Real Electric Vehicle Research Articles

Discrete‐time full‐order sensorless control of high‐speed interior permanent magnet synchronous motor in electric vehicle traction system

AbstractA discrete‐time full‐order sensorless control strategy for high‐speed interior permanent magnet synchronous motor (IPMSM) used in electric vehicle (EV) traction system is designed in this paper. While most speed and position observer (SPO) methods are developed in the continuous‐time domain, the presence of a unit‐time‐delay block existed in feedback loop and discretisation of the algorithm can lead to performance degradation during digital implementation. In this work, speed and angle are observed using the discrete‐time four‐order IPMSM model. A quadrature phase‐locked loop model with compensation is introduced, and its stability condition is established for the first time. Furthermore, to address the potential for sudden speed changes in vehicle applications, the parameter range is further refined to keep the estimation error within a specified range. The sliding mode observer is discretised and its stability is analysed using discrete Lyapunov theory. Additionally, the time sequence of interactive signals between SPO and field‐oriented control is thoroughly examined to ensure accurate time cycle. Finally, the proposed control strategy is validated and demonstrated through bench tests and real EV (LS6 of IM Motors) tests with a 190 kW IPMSM, which can improve the accuracy by 0.3 radians at 8000 rpm compared to traditional methods, and achieve stable control at 15,000 rpm on the test bench and at 120 kph with the EV.

Multi-Step Temperature Prognosis of Lithium-Ion Batteries for Real Electric Vehicles Based on a Novel Bidirectional Mamba Network and Sequence Adaptive Correlation

The battery systems of electric vehicles (EVs) are directly impacted by battery temperature in terms of thermal runaway and failure. However, uncertainty about thermal runaway, dynamic conditions, and a dearth of high-quality data sets make modeling and predicting nonlinear multiscale electrochemical systems challenging. In this work, a novel Mamba network architecture called BMPTtery (Bidirectional Mamba Predictive Battery Temperature Representation) is proposed to overcome these challenges. First, a two-step hybrid model of trajectory piecewise–polynomial regression and exponentially weighted moving average is created and used to an operational dataset of EVs in order to handle the problem of noisy and incomplete time-series data. Each time series is then individually labeled to learn the representation and adaptive correlation of the multivariate series to capture battery performance variations in complex dynamic operating environments. Next, a prediction method with multiple steps based on the bidirectional Mamba is suggested. When combined with a failure diagnosis approach, this scheme can accurately detect heat failures due to excessive temperature, rapid temperature rise, and significant temperature differences. The experimental results demonstrate that the technique can accurately detect battery failures on a dataset of real operational EVs and predict the battery temperature one minute ahead of time with an MRE of 0.273%.

Pedestrians' street-crossing decisions compared between conventional and electric vehicles

To safely cross a road before an approaching vehicle, pedestrians need to accurately judge the vehicle's motion. Recent studies from our lab demonstrated that the noise emitted by a vehicle conveys important information about its motion, particularly in the case of accelerating approaches. Here, we compared street-crossing decisions between conventional (ICEVs) and electric vehicles (EVs) with or without AVAS sounds. The experiment presented high-fidelity audiovisual VR simulations of approaching vehicles, based on recordings of real ICEVs and EVs. The presented velocity profiles included a large variation in acceleration rates, a = 0 to 4.4 m/s^2, and two velocities at the moment of the crossing decision (28 km/h versus 50 km/h). To investigate whether the lower sound level of EVs compared to ICEVs contributes to differences in crossing decisions, we additionally presented a condition in which the sound levels were matched between vehicle types. All experimental conditions were presented in two modality conditions (auditory-only and audiovisual). We compare the crossing decisions between vehicle types, velocity profiles and level profiles in terms of collision probabilities estimated from the measured psychometric functions. Preliminary results confirm differences between crossing decisions made in interaction with EVs compared to ICEVs, reduced in the level-matched conditions.

