Power Management Of Electric Vehicles Research Articles

Research on the Application of Virtual Reality Technology in the Design and Evaluation of Interactive Interfaces for Renewable Energy Systems

In this investigation, we delve deeply into the visual interaction design of electric vehicle power management systems, particularly how we can integrate sustainable development principles to enhance the user experience and promote environmentally friendly travel. Initially, we take a look at the present situation and challenges faced by electric vehicle power management systems. We identify shortcomings in traditional design approaches that fail to prioritize sustainability, such as low energy efficiency and challenges in accessing user-friendly information. To validate the effectiveness of our proposed method, we conduct case studies and user tests. The results reveal that incorporating sustainable development elements into the visual interaction design not only significantly boosts user satisfaction and trust in the power management system but also helps reduce energy consumption and environmental pollution.

Intelligent Power Management of Electric vehicle with onboard PV by ANN-based Model Predictive Control

This paper presents a novel configuration of Hybrid Electric Vehicle (HEV) with fuel cell as primary source and an on-board Photovoltaic (PV) array as secondary source. It incorporates an advanced Model Predictive Control (MPC) system that utilizes Artificial Neural Network (ANN) technology to control induction motor of electric vehicle. The system has three main parts: a fuel cell, an electrolyzer, and a PV array. Each part has a different job in supplying power to the vehicle. The PV array functions to supplement the fuel cell by providing power when ample irradiation is available. When the car isn't being used, the extra power from the solar panels goes to the electrolyzer. There, it turns into chemical energy, making hydrogen. This hydrogen is stored in the onboard hydrogen tank for future use. To fulfil the voltage needs of both the motor and energy sources, a quadratic bidirectional buck-boost converter (QBBC) is utilized. This converter efficiently manages the voltage output while ensuring compatibility with various energy sources. The system undergoes comprehensive analysis under different irradiance and speed conditions to evaluate its performance and efficiency. To make the solar power system more efficient and cost-effective, a Maximum Power Point Tracking (MPPT) algorithm is used. This algorithm helps get the maximum power from the PV system. An improved incremental conductance algorithm is used for this, making sure it accurately finds the best power point. The control strategy incorporates both outer voltage and inner current control, to effectively control the DC output voltage of the QBBC. The vehicle's drive system employs an indirect vector-controlled induction motor. To regulate the motor's speed effectively, an advanced ANN-MPC system is used, providing precise control and high efficiency. This paper introduces a novel approach for predictive torque control of AC machines, eliminating the need for weighting factors. An ANN estimator is used to accurately expect the motor speed, which improves the overall performance of the control system. By using MATLAB/SIMULINK to test how well the proposed Electric Vehicle (EV) setup works and make sure it's effective in real-life situations. Through this comprehensive approach, the paper contributes to advancing the development of efficient and intelligent electric vehicle systems..

Heat transfer analysis of Cu–H2O/Al2O3–H2O nanofluid flow in wavy/microchannels: A review

The miniaturization of electronic devices without compromising their heat dissipation capacities is the main concern due to the rapid evolution in power industries and engineering fields. The conventional methods of cooling or heating the devices are changed and old tactics of using conventional fluids for heat dissipation are replaced with nanofluids of strong thermal efficiency. In the present context, the experimental as well as theoretical studies of nanofluids (Cu–H2O/Al2O3–H2O) flow inside the wavy and microchannels are elucidated and discussed for different physical conditions. It is found that the use of Cu–H2O/Al2O3–H2O nanofluid improves the thermal efficiency of heat exchangers. The complex shapes and sizes of heat exchangers such as multilayer heat exchangers, heat exchangers with twisted and square shapes and multijet heat exchangers are considered effective coolants as compared with straight microchannel heat exchangers. The use of Cu–H2O/Al2O3–H2O nanofluids improves the overall heat transfer efficacy of electronic devices, and it is considered a promising coolant for various applications including aerospace (spacecraft and satellites), automobile (cooling the engines and power management in electric vehicles), renewable energy (solar plants), microelectronic devices (heat dissipation through the microprocessor and cooling the other components of devices) and modern heat exchangers of engineering domains.

