Browse Topic: Vehicle charging

Items (1,033)
Electric double-layer capacitors (EDLCs) store charge by adsorbing ions at the electrode-electrolyte interface, offering fast charge/discharge rates, high power density, minimal heat generation, and long cycle life. These characteristics make EDLCs ideal for memory backup in electronic devices and power assistance in electric and hybrid vehicles. However, their energy density is lower than that of batteries, necessitating improvements in electrical capacity and potential. Traditionally, activated carbon with a high specific surface area has been used, but recent research focuses on mesoporous carbon materials for better ion diffusion. This study uses resorcinol-formaldehyde-carbon cryogel (RFCC) with mesopores and organic electrolytes with a wider electrochemical window. Various RFCCs with different pore sizes were synthesized and evaluated. Comprehensive investigations into the pore structures and surface properties of both synthesized carbon gels and commercial mesoporous materials
Cheng, ZairanOkamura, TsubasaOhnishi, YutoNakagawa, Kiyoharu
The use of drum brakes in Battery Electric Vehicles (BEVs) offers numerous benefits, including energy efficiency, reduced brake dust emissions, and reliable performance under challenging weather conditions. The capability of regenerative braking reduces the friction brake application frequency in BEVs and therefore the brakes can be prone to corrosion and performance degradation especially considering conventional disc brake systems. The closed design of a drum brake prevents corrosion of the friction-components by sealing out water, dirt or snow. A common sealing concept is performed with a labyrinth between the gap of the rotating drum and the axle mounted backplate. A hermetical isolation of water and snow ingress into the drum cannot be achieved with this concept, so additional aerodynamic measures are necessary to deflect the air/water path and protect the inner brake components. Additionally, interfaces like wheel cylinders, electric park brake parts, brake shoe pins, and axle
Hennicke, TimKuthada, TimoBernhard, AdrianReichhart, LeanderWeber, EugenMoers, MichaelRettig, Marc
This study presents a control co-design method that utilizes a bi-level optimization framework for parallel electric-hydraulic hybrid powertrains, specifically targeting heavy-duty vehicles like class 8 semi-trailer trucks. The primary objective is to minimize battery energy consumption, particularly under high torque demand at low speed, thereby extending both battery lifespan and vehicle driving range. The proposed method formulates a bi-level optimization problem to ensure global optimality in hydraulic energy storage sizing and the development of a high-level energy management strategy. Two nested loops are used: the outer loop applies a Genetic Algorithm (GA) to optimize key design parameters such as accumulator volume and pre-charged pressure, while the inner loop leverages Dynamic Programming (DP) to optimize the energy control strategy in an open-loop format without predefined structural constraints. Both loops use a single objective function, i.e. battery energy consumption
Taaghi, AmirhosseinYoon, Yongsoon
This paper presents a highly integrated 4-in-1 power electronics solution for 800V electric vehicle applications, combining on-board charging (OBC), DC boost charging, traction drive, and high-voltage/low-voltage (HV/LV) power conversion in a single housing. Integration is achieved through the use of motor windings for charging and a custom-designed three-port transformer that magnetically couples HV and LV batteries while ensuring galvanic isolation. The system also employs a three-phase open-ended winding machine (OEWM) to support both single-(1P) and three-phase (3P) AC charging. A dual-bank DC/DC architecture allows for seamless integration of a redundant auxiliary power module (APM), enhancing functional safety and autonomy. In AC charging mode, the three-level (3L) T-type inverter operates as a Vienna rectifier for 3P charging and as a totem-pole power factor correction (PFC) circuit for 1P charging, with the motor windings utilized as PFC inductors. In DC boost charging mode
Wang, YichengTaha, WesamAnand, Aniket
Growth in the EV market is resulting in an unprecedented increase of electrical load from EV charging at the household level. This has led to concern about electric utilities’ ability to upgrade electrical distribution infrastructure at an affordable cost and sufficient speed to keep up with EV sales. Adoption of EVs in the California market has outpaced the national average and offers early insight for other regions of the United States. The Sacramento Municipal Utility District (SMUD) partnered with two grid-edge Distributed Energy Resource Management System (DERMS) providers, the OVGIP (recently incorporated as ChargeScape, a joint venture of Ford, BMW, Honda, and Nissan) and Optiwatt, to deliver a vehicle telematics-based active managed charging pilot. The pilot program, launched in Summer 2022 enrolled approximately 1,200 EVs over two years including Tesla, Ford, BMW, and GM vehicles. The goal of this pilot program was to evaluate the business case for managed charging to mitigate
