Electric Vehicle System Research Articles

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.

Energy Management for Hybrid Energy Storage System in Electric Based on Deep Deterministic Policy Gradient

In this paper, an intelligent control system design scheme based on deep deterministic policy gradient (DDPG) algorithm is proposed for the complex continuous action space problem in the hybrid energy storage system of electric vehicles. Firstly, the basic principle and internal logic of DDPG algorithm are introduced, including key elements such as Actor-Critic architecture, experience playback, target network, reward signal, policy gradient and value function update. Then, how to apply the DDPG algorithm to the industrial control system is described in detail. The Actor network learns the optimal strategy, the Critic network evaluates the value of the state-action pair, and uses the experience playback and the target network to improve the system stability and performance. Finally, the effect of the intelligent control system based on DDPG algorithm in complex environment is verified by simulation experiments. The results show that the system can effectively optimize the control strategy, improve the response speed and stability of the system, and has a good engineering application prospect.

Enhancing electric vehicle charging performance through series-series topology resonance-coupled wireless power transfer.

The current electric vehicles (EVs) market is experiencing significant expansion, underscoring the need to address challenges associated with the limited driving range of EVs. A primary focus in this context is the improvement of the wireless charging process. To contribute to this research area, this study introduces a circular spiral coil design that incorporates transceiver coils. First, an in-depth analysis is conducted using Ansys Maxwell software to assess the effectiveness of the proposed solution through the magnetic field distribution, inductance properties, and mutual inductance between receiver and transmitter coils. In the next step, a direct shielding technique is applied, integrating a ferrite core bar to reduce power leakage and enhance power transmission efficiency. The ferrite magnetic shielding guides magnetic field lines, resulting in a significant reduction in flux leakage and improved power transmission. Lastly, a magnetic resonance series (SS) compensation wireless system is developed to achieve high coupling efficiency and superior performance. The system's effectiveness is evaluated through co-simulation using Ansys Simplorer software. The results confirm the effectiveness of the proposed solution, showing its ability to transmit 3.6 kilowatts with a success rate approaching 99%. This contribution significantly advances the development of wireless charging systems for electric vehicles, addressing concerns and promoting global adoption.

IOT Based Battery Monitoring System For Electric Vehicle

IOT Based Battery Monitoring System For Electric Vehicle

Hybrid Electric Vehicle Powertrain Mounting System Optimization Based on Cross-Industry Standard Process for Data Mining

The meticulously engineered powertrain mounting system of hybrid electric vehicles plays a critical role in minimizing vehicle vibrations and noise, thereby enhancing the longevity of vital powertrain components. However, developing and designing such a system demands substantial time and financial investments due to intricate analysis and modeling requirements. To tackle this challenge, this study integrates data mining technology into the design and optimization processes of the powertrain mount system. The research focuses on the powertrain mounting system of a transverse four-cylinder hybrid electric vehicle, employing the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology to establish a data-mining prediction model for mounting stiffness. This model utilizes three data mining algorithms—Multi-SVR, MRTs, and MLPR—to assess their predictive accuracy concerning mounting system stiffness estimation. A comparative analysis reveals that the MRTs algorithm outperforms others as the most effective prediction model. The proposed predictive model elucidates the quantifiable correlation between vibration isolation performance and installation stiffness, overcoming complexities associated with traditional modeling approaches. Applying this model in powertrain mounting system design showcases the efficacy of the CRISP-DM-based approach, significantly enhancing design efficiency without compromising prediction accuracy.