Fast Charging Guidance and Pricing Strategy Considering Different Types of Electric Vehicle Users’ Willingness to Charge

As the penetration rate of electric vehicles (EVs) increases, how to reasonably distribute the ensuing large charging load to various charging stations is an issue that cannot be ignored. This problem can be solved by developing a suitable charging guidance strategy, the development of which needs to be based on the establishment of a realistic EV charging behaviour model and charging station queuing system. Thus, in this paper, a guidance and pricing strategy for fast charging that considers different types of EV users’ willingness to charge is proposed. Firstly, the EVs are divided into two categories: private cars and online ride-hailing cars. These categories are then used to construct charging behaviour models. Based on this, a charging decision model for EV users is constructed. At the same time, a first-come-first-served (FCFS) charging station queuing system is constructed to model the real-time charging situation in the charging station in a more practical way. Finally, a dynamic tariff updating model is used to obtain the optimal time-of-use tariff for each charging station, and then the tariffs are used to guide the fast-charging demand. By comparing the spatial and temporal distribution of charging demand loads at charging stations under different scenarios and considering whether the tariffs at each charging station play a guiding role, it is verified that the proposed strategy effectively optimises the balanced distribution of EV charging loads and alleviates the congestion at charging stations.

The transformative potential of blockchain technology in developing green supply chain: An evolutionary perspective on complex networks

Distrust is the main obstacle to the green transformation of the supply chain. Blockchain can rebuild trust among multiple participants by employing distributed information technologies. Based on complex network evolutionary game models, this paper tries to reveal the transformative potential of blockchain in promoting the green transformation of the supply chain. Simulation experiments with real electric vehicle data show that if adopting blockchain technology: i) the greenness of the supply chain can increase by 200% even without government policy intervention; by 75% when jointly optimizing the intensity of rewards and penalties; by 40% when exclusively adjusting the reward or penalty intensity; ii) the profits of both suppliers and manufacturers can be improved when they adopt green investment strategies, creating a “win-win” scenario. Besides, optimal government rewards and penalties are provided to accomplish various high levels of greenness in a blockchain supply chain network. This study not only quantifies the significant impact of blockchain technology on enhancing the greenness of supply chains, but also reveals the decision-making mechanisms of enterprises and government under varying degrees of greenness, providing theoretical guidance for enterprises and government to manage green blockchain supply chains.

Electric vehicle supply equipment monitoring and early fault detection through autoencoders

This paper presents a novel approach to detecting anomalies in Electric Vehicle charging unit power profiles using a combination of Autoencoders with LSTM techniques. This study presents a robust methodology, combining the two Machine Learning techniques, for early fault estimation in a real-world case study. The proposed methodology offers significant advantages over existing methods by providing a more comprehensive analysis of anomalous trends. To validate the effectiveness of the proposed methodology, the authors tested it on real Electric Vehicles charging power curves provided by an Italian Distribution System Operator recorded on a historical database and compared the performances with the ones of a traditional anomaly detection technique. The results of the study, tested on Electric Vehicles Supply Equipment or charging stations, demonstrate that the proposed approach is highly effective in detecting anomalous trends in Electric Vehicles charging profiles.

Co-estimation of state of charge and capacity for battery packs in real electric vehicles with few representative cells and physics-informed machine learning

Accurate state of charge and capacity estimation is crucial for battery packs in electric vehicles. However, the cell inconsistencies, computational complexity, temperature variations, and complex drive cycles all pose great challenges for the state of charge and capacity estimation of battery packs in field operation. This work aims to develop a framework for state of charge and capacity estimation of battery packs in real electric vehicles with few representative cells and physics-informed machine learning. By analyzing the battery data using a quantity-capacity diagram, the representative cells within the pack can be identified based on the relaxation terminal voltage online. Leveraging the physics information of batteries, the state of charge of the representative cells are monitored in a closed-loop manner. Then, state of charge and available capacity of battery pack are achieved based on the state of only two representative cells. We validate our method by evaluating its performance on the datasets of battery pack in real electric vehicles across different temperatures and drive cycles. The improvements of 83.60 % and 85.13 % in average root mean square errors can be achieved at real drive cycles under 30 °C and 15 °C. The results demonstrate that accurate and robust state estimation of battery packs can be achieved in field operations by utilizing the battery data of only two cells in laboratory conditions.

