Optimize Battery Performance Research Articles

Comparative Evaluation of Liquid Cooling-Based Battery Thermal Management Systems: Fin Cooling, PCM Cooling, and Intercell Cooling

The escalating demand for electric vehicles and lithium-ion batteries underscores the critical need for diverse battery thermal management systems (BTMSs) to ensure optimal battery performance. Despite this, a comprehensive comparative analysis remains absent. This study seeks to assess and compare the thermal and hydraulic performances of three prominent BTMSs: fin cooling, intercell cooling, and PCM cooling. Simulation models were meticulously developed and experimentally validated, with each system’s design parameters optimized under identical volumes to ensure equitable comparisons. In the context of fast-charging conditions, intercell cooling consistently met and even surpassed the desired target temperature, reducing the maximum temperature to 30.6°C with an increasing flow rate, while fin cooling faced challenges. Effective control of coolant temperature emerged as a critical factor for achieving optimal PCM cooling, with a potential reduction in temperature difference by 4.3 K. Despite exhibiting higher power consumption, intercell cooling demonstrated the most efficient cooling effect during fast charging. Considering the BTMS weight, fin cooling exhibited the lowest energy density, approximately half that of other methods. Addressing precooling and preheating conditions for high and low temperatures, the intercell method proved adept at meeting temperature requirements with minimal power consumption in significantly shorter durations. Conversely, the practicality of using PCM at high temperatures was deemed challenging.

Indirect Liquid Cooling for Battery Thermal Management: A Review

The thermal management system of batteries is a critical aspect of battery management for optimizing battery performance and lifespan. Research has rapidly progressed in battery thermal management in the past decade, mainly by adopting indirect liquid cooling methods. This article provides an in-depth overview of the recent developments in the application of indirect liquid cooling for battery thermal management, encompassing fundamental principles, types of fluids, and the strengths and weaknesses of this method. Various heat transfer techniques, such as convective heat transfer and heat generation, are also examined to comprehend effective ways of precisely managing battery temperature. The benefits and challenges of this indirect liquid cooling approach are evaluated, considering crucial criteria like safety. The results of this review demonstrate that the indirect liquid cooling method holds promise as an effective solution to address thermal challenges in batteries, with the potential to enhance overall battery performance and durability.

Effects of the cold plate with airfoil fins on the cooling performance enhancement of the prismatic LiFePO4 battery pack

To maintain the optimal battery performance, it is essential to develop an efficient battery thermal management system for keeping the battery operation temperature within the appropriate range. In this work, a novel design of cold plate featuring airfoil fin channel is developed as a highly effective cooling module of a prismatic LiFePO4 battery pack. The thermal performance of battery and the thermo-hydraulic performance of the cooling module for the cold plate featuring airfoil fin channel, U-type channel and serpentine channel are numerically investigated and compared to reveal the superiority of the novel cold plate. The results indicate that compared with the U-type channel and the serpentine channel, the airfoil fin channel can enhance the total heat dissipation amount by 3.47% and 4.64%, respectively. Besides, the airfoil fin channel can provide the smallest battery maximum temperature and temperature difference under the same coolant flow rate. The airfoil fin channel provides the smallest pressure loss and heat transfer coefficient but the largest overall thermo-hydraulic performance indictor in terms of j/f factor. Under the same requirement of battery cooling performance, the remarkably smaller power consumption is required for the airfoil fin channel compared with the U-type channel and serpentine channel.

Machine learning Approach to Battery Management and Balancing Techniques for Enhancing Electric Vehicle Battery Performance

The objective of this research is to apply machine learning techniques to optimize electric vehicle battery management and balance to attain maximum battery performance. Here, we will assess and compare the efficiency and accuracy of decision tree classifier, ANN, and Naive Bayes classifiers in: predicting two optimal charging and discharging battery management strategies, prediction of the battery’s performance and detecting any abnormalities in it. . In this context, one machine learning model will be proposed to have the most advantageous performance. The experimental results details that such findings are attained, and the precision rates, recall rates, and F1-scores attained by net models exceed 98%. Additionally, the decision tree, as well as the Naive Bayes classifier, have impressive performance, and their accuracy rates exceed 90%. Decision trees along with Naive Bayes classifiers have also important impacts on the identification of classification of battery’s responses through probabilistic classification in the former. Our findings have important implications for ensuring electric vehicle batteries are managed more properly for optimal performance. Additionally, based on the results obtained, they can be utilized to help relevant stakeholders in the EV industry and other industries with battery usage implement practical measures that optimize battery performance, increase battery life, and reduce the incurred operational expenses of electric vehicle fleets.

