Control Of Electric Vehicles Research Articles

Adaptive control of plug-in hybrid electric vehicles based on energy management strategy and dynamic programming algorithm

This study mainly analyses the fuel consumption of plug-in hybrid vehicles during operation. A new control method for automobiles based on energy management strategy and dynamic programming algorithm is proposed. The new method plans and analyses the minimum electricity consumption, and then uses dynamic programming algorithms to analyse this parameter. The research results indicated that the vehicle state was constantly changing with the variation of SOC value during driving. The energy mobilization of the vehicle was more obvious after adding dynamic programming strategy. The efficiency of the vehicle was relatively high in driving state 1, with a minimum value of 70%, which was about 20% higher than in driving state 4. The average fuel consumption in driving state 2 was 1.8L higher than in other driving states. The overall efficiency of automobiles after incorporating dynamic programming was improved, with a shorter time to reach the lowest efficiency point compared with not incorporating dynamic programming algorithms. The highest efficiency value was 7.86% higher than that of not incorporating dynamic programming models. The new control method can reduce energy consumption and improve the energy management and control effect. The study provides a better research direction for energy management and control of hybrid electric vehicles in the future.

Innovative Feature Analysis of Electric Vehicle Technology, Charging Infrastructure, Power management, and Control Methods

Lowering the dependence on fossil fuels and reducing pollution from greenhouse gas (GHG) emissions is incredibly achievable through electric vehicle (EVs) technology. EV technology is an innovation that uses electricity, rather than fossil fuels, to power and refuel (recharge) vehicles. The adoption and development of EVs should lead to a decline in future demand for fossil fuels, which are finite in supply and exhaustible. Inherent challenges in EV technology, such as inadequate supply of critical minerals, power grid overload, battery technology constraints, extended charging durations, insufficient charging infrastructures, high initial costs, and limited driving range, must be addressed. The technology of charging infrastructures cannot be over-emphasized in EV technology. EV technology, charging infrastructures, vis-à-vis the impact of their integration into the grid is investigated. Effective control strategies and power management systems (PMSs) are required to optimize energy use to improve EVs' efficiency and lifetime. This research uses comprehensive analysis methods to assess various control strategies, PMSs, and their effects on EV integration into the grid.

Coordinated torque vectoring and direct yaw control based on Kalman filter control allocation for a four in-wheel electric vehicle

This paper presents a novel hierarchical control architecture designed for a four in-wheel electric vehicle (EV), integrating longitudinal and lateral control strategies to optimize performance and lateral stability. On one hand, a high-level longitudinal controller is synthesized to track a desired speed profile and generate a total torque. On the other hand, a direct yaw control (DYC) strategy is employed for steerability and stability control to generate a yaw moment. To manage the total torque as well as the yaw moment, a control allocation layer is employed. Subsequently, the formulation of the control allocation problem is presented, incorporating a gain-scheduling Daisy chaining Kalman filter (DCKF) control allocation to deal with actuator dynamics and fault tolerance. Results demonstrate enhanced performance in normal and emergency driving. Quick adaptability in case of emergency is achieved without overshooting or undershooting, with a smooth response.

Energy Management System and Control of Plug-in Hybrid Electric Vehicle Charging Stations in a Grid-Connected Microgrid

In the complex environment of microgrid deployments targeted at geographic regions, the seamless integration of renewable energy sources meets a variety of essential challenges. These include the unpredictable nature of renewable energy, characterized by intermittent energy generation, as well as ongoing fluctuations in load demand, the vulnerabilities present in distribution network failures, and the unpredictability that results from unfavorable weather conditions. These unexpected events work together to disturb the delicate balance between energy supply and demand, raising the alarming threat of system instability and, in the worst cases, the sudden advent of damaging blackouts. To address this issue, a fuzzy logic-based energy management system has been developed to monitor, manage, and optimize energy consumption in microgrids. This study focuses on the control of diesel generators and utility grids in a grid-connected microgrid which manages and evaluates numerous energy consumption and distribution features within a specified system, e.g., building or a microgrid. An energy management system is suggested based on fuzzy logic as a swift fix for complications with effective and competent resource management, and its presentation is compared with both the grid-connected and off-grid modes of the microgrid. In the end, the results exhibit that the proposed controller outclasses the predictable controllers in dropping sudden variations that arise during the addition of sources of renewable energy, supporting the refurbishment of the constant system.

