Control Strategy For Electric Vehicles Research Articles

Traction Control and Total Driving Force Distribution Strategy for Electric Vehicles with Four in Wheel Motors based on PD-Fuzzy Control

In this paper, a torque distribution control for electric vehicles with an in-wheel motor installed for each wheel is proposed in order to manage the traction operation of the vehicle and avoid slip ratio from reach extreme values while the vehicle moves into slippery surfaces. The driving force control is the main controller in the proposed control system and its consist from two control loop. PD-Fuzzy logic and PI-control are applied as a wheel speed controller in the inner control loop with the aim of improving stability and support the traction operation of the wheels. To retain the total requested driving force, force distribution strategies are obtained when the vehicle moves on the road with a low friction coefficient surface. Also, it works as a yaw moment suppressor when the vehicle runs into split slippery surfaces which improves vehicle performance. The proposed vehicle dynamics and torque distribution control system are simulated using Simulink-MATLAB Program. The results of the simulation demonstrated the effectiveness of the proposed method.

A Novel Adaptive Cruise Control Strategy for Electric Vehicles Based on a Hierarchical Framework

Conclusive evidence has demonstrated the critical importance of adaptive cruise control (ACC) in relieving traffic congestion. To improve the performance of the ACC system, this paper proposes a novel ACC strategy for electric vehicles based on a hierarchical framework. Three main efforts have been made to distinguish our work from the existing research. Firstly, a sliding acceleration identification model is established based on the recursive least squares algorithm with multiple forgetting factors (MFF-RLS). Secondly, with vehicle following, economy, and comfort as the optimization objectives, the upper-level controller is developed based on the model predictive control (MPC) algorithm. Benefit from the identification of the sliding acceleration, the MPC controller holds better capability in accommodating environmental changes. Thirdly, an iterative learning lower-level controller is designed to control the driving and braking systems. Considering the efficiency of regenerative braking, the braking force distribution strategy is also designed in the lower-level controller. Simulation results show that, compared with the conventional MPC-based ACC strategy, the proposed strategy has similar performance in vehicle following, but it makes great improvements in comfort and economy. The specific features are that the vehicle acceleration and speed fluctuation are significantly reduced, and the energy consumption is also reduced by 2.05%.

A multi-objective power flow optimization control strategy for a power split plug-in hybrid electric vehicle using game theory

A multi-objective power flow optimization control strategy for a power split plug-in hybrid electric vehicle using game theory

Vehicle-vehicle energy mutual aid control strategy for electric vehicles

Vehicle-vehicle energy mutual aid control strategy for electric vehicles

Fuzzy predictive control strategy for plug-in hybrid electric vehicles over multiple driving cycles

Fuzzy predictive control strategy for plug-in hybrid electric vehicles over multiple driving cycles

Research on Control Strategy and Central Control System of Intelligent Electric Vehicle

With the continuous improvement of world science and technology, vehicle technology has also been greatly developed. Cars bring convenience to people’s travel, and occupy an increasingly high proportion in social life. Automobile plays an important role in the continuous progress of contemporary society and the change of human travel conditions. At the same time, the increasing number of cars also brings many problems: energy consumption and environmental pollution are worsening, traffic accidents are increasing year by year. According to the who survey, the number of casualties caused by traffic accidents in China is gradually increasing. High traffic accidents have caused irreversible losses to social development, people’s lives and property. It has become the development goal to improve the vehicle active safety, control the vehicle to automatically identify the road, drive according to the planned route and reduce the frequency of traffic accidents. In the traditional driving environment, people occupy a dominant position. In the face of complex and changeable traffic environment and a large number of traffic information, it is easy to make mistakes in driving and cause serious traffic accidents. Therefore, it is very important to extricate the driver from the complex and changeable traffic environment and make driving safer. The intelligent vehicle collects the information about the road and the vehicle itself through the sensor, and sends it to the central controller which controls the transverse and longitudinal movement of the electric vehicle. The central controller sends out corresponding instructions to control the horizontal and longitudinal operation of the vehicle according to the control strategy, so as to realize the control of the driving behavior of the vehicle, so that the vehicle can drive safely on the desired track at the expected speed, It can avoid driver fatigue driving behavior and traffic accidents caused by driving technology. Various sensors in the intelligent vehicle can obtain more vehicle information at the same time, and control the vehicle through the controller processing.

