Performance Of Electric Vehicles Research Articles

A novel dual-layer cascaded torque control mechanism for the improved drivetrain performance for electric vehicles

A novel dual-layer cascaded torque control mechanism for the improved drivetrain performance for electric vehicles

PROCESS MONITORING IN HYBRID ELECTRIC VEHICLES BASED ON DYNAMIC NONLINEAR METHOD

Highway third-level faults can significantly deteriorate the reliability and performance of hybrid electric vehicle (HEV) powertrains. This study presents a novel process monitoring method aimed at addressing this issue. We propose a multivariate statistical method based on dynamic nonlinear improvement, namely dynamic neural component analysis (DNCA). This method does not require the establishment of precise analytical models; instead, it only necessitates acquiring data from HEV powertrains. Through numerical simulation and real HEV experiments, we demonstrate the effectiveness of this approach in monitoring highway third-level faults. The testing outcomes demonstrate that DNCA outperforms traditional dynamic methods like dynamic principal component analysis (DPCA), conventional nonlinear methods such as kernel PCA (KPCA) and NCA, as well as traditional dynamic nonlinear methods like DKPCA.

A regulatory power split strategy for energy management with battery and ultracapacitor

Electric vehicle batteries face fast degradation due to the high frequency of charging/discharging cycles and great peak power demands. Lifetime, continuity of supply and power density of these batteries affect the performance of electric vehicles (EVs). Hybrid energy storage systems (HESS) offers a feasible solution by incorporating other energy storage elements like ultra-capacitor (UC) along with battery. Their combination provides higher efficiency and better performance in terms energy/power density. UC can behave like a power buffer when the EV is accelerating and regenerating. The HESS needs a controller that can split the available power between different sub systems as per demand. This paper presents a regulatory control strategy useful in HESS with battery and UC for the speed regulation of a brushless DC (BLDC) motor using a 3-port bidirectional DC-DC converter. The regulatory control strategy monitors the state of charge (SOC) of UC and a fuzzy logic controller regulates the power flow between HESS and the motor. Simulation in MATLAB validates the efficacy of the strategy. Simulation results and hardware evaluation confirm that the regulatory control scheme is effective in splitting the available power according to the load demand and achieves better energy efficiency.

Effect of Weight Distribution and Active Safety Systems on Electric Vehicle Performance.

This paper describes control methods to improve electric vehicle performance in terms of handling, stability and cornering by adjusting the weight distribution and implementing control systems (e.g., wheel slip control, and yaw rate control). The vehicle is first simulated using the bicycle model to capture the dynamics. Then, a study on the effect of weight distribution on the driving behavior is conducted. The study is performed for three different weight configurations. Moreover, a yaw rate controller and a wheel slip controller are designed and implemented to improve the vehicle's performance for cornering and longitudinal motion under the different loading conditions. The simulation through the bicycle model is compared to the experiments conducted on a rear-wheel driven radio-controlled (RC) electric vehicle. The paper shows how the wheel slip controller contributes to the stabilization of the vehicle, how the yaw rate controller reduces understeering, and how the location of the center of gravity (CoG) affects steering behavior. Lastly, an analysis of the combination of control systems for each weight transfer is conducted to determine the configuration with the highest performance regarding acceleration time, braking distance, and steering behavior.

Enhancing lithium-ion battery lifespan early prediction using a multi-branch vision transformer model

Accurately predicting the lifespan of lithium-ion batteries is crucial for effective battery management systems, particularly for ensuring the safe operation and proactive maintenance of electric vehicles. However, existing methods encounter challenges due to the early weak capacity aging trend in batteries. This study a novel approach using a multibranch vision transformer model to address early stage lifespan prediction issues. The proposed model leverages a multi-input data structure by considering various battery parameters during the charging and discharging phases. By employing distinct branch structures for different inputs, the model can separately extract features from individual input variables. Each vision transformer network was meticulously designed to extract high-dimensional global hidden features from the inputs and integrated to predict the battery lifespan. Comparative analyses against advanced baseline models and existing methods consistently demonstrate the superior performance and robustness of the proposed model. Compared with traditional vision transformer, the proposed model demonstrated a notable reduction in root mean square errors of 13.17 % and 6.32 % on the two public datasets, respectively, indicating the efficacy and reliability of our approach for accurately predicting the lifespan of LIBs during the early stages of capacity decline.