Liddell, ChelseaSchaefer, WalterDreffs, KoraMoul, JacobKay, CarolAswani, Deepak
As a crucial component of highway freight systems, tractor semitrailer vehicles play a key role in the transportation industry. However, their complex vehicle structure can lead to significant lateral instability during emergency obstacle avoidance, posing challenges to the vehicle's dynamic stability and safety. To enhance the emergency obstacle avoidance lateral stability of tractor semitrailer vehicles, a direct yaw moment lateral stability control strategy based on differential driving/braking is proposed. First, a 3-degree-of-freedom ideal linear dynamic model of the tractor-semitrailer is established, and its accuracy is validated. Then, a lateral stability control strategy for emergency obstacle avoidance is proposed. The upper-layer controller employs an improved feedforward differential model-free adaptive control (IMFAC) method to track the target yaw rate and vehicle sideslip angle, while the lower-layer controller focuses on optimizing tire load rate. Additionally, a drive
Guo, ShaozhongDou, Jingyang
Charging a battery electric vehicle at extreme temperatures can lead to battery deterioration without proper thermal management. To avoid battery degradation, charging current is generally limited at extreme hot and cold battery temperatures. Splitting the wall power between charging and the thermal management system with the aim of minimizing charging time is a challenging problem especially with the strong thermal coupling with the charging current. Existing research focus on formulating the battery thermal management control problem as a minimum charging time optimal control problem. Such control strategy force the driver to charge with minimum time and higher charging cost irrespective of their driving schedule. This paper presents a driver-centric DCFC control framework by formulating the power split between thermal management and charging as an optimal control problem with the goal of improving the wall-to-vehicle energy efficiency. Proposed energy-efficient charging strategy
Gupta, ShobhitKang, Jun-MoZhu, YongjieLee, ChunhaoZanardelli, Wesley
Accurate mass estimation is essential for commercial heavy-duty vehicles (HDVs) because fluctuating payloads significantly impact energy consumption. Precise vehicle mass estimates enhance the accuracy of energy consumption models, leading to more effective energy management systems and performance optimization strategies. For example, improved energy estimates can lead to more optimized routing and refueling schedules, improving operational efficiency and reducing costs. For electric HDVs, accurate mass estimates are crucial for battery sizing, range prediction, and optimized charge scheduling. While direct mass measurements may be obtained through external weight-in-motion or specialized onboard weighing systems, this paper focuses on methods that use data from Controller Area Network systems for alternative real-time predictions. The challenge lies in identifying a method that performs well under the highly variable and often sparse data conditions typical of HDV driving datasets
Jayaprakash, BharatEagon, MatthewFakhimi, SetayeshKotz, AndrewNorthrop, William
With Rapid growth of Electric Vehicles (EVs) in the market challenges such as driving range, charging infrastructure, and reducing charging time needs to be addressed. Unlike traditional Internal combustion vehicles, EVs have limited heating sources and primarily uses electricity from the running battery, which reduces driving range. Additionally, during winter operation, it is necessary to prevent window fogging to ensure better visibility, which requires introducing cold outside air into the cabin. This significantly increases the energy consumption for heating and the driving range can be reduced to half of the normal range. This study introduces the Ceramic Humidity Regulator (CHR), a compact and energy-efficient device developed to address driving range improvement. The CHR uses a desiccant system to dehumidify the cabin, which can prevent window fogging without introducing cold outside air, thereby reducing heating energy consumption. A desiccant system typically consists of two
Hamada, TakafumiShinoda, NarimasaKonno, YoshikiIhara, YukioIto, Masaki