Research on Fuzzy Control Strategy and Genetic Algorithm Optimization for Parallel Hybrid Electric Vehicle

In this paper, the fuzzy control strategy for parallel hybrid electric vehicle(HEV) is proposed. Based on fuzzy logic theory, SOC and torque demand scale factor are taken as the input, a fuzzy logic controller is designed. Combining with the rule-based control strategy(RBCS), the engine and motor output torque are gotten, so the whole energy control strategy is proposed. Then the genetic algorithm(GA) is used to get the optimal results: fuel consumption and emission level, to verify the accuracy of fuzzy control strategy.

Research on Adaptive Optimal Control Strategy of Parallel Plug-In Hybrid Electric Vehicle Based on Route Information

Research on Adaptive Optimal Control Strategy of Parallel Plug-In Hybrid Electric Vehicle Based on Route Information

Optimal Control Strategy for Parallel Plug-in Hybrid Electric Vehicles Based on Dynamic Programming

In this paper, the dynamic programming algorithm is applied to the control strategy design of parallel hybrid electric vehicles. Based on MATLAB/Simulink software, the key component model and controller model of the parallel hybrid system are established, and an offline simulation platform is built. Based on the platform, the global optimal control strategy based on the dynamic programming algorithm is studied. The torque distribution rules and shifting rules are analyzed, and the optimal control strategy is adopted to design the control strategy, which effectively improves the fuel economy of plug-in hybrid electric vehicles. The fuel consumption rate of this parallel hybrid electric vehicle is based on china city bus cycle (CCBC) condition.

Simulation Research on Regenerative Braking Control Strategy of Hybrid Electric Vehicle

This paper proposes a double layered multi parameters braking energy recovery control strategy for Hybrid Electric Vehicle, which can combine the mechanical brake system with the motor brake system in the braking process to achieve higher energy utilization efficiency and at the same time ensure that the vehicle has sufficient braking performance and safety performance. The first layer of the control strategy proposed in this paper aims to improve the braking force distribution coefficient of the front axle. On the basis of following the principle of braking force distribution, the braking force of the front axle and the rear axle is reasonably distributed according to the braking strength. The second layer is to obtain the proportional coefficient of regenerative braking, considering the influence of vehicle speed, braking strength, and power battery state of charge (SOC) on the front axle mechanical braking force and motor braking force distribution, and a three-input single-output fuzzy controller is designed to realize the coordinated control of mechanical braking force and motor braking force of the front axle. Finally, the AMESim and Matlab/Simulink co-simulation model was built; the braking energy recovery control strategy proposed in this paper was simulated and analyzed based on standard cycle conditions (the NEDC and WLTC), and the simulation results were compared with regenerative braking control strategies A and B. The research results show that the braking energy recovery rate of the proposed control strategy is respectively 2.42%, 18.08% and 2.56%, 16.91% higher than that of the control strategies A and B, which significantly improves the energy recovery efficiency of the vehicle.

An optimal control strategy design for plug-in hybrid electric vehicles based on internet of vehicles

This paper presents an approach to the design of an optimal control strategy for plug-in hybrid electric vehicles (PHEVs) incorporating Internet of Vehicles (IoVs). The optimal strategy is designed and implemented by employing a mobile edge computing (MEC) based framework for IoVs. The thresholds in the optimal strategy can be instantaneously optimized by chaotic particle swarm optimization with sequential quadratic programming (CPSO-SQP) in the mobile edge computing units (MECUs). The vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication are adopted in IoV to collect traffic information for a CPSO-SQP based optimization and transmit the optimized control commands to vehicle from MECUs. To guarantee real-time optimal performance, the communication delay in V2V and V2I is decreased via an alternative iterative optimization algorithm (AIOA) approach. The simulation results demonstrate the superior performance of the novel optimal control strategy for PHEV with 9% improvement, compared with the original strategy.