Model‐based adaptive control of modular DAB converter for EV chargers

AbstractThis paper presents the discrete‐time modelling and control of modular input‐parallel–output‐parallel (IPOP) dual‐active‐bridge (DAB) converters for electric vehicle (EV) charging. The proposed adaptive control system ensures adequate current‐sharing among parallel modules while minimizing DAB current stress by adopting dual phase‐shift modulation. Driven by the growing need for fast EV charging options, the paper highlights the importance of achieving top‐notch control performance, especially with varying load conditions. Specifically, it introduces a discrete‐time model for adjusting controller parameters adaptively, which simplifies the typically cumbersome manual tuning process associated with these systems. The proposed PI formulae are derived to satisfy specifications on the frequency domain as phase margin and the gain crossover frequency of the open loop gain transfer function, ensuring stability and robustness in operation. Moreover, the implementation of these formulae in discrete microcontrollers facilitates seamless PI autotuning for precise current, voltage, or power control. Notably, the proposed control strategy effectively mitigates current overshot issues commonly encountered during module engagement and shedding operations in modular EV chargers. To validate its efficacy, the proposed controller is evaluated through extensive testing and comparisons within the PLECS environment, particularly focusing on a two‐module IPOP‐DAB converter scenario, and including comparisons with classical offline model‐based pole placement methodology. Furthermore, real‐time hardware‐in‐the‐loop experiments are conducted to confirm the feasibility and performance of the proposed controller under realistic EV charging profiles.

Transfer learning based hybrid model for power demand prediction of large-scale electric vehicles

Accurately predicting the power demand of large-scale electric vehicles (EVs) is one of the key tasks of power grid operation optimization. However, this task is difficult to complete due to insufficient data and high randomness of power demand. To address this issue, this paper proposes a transfer learning based hybrid method for power demand prediction of large-scale EVs. Firstly, the linear trend of power demand is extracted by Multiple Linear Regression (MLR), and the nonlinear residual error is obtained by removing the trend from the original data. Secondly, the residual error is predicted via the bidirectional long short-term memory network (BiLSTM). Meanwhile, transfer learning is employed to improve the prediction accuracy of BiLSTM. The BiLSTM is pre-trained using the residual error data from building energy consumption and fine-tuned by the residual error data from EV power demand. Finally, the extracted trend of large-scale EVs’ power demand and the predicted residual errors are combined to obtain the final predicted results. To validate the proposed method, the power demand of a real EV charging station is predicted using the proposed method, and fifteen models are taken for comparison (including the most popular data-driven models). The experimental result indicates that the proposed method can improve the predictive performance by at least 14.23% compared with the comparative models in terms of symmetric mean absolute percentage error, effectively avoids negative transfer results, and significantly enhance the confidence level.

Low demand solution for (sub)urban personal transport by a direct hybrid (fuel cell-supercapacitors) source

For urban or suburban transport, the power demand of a personal car does not exceed 30 kW/ton, far below the power offered by regular cars designed for any ride. Moreover, it was formerly shown that the above power could be attained by a 10 kW fuel cell directly hybridized to a supercapacitor string able to deliver a 20 kW power peak. This theoretical fact, corresponding to low resource demand in the car manufacturing, was validated up to ½ scale on the bench. Within a research project in University of Lorraine, the target is to validate the low-resource demanding concept in a real hydrogen electric vehicle (fuel cell range extender Renault Kangoo) and to examine related aspects by combining engineering sciences to social and human sciences and economics. After reminding the former validation on a bench, the paper describes current work on how the electrical power is managed in the Renault H2 ZE Kangoo, by characterization of the fuel cell and thorough analysis of the transients of electrical and non-electrical variables available in the vehicle in various rides. Besides, risks and life cycle analysis of a one-ton hybrid vehicle have been evaluated in comparison with other 30 kW vehicles powered by either a fuel cell, or batteries or an internal combustion engine. Finally, social acceptability of the fuel cell cars by regular citizens is presented, revealing a positive opinion at fuel cells and hydrogen, however with a moderate readiness to its acquisition.