The driving capability and charging performance of electric vehicles (EVs) are continuously improving, with high-performance EVs increasing the voltage platform from below 500V to 800V or even 900V. To accommodate existing low-voltage public charging stations, vehicles with high-voltage platforms typically incorporate boost chargers. However, these boost chargers incur additional costs, weight, and spatial requirements. Most mature solutions add a DC-DC boost converter, which results in lower charging power and higher costs. Some new methods leverage the power switching devices and motor inductance within the electric drive motor to form a boost circuit using a three-phase current in-phase control strategy for charging. This approach requires an external inductor to reduce charging current ripple. Another method avoids the use of an external inductor by employing a two-parallel-one-series topology to minimize current ripple; however, this reduces charging power and increases the risk
Yuan, BaochengMa, YongXie, XiLiu, ShaoweiGuan, TianyuGe, KaiZheng, LifuXu, Xu
Electrifying truck fleets has the potential to improve energy efficiency and reduce carbon emissions from the freight transportation sector. However, the range limitations and substantial capital costs with current battery technologies imposes constraints that challenge the overall cost feasibility of electrifying fleets for logistics companies. In this paper, we investigate the coupled routing and charge scheduling optimization of a delivery fleet serving a large urban area as one approach to discovering feasible pathways. To this end, we first build an improved energy consumption model for a Class 7-8 electric and diesel truck using a data-driven approach of generating energy consumption data from detailed powertrain simulations on numerous drive cycles. We then conduct several analyses on the impact of battery pack capacity, cost, and electricity prices on the amortized daily total cost of fleet electrification at different penetration levels, considering availability of fast
Wendimagegnehu, Yared TadesseAyalew, BeshahIvanco, AndrejHailemichael, Habtamu
Battery safety is a paramount concern in the development of electric vehicles (EVs), as failures can lead to catastrophic consequences, including fires and explosions. With the rapid global adoption of EVs, understanding how battery cells perform under extreme conditions such as mechanical or thermal abuse is crucial for ensuring vehicle safety. This study investigates the abuse response of lithium-ion batteries under high-speed mechanical loading. Our research systematically examines the response of these cells at different states of charge (SOC) through controlled dynamic tests. These tests offer insights into the failure response of the cells. By analyzing the data, we gain a deeper understanding of the conditions that could trigger thermal runaway under mechanical abuse loadings, representative of EV crashes, a critical safety concern in EV battery systems. The experimental setup and methodologies are presented in this paper, alongside key findings that highlight the importance of
Patanwala, HuzefaKong, KevinChalla, VidyuDarvish, KuroshSahraei, Elham
In hybrid electric vehicles (HEVs), optimizing energy management and reducing system losses are critical for enhancing overall efficiency and performance. This paper presents a novel control strategy for the boost converter in hybrid electric vehicles (HEVs), aimed at minimizing energy losses and optimizing performance by modulating to a higher boost converter voltage only when necessary. Traditional approaches to boost converter control often lead to unnecessary energy consumption by maintaining higher voltage levels even when not required. In contrast, the proposed strategy dynamically adjusts the converter's operation based on real-time vehicle demands, such as driver input, Engine Start-Stop (ESS) events, Active Electric Motor Damping (AEMD), entry and exit transitions for Engine Fuel Cut-Off (DFCO), Noise-Vibration-Harshness (NVH) events like lash-zone crossing and other specific operational conditions. The control strategy leverages predictive algorithms and real-time monitoring
Basutkar, AmeyaHuo, ShichaoSullivan, ClaireBerger, DanielTischendorf, Christoph
The rapid expansion of the electric vehicle (EV) market has intensified the need for robust charging infrastructure. The quality of their experiences at public charging stations has become crucial to sustaining this transition. Key factors such as station accessibility, charging speed, and pricing transparency significantly affect user satisfaction. In Guangzhou, a China's major metropolitan city with an EV penetration rate exceeding 50%, this city offers an ideal context to assess the alignment between expanding EV infrastructure and user needs. This study examines user satisfaction with EV public charging stations in Guangzhou using a dataset of over 2,000 user comments from Amap. The comments are first processed using the Jieba segmentation library, with sentiment analysis conducted through the Natural Language Processing tool SnowNLP, categorizing comments by sentiment (419 positive, 156 neutral, and 1,690 negative). Term Frequency-Inverse Document Frequency(TF-IDF) is then applied