Robustification Through Minimax Dynamic Programing and Its Implication for Hybrid Vehicle Energy Management Strategies

Abstract Hybrid electric vehicle (HEV) control strategies are often designed around specific driving conditions. However, when driving conditions differ from the designed conditions, HEV performance can suffer. This paper develops a novel HEV energy management strategy (EMS) that is robust to uncertain driving conditions by augmenting a stochastic dynamic programing (SDP) controller with minimax dynamic programing (MDP). This combination of MDP and SDP has not previously been studied in the literature. The stochastic element uses a Markov chain model to represent driver behavior and is used to optimize the control for expected future driver behavior. The minimax element instead optimizes against potential worst-case (maximal cost) future driver behavior. The resulting EMS can be directly implemented on a vehicle. This method is demonstrated on a series hybrid electric bus model. Robustness to uncertain driving conditions is tested by simulating on a variety of heavy-duty vehicle drive cycles that differ from the drive cycle on which the EMS was trained. A single tuning parameter is used to balance the stochastic and minimax elements of the EMS, and a parametric study shows the effects of this tuning parameter. It was found that using minimax control could increase the vehicle fuel economy on multiple uncertain driving conditions, with a tradeoff of decreased fuel economy when the driving conditions match the designed conditions. That is, it offers an exchange of performance on the nominal driving conditions for performance on uncertain driving conditions.

Assessing the Impact of Electric Vehicle Fleets on a Multi-Microgrids System Under Different Operating Modes

The Global climate change and the lack of fossil fuels reserves have already had observable effects on the environment. Therefore, an undeniable investment is being made to respond these heavy challenges and to accelerate the momentum towards further embracing the electrification of transportation in smart microgrids, energy efficiency and clean energy production. The paper presents a novel cost-effective management of non-renewable resources of a multi-microgrid system under different operating modes (islanded, connected) with integrating renewable energy sources. Also, we investigate the impact of controlling vehicle-to-grid (V2G) operations on the multi-microgrid system by establishing a coordinated charging control strategy for plug-in electric vehicles (PEVs) with the aim of obtaining the maximum benefit from the grid as well as minimize the overall operating cost of the system. Both, the management of the multi-microgrid system and the EVs charging strategy have been formulated and solved using the Genetic Algorithm (GA), where the obtained results have shown that this method increases the quality, and the efficiency of obtained day-ahead scheduling solutions under any operating mode.

Regenerative Braking Control Strategy of Electric Vehicles Based on Braking Stability Requirements

Electric vehicles are effective way to solve energy and environmental problems, but the promotion and application of electric vehicles are suppressed by their limited endurance range seriously. The regenerative braking technology is an important method to increase the endurance range of the electric vehicle. During the braking process, the kinetic energy of the electric vehicle can be converted into electric energy and stored in the energy source device with the regenerative braking system, so the endurance range of the electric vehicle can be increased accordingly. In order to increase the efficiency of energy recovery, a regenerative braking strategy with the optimization distribution algorithm is proposed in this paper, and the braking forces of the front and rear axles are distributed optimally with variable ratios based on the braking strength. With the optimal braking force distribution ratio and related constraint conditions, the regenerative braking control strategy was designed to meet the braking stability and the maximum braking energy recovery. And then a simulation model of the braking control strategy was built with MATLAB/Simulink software, and the simulation tests on UDDS and NEDC cycle conditions were done to verify the effectiveness of the designed regenerative braking control strategy. Compared with the control strategy of ADVISOR software, the braking energy recovery efficiency was improved more than 51.9 % while maintaining the braking stability.

Research on control strategy of electric vehicle under braking state based on fuzzy control

Aiming at how to improve the efficiency of pure electric vehicles in the process of braking energy recovery and security problems, this paper takes brake laws ECER13 curve and ideal braking force distribution curve as constraint conditions, to design a kind of input is braking intensity z, battery SOC charge ratio, output for mechanical brake than kp, electric braking is larger than that of ki fuzzy control strategy, and with the fuzzy control strategy as the core to establish the model of regenerative braking, this model embedded in the ADVISOR of pure electric vehicles in the model, and the simulation under the condition of circulating CYC_UDDS. The results show that under the fuzzy control strategy, the effective recovery rate of regenerative braking energy increases by 5.4%, and the energy utilization rate of the vehicle increases by 1.9%.

