Electric Vehicle Battery Pack Charging Time Prediction

The transportation sector is under increasing pressure to reduce greenhouse gas emissions by decarbonizing its operations. One prominent solution that has emerged is the adoption of electric vehicles (EVs). As the electric vehicles market experiences rapid growth, the utilization of lithium-ion batteries (LiB) has become the predominant choice for energy storage. However, it is important to note that lithium-ion battery technology is sensitive to factors, like excessive voltage and temperature. Therefore, the development of an accurate battery model and a reliable state of charge (SOC) estimator is crucial to safeguard against the overcharging and over-discharging of the battery. Numerous studies have been conducted to address lithium-ion battery cell modeling and SOC estimations. These studies have explored variations in the number of RC networks within the model and different estimation methods. However, it is worth mentioning that the capacity of a single lithium-ion battery cell is relatively low and cannot be directly employed in electric vehicles. To meet the total capacity and voltage requirements for electric vehicles, multiple cells are typically connected in series or parallel configurations to form a battery pack. Surprisingly, this aspect has often been overlooked in previous research. To tackle this overlooked challenge, our study introduces a comprehensive battery pack model and an advanced Battery Management System (BMS). We then integrate these components into an electric vehicle model. Subsequently, we simulate the integrated EV-BMS model under the conditions of four different urban driving scenarios to replicate real-world driving conditions. The BMS that we have developed includes an Extended Kalman Filter (EKF)-based SOC estimation system, a mechanism for controlling coolant flow, and a passive cell-balancing algorithm. These components work together to ensure the safe and efficient operation of the battery pack within the electric vehicles.

The overconsumption of fossil fuels has drastically increased the emissions of toxic gases causing harm to the environment. Batteries are one of the most important components of Electric Vehicles. When dealing with bigger capacities and high power requirements, high power supplying battery packs are required, which consist of several batteries. These high capacity battery packs are prone to reach high temperatures during charging and discharging thus, giving rise to a lot of complications. Hence, the use of a battery management system is essential. It is responsible to optimize the battery pack which results in better and safer working of the battery pack. The main objective of this paper is to simulate a MATLAB/Simulink model of a Battery Management System (BMS) and analyze the different types of estimation methods of the main parameters of a battery management system. It also provides an idea on the most efficient and cost-friendly methods to implement in a BMS. The main parameters include the balancing of the battery pack, state of charge (SoC) estimation methods, state of health (SoH) estimation, charging and discharging control and temperature monitoring, which help improve the performance of the battery.

With the rapid rise in electric vehicles (EVs), intelligent Systems are being developed that will facilitate battery reliability, improve performance and optimize safety while operational. This paper includes an advanced Battery Management System (BMS) that monitors battery charge in real-time and takes proactive measures in preventing fire. The BMS will be used for real-time monitoring of critical parameters of the battery, such as voltage, current drain, temperature, and state of charge (SOC). These will enable accurate control and extension of the life of battery packs. Each BMS will be connected to a monitoring unit that will process sensors for abnormal thermal and electrical states, which may cause hazardous situations. To minimize safety risks associated with EV battery packs, the BMS will include a module for early detection and prevention of fire by monitoring thermal runaway and taking pre-emptive actions, i.e., isolating circuits and alerting. Intelligent algorithms will be included for effective energy management and efficient performance for various battery states of charge. These will be integrated into an enhanced safety system for overall safety and optimal charge/discharge cycles. This model is intended to provide a robust, scalable and cost-effective solution to the next generation of electric mobility while promoting greater user safety and creating sustainable transport systems.

Lithium-ion battery electro-thermal modelling and internal states co-estimation for electric vehicles

The expected widespread adoption of electric vehicles (EVs) in the near future brings new challenges as for example that of scheduling the charging times under limited power and other technological constraints. In this paper, we tackle a scheduling problem derived from a EV charging model and control system recently proposed to organize the charging times of a large fleet of EVs. This problem may be formalized as that of scheduling a set of tasks with given processing times and due dates on a machine whose capacity varies over time. We first introduce a schedule builder and study its main properties. Then, we propose a genetic algorithm that exploits the schedule builder as decoding algorithm. Experimental results show the suitability of our approach.

