Electric Vehicle Battery Management Research Articles

Electric Vehicle Battery Management System with Charge Monitor and Fire Protection

This paper outlines the development and execution of an Electric Vehicle Battery Management System (BMS) outfitted with charge surveillance and fire prevention functionalities. As the utilization of electric vehicles (EVs) continues to grow, ensuring the safety and optimal functioning of battery packs becomes increasingly crucial. The suggested BMS merges sophisticated monitoring algorithms with fire suppression systems to alleviate risks linked with overcharging and thermal runaway incidents. Through thorough examination and validation procedures, our findings underscore the efficacy of the implemented BMS in ameliorating battery performance and guarding against potential dangers, thereby fostering the widespread embrace of electric mobility

Optimizing Electric Vehicle Battery Management Systems for Enhanced Performance and Efficiency in the Automotive Sector

Automotive means of transportation has become the major and cheapest means transportation in Nigeria. This fact has put more pressure on manufacturers to provide customers with high quality Automobiles with the aim of making profits. Nowadays, manufacturers are faced with a major problem in the management of information within a manufacturing plant and creation of effective relationship with customers. As an approach to solving these problems, a detailed analysis is carried out on the existing system to find out the strength and weaknesses of the existing system. And based on the requirements generated, the new system is designed. An Object Oriented design approach is used to describe the various units (modules) that make up the system in terms of classes and objects. The design and development of an Automotive Plant Management system for a manufacturing company is a project developed with PHP, MySQL, Ajax, HTML, JavaScript and Jquery Plugins, it provided better and more efficient management of information generated from Human Resource, Inventory, Company Finance and also Customer Relationship Management In recent years, the surge in the adoption of electric vehicles has played a vital role in reducin fossil fuel consumption and greenhouse gas emissions. However, limited cross-national research has been conducted on the determinants of electric vehicle adoption in developing and developed countries. This study examines the factors influencing the intention to adopt electric vehicles in India (378 participants) and Spain (265 participants). This study develops an integrated model that combines the unified theory of acceptance and use of technology (UTAUT2) and the value-belief-norm (VBN) model while accounting for the impact of national culture. The model is tested using structural equation modeling. The results indicate the integrated UTAUT2-VBN model is a valuable tool for explaining the differences in adoption intention across cultures. Moreover, the national cultural system plays a significant moderating role in most relationships within the model. This study offers valuable insights into the factors influencing electric vehicle adoption in different cultural contexts, which can inform policies and strategies to promote sustainable transportation.

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.

An improved Cauchy robust correction-sage Husa extended Kalman filtering algorithm for high-precision SOC estimation of Lithium-ion batteries in new energy vehicles

The accurate estimation of battery State of Charge (SOC) is a key technology in the research of electric vehicle battery management systems. In order to solve the problem of inaccurate noise estimation in nonlinear systems, an improved Cauchy robust correction-Sage Husa extended Kalman filtering (CRC-SHEKF) algorithm is proposed for high-precision SOC estimation of lithium-ion batteries in new energy vehicles. Considering the polarization effect of the battery, the FFRLS algorithm is used for online parameter identification of the Dual Polarization model. Using robust data correction methods, the Cauchy robust function is simplified for real-time correction of the covariance matrix Q of system state noise and the covariance matrix R of the observed noise in the filtering process and combined with SHEKF for SOC estimation. The experimental results show that under different temperature conditions and complex working conditions, the proposed CRC-SHEKF algorithm has the minimum mean absolute error (MAE), root mean square error (RMSE), and maximum error (MAX). Under the condition of the Beijing bus dynamic stress test (BBDST) at 15 °C, the MAE, RMSE, and MAX of the CRC-SHEKF algorithm are 0.392 %, 0.716 %, and 0.945 %, with the computing time of only 4.839 s. The algorithm proposed in this article has high accuracy and robustness, and has practical application value, providing a reference for the application of lithium battery condition monitoring.

Comparative analysis of equivalent circuit battery models for electric vehicle battery management systems

Lithium-ion batteries need to be controlled by a Battery Management System (BMS) to operate safely and efficiently. BMS controls parameters, such as current, voltage, temperature, state of charge (SoC),state of health (SoH), state of power (SoP) and etc. The battery models and several prediction algorithms that the BMS uses to carry out these checks are essential to the system's performance. Therefore, the battery model is crucial to the BMS. This model is used to optimize the performance, capacity, lifetime and safety of the battery. Using the accurate battery model for BMS and electric vehicles can improve energy efficiency, extend battery life and reduce safety risks. Therefore, it is important that the model can accurately reflect the battery behavior under different load conditions. In this study, the performance of Rint, Partnership for a New Generation of Vehicles (PNGV), Thevenin, and Dual Polarization (DP) battery models, which are widely known in the literature, to simulate static and dynamic voltage behavior is compared. A 18650 NMC battery was used for this purpose, and Hybrid Pulse Power Characterization (HPPC), Dynamic Stress Test (DST), Worldwide Harmonised Light Vehicle Test Procedure (WLTP), and Constant Current (CC) discharge tests were performed. The performance of the models for the four tests is compared. The maximum error values for WLTP are 2.98 % in Rint, 1.32 % in PNGV, 2.80 % in Thevenin, and 1.09 % in DP. Comparing the performances of models for all tests, it is found that the DP model is the most accurate model under both constant and dynamic current conditions.

