Electric Vehicle System Research Articles

Heat transport characteristics of a flat-evaporator loop heat pipe for use in a thermal management system of electric vehicles

In this work, a flat-evaporator loop heat pipe (FELHP), with a 100 cm2 rectangular thermal interface and an overall length of approximately 2 m was devised for the thermal management of electric vehicles. A simple rectangular-cuboid evaporator was devised using a novel stepped-flat wick structure, and the heat transport characteristics were systematically investigated in terms of the heat load, relative height of the evaporator to the condenser (i.e., position), heat sink temperature, and working fluid. In particular, water-ethanol binary mixtures with different compositions were tested to find an optimum low-freezing-temperature working fluid for use in thermal management system of the electric vehicles driving outdoors. Over the tested heat load range, a significant position dependence of the operating temperature was observed with a thermal resistance change from 0.21 K/W ± 0.02 K/W to 0.14 K/W ± 0.01 K/W for a 0.4 m change in the position. In contrast, the operating temperature of the FELHP was insensitive to changes in the heat sink temperature. A binary mixture containing 20% water and 80% ethanol (by volume) resulted in comparable heat transport characteristics to those of pure water but had a much lower freezing temperature (e.g., a thermal resistance of 0.25 K/W ± 0.03 K/W at 450 W and a freezing temperature of approximately -59 °C), demonstrating that this was an optimum composition of the water-ethanol mixture working fluid of the FELHP.

Smart Charging System for Electric Vehicles

Smart Charging System for Electric Vehicles

Research on refrigerant charge determination under different compressor speed and its effects on the performance of transcritical CO2 air-conditioning heat pump system in electric vehicle

CO2 is assumed to be one of the most potential refrigerant alternatives for electric vehicles for its excellent properties. However, the charge determination of CO2 in current studies remain controversial. In this study, a transcritical CO2 air-conditioning heat pump system was established and experimentally tested to analyze the optimal charge amount. Based on two conflicting methods of charge determining proposed by the preceding research, this paper substantiated the existing controversy and subsequently proposed a more comprehensive method. The effects of different refrigerant charge on the system characteristics were investigated. The influence of the compressor speed on the optimal refrigerant charge and system characteristics was also analyzed. It was found that the optimal charge plateau occurred from refrigerant of 500 g–580 g at the compressor speed of 3000 r·min−1. However, the optimal charge declined with the increment of compressor speed from 3000 r·min−1 to 4500 r·min−1. Among three models, Hughmark's model was proved to be the most appropriate for the theoretical calculation of optimal charge within an error of 6.09%. Further study illustrates that refrigerant mass in high-pressure pipe and intermediate heat exchanger/accumulator (IHX/A) accounted for the main proportion about 52.6%–55.14%.

Sliding Mode Control of an Electric Vehicle Driven by a New Powertrain Technology Based on a Dual-Star Induction Machine

This article examines a new powertrain system for electric vehicles based on the dual-star induction machine, presented as a promising option due to its significant advantages in terms of performance, energy efficiency, and reliability. This system could play a key role in the evolution of electro-mobility technology. The dual-star induction machine reduces electromagnetic torque fluctuations, limits current harmonics, improves power factor, and enables half-speed operation. Our study focuses on the control strategy and operation of the traction chain for electric vehicles propelled by the dual-star induction machine (DSIM) using Matlab software with version 2017. We integrate the battery as the main energy source, along with three-level static converters for energy conversion in the vehicle’s four operating quadrants. We have opted for sliding mode control, which has proven to be feasible and robust against external disturbances. Although we have modeled driver behavior, we consider it as an aspect of control, to which we add the driving profile to guide our evaluation of the control to be used for vehicle operation. The results of our study demonstrate the reliability and robustness of DSIM for electric vehicle motorization and speed control. Promoting this technology is essential to improve the overall performance and efficiency of electric vehicles, especially in traction and braking modes for energy recovery. This underscores the importance of DSIM in the sustainable development of the electric transportation system.

State of charge estimation of electrical vehicle battery by Using Box Particle Filter Signal Tracking Method

ABSTRACT This paper concentrates on the effective study of the online state of charge (SoC) estimation of lithium-ion batteries used in electric vehicles (EVs). Box Particle filters (BPF) are commonly used in the estimation of multivariate systems under noisy environments. In this study, a Joint-estimator is proposed which uses the forgetting factor recursive least square (FF-RLS) method for the 1-RC battery model online parameter estimation and the lithium-ion battery SoC is estimated using battery terminal voltage signal tracking with (BPF). The implemented method is compared with a Sequential Importance Resampling Particle Filter (SIR-PF) which shows that the proposed BPF has reduced the number of particles and computational time and maintained the efficiency comparable to the SIR-PF. The BPF performance is further evaluated at assorted voltage signal tracking accuracy, the number of boxes and the initial dimension of the boxes. The BPF method requires less number of boxes as compared to the SIR-PF with compatible efficiency. Therefore, BPF can be used as an alternative to the complex algorithms used in the SoC estimation in battery management systems (BMS) of electric vehicles.

