Control Strategy For Electric Vehicles Research Articles

Composite braking control strategy for electric vehicles based on different adhesion coefficients

Composite braking control strategy for electric vehicles based on different adhesion coefficients

Offline and Online Exergy-Based Strategies for Hybrid Electric Vehicles

Abstract Exergy-based control strategies for ground hybrid electric vehicles (HEVs) enable to pursue unconventional optimization goals that are inaccessible when standard energy-based modeling frameworks based on fuel consumption minimization are used. In this work, we formulate and solve offline and online exergy-based optimization strategies for military HEVs aimed at the minimization of genset exergy destruction and thermal emissions to increase vehicle efficiency and minimize the risk of thermal imaging detection, respectively. We refer to the offline version of these strategies as exergy minimization strategies (ExMSs). Adaptive ExMSs (A-ExMSs) are then formulated for online implementation. Moreover, charge increasing (CI) ExMSs and A-ExMSs are developed to charge the battery as much as possible during a driving mission that is followed by a silent watch phase. To assess the performance of the proposed strategies, the results obtained by the ExMSs and A-ExMSs are compared to the benchmark solutions obtained by Dynamic Programming.

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.

A learning‐based hierarchical energy management control strategy for hybrid electric vehicles

AbstractIn this work, a novel energy management control framework is developed for hybrid electric vehicles (HEVs) driving in car‐following scenarios. In order to enhance the energy efficiency while maintaining the driving safety, a hierarchical control approach consisting of an upper level speed tracking control scheme and a lower level energy management control strategy is proposed. For the upper level tracking control system, an iterative learning model predictive control (ILMPC) scheme is developed to guarantee the tracking performance and the driving safety simultaneously. Additionally, a model predictive control (MPC) algorithm is adopted at the lower level to optimize the torque distribution in real‐time based on the driving cycles generated by the upper level control system. With the proposed hierarchical control framework, HEVs are able to improve the energy efficiency significantly by taking the advantages of the operational repeatability. The convergence of the proposed control strategy is analyzed rigorously, and its effectiveness is illustrated through numerical simulations.

Optimal Economic Analysis of Battery Energy Storage System Integrated with Electric Vehicles for Voltage Regulation in Photovoltaics Connected Distribution System

The integration of photovoltaic and electric vehicles in distribution networks is rapidly increasing due to the shortage of fossil fuels and the need for environmental protection. However, the randomness of photovoltaic and the disordered charging loads of electric vehicles cause imbalances in power flow within the distribution system. These imbalances complicate voltage management and cause economic inefficiencies in power dispatching. This study proposes an innovative economic strategy utilizing battery energy storage system and electric vehicles cooperation to achieve voltage regulation in photovoltaic-connected distribution system. Firstly, a novel pelican optimization algorithm-XGBoost is introduced to enhance the accuracy of photovoltaic power prediction. To address the challenge of disordered electric vehicles charging loads, a wide-local area scheduling method is implemented using Monte Carlo simulations. Additionally, a scheme for the allocation of battery energy storage system and a novel slack management method are proposed to optimize both the available capacity and the economic efficiency of battery energy storage system. Finally, we recommend a day-ahead real-time control strategy for battery energy storage system and electric vehicles to regulate voltage. This strategy utilizes a multi-particle swarm algorithm to optimize economic power dispatching between battery energy storage system on the distribution side and electric vehicles on the user side during the day-ahead stage. At the real-time stage, the superior control capabilities of the battery energy storage system address photovoltaic power prediction errors and electric vehicle reservation defaults. This study models an IEEE 33 system that incorporates high-penetration photovoltaics, electric vehicles, and battery storage energy systems. A comparative analysis of four scenarios revealed significant financial benefits. This approach ensures economic cooperation between devices on both the user and distribution system sides for effective voltage management. Additionally, it encourages trading activities of these devices in the power market and establishes a foundation for economic cooperation between devices on both the user and distribution system sides.

A Fast-Computing Path Tracking Control Strategy for Autonomous Multiaxle Electric Vehicle Considering Safety and Stability

A Fast-Computing Path Tracking Control Strategy for Autonomous Multiaxle Electric Vehicle Considering Safety and Stability

An adaptive fuzzy coordinated control strategy for hybrid electric vehicles considering clutch wear and engine temperature variation

AbstractAn improved method of clutch coordinated control based on the Kalman filter was proposed to solve the problem that the existing mode switching strategy of hybrid electric vehicles could not adapt to engine temperature changes and clutch wear. First, taking advantage of the relationship between the torque transmitted by the clutch and the starting resistance of the engine, combined with the characteristics of the clutch, the clutch wear was roughly calculated. Accordingly, the control strategy of the clutch in the existing mode switching was improved to adapt to the clutch wear. The adaptive control strategy proposed for clutch wear included the fuzzy control module of the initial engagement pressure, the fuzzy inference module of the clutch engaging pressure change, the clutch wear estimation module and so on. Second, the Kalman filter was used to process the results to improve the estimation accuracy of clutch wear. The engine starting resistance related to starting speed and temperature was modeled to enhance the adaptability of the control strategy to engine temperature. Finally, the designed control strategy was verified in simulation. The results show that the improved control strategy can complete the mode switching when the engine temperature is variable and the clutch is worn. The maximum impact degree increased from 5 m/s3 without wear to 8.5 m/s3 with wear, but it is still less than the index limit, and it can be considered that the proposed strategy can achieve the desired control effect. The fuzzy control algorithm proposed enhances the vehicle's ride comfort during mode switching from pure electric driving to hybrid driving.