Comparative Analysis of Commonly Used Machine Learning Approaches for Li-Ion Battery Performance Prediction and Management in Electric Vehicles

The significant role of Li-ion batteries (LIBs) in electric vehicles (EVs) emphasizes their advantages in terms of energy density, being lightweight, and being environmentally sustainable. Despite their obstacles, such as costs, safety concerns, and recycling challenges, LIBs are crucial in terms of the popularity of EVs. The accurate prediction and management of LIBs in EVs are essential, and machine learning-based methods have been explored in order to estimate parameters such as the state of charge (SoC), the state of health (SoH), and the state of power (SoP). Various machine learning techniques, including support vector machines, decision trees, and deep learning, have been employed for predicting LIB states. This study proposes a methodology for comparative analysis, focusing on classical and deep learning approaches, and discusses enhancements to the LSTM (long short-term memory) and Bi-LSTM (bidirectional long short-term memory) methods. Evaluation metrics such as MSE, MAE, RMSE, and R-squared are applied to assess the proposed methods’ performances. The study aims to contribute to technological advancements in the electric vehicle industry by predicting the performance of LIBs. The structure of the rest of the study is outlined, covering materials and methods, LIB data preparation, analysis, the proposal of machine learning models, evaluations, and concluding remarks, with recommendations for future studies.

Review of Cell-Balancing Schemes for Electric Vehicle Battery Management Systems

The battery pack is at the heart of electric vehicles, and lithium-ion cells are preferred because of their high power density, long life, high energy density, and viability for usage in relatively high and low temperatures. Lithium-ion batteries are negatively affected by overvoltage, undervoltage, thermal runaway, and cell voltage imbalance. The minimisation of cell imbalance is particularly important because it causes uneven power dissipation by each cell and, hence, temperature distribution that adversely impacts the battery lifetime. Several papers in the literature proposed advanced cell-balancing techniques to increase the effectiveness of basic cell-balancing approaches, reduce power losses, and reduce the number of components in balancing circuits. The new developments and optimisations over the last few years have been particularly intense due to the increased interest in battery technologies for several end-use applications. This paper reviews and discusses recent cell-balancing techniques or methods, covering their operating principles and the optimised utilisation of electrical components.

Beyond Limits: A Brief Exploration of Fault Detection and Balancing in Lithium-ion Battery Technology

The process of achieving balance among sequentially connected cells is crucial to prevent excessive charging or discharging, and it also improves the overall energy capacity. This article discusses various algorithms created for equalizing cell charge within a battery management system (BMS). Proper cell balancing is indispensable for upkeeping lithium-ion battery (LiB) packs. Within the BMS, identifying faults is of utmost importance. This encompasses detecting, isolating, and estimating faults. To prevent batteries from operating in unsafe ranges, it is vital to ensure the accurate functioning of current, voltage, and temperature sensors. Accurate fault diagnosis is pivotal for the optimal operation of battery management systems. In the context of electric vehicle battery management systems, precise measurement of current, voltage, and temperature is greatly relied upon to estimate the State of Charge (SOC) and overall battery health. Swiftly identifying early failures can mitigate safety hazards and minimize damage. Nevertheless, effectively pinpointing these initial failures using genuine operational data from electric vehicles remains a intricate task. This paper presents an analysis of different algorithms for detecting balancing-related faults, covering both methods based on models and those not reliant on models. The strengths and weaknesses of the evaluated algorithms, along with upcoming challenges in the realm of balancing and fault detection for LiBs, are also discussed in this document.

