Vehicle Battery Research Articles

How new nanomaterials can improve the performance of lithium batteries

Abstract In recent years, lithium batteries have become energy storage methods in many fields for their advantages of high energy density, and many fields such as civil electric vehicles, electronic products, and aerospace rely on lithium batteries. At present, the performance demand for lithium batteries in electric vehicles is increasing, and finding ways to improve the performance of lithium batteries has become a top priority. Since silicon has the highest theoretical specific capacity, carbon nanotube technology is relatively mature, and the research and development prospects of new negative electrode materials for nano-batteries are broad. This paper analyzes the different influences of nano-silicon-based materials, carbon nanotubes modified by new materials and new negative nano-lithium materials on lithium batteries from nanomaterials. And summarize their respective roles to help readers further understand the application of nanomaterials in lithium batteries. It is designed to help readers further understand the application of nanomaterials in lithium batteries.

КОМП’ЮТЕРНЕ ТА ФІЗИЧНЕ МОДЕЛЮВАННЯ БЕЗДРОТОВОГО ЗАРЯДНОГО ПРИСТРОЮ ЄМНІСНОГО ТИПУ ДЛЯ МАЛОГАБАРИТНОГО ЕЛЕКТРОТРАНСПОРТНОГО ЗАСОБУ

The work considers one of the possible approaches to the creation of a capacitive type wireless charger, which allows for non-contact charging of batteries of small-sized vehicles: electric carts, electric scooters, electric scooters. The basis of the development of such a device is the operation of the hybrid DC-DC converter circuit, which operates at a high frequency (550 kHz), transfers energy to the charging circuit using two capacitive elements and allows receiving a constant regulated voltage on the load, which can be either lower or and more input DC voltage. A prototype of such a device has been developed and the results of its experimental research are given, which confirm the possibility of using this scheme for wireless charging of the battery. A Simulink computer model of such a device has been developed, which takes into account the real values of all parameters of the developed prototype and uses the basic model of a lithium-ion battery. According to the results of the calculations, it was determined that for the developed device of the capacitive type, and the largest losses occur in the inductor coils, the value of efficiency, which characterizes the efficiency of the process of transferring electrical energy from the primary power source to the battery, at the initial stage of its charging (when the parameter of the state of the battery SOC =50%) is 80%. References 10, figures 5.

A Shared Charging Channel for Power and Auxiliary Batteries in Electric Vehicles

A Shared Charging Channel for Power and Auxiliary Batteries in Electric Vehicles

Mechanical vibration's effect on thermal management module of Li-ion battery module based on phase change material (PCM) in a high-temperature environment

PCM-based systems play a crucial role in managing the thermal conditions of electric vehicle (EV) battery modules, a critical aspect in ensuring their optimal performance. However, an often overlooked facet in current research is the impact of mechanical vibration on EV battery modules during real-world driving scenarios. Addressing this gap, the current article takes a pioneering approach, drawing inspiration from the groundbreaking study by Du and Chen in 2023. This study delves into the effects of mechanical vibration on a PCM-based Battery Thermal Management System (BTMS) specifically adapted for battery modules operating in high-temperature environments. The results, shedding light on a previously underestimated factor, unveil the remarkable ability of mechanical vibration to ameliorate the surge in temperature within the battery module, particularly under high discharge rates. An unprecedented revelation surfaces as mechanical vibration emerges as a factor influencing the thickness of the encapsulated PCM, offering a unique avenue for augmenting the energy density of the powertrain. In this challenging thermal environment, mechanical vibration fosters temperature uniformity among batteries and curtails heat accumulation within the battery module. Remarkably, when the vibration amplitude surpasses a critical threshold, the impact of amplitude variations becomes negligible. Moreover, the intricate relationship between vibration frequency and temperature change complicates the scenario, with medium to high frequencies demonstrating a propensity to enhance the management system's overall thermal performance. These groundbreaking findings transcend the theoretical realm, offering tangible insights with immense implications for practical design and application. By recognizing and harnessing the potential benefits of mechanical vibration, engineers and designers can optimize BTMS, paving the way for more efficient and resilient EV battery modules.