Energy-efficient operation of the thermal management system in electric vehicles via integrated model predictive control

The thermal management system (TMS) in electric vehicles (EVs) is a comprehensive system that integrates an air conditioning system for the cabin, a temperature control system for the battery, and a cooling system for the motor. The currently used PI control strategy can only meet the basic TMS functions and cause high energy consumption. In this paper, we present a novel model predictive control (MPC) strategy for the TMS to optimize operational performance in real time. Different from the independent PI control for the individual components, MPC can predict future operation conditions and provide the optimal operating inputs in advance. A complete control-oriented model for MPC is developed, and the MPC strategy is designed to minimize the total power consumption of the TMS under the control-oriented model and constraints. The evaluation is carried out under several cases including the fixed ambient temperature, realistic ambient temperature, and different vehicle speeds. The results showed that the novel MPC strategy saved energy consumption by 5.9%–10.3% in these cases when compared to the PI strategy, demonstrating the effectiveness and feasibility of the proposed MPC control.

Fault Detection and Diagnosis in Electric Vehicle Systems using IoT and Machine Learning: A Support Vector Machine Approach

This research examines blame discovery and determination in electric vehicle (EV) frameworks utilizing Internet of Things (IoT) information and machine learning calculations, centring on a Support Vector Machine (SVM) approach. The study points to improving the unwavering quality and security of EV operations by precisely recognizing and diagnosing flaws in real time. The test comes about illustrates the adequacy of the SVM-based approach, with an exactness of 95%, accuracy of 94%, review of 96%, and F1-score of 95%. Comparative investigation with elective calculations such as k-Nearest Neighbors, Decision Tree, and Random Forest underscores the predominant execution of SVM in blame discovery and determination. The SVM calculation shows negligible misclassifications over distinctive blame classes, highlighting its strength and viability. This inquiry contributes to the headway of blame location strategies in EV frameworks and gives profitable bits of knowledge into the commonsense usage of machine learning strategies for improving framework unwavering quality. Moving forward, the discoveries clear the way for assist investigations in optimizing blame location systems and expanding their appropriateness to other spaces such as mechanical mechanization and renewable vitality frameworks.

Energy management strategy for photovoltaic powered hybrid energy storage systems in electric vehicles

Nowadays, electric vehicles (EVs) using additional energy sources frequently deliver a safe ride without concern about the distance. The energy sources including a battery, an ultra-capacitor (UC), and a photovoltaic (PV) are considered in this research for driving the EV. Vehicles that only use battery-oriented technologies experience problems with charging and quick battery discharge. EVs are used with an ultracapacitor to decrease the quick discharge effects and increase the lifetime of the battery. Furthermore, bidirectional DC-DC converters are a type of power electronics device used to verify the smooth transfer of generated power from energy sources to the motor throughout various stages of the driving cycle. Therefore, this study proposes a perturb and observe (P&O) energy management control technique based on tuna swarm optimization (TSO). The suggested TSO-P&O completely uses UC while regulating the battery because it lowers dynamic battery charging and discharging currents. Due to the aforementioned aspect, the suggested TSO-P&O increases battery life and demonstrates a very dependable, long range power source for an electric car. The TSO-P&O technique achieves the EVs by obtaining the maximum speed of 91.93 km/hr. with a quicker settling time of 4,930 ms when compared with the existing zero-fuel zero-emission (ZFZE) method.