State of Health Estimation for Lithium-Ion Batteries Under Arbitrary Usage Using Data-Driven Multimodel Fusion

Accurately estimating the state of health (SoH) of batteries is indispensable for the safety, reliability, and optimal energy and power management of electric vehicles. However, from a data-driven perspective, complications, such as dynamic vehicle operating conditions, stochastic user behaviors, and cell-to-cell variations, make the estimation task challenging. This work develops a data-driven multi-model fusion method for SoH estimation under arbitrary usage profiles. All possible operating conditions are categorized into six scenarios. For each scenario, an appropriate feature set is extracted to indicate the SoH. Based on the obtained features, four machine learning algorithms are applied individually to train SoH estimation models using time-series data. In addition to the estimates at the current time step, a histogram data-based and online adaptive model is taken from previous work for predicting the next-step SoH. Then, a Kalman filter is applied to systematically fuse the results of all the estimation and prediction models. Experimental data collected from different types of batteries operated under diverse profiles verify the effectiveness and practicability of the developed method, as well as its superiority over individual models.

A decentralized optimization approach to the power management of electric vehicles parking lots

To reduce the transportation sector's environmental impact, a transition to electric mobility is being witnessed worldwide. However, to avoid any issues on the grid, the charging process of electric vehicles (EVs) must be carefully managed. One valid solution to this problem is aggregating EVs and managing their collective charge and discharge. Parking lots equipped with charging stations are likely to become more prevalent in the future. Careful management of such infrastructures is essential to avoid local overloads and to provide flexibility to the electrical grid. In this context, this paper formalizes an optimization model for the optimal power management of a smart PL, with the objective of minimizing the power withdrawn from the grid while considering customers’ needs. The main novelties regard how the PL is modeled and how the solution to the optimization problem is achieved. In fact, as far as the modeling is concerned, both single- and three-phase EVs are considered, and batteries are modeled via a non-linear charging profile. As regards the solution method, an augmented Lagrangian-based decentralized model predictive control technique is proposed. The adopted algorithm has been tested on a smart PL in a Genova Municipality (Italy) and its performance has been evaluated by implementing two different scenarios: in the first one only 5 EVs are considered, while the second one regards a scalability analysis where the number of served EVs is increased to 40. The conducted tests confirm that the proposed algorithm can provide a computationally efficient solution, while preserving an accurate model.

Enhancing Performance of Hybrid Electric Vehicle using Optimized Energy Management Methodology

The fuel consumption and the fuel management strategy (PMS) of the hybrid electric vehicle are closely linked (HEV). In this study, a hybrid power management technique and an adaptive neuro-fuzzy inference (ANFIS) method are established. Artificial intelligence represents a huge improvement in electricity management across different energy sources (AI). The main energy source of the hybrid power supply is a proton exchange membrane fuel cell (PEMFC), while its electrical storage devices are a battery bank and an ultracapacitor. The hybrid electric vehicle's power management strategy (PMS) and fuel consumption are closely related (HEV). In this paper, an adaptive neuro-fuzzy inference and hybrid power management strategy (ANFIS) approach is developed. A significant advance in electricity management across multiple energy sources is artificial intelligence (AI). The proton exchange membrane fuel cell (PEMFC) serves as the primary energy source of the hybrid power supply, and the ultracapacitor and battery bank serve as its electrical storage components.

