Electric Vehicle Battery System Research Articles

A new method for thermal runaway warning of electric vehicle charging

Abstract With the large-scale development of electric vehicle (EV) in China, the frequent occurrence of EV fire accidents has attracted the attention of insiders to the fault monitoring and early warning during the charging process of EV. According to data from the National Emergency Management Ministry, there were over 3,000 EV fire accidents nationwide in 2023, underscoring the urgency for enhanced fault monitoring and early warning measures. In response to these issues, this article proposes an EV thermal runaway early warning method based on Temporal Convolutional Network (TCN) and Gated Recurrent Unit (GRU) by combining the two technologies. The method aims to improve EV safety by monitoring battery status for early thermal runaway warning. First, the historical normal charging data of the battery are preprocessed and feature extracted using the TCN, and then features fed into GRU for time-series modeling and forecasting. This combined model not only demonstrates high prediction accuracy and stability but also swiftly responds to abnormal conditions during charging, effectively preventing thermal runaway accidents and ensuring charging safety. Furthermore, this model possesses excellent generalization capabilities and can adapt to different types and specifications of EV battery systems.

Estimation of SOC for Li-ion battery-powered three-wheeled electric vehicle using machine learning methods

Abstract The Battery Management System (BMS) serves as the heart of the electric vehicle system, in which estimating the state of charge (SOC) is the crucial part of the BMS to ensure the durability, reliability, and sustainability of the battery pack. Due to its nonlinear characteristics, accurately estimating the SOC for a slow degradation of the charge is highly cumbersome. The literature provides a series of machine learning algorithms (MLA) to estimate and predict the SOC of lithium-ion (Li-ion) battery systems for electric vehicle (EV) applications. The literature has proposed various MLA, coulomb counting, and different Kalman filter methods to address this challenge and estimate the SOC of Li-ion battery systems for EV applications. This research looks at the differences and similarities between the coulomb counting method, the unscented Kalman filter method, and a number of machine learning algorithms. These include linear regression, polynomial linear regression, support vector regression, decision trees, random forests, artificial neural networks (ANN), and long short-term memory (LSTM). The goal is to assess the MLAs' accuracy in estimating battery SOC. Analyzing model errors optimizes the battery's performance parameter. We identify ANN and LSTM as the two most efficient methods for accurate SOC estimation in an EV-operated BMS system by evaluating the performance indices of the aforementioned machine learning methodologies. Once again, the LSTM model for SOC estimation has proven to be highly accurate in analyzing the discrepancy between the actual and predicted traveling ranges of the designed prototype. We design a MATLAB/SIMULINK EV powertrain by collecting realtime data from the Li-ion battery pack, analyzing the SOC variation data, and using the previously mentioned MLA in the Python platform to estimate the SOC and its accuracy. It highlights the effectiveness of advanced MLAs in improving SOC estimation, thereby enhancing the performance and reliability of EV battery systems.

Heat Transfer Modeling and Optimal Thermal Management of Electric Vehicle Battery Systems

Heat Transfer Modeling and Optimal Thermal Management of Electric Vehicle Battery Systems

Superior thermal response of additive manufactured porous stainless steel with carbon nanotubes

The temperature rise in the battery cells during continuous operation leads to a thermal runaway that affects the life and performance of electric vehicles. For such reasons, heat sinks with high thermal conductivity will be of utmost importance in maintaining optimal operating temperatures. This paper presents the development of a 3D-printed SS 316L porous structure with carbon nanotubes coated on it through a chemical vapour deposition method to enhance thermal management. The carbon nanotubes (CNTs)-coated porous Stainless Steel (SS) structure showed enhanced thermal performance. It absorbed heat 7 % faster than the non-coated and dissipated heat 138 % more at a heat input of 36.5 W. For the fixed 120-second time, a temperature difference existed of 3.9 °C during heating and up to 4.4 °C during cooling. These results demonstrate that the CNT coating dramatically improves the thermal response of the porous SS structure. This original approach for integrating CNT coatings with 3D-printed porous SS structures opens up a potential avenue for the efficient design and manufacturing of a heat sink, thus increasing the reliability and durability of the battery systems in electric vehicles.