Selection of energy storage systems for a special purpose rail vehicle based on simulation analysis

The issue of power supply to electric rail vehicles leads to a separation of the rail network into electrified and unelectrified portions, where the sections lacking electrification exclude the operation of electric rail vehicles powered from the overhead lines. The potential solution to this problem was found in adding energy storage systems to electric rail vehicles, to allow them some range of travel beyond the electrified lines. A simulation analysis of a special purpose rail vehicle travelling across a non-electrified section of railway line was conducted to assess the energy consumption rate and the necessary energy storage capacity. Three energy storage solutions were simulated, showing the travel range they can provide, with the aim of finding the lowest battery capacity solution that would still allow the vehicle to safely complete the simulated drive. The final selection of energy storage system capacity was done based on the assumed expected range outside electrified railway weighed against the mass and cost of the extra energy storage system added to the vehicle. For a vehicle with a mass of 65 tons a battery system with a capacity of 600 Ah was found to be sufficient.

Doping Effects on Ternary Cathode Materials for Lithium-Ion Batteries: A Review.

The ongoing advancements in lithium-ion battery technology are pivotal in propelling the performance of modern electronic devices and electric vehicles. Amongst various components, the cathode material significantly influences the battery performance, such as the specific capacity, capacity retention and the rate performance. Ternary cathode materials, composed of nickel, manganese, and cobalt (NCM), offer a balanced combination of these traits. Recent developments focus on elemental doping, which involves substituting a fraction of NCM constituent ions with alternative cations such as aluminum, titanium, or magnesium. This strategic substitution aims to enhance structural stability, increase capacity retention, and improve resistance to thermal runaway. Doped ternary materials have shown promising results, with improvements in cycle life and operational safety. However, the quest for optimal doping elements and concentrations persists to maximize performance while minimizing cost and environmental impact, ensuring the progression towards high-energy-density, durable, and safe battery technologies.

State of health estimation method based on real data of electric vehicles using federated learning

Accurately estimating the state of health (SOH) of lithium-ion batteries (LIBs) is of paramount importance for the development and operation of electric vehicles, as it directly impacts driving performance. Currently, there are numerous methods for estimating SOH, but only some are suitable for real-world data, most existing methods for estimating SOH use data of lithium-ion battery cells from laboratory operations to train and test. Firstly, obtaining data usage of battery from the real world takes work. Secondly, the model for estimating SOH must be representative and generalized because they are trained on separate data for each electric vehicle. Lastly, collecting data for centralized training may lead to huge data uploads This study goes beyond that by using battery data from real-world vehicle operations. This study is based on 10 in-service electric trucks over 3 years, adopting methods such as utilizing data acquisition technology from battery management systems (BMS) and other controllers, as well as vehicle networking data reporting technology, to propose a SOH estimation method utilizing a new machine learning approach based on artificial neural network (ANN) and federated learning (FL) to predict the SOH of lithium batteries in real-world scenarios. This study developed an ANN local model and compared it with the popular long short-term memory (LSTM) model, finding that the ANN model is relatively more suitable as a local model for electric vehicles. In addition, we apply the FL framework to train the model to reduce the amount of uploaded electric vehicles’ battery data. We verified the results of the model on experimental data. Meanwhile, we analyzed the model on actual data by comparing its mean absolute and relative errors. The results show that federated training method achieves better generalization in predicting SOH compared to centralized training method and another traditional machine learning models. This study provides an effective solution of electric vehicle batteries SOH prediction and offers new ideas and methods for the generalization of traditional machine learning models.

Estimation of battery temperature during drive cycle operation by the time evolution of voltage and current

During the operation of electric vehicles, accurate temperature monitoring of lithium-ion batteries by battery management system is crucial to their safety. However, temperature sensors possess limitations as they necessitate regular calibration to avoid inaccuracies, which can be challenging to accomplish in certain scenarios. As an alternative, a general sensorless temperature estimation framework is developed in this study to either serve as a complementary approach alongside sensors or function independently to ensure reliable temperature measurement. The proposed framework relies solely on data regarding battery voltage and current measurements, and this information is employed to determine the parameters of the equivalent circuit model via the vector-type recursive least squares algorithm in the first step. In the second step, these parameters and the voltage/current data serve as the input to the deep neural network (DNN) to estimate the instantaneous battery surface temperature during dynamic driving cycles under various ambient temperatures and degradation scenarios. To reflect the effect of past battery operation on the current temperature, a newly defined current integral function is proposed, by which the input time length of the DNN model is suggested. Four combinations of the model input are studied, and the best input option yields an average estimation error of the battery temperature that falls below 0.5 °C.

