Electric Vehicle Battery Research Articles

Zero Torque Integrated On-Board EV Battery Charger With Enhanced Winding Filter Inductance

Zero Torque Integrated On-Board EV Battery Charger With Enhanced Winding Filter Inductance

Evonik expands the TEGO® Therm range with fire-resistant coatings for EV battery housings

Evonik expands the TEGO® Therm range with fire-resistant coatings for EV battery housings

Efficient state of charge estimation of lithium-ion batteries in electric vehicles using evolutionary intelligence-assisted GLA–CNN–Bi-LSTM deep learning model

The battery's performance heavily influences the safety, dependability, and operational efficiency of electric vehicles (EVs). This paper introduces an innovative hybrid deep learning architecture that dramatically enhances the estimation of the state of charge (SoC) of lithium-ion (Li-ion) batteries, crucial for efficient EV operation. Our model uniquely integrates a convolutional neural network (CNN) with bidirectional long short-term memory (Bi-LSTM), optimized through evolutionary intelligence, enabling an advanced level of precision in SoC estimation. A novel aspect of this work is the application of the Group Learning Algorithm (GLA) to tune the hyperparameters of the CNN–Bi-LSTM network meticulously. This approach not only refines the model's accuracy but also significantly enhances its efficiency by optimizing each parameter to best capture and integrate both spatial and temporal information from the battery data. This is in stark contrast to conventional models that typically focus on either spatial or temporal data, but not both effectively. The model's robustness is further demonstrated through its training across six diverse datasets that represent a range of EV discharge profiles, including the Highway Fuel Economy Test (HWFET), the US06 test, the Beijing Dynamic Stress Test (BJDST), the dynamic stress test (DST), the federal urban driving schedule (FUDS), and the urban development driving schedule (UDDS). These tests are crucial for ensuring that the model can perform under various real-world conditions. Experimentally, our hybrid model not only surpasses the performance of existing LSTM and CNN frameworks in tracking SoC estimation but also achieves an impressively quick convergence to true SoC values, maintaining an average root mean square error (RMSE) of less than 1 %. Furthermore, the experimental outcomes suggest that this new deep learning methodology outstrips conventional approaches in both convergence speed and estimation accuracy, thus promising to significantly enhance battery life and overall EV efficiency.

Unveiling barriers to the integration of blockchain‐based circular economy and Industry 5.0 in manufacturing industries: A strategic prioritization approach

AbstractAlthough Industry 4.0 plays the role of a catalyst in the transition from a linear economy to a circular economy (CE), some sustainability goals are neglected by prioritizing machines over humans. Industry 5.0 insists on combining technology with human values to achieve sustainable development goals. Therefore, the integration of CE and Industry 5.0 leads to sustainable synergy in various industries, especially in manufacturing industries. However, their integration is always associated with challenges and barriers. This study identifies a set of these barriers in the electric vehicle battery production industry from the financial, technical, supply chain, cultural, and regulatory and processes perspectives through a deep literature review. The fuzzy weighted influence non‐linear gauge system method is used to analyze the relationship between identified barriers and their prioritization. Finally, we leverage the knowledge of four experts from an electric vehicle battery manufacturing company in Southeast Asia to validate the proposed approach and to rank the barriers. The results demonstrate that immature technology providers are the biggest obstacle in the integration of blockchain‐based CE and Industry 5.0 in the electric vehicle battery manufacturing industry.

Classification of cathode materials for lithium-ion batteries of new energy electric vehicles based on safety and dynamic performance

The severe environmental pollution caused by fossil fuels has driven the demand for new energy vehicles. The choice of cathode materials for lithium-ion batteries is a major difficulty to be overcome in the development of new energy vehicles. Based on the performance characteristics of new energy vehicle batteries in actual use, this paper classifies the six most widely used cathode electrode materials in the new energy vehicle market. Firstly, the temperature data of power batteries with different cathode materials in the overcharge experiment and acupuncture experiment were collected to characterize the safety performance of power batteries. Then the maximum specific discharge capacity and operating voltage data of the battery were collected, and the maximum specific discharge energy of the battery was calculated to study the dynamic performance of the vehicle. After that, the safety performance and dynamic performance of each index is compared. Finally, according to the safety performance and dynamic performance of the corresponding power battery, the six cathode materials are divided into three types: power type, safety type, and balanced type. According to the classification results, it can be concluded that the design of balanced cathode materials with both safety and dynamic performance is the best development route for future new energy vehicle batteries.