Efficiency Analysis of Hybrid Extreme Regenerative with Supercapacitor Battery and Harvesting Vibration Absorber System for Electric Vehicles Driven by Permanent Magnet Synchronous Motor 30 kW

This research presents an approach to the hybrid energy harvesting paradigm (HEHP) based on suspended energy harvest. It uses a harvesting vibration absorber (HVA) with an SC/NMC-lithium battery hybrid energy storage paradigm (SCB-HESP) equipped regenerative braking system (SCB-HESP-RBS) for electric vehicles 2 tons in gross weight (MEVs) driven by a 30 kW permanent magnet synchronous motor (PMSM). During regenerative braking, the ANN mechanism controls the RBS to adjust the switching waveform of the three-phase power inverter, and the braking energy transfers to the energy storage device. Additionally, a supercapacitor (SC) equipped with HVA can absorb energy from vehicle vibrations and convert it into electrical energy. The energy-harvesting efficiency of MEV based on SCB-HESP-RBS using HVA suspended energy harvesting enhances the efficiency maximum to 50.58% and 15.36% in comparison to MEV with only-HVA and SCB-HESP-RBS, respectively. Further, the MEV with SCB-HESP-RBS using HVA has a driving distance of up to 247.34 km (22.5 cycles) when compared with SCB-HESP-RBS (214.40 km, 19.5 cycles) and only-HVA (164.25 km, 15 cycles).

Self-Diagnostic Opportunities for Battery Systems in Electric and Hybrid Vehicles

The number of battery systems is also growing significantly along with the rise in electric and hybrid car sales. Different vehicles use different types and numbers of batteries. Furthermore, the layout and operation of the control and protection electronics units may also differ. The research aims to develop an approach that can autonomously detect and localize the weakest cells. The method was validated by testing the battery systems of three different VW e-Golf electric vehicles. A wide-range discharge test was performed to examine the condition assessment and select the appropriate state of charge (SoC) for all three vehicles. On the one hand, the analysis investigated the cell voltage deviations from the average; the tests cover deviations of 0 mV, 12 mV, 60 mV, 120 mV, and 240 mV. On the other hand, the mean value calculation was used to filter out possible erroneous values. Another important aspect was examining the relationship between the state of charges (SoC) and the deviations. Therefore, the 10% step changes were tested to see which SoC level exhibited more significant voltage deviations. Based on the results, it was observed that there are differences between the cases, and the critical range is not necessarily at the lowest SoC level. Furthermore, the load rate (current) and time of its occurrence play an important role in the search for a faulty cell. An additional advantage of this approach is that the process currently being tested on the VW e-Golf can be relatively simply transferred to other types of vehicles. It can also be a very useful addition for autonomous vehicles, as it can self-test the cells in the system at low power consumption.

A comparative study on the effects of multi-walled carbon nanotubes and graphene nanoplates incorporated for improved thermal conductivity and dielectric properties of polyvinyl chloride

In the present work, the improvement in the thermal conductivity and dielectric behavior in the frequency range of 0.025–5 MHz of polyvinyl chloride (PVC) films incorporated with graphene nanoplates (GNs) and multi-walled carbon nanotubes (CNTs) were investigated. The neat PVC and PVC composites consisting of different filler ratios of 0.25, 0.5, 0.75, and 1 wt% were synthesized using the casting technique. The increase in the concentration of GNs or CNTs up to 0.75 wt% enhanced the dielectric constant, ac conductivity, q-factor, F-factor, thermal conductivity, and total impedance of PVC composites. The values of the investigated parameters are slightly higher for PVC-CNTs composites compared to PVC-GNs composites. A further increase in concentration to 1 wt% reduces these parameters, but still more than PVC consisting of 0.5 wt% fillers. The mechanism of correlated barrier hopping (CBH) could be successfully used to describe the AC electrical conductivity of both PVC-GNs and PVC-CNTs composites thin films. Furthermore, the glass transition and decomposition temperatures were determined and found to be dependent on the filler ratio and type, and the thermal stability was increased after adding the GNs or CNTs nanofillers. These findings have drawn special attention to developing PVC composites that can be used in various industrial fields including flexible and wearable technologies, power devices, supercapacitors, electric vehicle battery systems, and aerospace components.