A Framework for Managing and Monitoring the Battery Health Parameters of Rental Electric Vehicles

The rapid adoption of electric vehicles (EVs) has paved the way for a sustainable transportation future. However, ensuring the safety and reliability of rental EVs, particularly regarding their battery health, presents unique challenges. This paper proposes a comprehensive framework for monitoring and managing rental EV batteries, encompassing real-time data acquisition, advanced analytics, and proactive maintenance strategies. By implementing such a framework, rental EV providers can optimize battery performance, extend lifespan, and guarantee the safety and convenience of their customers. The surging popularity of rental electric vehicles (EVs) poses unique challenges in ensuring their safety and reliability, particularly concerning battery health. This paper presents a comprehensive framework for proactive monitoring and management of rental EV batteries. The framework encompasses real-time data acquisition of key battery parameters, advanced analytics for predicting degradation and identifying influencing factors, and data-driven maintenance strategies like cell balancing and optimized charging. Implementing this framework empowers rental EV providers to maximize battery lifespan, enhance customer experience, and contribute to a sustainable transportation future. This leads to improving the factors and the process to monitor the battery health parameters of the rental electric vehicle. By adopting the various monitoring algorithms to measure and monitor the battery health parameters such as State of Charge, State of Health, battery voltage, balancing current, and temperature are monitored by the lifespan of the battery is extended and the safety and reliability of the battery is ensured by monitoring the temperature.

Exploring Electric Vehicle Battery Lifespan: Implications and Strategies for Sustainable Mobility

The global transition towards electric vehicles (EVs) represents a profound evolution in consumer behavior, driven by a growing awareness of environmental sustainability and the imperative to reduce carbon emissions. The lifespan of EV batteries is central to the success and widespread adoption of EVs. This critical factor influences not only EVs' operational efficiency and reliability but also the automotive industry's overall sustainability. This research paper delves deeply into the intricate web of factors that impact EV battery lifespan, ranging from the fundamental principles of battery chemistry to the practical considerations of user behavior and maintenance practices. By dissecting these complexities, we aim to provide a comprehensive understanding of the mechanisms underlying battery degradation and longevity. Moreover, we explore manufacturers' innovative strategies and technological advancements to optimize battery performance, enhance lifespan, and mitigate environmental impact. Through this exploration, we seek to offer valuable insights and practical recommendations for industry stakeholders, policymakers, and consumers to accelerate the transition toward a more sustainable transportation ecosystem. By empowering stakeholders with knowledge and tools to maximize the potential of EVs, we can pave the way for a future characterized by cleaner air, reduced greenhouse gas emissions, and greater energy independence.

Optimizing Lithium-Ion Battery Modeling: A Comparative Analysis of PSO and GWO Algorithms

In recent years, the modeling and simulation of lithium-ion batteries have garnered attention due to the rising demand for reliable energy storage. Accurate charge cycle predictions are fundamental for optimizing battery performance and lifespan. This study compares particle swarm optimization (PSO) and grey wolf optimization (GWO) algorithms in modeling a commercial lithium-ion battery, emphasizing the voltage behavior and the current delivered to the battery. Bio-inspired optimization tunes parameters to reduce the root mean square error (RMSE) between simulated and experimental outputs. The model, implemented in MATLAB/Simulink, integrates electrochemical parameters and estimates battery behavior under varied conditions. The assessment of terminal voltage revealed notable enhancements in the model through both the PSO and GWO algorithms compared to the non-optimized model. The GWO-optimized model demonstrated superior performance, with a reduced RMSE of 0.1700 (25 °C; 3.6 C, 455 s) and 0.1705 (25 °C; 3.6 C, 10,654 s) compared to the PSO-optimized model, achieving a 42% average RMSE reduction. Battery current was identified as a key factor influencing the model analysis, with optimized models, particularly the GWO model, exhibiting enhanced predictive capabilities and slightly lower RMSE values than the PSO model. This offers practical implications for battery integration into energy systems. Analyzing the execution time with different population values for PSO and GWO provides insights into computational complexity. PSO exhibited greater-than-linear dynamics, suggesting a polynomial complexity of O(nk), while GWO implied a potential polynomial complexity within the range of O(nk) or O(2n) based on execution times from populations of 10 to 1000.