Reduced global oil and gas resources, as well as environmental concerns, have prompted the automotive industry to produce more fuel-efficient and cleaner vehicles in order to save money and protect the environment. In addition to concerns about energy security and greenhouse gas emissions, plug-in hybrid electric vehicles (PHEVs) are also becoming more popular due to their low cost of ownership and low power consumption. As electric vehicle battery capacity and range of EVs improve, electric vehicles may require fast charging. Photovoltaic (PV) and wind energy are proposed as energy sources in this paper.The MPPT control is used to provide maximum power tracking for PV System, as well as to transmit that maximum power to the batteries of a Hybrid Energy system via the Battery Management System (BMS). Energy produced by solar PV and wind power systems can be stored in energy storage systems and used for propulsion, and a battery management system (BMS) helps to track the battery’s Temperature, Current, Voltage, and State of Charge (SOC).Basically the paper deals with the increasing the range of the Battery using Hybrid PV Wind system with Battery Management System where PV system and Wind system are integrated using Battery Management System. The main reason BMS is used is to check the State of Charge(SOC) of the battery because the battery is very costly in the EVs so, the utmost care is given to the Battery.

Growing response for Electric Vehicles (EV) across the world is an implication of techno-economical efforts targeted to mitigate the challenges related to fossil fuels. Energy storage powering EVs is a very critical component. A battery pack used as energy storage in EVs uses many battery cells connected in series and parallel. These battery cells need close monitoring and management system during its operation in EVs. Such a system referred to as ‘Battery Management System’ (BMS) ensures a safe operating envelope while increasing battery power delivery capabilities and improving lifetime. The cell voltage balancing along with the State of Charge (SOC) and State of Health (SOH) monitoring are some of the critical functions of BMS. For EVs to become the best techno-commercial alternative for gasoline-based vehicles, BMS and battery packs will play a very crucial role. This paper highlights the state of the art of BMS and illustrates the passive cell balancing network design for Lithium-Iron-Phosphate (LiFePO4) batteries based on Digital Signal Processor (DSP) TMS320F28379D controller. Some key simulation and hardware results are presented to demonstrate the SOC estimation using the Coulombs counting method and battery cell balancing mechanism.

Research in the field of Electric Vehicles (EV) is significantly increasing. EV is powered by the battery pack. Therefore, an improvement in the accuracy of the battery model is required. Batteries have several operational challenges, which required a proper Battery Management System (BMS) to achieve optimal performance. This paper provides an analysis of EV batteries and a study on battery management systems in EV applications. This review focuses on many issues, challenges, and problems of Electric Vehicles. This review enhances the development effort of the advance Battery Management system. The purpose of modelling is to detect internal variables such as state of charge (SOC) and internal resistance, calculated based on external variables such as battery voltage, current, and temperature. BMS is responsible for safe operation, performance and quality of life under various charge/discharge, and environmental conditions. When designing a BMS, cell voltage, temperature, state of charge (SOC), state of health (SOH) should be controlled and maintained. Accurate battery models are required for the proper design and operation of battery-powered systems. This paper shows a battery model based on the Simulink and simscape of MATLAB, to build and parameterize the Li-ion battery model. The performance of the battery model is tested in the Urban Dynamometer Driving Schedule (UDDS) cycle. The test results obtained from a Li-ion polymer battery were compared with data provided for the models.

The main barriers in the widespread adoption of electric vehicles (EVs) include limited autonomy, high up-front cost, low availability of charging infrastructure, and long charging time. Wide bandgap (WBG) semiconductors are key industry players in the electronics circuit design because they are advantageous in terms of high operating temperature, high efficiency, and low volume and weight. The WBG devices improve power density and allow power electronics (PE) circuits to reach operating points and temperatures that have not been considered before. Currently, SiC and GaN are the most viable WBG semiconductor candidates to replace Si-based devices. However, the cost, packaging limitations, reliability, safety, low manufacturing, and demand level of WBGs are issues that should be addressed; the integration of these devices into the automotive power electronic (APE) systems can then successfully be realized. This paper presents an industrial oriented overview of WBG power semiconductors including their advantages, recent progress, challenges, and development partnerships.

