Stability Of Electric Vehicle Research Articles

Distributed-Drive Vehicle Lateral-Stability Coordinated Control Based on Phase-Plane Stability Region

The lateral stability control of vehicles is one of the most crucial aspects of vehicle safety. This article introduces a coordinated-control strategy designed to enhance the handling stability of distributed-drive electric vehicles. The upper controller uses active front steering and direct yaw moment-control controllers designed based on sliding-mode control theory. The lower controller optimally allocates control inputs to the upper controller, considering factors such as load transfer and tire load rate. It divides the stability region by relying on the phase plane and develops a coordinated-control strategy based on the degree of deviation of the vehicle state from the stability region. The results of the simulation experiments demonstrate that the proposed control strategy effectively improves handling stability under extreme working conditions.

Long Downhill Braking and Energy Recovery of Pure Electric Commercial Vehicles

The thermal decay of the brake has a great impact on the long downhill braking stability of pure electric commercial vehicles. Based on the road slope and using the fuzzy control method, the motor regenerative braking force and friction braking force distribution strategies were designed to reduce the friction braking force, improve the braking stability and recover the braking energy. By establishing road driving conditions with different slopes, numerical analysis methods are used to verify the proposed control strategy. The results show that the vehicle maintains a constant speed downhill at 30 km/h under the condition of 6% constant slope driving, and the braking energy recovery rate reaches 50.93% under 60% initial battery SOC, 50.89% under 70% initial battery SOC, and 50.81% under 80% initial battery SOC. The speed of the vehicle fluctuates slightly under the driving condition of an 18 km long variable slope distance, but the power torque of the electric mechanism can still be maintained at a constant speed of 30 km/h by adjusting the electric mechanism, and the braking energy recovery rate reaches 49.96%. During the downhill driving at a constant speed, the friction braking force does not participate in braking, and the recuperation rate of braking is determined by the slope and the magnitude of braking deceleration.

ICNCS: Internal Cascaded Neuromorphic Computing System for Fast Electric Vehicle State-of-Charge Estimation

Accuracy and speed of Lithium-ion battery state of charge (SOC) estimation determine the reliability and stability of electric vehicle (EV), as well as promoting the development of smart home energy management system. Existing SOC estimation approaches embedded in commercial EVs still suffer from limitations of low precision, effectiveness, and relatively low robustness. To address these issues, we propose an internal cascaded neuromorphic computing system (ICNCS) via memristor circuits for EV SOC estimation. Specifically, we use three circuit modules to facilitate the design of the proposed ICNCS. Firstly, a bidirectional gated recurrent unit (Bi-GRU) circuit module is designed, enabling adequate feature extraction from the time-contextual battery information. Secondly, an attention circuit module is proposed to distinguish the useful and unimportant information related to SOC estimation. Thirdly, the Kalman filter module is constructed to eliminate the random noise caused by data transmission and processing. Finally, a series of experiments and analysis demonstrate that the proposed ICNCS has good performances in terms of accuracy (the lowest achievable RMSE and MAE are 0.853 and 0.711, respectively), time efficiency (approximately 16 20 times), and robustness (anti-noise capacity), indicating an advancement in consumer electronics applications.

Improved sliding mode extension control of vehicle active front wheel steering based on extended state observer

In order to improve the handling stability of electric vehicles, a new active front-wheel steering (AFS) control method was proposed. Firstly, parametric uncertainty and external interference in vehicle dynamics are summarized as a nonlinear interference term in vehicle model. An extended state observer (ESO) is designed to observe and compensate the nonlinear interference terms in real time, so as to improve the accuracy and control effect of the model. Secondly, in order to further increase the convergence speed and effectively suppress the chattering phenomenon in sliding mode control without affecting its robustness and reaching speed, an integral exponential fast terminal sliding mode controller (IEFTSMC) based on fast exponential reaching law (FERL) is designed. The problem that the control effect of some regions becomes worse when a single control algorithm is used for global region control in traditional AFS control is addressed. By combining extension theory with sliding mode control method, an improved sliding mode extension control is designed to improve the effect of AFS global control. Finally, comparative simulation tests are carried out on the CarSim/Simulink co-simulation platform. The results show that compared with the traditional FTSMC, the improved sliding mode extension control method based on ESO can not only suppress chattering more effectively, but also smoother the response curve. It also has good control effect when there is external disturbance. The effectiveness and robustness of the control strategy are verified.