A novel power quality-improved high-step-up-gain Luo converter-powered BLDC motor drive with model reference adaptive controller for electric vehicles

A novel power quality-improved high-step-up-gain Luo converter-powered BLDC motor drive with model reference adaptive controller for electric vehicles

Integrated Control of Four-Wheel Steering, Electronic Stability Control and Active Aerodynamic Control for Electric Vehicles

Integrated Control of Four-Wheel Steering, Electronic Stability Control and Active Aerodynamic Control for Electric Vehicles

Orderly Charging Control of Electric Vehicles: A Smart Meter-Based Approach

Orderly Charging Control of Electric Vehicles: A Smart Meter-Based Approach

Coordinated Control of Differential Drive-Assist Steering and Direct Yaw Moment Control for Distributed-Drive Electric Vehicles

Direct yaw moment control (DYC) and differential drive-assist steering (DDAS) for distributed-drive vehicles are both realized by allocating the in-wheel motor torque. To address the interference caused by overlapping control objectives, this paper proposes a multilayer control strategy that integrates DYC and DDAS, consisting of an upper controller, a coordinated decision layer, and a torque distribution layer. The upper controller, designed based on the vehicle’s dynamic characteristics, incorporates an adaptive fuzzy control DYC system and a dual PID control DDAS system. The coordinated decision layer is developed utilizing a phase-plane dynamic weighting method, delineating region boundaries by applying the double-line and limit cycle methods. The torque distribution strategy is formulated considering motor peak torque and road adhesion conditions. Multi-condition joint simulation experiments indicate that the proposed multilayer control strategy, integrating the advantages of DYC and DDAS, reduces peak steering wheel torque by approximately 10%, peak yaw rate by around 25%, peak sideslip angle by roughly 29%, and peak sideslip angle rate by about 19%, significantly improving driving stability and maneuvering flexibility.

Optimization of Power Control for Autonomous Hybrid Electric Vehicles with Flexible Power Demand

Abstract Technology advancement for on-road vehicles has gained significant momentum in the past decades, particularly in the field of vehicle automation and powertrain electrification. The optimization of powertrain controls for autonomous vehicles typically involves a separated consideration of the vehicle's external dynamics and powertrain dynamics, with one key aspect often overlooked. This aspect, known as flexible power demand, recognizes that the powertrain control system does not necessarily have to precisely match the power requested by the vehicle motion controller at all times. Leveraging this feature can lead to control designs achieving improved fuel economy by adding an extra degree of freedom to the powertrain control while maintaining safety and drive comfort. The present research investigates the use of an Approximate Dynamic Programming (ADP) approach to develop a powertrain controller, which takes into account the flexibility in power demand within the ADP framework. The concept of reachable sets is incorporated into the ADP framework to ensure safety, improve ride comfort, and enhance the accuracy of the optimization solution. The formulation is based on an autonomous hybrid electric vehicle (HEV), while the methodology can also be applied to other types of vehicles. It is also found that necessary customization of the ADP algorithm is needed for this particular control problem to prevent convergence issues. Finally, a case study is presented to evaluate the effectiveness of flexible power demand, as addressed by the ADP method. The experiment demonstrates a 14.1% improvement in fuel economy compared to a scenario without flexible power demand.

Combined LKA and DYC Control for Electric Vehicle With a Domain-Centralized E/E Architecture Based on Software-Defined Networking

Combined LKA and DYC Control for Electric Vehicle With a Domain-Centralized E/E Architecture Based on Software-Defined Networking

EV battery soH, soC monitor on the edge of speed control and fire protection

Lithium batteries are the most common energy storage devices in items such as electric vehicles, portable devices, and energy storage systems. However, if lithium batteries are not continuously monitored, their performance could degrade, their lifetime become shortened, or severe damage or explosion could be induced. To prevent such accidents, we propose a lithium battery state of health monitoring method and state of charge estimation algorithm based on the state of health results. And also speed control in electric vehicles is mandatory as it is used to influence the rotational speed of motors and machinery. This has a direct effect on the operation of the machine and is crucial for the quality and outcome of the work. Li-ion batteries have a lot of energy in them, and thermal runaway accelerates quicker the more power is in the battery itself. If a battery is fully charged and something happened inside it, then thermal runaway would happen really quickly. To overcome this, fire protection of electric vehicle is necessary.

Enhanced PD Controller for Speed Control of Electric Vehicle Based on Gorilla Troops Algorithm

Enhanced PD Controller for Speed Control of Electric Vehicle Based on Gorilla Troops Algorithm

Energy saving and speed control in autonomous electric vehicle using enhanced manta ray foraging algorithm optimized intelligent systems

The integration of autonomous vehicles (AVs) with hybrid electric vehicles (HEVs) aims to address the problem of optimizing energy economy while improving reliability and safety. By using intelligent driver support technologies and sophisticated control algorithms, this method aims to lower emissions and fuel consumption. Therefore, the HEVs develop an intelligent driver aid system that integrates an energy management system (EMS) with adaptive cruise control (ACC). To maximize energy use, the system integrates HEVs with autonomous vehicle technologies. In order to maintain safe distances from the lead vehicle, identify the desired acceleration, and switch between speed and distance control, the ACC uses a deep learning system in conjunction with a tilt integral derivative (TID) controller. This ensures stability. Based on ACC commands, the EMS controls energy usage. An enhanced manta ray foraging optimization (EMRFO) technique is used to significantly enhance speed control and energy economy. Simulink/MATLAB analysis of the suggested model shows notable gains in energy consumption control, particularly with the ACC with deep neural network (DNN) system, and considerable reductions in driving risks. In addition, the system precisely controls engine torque to maintain a 72.75 % State of Charge (SoC) and an average engine energy efficiency of 32.36 %. To validate the performance, the proposed approach is put into practice in real-time configuration. The configuration for the experiment produced an ideal efficiency of 31.99 % with minimal error using the proposed method.