Research and evaluation of the arrangement of heat exchangers in electric vehicle thermal management systems for CO2 refrigerant

The application of transcritical CO2 cycle in the thermal management system of electric vehicles has become increasingly prevalent, but the existing thermal management system architecture has not been optimized to specifically enhance the performance of CO2 refrigerant. This paper analyzes the impact of various heat exchanger arrangements on CO2 thermal management system and evaluates system configurations based on a new performance index, cycle coefficient of performance. The effects of air temperature, ambient temperature, and heat load on system performance are conducted with a high precision model. The chiller is connected in parallel-series with indoor heat exchangers, resulting in a maximum coefficient of performance improvement of 27.89% at an indoor air inlet temperature of 10 °C, and a maximum coefficient of performance improvement of 21.69% at an ambient temperature of 5 °C, under simultaneous heating conditions. The radiator is placed on the windward of gas cooler, leading to a maximum coefficient of performance increase of 4.85% in heating mode, but a maximum coefficient of performance decrease of 9.84% in cooling mode. The evaluation of these configurations across different operating cycles reveals that the parallel-series connection is well-suited for the new European driving cycle, showing an 18.4% improvement in cycle coefficient of performance compared to the series-parallel connection. The windward arrangement of radiator proves effective for highway fuel economy test cycle in winter and the new European driving cycle in summer. This study provides effective instruction for configuration design and evaluation of transcritical CO2 vehicle thermal management systems.

Stand-alone PV-SOFC-battery power system based on ANFIS controller for electric vehicle charging

Purpose This study aims to offer a hybrid stand-alone system for electric vehicle (EV) charging stations (CS), an emerging power scheme due to the availability of renewable and environment-friendly energy sources. This paper presents the analysis of a photovoltaic (PV) with an adaptive neuro-fuzzy inference system (ANFIS) algorithm, solid oxide fuel cell (SOFC) and a battery storage scheme incorporated for EV CS in a stand-alone mode. In previous studies, either the hydrogen fuel of SOFC or the irradiance is controlled using artificial neural network. These parameters are not controlled simultaneously using an ANFIS-based approach. The ANFIS-based stand-alone hybrid system controlling both the fuel flow of SOFC and the irradiance of PV is discussed in this paper. Design/methodology/approach The ANFIS algorithm provides an efficient estimation of maximum power (MP) to the nonlinear voltage–current characteristics of a PV, integrated with a direct current–direct current (DC–DC) converter to boost output voltage up to 400 V. The issue of fuel starvation in SOFC due to load transients is also mitigated using an ANFIS-based fuel flow regulator, which robustly provides fuel, i.e. hydrogen per necessity. Furthermore, to ensure uninterrupted power to the CS, PV is integrated with a SOFC array, and a battery storage bank is used as a backup in the current scenario. A power management system efficiently shares power among the aforesaid sources. Findings A comprehensive simulation test bed for a stand-alone power system (PV cells and SOFC) is developed in MATLAB/Simulink. The adaptability and robustness of the proposed control paradigm are investigated through simulation results in a stand-alone hybrid power system test bed. Originality/value The simulation results confirm the effectiveness of the ANFIS algorithm in a stand-alone hybrid power system scheme.

Research on the Performance Characteristics of a Waste Heat Recovery Compound System for Series Hybrid Electric Vehicles

In this paper, a waste heat recovery compound system for series hybrid electric vehicles is established. The existing components of vehicle air conditioning are used in the organic Rankine cycle (ORC) to realize miniaturization. The waste heat recovery compound system is constructed using GT-SUITE, and the objective of the analysis is to increase the power output and engine thermal efficiency increase ratio (ETEIR). The effects of the expander speed, pump speed, working fluid mass flow rate, and working fluid type on the waste heat recovery compound system are analyzed. The simulation results show that the optimal schemes for the ORC system and compound system corresponding to the expander speed and pump speed are 1000 pm, 2500 rpm, 1200 rpm, and 2500 rpm, respectively. Compared with the ORC system, the maximum power output of the compound system with the same working fluid in three states (1500 rpm, 2500 rpm, and 3500 rpm) of the engine is increased by 21.67%, 24.05%, and 28.23%, respectively. Working fluid supplies of 0.4 kg/s, 0.4 kg/s, and 0.6 kg/s in the three engine states are also considered the best solutions. The working fluid R1234yf and R1234ze are the preferred choices for a waste heat recovery compound system, which have a high system power output and ETEIR and are environmentally friendly.