Research on SOC estimation of lithium-ion batteries based on robust full order proportional integral observer

Accurate state of charge (SOC) estimation of lithium-ion batteries is vital for the reliable and safe operation of electric vehicles. This paper introduces a novel SOC estimation method, the robust full-order proportional integral observer (RF-PIO), designed to overcome SOC estimation misalignment challenges in electric vehicles during high load shocks and temperature fluctuations. These challenges arise from the degradation of proportional integral observer stability. The RF-PIO method incorporates a novel proportional link into the disturbance estimation, significantly improving the observer's robustness without increasing computational complexity. This enhancement allows for more accurate tracking of model parameters under dynamic conditions. Extensive comparison experiments, in conjunction with a variety of driving data, validate the RF-PIO method. The results indicate that the SOC estimation error remains below 2.11%, even amidst unknown model parameters and high-frequency noise interference.

Marketing Events Mediating BEV Features and Operating Costs: Impact on Purchase Intentions

This research aims to find out the lack of influence of Batery Electric Vehicle (BEV) features and vehicle operating perspective costs on the interest in purchasing electric cars with marketing events as a mediation and review from an Islamic. This devotional population is a society over 21 years old that is domiciled in Jabodetabek. The sample in this study was made up of people who drove more than three times in one week, for a total of 100 respondents. The sampling technique uses a non-probability method with purposive sampling techniques through the distribution of questionnaires with Google forms. The analytical method used is the Partial Least squares Structural Equation Modeling method (PLS-SEM). The results of the study show that Feature influences Purchase Intention, Feature influences Marketing Event, Vehicle Operating Cost influences Purchase Intention, Marketing Event influences Purchase Intentions, Marketing Event mediates the influence of Feature on Purchase Intentions, Features, Vehicle Operating Costs, Marketing Events, and Purchase Intention BEV cars among the people in Jabodetabek. The implications of the results of this study present that purchase intention towards BEVs encourages producer management to optimally utilize all the variables analyzed in this study proportionally.

Multi-Strategical Thermal Management Approach for Lithium-Ion Batteries: Combining Forced Convection, Mist Cooling, Air Flow Improvisers and Additives

Maintaining the peak temperature of a battery within limits is a mandate for the safer operation of electric vehicles. In two-wheeler electric vehicles, the options available for the battery thermal management system are minuscule due to the restrictions imposed by factors like weight, cost, availability, performance, and load. In this study, a multi-strategical cooling approach of forced convection and mist cooling over a single-cell 21,700 lithium-ion battery working under the condition of 4C is proposed. The chosen levels for air velocities (10, 15, 20 and 25 m/s) imitate real-world riding conditions, and for mist cooling implementation, injection pressure with three levels (3, 7 and 14 bar) is considered. The ANSYS fluent simulation is carried out using the volume of fluid in the discrete phase modelling transition using water mist as a working fluid. Initial breakup is considered for more accurate calculations. The battery’s state of health (SOH) is determined using PYTHON by adopting the Newton–Raphson estimation. The maximum temperature reduction potential by employing an airflow improviser (AFI) and additives (Tween 80, 1-heptanol, APG0810, Tween 20 and FS3100) is also explored. The simulation results revealed that an additional reduction of about 11% was possible by incorporating additives and AFI in the multi-strategical approach. The corresponding SOH improvement was about 2%. When the electric two-wheeler operated under 4C, the optimal condition (Max. SOH and Min. peak cell temp.) was achieved at an air velocity of 25 m/s, injection pressure of 7 bar with AFI and 3% (by wt.) Tween 80 and a 0.1% deformer.

Active Equalization of Lithium Battery Pack with Adaptive Control based on DC Energy Conversion Circuit

Background:: How to solve the inconsistency of battery pack is a key point to ensure reliable operation of electric vehicles. Battery equalization is an effective measure to address the inconsistency. Passive equalization method has poor efficiency and thermal management problems. Average voltage equalization method is only suitable for situations where there is a significant voltage difference between batteries. The SOC-based equalization method is relatively difficult and may inevitably lead to the accumulation of errors during the process. Objective:: In order to avoid the disadvantages of traditional control methods, a new control method is proposed to improve the accuracy and self-adaptation of active equalization, which is easy to be realized without online calculation. Methods:: Cascaded bidirectional Buck-boost circuit is adopted as the novel equalization topology. Based on fuzzy PID theory, an adaptive digital-analog hybrid control strategy based on fuzzy PID is proposed in this paper. Parameter design of the fuzzy PID controller is carried out. A battery equalization system based on cascaded bidirectional Buck-boost circuit is designed and developed. Experimental verification is conducted on relevant hardware platforms. Results:: An adaptive digital-analog hybrid control strategy based on fuzzy PID is proposed. Compared to passive equalization, this proposed method provides high efficiency. Regarding traditional voltage control, the method improves control reliability and flexibility. Compared to the average voltage equalization method, the approach needs less convergence time. Moreover, the control method is much easier to realize than the SOC-based equalization method. Conclusion:: By using the presented adaptive control based on DC energy conversion circuit, the degree of self-adaptation of the equalization process has been obtained as higher and the inconsistency as smaller.