Challenges in using Electrocoagulation Process in Removal of Nickel Metal in Wastewater: a Literature Review

In recent years, the surge in nickel production, driven by the growing demand for electric vehicle batteries, has raised concerns regarding environmental consequences. The nickel mining and processing industries contribute to increased nickel levels in wastewater, presenting a serious threat to aquatic ecosystems and human health. This article emphasizes the urgency of developing effective technologies for treating nickel-contaminated wastewater. Electrocoagulation emerges as a promising method, providing high efficiency, minimal sludge production, and cost-effectiveness. The article critically and systematically reviews the potential of the electrocoagulation process in nickel removal from wastewater. In the review, we identify and analyze nearly 32 studies published from 2013 to 2023. We discuss contaminant removal mechanisms and analyze trends in the use of operational parameters. This article identifies the most commonly applied conditions: aluminum electrodes, inter-electrode spacing ≥ 1 cm, current density ≤ 10 mA/cm², initial pH 6 ≤ pH < 11, electrolysis time < 60 min, batch operation, and initial nickel concentration > 50 mg/L. This comprehensive review serves as a foundational resource for advancing electrocoagulation technology in the removal of heavy metals from nickel wastewater.

Optimizing public space design through odd-even policy: Reducing traffic congestion and pollution in DKI Jakarta

Background: The Provincial Government of DKI Jakarta issued Governor Regulation No. 155 of 2018 and its amendments regarding Traffic Restrictions with the Odd-Even System. This regulation aims to reduce congestion levels on 16 main roads. By decreasing congestion, the expected outcome is a reduction in air pollution levels in DKI Jakarta. However, there is concern that the implementation of Governor Regulation No. 155 of 2018 might lead to an increase in conventional car purchases, as affluent individuals may buy two cars with odd and even license plates. This choice is driven by the continued use of fossil fuel-based energy. This study aims to evaluate the effectiveness of Governor Regulation No. 155 of 2018 in reducing traffic congestion and air pollution in DKI Jakarta. Additionally, the study seeks to understand the relationship between the implementation of the odd-even traffic restriction system and electric vehicle sales. Methods: The research was conducted by analyzing secondary data obtained from government agencies and other institutions. Furthermore, a questionnaire survey was administered to individuals active on the 16 main roads where the odd-even policy is enforced. The study also analyzed the purchase levels of electric cars, which is one method for Jakarta residents to avoid fines on odd-even roads. Result: Based on the research findings, Governor Regulation No. 155 of 2018 and its amendments have not yet effectively reduced traffic congestion and air pollution in DKI Jakarta. Conclusion: The increase in electric car purchases potentially introduces new issues, such as the unsustainable management of nickel mines required for electric vehicle battery production. Therefore, other sustainable strategies are needed to address traffic congestion and air pollution. Novelty/Originality of the study: This study shows that Governor Regulation No. 155 of 2018 has yet to reduce congestion and air pollution in DKI Jakarta effectively. In addition, the increase in the purchase of electric cars as a solution to avoid fines has raised new problems related to the environmental impact of electric vehicle battery production.

Improving electric vehicle battery cooling efficiency with nanofluid and vibration integration: a novel thermal management approach

Improving electric vehicle battery cooling efficiency with nanofluid and vibration integration: a novel thermal management approach

As a service or a product? A comparison of electric vehicle battery supply models

In response to the challenges posed by high costs and rapid degradation of electric vehicle (EV) batteries, Battery as a Service (BaaS) is introduced as a new EV battery supply model, alongside the traditional model of Battery as a Product (BaaP). Under this background, how to choose a battery supply model is a major strategic problem. Through game theory approach, we analyze and compare the BaaS and BaaP models under a durable goods framework. Firstly, we find that the BaaS model is more profitable than the BaaP model when battery cost or degradation rate is excessively high. The firm under BaaS can relieve cost pressures by selectively retaining used batteries and reducing the price threshold for consumers to acquire batteries. Secondly, through premature disposal of used batteries, a firm can endogenously determine the quality of battery service, making the BaaS model more profitable when battery cost or degradation rate is excessively low. Thirdly, we show that the firm under BaaS always benefits from the decrease of battery degradation rate. However, a lower battery degradation rate may reduce the firm’s profit under BaaP because of the cannibalization effect from the secondary market.

