Multiple Power Sources Research Articles

Optimization for fuel consumption and TCO of a heavy-duty truck with electricity-propelled trailer

As renewable energy grows in the heavy-duty truck sector, exploration of diverse solutions with varied energy types and powertrain configurations becomes a necessity. While most studies focus on powertrain configurations in tractors, this study takes a different approach: propose and evaluate new plug-in hybrid configurations with multiple power sources for heavy-duty trucks. The tractor retains its engine powertrain, while the trailer is equipped with electric axle(s), forming a fuel-electricity hybrid propulsion system. The specifications of the electric propulsion system are optimized, using a multi-objective particle swarm optimization algorithm and dynamic programming control algorithm to evaluate the energy consumption and total cost of ownership (TCO). The results highlight the potential of the configuration with three electric axles and single reduction gear in regard to vehicle energy consumption. Conversely, the configuration with one electric axle and a two-speed transmission exhibits the lowest TCO. Under the China World Transient Vehicle Cycle, the fuel consumption of the diesel truck is reduced by up to 44.59 % with the use of the optimized hybrid configuration. The truck's TCO can be then reduced by up to 832 thousand CNY in one-million-kilometer operation. For an actual road operation, these figures change to 34.79 % and 284 thousand CNY, respectively.

Optimal Rule-Interposing Reinforcement Learning-Based Energy Management of Series—Parallel-Connected Hybrid Electric Vehicles

P2–P3 series–parallel hybrid electric vehicles exhibit complex configurations with multiple power sources and operational modes, presenting a difficulty in developing efficient energy management strategies. This paper takes a P2–P3 series–parallel hybrid power system-KunTye 2DHT system as the research object and proposes a deep reinforcement learning framework based on pre-optimized energy management to improve the energy consumption performance of the hybrid electric vehicles. Firstly, a control-oriented model is established based on its system configuration and characteristics. Then, the optimal distribution of the motor energy under different operating modes is pre-optimized, which aims to reduce the energy management task’s dimensionality by equating two motors as an equivalent motor. Subsequently, based on real-time traffic information under connected conditions, deep reinforcement learning is utilized to optimize the optimal operating modes of the hybrid system and the optimal distribution between the engine and equivalent motors. Combining the pre-optimized results, the optimal energy distribution between the engine and the two motors in the system is achieved. Finally, performance comparisons are made between the predictive control and the traditional Dynamic Programming and Adaptive Equivalent Consumption Minimization Strategy, revealing the proposed optimization algorithm’s promising potential in reducing fuel consumption.

Research on multi-source separation method of partial discharge based on ABC-JADE

Abstract The presence of multiple PD power sources in the switchgear will produce mixed PD signals, which will bring difficulties to the subsequent signal identification work. To solve this problem, this paper uses the exponential signal model to perform hybrid separation and makes ABC (Artificial Bee Colony Algorithm)-JADE (Joint Approximative Diagonalization of Eigen matrices), i.e., the ABC algorithm is a part of the JADE algorithm, and the ABC algorithm introduces the foraging behavior of bees in the search space, which can help the algorithm better explore the solution space. The JADE algorithm is a differential evolution algorithm, which can adjust the algorithm parameters through an adaptive mechanism. In this paper, the similarity coefficient and amplitude relative error index are used to evaluate the signal separation performance, and the experimental results show that the proposed JADE-ABC algorithm can effectively separate mixed signals. The amplitude relative error and phase accuracy of the separated signal and the source signal are within a certain range, and have a certain degree of robustness.

Coordinated voltage control for large-scale wind farms with ESS and SVG based on MPC considering wake effect

The wake effect reduces the wind speed at downstream wind turbines (WTs), making it necessary for the central controller to collect wind power generation data from each WT. However, wind farms (WFs) face a more complex problem in maintaining the voltage stability at the WT terminal while following the transmission system operator (TSO) instructions due to the information collection as well as the possible data loss during transmission. Therefore, this study proposes a coordinated control method for WTs and multiple power sources based on model predictive control under wake disturbance conditions, aiming to reduce the average voltage deviation in WT terminals and go close to the rated voltage and ensure effective compliance with TSO commands in large-scale WFs. Accordingly, the Jensen wake model was utilized to accurately calculate the available active and reactive power limits for each WT. Energy storage systems and static Var generators were modeled to coordinate and maintain the voltage in all WT terminals within the feasible range, providing peak shaving and valley filling support to reduce wind energy waste and shortfall, thereby enhancing the economic and operational reliability of WF. Further, the effectiveness of the proposed method was validated in MATLAB/Simulink.