ANALISIS SPESIFIKASI TEKNIS SISTEM PENGEMUDIAN ELEKTRIK PADA KENDARAAN LISTRIK BERBASIS BATERAI KOMERSIAL POPULER

The increase in the number of motor vehicles in Indonesia has elevated the negative impact on the environment, both in terms of air pollution and global warming. According to the Ministry of Transportation (Kemhub), the number of motor vehicles in Indonesia reached 141.99 million units in 2021. The electrification of motor vehicles is one of the solutions to reduce emissions from the motor vehicle sector. This is supported by Presidential Regulation No. 55/2019 to stimulate the growth of the national battery-based electric vehicle (BEV) industry. This paper reviews battery-based electric vehicle (BEV) technology worldwide, especially four-wheeled vehicles with a high total population. The research was conducted through online surveys in 2023 using international data sources and the Association of Indonesian Automotive Industries (GAIKINDO), resulting in a database of 33 battery-based electric vehicles. From this database, it was found that the majority of electric cars purchased by the public are hatchback and sport utility vehicle (SUV) types. In terms of electric drive systems, the power capacity range of electric motors used by these vehicles is between 20 - 150 kW for hatchbacks and 100 - 406 kW for SUVs. The most commonly used electric motor type, constituting about 64%, is the Permanent Magnet Synchronous Motor (PMSM). About 70% of these vehicles use lithium-ion battery technology. The technical specifications of these popular electric cars are expected to assist stakeholders in quickly understanding the technical specifications of electric drive systems in battery-based electric vehicles, serving as a benchmark for designing electric cars suitable to meet domestic needs.

Control strategy and parameter optimization of compound energy storage device for pure electric vehicle

In order to solve the problem of insufficient power of battery of pure electric vehicle, we study the energy storage system of electric vehicle. According to the characteristics and objectives of the complex energy storage device, we design its working mode, and propose a fuzzy control strategy based on speed and current limitation. In order to further improve the vehicle performance and reduce the output current of the battery, the key parameters of the composite energy storage device are linearly optimized. The simulation results show that: The acceleration time of pure electric vehicles equipped with composite energy storage devices is shortened by 12%, the braking energy recovery efficiency is increased by 39%, the power consumption of 100 kilometers is reduced by 8.55%, and the output current of the battery is significantly reduced, effectively extending the service life of the battery and the driving range of the vehicle.

A Survey of Wireless Charging System for Electric Vehicles and Fire Protection

Wireless charging for electric vehicles offers convenient power replenishment without physical connections. Fire protection measures are integral to ensure safety during wireless charging, mitigating potential risks of overheating or electrical malfunctions. The integration of wireless charging systems for electric vehicles (evs) alongside effective fire protection measures presents a promising avenue towards enhancing the sustainability and safety of urban transportation. In this survey work, we meticulously examine the state-of-the-art advancements, methodologies, and challenges pertaining to the fusion of wireless charging technologies with robust fire protection mechanisms for evs. In this survey encapsulates a synthesis of methodologies, technical architectures, and regulatory frameworks, providing researchers and practitioners with valuable insights to navigate the intricate landscape of wireless charging systems for evs and fire protection

Investigation of Charging Technologies for Electric Vehicles

In today's world, internal combustion engine vehicles are widely used. These vehicles cause harmful emissions as they use fossil fuels for engine propulsion and vehicle movement. To address this issue, electric vehicles have been considered as a solution. In electric vehicles, electric motors are used instead of traditional internal combustion engines and the necessary electrical energy is supplied from battery packs. In this way, electric vehicles do not cause harmful emissions to the environment. If the energy required for electric vehicle charging stations is obtained from renewable energy systems, electric vehicles will be zero-emission vehicles. Battery packs, that provide energy for electric vehicles, typically consist of rechargeable Lithium-ion battery groups and they can be charged using fast charging technology. In this study, the future of transportation, electric vehicles and the standards and technologies used in electric vehicle charging stations have been examined. Additionally, an electric vehicle system has been designed using MATLAB/Simscape.

A virtual test rig for performance and efficiency assessment of heat pump thermal management system for battery electric vehicles

In a Battery Electric Vehicle (BEV), the optimization of its thermal state plays a crucial role in the achievement of relevant vehicle targets such as range, passenger comfort, and recharging times, as well as varying external ambient conditions. Therefore, selecting the most proper solution between the many possible architectures to manage the different thermal loads of a BEV needs the support of a robust simulation platform. In such a framework, a comprehensive numerical twin of an electric city car was developed in the GT-Suite environment and validated against experimental data. Through that, the promising performance of a Heat Pump (HP) based thermal layout has been demonstrated, against a standard Air Conditioning system integrated with a PTC heater, on a wide range of external ambient temperatures (reaching up to +20 % driving range improvement in cold conditions). The present layout proposed a sustainable alternative to the most common R134a as working fluid (i.e., propane) and the integration of multiple heat sources. In addition, different battery preconditioning strategies have been tested, tailored to the selected real-world mission profiles, obtaining a remarkable gain in terms of driving range for the HP system in cold conditions (up to 6 % at −10 °C and 4 % at −20 °C).