Explicit hybrid MPC for the lateral stabilization of electric vehicle system

This paper presents a hybrid Model Predictive Control (hMPC) approach to improve Electric Vehicle (EV) stability when cornering or in high-risk driving conditions. By using approximations of tire force characteristics, the EV system proposed in this study is considered as a Piecewise Affine (PWA) system which is a class of Hybrid Dynamical Systems (HDSs). First, a hybrid modeling of the studied system is developed within the PWA framework. Then, the synthesis of an explicit hybrid MPC law, based on the integration of the Active Front-wheel Steering control method (AFS) and Direct Yaw moment Control method (DYC), can be established. The purpose of the PWA-MPC is to force the yaw rate and sideslip angle of the EV system to follow the given references to guarantee lateral stability. The calculation of the control laws of this proposed approach is performed offline using multiparametric programming. Finally, this paper investigates the effectiveness of the designed controller by performing a set of simulations to analyze the efficiency and robustness of the proposed approach. A comparative study is addressed in this work to demonstrate the efficiency of the proposed hybrid MPC controller compared to the conventional MPC approach which considers the linear model of the system. Another simulations are carried out to assess the robustness of the proposed control, important disturbances and uncertainties were incorporated such as the change of the additional mass, the change of terrain and the high velocity. The simulation results show that the PWA EV system model with the designed controller is able to ensure lateral stability by keeping the sideslip angle and yaw rate tracking the ideal values.

A Novel Event-Triggered Torque Vectoring Control for Improving Lateral Stability and Communication Resource Consumption of Electric Vehicles

A Novel Event-Triggered Torque Vectoring Control for Improving Lateral Stability and Communication Resource Consumption of Electric Vehicles

A Safety Requirements Adaptive NMPC Strategy for Electric Vehicle Stability Control with Computationally Efficient Optimization

A Safety Requirements Adaptive NMPC Strategy for Electric Vehicle Stability Control with Computationally Efficient Optimization

Developing an Apparatus to Analyze Volume Changes in a Pouch Battery Cell

Recently, there has been a significant increase in demand for lithium-ion batteries due to the growing demand for eco-friendly mobility. However, to ensure the stability of electric vehicles during operation, research on the structural and material characteristics of batteries is essential. The Battery Management System (BMS), responsible for managing battery degradation and performance, is directly impacted not only by the electrochemical characteristics but also by the structural and material stability of the battery. As the demand for high-density battery modules increases, the volume change of batteries after degradation can generate significant internal stresses, making it crucial to develop the vehicle's BMS in relation to this issue. Therefore, this study aims to measure the characteristics of batteries volume changes during degradation under various pressure conditions from the cell level. Ultimately, we propose a homogenized model that can predict the degradation characteristics of a battery pack subjected to pressure during charging and discharging. An apparatus capable of measuring the volume changes of batteries is developed for the modelling.Keywords: Battery volume changes, pouch cell, modeling, mechanical properties, compression test, apparatus

Anti-slip regulation method for electric vehicles with four in-wheel motors based on the identification of slip ratio

To improve the stability and acceleration of electric vehicles with four in-wheel motors under various road conditions, an anti-slip regulation method considering the identification of slip ratios is proposed. In this paper, the model of the whole vehicle is built, and the road identifier based on the tire model established by Burckhardt is designed. Then, an anti-slip controller that utilizes the variable universe fuzzy proportional integral derivative (PID) algorithm is established to adjust the driving torques of the four in-wheel motors according to the road conditions. The simulation results show that the designed control strategy can quickly and accurately identify the optimal slip ratio under each typical road condition and make the tire slip ratio approach the optimal slip ratio in a short time, so as to effectively improve the driving stability and dynamic performance of the vehicle.