Research on gear whine evaluation method based on transmission error sensitivity

Abstract The issue of noise control for electric vehicles has gradually become prominent, and gear whine noise, as a key factor, is crucial for improving the overall performance and quality of vehicles. This paper discusses the transmission error, a key parameter leading to gear whine, and analyzes the influence of load and gear micro modification on the distribution of transmission error, to achieve comparison and optimization of different axle plans. In addition, by statistical analysis of the distribution of manufacturing errors, it is concluded that they accord with a normal distribution. Then, the weighted transmission error is obtained considering error probability distribution, which provides a new approach for evaluating the sensitivity of gear whine noise to manufacturing errors, in order to solve the issue of noise consistency in large-scale production.

A novel bald eagle search optimised ANFIS controlled high gain sepic converter-based efficient regenerative charging control for electric vehicles

A novel bald eagle search optimised ANFIS controlled high gain sepic converter-based efficient regenerative charging control for electric vehicles

An interleaved converter topology and optimized controller for electric vehicle drive utilizing solar photovoltaic system

An interleaved converter topology and optimized controller for electric vehicle drive utilizing solar photovoltaic system

Switched capacitor voltage boost converter for BLDC motor speed control of electric vehicles

BLDC motors are extensively used in various industries, including CNC machine tools, industrial robots, and electric vehicles. Because of their compact size, high efficiency, high torque-to-power ratios, and low maintenance requirements due to their brushless operation, BLDC motors are the backbone of many industrial automation systems. However, they pose significant challenges when it comes to speed control. In this study, the speed of BLDC motors is controlled by PI-based speed controllers. In the described method, the BLDC motor is commutated using Hall Effect sensors. A proposed approach uses PI to regulate the speed of BLDC motors in an open-loop PWM method. The speed-controlled BLDC motor is analysed using MATLAB/simulink. The hardware of the proposed system is also implemented.

Enhancement of energy utilization efficiency and speed control of autonomous electric vehicles (AEVs): A hybrid approach

Enhancement of energy utilization efficiency and speed control of autonomous electric vehicles (AEVs): A hybrid approach

Fault-Tolerant Control Study of Four-Wheel Independent Drive Electric Vehicles Based on Drive Actuator Faults

Failure of any of the drive systems in a Four-Wheel Independent Drive (4WID) electric vehicle may affect the control performance and driving safety of the whole vehicle. Therefore, in this paper, a fault-tolerant controller (FTC) for 4WID electric vehicles considering drive actuator failures is proposed. First, a comprehensive characterization of multiple fault types is achieved by establishing a generalized fault model and designing a comprehensive fault factor. Second, based on the comprehensive fault factor, an LPV model with faults is constructed. Further, a fault-tolerant controller based on LPV/H∞ output feedback is designed by combining the weighting function. Finally, the effectiveness of the FTC in this paper is verified by simulation and hardware-in-the-loop (HIL) experiments. The experimental results show that the FTC designed in this paper can improve the stability of the vehicle traveling while ensuring tracking accuracy when the drive system fails.

Investigating intentions and barriers in adopting decentralized home energy management systems: A justice dimension of demand flexibility

The integration of a decentralized home energy management system (HEMS) marks a pivotal advancement in the pursuit of enhanced energy efficiency and sustainability within modern households. While numerous studies focus on developing efficient and innovative programs from technical perspectives, the willingness of individuals to adopt these systems is equally crucial for achieving widespread adoption and ultimately creating a more energy-efficient society. Based on a large-scale online survey with 1,196 participants in California in 2021, we investigated the intentions related to the adoption of decentralized HEMS, particularly considering demand flexibility encompassing both air-conditioning (AC) and electric vehicles (EV) control, specifically focusing on socio-demographic disparities. Our analysis found greater openness for allowing HEMS to control AC usage compared to scheduling EV charging, possibly due to immediate comfort needs or trust in AC predictability. Low-income households showed less flexibility in adjusting both AC and EVs, while high-income households were less likely to decrease EV charging. Furthermore, homeowners exhibited greater flexibility compared to renters. Disparities between different racial backgrounds in EV charging time-shifting were more pronounced than in AC aspects. Our findings indicated that vulnerable populations may lack the flexibility and resources necessary to shift their energy consumption patterns effectively, potentially amplifying energy-related disparities and exacerbating their energy burden. Policy recommendations highlight the need for multifaceted approaches in addressing demand flexibility and energy management, especially with emerging technologies like EVs, to ensure equitable strategies for promoting sustainable energy practices across diverse communities.