Real-time and hardware in the loop validation of electric vehicle performance: Robust nonlinear predictive speed and currents control based on space vector modulation for PMSM

The transport industry's environmental impact has prompted various stakeholders to focus more on electric vehicles as a potential solution. Extensive research on electric vehicle motor control reveals their complexity, highlighting the crucial role of the traction motor control mechanism being one of their essential subsystems. This study presents a novel approach to control the drive system of an electric vehicle's permanent magnet synchronous motor, using a robust non-linear model predictive control in a cascaded structure combined with a space vector modulation technique. The formulation of the robust non-linear model predictive control law involves optimizing a new cost function with the inclusion of an integral action in the controller to ensure zero steady-state error. An important aspect of this control approach is its ability to enhance robustness without needing information about external perturbations and parameter uncertainties. Moreover, the space vector modulation technique is designed to address the issue of current harmonic distortion by carefully selecting appropriate switching vectors. The appropriate selection for space voltage vectors for every sampling period maintains a consistent switching frequency. As a result, the SVM can enhance torque-rippled regulation and accomplish effective flux and torque control. The performance of the proposed control system is evaluated through a series of numerical simulations using MATLAB/Simulink. The simulation results are analyzed and compared to assess the effectiveness of the proposed control system in terms of tracking accuracy, disturbance rejection, robustness against parameter uncertainties, reduction of torque, and current ripples. To further validate the numerical simulation results, a hardware-in-the-loop (HIL) setup is established using the OPAL-RT platform. Furthermore, experimental validation on a PMSM utilizing the dSPACE DS1202 board is carried out, demonstrating the efficacy of the proposed control system. These findings validate the suggested control technique's robustness and efficiency.

Legal Cooperation of Kazakhstan with the BRICS Countries on the Production and Operation of Medical Electric Vehicles with Artificial Intelligence Technologies

This scientific article is devoted to the study and analysis of legal relations between the Republic of Kazakhstan and the BRICS countries in the field of the production and operation of medical electric vehicles with artificial intelligence technologies. Particular attention is paid to legislative measures that promote the formation and development of a new industrial sector, such as the production of medical electric vehicles with artificial intelligence technologies. The research uses a number of methods, including studying empirical data, comparative legal analysis, synthesis, generalization, and scientific forecasting. The article proposes legislative measures to solve the problems facing the medical electrical machinebuilding industry and the unmanned medical electric vehicle industry, as well as the difficulties of integrating automation and digitalization into the production process of transportation plants in Kazakhstan and the BRICS countries. In the order of forecasting, the authors propose the adoption of several laws that are relevant to the issue under consideration. These proposals include the signing of new international cooperation agreements between Kazakhstan and the BRICS countries aimed at the introduction of digitalization at machine-building plants in Kazakhstan for the production of medical electric vehicles equipped with artificial intelligence technologies.

Greases for electric vehicle motors: Bearing friction torque under driving cycle conditions and the thickener effect on oil release

Performance of Lithium Complex (LiX), Polyurea (PU), and Polypropylene (PP) greases in SKF6208 bearings subjected to driving cycle conditions for 28 days (equivalent to 23,000 km of electric vehicle operation) was studied by continuously measuring bearing friction torque and temperature. The energy dissipation was correlated to the differences in oil bleeding and rheology for the three greases. Evolution of the friction torque, friction torque hysteresis, and changes in grease rheology were dominated by the oil release property. The latter was determined by the thickener system and its particular response to the conditions imposed by the driving cycle. A quantitative estimate of the carbon footprint from using these greases to lubricate bearings under driving cycle conditions is also presented.

Estimating the Optimal State of Charge for Electric Car Batteries Using an Extended Kalman Filter

The efficient management of battery state of charge (SOC) is crucial for maximizing the performance, range, and longevity of electric vehicle (EV) batteries. This paper presents a novel approach for estimating the optimal state of charge of electric car batteries using an Extended Kalman Filter (EKF). The EKF is a recursive algorithm that combines measurements from various sensors with a dynamic battery model to estimate the current SOC and predict future SOC values with high accuracy. The paper provides a detailed explanation of the EKF algorithm and its application to battery SOC estimation, highlighting its ability to handle nonlinearities, uncertainties, and measurement noise inherent in battery systems. Furthermore, this research presents a simulation-based validation of the proposed EKF approach using real-world driving data from electric vehicles. The simulation results demonstrate the effectiveness of the EKF algorithm in accurately estimating the SOC of electric car batteries under various operating conditions, including different driving patterns, temperatures, and battery degradation scenarios.