Lithium-ion (Li-ion) batteries have gained considerable attention in the Electric Vehicle (EV) industry due to their high energy density, better lifespan, and higher nominal voltage.However, accurately estimating the State of Charge (SOC) and State of Health (SOH) for Li-ion batteries remains challenging due to its aging and nonlinear behaviour.This paper explores Battery Management System (BMS) models potential incorporating Artificial Intelligence (AI) estimation techniques, particularly Deep Learning (DL), to improve SOC and SOH model estimations.This research paper summarized and analyzed current BMS approaches by identify the potential gaps in existing research focus and propose another technique for further exploration in the EV Li-ion battery.Currently, there is a research gap in the existing studies, especially in the application of DL for SOC and SOH estimation.and underscores the need for more comprehensive exploration and refinement of DL methods.Future research should address these gaps to advance the integration of DL into BMS to ensure robust and reliable SOC and SOH estimations.Because of its features and capacity to improve SOC and SOH estimating health models accurately, deep learning has a lot of potential for studying SOC & SOH in BMS.As a result, there is opportunity to investigate the DL technique further in order to thoroughly and clearly examine the correctness of SOC & SOH model estimations in BMS.Index Terms-Battery management system (BMS), lithium-ion, artificial intelligence, state of charge (SOC), state of health (SOH) I. INTRODUCTIONDue to rising concerns about environmental pollution and global warming, modern society has made sustainability one of its top priorities.To ease these worries and move forward to a sustainable future, global energy strategies emphasise the switch from fossil fuels to renewable energy (RE) sources.The ability of RE to be efficiently used to manage energy consumption and cut carbon emissions makes it essential in the commercial, industrial, and residential sectors [1].Major contributors to carbon emissions include electricity and heat production, transport, manufacturing and construction, as well as agriculture [2].The transportation sector is among the leading causes of environmental pollution, contributing over one-third carbon dioxide emissions, of which vehicle transportation accounts for over 70% [3].Technological disruptions in the transportation sector facilitate decarbonisation because of rising environmental pollution and concerns about global warming.This transformation includes a move toward adopting electric vehicles (EVs), which have several benefits, such as shorter payback period and longer lifespan, helping to reduce CO2 emissions and change the way transportation will be in the future [4].With the promise of lower emissions and less reliance on oil, EVs have drawn much interest as a leading the way for environmental sustainability and emissions-free mobility [4], [5].Artificial intelligence (AI) is the most fascinating and discussed technology in the current decade for its nature to mimic human intelligence.The field of AI has shown an upward trend of growth in the 21 st century (from 2000 to 2015) [6].Artificial Intelligence (AI) is the study of creating machines that can perceive, analyze, comprehend, and react like humans.Simply said, the ultimate goal of artificial intelligence (AI) is to extend and argue humanity's capacity and efficiency in the work of changing nature and regulating society through intelligent machines.Simultaneously, artificial intelligence technologies that enhance the system's overall capabilities have significant importance.The legal standing of AI technology is examined in this study.In the current societal development stage, AI is rapidly developing its capabilities as a future technology.This research study aims to provide an overview of AI and explain how it relates to the concepts of SOC and SOH.A difficulty at the junction of AI is data integration.AI finds extensive application in the automotive, logistics, healthcare, stock trading, robotics, finance, transportation, and educational domains.AI techniques provide promise for the vehicle, the infrastructure, the driver, or the transport user-and especially for the interactions among them.Transportation is changing as a result of the EV industry's adoption of AI, particularly deep learning (DL) and machine learning (ML), which enhances user experience, efficiency, sustainability, and safety.

The rapid adoption of electric vehicles (BEVs) has increased the need to understand how fast-charging strategies influence long-distance travel times under real-world conditions. While most manufacturers specify maximum charging power and standardized driving ranges, these figures often fail to reflect actual highway operation, particularly in adverse weather. This study addresses this gap by analyzing the fast-charging behaviour, net battery capacity and highway energy consumption of 62 EVs from different market segments. Charging power curves were obtained experimentally at high-power DC stations, with data recorded through both the charging infrastructure and the vehicles’ battery management systems. Tests were conducted, under optimal conditions, between 10% and 90% state of charge (SoC), with additional sessions performed under both cold and preconditioned battery conditions to show thermal effects on the batteries’ fast-charging capabilities. Real-world highway consumption values were applied to simulate 1000 km journeys at 120 km/h under cold (−10 °C, cabin heating) and mild (23 °C, no AC) weather scenarios. An optimization model was developed to minimize total trip time by adjusting the number and duration of charging stops, including a 5 min detour for each charging session. Results show that the optimal charging cutoff point consistently emerges around 59% SoC, with a typical deviation of 10, regardless of ambient temperature. Charging beyond 70% SoC is generally inefficient unless dictated by charging station availability. The optimal strategy involves increasing the number of shorter stops—typically every 2–3 h of driving—thereby reducing total trip.