Evaluation Method of Average House Demand in Iraq’s Middle Territories

Iraqi householders are commonly interesting of hybrid power systems due to the considerable shortage of grid utility to meet there simple daily residential demands. Hybrid power systems that based on combination of multiple power sources (solar panels) and (diesel or gasoline) power generators are most favorable to use there, due to the small sizes and comparatively cheap initial prices of such systems. Renewable energy sources (RES) are intermittent in nature and change seasonally and daily, which make the hourly function evaluation, of electricity demand, is very important in the design of power systems based on these sources. In this work, average house electricity demand of Iraq’s middle territories is estimated, by considered Baghdad average house demand as representative for this region, in terms of appliances sizes and annual distribution of daily operation hours. Also, solar system is designed based on the estimated consumption profile. It’s found that the estimated profile with and without two tons of refrigeration air-conditioner system provide close results to the real values of Baghdad average house demand. Beside its reliability, independence and environmentally advantages, solar system that designed here show considerably cost effective than the private business diesel generator and discontinuous grid service in this area.

Low-carbon oriented planning of shared photovoltaics and energy storage systems in distribution networks via carbon emission flow tracing

With the targets of carbon peaking and carbon neutrality, carbon emission reduction measures have become significant issues in new-type power systems. Distribution networks (DNs), which multiple power sources and flexible loads access to, play an essential role in exploiting the potential of reducing carbon emissions on the demand side. To achieve a global carbon emission reduction considering the carbon quota of each customer, shared photovoltaics (PVs) and energy storage systems (ESSs) are allocated with a centralized calculation and optimization conducted by DNs. To solve two key points in demand-side planning of shared PVs and ESSs in distribution networks, i.e., the accuracy of carbon emission flow (CEF) calculation and carbon quota-oriented optimization planning, this paper proposes a low-carbon oriented planning method for shared PVs and ESSs via CEF tracing. Firstly, a novel CEF calculation method is proposed based on the forward current sweep and backward voltage sweep algorithm, which considers the tracing of reactive power and power losses. Next, carbon emission index and economic index of allocating shared PVs and ESSs are defined. Subsequently, a bi-level optimization model is presented, considering the excess carbon emissions of each load based on carbon accounting and carbon quotas. Finally, the superiority of the proposed method is verified through a modified IEEE 33-node distribution network, and the effects of various investment constraints and carbon quotas are discussed.

Consecutive multimaterial printing of biomimetic ionic hydrogel power sources with high flexibility and stretchability

Electric eel is an excellent example to harness ion-concentration gradients for sustainable power generation. However, current strategies to create electric-eel-inspired power sources commonly involve manual stacking of multiple salinity-gradient power source units, resulting in low efficiency, unstable contact, and poor flexibility. Here we propose a consecutive multimaterial printing strategy to efficiently fabricate biomimetic ionic hydrogel power sources with a maximum stretchability of 137%. The consecutively-printed ionic hydrogel power source filaments showed seamless bonding interface and can maintain stable voltage outputs for 1000 stretching cycles at 100% strain. With arrayed multi-channel printhead, power sources with a maximum voltage of 208 V can be automatically printed and assembled in parallel within 30 min. The as-printed flexible power source filaments can be woven into a wristband to power a digital wristwatch. The presented strategy provides a tool to efficiently produce electric-eel-inspired ionic hydrogel power sources with great stretchability for various flexible power source applications.