Nonlinear Magnetic Model of IPMSM Based on the Frozen Permeability Technique Utilized in Improved MTPA Control

In this paper, the enhanced nonlinear magnetic model of the low voltage interior permanent magnet synchronous machine (IPMSM) is developed using the frozen permeability (FP) technique in finite element analysis (FEA) FEMM 4.2 software. The magnetic model is derived by obtaining flux saturation maps for a wide range of dq stator currents. Furthermore, the FEA FP technique accounts for the corresponding offset in the flux maps due to the excitation of the permanent magnets, and well as for fitting the coefficients for the curve-fitting procedure. In order to demonstrate the usefulness of the proposed magnetic model, a nonlinear control strategy based on the maximum torque per ampere (MTPA) optimal algorithm for IPMSM is employed. The magnetic model and the MTPA control strategy are validated through a variety of computer simulations based on FEMM 4.2 and MATLAB R2023a software, as well as on a real IPMSM electric vehicle (EV) traction drive experimental setup.

A voltage reconstruction model for lithium-ion batteries considering the polarization process

Health assessment is one of the most crucial components in lithium-ion battery (LIB) management. However, traditional methods often require disassembling LIBs, which are inconvenient for implementation. On the contrary, non-destructive diagnostics can obtain the aging information of LIBs without causing damage to batteries, which is beneficial for the periodic inspection and secondary use of LIBs in other fields. Therefore, accurate non-destructive diagnostics is necessary in practical applications. This paper proposes a new voltage reconstruction model considering the polarization process to get the battery internal information. Through matching the equilibrium potentials of the positive and negative electrodes, combined with an equivalent circuit model, this voltage reconstruction model can accurately simulate the whole charging and discharging processes of LIBs. The parameters with clear physical interpretation in the model can be quickly and accurately identified within the MATLAB software, and these parameters can be used to quantitatively identify the degradation modes of LIBs. Finally, the post-mortem analysis of the aged LIB is used for verification. The results show that the proposed model could precisely reflect the battery internal ageing information and could be used for online diagnostics in real electric vehicles.

A Data-Driven Comprehensive Battery SOH Evaluation and Prediction Method Based on Improved CRITIC-GRA and Att-BiGRU

The state-of-health (SOH) of lithium-ion batteries has a significant impact on the safety and reliability of electric vehicles. However, existing research on battery SOH estimation mainly relies on laboratory battery data and does not take into account the multi-faceted nature of battery aging, which limits the comprehensive and effective evaluation and prediction of battery health in real-world applications. To address these limitations, this study utilizes real electric vehicle operational data to propose a comprehensive battery health evaluation indicator and a deep learning predictive model. In this study, the battery capacity, ohmic resistance, and maximum output power were initially extracted as individual health indicators from actual vehicle operation data. Subsequently, a methodology that combines the improved criteria importance through inter-criteria correlation (CRITIC) weighting method with the grey relational analysis (GRA) method is employed to construct the comprehensive battery health evaluation indicator. Finally, a prediction model based on the attention mechanism and the bidirectional gated recurrent unit (Att-BiGRU) is proposed to forecast the comprehensive evaluation indicator. Experimental results using real-world vehicle data demonstrate that the proposed comprehensive health indicator can provide a thorough representation of the battery health state. Furthermore, the Att-BiGRU prediction model outperforms traditional machine learning models in terms of prediction accuracy.