Modeling of an all-solid-state battery with a composite positive electrode

All solid-state batteries are considered as the most promising battery technology due to their safety and high energy density. This study presents an advanced mathematical model that accurately simulates the complex behavior of all-solid-state lithium-ion batteries with composite positive electrodes. The partial differential equations of ionic transport and potential dynamics in the electrode and electrolyte are solved and reduced to a low-order system with Padé approximation. Moreover, the imperfect contact and the electrical double layers at the solid-solid interface are also taken into consideration. Subsequent experiments are conducted for the blocked cell and half-cells to extract parameters. Next, the parameterized model is validated with extensive experimental data from NCM811/LPSC/Li4.4Si batteries, illustrating the superior capability of predicting cell voltage with an average RMSE of 19.5 mV for the discharging/charging phases and 2.8 mV for the end of relaxation under a total of 15 conditions. From the simulations, it can be concluded that the limiting factors for battery performance are overpotentials caused by concentration polarization within positive particles and interface reactions. Finally, through a parameter sensitivity analysis, we offer strategic guidelines for optimizing battery performance, thus enhancing the development efficiency of ASSBs.

Exploring the feasibility of sodium alginate as a binder in aqueous zinc-ion batteries incorporating α-MnO2 nanorod cathodes

Batteries with good stability and capacity are required to maximize the role of electrical energy storage. The use of a binder is essential for maintaining the structural integrity of the electrodes and optimizing battery performance. Polyvinylidene fluoride (PVDF) has been a commonly used binder; however, its high toxicity and the expensive solvent (N-methyl-2 pyrrolidone) used in its processing pose concerns. In this study, we show that as an alternative to replace PVDF, to some extent, sodium alginate (SA) demonstrates better electrochemical performance owing to its cross-linking with Zn2+ ions, which maintains the stability of the a-MnO2 electrode in zinc-ion batteries (ZIBs). The strong and reversible chemical bonding of the binder with the active material causes the binder to fill the cracks occurring during electrochemical cycling and self-repairing these cracks upon cycling. After prolonged cycling, the electrode with SA as the binder exhibited better stability than that with PVDF. Furthermore, the rate capability test also suggested that the electrode with the SA binder recovered well after cycling at high current rates. This study highlights the potential of SA as an alternative, self-healing, and environmentally friendly binder for aqueous ZIB applications, thereby opening avenues for maximizing its usage in energy-storage systems.

Experimental study on 18650 lithium-ion battery-pack cooling system composed of heat pipe and reciprocating air flow with water mist

Lithium-ion batteries (LIBs) are critical power sources for portable devices, electric vehicles, power grids, and energy-storage systems. Effective thermal management is mandatory for achieving optimal battery performance. Although the air-cooling method has been investigated extensively owing to its simple structure, convenient maintenance, and low cost, it is insufficient for cooling large-scale battery packs because of its limited air-cooling capacity and unsatisfactory temperature uniformity. Thus, a reciprocating spray cooling system comprising a reciprocating air flow channel, heat pipe array, and spray nozzle is proposed herein for the efficient cooling of 18,650 LIB packs. The effects of the inlet air speed, spray frequency, and spray duty cycle on the cooling performance of the reciprocating mist cooling system and the corresponding optimal operating parameter range are analyzed. Additionally, the competitive mechanism between forced air convection and evaporative cooling is discussed. The results indicate that, compared with the case of natural convection cooling, the temperature increment of the battery pack decreases by 32.1 % under reciprocating mist cooling. Meanwhile, the maximum temperature difference reduces to approximately 3.41 °C with 3C of discharge. The spray frequency minimally affects the overall heat dissipation but induces surface temperature fluctuations in individual batteries. Increasing the spray duration per 10 s in a single spray duty cycle can reduce the average temperature of the battery pack by approximately 2 °C. Additionally, the power-allocation scheme affects the heat dissipation of the system. Decreasing the flow rate of spray cooling water and increasing the inlet air speed promote the evaporation of water mist inside the system, thus further improving heat dissipation. This study provides new insights into the design of air-cooled thermal management systems for battery packs.