Prediction Method of PHEV Driving Energy Consumption Based on the Optimized CNN BiLSTM Attention Network

In the field of intelligent transportation, the planning of traffic flows that meet energy-efficient driving requirements necessitates the acquisition of energy consumption data for each vehicle within the traffic flow. The current methods for calculating vehicle energy consumption generally rely on longitudinal dynamics models, which require comprehensive knowledge of all vehicle power system parameters. While this approach is feasible for individual vehicle models, it becomes impractical for a large number of vehicle types. This paper proposes a digital model for vehicle driving energy consumption using vehicle speed, acceleration, and battery state of charge (SOC) as inputs and energy consumption as output. The model is trained using an optimized CNN-BiLSTM-Attention (OCBA) network architecture. In comparison to other methods, the OCBA-trained model for predicting PHEV driving energy consumption is more accurate in simulating the time-dependency between SOC and instantaneous fuel and power consumption, as well as the power distribution relationship within PHEVs. This provides an excellent framework for the digital modeling of complex power systems with multiple power sources. The model requires only 54 vehicle tests for training, which is significantly fewer than over 2000 tests typically needed to obtain parameters for power system components. The model’s prediction error for fuel consumption under unknown conditions is reduced to 5%, outperforming the standard error benchmark of 10%. Furthermore, the model demonstrates high generalization capability with an R2 value of 0.97 for unknown conditions.

Performance Analysis of MPBC with PI and Fuzzy Logic Controllers Applied to Solar Powered Electric Vehicle Application

One of the more complicated cases is managing the energy between multiple power sources that are utilized to power electric cars (EVs). Power management is often carried out following the load requirements of electric vehicles. By considering the speed and current values of the motor a novel controller is modeled named as Measurement of parameter-based controller (MPBC) which is used to obtain the smooth transition between two passive energy sources battery and Supercapacitor (SCap). Further, the proposed MPBC is combined with fuzzy logic (FLC) and proportional-integral (PI) controllers, forming two different hybrid controllers named MPBC+FLC and MPBC+PI, utilized to attain proper power management. The main function of traditional controllers FLC/PI is to generate the pulse signals to the switches present in the bidirectional converters at both battery and SCap end. On the other hand, the MPBC is utilized to control the pulse signals based on the current and speed values of the electric motor. Futcher's final MATLAB/Simulink model is built with the proposed control technique with two hybrid controllers by considering different power-generating conditions of the PV array, to know the effectiveness of the individual model.

Pre-optimization-assisted deep reinforcement learning-based energy management strategy for a series–parallel hybrid electric truck

P2-P3 series-parallel hybrid electric vehicles (HEVs) feature intricate topologies with multiple power sources and multiple working modes, posing a challenge for developing effective energy management strategies (EMSs). This paper introduces a framework that combines deep reinforcement learning (DRL) with pre-optimized energy management to address this challenge. Considering the characteristics of HEVs, the framework incorporates the pre-optimized equivalent motor and pre-optimized mode selection rule into a deep deterministic policy gradient (DDPG) loop. The pre-optimization of the equivalent motor aims to reduce the energy management task’s dimensionality by equating two motors as the equivalent motor, while the pre-optimized mode selection rule systematically integrates mode selection and energy allocation into the EMS. The study assumes HEVs operate in connected urban environments and incorporates middle-horizon traffic information to enhance EMS performance. Additionally, the study integrates the frequent engine start-stop characteristics of HEVs into EMS design. Simulation results demonstrate that the fuel economy of the proposed EMSs in this study ranges from 87.2% to 90.7% of the DP-based EMS. The inclusion of an engine start-stop penalty significantly reduces the number of engine start-stops without compromising fuel economy.

