scholarly journals Impact of Powertrain Components Size and Degradation Level on the Energy Management of a Hybrid Industrial Self-Guided Vehicle

Energies ◽  
2020 ◽  
Vol 13 (19) ◽  
pp. 5041 ◽  
Author(s):  
Amin Ghobadpour ◽  
Ali Amamou ◽  
Sousso Kelouwani ◽  
Nadjet Zioui ◽  
Lotfi Zeghmi
Keyword(s):  
Energy Management ◽  
High Efficiency ◽  
Practical Implementation ◽  
Energy Management Strategy ◽  
Component Sizing ◽  
Range Extender ◽  
Stop And Go ◽  
Acceleration And Deceleration ◽  
Battery Degradation ◽  
High Level

This paper deals with the design of an energy management strategy (EMS) for an industrial hybrid self-guided vehicle (SGV), considering the size of a fuel cell (FC) stack and degradation of a battery pack. In this context, first, a realistic energy model of the SGV was proposed and validated, based on experiments. This model provided a basis for individual components analysis, estimating energy requirements, component sizing, and testing various EMSs, prior to practical implementation. Second, the performance of the developed FC/battery SGV powertrain was validated under three EMS modes. Each mode was studied by considering four different FC sizes and three battery degradation levels. The final results showed that a small FC as a range extender is recommended, to reduce system cost. It is also important to maintain the FC in its high efficiency zones with a minimum ON/OFF cycle, leading to efficiency and lifetime enhancement of FC system. Battery SOC have to be kept at a high level during SGV operation, to support the FC during SGV acceleration. In order to improve the SGV’s overall autonomy, it is also important to minimize the stop and go and rotational SGV motion with appropriate acceleration and deceleration rate.

scholarly journals Development of Global Optimization Algorithm for Series-Parallel PHEV Energy Management Strategy Based on Radau Pseudospectral Knotting Method

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3268
Author(s):  
Kegang Zhao ◽  
Jinghao Bei ◽  
Yanwei Liu ◽  
Zhihao Liang
Keyword(s):  
Global Optimization ◽  
Energy Management ◽  
Computational Efficiency ◽  
Management Strategy ◽  
Hybrid Electric Vehicles ◽  
Minimum Principle ◽  
Management Strategies ◽  
Energy Management Strategy ◽  
Pontryagin Minimum Principle ◽  
Component Sizing

The powertrain model of the series-parallel plug-in hybrid electric vehicles (PHEVs) is more complicated, compared with series PHEVs and parallel PHEVs. Using the traditional dynamic programming (DP) algorithm or Pontryagin minimum principle (PMP) algorithm to solve the global-optimization-based energy management strategies of the series-parallel PHEVs is not ideal, as the solution time is too long or even impossible to solve. Chief engineers of hybrid system urgently require a handy tool to quickly solve global-optimization-based energy management strategies. Therefore, this paper proposed to use the Radau pseudospectral knotting method (RPKM) to solve the global-optimization-based energy management strategy of the series-parallel PHEVs to improve computational efficiency. Simulation results showed that compared with the DP algorithm, the global-optimization-based energy management strategy based on the RPKM improves the computational efficiency by 1806 times with a relative error of only 0.12%. On this basis, a bi-level nested component-sizing method combining the genetic algorithm and RPKM was developed. By applying the global-optimization-based energy management strategy based on RPKM to the actual development, the feasibility and superiority of RPKM applied to the global-optimization-based energy management strategy of the series-parallel PHEVs were further verified.

scholarly journals Minimum Energy Management Strategy of Equivalent Fuel Consumption of Hybrid Electric Vehicle Based on Improved Global Optimization Equivalent Factor

Energies ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 2076 ◽  
Author(s):  
Xixue Liu ◽  
Datong Qin ◽  
Shaoqian Wang
Keyword(s):  
Energy Management ◽  
Fuel Economy ◽  
Hybrid Electric Vehicle ◽  
High Efficiency ◽  
Energy Management Strategy ◽  
Target Value ◽  
Equivalent Fuel ◽  
Equivalent Factor ◽  
Equivalent Fuel Consumption ◽  
Engine Operating Point