Synthesis and characterization of acrylamide/zeolite nanocomposite hydrogels for aqueous zinc-ion batteries

Polyacrylamide (PAM)/zeolite nanocomposite hydrogels were synthesized as novel gel polymer electrolytes (GPEs) for flexible zinc-ion batteries. The hydrogels were prepared by in-situ free radical polymerization of acrylamide in the presence of dispersed zeolite nanoparticles. Fourier transform infrared spectroscopy confirmed the formation of crosslinked PAM and its interaction with zeolite nanoparticles. Scanning electron microscopy revealed increased porosity and improved zeolite dispersion with higher nanoparticle content. Thermogravimetric analysis indicated enhanced thermal stability with zeolite incorporation. The compressive modulus increased from 0.25 MPa for the neat hydrogel to 1.12 MPa for the nanocomposite containing 20 wt% zeolite, demonstrating improved mechanical strength. Electrochemical impedance spectroscopy showed that the ionic conductivity increased from 0.75 mS/cm for pristine PAM hydrogel to 1.85 mS/cm for the nanocomposite hydrogel with 15 wt% zeolite, attributed to increased porosity and zeolite's ion-exchange capacity. Cyclic voltammetry revealed faster redox kinetics with increasing zeolite content. Galvanostatic cycling demonstrated that the PAM/zeolite nanocomposite GPE with 15 wt% zeolite delivered the optimal zinc-ion battery performance, with a high initial discharge capacity of 125 mAh/gZn, coulombic efficiency exceeding 98 %, and capacity retention of 87 % after 100 cycles. The results indicate that zeolite incorporation significantly enhanced the thermal, mechanical and electrochemical properties of the PAM hydrogels for application as GPEs for flexible zinc-ion batteries.

Zn dendrite suppression and solid electrolyte interface control using N-allylthiourea as an electrolyte additive for aqueous Zn-ion batteries

Despite being promising candidates for Li-ion batteries, the development of aqueous Zn-ion batteries is still impeded by inherent unresolved problems such as dendrite growth and side reactions on the anode side. In this study, a stabilized Zn-electrolyte interface is established using N-allylthiourea (ATU) as an additive in mildly acidic electrolytes because of its strong affinity towards Zn2+ ions and the Zn metal. Through this favorable electrostatic adsorption, side reactions owing to water molecules are inhibited, and uniform Zn deposition is achieved, leading to highly reversible stripping/plating performance. Consequently, a stable cycling performance of up to 500 h at a current density of 1 mA cm–2 and an areal capacity of 1 mAh cm–2 is demonstrated for Zn symmetric cells. Furthermore, a Zn|VO2 full cell validates the possible practical use of ATU where it delivers a promising cycling performance of up to 300 cycles at a current rate of 1C (200 mA g–1). This study contributes insights into the role of additives in optimizing battery performance and creates opportunities for further exploration of similar compounds in pursuit of sustainable energy storage systems.

Critical Binder-to-Powders Coverage Ratio for Faster Graphite/SiOx Electrode Formulation Optimization.

Mastering electrodes' formulations is a complex and tedious task, because for each composition of electroactive material(s) it is necessary to adjust the inactive additives nature and content to optimize battery performance. In this direction, the amount of binder is proposed to be adjusted to the surface developed by all of the powders involved in the composition of the electrode, i.e., the electroactive materials and electronic conductive additives. This concept, introduces here as binder-to-powders coverage ratio, relies upon the micromechanical models developed in the field of polymer-based composite materials. The validity of this new electrode formulation parameter is shown here for two different SiOx/Graphite blends, which differ in the type of graphite, and for blends of two different binders, polyacrylic acid and styrene-butadiene rubber. At the optimal coverage ratio, a satisfactory capacity retention is obtained in full cell with an ethylene carbonate free and fluoroethylene carbonate rich electrolyte.

Smart Lithium-Ion Battery Monitoring in Electric Vehicles: An AI-Empowered Digital Twin Approach

This paper presents a transformative methodology that harnesses the power of digital twin (DT) technology for the advanced condition monitoring of lithium-ion batteries (LIBs) in electric vehicles (EVs). In contrast to conventional solutions, our approach eliminates the need to calibrate sensors or add additional hardware circuits. The digital replica works seamlessly alongside the embedded battery management system (BMS) in an EV, delivering real-time signals for monitoring. Our system is a significant step forward in ensuring the efficiency and sustainability of EVs, which play an essential role in reducing carbon emissions. A core innovation lies in the integration of the digital twin into the battery monitoring process, reshaping the landscape of energy storage and alternative power sources such as lithium-ion batteries. Our comprehensive system leverages a cloud-based IoT network and combines both physical and digital components to provide a holistic solution. The physical side encompasses offline modeling, where a long short-term memory (LSTM) algorithm trained with various learning rates (LRs) and optimized by three types of optimizers ensures precise state-of-charge (SOC) predictions. On the digital side, the digital twin takes center stage, enabling the real-time monitoring and prediction of battery activity. A particularly innovative aspect of our approach is the utilization of a time-series generative adversarial network (TS-GAN) to generate synthetic data that seamlessly complement the monitoring process. This pioneering use of a TS-GAN offers an effective solution to the challenge of limited real-time data availability, thus enhancing the system’s predictive capabilities. By seamlessly integrating these physical and digital elements, our system enables the precise analysis and prediction of battery behavior. This innovation—particularly the application of a TS-GAN for data generation—significantly contributes to optimizing battery performance, enhancing safety, and extending the longevity of lithium-ion batteries in EVs. Furthermore, the model developed in this research serves as a benchmark for future digital energy storage in lithium-ion batteries and comprehensive energy utilization. According to statistical tests, the model has a high level of precision. Its exceptional safety performance and reduced energy consumption offer promising prospects for sustainable and efficient energy solutions. This paper signifies a pivotal step towards realizing a cleaner and more sustainable future through advanced EV battery management.