Multi-Agent Deep Reinforcement Learning-Based Multi-Objective Cooperative Control Strategy for Hybrid Electric Vehicles

Multi-Agent Deep Reinforcement Learning-Based Multi-Objective Cooperative Control Strategy for Hybrid Electric Vehicles

A unified anti-slip cruise control strategy for electric vehicles

A unified anti-slip cruise control strategy for electric vehicles

High-precision Prediction Method of Electric Vehicle Trading Power based on Neural Network

Introduction: Electric vehicles have become a development trend due to their good environmental protection and energy saving, etc. Prediction of electric vehicle charging volume can help relevant departments optimize power supply, service, and construction. Methods: In this paper, the Support Vector Machine (SVM) model and the combined Long Short Term Memory (LSTM) and Support Vector Regression (SVR) prediction model are constructed for the prediction of charging capacity, and simulated by actual trading power data in Hubei Province. Results: The results show that the prediction effect of the two methods is good, and the LSTMSVR algorithm is judged to have better performance and less error in predicting the fluctuation of transaction power. Conclusion: LSTM-SVR can be used as a charging prediction method to provide a reference basis for the power control strategy of electric vehicles charging management platform, which is conducive to the healthy development of electric vehicles industry.

Research on Automatic Optimization of a Vehicle Control Strategy for Electric Vehicles Based on Driver Style

In order to reduce energy consumption, improve driving mileage, and make vehicles adopt driver styles, research on automatic optimization of control strategy based on driver style is conducted in this paper. According to the structure of the powertrain, the vehicle control strategy is designed and a driver-style recognition model based on fuzzy recognition is added to the rule-based control strategy to improve the driver adaptation of the vehicle. In order to further improve the energy-saving effect of the strategy, the control strategy based on driver style is automatically optimized by the Isight optimization platform to make the strategy reach optimum. The test results show that the strategy based on driver style is able to adapt to different styles of drivers and the economy of the vehicle is improved by 2.06% compared with pre-optimization, which validates the effectiveness of the strategy.

Research on regenerative braking control of electric vehicles based on game theory optimization.

The energy-efficient, clean, and quiet attributes of electric vehicles offer solutions to conventional challenges related to resource scarcity and environmental pollution. Consequently, thorough research into harmonizing energy recuperation during braking, enhancing vehicle stability, and ensuring occupant comfort in electric vehicles is imperative for their effective advancement. The study introduces a regenerative braking control strategy for electric vehicles founded on game theory optimization to enhance braking performance and optimize braking energy utilization. Develop a regenerative braking control approach based on the dynamic model of an electric vehicle equipped with hub motors. Employing game theory, we establish participants, control variables, strategy sets, benefit functions, and constraints to optimize the coefficient K for regenerative braking. The efficacy and superiority of the control strategy model are validated through joint simulations using Matlab/Simulink and AVL Cruise. Research findings indicate: (1) Speed tracking error remains below 3% in both NEDC and CLTC-P simulations, underscoring the effectiveness of the dynamic model and control strategy devised in this study. (2) The energy recovery rate achieved by the game theory-based optimization strategy surpasses that of the Cruise self-contained strategy and fuzzy control strategy by 18.06% and 4.5% in the NEDC simulation, and by 13.48% and 3.85% in the CLTC-P simulation, respectively. The adhesion coefficient curves implemented on the front and rear axles, derived from the game theory optimization control strategy, closely approximate the ideal adhesion coefficient curve, leading to a substantial enhancement in the car's braking stability. The degree of jerk magnitude regulated by the game theory optimization strategy consistently falls within the ±3 m/s³ threshold, resulting in a considerable enhancement in the comfort of vehicle occupants. These outcomes underscore the efficacy of the game theory-based optimized control strategy in enhancing energy recovery, braking stability, and comfort throughout the braking process of the vehicle.