Loss model based controller of fuzzy DTC driven induction motor for electric vehicles using optimal stator flux

Adoption of electric vehicles (EVs) is playing a major role in achieving green energy; however, this suffers from energy density range anxiety. Induction motors (IM) are designed to operate most efficiently under rated conditions. However, for EV applications, it operates at partial load at a constant speed, which reduces its efficiency when operated with nominal flux. In the long run, a lot of battery energy is consumed, which increases the running cost of the vehicle. This paper presents an energy efficient motor drive for EV applications with the objective of reducing battery energy consumption by an optimal reference flux. The optimal value of the stator flux reduces the difference between constant and variable losses, which improves the efficiency and driving range of the vehicle. The optimal flux selection strategies used in the current literature are mostly based on a search control approach, which has convergence issues, is highly complex, and is difficult to implement. In this paper, the Teamwork optimization algorithm (TOA) is used for optimal flux selection under a dynamic driving scenario. The method is examined with realistic EV data under various operating conditions and validated using standard drive cycles. The analysis of efficiency, energy savings, and power input confirms the superiority of the proposed method over other existing methods.

A Heuristic-Driven Charging Strategy of Electric Vehicle for Grids with High EV Penetration

The widespread adoption of electric vehicles (EVs) poses challenges associated with charging infrastructures and their impact on the electrical grid. To address these challenges, smart charging approaches have emerged as a key solution that optimizes charging processes and contributes to a smarter and more efficient grid. This paper presents an innovative multi-objective optimization framework for EV smart charging (EVSC) using the Dynamic Hunting Leadership (DHL) method. The framework aims to improve the voltage profile of the system in addition to eliminating voltage violations and energy not supplied (ENS) to EVs within the network. The proposed approach considers both residential EV chargers and parking stations, incorporating realistic EV charger behaviors based on constant current charging and addressing the problem as a mixed integer non-linear programming (MINLP) problem. The performance of the optimization method is evaluated on a distribution network with varying levels of EV penetration connected to the chargers in the grid. The results demonstrate the effectiveness of the DHL algorithm in minimizing conflicting objectives and improving the grid’s voltage profile while considering operational constraints. This study provides a road map for EV aggregators and EV owners, guiding them on how to charge EVs based on preferences while minimizing adverse technical impacts on the grid.

Charging patterns analysis and multiscale infrastructure deployment: based on the real trajectories and battery data of the plug-in electric vehicles in Shanghai

The rapid development of transportation electrification aims to achieve a fuel-free and zero-carbon emission world. Electric vehicles (EVs) are booming worldwide, especially in China. Even if EVs' cost decreases and performances make significant progress, the convenience and deployment problem of charging infrastructure is still the key restriction for large-scale adoption. Therefore, this study proposes a real-EV-data-based model with the aim of optimal charging infrastructure deployment oriented to charging convenience and charging demand. Traveling and charging behaviors based on the real EV data from Shanghai, where the EV penetration rate is highest in China, are revealed for the first time to reflect the interactions between drivers and infrastructures. Especially, the charging behaviors in Shanghai are beneficial for the battery life of EVs. The deployment and related optimization models are then established to meet the charging demand and promote quantified convenience. Moreover, infrastructure deployment considering both the charging type and the scale can be programmed. The proposed capacity-removal heuristic algorithm have advantages in robustness for deployment solving. The deployment results promote the charging convenience by 119.4%, and the expectation of the average distance to the nearest charger is decreased by 32.0%. For the slow charger, fast charger and mixed charger deployment schemes, the charging experience of drivers can all be improved by over 10% and the return analysis based on the investment cost and charging service fee shows a good rate of return with 8.38% and 18.5%. Besides, scenario analysis shows that charging station deployment with higher-power and larger-capacity chargers is the future trend.