Optimizing Electric Vehicle Battery Performance: Comprehensive Study of Battery Heating Challenges and Sustainable Battery Swapping System

This paper conducts a comprehensive study on battery heating challenges in electric vehicles (EVs) and proposes a sustainable battery swapping system to optimize battery performance. With the increasing demand for EVs and the necessity for extended driving ranges, notable progress has been achieved in battery technologies. However, battery heating remains a crucial concern, particularly in cold weather conditions. This research thoroughly examines various factors contributing to battery heating, including battery size, chemistry, design, and ambient conditions. Diverse battery heating techniques, such as insulation and thermal management systems, are extensively analyzed. Underlining the significance of effective battery heating solutions, this study emphasizes the importance of maintaining an optimal battery temperature to enhance performance and prolong battery life. Additionally, the paper introduces a sustainable battery swapping system controlled by a Blynk app and Node MCU controller, facilitating seamless battery replacement and promoting the integration of renewable energy sources. This system ensures uninterrupted EV operation while providing users complete control and real-time monitoring capabilities via mobile devices, revolutionizing the EV charging paradigm.

Designing a battery Management system for electric vehicles: A congregated approach

In many high-power applications, such as Electric Vehicles (EVs) and Hybrid Electric Vehicles (HEVs), Battery Management System (BMS) is needed to ensure battery safety and power delivery. BMS performs cell balancing (CB), State of Charge (SoC) estimation, monitoring, State of Health (SOH) estimation, and protective operation. To safeguard and optimize battery performance, required essential functionalities must be enabled. To optimize battery system performance, safety, and longevity, including temperature management, cell balancing, and communication protocols. This article provides an architecture that includes both a hardware demonstration and a simulation of the continuous on-time control approach for systematic measurement of Voltage, current, and temperature in EVs. A single-ended primary-inductance converter (SEPIC) DC-DC converter, Analogue Front End (AFE), and balancing circuits provide the optimal supply for proposed congregated BMS process. The effectiveness of proposed congregated design performance is validated through meter and sensor measurements for voltmeter, current, and temperature. The numerical error between proposed BMS and conventional measurements is relatively low for voltage (0.09 V), current (0.05 A), and temperature (0.09 °C). In the end, the simulated results and hardware results are benchmarked that the proposed congregated BMS design can regulate temperature, prevent overcharging and over-discharging, and balance the battery cells inside a given battery module.

Process insights with physics-inspired data-driven modeling- example of battery electrode processing

Lithium-ion batteries are essential for a wide range of applications due to their high energy density and rechargeability. However, their production and performance improvement often rely on time-consuming and expensive experiments. Typically, simulation analyzes is used to build process models to optimize battery performance and lifetime. However, simulations cannot always take into account the limitations of the manufacturing process. As a result, the process parameters determined by such a model remain largely theoretical. For the efficient production of lithium-ion batteries, an understanding of the relationships between physical quantities is essential to meet optimized performance and safety standards. However, these relationships are quite complex, making it difficult for traditional methods to determine the physical model. Alternatively, artificial intelligence methods can be beneficial. This work uses a novel gray-box modeling technique that incorporates physical knowledge and empirical data. To achieve this goal, we combine a deep neural network with a genetic algorithm to determine the existing physical relationships within the data and then estimate the final model for the system.

How Does Stacking Pressure Affect the Performance of Solid Electrolytes and All‐Solid‐State Lithium Metal Batteries?