Safe and Energy-Efficient Car-Following Control Strategy for Intelligent Electric Vehicles Considering Regenerative Braking

Intelligent electric vehicles (IEVs) have attracted more and more attention benefitting from the characteristics of high degree of safety and energy efficiency. This paper proposes an adaptive cruise control framework considering regenerative braking to improve safety and energy efficiency of IEVs during the car-following process. At first, a coupled and nonlinear dynamic model of IEVs system is constructed, which is mainly composed of a powerful battery, an electric motor, a single-speed transmission, and a hydraulic braking system. Then, an adaptive fuzzy sliding mode high-level controller is designed to accurately obtain the desired longitudinal acceleration of IEVs, in which the fuzzy logic is utilized to approximate the switching control item of the sliding mode control for chattering free. And the stability of high-level controller is proven by the Lyapunov theory. In the lower-level controller, traction control and brake control are designed to track the desired acceleration produced by the high-level controller, in addition, a novel regenerative braking strategy is presented to maximize the braking energy recovery. Finally, the simulation results indicate that the proposed control scheme has the excellent performance of longitudinal tracking and braking energy recovery with no loss of safety.

Research on Control Strategy of Hybrid Electric Vehicle

Focusing on optimizing the energy management strategy of the hybrid power system and improving the fuel economy, this paper mainly studies the determination of the optimal control target driving comfort, and obtains the optimal control law according to the optimal control theory, and designs the controller. Finally, in order to make the system control indicators reach the research goals, an observer is established for tracking observation.

Research on Novel Fuzzy Control Strategy of Hybrid Electric Vehicles Based on Feature Selection Genetic Algorithm

Research on Novel Fuzzy Control Strategy of Hybrid Electric Vehicles Based on Feature Selection Genetic Algorithm

Dual-mode regenerative braking control strategy of electric vehicle based on active disturbance rejection control

The traditional regenerative braking control strategy usually uses the torque control mode and does not perform closed-loop control on the charging current, when the vehicle needs to be charged with a small current, the regenerative braking system cannot work effectively. The dual-mode regenerative braking control strategy proposed in this paper unifies the closed-loop control of regenerative current and the closed-loop control of regenerative torque. Especially when the battery is in a state of high charge or the temperature of the battery is too high or too low, this strategy can ensure charging safety, regeneration efficiency, and ride comfort. In the current closed-loop control mode, this proposal uses the ADRC controller to dynamically adjust the motor torque to achieve the purpose of accurately controlling the regenerative current. This method does not need to change the original vector control frame of the motor, which is convenient for engineering applications. The designed regenerative control strategy is verified through typical braking simulation. Bench tests are carried out and the results validate the feasibility and effectiveness of the designed strategy. Based on the realization of the safety of charging and the vehicle ride comfort, the proposed regenerative braking control strategy can achieve higher regeneration efficiency under the dynamical limitation of battery charging current, which further expands the operating range of the regenerative braking system.

A Customized Voltage Control Strategy for Electric Vehicles in Distribution Networks With Reinforcement Learning Method

The increasing electric vehicles (EVs) at charging stations will impose great challenges on the conventional voltage control in distribution networks. In this article, a two-stage voltage control strategy based on deep reinforcement learning is proposed to mitigate voltage violations caused by the uncertainty of EVs and load. In the first stage, the charging demand of EVs is predicted based on trip chain theory and simulated by Monte Carlo simulation. The optimal power flow is then performed to determine the day-ahead dispatch of on-load tap changer and capacitor banks. In the second stage, the real-time voltage control problem is formulated as a Markov Game considering both reactive power control and vehicle to grid modes of EVs. The problem is solved by the deep deterministic policy gradient algorithm to develop a well-trained control strategy that can be implemented online. Moreover, a novel customized charging criterion is proposed to conduct the charging behavior of EVs and guarantee full charging at the departure time. The proposed approach is tested on the IEEE 33-bus and 123-bus distribution systems and comparative simulation results show the effectiveness in addressing voltage problems.