivability while significantly reducing hazardous emissions. This is achieved through optimal power flow solution via energy management strategy (EMS) which efficiently handles energy distribution from the different energy sources of a HEV. In this paper, a through-the-road (TtR) HEV configuration with fuzzy logic-based EMS is proposed. Fuzzy logic is applied in the main control block of the vehicle with a pair of membership functions assisting the power flow controller to select the appropriate power distribution by the hybrid drivetrain based on available resources in real time. The EMS operates in hybrid mode blended control strategy to achieve minimum fuel consumption for the desired trip by prioritising the electrical drivetrain over the internal combustion engine (ICE) for power distribution to the wheels. A Simulink model was constructed in MATLAB® to represent the TtR HEV equipped with in-wheel motors (IWM) in the rear wheels. A fuzzy logic-based EMS controller has been synthesised. The power flow in the TtR HEV is decided based on current vehicle speed and the global discharge rate (GDR) value derived from the current state-of-charge (SOC) of the battery and remaining trip distance. The proposed controller performs well on standard drive cycles and offers up to 62% improvement in fuel consumption compared to the reference model which uses rule-based EMS. Comparisons against other published models are equally encouraging, especially on high average speed drive cycles with up to 19.8% improvements in fuel consumption. [ABSTRACT FROM AUTHOR]

Abstract A practical dual-motor coaxial coupling propulsion system for electric city-buses and an extraction method for optimal energy management strategy are proposed. The powertrain configuration of the dual-motor coaxial coupling propulsion bus (DMCEB) is illustrated. To research the energy-saving strategy of DMCEB, the comprehensive state and cost functions are established. Then dynamic programming (DP) algorithm is adopted to find the global optimal energy management strategy including the transmission shift schedule, the torque split ratio and the operating points of the two motors. Since the DP-based strategy is hardly implemented in real vehicle directly, a novel application-oriented optimal rule-based strategy is extracted from the DP results by utilizing non-linear support vector machine (SVM) classifier, K-means clustering and piecewise polynomial fitting methods. The extracted strategy is used to redesign and improve the preliminary rule-based strategy, which can decouple the online calculation and offline application parts. The simulation results demonstrate that the extracted strategy is only around 10% worse than that based on DP results. However, it can be executed online easily due to its simplified polynomial form. [ABSTRACT FROM AUTHOR]

Abstract The most challenges for the hybrid energy storage system made up of the battery and super capacitor (SC) are the reasonable energy management strategy (EMS) and real-time implementation. Therefore, a variable-step multistep prediction MPC-based energy management strategy is proposed in this paper, which minimizes the system energy losses of the whole operating process and ensures the battery current and SC SOC in a suitable range. In addition, the neural networks (NN) are applied in this paper for real-time implementation, which are trained by using MPC optimization results. To do this, the loss models of the battery, SC and DC/DC converter are built and Simulation is carried out in MATLAB/Simulink, which shows that the proposed EMS can keep the SC SOC in a suitable range. At the same time, the proposed online energy management method can achieve excellent results of MPC optimization. [ABSTRACT FROM AUTHOR]

Abstract A traditional logic threshold control strategy (LTCS) is proposed but simulation results show control effects have a large gap with pseudo-spectral optimal results. To narrow this gap, the optimization is analyzed, the power relationship between the hybrid energy storage system (HESS) and the vehicle load is determined and the optimal power distribution between the battery and the ultra-capacitor is obtained. A near-optimal LTCS is presented using valuable control rules extracted by analyzing optimal control actions. The near-optimal LTCS can fully use high power characteristics of ultra-capacitor, avoid battery being damaged by high charge and discharge current, and make battery SOC consumption decreased by 2.0%. The hardware-in-the-loop (HIL) experiment is carried out and further validates that the near-optimal LTCS is real-time and reliable. [ABSTRACT FROM AUTHOR]