Reducing the effect of engine failure on the aerodynamic performance of the light transport aircraft model

Recently, aircraft engine manufacturers have shown increased interest in developing hybrid powerplants, which are a combination of gas turbine engines (GTE) with electric motor-generators. The use of the hybrid powerplant makes it possible to increase the fuel efficiency of an airplane, as well as to create new configurations with improved aerodynamic and thrust characteristics. The fuel efficiency improvement is achieved as a result of optimizing the powerplant operation mode to meet the cruising flight requirements, compensating insufficient power during the takeoff and go-around procedures by activating battery-powered electric motors. The creation of new configurations with improved performance can be ensured due to the synergetic effect of the propeller-airframe interaction. Successful flight tests of the hybrid powerplant prototypes in light aircraft configurations allow us to rely on their possible application in the future regarding the projects of new propeller-driven aircraft. The potential benefits of using new powerplants on local airlines can lead to both fuel savings and carbon emission reduction. Short-term maintaining a safe flight mode is also practical in case of one engine failure when using multiple power sources. The power, generated by an electric generator connected to the running engine, can be used both for the electric motor drive of the tip propellers and for rotating the thrust producer of the failed engine. The paper presents the study results of the critical engine failure effect on the aerodynamic performance of the light transport aircraft model obtained as under available electrical transmission as under non-available one between a running and a failed engine. Experimental studies were carried out in a low-speed wind tunnel T-102 TsAGI. The simulation of the electric transmission operation was carried out by setting the operation mode of two power-plant simulators corresponding to the half value of the load factor of one engine propeller Bo in the take-off mode.

Application Design of A Multiple Power Source Monitoring System Based On The Internet Of Things (IOT)

At the present time, technology is developing rapidly in various scientific fields. People continue to develop and research the latest technologies to make human life easier. One of them is in the field of IoT (Internet of Things) technology. The use of electrical devices in each power source has different electrical power consumption. And this often happens, so when using electrical equipment, each power source is a tool for monitoring the use of electrical power, so that the use of electrical power in multiple power sources is in accordance with the required power. Therefore, a tool is designed that can facilitate the performance of activities to monitor the use of electric power, the results of which can be displayed through LCD 16X2 and can be informed through the Internet. The purpose of this research is to design an IoT-based electric power monitoring system to facilitate the monitoring of electric power consumption in multiple IoT-based power sources. The method used in data collection is quantitative method. With the collection of several components needed, which are designed in this study such as voltage sensors, Ethernet shields, hub switches and Arduino Uno microcontrollers. This research will monitor IoT-based power, and can be monitored via the Internet in the form of a graphical display on a desktop application or in the form of a monitoring web page. The results showed that the average error value in testing the voltage sensor was 0.02%, the sensor for current readings had an error value of 0.19%, and the value of power was 0.18%. So it can be concluded that by having a fairly small difference and error, this tool is said to be quite good and then by testing the monitoring application system on the web page that is made capable of sending, storing and displaying data on the monitoring web page in real time every minute even with different power sources, in tests conducted with two different power sources and can be measured properly, so that testing the web monitoring application can be said to be good because it can monitor electrical power of multiple power sources.

Ultrasonic Manipulation of Hydrodynamically Driven Microparticles in Vessel Bifurcation: Simulation, Optimization, Experimental Validation, and Potential for Targeted Drug Delivery.

Microrobots driven by multiple external power sources have emerged as promising tools for targeted drug and stem cell delivery in tissue regeneration. However, navigating and imaging these devices within a complex colloidal vascular system at a clinical scale is challenging. Ultrasonic actuators have gained interest in the field of non-contact manipulation of micromachines due to their label-free biocompatible nature and safe operation history. This research presents experimentally validated simulation results of ultrasonic actuation using a novel ultrasonic transducer array with a hemispherical arrangement that generates active traveling waves with phase modulation. Blood flow is used as a carrier force while the direction and path are controlled by blocking undesirable paths using a highly focused acoustic field. In the experiments, the microrobot cluster was able to follow a predefined trajectory and reach the target. The microrobot size, maximum radiation pressure, and focus position were optimized for certain blood flow conditions. The outcomes suggest that this acoustic manipulation module has potential applications in targeted tumor therapy.