Return and volatility spillovers between the raw material and electric vehicles markets

This paper investigates the return and volatility spillovers between the upstream electric vehicles (EV) battery raw materials market and the individual downstream EV producers. The study uses the daily stock returns of two lithium producers and a new model in the GARCH family to capture the jump component of volatility in the EV battery raw materials market. Return and volatility spillovers are studied using an EGARCH(1,1) model including the excess stock returns of lithium producers in the mean equation and their jump component intensity in the variance equation. The results indicate that jumps exist in the EV battery raw materials market and that there exist significant return spillovers between lithium and EV producers. However, this paper did not find any strong evidence of the existence of volatility spillovers between these two markets through lithium unexpected news.

Comparing the circularity and life cycle environmental performance of batteries for electric vehicles

Batteries account for a significant share of the life cycle impact of electric vehicles (EV). Nonetheless, the circularity and environmental performance of EV batteries remains underexplored in the literature. This paper compares the circularity (Circularity Index and Product Circularity Indicator) and environmental performance (Global Warming Potential (GWP) and Abiotic Depletion Potential of minerals (ADPm)) of lithium nickel manganese cobalt (NMC) and lithium iron phosphate (LFP) batteries subjected to pyrometallurgy and hydrometallurgy recycling. Lifetime extension, improved energy efficiency, and material recovery ratios were also calculated to identify the optimal battery design. The findings show that NMC batteries are 6–25% more circular and environmentally sustainable (<4–6% GWP and <13–16% ADPm) than LFP batteries, primarily due to better material recovery ratios. Moreover, the longer lifetime of LFP does not sufficiently offset resource and environmental impacts. Finally, the study discusses the results to support circular and environmental innovation in EV battery design.

Sizing of Energy Storage Systems in Electric Vehicles based on Battery-Supercapacitor Technology

The purpose of this research is to determine the ideal hybrid energy storage system (HESS) size with the goal to improve the effectiveness and efficiency of combined battery and supercapacitor energy storage in electric vehicles. The research uses Mixed Integer Linear Programming (MILP) to determine the most suitable configurations using simulation data from a modeled electric vehicle. The results show that MILP works at identifying the specific capacity needs of the storage system, which change based on the vehicle's power and energy capacity. The innovation provides a paradigm for the development of sustainable and highly efficient electric vehicles in the future, while also enhancing the functionality of current electric vehicles.

Power Allocation based on ANN for Hybrid Battery and Supercapacitor Storage System in EV

The paper focuses on presented an ArtificialNeural Network (ANN) approach to allocate power for a hybridenergy storage system (HESS) in an Electric Vehicle (EV). TheHESS is comprised of a battery and supercapacitor, and theANN algorithm aims to optimize power allocation between thesetwo energy storage devices. The data for ANN training wasbased on cost optimization-based power allocation fromprevious research. While optimization can often take highcomputational resource and time, it is expected that a welltrained ANN can allocate power for the EV HESS more quickly.In this research, the inputs to the ANN are the required powerderived from the drive cycle, energy and power capacity of thebattery and supercapacitor, and state of charge (SoC) of thebattery and supercapacitor. The trained ANN was trained withvarious inputs not used in the training and it shows satisfactoryperformance.