Optimal routing and power management of electric vehicles in coupled power distribution and transportation systems

With the increasing penetration level of electric vehicles (EVs) in the transportation network, it is necessary to control the charging and discharging process of EVs to avoid operational issues in the power grid. Therefore, in this paper, a two-stage framework is presented for EVs optimal routing and their active and reactive power control in the distribution grid. In the first stage, the EVs mobility in the transportation system is taken into consideration. On this basis, EV daily trips in the transportation network are modelled using the trip chain method. Also, the Dijkstra algorithm is utilized for optimal routing with the shortest travel time between the origin and the destination node. Furthermore, the effects of ambient temperature, traffic congestion and road type on the EVs energy consumption are assessed. In the second stage, optimal active and reactive power exchange of EVs with the distribution grid is carried out. Thus, a mixed-integer linear programming (MILP) model is proposed that simultaneously considers EV owners’ and the distribution network operator's benefits as the optimization goals. The EV battery degradation cost due to the discharging of active power is also integrated into the objective function of the optimization problem. The proposed framework is implemented on a standard IEEE 33-bus system coupled with a 30-node transportation network. The simulation results show that the presented method can reduce the loss cost of the distribution grid and benefit EV owners compared to different charging strategies. In addition, with the battery technology development, the proposed framework can significantly improve the distribution system operation, decrease environmental issues, and reduce EV owner’s costs.

Pricing and energy management of EV charging station with distributed renewable energy and storage

Current studies have shown that the rapid increasing of electric vehicles (EVs) may bring in great impact to the power grid, especially to the directly-connected EV charging station. Considering the cost sensitivity of EV owner and the controllability of the charging process, the pricing and charging power management may be the most effectiveness adjusting mechanisms to reduce the negative impact to the EV charging station. Therefore, we consider the combined scheduling of pricing and power management for EVs in the charging station. First, due to the uncertain EV arrivals, charging requirements and distributed renewable power generation, this problem is represented as a Markov decision process. Second, a bilevel optimization framework is designed for the formulated stochastic programming model. The outer optimization is solved by rollout method while the inner optimization is represented as a mixed-integer programming and solved by typical commercial tools. At last, the operation enhancement and operation benefit improvement of this method are demonstrated by comparing the turnover and the detailed power management of different policies.

Hybrid PV Solar, PEM Fuel Cell and Battery with Active Power Management for Electric Vehicles.

Hybridization of Different electrical sources helps to enhance the overall performance of electric vehicles. Due to the advantages of hybridization of power sources, in this work three energy sources such as Fuel cell (FC), Battery and solar have been considered to supply power adequately to electric vehicles. This paper focuses on power management of these three energy sources during vehicle driving. Because of their different characteristic, power sharing at different time and condition is different and the proposed control strategy demonstrates the active power sharing of the three energy sources. The feasibility of the proposed system is verified through computer simulation using matlab/simulink software. The demonstration of simulation results shows the performance of electric drive system is more satisfactory over the entire drive operation, fulfilling the power management rules and load requirement.

Optimization-based method to develop practical driving cycle for application in electric vehicle power management: A case study in Shenyang, China

In this study, a novel method for the construction of a driving cycle based on a two-layer optimization process is proposed with a case study in Shenyang, China. First, the statistical data is obtained and divided into many micro-trips, namely the speed profiles between two successive stops; then, three representative parameters are derived from the vehicular model. Second, the development of the driving cycle is transferred to an optimization problem, and a two-layer optimization method is proposed to construct the typical driving cycle. In the first layer, the optimal combination of micro-trips is determined using a genetic algorithm (GA) with varying quantity of micro-trips, whereas in the second layer, the best quantity of micro-trips is determined according to the speed–acceleration probability distribution (SAPD) and average energy consumption (AEC). The results indicate that the proposed method can produce a more representative driving cycle, 2.49% closer to the statistical data than the traditional Markov chain method. Finally, the established driving cycle is applied to power management design with three different vehicle types. The results indicate that the established driving cycle can help in reducing the energy cost by up to 19.8% under the real-world Shenyang driving condition.

Guest Editors’ Introduction: Special Issue on Energy and Power Management for Electric Vehicles

Electric vehicles (EVs) have many fundamental advantages over internal combustion engine vehicles. The EV idea is not new. As early as 1891, William Morrison of Des Moines, Iowa, already built the first successful EV in the United States. However, internal combustion engines almost completely replaced electric powertrains in the production of vehicles for the past 100 years. Thanks to the recent evolution of lithium-ion batteries and strong demand for zero-emission vehicles, EVs have successfully repositioned, demonstrating commercial success. Nevertheless, EVs still have challenges in terms of the battery weight, fully charged driving range, costs, etc.