Abstract The Hybrid Electric Vehicle (HEV) seems to be one of the most promising short-term solution to improve the sustainability on the transportation sector. As well-known, the numerical analyses can give a substantial contribute during the preliminary vehicle design. In this context, the development of the Energy Management Strategy (EMS) represents the most challenging task. In this paper, an on-line local optimization EMS for a parallel/series hybrid vehicle is proposed to minimize the CO 2 emissions. The proposed EMS, implemented in a dynamic simulation platform, is compared to the well-assessed off-line Pontryagin's Minimum Principle (PMP). Firstly, the main differences regarding the energy management are highlighted in detail. Then, the EMSs are assessed in terms of CO 2 emissions, putting into evidence that the proposed on-line strategy involves limited penalizations (3-4%) compared to the PMP target. [ABSTRACT FROM AUTHOR]

As the global environment and energy problems become increasingly serious, electric vehicles are expected to play an important role in current and future transportation. In this paper, the configuration of a coaxial series dual-motor coupled propulsion system is analysed, and the optimization objective function is obtained for a power management strategy in which the control strategy is extracted using Dynamic Programming (DP). The simulation model of the powertrain is tested in MATLAB with different strategies under standard driving cycles. The control strategies extracted from DP (DP-based strategy) reduces the energy consumption by 8.8% compared with the traditional proportional control strategy which simply distributes torques of the two electric motors according to default ratios. [ABSTRACT FROM AUTHOR]

One of the reasons which cause poor fuel consumption of plug-in hybrid electric vehicle (PHEV) is that working modes switch frequently. In order to solve this problem, the single-shaft PHEV and fuel consumption are regarded as research object and research goal, respectively. The method which integrated many modes into one module is used to improve fuel economy of PHEV. And this paper proposed mode integration algorithm based PHEV energy management strategy. Simulation results indicate that this paper proposed mode integration algorithm based energy management strategy can improve fuel economy largely and it's meaningful for researching and development of PHEV. [ABSTRACT FROM AUTHOR]

Energy management strategy is vital for a plug-in hybrid electric vehicle and in this paper, a strategy based on stochastic model predictive control is proposed. Firstly, Markov Chain Monte Carlo Simulation is used to predict velocity sequences in the 10-second horizon followed by post-processing like average filtering, quadratic fitting, etc. which is meant to moderate fluctuations of the results. The RMSE is controlled around 2.4357 Km/h. Moreover, dynamic programming is adopted to construct a benchmark strategy and also to act as the rolling optimization part of SMPC-based strategy. The results show that the fuel economy of the strategy based on SMPC is around 13 percent worse than that on DP. However, with 14.7 L/100 km as fuel consumption, it is still within reasonable ranges. [ABSTRACT FROM AUTHOR]

In this paper, a simulation model of power-split hybrid electric vehicle is established and a type of energy management strategy based on optimum-fuel-economy curve is proposed as well. The model contains blocks which can simulate the real physical components, so its accuracy is improved. Power performance and fuel economy are tested by simulation experiment at NEDC and HWYCOL cycle. The result shows the model is correct and effective. What's more, it uses the energy management strategy, so that the fuel consumption per 100 km is 4.56 in NEDC cycle and 4.42 in HWYCOL cycle. [ABSTRACT FROM AUTHOR]

An energy management strategy based on dynamic programming for a plug-in serial-parallel hybrid electric bus is proposed. The sets of comprehensive cost function, five control variables and mesh accuracies are illustrated. Then the fuel economy is discussed, followed by the sensitivity analysis of this strategy on different lengths of driving cycle input and dissimilar control variable settings. It can be concluded that scaling the length of driving cycle and battery capacity have little effect on control laws output while optimum control variable settings should combine exact characters of driving cycles and driving components of the bus. [ABSTRACT FROM AUTHOR]

The use of concentrating solar technologies for supplying the heat and power demand of a typical dairy factoryis investigated in this paper.A yearly-based performance analysis iscarried out considering different values of solar field collecting area and thermal energy storage capacity with reference to a typical meteorological data setof a Sardinian location. Specific simulation models are developed for each section of the plant. Moreover, a novel energy management strategy isdeveloped for the determination of the priority order between thermal and electrical demand. The results demonstrate that concentrating solar technologies could be a promising option if power and heat are both required. In particular, the presence of the energy storage section provides important flexibility features to the plant and by suitably setting the control variable, the energy management strategy allows to give priority to the heat or to the electrical demand of the dairy. [ABSTRACT FROM AUTHOR]