Dielectric flow- and tab-based battery thermal management system for EV high performance application

From the perspectives of security, durability, and proper operation, effective temperature control via thermal management systems (TMSs) on lithium-ion batteries is crucial for electric vehicle use. In this regard, a novel TMS based on dielectric flow and tab cooling was presented in this paper. A functional prototype of the system was developed and experimentally tested for performance analysis. A thermal model was built on computational fluid dynamics software, and a comparative analysis of the initial and an optimised geometry of the prototype was carried on after validation. Results demonstrate the adequate response of the system. Thermally speaking, temperature decrease was noticed during operation, and the internal and among-cells thermal gradient was preserved into the recommended span. Besides, the used dielectric fluid assisted with a low auxiliary consumption due to its particular viscosity. With the new geometry, the dielectric flow duct volume was reduced by 79.8%, and the system particularities were enhanced due to heat dissipation improvement: operating temperatures were even lower in every scenario examined. The temperature difference among cells was reduced by 15%. Thermal dispersion within the cells was still below the limit, despite being increased. Moreover, the necessary pumping energy consumption was below 0.004% of the exchanged battery module’s energy, demonstrating the TMS efficiency. Thus, the application potential of the dielectric tab cooling-based system for electric vehicles was highlighted.

Study on the effect of low-temperature cycling on the thermal and gas production behaviors of Ni0.8Co0.1Al0.1/graphite lithium-ion batteries

With their high energy density and long cycle life, lithium-ion batteries (LIBs) are currently the most promising electrochemical energy storage system for electric vehicles. However, their safety and cycling performance can be significantly compromised in low-temperature environments due to lithium plating and other factors. Therefore, it is essential to study the safety of aging batteries. In this study, we focused on a commercially available high-specific-energy Ni0.8Co0.1Al0.1 cathode system battery and simulated the aging of the battery by long-term cycling at 0 ℃. This study compares and analyzes the samples on the basis of the previous ones in terms of heat production power, thermal runaway (TR) characteristics and gas production characteristics. It reveals the influence of low-temperature aging on the comprehensive performance of lithium batteries in a more comprehensive way. The results show that the charging pattern of all three characteristics is related to the aging degree of the battery. In terms of the thermal characteristics, the relative change in specific heat capacity of the aged battery is only between 0.32 % and 1.08 % compared to that of a fresh battery, so the effect of ageing on the specific heat capacity of the battery is negligible. The average heat generation power of batteries with different state of health (SOH) is not monotonically increasing with the decrease of SOH, the threshold of dramatic increase is SOH = 80 %. In terms of TR characteristics, the onset temperature of exothermic reactions (TOER) of aged batteries is significantly lower than that of fresh batteries. With the decrease of SOH, TOER also decreases gradually. Compared with batteries with different SOHs, the more seriously aged the battery safety valve opens earlier, and the TR occurs earlier. In terms of the TR gas production characteristics, the more severely aged the battery, the earlier the safety valve opens compared to a fresh battery. The most gas is produced when SOH = 70 %, which increases the risk of catastrophic damage.

Fuzzy Neural Network PID-Based Constant Deceleration Control for Automated Mine Electric Vehicles Using EMB System.

It is urgent for automated electric transportation vehicles in coal mines to have the ability of self-adaptive tracking target constant deceleration to ensure stable and safe braking effects in long underground roadways. However, the current braking control system of underground electric trackless rubber-tired vehicles (UETRVs) still adopts multi-level constant braking torque control, which cannot achieve target deceleration closed-loop control. To overcome the disadvantages of lower safety and comfort, and the non-precise stopping distance, this article describes the architecture and working principle of constant deceleration braking systems with an electro-mechanical braking actuator. Then, a deceleration closed-loop control algorithm based on fuzzy neural network PID is proposed and simulated in Matlab/Simulink. Finally, an actual brake control unit (BCU) is built and tested in a real industrial field setting. The test illustrates the feasibility of this constant deceleration control algorithm, which can achieve constant decelerations within a very short time and maintain a constant value of -2.5 m/s2 within a deviation of ±0.1 m/s2, compared with the deviation of 0.11 m/s2 of fuzzy PID and the deviation of 0.13 m/s2 of classic PID. This BCU can provide electric and automated mine vehicles with active and smooth deceleration performance, which improves the level of electrification and automation for mine transport machinery.