Enhancing passive cell balancing techniques for electric vehicles

This research endeavors to enhance the efficiency and functionality of electric vehicle (EV) energy storage systems by scrutinizing the state of charge (SOC) across three batteries. Utilizing Matlab Simulink, a comprehensive battery management system (BMS) integrating passive cell balancing techniques has been meticulously devised. Additionally, a pioneering protective mechanism has been proposed to prevent undue discharges and overcharges, thus ensuring the longevity and reliability of EV battery systems. The study's findings underline the significance and applicability of the developed model in facilitating prospective enhancements within the realm of EV energy storage systems. By meticulously analyzing SOC dynamics across multiple batteries, the study offers valuable insights into optimizing battery utilization, prolonging battery lifespan, and enhancing overall system performance. Moreover, the integration of passive cell balancing techniques within the BMS framework represents a noteworthy advancement in battery management strategies. This approach not only promotes uniformity in SOC distribution among battery cells but also minimizes energy losses and mitigates the risk of cell degradation, thereby bolstering the operational efficiency and durability of EV energy storage systems. Furthermore, the proposed protective mechanism serves as a proactive measure to safeguard against potential damage caused by extreme operating conditions, such as overdischarge and overcharge events. By implementing real-time monitoring and control mechanisms, the protective system ensures the safe and reliable operation of EV batteries under varying driving conditions, thereby enhancing both safety and performance aspects. In conclusion, this research contributes to the ongoing efforts aimed at advancing the state-of-the-art in EV energy storage systems, holding immense potential for fostering innovation and driving sustainable progress in the field of electric mobility.

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.

Review on Laser Welding Technology on Aluminum and Copper Dissimilar Materials for Secondary Batteries Assembly

Electric vehicles have recently taken center stage, driven by growing environmental concerns, with a key focus on ensuring the reliability of their batteries. The materials used in the electrical connections of secondary batteries predominantly consist of Al and Cu alloys, necessitating the joining of both similar and dissimilar materials. This paper presents a technical review on laser welding technology applied to the challenging Al/Cu dissimilar metal combination. The complexity arises from the formation of brittle phases in intermetallic compounds and the low absorption of laser beams by the base materials. The characteristics of various laser welding modes and their effective monitoring through deep learning are explained in detail. The development of real-time laser welding monitoring technology is crucial to increase productivity and enable the mass production of reliable electric vehicle battery systems.

Aging behavior of an electric vehicle battery system considering real drive conditions

In this article, a comprehensive study of the aging behavior of an electric vehicle battery pack considering the vehicle's operation under real driving conditions, such as ambient temperature, humidity, and solar irradiation conditions, in all different months of the year for a 10-year time horizon with two different working scenarios is investigated for the first time. The vehicle duty cycle represents a personal vehicle traveling once at 7:00 AM from home to work and returning at 5:00 PM from work to home for scenario 1. For the second scenario, 8:30 AM is set for outbound trips, and 6:30 PM is set for return trips via the WLTP driving cycle. The target temperature for the cabin and battery pack are set to 22 °C and 28 °C, respectively. The results indicate the highest energy consumption is for the cold season. The EV's energy consumptions in scenario 2 for outbound and return trips were 20 % less and 0.6 % more than the EV's energy consumptions in scenario 1 for outbound and return trips, respectively. Next, the battery aging was evaluated for ten years, resulting in 7.16 % and 6.99 % total capacity loss for scenario 1 and scenario 2, respectively.

Micro-short circuit fault diagnosis of lithium-ion battery based on voltage curve similarity ranking volatility

ABSTRACT An electric vehicle battery system often consists of multiple battery cells that are interconnected in series and parallel. If a micro-short circuit failure occurs in one of the battery cells, it may cause a serious internal short circuit or even thermal runaway. Consequently, it is extremely important to detect micro-short circuit faults in batteries. This paper presents a novel approach for diagnosing faults in lithium-ion batteries based on the similarity ranking fluctuation rate of voltage curve, and verify the feasibility of the method through a series of micro-short circuit experiments containing an external parallel variable resistance device. This approach does not require complete charge-discharge curves. Firstly, the charging voltage data is preprocessed using the Variational Mode Decomposition algorithm, after which the Dynamic Time Warping technique is employed to calculate the similarity between the charging voltage of each battery and a reference battery. Then a ranking fluctuation rate of voltage curve similarity is established, and the Grubbs criterion is utilized to identify batteries exhibiting abnormal fluctuation rate within the ranking. Finally, the effectiveness of the proposed method is validated by analyzing actual battery signals collected from faulty vehicles. The results demonstrate that the method accurately identifies batteries with micro-short circuit faults.