A parallel hybrid electric vehicle (PHEV) is used to investigate the fuel economy effect of the equivalent fuel consumption minimization strategy (ECMS) with the equivalent factor as the core, where the equivalent factor is the conversion coefficient between fuel thermal energy and electric energy. In the conventional ECMS strategy, the battery cannot continue to discharge when the state of charge (SOC) is lower than the target value. At this time, the motor mainly works in the battery charging mode, making it difficult to adjust the engine operating point to the high-efficiency zone during the acceleration process. To address this problem, a relationship model of the battery SOC, vehicle acceleration a, and equivalent factor S was established. When the battery SOC is lower than the target value and the vehicle demand torque is high, which makes the engine operating point deviate from the high-efficiency zone, the time that the motor spends in the power generation mode during the driving process is reduced. This enables the motor to drive the vehicle at the appropriate time to reduce the engine output torque, and helps the engine operate in the high-efficiency zone. The correction function under US06 condition was optimized by genetic algorithm (GA). The best equivalent factor MAP was obtained with acceleration a and battery SOC as independent variables, and the improved global optimal equivalent factor of ECMS was established and simulated offline. Simulation results show that compared with conventional ECMS, the battery still has positive power output even when the SOC is less than the target value. The SOC is close to the target value after the cycle condition, and fuel economy improved by 1.88%; compared with the rule-based energy management control strategies, fuel economy improved by 10.17%. These results indicate the effectiveness of the proposed energy management strategy.

Energy Management Strategy for a Fuel Cell E-REV Based on Minimum Power Loss Algorithm

2012 ◽  
Vol 510 ◽  
pp. 603-608
Author(s):  
Jin Yu Hu ◽  
Ke Song ◽  
Tong Zhang
Keyword(s):  
Fuel Cell ◽  
Energy Management ◽  
Power Loss ◽  
Management Strategy ◽  
Energy Management Strategy ◽  
Fuel Cell Stack ◽  
Minimum Power ◽  
Range Extender ◽  
Set Up ◽  
Different Characteristics

Aim at the different characteristics from general fuel-cell vehicles of extended-range electric vehicles (E-REVs) with a fuel-cell stack as the Range Extender (RE), an energy management strategy based on minimum power loss algorithm is presented, which considers the efficiency of the fuel-cell stack and the charging and discharging efficiency of battery. The strategy is realized by neural network, simulated with the E-REV model, which is set up with ADVISOR. And a longer driving range is obtained.

scholarly journals A Verification of High-efficiency DC Micro-grid Power Systems with High-performance Power Converters and Energy Management Strategy

2014 ◽  
Vol 18 ◽  
pp. 47-52 ◽  
Author(s):  
Tomokazu Mishima ◽  
Ittetsu Taniguchi ◽  
Hisashi Tamaki ◽  
Youichi Kitagawa ◽  
Kouji Yutani ◽  
...  
Keyword(s):  
Power Systems ◽  
Energy Management ◽  
Management Strategy ◽  
High Performance ◽  
High Efficiency ◽  
Power Converters ◽  
Energy Management Strategy ◽  
Micro Grid

scholarly journals Research on a Plug-In Hybrid Electric Bus Energy Management Strategy Considering Drivability

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 2177 ◽  
Author(s):  
Ye Yang ◽  
Youtong Zhang ◽  
Jingyi Tian ◽  
Si Zhang
Keyword(s):  
Real Time ◽  
Energy Management ◽  
Management Strategy ◽  
High Efficiency ◽  
Ride Comfort ◽  
Mode Switching ◽  
Energy Management Strategy ◽  
Power Sources ◽  
Optimal Energy ◽  
Hybrid Electric

Plug-in hybrid electric buses (PHEBs) is some of the most promising products to address air pollution and the energy crisis. Considering the switching between different working modes often bring aboutsudden changes of the torque and the speed of different power sources, which may lead to the instability of the power output and affect the driving performance and ride comfort, it is of great significance to develop a real-time optimal energy management strategy for PHEBs to achieve the optimization of fuel economy and drivability. In this study, the proposed strategy includes an offline part and an online part. In the offline part, firstly, the energy conversion coefficient s(t) is optimized by linear weight particle swarm optimization algorithm (LinWPSO), then, the optimization results of s(t) are converted into a 2-dimensional look-up table. Secondly, combined with three typical driving cycle conditions, the gear-shifting correction and mode switching boundary parameters that affect the drivabilityof the vehicle are extracted by dynamic programming (DP) algorithm. In the online part, combined with the s(t), the gear-shifting correction and mode switching boundary parameters which are obtained through offline optimization, the real-time energy management strategy is proposed to solve the trade-off problem between minimizing the fuel consumption and improving the drivability and riding comfort. Finally, the proposed strategy is verified with simulation, the results show that the proposed strategy can guarantee the engine and the electric motor (EM) work in the high-efficiency area with optimal energy distribution while keeping drivability in the variation of driving circle. The overall performance is improved by 18.54% compared with the rule-based control strategy. The proposed strategy may provide theoretical support for the optimal control of PHEB.