Integration of multiple sources for fuel cell hybrid electric vehicles using single inductor multi-input converter

Fuel Cell Hybrid Electric Vehicles (FCHEV) use batteries and Photo Voltaic (PV) arrays to overcome Fuel Cell (FC) limitations like slow reaction times and low power densities. In a traditional power conversion system, three converters link FC, PV array, and battery to the output load. Despite using the FC, PV array, and battery, it has three inductors and several switches. Each switch also endures high voltage stress, possibly lowering power density and efficiency. This paper proposes a single-inductor multi-input converter (SI-MIC) that achieves 550 V as output where the input is variable. The fuzzy logic controller enables the integration of multiple power sources by selectively turning the ON and OFF switches in various operating modes. Fewer active switches during the powering process make it possible to employ semiconductors with a lower voltage. Thus, the converter generates high power with low conduction and switching losses. The state space model verifies the validity and stability of the converter under various operating modes.

Experimentation of Multi-Input Single-Output Z-Source Isolated DC–DC Converter-Fed Grid-Connected Inverter with Sliding Mode Controller

Converting devices are quickly becoming the most important part of renewable energy-producing systems that are linked to the grid. Applications that are linked to the grid are the most common place to find usage for two-port power converters that are built using single-input and single-output (SISO) ports. The incorporation of SISO power converters into the grid-connected hybrid system results in an increase in both its size and its cost. Multiple power sources may be connected to a single DC bus by means of hybrid power systems, which make use of multi-input power converters. To combine the hybrid wind and PV system with a common DC bus, this study suggests an isolated multi-input single-output (IMISO) Z-Source converter. It has been determined that the suggested system performs well in spite of dynamic load fluctuations and shifting input voltage circumstances. The sliding mode controller (SMC) has also been used to control a single-phase five-level (SPFL) inverter. The purpose of developing the laboratory prototype model was to verify the proposed IMISO Z-source converter-fed single-phase five-level (SPFL) inverter in the context of the circumstance that is being investigated.

Power Management Strategy of Hybrid Fuel Cell Drones for Flight Performance Improvement Based on Various Algorithms

In recent years, there has been a growing demand for multipurpose drones that can handle surveillance, environmental monitoring, and urgent deliveries. This trend has spurred the need for increased power and longer flight times for drones. Hence, many researchers introduced various hybrid systems to enhance endurance. In particular, a hybrid system that integrates solar cells, fuel cells, and batteries can substantially enhance a drone’s endurance. However, linking multiple power sources necessitates a control strategy that prioritizes safety and durability. It is also essential to analyze the control characteristics of each component as the dynamic behavior of individual components, coupled with environmental factors, significantly impacts the overall dynamic characteristics of drone systems. This study introduces a PEMFC–battery drone model. The model’s dynamic characteristics can be evaluated based on changes in environmental conditions and the control strategies of primary components. The validity of this model is confirmed by analyzing the dynamic characteristics of drone systems. As a result, the MRAC logic applied to the flight-level control and thrust motor of the drone was found to amplify the characteristics of the underlying PI and IP controllers. These control characteristics can lead to the development of control strategies for improving the flight performance or power durability of the aircraft by being properly applied to the flight environment of the drone.

Performance analysis of AI-based energy management in electric vehicles: A case study on classic reinforcement learning

Reinforcement learning-based (RL-based) energy management strategy (EMS) is considered a promising solution for the energy management of electric vehicles with multiple power sources. Research and applications of reinforcement learning and deep reinforcement learning in energy management issues are emerging. However, previous studies have not systematically examined the essential elements of RL-based EMS. This paper presents a performance analysis of RL-based EMS in a Plug-in Hybrid Electric Vehicle (PHEV) and Fuel Cell Electric Vehicle (FCEV). The performance analysis is developed in four aspects: algorithm, perception and decision granularity, hyperparameters, and reward function. The results show that the Off-policy algorithm effectively develops a more fuel-efficient solution within the complete driving cycle compared with other algorithms. Improving the perception and decision granularity reduces the frequency of tabular-based policy updates but better balances battery power and fuel consumption. Setting a high initial SOC in training will effectively improve the performance of RL-based EMS. The construction of an equivalent energy loss reward function for RL-based EMS based on instantaneous State of Charge (SOC) variation should be approached with caution. This approach is highly sensitive to parameters and is more likely to lead to violations of SOC constraints. In contrast, an equivalent energy reward function based on overall SOC variation is a safer alternative.