Two-Phase Cooling System for Electric Vehicle Battery Based on a 3D Pulsating Heat Pipe

The primary objective pursued in this research is the creation and thorough evaluation of an inventive cooling system designed to uphold optimal temperatures within the batteries employed in electric vehicles. Nowadays, the prevailing equipment underpinning electrical motion hinges on Lithium-Ion cells. These cells frequently necessitate the expeditious delivery of substantial power, thereby giving rise to a consequential generation of heat. Consequently, there is an emergence of elevated operational temperatures, potentially causing noteworthy declines in battery performance, or, in extreme cases, operational failures. Furthermore, deviating from the recommended temperature range (20–40 °C) significantly expedites the aging process of the battery and elevates the probability of premature malfunction. In response to these crucial challenges, the implementation of a battery thermal management system assumes a pivotal role in maximizing battery efficiency. Within the framework of this investigation, we propose the adoption of a cooling system founded on a three-dimensional pulsating heat pipe as the designated thermal management technology for a battery pack. The preliminary phase of our study involved the assessment of the suggested pulsating heat pipe’s performance. Following this, the efficacy of the pulsating heat pipe is subjected to rigorous scrutiny through practical experimentation on an authentic electric battery. The empirical findings conclusively highlight the substantial cooling capacity of the proposed system, thereby presenting a robust and efficacious solution for the thermal management challenges faced by electric vehicle batteries.

A Smart and Efficient Cooling System Design for High-Performance Electric Vehicle Battery Packs

A Smart and Efficient Cooling System Design for High-Performance Electric Vehicle Battery Packs

Strategies and models for optimal EV charging station site selection

Abstract In numerous countries worldwide, adopting electric vehicles (EVs) is gaining momentum as a proactive measure to mitigate the detrimental environmental impacts of traditional fuel-powered automobiles. This shift drives exponential growth in the adoption of EVs, prompting the need for comprehensive analysis to optimize charging infrastructure requirements. Developing reliable and sustainable charging infrastructure depends on practical and strategic site selection of EV charging stations. The main challenge is finding a charging solution that maximizes efficiency within limited financial resources. The present review critically assesses methodologies for selecting optimal EV charging station sites, considering technical, environmental, social, and economic factors. Special emphasis is given to social factors such as population density and service accessibility, as well as technical factors like vehicle battery life, charging time, and grid capacity. Environmental impact and feasibility are also vital criteria under evaluation. Through a synthesis of insights from various studies, this review provides a comprehensive overview of the existing models used in EV charging infrastructure site selection. The findings contribute valuable insights for decision-makers, city planners, and other stakeholders in creating sustainable EV charging networks amidst the dynamic landscape of electric mobility.

Startup characteristics of a water loop heat pipe with dual heat sources for battery thermal management system in electric vehicle

Abstract The usage of electric vehicles has significantly reduced emissions of greenhouse gases and other pollutants. However, the high heat release generated by the electric vehicle batteries poses a challenge. To solve this problem, scientists have created a passive cooling thermal management system specifically for electric vehicle based on heat pipes, particularly loop heat pipes. A battery pack often consists of several battery modules, which results in multiple heat sources being dispersed according to their power capacity. Startup behavior of loop heat pipe has been investigated extensively in the literature. However, most of the studies use only one heat source. This paper aims to fill the research gap, particularly when the system is implemented in dual heat sources managed by only one evaporator. To achieve the research objectives, a custom loop heat pipe was constructed. This cooling system’s design is briefly described. The evaporator is made of copper, deionized water was selected as the working fluid because of its high merit number, which indicates strong performance as a heat pipe working fluid and the stainless-steel wire mesh serves as the porous wick. Battery simulator was built using aluminum material and a cartridge heater to mimic the heat produced by the battery. Two case studies were done. First, only one battery simulator was used. Second, two battery simulators were placed on both sides of the evaporator. A type-K thermocouple attached to the NI DAQ 9214 module was used to measure the temperature while the electric heat load varied between 10 W and 50 W. The study investigated the interaction between the heat load distribution and the startup behavior of the loop heat pipe. Startup behavior is crucial for the performance of the loop heat pipe. Based on the experimental results, the loop heat pipe demonstrates outstanding startup performance. It can effectively initiate operation even at a minimal heat load as low as 30 W for the first and second case study. The findings of the study indicate that the dual heat source arrangement effectively mitigates overshoot temperatures and enhances heat transfer performance by increasing the contact area.

Deep reinforcement learning-based scheduling for integrated energy system utilizing retired electric vehicle battery energy storage

Retired electric vehicle batteries (REVBs) retain substantial energy storage capacity, holding great potential for utilization in integrated energy systems. However, the dynamics of supply and demand, alongside battery safety constraints, present challenges to the optimal dispatch of energy. This paper proposes a hybrid system including thermal and electric energy employing REVB as the energy storage component. This system relies on photovoltaics (PV) technology to harness solar energy and convert it into thermal and electrical energy for supplying users. The REVB, as the core component of the system, functions to regulate the allocation of energy by managing its charging and discharging behavior. To explore the optimal energy scheduling strategy, a two-stage deep reinforcement learning (DRL) optimization method is presented. In the first phase, a deep deterministic policy gradient (DDPG) method is introduced to determine the REVB's behavior, and in the second phase, a competitive bidding mechanism is presented to optimally distribute energy. Simulation results, validated with real scenarios and data, achieve a reduction of 35.2 % in energy dissipation and 4.6 % in users' expenditure for purchasing energy compared to standard strategy. Simultaneously, experiments have shown that REVB is nearly as efficient as new battery as energy storage component, with energy wastage only about 1.1 % higher and users' expenditure less than 1 % higher.