Deep Learning Approach for Operational Transfer Path Analysis: Case Study of Electric Vehicles

This paper presents a new approach to fault diagnosis of the drivetrains of the electric vehicle. Most commercially available electric vehicles do not have accelerometers on electric drivetrains making it difficult to detect fault characteristics of the drivetrains of the electric vehicle, whereas accelerometers exist on the driver's seat. The proposed approach’s key idea is based on the operational transfer path analysis that determines the transfer function between the source and receiver. The transfer function is derived by training a deep learning model. The deep learning model converts the driver's seat vibration signals into drivetrains vibration signals. The validity of the proposed approach is evaluated using data from the durability test of real electric vehicles. It is anticipated that the proposed approach is effective to diagnose electric vehicle drivetrains subjected to external noise conditions.

Enhance Auditory Experience by Identifying User Needs and Classifying User Types While Driving Electric Vehicles

The auditory experience of driving electric vehicles (EVs) has been studied as a means of conveying vehicle status information and improving user satisfaction. However, the driving experience and user preferences may vary depending on the driving context, including user characteristics. Thus, this study aims to 1) classify users based on their characteristics and 2) investigate their auditory experience preferences while driving EVs. For this purpose, 40 participants conducted questionnaires about their characteristics and performed a think-aloud task while driving 15.6 km in real EVs. As a result, three user characteristics and two user types were identified using factor analysis and K-means clustering, respectively. Text-formed think-aloud data were analyzed through network analysis to obtain insights for designing usercentered driving sound for EVs. These findings can contribute to the strategic management of EV sound design.

Optimized Passivity-based Control of a Hybrid Electric Vehicle Source using a Novel Equilibrium Optimizer

Passivity-based control is a well-known strategy for synthesizing stable controllers because it models and manages systems based on their global state space equations. It also permits proving the system's stability by making it passive, which is an undeniable advantage over traditional controllers that have limitations in terms of applications, saturation, and the inability to verify stability. Previous research used nonlinear control based on passivity method to control the energy distribution between the embedded sources, they did not investigate the impact of the dumping matrix parameter, which was typically considered to be a positive random value. In contrast, the results of this investigation show that it significantly affects system behaviour, especially in the transient-state domain. The primary objective of this study is to develop and evaluate an energy management strategy for hybrid electric vehicles powered by Fuel Cells as the primary source and Supercapacitors as the secondary source. The fast response with less overshoot is ensured by optimizing the damping matrix parameter using a well-known optimization technique named Novel Equilibrium Optimizer. The proposed technique is successfully demonstrated in a Matlab® simulation environment, and the control system exhibits robust dynamic behaviour. Finally, despite the complexity of this technique's mathematical proof, it provides a simple, efficient feedback control law for a real electrical vehicle application.

Electric Vehicles Lithium-Polymer Ion Battery Dynamic Behaviour Charging Identification and Modelling Scheme

Lithium-ion batteries are considered the substantial electrical storage element for electric vehicles (EVs). The battery model is the basis of battery monitoring, efficient charging, and safety management. Non-linear modelling is the key to representing the battery and its dynamic internal parameters and performance. This paper proposes a smart scheme to model the lithium-polymer ion battery while monitoring its present charging current and terminal voltage at various ambient conditions (temperature and relative humidity). Firstly, the suggested framework investigated the impact of temperature and relative humidity on the charging process using the constant current-constant voltage (CC-CV) charging protocol. This will be followed by monitoring the battery at the surrounding operating temperature and relative humidity. Hence, efficient non-linear modelling of the EV battery dynamic behaviour using the Hammerstein-Wiener (H-W) model is implemented. The H-W model is considered a black box model that can represent the battery without any mathematical equivalent circuit model which reduces the computation complexity. Finally, the model beholds the boundaries of the charging process that not affecting on the lifetime of the battery. Several dynamic models are applied and tested experimentally to ensure the effectiveness of the proposed scheme under various ambient conditions where the temperature is fixed at 40°C and the relative humidity (RH) at 35%, 52%, and 70%. The best fit using the H-W model reached 91.83% to describe the dynamic behaviour of the battery with a maximum percentage of error 0.1V which is in good agreement with the literature survey. Besides, the model has been scaled up to represent a real EV and expressed the significance of the proposed H-W model.