Optimizing fault diagnosis for electric vehicle battery systems: Improved Giza pyramids construction and advanced gradient boosting decision trees

This manuscript proposes an online multi-fault diagnostic technique for the series string batteries in electric vehicles to identify and diagnose external/internal short circuits, sensor faults, and connection fault detection. The proposed technique combines the Improved Giza Pyramids Construction and Gradient Boosting Decision Trees. Many variables, including road conditions, electromagnetic interference, and driving habits, may complicate the battery fault system, multi-parameter or non-linear coupling, making it challenging to diagnose faults properly by using a single parameter in the real operation of an electric vehicle. The voltage fluctuation data is collected by simulating a battery discharging and charging investigation in a vibration setting. By deconstructing and rebuilding the discrete wavelet transform, the proposed approach removes voltage signal noise. To categorize the fault state, the voltage change, co-variance and variance matrixes, and voltage characteristics are employed as input-values in the improved Giza pyramids construction, and gradient boosting decision trees technique. The Improved Giza pyramids construction technique is utilized to detect fault signals and determine the degree of the defect. The cell faults are separated from other faults using the Gradient Boosting Decision Trees approach by identifying the surrounding voltages with fault flags. Furthermore, the correlation coefficient of the nearby voltage difference and current isolates connection and voltage sensor defects. The multi-fault diagnosis approach may prevent erroneous fault detection and offer high resilience to battery inconsistencies and normal measurement errors of state of health, ambient temperature, and state of charge. The projected control theme is evaluated on the MATLAB platform and its performance is assessed. The performances of the proposed method are compared with existing techniques like Artificial Bee Colony, Differential Evolution, Improved Giza Pyramids Construction and Particle Swarm Optimization. The efficiency of the Artificial Bee Colony, Differential Evolution, Particle Swarm Optimization, and Improved Giza Pyramids Construction and proposed technique is 82.237 %, 79.265 %, 87.1029 %, 89.023 % and 95.4501 %.

The multi-variable stepwise algorithm for internal short circuit detection in a serial battery pack with inconsistent state of health

Internal short circuit of cells is one of the main causes of thermal runaway in electric vehicle battery systems. Therefore, one of the most effective ways to prevent thermal runaway is to detect and identify internal short-circuit lithium-ion batteries before thermal runaway using a battery management system. This paper investigates the detection and identification of internal short circuits in batteries by proposing a multi-variable stepwise analysis (MSA) method. The MSA method is proposed for detecting and identifying faulty batteries by combining horizontal and vertical comparison methods and aging cells' ohmic internal resistance variation characteristics. A less consistent pack containing aging cells was designed to perform internal short-circuit experiments. Based on setting the appropriate threshold and moving the window frame horizontally, comparing the deviation degree of the ohmic internal resistance of each cell in the battery pack and the average ohmic internal resistance of the normal battery, the aging battery in the battery pack can be effectively identified. State of health (SOH) is the percentage remaining of the battery's actual maximum capacity value. The deviation degree of ohmic internal resistance of aging batteries with SOH of 92% and 80% is maintained at more than 15% and 45%. For early internal shorts with an equivalent internal short-circuit resistance of 100 Ω, the internal short-circuit detection time is 3896 s. For the short circuit in the middle and later periods (<10Ω), the MSA algorithm can achieve rapid internal short-circuit detection within the 50 s window, reducing the risk of thermal runaway. The results verified that the method could effectively identify aging cells within the battery pack and detect internal short circuits for other cells, reducing false positives and effectively preventing thermal runaway.

Batteries on the Move: Navigating Challenges, Expanding Horizons for Indian EVs

In India, the widespread adoption of swappable battery systems for electric vehicles faces significant hurdles such as regulatory gaps, infrastructure limitations, technological constraints, economic uncertainties, and environmental complexities. Overcoming these challenges demands a cohesive strategy involving thorough regulatory assessment, infrastructure expansion, tech collaboration, financial scrutiny, and environmental evaluations. This holistic approach is key to unlocking the untapped potential of swappable batteries, paving the way for an innovative, eco-friendly electric mobility landscape in India. This research paper aims to provide valuable insights for policymakers, industry players, and researchers grappling with the adoption of swappable battery systems in India's EV sector.