Numerical Simulation of Aerodynamic Characteristics of Electric Vehicles with Battery Packs Mounted on Chassis

Aerodynamic characteristics are of great significance to the fuel economy and handling the stability of electric vehicles. The battery pack of electric vehicles has a huge structure and is usually arranged in the chassis area of the vehicle, which inevitably occupies the space at the bottom of the vehicle and affects the aerodynamic characteristics of the vehicle. To study the effect of the power battery pack installed in the chassis on the aerodynamics characteristics of the electric vehicle, the Computational Fluid Dynamics (CFD) method is used to study the flow and pressure fields of the SAE (Society of Automotive Engineers) hierarchical car model with battery packs mounted on chassis. The influence of the structure parameters of the battery pack on the automobile’s aerodynamics are also analyzed in detail. Based on the simulation results, it can be seen that the battery pack installed on the chassis has a great impact on the flow and pressure field at the bottom and tail of the vehicle, causing the drag coefficient and lift coefficient to increase. The structural parameters of the battery pack have contradictory effects on the drag and lift coefficients. As the length of the battery pack increases, the drag coefficient decreases, and the lift coefficient increases. As the battery pack width and height increase, the drag coefficient increases, and the lift coefficient decreases. The research results provide a reference for the optimization of the aerodynamic characteristics of electric vehicles with battery packs mounted on chassis.

친환경 전기자동차 고전압 절연안정성 측정시스템에 관한 연구

친환경 전기자동차 고전압 절연안정성 측정시스템에 관한 연구

Coordinated control of AFS and DYC for electric vehicles with mechanical elastic electric wheels considering the roll effects and uncertain parameters

In order to improve the lateral stability of in-wheel motors electric vehicles (IMEV) equipped with mechanical elastic electric wheels (MEEW) under extreme conditions, this paper proposes an integral sliding mode control (ISMC) algorithm based on the coordination of active front steering (AFS) and direct yaw moment control (DYC) considering the roll effects and the uncertainty of tire cornering stiffness. Firstly, the AFS control algorithm based on adaptive integral terminal sliding mode is proposed, which uses radial-basis-function neural network (RBFNN) to adaptively approximate the integrated nonlinear unknown disturbance. Secondly, considering the roll effects and uncertain cornering stiffness, a mismatched uncertain system for integrated control of AFS and DYC is established. An integral sliding mode control algorithm based on linear matrix inequality (LMI) is designed, and the asymptotic stability of sliding mode dynamics is proved. Further, a constraint optimization algorithm is used to distribute the additional yaw moment. Then, a coordinated control strategy based on the elliptical stable region of the phase plane and the rollover index is designed to activate the integrated controller of AFS and DYC at the right time. Finally, the effectiveness of the control algorithm is verified by high-fidelity CarSim-Matlab simulations. The results show that the proposed controller can effectively and robustly ensure the vehicle lateral stability under extreme conditions.

Negative dynamics effect of in-wheel switched reluctance motor with inclined airgap eccentricity on handing stability for electric vehicle

This paper focuses on the negative effect of in-wheel switched reluctance motor (SRM) with inclined airgap eccentricity for the handling stability of electric vehicle (EV). Firstly, the radial electromagnetic force of the SRM under inclined airgap eccentricity is deduced based on the Maxwell stress tensor and airgap permeability correction coefficient, and the calculation results are verified by the built measurement device. Then, a dynamics model of in-wheel motor driving EV is constructed according to the response relationship of driving wheels. Finally, the influence of unbalanced radial torque (URT) on the negative dynamic effect of EV is analyzed with angular step input and sine input respectively. The simulation results show that the EV will deviate from the expected trajectory due to the impact of URT, which will threaten driving safety in vehicle steering or overtaking. In addition, the vehicle yaw velocity will deteriorate with the increase of the driving speed and turning angle, which will affect the dynamics response of vehicle handling stability. This research, starting from the coupling relationship between in-wheel motor response characteristics and vehicle driving conditions, can provide theoretical support for the popularization and application for in-wheel motor driving EVs.