IoT-enabled advanced monitoring system for tubular batteries: Enhancing efficiency and reliability

Monitoring battery health is crucial for predicting the lifespan of batteries in electric vehicles. Real time voltage, current, and operating temperature are the key factors influencing longevity. An efficient data acquisition (DAQ) system becomes essential for accurately monitoring real-time battery operating parameters. For commercially viable three-wheeled electric vehicles, a cost-effective yet highly accurate DAQ is imperative. Current commercially available lead-acid three-wheeled electric vehicles lack a dedicated DAQ for monitoring battery health. In this investigation, we address this gap by developing a low-cost, multi-channel DAQ tailored for such electric vehicles, integrated with a cloud server for subsequent data analysis and Remaining Useful Life (RUL) prediction. The implemented prototype excels in measuring individual battery terminal voltage and battery pack voltage under operating conditions, employing the node voltage subtraction method. It also features real-time detection of high current discharges up to 30A using a Hall effect-based current sensor. A custom temperature sensor probe was designed to detect battery electrolyte temperatures up to 60 °C, with multiple probes operating in multiplexed mode to reduce circuit complexity. The system is proficient in storing recorded data locally and in the cloud. A mobile device platform has been developed and interfaced with the system for real-time monitoring of battery parameters. After testing multiple prototypes in running vehicles for over 1000h without failure, the system demonstrated a high accuracy level of 98.6 %. Multiple prototypes were designed to validate the measured accuracy.

An Intelligent Optimization Scheme for LoRaWAN-Based Electric Vehicle Batteries Monitoring System Located in Warehouses

&lt;div&gt;This article presents an optimization scheme for LoRaWAN-based electric vehicle batteries monitoring system located in warehouses by utilizing techniques to optimize packet delivery and power settings. Utilizing simulations, we identify that system optimization largely depends on network traffic, influenced by active users and the adoption of the pure ALOHA protocol. We define a reward metric based on the packet delivery rate and power efficiency, aiming for settings that yield the maximum reward. Our approach includes duty cycle management to minimize network traffic and maximize throughput, especially critical when handling urgent data from batteries. Traffic management based on the number of critical batteries in the warehouse also plays a crucial role. Predictive modeling of future traffic further refines power settings for optimal performance. The proposed system, tested through simulations, shows an average of 31% higher reward compared to traditional methods without duty cycle management.&lt;/div&gt;

A novel development of h-CSLQFL controller for optimal sizing and economic pricing of battery in electric vehicle system

This paper proposes a novel design of h-CSLQFL controller for reducing the battery size (kWh) and battery cost (US$/kWh) of electric vehicle (EV) system. The h-CSLQFL controller is a hybrid controller which is formed by the combination of Cuckoo search optimization (CSO), linear quadratic regulator (LQR), and fuzzy logic controller (FLC). The higher battery cost is one of the biggest challenges with EV due to which a significant number of consumers cannot afford to purchase it. The replacement of conventional vehicles with EV creates ecofriendly and sustainable solutions. For attaining the optimal reduced cost of battery, a strategy has been demonstrated by framing a multi-objective function which is controlled with h-CSLQFL controller. It commences with the design of mathematical model of EV in which supply voltage and current are the input parameters whereas the speed and torque are the output parameters. Thereafter, LQR controller was designed and implemented for analyzing the performance parameter of EV. Afterwards, CSO controller in support with LQR controller has been formulated in closed loop manner. At the end, the multi-objective function is controlled with LQR based CSO and LQR controller. It is observed that the least optimal economic cost and size of EV battery have been attained with h-CSLQFL controller in comparison to the LQR based CSO and LQR controller. In addition to this, the least risk factor and reduced sensitivity have been also attained with the proposed approach. The validation of the proposed h-CSLQFL controller has been done on a hardware set up.

Co-estimation of state-of-charge and capacity for series-connected battery packs based on multi-method fusion and field data

Accurate state-of-charge (SOC) and capacity estimations are of great importance for the performance management, predictive maintenance, and safe operation of lithium-ion battery packs in electric vehicles (EVs). However, it is quite challenging to estimate real-world large-sized EV battery packs due to the unpredictable operating profiles and large measurement disturbances. This article proposes an adaptive onboard SOC and capacity co-estimation framework, which incorporates a multi-timescale hierarchy and integrates multiple individual methods adaptively to practical driving profiles. First, this framework considers the most evident inconsistency between battery cells and periodically screens the weakest cells in a long timescale (week-level). Subsequently, the SOC of the battery pack is accurately estimated in a short timescale (in real-time) based on multi-method fusion. Finally, the capacity of the battery pack is periodically calibrated in a medium timescale (minute-level) based on an adaptive state filter and reliable SOC estimation. Both the laboratory and field data were used for validation, and the results demonstrated the proposed method achieved accurate SOC and capacity estimations of large-sized EV battery packs, with the maximum root mean squared errors of <0.7 % and <3.2 %, respectively, and it was run five times faster than the multi-cell model-based method.