Managing Electric Vehicles

<fig orientation="portrait" position="float" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <graphic orientation="portrait" position="float" xlink:href="henke-2874224.tif"/> </fig> The highlight of this issue is the Special Issue on Energy and Power Management for Electric Vehicles. Though electric vehicles are a reality, particularly the power management of electric vehicles bears a large potential for improvement. For the foreseeable future, there will be no revolutions in battery energy and power densities. It is therefore important to smartly and efficiently use the available energy.

Energy efficiency hybrid power management of electric vehicle (EV) charging through photovoltaic (PV) and micro grid (MG)

To Analyze wireless charging system of solar PV, panel concept with electric vehicle (EV). In this developed type is a combination of solar photovoltaic (PV) and Micro grid. In this type, designed two type of operation initially the EV Charging with micro grid input supply and PV Panel. If any fault occur or grid power failure the EV battery system charge through PV panel ultimately the battery sys-tem always on line charging either grid source or PV arrays panel. The proposed methods are integrated with bidirectional converter back-to-back converter with grid source. This paper focus into the power enhancement depends upon the irradiation of solar panels, so that the conversion of electrical energy is in accordance with the needs of electric vehicle attain maximum as possible. The design type of EV wireless charging methodology analyzed using MATLAB/Simulink is to get the effective power enhancement and charging to the electric vehicle continuous on line charging system.

A hybrid electric vehicle energy optimization strategy by using fueling control in diesel engines

This paper addresses a control scheme for a parallel hybrid vehicle powertrain by introducing fueling control techniques. Since a diesel engine is involved in the proposed configuration, the control of fuel injection mass and timing becomes a crucial issue. In this study, these two variables are selected as control inputs for the hybrid powertrain system. Meanwhile, an optimization-based control strategy is designed to solve the hybrid electric vehicle power management problem by incorporating engine brake specific fuel consumption characteristics with regard to fuel injection control variables. To show the advantages of the proposed control scheme, another optimization-based strategy with fixed fuel injection timing is developed and implemented for comparison. The influence of NOx emission is also considered in control strategy and simulation results to show that the proposed fuel control technique has limited impact on NOx emission but imposes a considerable improvement on fuel saving.

Two alternative robust optimization models for flexible power management of electric vehicles in distribution networks

This paper presents a robust optimization problem of the flexible bidirectional power management of a smart distribution network and harmonic compensation of nonlinear loads using electric vehicles (EVs) equipped with bidirectional chargers. The base deterministic model of the proposed problem is as mixed-integer nonlinear programming (MINLP), having the objective function to minimize the economic and technical indices subject to harmonic load flow equations, EVs constraints, system operation and harmonic indices limits. This model is converted to a mixed-integer linear programming (MILP) model in the next step. In the proposed MILP model, the active, reactive and apparent loads, electrical energy, reactive power and harmonic current prices, as well as EVs characteristics, are considered uncertain parameters. Accordingly, two alternative robust optimization approaches have been implemented for the conditions of having both the probability distribution function or the bounded uncertainty in the proposed MILP problem model. The proposed model is tested on distribution networks to demonstrate its efficiency and performance. The results show that the MINLP model can be substituted by the proposed high-speed MILP model. In addition, the capacity of the injecting power of EVs is reduced in the worst-case scenario with respect to the scenario that is used in the deterministic model, while the consumed power of loads and EVs and energy price increases in this scenario. Finally, the payment of EV owners is reduced by considering EVs power and harmonic control.