An ECC-Based Authentication Protocol for Dynamic Charging System of Electric Vehicles

Dynamic wireless charging emerges as a promising technology, effectively alleviating range anxiety for electric vehicles in transit. However, the communication between the system’s various components, conducted over public channels, raises concerns about vulnerability to network attacks and message manipulation. Addressing data security and privacy protection in dynamic charging systems thus becomes a critical challenge. In this article, we present an authentication protocol tailored for dynamic charging systems. This protocol ensures secure and efficient authentication between vehicles and roadside devices without the help of a trusted center. We utilize a physical unclonable function (PUF) to resist physical capture attacks and employ the elliptic curve discrete logarithm problem (ECDLP) to provide forward security protection for session keys. We validated the security of our proposed scheme through comprehensive informal analyses, and formal security analysis using the ROR model and formal analysis tool ProVerif. Furthermore, comparative assessments reveal that our scheme outperforms other relevant protocols in terms of efficiency and security.

Bi-LSTM predictive control-based efficient energy management system for a fuel cell hybrid electric vehicle

Energy management systems (EMS) in hybrid electric vehicles play a vital role in achieving optimal energy utilization, improved fuel efficiency, lower emissions, and improved vehicle performance. Thus, this paper proposes a Bidirectional Long-Term Memory (Bi-LSTM) model based on an efficient EMS for a hybrid electric vehicle that manages the powertrain elements of the IC engine, electric motor(s), fuel cell, and energy storage system. The proposed EMS also continuously monitors various parameters, including vehicle speed, driver inputs, battery state of charge, engine load, and environmental conditions, to make real-time decisions on power distribution and operation modes. Further, the proposed system leverages the capabilities of the Bi-LSTM model to capture intricate temporal dependencies and bidirectional context in driving patterns using the real-time dataset. Empirical studies are conducted to substantiate the efficacy of the proposed energy management strategy vis-à-vis diverse controllers, encompassing Model Predictive Control and Sliding Mode Control. Real-world driving data is employed as the basis for these investigations, aiming to provide a robust assessment of the proposed energy management approach in practical scenarios. Results demonstrate the efficacy of the suggested methodology compared to traditional EMS approaches, showcasing improvements in energy utilization and reduced environmental impact. Finally, this research emphasizes a comparative analysis of the proposed topology with the existing approaches in a real-world fuel cell hybrid electric vehicle system. The findings provide insights into the feasibility of deploying intelligent EMS strategies for improved sustainability and efficiency in a modern energy-efficient environment.

Cooling performance and optimization of a thermal management system based on CO2 heat pump for electric vehicles

Efficient integrated thermal management scheme is crucial for improving the driving range, thermal comfort, and safety of electric vehicles. Compared with the synthetic refrigerant heat pump indirect cooling/heating on the vehicle electrical system, the natural refrigerant CO2 heat pump direct thermal controlling integrated into the thermal management system has advantages in the thermal cycle performance and environmental friendliness. The present study proposed a thermal management system based on CO2 heat hump with multiple working modes for enhancing vehicle energy utilization coefficient. The system thermal performance under the World Light Vehicle Test Cycle was investigated via energy and exergy analyses. The operational control strategies of the thermal management system were optimized for the thermal controls of the cabin, battery and motor. The results show that the minimum exergy destruction of the thermal system occurs at the optimal discharge pressure corresponding to the highest coefficient of performance. Ensuring two-phase latent heat transfer for battery cooling is the most effective measure to maintain battery temperature uniformity via dynamic collaborative regulation of expansion valves’ openings and compressor speed. Compared with constant expansion valve’s opening, the coefficient of performance of the system can be improved by a maximum of 10% with the saturated CO2 vapor at the outlet of battery cooling plate. Due to the low thermal capacitance and minimal thermal inertia of driving motor, its cooling performance is significantly influenced by the heat generation rate and refrigerant flow. Optimal motor cooling efficiency is achieved when controlling the refrigerant quality to 0.85 at the cooling passage outlet. Under the World Light Vehicle Test Cycle, the thermal management system effectively regulated the temperatures across different operating modes while operating at optimal discharge pressures.