Guo, HaifengOu, Shiqi (Shawn)Jing, HaoQi, HaoShi, Lanxin
aThe lengthy charging time of lithium-ion batteries for electric vehicles (EVs) significantly affect their acceptance. Reducing charging time requires high-power fast charging. However, such fast charging can trigger various side reactions, leading to safety and durability issues. Among these, lithium plating is a major concern as it can reduce battery capacity and potentially cause internal short circuits or even thermal runaway. Currently, multi-stage constant current charging (MCCC) protocols are widely adopted. However, the difficulty in effectively detecting lithium plating during the MCCC process significantly limits the charging power. Therefore, it is urgent to explore a method to detect lithium plating during the MCCC process. In this study, the impedance evolution during the MCCC procedure was first investigated. Then a method based on the impedance variation patterns was proposed to detect lithium plating. Besides, the reason for the behavior of impedance changes was further
Shen, YudongWang, XueyuanWu, HangWei, XuezheDai, Haifeng
The added connectivity and transmission of personal and payment information in electric vehicle (EV) charging technology creates larger attack surfaces and incentives for malicious hackers to act. As EV charging stations are a major and direct user interface in the charging infrastructure, ensuring cybersecurity of the personal and private data transmitted to and from chargers is a key component to the overall security. Researchers at Southwest Research Institute® (SwRI®) evaluated the security of direct current fast charging (DCFC) EV supply equipment (EVSE). Identified vulnerabilities included values such as the MAC addresses of both the EV and EVSE, either sent in plaintext or encrypted with a known algorithm. These values allowed for reprogramming of non-volatile memory of power-line communication (PLC) devices as well as the EV’s parameter information block (PIB). Discovering these values allowed the researchers to access the IPv6 layer on the connection between the EV and EVSE
Kozan, Katherine
The surge in electric vehicle usage has expanded the number of charging stations, intensifying demands on their operation and maintenance. Public charging stations, often exposed to harsh weather and unpredictable human factors, frequently encounter malfunctions requiring prompt attention. Current methods primarily employ data-driven approaches or rely on empirical expertise to establish warning thresholds for fault prediction. While these approaches are generally effective, the artificially fixed thresholds they employ for fault prediction limit adaptability and fall short in sensitivity to special scenarios, timings, locations, and types of faults, as well as in overall intelligence. This paper presents a novel fault prediction model for charging equipment that utilizes adaptive dynamic thresholds to enhance diagnostic accuracy and reliability. By integrating and quantifying Environmental Influence Factors (EF), Scenario Influence Factors (SF), Fault Severity Factors (FF), and
Wang, HaoWang, NingLi, YuanTang, Xinyue
Efficient and robust optimization frameworks are essential to develop and parametrize battery management system (BMS) controls algorithms. In such multi-physics application, the tradeoff between fast-charging performance and aging degradation needs to be solved while simultaneously preventing the onset of thermal runaway. To this end, a multi-objective optimization framework was developed for immersion-cooled battery systems that provides optimal charging rates and dielectric flowrates while minimizing aging and charging time objectives. The developed production-oriented framework consists of a fully coupled, lumped electro-thermal-aging model for cylindrical cells with core-to-surface and immersion-cooling heat transfer, the latter controlled by the dielectric fluid flowrate. The modeled core temperatures are inputs to a semi-empirical aging degradation model, in which a fast-aging solver computes the updated capacity and internal resistance over multiple timescales, which in turn
Suzuki, JorgeTran, Manh-KienTyagi, RamavtarMeshginqalam, AtaZhou, ZijieNakhla, DavidAtluri, Prasad