scholarly journals Mapping Fuzzy Energy Management Strategy for PEM Fuel Cell–Battery–Supercapacitor Hybrid Excavator

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3387 ◽  
Author(s):  
Hoai Vu Anh Truong ◽  
Hoang Vu Dao ◽  
Tri Cuong Do ◽  
Cong Minh Ho ◽  
Xuan Dinh To ◽  
...  
Keyword(s):  
Fuel Cell ◽  
Energy Management ◽  
Power Distribution ◽  
Management Strategy ◽  
Internal Combustion Engines ◽  
High Efficiency ◽  
Proton Exchange ◽  
High Peak Power ◽  
Energy Management Strategy ◽  
Power Sources

By replacing conventional supplies such as fossil fuels or internal combustion engines (ICEs), this paper presents a new configuration of hybrid power sources (HPS) based on the integration of a proton-exchange membrane fuel cell (PEMFC) with batteries (BATs) and supercapacitors (SCs) for hydraulic excavators (HEs). In contrast to conventional architectures, the PEMFC in this study functions as the main power supply, whereas the integrated BAT–SC is considered as an auxiliary buffer. Regarding shortcomings existing in the previous approaches, an innovative energy management strategy (EMS) was designed using a new mapping fuzzy logic control (MFLC) for appropriate power distribution. Comparisons between the proposed strategy with available approaches are conducted to satisfy several driving cycles with different load demands and verify the strategy’s effectiveness. Based on the simulation results, the efficiency of the PEMFC when using the MFLS algorithm increased up to 47% in comparison with the conventional proposed EMS and other approaches. With the proposed strategy, the HPS can be guaranteed to not only sufficiently support power to the system even when the endurance process or high peak power is required, but also extend the lifespan of the devices and achieves high efficiency.

scholarly journals Fuzzy control of dual storage system of an electric drive vehicle considering battery degradation

2020 ◽  
Vol 11 (1) ◽  
pp. 30-40
Author(s):  
Kemal Keskin ◽  
Burak Urazel
Keyword(s):  
Electric Drive ◽  
Fuzzy Inference ◽  
Storage System ◽  
Threshold Method ◽  
Energy Management Strategy ◽  
Inference System ◽  
Battery Degradation ◽  
High Level ◽  
Logic Threshold ◽  
Different Characteristics

In this manuscript, fuzzy logic energy management strategy for dual storage system inclu\-ding supercapacitors and battery is proposed in order to prolong battery lifespan and enhance the range of electric drive vehicle (EDV). First an EDV model and three drive cycles (NEDC, UDDS, and NREL) are established in Matlab/Simulink. Then a fuzzy inference system is designed considering three inputs: power demand, state of charge (SOC) of battery and SOC of supercapacitors. An output, which refers to split ratio between supercapacitors and battery power, is determined. Fuzzy rules are constituted in order to decrease not only high level battery current but also number of charge/discharge cycle of battery which are the main factors of battery deterioration. For a performance verification of the proposed method, three drive cycles with different characteristics are considered. Obtained results are compared to two other strategies; one of them is battery only system and the other one is dual storage system managed by logic threshold method. It is shown that the proposed method delivers better and robust performance to prolong battery lifespan.

A novel energy management strategy to reduce gird dependency using electric vehicles storage in coordination with solar power

2021 ◽  
pp. 1-17
Author(s):  
J.T. Ramalingeswar ◽  
K. Subramanian
Keyword(s):  
Energy Management ◽  
Fuzzy Controller ◽  
Probabilistic Approach ◽  
Economic Benefits ◽  
Solar Pv ◽  
Time Zone ◽  
Energy Management Strategy ◽  
Battery Degradation ◽  
Water Filling Algorithm ◽  
The Impact

The effective coordination of solar photovoltaic (solar PV) with Electrical Vehicles (EV) can substantially improve the micro grid(MG) stability and economic benefits. This paper presents a novel Energy Management System (EMS) that synchronizes EV storage with Solar PV and load variability. Reducing grid dependency and energy cost of the MGs are the key objectives of the proposed EMS. A smart EV prioritization based control strategy is developed using fuzzy controller. Probabilistic approach is designed to estimate the EV usage expectancy in the near time zone that helps smart decision on choosing EVs. Minimizing battery degradation and maximizing EV storage exploitation are the key objectives of EV prioritization. On the other hand, Water Filling Algorithm (WFA) is used for Optimal Storage Distribution (OSD) in each zone of energy need for load flattening. The proposed EMS is implemented in a real time on-grid MG scenario and different case studies have been investigated to realize the impact of proposed EMS. A comprehensive cost analysis has been conducted and the efficacy of the proposed EMS is analysed.

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