All‐solid‐state lithium metal batteries (ASSLMBs) with solid electrolytes (SEs) have emerged as a promising alternative to liquid electrolyte‐based Li‐ion batteries due to their higher energy density and safety. However, since ASSLMBs lack the wetting properties of liquid electrolytes, they require stacking pressure to prevent contact loss between electrodes and SEs. Though previous studies showed that stacking pressure could impact certain performance aspects, a comprehensive investigation into the effects of stacking pressure has not been conducted. To address this gap, we utilized the Li6PS5Cl solid electrolyte as a reference and investigated the effects of stacking pressures on the performance of SEs and ASSLMBs. We also developed models to explain the underlying origin of these effects and predict battery performance, such as ionic conductivity and critical current density. Our results demonstrated that an appropriate stacking pressure is necessary to achieve optimal performance, and each step of applying pressure requires a specific pressure value. These findings can help explain discrepancies in the literature and provide guidance to establish standardized testing conditions and reporting benchmarks for ASSLMBs. Overall, this study contributes to the understanding of the impact of stacking pressure on the performance of ASSLMBs and highlights the importance of careful pressure optimization for optimal battery performance.

Ionic Conductivity of the Li6PS5Cl0.5Br0.5 Argyrodite Electrolyte at Different Operating and Pelletizing Pressures and Temperatures

All-solid-state lithium batteries (ASSLBs) using argyrodite electrolyte materials have shown promise for applications in electric vehicles (EVs). However, understanding the effects of processing parameters on the ionic conductivity of these electrolytes is crucial for optimizing battery performance and manufacturing methods. This study investigates the influence of electrolyte operating temperature, electrolyte operating pressure, electrolyte pelletization pressure, and electrolyte pelletizing temperature on the ionic conductivity of the Li6PS5Cl0.5Br0.5 argyrodite electrolyte (AmpceraTM, D50 = 10 µm). A specially designed test cell is employed for the experimental measurements, allowing for controlled pelletization and testing within the same tooling. The results demonstrate the significant impact of the four parameters on the ionic conductivity of the argyrodite electrolyte. The electrolyte operating temperature has a more pronounced effect than operating pressure, and pelletizing temperature exerts a greater influence than pelletizing pressure. This study provides graphs that aid in understanding the interplay between these parameters and achieving desired conductivity values. It also establishes a baseline for the maximum pelletizing temperature before undesirable degradation of the electrolyte occurs. By manipulating the pelletizing pressure, operating pressure, and pelletizing temperature, battery engineers can achieve the desired conductivity for specific applications. The findings emphasize the need to consider operating conditions to ensure satisfactory low-temperature performance, particularly for EVs. Overall, this study provides valuable insights into processing and operating conditions for ASSLBs utilizing the Li6PS5Cl0.5Br0.5 argyrodite electrolyte.

Temperature-Based State-of-Charge Estimation Using Neural Networks, Gradient Boosting Machine and a Jetson Nano Device for Batteries

Lithium-ion batteries are commonly used in electric vehicles, mobile phones, and laptops because of their environmentally friendly nature, high energy density, and long lifespan. Despite these advantages, lithium-ion batteries may experience overcharging or discharging if they are not continuously monitored, leading to fire and explosion risks, in cases of overcharging, and decreased capacity and lifespan, in cases of overdischarging. Another factor that can decrease the capacity of these batteries is their internal resistance, which varies with temperature. This study proposes an estimation method for the state of charge (SOC) using a neural network (NN) model that is highly applicable to the external temperatures of batteries. Data from a vehicle-driving simulator were used to collect battery data at temperatures of 25 °C, 30 °C, 35 °C, and 40 °C, including voltage, current, temperature, and time data. These data were used as inputs to generate the NN models. The NNs used to generate the model included the multilayer neural network (MNN), long short-term memory (LSTM), gated recurrent unit (GRU), and gradient boosting machine (GBM). The SOC of the battery was estimated using the model generated with a suitable temperature parameter and another model generated using all the data, regardless of the temperature parameter. The performance of the proposed method was confirmed, and the SOC-estimation results demonstrated that the average absolute errors of the proposed method were superior to those of the conventional technique. In the estimation of the battery’s state of charge in real time using a Jetson Nano device, an average error of 2.26% was obtained when using the GRU-based model. This method can optimize battery performance, extend battery life, and maintain a high level of safety. It is expected to have a considerable impact on multiple environments and industries, such as electric vehicles, mobile phones, and laptops, by taking advantage of the lightweight and miniaturized form of the Jetson Nano device.