Rechargeable lithium batteries are widely used in the electric vehicle industry due to their long lifespan and high energy density. However, after long-term repeated charging and discharging, various electrochemical reactions inside lithium batteries can lead to performance degradation and even cause battery fires. Estimating the health status and predicting the remaining life of lithium batteries can provide insights into their future operating conditions, which is crucial for achieving fault warnings and ensuring the safe operation of battery-related equipment. In terms of predicting the health status of lithium batteries, this paper proposes a method based on an improved Long Short-Term Memory (LSTM) for health status estimation. This method first employs nearest neighbor component analysis to eliminate feature redundancy among the multidimensional health factors of the battery. Then, a differential grey wolf optimization algorithm (DEGWO) is used to globally optimize the
K, Meng Zi
The operating temperature of lithium-ion battery (LIB) cells significantly influences their degradation behavior. In indirect liquid cooling systems, temperature variations within a Battery Electric Vehicle (BEV) LIB module are inevitable due to the increasing downstream temperature of the cooling medium as it absorbs heat. This leads to reduced temperature differentials between the cooling medium and the LIB cells. As a result, LIB cells located further along the flow path experience higher average temperatures than those at the front. Typically, a maximum average cell temperature difference of 5 K within LIB modules is considered acceptable. However, results from a conventional cooling system indicate that, when fast charging is exclusively used, this can lead to a 15.5 % difference in the total ampere-hours passed before the End-of-Life (EOL) is reached for the front and back LIB cells. To address this issue, a switchable thermal management system for the traction battery is
Auch, MarcusWeyershäuser, KonstantinKuthada, TimoWagner, Andreas
Battery health status and driving rangeof electric vehicles (EVs) are critical factors in determining their market penetration. Choosing an optimal charging strategy—specifying how, when, and for how long to charge based on the driver’s travel behavior—can significantly mitigate battery degradation and extend battery life. This study introduces an EV powertrain system energy model designed to enhance the prediction accuracy of battery status under real-world driving conditions. By integrating with the Q-learning approach, this studyprovides tailored recommendations on charging behaviors, including charger type, start time, and charging duration. This study innovatively considers the rental costs caused by the battery capacity not being able to meet the daily driving range. Simulating a typical three-year usage scenario for an average driver in New England, the results indicate that thecharging strategy proposed by this study reduces battery degradation rates by 1.53‰, 3.57‰, and 7.68
Wang, JiayiJing, HaoOu, Shiqi (Shawn)Lin, Zhenhong
As the United States Army explores electrified tactical vehicles, wireless power transfer (WPT) has emerged as a promising recharging method. WPT allows multiple vehicles to recharge while in proximity of a charging station based on a mobile platform. This study examines the requirements of WPT by analyzing geo-location data from over 400 tactical vehicles at the National Training Center. The data was extracted, cleaned, and analyzed to identify periods when vehicles were close enough for effective WPT. The analysis quantifies the amount of time vehicles spend in proximity and their average distance apart, both while stationary and moving, to establish initial WPT requirements. These results were combined with energy consumption rates to estimate the power throughput of a WPT system. Vehicles were found to be stationary and close to other vehicles for most of the day, making WPT a practical solution in those situations. Although the analysis found that WPT is feasible during convoys
Mittal, VikramEl Ouadi, Ameir
Impact resistance is crucial for assessing charging pile safety and reliability. This study proposes a prediction model, called GA-BP neural network, which achieved prediction errors below 5% and reduced computation time by over 95% in comparison to finite element analysis (FEA). Initially, the charging pile impact test platform is constructed, and a matching finite element simulation model is developed. The correctness of the simulation model is then verified by integrating the experimental findings. Furthermore, the Latin hypercube approach is used to create 200 sets of simulation schemes, and using the Python programming language, the impact resistance performance indicators of charging piles are automatically collected. Next, a genetic algorithm is used to optimize the initial weight and bias of the BP neural network, lastly, fine-tune the hyperparameters in the neural network to develop a prediction model for the impact resistance performance of the charging pile. The GA-BP model