A novel battery abnormality diagnosis method using multi-scale normalized coefficient of variation in real-world vehicles

Accurate and efficient diagnosis of battery voltage abnormality is crucial for the safe operation of electric vehicles. This paper proposes an innovative battery voltage abnormality diagnosis method based on a normalized coefficient of variation in real-world electric vehicles. Vehicle and laboratory data are collected and analyzed, with joint preprocessing to improve data quality, and battery voltages are log-transformed to improve the contribution of anomalous voltage fluctuations. The normalized coefficient of variation is proposed to detect the fluctuation inconsistency of cell voltage, and the risk coefficient rule is formulated by Z-score and normalization. Furthermore, the validity and robustness are verified by laboratory and real-world battery faults. The results demonstrate that the optimal slide step and calculation window for real-world under-voltage fault are 10 and 40, and those for laboratory lithium plating and real-world thermal runaway are both 10 and 50, respectively. More importantly, this study introduces a battery abnormality diagnosis strategy based on the vehicle T-box, anticipated to be widely implemented to ensure the safety of real-vehicle operations. This method not only enhances the accuracy and efficiency of detecting electric vehicle battery abnormalities, but also offers a practical solution to prevent battery related faults.

Enhancing Electric Vehicle Performance with a Hybrid PI-Sliding Mode Controller for Battery Supercapacitor Integration

Nowadays, most of the works are based on electric vehicle usage for sustainable transportation using traditional energy storage device, such as battery. Usage of batteries in electric vehicles is having several disadvantages, for example, life span, temperature, and charge estimation. In this paper, a novel control scheme for battery and supercapacitor- (SC-) based hybrid energy storage system (HESS) using hybrid proportional and integral- (PI-) sliding mode control (SMC) for electric vehicle (EV) applications is introduced and implemented. This HESS with hybrid controller proves the usage of batteries in EVs to its fullest potential. The conventional control strategy for HESS follows two-loop voltage and current PI controllers with low-pass filter (LPF) and involves tuning of multiple control parameters with variations of source and load disturbances. Performance of the system is affected by tuning PI controller constants. A slow response time with linear PI controllers is long which is not advisable for starting and sudden jerk conditions of EVs. Moreover, the PI controller performance is affected by the system parameter variations during load changes. And these parameters are dynamic in nature due to nonideal conditions. In this paper, a hybrid PI-sliding mode controller (SMC) scheme is designed to control the bidirectional DC-DC converters to overcome the drawbacks of aforementioned issues. The combined PI-SMC controller reduces the tuning effort and reduces the effect of shift in operating point in controller performance. Linear modeling is done using small signal analysis for each subsystems. Permanent magnet synchronous machine (PMSM) is used as electric vehicle. The entire system and its controllers are simulated using MATLAB-Simulink, and detailed comparison is carried between conventional PI and proposed hybrid PI-SMC scheme to regulate the DC link voltage. The results are tabulated and show that the hybrid PI-SMC scheme outperforms in transient and steady-state conditions than the traditional PI controller. A scaled hardware prototype of 48 W set-up is developed using dSPACE-1104, and the experimental results have been carried out to verify the proposed system’s feasibility.

OptimHome: A Shrinking Horizon Control Architecture for Bidirectional Smart Charging in Home Energy Management Systems

This study aims to develop an adaptable home energy management system capable of integrating the bidirectional smart charging of electric vehicles. The final goal is to achieve a user-defined objectives such as cost minimization or maximizing renewable self-consumption. Industrialwise, the present work yields valuable outcomes in identifying operational frameworks and boundary conditions. Optimal scheduling benefits both users and the electric network, thus enhancing grid utilization and increasing renewable energy integration. By coordinating power interactions with dynamic time-of-use tariffs, the energy management system minimizes user costs and aids the grid by cutting peak hour energy consumption. Charging and discharging operations in electric vehicles comply with energy level constraints outlined by bidirectional charging protocols. The proposed approach ensures the scheduling of cycles that minimize detrimental effects on battery health when evaluating an economically ageing mechanism. Compared to uncontrolled charging, optimal scheduling resulted in a significant reduction in the total operational cost of the dwelling. Trade-off conditions between renewable integration and potential savings are identified and numerically evaluated by means of multiobjective optimization. In contrast to scheduling-based models, the proposed architecture possesses the ability to iteratively adapt decision variables in response to system changes, thus responding effectively to external stochastic uncertainty.