• A near-zero energy community energy system with hybrid energy storage is proposed. • An energy management strategy combined with fuzzy logic method is schemed. • A collaborative optimization model of system design and operation is built. • The variation of different hydrogen vehicle charging modes on the system is analyzed. A near-zero energy community energy system is an effective way to reduce fossil energy consumption and achieve a decarbonized and clean energy supply. However, the utilization proportion of renewable energy in the system is low, which is due to the lack of mature theoretical methods for energy management and optimization design. Therefore, this paper first proposes a near-zero energy community energy system that combines hydrogen storage, electricity storage, and heat storage. Second, considering the health state of energy storage, an energy management strategy is proposed that uses fuzzy logic to allocate electricity to hydrogen storage and electricity storage. On this basis, the novel energy system is applied to a diversified near-zero energy community that considers hydrogen vehicle loads. Subsequently, a multi-objective collaborative optimization method is used to design the system configuration and its related operation. The results indicate that the renewable energy proportion of the novel system is greater than 80%. The annual carbon emissions, annual total costs, and total grid interactions of the system are reduced by up to 155.3 t (8.2%), 41.6 k$ (2.3%), and 223.1 MWh (13.8%), respectively. This demonstrates the advantages of the new energy management strategy when compared with the fixed priority strategy. [ABSTRACT FROM AUTHOR]

Abstract: This work relates the study of system performance in operational conditions for an isolated micro-grid powered by a photovoltaic system and a wind turbine. The electricity produced and not used by the user will be accumulated in two different storage systems: a battery bank and a hydrogen storage system composed of two PEM electrolyzers, four pressurized tanks and a PEM fuel cell. One of the main problems to be solved in the development of isolated micro-grids is the management of the various devices and energy flows to optimize their functioning, in particular in relation to the load profile and power produced by renewable energy systems depending on weather conditions. For this reason, through the development and implementation of a specific simulation program, three different energy management systems were studied to evaluate the best strategy for effectively satisfying user requirements and optimizing overall system efficiency. [Copyright &y& Elsevier]

Abstract: In this thesis, the operation mode of a kind of Parallel-Series HEV (Hydraulic Energy-saving Vehicles) is analyzed, the system efficiency models for the HEV at the charging and discharging mode were proposed based upon the engine efficiency map, motor/generator efficiency map and battery efficiency map. The maximum system discharging efficiency is taken as the optimal objective of the discharging operation mode, while the charging operation mode, according to different charge states of the storage energy, takes the maximum system charging efficiency and the maximum product between the system charging efficiency and the charging power as the optimal objective respectively; and the energy management strategy of the drive hybrid system optimized and studied, resulting in the best controlling torque and speed of the engine, electric motor and dynamo of vehicles under different operation circumstances. The emulation result of the fuel economical efficiency shows that the fuel consumption of this drive hybrid system under the cycle operation mode of NEDC is reduced by 36.94%. [Copyright &y& Elsevier]

A discussion on how to manage the hybrid electric vehicle powertrain is dealt with in this paper. An approach to analyse the optimal operation of a power-split hybrid electric vehicle is suggested on the basis of multi-objective optimization. A quasi-static powertrain model is developed and an entire space of operational choices is explored in order to find an attainable set of the optimal operation candidate group. Using this concept, two types of simulation are developed: the first simulation is based on the minimization of the equivalent fuel consumption and the latter simulation uses dynamic optimization. By analysing and comparing the results, choosing a single optimal operation point by minimizing the equivalent fuel consumption was found to give almost as high an energy efficiency as the theoretical maximum performance obtained by optimal control simulation. The results did not show a noticeable difference except in the case of high-power load conditions. From this analysis, a threshold is also proposed for vehicle operation in the pure electric vehicle mode. [ABSTRACT FROM AUTHOR]