Jiang, BingyunHu, PengLiu, ZhenyuYuan, PengfeiLiu, Hui
For the heat dissipation design of charging equipment for electric vehicles, a study is conducted on the thermal performance and its influencing factors of a specific alternating current (AC) charging device. First, based on heat dissipation theory and CFD simulation software, the corresponding finite element model is established and verified through experiments. Next, using the verified finite element model and applying the orthogonal experimental method, the factors influencing the heat dissipation performance of the AC charging pile, such as ambient temperature, output current of the AC charging pile, and surface radiation characteristics, are investigated. Finally, a prediction model for the maximum temperature of the main board is established using the response surface method (RSM), and the effects of each factor on the maximum main board temperature are analyzed, enabling rapid prediction of the heat dissipation performance of the AC charging pile. The analysis of the orthogonal
Tang, YuYan, ChongjingLu, FeifeiJiang, BingyunBao, YidongHu, Peng
Exhaust emissions from congested road segments constitute a significant source of urban air pollution. Resolving traffic congestion throughout the road network presents considerable challenges. However, alleviating tailpipe emissions on congested roads can be achieved by increasing the proportion of electric vehicles (EVs) in the traffic flow. Therefore, we propose a method for optimizing the layout of EV charging stations based on urban road networks congestion tracing. This method traces congestion sources through similarity between road networks, and evaluates the installation potential value of adjacent candidate installation points using the congestion contribution degree of the road segment as an indicator. The analysis is conducted on 100 routes within the Qinhuai district of Nanjing city, using spatiotemporal similarity metrics. The utilization of point-of-interest and traffic data from online mapping sources overcomes the complexity of road network structure and the sparsity
Zeng, WenyiJian, LuHu, Xiaojian
This SAE Technical Information Report (TIR) is based on the initial assumption that a system level standard covering all aspects of EV charging components and use cases would grow to be too large for one comprehensive document. Hence, the SAE J3271 work group was launched with five subtopics that could be subsections of one standard or a separate document for each topic. In this document, all five subtopics are compiled into one document. The recommended practice level documents will follow this structure, with added detail and streamlined content. The five subtopics that are now subsection numbers in this document include: SAE J3271/1 (see 5.1) Electromechanical Coupler Specifications SAE J3271/2 (see 5.2) Communication and Controls SAE J3271/3 (see 5.3) Cables/Cable Handling, Cooling, and Automated Connection Systems SAE J3271/4 (see 5.4) Use Cases Including Grid Interconnection, Black Start, and Bidirectional Power Transfer SAE J3271/5 (see 5.5) Interoperability Testing Requirements
Hybrid - EV Committee
With current and future regulations continuing to drive reductions in carbon dioxide equivalent (CO2e) emissions in the on-road industry, the off-road industry is also likely to be regulated for fuel and CO2e savings. This work focuses on converting a heavy-duty off-road material handler from a conventional diesel powertrain to a plug-in series hybrid, achieving a 49% fuel reduction and 29% CO2e reduction via simulation. Control strategies were refined for energy savings, including a regenerative braking strategy to increase regenerative braking and a load-following hydraulic strategy to decrease electrical energy consumption. The load-following hydraulic control shuts off the hydraulic electric machine when it is not needed—an approach not previously seen in a load-sensing, pressure-compensated system. These strategies achieved a 24.1% fuel savings, resulting in total savings of 61% in fuel and 41% in CO2e in the plug-in series compared to the conventional machine. Beyond control
Goodenough, BryantCzarnecki, AlexanderRobinette, DarrellWorm, JeremySubert, DavidKiefer, DylanHeath, MatthewBrunet, BobKisul, RobertLatendresse, PhilWestman, JohnBlack, Andrew