Direct regeneration of high-value LiFePO4 cathode materials with nitrogen doped carbon coating

Lithium iron phosphate (LiFePO4) batteries are currently becoming the primary power system for electric vehicles. However, after the life period, the heavy amounts of electric cars would give rise to a great number of scrapped LiFePO4 batteries, which inevitably causes serious environmental pollution and resources waste problems. Therefore, it is much imperative and urgent to develop the green technology for regenerating and recycling these scrapped LiFePO4 batteries. Herein, we reported an efficient and straightforward solid-phase strategy to achieve the direct regeneration of scrapped LiFePO4 cathode materials. The decommissioned LiFePO4 was successfully regenerated by adding lithium carbonate and melamine under the heat treatment condition, and the battery performance was repaired. The electrochemical performance of regenerated LiFePO4 is even higher than that of the unused LiFePO4 at 0.05 C. Moreover, the regenerated LiFePO4 also exhibits excellent capacity preservation rate of 99.03 % after 200 cycles and outstanding rate capability at 5 C with a discharge capacity of 116 mAh/g. The systematical study demonstrates that the nitrogen-doped carbon coatings play vital role in improving the properties of regenerated LiFePO4 cathode materials. This work provides a potential approach to resolve the scrapped LiFePO4 cathode materials to meet the coming retired tide of lithium ion batteries.

Employment of Artificial Intelligence (AI) Techniques in Battery Management System (BMS) for Electric Vehicles (EV): Issues and Challenges

Rechargeable Lithium-ion batteries have been widely utilized in diverse mobility applications, including electric vehicles (EVs), due to their high energy density and prolonged lifespan. However, the performance characteristics of those batteries, in terms of stability, efficiency, and life cycle, greatly affect the overall performance of the EV. Therefore, a battery management system (BMS) is required to manage, monitor and enhance the performance of the EV battery pack. For that purpose, a variety of Artificial Intelligence (AI) techniques have been proposed in the literature to enhance BMS capabilities, such as monitoring, battery state estimation, fault detection and cell balancing. This paper explores the state-of-the-art research in AI techniques applied to EV BMS. Despite the growing interest in AI-driven BMS, there are notable gaps in the existing literature. Our primary output is a comprehensive classification and analysis of these AI techniques based on their objectives, applications, and performance metrics. This analysis addresses these gaps and provides valuable insights for selecting the most suitable AI technique to develop a reliable BMS for EVs with efficient energy management.

In-situ monitoring of Al/Cu dissimilar laser welding process using optical emission spectroscopy (OES)

Aluminum (Al) and copper (Cu) dissimilar laser welding is an in-demand process in the manufacture of secondary battery systems for electric vehicles. However, the absence of a robust in-situ monitoring technique for this process reduces its efficiency and increases the potential risk to the battery system. Laser-induced plasma (LIP) has been extensively studied in various laser processes because it provides crucial information regarding the interaction between the laser beam and material. In this study, the measurement of LIP was conducted using optical emission spectroscopy (OES) during the Al and Cu dissimilar welding process to gain a comprehensive understanding of the process. Moreover, the feasibility of OES as a monitoring tool for dissimilar laser welding of Al and Cu was evaluated. Plasma parameters such as emission line intensity, electron temperature, and number density were effectively used to diagnose the welding state (non-welding, threshold welding, or Cu-penetrated welding). In addition, a strong correlation between the weld geometry and intensity of the Cu emission line was found. This study demonstrates the considerable potential of OES-based in-situ monitoring system applicable for high-demand dissimilar laser welding processes.

Compliance Assessment of the Spatial Averaging Method for Magnetic Field Leakage from a Wireless Power Transfer System in Electric Vehicles

Wireless power transfer (WPT) via magnetic resonance offers efficient electrical power transfer, making it an increasingly attractive option for charging electric vehicles (EVs) without conventional plugs. However, EV charging requires a transfer power in order of kW or higher, resulting in a higher-leaked magnetic field than conventional wireless systems. The leaked magnetic field is nonuniform, and the assessment in terms of the limit prescribed in the guideline is highly conservative because it assumes that a person standing in free space is exposed to a uniform field. In such cases, an assessment should be performed using the limits of the internal electric field, as it is more relevant to the adverse health effects, whereas its evaluation is time-consuming. To mitigate this over-conservativeness, international product standards introduce a spatial averaging method for nonuniform exposure assessment. In this study, we investigate assessment methods, especially for measurement points of nonuniform magnetic field strength leaked from the WPT system. Various spatial averaging methods are correlated with the internal electric field derived from electromagnetic field analysis using an anatomically based human body model. Our computational results confirm a good correlation between the spatially averaged magnetic and internal electric fields. Additionally, these methods provide an appropriate compliance assessment with the exposure guidelines. This study advances our understanding of the suitability of spatial averaging methods for nonuniform exposure and contributes to the smooth assessment in WPT systems.