State-of-charge estimation for supercapacitors based on salp swarm algorithm-optimized high and low degree cubature Kalman filters considering temperature uncertainty

The accurate estimation of state-of-charge (SOC) is crucial for the supercapacitor (SC) management system of electric vehicles. In this paper, a salp swarm algorithm (SSA)-optimized high and low degree cubature Kalman filters (CKFs) for SC SOC estimation is proposed. Firstly, the key parameters are identified for the Thevenin model by SSA under SC test data. Secondly, a temperature-varied model with temperature uncertainty is constructed by polynomial fitting. Thirdly, an adaptive rule based on SSA is designed to regulate the noise covariance matrix information in real time. Combining this rule and CKFs, SSA-based generalized high-degree CKF (SSA-GHCKF) and SSA-based low-degree CKF (SSA-CKF) are developed with the minimum prediction error as the objective. Finally, the effectiveness of improved CKFs is evaluated by the urban dynamometer driving schedule in varying temperatures. The simulation results show that the root mean square errors of SOC estimation by CKF, GHCKF, and SSA-CKF are kept within 1.4553 %, 1.2880 % and 1.2152 %, while for SSA-GHCKF, this metric is merely 1.1933 %. The proposed SSA-GHCKF enables superior accuracy and robustness for SOC estimation at extreme temperatures.

Design and Implementation of a Wireless Power Transfer System for Electric Vehicles

Wireless power transfer (WPT) systems, which have been around for decades, have recently become very popular with the widespread use of electric vehicles (EVs). In this study, an inductive coupling WPT system with a series–series compensation topology was designed and implemented for use in EVs. Initially, a 3D Maxwell (ANSYS Electromagnetics Suite 18) model of the system was generated. The impact of individual parameters on the coupling coefficient was analyzed through systematic variations in each parameter’s values. As a result, a system with a higher coupling coefficient was obtained. Using this system, three distinct load cases were investigated for their efficiency in the Simplorer (ANSYS Electromagnetics Suite 18) circuit. Subsequently, a prototype of the system was constructed, and the experimental results were compared with the model’s results. This study shows that both the output power and the efficiency of the system increase as the load resistance increases. The results obtained in this study are anticipated to offer valuable insights for the enhancement of WPT system design.

A novel hybrid liquid-cooled battery thermal management system for electric vehicles in highway fuel-economy condition

A hybrid liquid cooling system that contains both direct and indirect liquid cooling methods is numerically investigated to enhance the thermal efficiency of a 21700-format lithium-ion battery pack during the discharge operation. One of the most significant challenges that liquid-based direct cooling systems face is the filling of the heat capacity of the coolant during the cooling process, hindering the effective dissipation of heat generated from batteries. This paper aims to introduce a battery thermal management system to address this issue. In the present study, the influences of immersion and flow cooling methods using HFE-7100 and water (as working fluids), inflow velocities of cooling channel and tubes, parallel and counter flow, battery cell space, and highway fuel-economy test on the temperature distribution of batteries were studied comprehensively. Results highlight that immersion cooling with water outperforms due to the lower thermal conductivity of HFE-7100. In addition, Flow cooling significantly reduces the battery pack's highest temperature and non-uniformity compared to immersion. According to the numerical results, using cooling tubes as an indirect cooling system integrated with the direct flow cooling method can remarkably improve the thermal efficiency of the battery pack. Moreover, results exhibited that counterflow between cooling tubes/channels can keep the highest temperature and temperature variance of Li-ion battery package below 32.15 °C and 3.04 °C, respectively. Varying battery cell spaces shows that a 14 mm space reduces the battery package's highest temperature by 1.54 °C compared to a 10 mm space. Finally, highway fuel-economy condition is applied to the simulations of this study, illustrating the effect of an innovative hybrid battery thermal management system for a 21700-type Li-ion battery pack.