This research investigates how distributed energy resources (DERs) and electric vehicles (EVs) affect distribution networks. With sensitivity analysis, the research focuses on how these integrations affect load profiles. The research focuses on sizing of various DERs and EV charging/discharging strategies to optimize the load profile, voltage stability, and network loss minimization. System parameters including load profile, EV charging pattern, weather conditions, DER sizes, and electricity pricing are analyzed to quantify their individual and combined impacts on load variability. However, with increased capacity of DERs, network losses increase. A mathematical model with system and operational constraints has been developed and simulated in MATLAB Simulink environment, validation of the proposed approach in improving the load profile, and reduction in network losses, with the intermittent power generation from DERs and EV integration. Simulation result shows that optimal capacity of
Khedar, Kamlesh KumarGoyal, Govind RaiSingh, Pushpendra
The safety of power batteries is an important issue that has attracted widespread attention in new energy vehicle technology. In this paper, Generative Adversarial Networks (GAN) are introduced, and the data generation and fault diagnosis of power battery life-cycle data are carried out. GAN is composed of a pair of generators and discriminators, combining signal processing with neural networks, using the discriminator architecture based on Fourier transform and the generator architecture based on wavelet transform, so that the neural network can learn the characteristics of power battery life-cycle data from the perspective of time and frequency domain, and use the good performance of wavelet transform in data denoising and repair to generate high-quality and low-noise data, and use Fourier transform to target the characteristics of periodicity. Identify and distinguish the periodic characteristics and time-frequency domain data characteristics in the generated data and laboratory
Tan, PiqiangYang, AojiLiu, XiangYao, Chaojie
After the defected gears are determined, a novel method, combined with wavelet packet decomposition, complementary ensemble empirical mode decomposition with adaptive noise and singular value decomposition, is put forward. It is utilized to exclude disturbance of irrelevant signals that generated by the defect gears. Firstly, wavelet packet decomposition is used to extract the defect signals and retain original features. The processed signal is called S1 and the irrelevant frequency bands could be filtered out. Secondly, complementary ensemble empirical mode decomposition with adaptive noise decomposes S1 into a series of intrinsic modal functions. The correlations between S1 and intrinsic modal functions are analyzed. The intrinsic modal functions that are highly correlated with S1 are screened out and reconstructed into a new signal, called S2. The disturbance of irrelevant signals could be further filtered out, but some of them still disturb the judgement. Thirdly, singular value
Gu, JunqingZuo, YueyunZhang, NiDeng, FengWu, Xiaolong
Accurate and reliable SOC estimation plays a vital role in the engineering application and development of LIBs. A multi-time scale joint algorithm combining FFRLS and AEKF is introduced in this paper. The FFRLS algorithm is employed for online parameter identification of a second-order resistance-capacitance ECM, while the AEKF algorithm estimates the SOC. To account for the time-varying nature of model parameters and SOC, different sampling periods are selected, enabling the parameter identification and SOC estimation processes to operate on distinct time scales. Experimental results demonstrate that, under constant current conditions at room temperature, the multi-time scale FFRLS-AEKF joint algorithm can maintain a high level of accuracy while reducing the computational burden, with MAE and RMSE values of 0.0111 and 0.0129, respectively. Simultaneously, a public data set is used to prove the application of the algorithm in complex operating conditions, and the computed results of
Liang, DanYang, BoLiu, BingLiu, ShuaiCao, Chang
In cold environments, it is slow and risky for charging rate of electric heavy-duty trucks due to lithium plating. Common heating-charging methods overlook the complex dynamics between current, temperature, and battery aging, which need to be further improved. This study presents a tailored thermal management strategy for low-temperature battery charging, analyzing heating performance and battery improvement effect on the fast-charging performance. The data-driven multi-tiered power heating strategy based on a customer electro-thermal-aging model was proposed to minimize charging time and costs. The heating power combinations have been optimized by a particle swarm optimization algorithm, which outperforms conventional methods that aim to reach a set temperature. The optimized strategy reduced charging time by 11% and battery life degradation by only 0.0512%, enhanced the efficiency of cold-weather fast charging for electric trucks.