A High-Efficiency High Step-Up Interleaved Converter with a Voltage Multiplier for Electric Vehicle Power Management Applications

This paper proposes a novel high-efficiency high-step-up interleaved converter with a voltage multiplier, which is suitable for electric vehicle power management applications. The proposed interleaved converter is capable of achieving high step-up conversion by employing a voltage-multiplier circuit. The proposed converter lowers the input-current ripple, which can extend the input sources lifetime, and reduces the voltage stress on the main switches. Hence, large voltage spikes across the main switches are alleviated and the efficiency is improved. Finally, a prototype circuit with an input voltage of 24 V, an output voltage of 380 V, and an output rated power of 1 kW is implemented and tested to demonstrate the functionality of the proposed converter. Moreover, satisfying experimental results are obtained and discussed in this paper. The measured full-load efficiency is 95.2%, and the highest measured efficiency of the proposed converter is 96.3%.

Intelligent Power Management of Electric Vehicle with Li-Ion Battery

The paper aims the power management of Li-ion battery on the electric vehicle to record motor-using behaviors for each electric vehicle in a cloud server. After retrieving information from each electric vehicle, developed algorithm will be able to conclude a proper charging scheme for each electric car according to their using history. Therefore, optimizing rapid charging scheme become possible, and the cloud information can also assist power grid to manage power flow under crucial circumstances. To do integrated management of batteries, the design starts from setting up LiFePO4 batteries’ charging/discharging characteristic curve under different load. Considering the mobility of battery information access, the work proposes a cloud server work as database and computing center. The more information collected by the cloud, the more precise of the battery-using model is. The database collects data including battery SOC, battery voltage, discharging current, temperature…etc. The battery life simulation also works in the cloud by the information about SOC, open-circuit voltage, discharging current and temperature. According to cloud data analysis, charging current 5C/10C/20C under rapid charging grid’s load monitoring is possible; the process of environmental development and hardware planning can train students to use hardware, firmware and software design platform for the skills development for future employment.

Design of Power Management for Electric Vehicle

Design of Power Management for Electric Vehicle

Forward power-train energy management modeling for assessing benefits of integrating predictive traffic data into plug-in-hybrid electric vehicles

In this paper, a forward power-train plug-in hybrid electric vehicle model with an energy management system and a cycle optimization algorithm is evaluated for energy efficiency. Using wirelessly communicated predictive traffic data for vehicles in a roadway network, as envisioned in intelligent transportation systems, traffic prediction cycles are optimized using a cycle optimization strategy. This resulted in a 56–86% fuel efficiency improvements for conventional vehicles. When combined with the plug-in hybrid electric vehicle power management system, about 115% energy efficiency improvements were achieved. Further improvements in the overall energy efficiency of the network were achieved with increased penetration rates of the intelligent transportation assisted enabled plug-in hybrid electric vehicles.

Hybrid Electric Vehicle Power Management Solutions Based on Isolated and Nonisolated Configurations of Multilevel Modular Capacitor-Clamped Converter

This paper presents the various configurations of a multilevel modular capacitor-clamped converter (MMCCC), and it reveals many useful and new formations of the original MMCCC for transferring power in either an isolated or nonisolated manner. The various features of the original MMCCC circuit are best suited for a multibus system in future plug-in hybrid or fuel-cell-powered vehicles' drive train. The original MMCCC is capable of bidirectional power transfer using multilevel modular structure with capacitor-clamped topology. It has a nonisolated structure, and it offers very high efficiency even at partial loads. This circuit was modified to integrate single or multiple high-frequency transformers by using the intermediate voltage nodes of the converter. On the other hand, a special formation of the MMCCC can exhibit dc outputs offering limited isolation without using any isolation transformer. This modified version can produce a high conversion ratio from a limited number of components and has several useful applications in providing power to multiple low-voltage loads in a hybrid or electric automobile. This paper will investigate the origin of generating ac outputs from the MMCCC and shows how the transformer-free version can be modified to create limited isolation from the circuit. In addition, this paper will compare various modified forms of the MMCCC topology with existing dc-dc converter circuits from compactness and component utilization perspectives.