Optimal Regenerative Braking Control Strategy for Electric Vehicles Based on Braking Intention Recognition and Load Estimation

Optimal Regenerative Braking Control Strategy for Electric Vehicles Based on Braking Intention Recognition and Load Estimation

Dynamic coordinated control strategy of a dual-motor hybrid electric vehicle based on clutch friction torque observer

The hybrid power system with dual motors and multiple clutches experiences significant torque fluctuation during mode switching process due to the different torque response characteristics of the motor and engine. To address this issue, this paper focuses on the estimation of clutch friction torque and the development of dynamic coordinated control strategies for the components. Firstly, based on the dynamic model of the novel dual-motor hybrid electric vehicle, a torque observer based on the Kalman filter algorithm is developed to predict the friction torque generated in the clutch sliding friction stage. Secondly, the control strategies are developed for the mode switching process from single-motor to dual-motor and from dual-motor to parallel drive on a co-simulation platform. Thirdly, a power level Hardware-In-the-Loop test platform is built, and the performance of the designed control strategies is verified by the HIL platform. The results show that for the mode switching process from dual-motor to parallel drive, compared with the control strategy using the engine target speed, the control strategy based on engine idle speed proposed in this paper reduces the clutch sliding friction work and the maximum longitudinal jerk of the vehicle by 42.5% and 25.4%, respectively.

Research on car-following control and energy management strategy of hybrid electric vehicles in connected scene

To address the comprehensive optimization problem of driving performance and fuel economy in the driving process of hybrid electric vehicles (HEV) in the car-following scene in the connected environment, an energy management strategy (EMS) based on front vehicle speed prediction and ego vehicle speed planning is designed by combining intelligent transportation system (ITS) technology. The front vehicle speed predictor is first established based on the long short-term memory neural network (LSTM). Then, based on the predicted speed of the front car, the predictive cruise control (PCC) strategy is designed for realizing the speed control in the car-following scene by combining it with the adaptive cruise control (ACC). Finally, based on the planned vehicle speed, deep reinforcement learning (DRL)-based EMS is used to optimize the power distribution among different power components of HEVs. The analysis of simulation results under the SUMO-Python joint simulation platform verifies the proposed strategy.

Research on Mode Switching and Energy Management of Hybrid Electric Vehicle

Aiming at the optimization of mode switching control and energy management strategy for parallel hybrid electric vehicles, a mode switching control method and A-ECMS steady-state energy management strategy is proposed. Firstly, in order to effectively suppress the impact caused by mode switching, and a fuzzy controller is designed for clutch engagement displacement. Then, on the basis of ensuring the smoothness of mode switching, A-ECMS control strategy based on SOC feedback is established. Finally, Isight and Cruise are used to build a co-simulation platform, and multi-island genetic algorithm is used to study the influence of initial value selection of equivalent factor on the controller. The results show that the proposed mode switching control and energy management control strategy, combined with A-ECMS strategy, can effectively improve the vehicle fuel economy on the basis of ensuring the mode switching smoothness.

A Multi-objective Optimization Control Strategy of a Range-Extended Electric Vehicle for the Trip Range and Road Gradient Adaption

A Multi-objective Optimization Control Strategy of a Range-Extended Electric Vehicle for the Trip Range and Road Gradient Adaption

A Novel Bilevel Electromechanical Compound Braking Coordinated Control Strategy for Electric Vehicles

Due to the difference of response time and braking type between the motor and the pneumatic braking system, it is still difficult to coordinate the motor braking and the pneumatic braking to ensure the vehicle stability and maximal energy regeneration. To address this challenge, a bilevel electromechanical compound braking coordinated control strategy for electric vehicles is proposed considering general and emergency braking state. First, in general braking state, considering the delay characteristics of the pneumatic braking system, a Lagrange quadratic interpolation prediction algorithm is designed to start the pneumatic braking system in advance. Second, in emergency braking state, a model predictive control method is proposed to optimize the braking torque distribution while controlling the wheel slip ratio in a stable range. In order to obtain the optimal control effect, a modified adaptive cuckoo search algorithm is put forward, in which three adaptive impact factors are added. Finally, the proposed control strategy is verified under three road conditions and compared with the conventional control strategy. The results demonstrate significant improvements under gravel road condition, including a 7% increase in energy recovery efficiency, a 92.1% enhancement in the following effect of pneumatic braking torque, and a 43.5% reduction in wheel fluctuation.

New control methodology of electric vehicles energy consumption optimization based on air conditioning thermal comfort

This article discusses the new control methodology of vehicle energy consumption optimization for thermal comfort. To address the issues of exceeding boundaries and redundancy in the application of PMV(Predicted Mean Vote) indicators in vehicles, three different control strategies for electric vehicles are proposed. To address the issue of inaccurate prediction results caused by considering air conditioning energy consumption as a part of vehicle driving energy consumption, a method combining air conditioning average power method is proposed to estimate the remaining range of vehicles. The impact of air conditioning energy consumption on the estimation results of vehicle remaining range under three control strategies is analyzed. Finally, the simulation and experimental results demonstrate the differences in the energy consumption values of the entire vehicle under the three modes, providing a solution for balancing energy saving and comfort control in the use of electric vehicles. In addition, the experimental results show that the error rate between the actual value of estimating the remaining driving distance of vehicles using the average power method of air conditioning and the simulated data is less than 3%. The proposed algorithm provides new ideas and methods for estimating the remaining driving range of electric vehicles.

Optimal Energy Management and Control Strategies for Electric Vehicles Considering Driving Conditions and Battery Degradation

Optimal Energy Management and Control Strategies for Electric Vehicles Considering Driving Conditions and Battery Degradation