Lin, JieweiJiang, FeifanDai, HuweiSun, LeiLiu, BaoguoLi, ShiboZhang, Junhong
To investigate the characteristics of a battery direct-cooling thermal management system integrated with the passenger compartment air-conditioning in a range-extended hybrid electric vehicle (REV), a model of the vehicle’s direct-cooling and liquid-cooling thermal management systems was established in GT-SUITE software. The findings are as follows: (1) Under high-temperature fast-charging conditions, the direct-cooling thermal management system exhibited improved performance indicators compared to the liquid-cooling system. Specifically, the charging time was reduced by 3.8%, the maximum heat exchange power increased by 27.33%, the battery temperature decreased by 2.37°C, the thermal decay rate was only 6%, and the average system energy efficiency ratio increased by 8.37%. (2)The outlet pressure of the direct-cooling plate significantly affected the temperature reduction of the battery pack during high-temperature fast-charging. The results indicated that within a certain range, a
Li, Li-JieSu, ChuqiWang, Yi-PingYuan, Xiao-HongLiu, Xun
With the rapid development of new energy vehicles, lithium-ion batteries (LIBs) have been widely used in the automotive sector. The performance and safety of LIBs in electric vehicles (EVs) are significantly influenced by operating temperature, making the development of an effective battery thermal management system (BTMS) crucial. In recent years, phase change material (PCM)-based BTMS technology has been recognized as one of the most promising solutions. Compared to traditional air and liquid cooling systems, PCM cooling technology exhibits superior cooling performance due to its large latent heat and efficient heat dissipation capabilities, while also eliminating the need for additional pump power consumption. Therefore, in-depth research on PCM cooling technology is of significant academic and practical value for enhancing the effectiveness and safety of power battery thermal management. This study investigates the effects of thermal conductivity, melting point, and thickness of
Lv, Kang-MinSu, Chu-QiWang, Yi-PingYuan, Xiao-HongLiu, Xun
Sodium-ion batteries (SIBs) make their marks in energy storage and electric vehicles due to their abundant reserves, cost-effectiveness, environmental resilience, and high safety. However, maintaining high battery performance in intricate operating conditions is challenging, which necessitates precise control based on timely and accurate acquisition of operation parameters, especially for the state of charge (SOC). Equivalent circuit model (ECM) is the most widely used in the evaluation of SOC. In this work, a 2nd-order resistor-capacitor ECM (2ORC-ECM) is chosen because of its balance between accuracy and computational efficiency. Furthermore, dynamic parameters in the 2ORC-ECM are accurately identified online by introducing an enhanced recursive least squares method with a forgetting factor. Finally, the proposed method is carried out based on the measured data of commercial SIBs. The results show that the proposed method can mitigate data saturation effectively while ensuring high
Qi, HonghaoPan, LyumingXu, XiaoqianRao, HaoyaoYu, YueshengLiu, XiangchiZhu, YifeiYang, CanWu, WeixiongLi, YubaiLi, WenjiaZeng, LinXu, QianRen, JiayouWei, Lei
Fast chargers are necessary for the success of vehicle electrification. These devices can achieve a battery charge rate greater than 4C, significantly increasing the amount of heat generated by the battery. Additionally, the operating temperature of the storage device directly influences the device’s efficiency and lifespan. Given the importance of operation temperature, the Battery Management System (BMS) plays a key role in mitigating heat generation and degradation effects. Despite BMS optimizing battery operation under all possible conditions, the use of fast chargers in extremely hot and cold environments still lowers overall efficiency. In these two worst-case scenarios, the thermal system must manage the ideal charging temperature by consuming part of the energy supplied by the charger. The present work aims to evaluate the charging energy efficiency and time with fast charger utilization, considering the Brazil’s minimum and maximum temperatures registered in 2020. In order to
Pires, Rodrigo AlonsoPontes, Diego AugustoSouza, Rafael BarbosaOliveira, Matheus Leonardo AraújoRodrigues, Luiz Fernando AlvesFernandes, HederMaia, Thales Alexandre Carvalho
Heavy-duty vehicles, particularly those towing higher weights, require a continuous/secondary braking system. While conventional vehicles employ Retarder or Engine brake systems, electric vehicles utilize recuperation for continuous braking. In a state where HV Battery is at 100% of SOC, recuperated energy from vehicle operation is passed on to HPR and it converts electrical energy into waste heat energy. This study focuses on identification of routes which are critical for High Power Brake Resistors (HPRs), by analyzing the elevation data of existing charging stations, the route’s slope distribution, and the vehicle’s battery SOC. This research ultimately suggests a method to identify HPR critical vehicle operational routes which can be useful for energy efficient route planning algorithms, leading to significant cost savings for customers and contributing to environmental sustainability.
Thakur, ShivamSalunke, OmkarAmbuskar, MandarPandey, Lokesh
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