A Lightweight Deep-Learning Algorithm for Welding Defect Detection in New Energy Vehicle Battery Current Collectors

A Lightweight Deep-Learning Algorithm for Welding Defect Detection in New Energy Vehicle Battery Current Collectors

A Case Study on a Degradation Prediction Algorithm for Electric Vehicle Battery with LSTM-AutoEncoder

A Case Study on a Degradation Prediction Algorithm for Electric Vehicle Battery with LSTM-AutoEncoder

Privacy-preserving State of Health prediction for electric vehicle batteries: A comprehensive review

This work presents an in-depth systematic literature review of privacy-preserving machine learning techniques for battery State of Health (SoH) prediction in electric vehicles (EVs). Our review methodically analyses a corpus of 43 research articles and publications spanning from 2012 to 2024 which provides a comprehensive landscape of the field’s evolution and current trends. This review covers a range of machine learning techniques and uniquely addresses first-of-its-kind privacy needs specific to SoH prediction for EVs, with a detailed examination of the integration of privacy-preserving methods. We also delve into the key challenges associated with data processing and feature engineering, and how these challenges are managed. The methodology employed includes not only the identification but also a thematic analysis of the literature, focusing on how these techniques balance data confidentiality with analytical performance. We proposed a privacy preserving system model for SoH prediction. The review is further enriched by various real-time applications and case studies, illustrating the practical implementation and efficacy of the discussed methods. This systematic approach provides a well-rounded view of the existing solutions and highlights gaps for future research, offering invaluable insights for both academic and industrial stakeholders in the domain of electric vehicle technology.

Lithium battery state of health estimation using real-world vehicle data and an interpretable hybrid framework

Accurate point estimation and uncertainty estimation of the state of health (SOH) of battery systems are crucial for alleviating user range anxiety and preventing battery safety incidents. On top of approximately 28 million real-world electric vehicle operation data samples, this paper embeds the categorical boosting algorithm as the base learner of the natural gradient boosting, proposing a novel interpretable N-CatBoost hybrid framework to achieve precise point estimation and uncertainty estimation of the battery SOH. Deep charging segments are selected to calculate the initial capacity, and the nonlinear decay trend of the capacity is derived through the Savitzky-Golay filter. Based on the influencing factors of capacity degradation, health features characterizing battery aging are extracted from electric vehicle data as model inputs. The model's hyperparameters are optimized using the particle swarm optimization algorithm, and it is compared with seven other popular machine learning algorithms. The results indicate that the proposed N-CatBoost model achieves the highest estimation accuracy, with mean absolute percentage error and root mean square error of 0.817 % and 1.156 Ah, respectively. In addition, the Shapley additive explanation method is utilized to make the model interpretable, providing full transparency of all predicted values. More importantly, the developed N-CatBoost model can achieve uncertainty estimation of its predictions, with 100 % of the actual capacity values successfully falling within the model's 99 % prediction interval. Therefore, the proposed N-CatBoost model is a reliable and effective method with great potential for deployment on the cloud side for the SOH estimation of batteries in large-scale vehicles.

Service capability evaluation of electric vehicle charging and battery swapping stations: A game theory‐based combination weighting method

Service capability evaluation of electric vehicle charging and battery swapping stations: A game theory‐based combination weighting method

Electrically Powered Vehicles Battery Management System: Cooling for Parked Vehicles

Electrically Powered Vehicles Battery Management System: Cooling for Parked Vehicles

Disassembly technologies of end-of-life automotive battery packs as the cornerstone for a circular battery value chain: A process-oriented analysis

Battery-powered electromobility is essential for achieving the climate goals set out in the Paris Climate Agreement. Using recycled materials is important not only because of the high carbon footprint of virgin materials, but also because of supply chain dependencies. In the automotive traction battery recycling process, the disassembly step is crucial for reusing components and recovering recyclates with high purity. Therefore, this paper will comprehensively analyze the different disassembly technologies for end-of-life electric vehicle batteries on the basis of a systematic literature review. The analysis will address non-destructive, semi-destructive and destructive disassembly technologies as well as supporting technologies such as artificial intelligence and vision systems. In addition, the advantages and challenges of the different technological approaches and the disassembly process itself will be discussed. Ultimately, implications for policy makers and industry players will be highlighted to facilitate the entire value chain of automotive traction battery recycling with robust disassembly technologies.