Coordinated control algorithm of lateral stability of hybrid electric commercial vehicle

Focusing on the problem of mutual interference between the vehicle controller and the lateral stability control system in the stability control of hybrid electric vehicles (HEV), this study proposes a coordinated control algorithm for hybrid power systems and lateral stability, which can meet the real-time control requirements of the vehicle stability. First, the overall architecture of coordinated control between the hybrid power system and the vehicle lateral stability control system is designed. The judgment logic of lateral stability control system intervention and exit is determined on this basis. Second, in the upper layer of the control algorithm, the model predictive control algorithm is used to calculate the additional yaw moment required to maintain stability control under lateral instability of the vehicle with the vehicle two-degrees-of-freedom model as a reference. The motor output torque control and braking force distribution are then carried out in the lower layer of the control algorithm. The target braking wheel is determined according to the oversteering and understeering state of the hybrid vehicle. The additional yaw torque distribution control is formulated based on the quadratic programing algorithm. The strategy is to perform dynamic compensation for unstable or critically unstable vehicles to solve the optimal control problem under multiple constraints. Finally, through Matlab/Simulink and Trucksim co-simulation and hardware-in-the-loop test, the validity and real-time performance of the control algorithm proposed are verified to solve the interference problem in the lateral stability control process of HEVs and further improve the stability of vehicle rideability and active safety.

State of Health Estimation for Lithium-Ion Batteries Using IAO–SVR

The state of health (SOH) of lithium-ion batteries (LIBs) needs to be accurately estimated to ensure the safety and stability of electric vehicles (EVs) while in operation. In this paper, we proposed a SOH estimation method based on Improved Aquila Optimizer (IAO) and Support Vector Regression (SVR) to achieve an accurate estimation of SOH. During the charging and discharging phases of the battery, we analyzed the trends in current, voltage, and energy, then extracted four features. We used the Kendall coefficient and gray relational grade to prove that features and SOH were highly correlated. On the other hand, IAO was used to optimize the penalty factor and kernel function parameters of the SVR to further improve the generalization and mapping ability. The proposed method was verified under different operating conditions using the CACLE battery data set; the results show that high accuracy can be achieved in SOH estimation via IAO–SVR, and the estimation error of mean MAE is remaining within 2%.

Aging Mitigation for Battery Energy Storage System in Electric Vehicles

Battery energy storage systems (BESS) have been extensively investigated to improve the efficiency, economy, and stability of modern power systems and electric vehicles (EVs). However, it is still challenging to widely deploy BESS in commercial and industrial applications due to the concerns of battery aging. This paper proposes an integrated battery life loss modeling and anti-aging energy management (IBLEM) method for improving the total economy of BESS in EVs. The quantification of BESS aging cost is realized by a multifactorial battery life loss quantification model established by capturing aging characteristics from cell acceleration aging tests.Meanwhile, a charging event analysis method is proposed to deploy the built life loss model in vehicle BESS management. Two BESS active anti-aging vehicle energy management models: vehicle to grid (V2G) scheduling and plug-in hybrid electric vehicle (PHEV) power distribution, are further designed, where the battery life loss quantification model is used to generate the aging cost feedback signals. The performance of the developed method is validated on a V2G peak-shaving simulation system and a hybrid electric vehicle. The work in this paper presents a practical solution to quantify and mitigate battery aging costs by optimizing energy management strategies and thus can further promote transportation electrification.

Advanced Vertical and Lateral Coupling Dynamics Control for a Four In-wheel Motor Drive Electric Vehicle

This paper presents an advanced vertical and lateral coupling dynamics control for stabilizing the vertical and lateral motion of a four in-wheel motor drive electric vehicle. The novelty is to investigate the possibilities of enhancing the lateral stability of electric vehicle through the correct control of their suspension system, and further improve the suspension system performances using direct yaw control (DYC) without having a contradictory effect between the vertical and lateral controllers. There are two main objectives; the first is to improve ride comfort, road holding and vehicle lateral handling performance simultaneously by using a new combined full vehicle suspension system, and the second is to strengthen electric vehicle lateral handling performance by using a sliding mode technique for controlling the direct yaw moment. For vehicle suspension system control, we are seeking to design a skyhook control to control the suspension damper and a sliding mode control to stabilize the position of the sprung masses of the in-wheel damper vibration absorber (DVA). To solve the problems caused by the chattering phenomena and ensure the exponential stability of the sprung mass, the continuous singular terminal sliding-mode controller is selected to design the sliding mode control used as a DVA controller. Simulation results show that the coordinated control between DYC and combined full vehicle suspension system under several turns' maneuvers and under random road excitations, can stabilize electric vehicle lateral motion, enhance the vehicle ride comfort, road holding, enhance the rollover resistance performance and satisfy the main suspension performances simultaneously.