Data simulation of the impact of ball strikes on the bottom of electric vehicle battery packs based on finite element analysis

As a key component, electric vehicle battery packs may suffer serious consequences from external impacts, but there is currently a lack of comprehensive data and simulation studies to evaluate the impact resistance of battery pack structures. Therefore, this article establishes a three-dimensional model of the electric vehicle battery pack through finite element analysis method, taking into account the elastic and geometric nonlinear characteristics of the material. In the ball hitting simulation, appropriate impact dynamic loads were selected to simulate the actual response and deformation of the battery pack bottom under impact, in order to ensure the accuracy and reliability of the simulation results. By analyzing the simulation results, the deformation, stress, and strain distribution at the bottom of the battery pack under ball impact were obtained, as well as the related variation patterns. It was observed that the battery pack underwent significant deformation under impact load, and stress concentration also occurred in certain areas. By analyzing the stress and strain distribution obtained, the weak areas of the battery pack can be identified, and corresponding improvement measures can be proposed to enhance its impact resistance and structural strength. These simulation results also provide reference for the optimization design of the battery pack, helping to further improve its overall working performance.

Advancing battery state of charge estimation in electric vehicles through deep learning: A comprehensive study using real-world driving data

Accurately estimating the State of Charge (SOC) in Electric Vehicles (EVs) is critical for battery management and operational efficiency. This paper presents a Deep Learning (DL) approach to address this challenge, utilizing Feed-Forward Neural Networks (FFNN) to estimate SOC in real-world EV scenarios. The research used data from 70 driving sessions with a BMW i3 EV. Each session recorded key factors like voltage, current, and temperature, providing inputs for the DL model. The recorded SOC values served as outputs. We divided the dataset into training, validation, and testing subsets to develop and evaluate the FFNN model. The results demonstrate that the FFNN model yields minimal errors and significantly improves SOC estimation accuracy. Our comparative analysis with other machine learning techniques shows that FFNN outperforms them, with an approximately 2.87 % lower root mean square error (RMSE) compared to the second-best method, Extreme Learning Machine (ELM). This work has significant implications for electric vehicle battery management, demonstrating that deep learning methods can enhance SOC estimation, thereby improving the efficiency and reliability of EV operations.

Centrally-chosen versus user-selected swaps: How the selection of swapping stations impacts standby battery inventories

Swapping depleted batteries of electric vehicles promises much better driving-to-total-travel-time ratios than plug-in charging. Nonetheless, large-scale battery swapping systems have not successfully established yet. One obstacle, on top of the high infrastructure cost, is certainly the additional invest into extra standby batteries that await their swaps at stations. Existing research is focused on systems in which users decide individually where they want to swap batteries. This system requires significant standby battery inventories to protect against uncertain swapping demand. This paper evaluates another system where users must register their trips on a central platform, so that battery swaps can be coordinated based on central optimization results. To benchmark central optimization and user choice regarding their impact on standby battery inventories, we formulate the min-battery swapping problem: For a given set of vehicle trips, this optimization problem minimizes the number of standby batteries, distributes them in a given station network, and derives detailed swapping plans to feasibly power all trips. First, we present a thorough analysis of computational complexity. Then, we provide an efficient mixed-integer programming formulation that is adaptable to different swapping policies and (when fed into a default solver) solves instances with up to 200 trips to proven optimality in just a few seconds. Our computational study reveals that central optimization promises a significant reduction of standby battery inventories. This potential is shown to increase if swaps of not yet fully-charged batteries are allowed and swaps are pooled at a reduced number of swapping stations.