Optimization and intelligent power management control for an autonomous hybrid wind turbine photovoltaic diesel generator with batteries

In this paper, a critical issue related to power management control in autonomous hybrid systems is presented. Specifically, challenges in optimizing the performance of energy sources and backup systems are proposed, especially under conditions of heavy loads or low renewable energy output. The problem lies in the need for an efficient control mechanism that can enhance power availability while protecting and extending the lifespan of the various power sources in the system. Furthermore, it is necessary to adapt the system's operations to variations in climatic conditions for sustained effectiveness. To address the identified problem. It is proposed the use of an intelligent power management control (IPMC) system employing fuzzy logic control (FLC). The IPMC is designed to optimize the performance of energy sources and backup systems. It aims to predict and adjust the system's operating processes based on variations in climatic conditions, providing a dynamic and adaptive control strategy. The integration of FLC is specifically emphasized for its effectiveness in balancing multiple power sources and ensuring a steady and secure operation of the system. The proposed IPMC with FLC offers several advantages over existing strategies. Firstly, it showcases enhanced power availability, particularly under challenging conditions such as heavy loads or low renewable energy output. Secondly, the system protects and extends the lifespan of the power sources, contributing to long-term sustainability. The dynamic adaptation to climatic variations adds a layer of resilience to the system, making it well-suited for diverse geographical and climatic conditions. The use of realistic data and simulations in MATLAB/Simulink, along with real-time findings from the RT-LAB simulator, indicates the reliability and practical applicability of the proposed IPMC strategy. Efficient load supply and preserved batteries further underscore the benefits of the fuzzy logic-based control strategy in achieving a well-balanced and secure system operation.

Micro-Grid Line Protection Method Based on Current Frequency Variability

Due to the access of multiple distributed power sources (DG) in the Micro-Grid, the original radiation topology is changed, making the traditional distribution network line protection methods no longer applicable; and most of the existing Micro-Grid line protection methods discuss their grid-connected and Islanded operation separately, resulting in a complex and variable line protection configuration. By analysing the transient current characteristics provided by the DG side and the network side of the Micro-Grid during grid-connected operation and the transient current characteristics provided by different types and numbers of DG on both sides during Islanded operation, it is concluded that there are significant differences in the frequency characteristics of the first-mode components of the current provided on both sides of the protected section of the Micro-Grid line during a fault. As a result, a Micro-Grid line longitudinal differential protection method based on the frequency similarity of the current first-mode quantities is proposed to achieve the identification and removal of faults inside and outside the protected section. A 380 V Micro-Grid simulation model is set up in MATLAB software and the proposed method is tested in a large number of experiments to enable it to operate correctly under different operating modes, different fault locations, and different transition resistances of the Micro-Grid, and to adapt to different operating conditions.

Non linear model predictive control strategy for the energy management of a P4 parallel hybrid electric vehicle

In this paper, the energy management strategies (EMS) as main fuel saving approaches are studied for a P4 parallel hybrid electric vehicle (HEV). The multiple power sources of the analysed HEV (one thermal engine and two electric motors) and the different vehicle driving conditions increase the complexity in designing an optimal EMS. To efficiently solve the fuel minimization problem, a non linear Model Predictive Control (NL-MPC) is proposed as energy optimization strategy of the examined HEV. First, a vehicle simulation model is developed in Matlab/Simulink environment. A NL-MPC-controller is designed, implemented into the adopted code and coupled to the vehicle model. The effectiveness of developed NL-MPC approach is evaluated in two different driving cycles, also including various initial battery State of Charge. A comparison with a well-recognized real-time EMS strategy, namely heuristic/rule based (RB) approach, is performed over WLTC and a Real Driving Cycle (RDC). The numerical outcomes demonstrate the capability of NL-MPC controller at significantly improving the fuel consumption with respect to the RB strategy (maximum advantage of 9% and 15% over WLTC and RDC), thus providing an excellent and robust method in the HEV powertrain control with satisfactory performance.