A Novel Braking Control Strategy for Hybrid Electric Buses Based on Vehicle Mass and Road Slope Estimation

Proper braking force distribution strategies can improve both stability and economy performance of hybrid electric vehicles, which is prominently proved by many studies. To achieve better dynamic stable performance and higher energy recovery efficiency, an effective braking control strategy for hybrid electric buses (HEB) based on vehicle mass and road slope estimation is proposed in this paper. Firstly, the road slope and the vehicle mass are estimated by a hybrid algorithm of extended Kalman filter (EKF) and recursive least square (RLS). Secondly, the total braking torque of HEB is calculated by the sliding mode controller (SMC), which uses the information of brake intensity, whole vehicle mass, and road slope. Finally, comprehensively considering driver's braking intention and regulations of the Economic Commission for Europe (ECE), the optimal proportional relationship between regenerative braking and pneumatic braking is obtained. Furthermore, related simulations and experiments are carried out on the hardware-in-the-loop test bench. Results show that the proposed strategy can effectively improve the braking performance and increase the recovered energy through precise control of the braking torque.

Real-Time Predictive Control of Path Following to Stabilize Autonomous Electric Vehicles Under Extreme Drive Conditions

A novel real-time predictive control strategy is proposed for path following (PF) and vehicle stability of autonomous electric vehicles under extreme drive conditions. The investigated vehicle configuration is a distributed drive electric vehicle, which allows to independently control the torques of each in-wheel motor (IWM) for superior stability, but bringing control complexities. The control-oriented model is established by the Magic Formula tire function and the single-track vehicle model. For PF and direct yaw moment control, the nonlinear model predictive control (NMPC) strategy is developed to minimize PF tracking error and stabilize vehicle, outputting front tires’ lateral force and external yaw moment. To mitigate the calculation burdens, the continuation/general minimal residual algorithm is proposed for real-time optimization in NMPC. The relaxation function method is adopted to handle the inequality constraints. To prevent vehicle instability and improve steering capacity, the lateral velocity differential of the vehicle is considered in phase plane analysis, and the novel stable bounds of lateral forces are developed and online applied in the proposed NMPC controller. Additionally, the Lyapunov-based constraint is proposed to guarantee the closed-loop stability for the PF issue, and sufficient conditions regarding recursive feasibility and closed-loop stability are provided analytically. The target lateral force is transformed as front steering angle command by the inversive tire model, and the external yaw moment and total traction torque are distributed as the torque commands of IWMs by optimization. The validations prove the effectiveness of the proposed strategy in improved steering capacity, desirable PF effects, vehicle stabilization, and real-time applicability.

A Real-Time NMPC Strategy for Electric Vehicle Stability Improvement Combining Torque Vectoring With Rear-Wheel Steering

On low-friction surfaces, vehicle stability is significantly influenced by nonlinear vehicle dynamics. To realize stability improvement, a real-time nonlinear model predictive control (NMPC) strategy that combines torque vectoring control (TVC) and rear-wheel steering (RWS) is proposed. A multitarget optimization objective function that considers system state tracking, stability, control constraints, and actuation energy consumption is established. Then, a fast-solving algorithm based on Pontryagin’s minimum principle (PMP) is proposed to reduce the heavy computational burden of NMPC and satisfy the real-time computation requirement of vehicular applications. The effectiveness of the proposed fast-solving algorithm is verified by comparing its closed-loop performance with interior point optimization (IPOPT). A series of hardware-in-the-loop (HIL) experiments with the real human driver is conducted to verify the effectiveness of the proposed fast-solving algorithm and combined control strategy. Furthermore, the HIL results show that the proposed combined control strategy can improve a vehicle’s handling stability on a low-friction surface and has a better performance and lower actuation energy consumption than the TVC-only method.