Electric vehicles (EVs) are the fastest-growing type of transport. Battery packs are a key component in EVs. Modern lithium-ion battery cells are characterized by low self-discharge current, high power density, and durability. At the same time, the battery management system (BMS) plays a pivotal role in ensuring high efficiency and durability of battery cells and packs. The BMS monitors and controls the battery charge and discharge to ensure EV safety and optimum operation. This paper is devoted to analyzing BMS circuitry configurations and algorithms. The analysis includes circuit solutions and algorithms for implementing the main BMS functions, such as parameter monitoring, protection, cell balancing, state estimation, charging and discharging management, communication, and data logging. The paper provides insights into the recent research literature on BMS, and the advantages and disadvantages of methods for implementing BMS functions are compared. The paper also discusses the application of artificial intelligence technologies and aspects of further work on next-generation BMS technologies.

As the world moves toward greenhouse gas reduction, there is increasingly active work around Li-ion chemistry-based batteries as an energy source for electric vehicles (EVs), hybrid electric vehicles (HEVs) and smart grids. In these applications, the battery management system (BMS) requires an accurate online estimation of the state of charge (SOC) in a battery pack. This estimation is difficult, especially after substantial battery aging. In order to address this problem, this paper utilizes SOC estimation of Li-ion battery packs using a fuzzy-improved extended Kalman filter (fuzzy-IEKF) for Li-ion cells, regardless of their age. The proposed approach introduces a fuzzy method with a new class and associated membership function that determines an approximate initial value applied to SOC estimation. Subsequently, the EKF method is used by considering the single unit model for the battery pack to estimate the SOC for following periods of battery use. This approach uses an adaptive model algorithm to update the model for each single cell in the battery pack. To verify the accuracy of the estimation method, tests are done on a LiFePO4 aged battery pack consisting of 120 cells connected in series with a nominal voltage of 432 V.

As one of the most important features representing the operating state of power battery in electric vehicles (EVs), state of charge (SOC) and capacity estimation is a crucial assessment index in battery management system (BMS). This paper presents a fusion method of SOC and capacity estimation with identified model parameters. The equivalent circuit model (ECM) parameters are obtained online by variable forgetting factor recursive least squares (VFFRLS), which is based on incremental ECM analysis to respond to the inconsistent rates of parameters variation. The independent open-circuit voltage (OCV) estimation way is designed to reduce the effect of mutual coupling between OCV and ECM parameters. Based on the identified ECM parameters and OCV, a dual adaptive H infinity filter (AHIF) combined with strong tracking filter (STF) is proposed to estimate battery SOC and capacity. A new quadratic function as capacity error compensation is introduced to represent the relationship between capacity and OCV. The adaptive strategy of the AHIF can adjust noise covariance and restricted factor, while the STF can regulate prior state covariance by adding suboptimum fading factor. The results of experiment and simulation show the merits of proposed approach in SOC and capacity estimation.

Electric vehicles (EVs) rely on a battery pack as their primary energy source, making it a critical component for their operation. To guarantee safe and correct functioning, a Battery Management System (BMS) is employed, which uses variables such as State of Charge (SOC) to set charge/discharge limits and to monitor pack health. In this article, we propose a Multilayer Perceptron (MLP) network to estimate the SOC of a 14.8 V battery pack installed in a robotic vacuum cleaner. Both offline and online (real-time) tests were conducted under continuous load and with rest intervals. The MLP’s output is compared against two commonly used approaches: NARX (Nonlinear Autoregressive Exogenous) and CNN (Convolutional Neural Network). Performance is evaluated via statistical metrics, Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE), and we also assess computational cost using Operational Intensity. Finally, we map these results onto a Roofline Model to predict how the MLP would perform on an automotive-grade microcontroller unit (MCU). A generalization analysis is performed using Transfer Learning and optimization using MLP–Kalman. The best performers are the MLP–Kalman network, which achieved an RMSE of approximately 13% relative to the true SOC, and NARX, which achieved approximately 12%. The computational cost of both is very close, making it particularly suitable for use in BMS.