Capacitor-Based Active Cell Balancing for Electric Vehicle Battery Systems: Insights from Simulations

Abstract Cell balancing, a critical aspect of battery management in electric vehicles (EVs) and other applications, ensures a uniform state of charge (SOC) distribution among individual cells within a battery pack, enhancing performance and longevity while mitigating safety risks. This paper examines the effectiveness of capacitor-based active cell-balancing techniques using simulations under dynamic loading conditions. Utilising MATLAB and Simulink, various circuit topologies are evaluated, considering real-world cell parameters and open-circuit voltage (OCV) curve modelling. Results indicate that advanced configurations, such as double-tiered switched-capacitor balancing, offer improved balancing speed and efficiency compared to conventional methods. However, challenges such as transient events during charging and discharging phases underscore the need for further research. By leveraging simulations and experimental data, researchers can refine cell-balancing strategies, contributing to the development of safer, more efficient battery systems for EVs and beyond. This study underscores the importance of systematic analysis and optimisation in advancing cell-balancing technology for future energy-storage applications.

A Fault Detection Method for Electric Vehicle Battery System Based on Bayesian Optimization SVDD Considering a Few Faulty Samples

A Fault Detection Method for Electric Vehicle Battery System Based on Bayesian Optimization SVDD Considering a Few Faulty Samples

Numerical investigation on the thermal management of Li-ion batteries for electric vehicles considering the cooling media with phase change for the auxiliary use

This study investigates the thermal management of a battery module of an electric vehicle for the recovery of waste heat. In the evaluation of this waste heat, a two-phase flow system is used to provide a more effective heat transfer. The battery system was formed of 6 serial connected prismatic Lithium-Ion cells. It was aimed to design a 134.55 kW electric vehicle battery system. For this aim, 360 battery modules were used. The maximum amount of waste heat obtained from these modules was recorded as 6.192 kW. Since it is aimed to use this waste heat in the auxiliary cycles such as air conditioning and extra electricity production, R600a was used as the cooling media. A parametric study was conducted for different discharging rates (C) and depths of discharging (DoD) through computational fluid dynamics (CFD) for performing the thermal analysis of the battery module. The heat transfer coefficient was determined ranging between 1.10 and 5.49 kWm−2 K−1. The cooled module temperature was recorded between 290.93 K and 317.31 K, whereas it was recorded as 332.57 K for the non-cooled system. For secondary purposes, the refrigerant mass rate ranging between 0.2760 kgs−1 and 0.6559 kgs−1 was obtained in a temperature range of 283 K and 313 K.

Smart Solar Powered Battery System for EV for IoT Voltage Monitoring

Abstract: This paper presents a Smart Solar-Powered Battery System for Electric Vehicles (EVs) enhanced with Internet of Things (IoT) voltage monitoring, featuring a robust combination of components. Utilizing ESP32, Solar Panels, Breadboard, PCB, Voltage Sensor, Liquid Crystal Display, BJT, Battery, Jumper Wires, USB Cable, Arduino IDE, and BlynkIoT, this system embodies a sustainable and efficient EV charging infrastructure. The integration of solar panels within the system capitalizes on solar energy, diminishing the environmental impact of EV charging while reinforcing grid resilience. The high-capacity battery and IoT voltage sensors ensure continuous and reliable power supply to EVs, facilitated by remote monitoring through the BlynkIoT platform. This smart and grid-independent solution promises to redefine electric vehicle charging infrastructure, mitigating reliance on traditional power sources. In summary, the Smart Solar-Powered Battery System, comprising these pivotal components, demonstrates the potential to revolutionize EV charging, aligning with the global shift towards sustainable and ecofriendly transportation solutions.

Improved vµ-LMS-Controlled Single-Phase Grid Tied PV Powered Electric Vehicle Battery System with High Gain DC-DC Converter and Power Management

The increasing solar photovoltaic (PV) and EV charging applications create power quality issues at utility grid. It requires efficient control thus, in this paper, single-phase grid-tied PV powered electric vehicle battery (EVB) system is proposed with reliable control and power management. The presented work offers efficient EVB charging either from PV power or utility to handle PV impacts on the utility. During peak power demand, the proposed power management offers less burden on utility and allows EVB to feed increased load along with changing PV power. In work, a recent high-gain DC-DC boost converter topology is utilized thereby making smaller household PV arrays in grid-tied mode with DC-AC voltage source converter (VSC). Moreover, a new improved variable step-parameter ‘µ’ least mean square (IvµLMS) adaptive control is proposed and compared for efficient control of VSC with various nonlinear load and PV irradiance variations. The IvµLMS offers good convergence, less burden on controller and improved dynamic responses under various conditions. As per IEEE-519 standard, the PV power is fetched to grid at unity power factor (UPF) with low total harmonic distortion (THD). The proposed grid tied EVB system, and its controls are modelled in MATLAB/Simulink environment and test results are analyzed and validated on a laboratory prototype.