Statistical analysis of trends in Battery Electric Vehicles: Special reference to vehicle weight reduction, electric motor, battery, and interior space dimensions

Electric vehicles (EVs) are increasingly being used, as they are more environmentally friendly than conventional vehicles with internal combustion engines (ICE). Battery electric vehicles (BEVs) can be said to have zero exhaust emissions only if the electricity used to drive these vehicles is obtained in an environmentally friendly way. It is common knowledge that BEVs have a significantly higher overall mass than conventional vehicles. The significantly higher total vehicle weight of BEVs can have various adverse effects on energy consumption during movement and the vehicle's dynamics. In contrast to the negative aspects of BEVs, there are also positive aspects that are primarily related to the comfort of drivers and passengers, considering the main fact that they do not require the presence of a floor tunnel. In this paper, trends related to BEVs in the previous five years were statistically analysed. Changes in average sizes related to BEVs are shown, primarily internal dimensions that can be of crucial importance when deciding between BEVs and conventional vehicles. In the paper itself, other important trends are presented, both for the electric motor itself and for the batteries used in BEVs.

Distributed state-of-charge and power balance estimation for aggregated battery energy storage systems with EV aggregators

Aggregated battery energy storage systems (ABESSs) play an important role in smart grids. This study considers distributed ABESSs containing electric vehicle (EV) aggregators and battery energy storage systems (BESSs). Due to randomness and uncertainty caused by EV aggregators, the distributed ABESSs face heterogeneous random communication failures between the EV aggregators and the BESSs. Aiming to solve this problem, this study proposes a distributed state-of-charge (SoC) and power balance estimation strategy for ABESSs with an event-triggered mechanism. Different from the traditional SoC balance problem definition in the BESSs, this study introduces an innovative balance convergence condition to solve the problem of power imbalance between the EV aggregators and the BESS units. In addition, an adaptively robust estimation algorithm is designed to estimate the precise value of SoC and power under heterogeneous random failures caused by EV aggregators. Further, a distributed periodic dynamic event-triggered mechanism is developed considering redundant messages in the ABESS communication channels. This mechanism can both handle the Zeno phenomenon and obtain a larger minimum time interval with fewer sent measurements. The simulation results demonstrate the effectiveness of the proposed distributed approach in both the discharging and charging modes of ABESSs.

Ecological Motorcycle Taxi, incorporation of an electrolyzer as an alternative for the suppression of pollutants and noise reduction

Environmental pollution from transportation has been a fundamental challenge for humanity throughout history, with conventional fuels contributing significantly to environmental degradation. This project seeks to suppress pollution and reduce noise generated by motorcycle taxis, whether for personal and/or private use. The main objective of the research work was to implement an electrolyzer in the combustion system of a motorcycle taxi to suppress environmental pollution and reduce noise. Methods included vehicle preparation, electrolyzer installation, battery adjustment, hydrogen generation, emissions measurement, safety, and data collection. As a result, the electrolyzer was implemented in the single-cylinder motorcycle taxi; The noise limit allowed at work in Peru is 85 dB, which is equivalent to 100 % noise at most; When using gasoline, a value of 76.4 dB was obtained, equivalent to 89.88 % noise, and when using hydrogen, a value of 71.7 dB was obtained, equivalent to 84.35 % noise, therefore, the total noise reduction was 4.70 dB (5.53 %) than established. The discussion highlighted that hydrogen is a clean fuel, with zero emissions and only emitting water vapor instead of polluting gases [5]. The conclusion highlights the critical importance of the research, supported by the total reduction of 5.53 % of noise with this new technology and in turn external sources support that using hydrogen produced a smoother and quieter combustion; Regarding the useful life of the engine, due to the cleaner and more efficient combustion, the accumulation of waste was reduced, which benefits reducing the wear of internal components and the need for costly maintenance.

Joint Concern over Battery Health and Thermal Degradation in the Cruise Control of Intelligently Connected Electric Vehicles Using a Model-Assisted DRL Approach

Eco-driving aims to enhance vehicle efficiency by optimizing speed profiles and driving patterns. However, ensuring safe following distances during eco-driving can lead to excessive use of lithium-ion batteries (LIBs), causing accelerated battery wear and potential safety concerns. This study addresses this issue by proposing a novel, multi-physics-constrained cruise control strategy for intelligently connected electric vehicles (EVs) using deep reinforcement learning (DRL). Integrating a DRL framework with an electrothermal model to estimate unmeasurable states, this strategy simultaneously manages battery degradation and thermal safety while maintaining safe following distances. Results from hardware-in-the-loop simulation testing demonstrated that this approach reduced overall driving costs by 18.72%, decreased battery temperatures by 4 °C to 8 °C in high-temperature environments, and reduced state-of-health (SOH) degradation by up to 46.43%. These findings highlight the strategy’s superiority in convergence efficiency, battery thermal safety, and cost reduction compared to existing methods. This research contributes to the advancement of eco-driving practices, ensuring both vehicle efficiency and battery longevity.