Remaining Useful Life Prediction of Lithium‐Ion Batteries Based on a Combination of Ensemble Empirical Mode Decomposition and Deep Belief Network–Long Short‐Term Memory

The prediction of remaining useful life (RUL) for lithium‐ion batteries is a critical component of electric vehicle battery management systems. However, during the aging process, batteries exhibit an overall declining trend in capacity curves, coupled with capacity regeneration and localized fluctuations. Directly modeling this degradation trend based on the original capacity curve proves challenging, leading to reduced accuracy in RUL prediction. This article introduces a hybrid method to enhance the precision of battery RUL prediction. Utilizing the ensemble empirical mode decomposition technique, the battery's capacity degradation sequence is decomposed into intrinsic mode functions (IMFs) with varying degrees of fluctuations, along with a residue that characterizes the battery's overall declining trend. Subsequently, deep belief networks and long short‐term memory networks are established to predict the residue and IMFs separately. The combined results from these models yield the final battery RUL prediction. Finally, the effectiveness of this approach is validated on the NASA battery dataset, with diverse training periods and prediction time steps. Experimental results demonstrate that the root mean square error of predictions for all four batteries remains below 2%.

Automatic Electric Vehicle Charging Station

Our Automatic Electric Vehicle Charging station is a autonomous power supply unit which works without the need for human attendants. The system is designed to facilitate the existing power grid infrastructure. The system employs the Raspberry Pi as our primary controller board and hosts the User Interface on the LCD Display mounted on the Charger Housing. The Pi Board interfaces with our custom Pilot PCB to communicate with the Electric Vehicle Battery Management Systems to negotiate the charging speed, capacity of the car battery and perform safety checks. The user can choose AC level 1, 2 or DC level 3 charging. The system uses a 30A relay circuit to isolate the battery from the power supply until all the necessary checks and payment processes are completed. RazorPay is our real time Payment gateway for payment transactions. Once the user has completed the formalities the charging process will begin. We also have a charging monitoring system developed that will help users track the battery charge percentage and charging time remaining on the LCD display. The station has emergency Kill Switches and Emergency Stop buttons mounted on the housing to address safety concerns that arise on the advert of damaged high power output utilities, when engaged the entire system will kill all the power inputs from the grid so that any fault can be safely repaired without risk of electrocution. For our implementation and testing we have built a 18650 NMC chemistry 3S5P Battery pack with a 3S BMS. The unit utilizes level 3 AC charging SAE (Society of Automotive Engineering) standards to ensure the safety and quality of the service.

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.

Cybersecurity enabled electric vehicle charging and battery management system

Electric vehicles (EVs), which are become easier to get, are displacing conventional gasoline-powered cars. Because of this, there is a great demand for electric vehicle charging systems (EVCS), which has led to a major expansion of the public and private EVCS infrastructure. The threats related to cybersecurity have significantly increased as a result of the EVCS’s growing network. In this light, this article offers an examination of the cybersecurity threats associated with the network of EVCS. First, recent trends in EV adoption, charging use cases, and the most recent advancements in the EVCS network give context for the research subject. Second, while describing cybersecurity features of EVCS, possible cyberattack scenarios as well as vulnerabilities centered on infrastructure and protocols have been taken into account. Thirdly, risks in EVCS have been confirmed through real-time data-centric analysis of EVs.

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.

Electric vehicle battery capacity degradation and health estimation using machine-learning techniques: a review

Abstract Lithium-ion batteries have an essential characteristic in consumer electronics applications and electric mobility. However, predicting their lifetime performance is a difficult task due to the impact of operating and environmental conditions. Additionally, state-of-health (SOH) and remaining-useful-life (RUL) predictions have developed into crucial components of the energy management system for lifetime prediction to guarantee the best possible performance. Due to the non-linear behaviour of the health prediction of electric vehicle batteries, the assessment of SOH and RUL has therefore become a core research challenge for both business and academics. This paper introduces a comprehensive analysis of the application of machine learning in the domain of electric vehicle battery management, emphasizing state prediction and ageing prognostics. The objective is to provide comprehensive information about the evaluation, categorization and multiple machine-learning algorithms for predicting the SOH and RUL. Additionally, lithium-ion battery behaviour, the SOH estimation approach, key findings, advantages, challenges and potential of the battery management system for different state estimations are discussed. The study identifies the common challenges encountered in traditional battery management and provides a summary of how machine learning can be employed to address these challenges.

Digital twin for electric vehicle battery management with incremental learning

The current Industry 4.0 revolution promotes the use of cyber–physical systems to enhance manufacturing and other industrial processes via automation, real-time analysis, etc. Data communication between individual systems plays an important role in this revolution’s success. As defined by researchers, Digital Twin is the digital representation of a physical system that enables predictive maintenance. Due to the increase in environmental pollution, battery-powered electric vehicles (EVs) are regarded as the urgent solution to internal combustion engines in the transportation business, despite obstacles such as safety concerns and range estimation. State of Health (SoH) and State of Charge (SoC) are two battery metrics that, when precisely anticipated, permit safer and longer battery use. Predicting these parameters online is computationally and financially expensive. Alternately, some of these factors could be predicted in the cloud rather than on the vehicle, hence cutting costs. Consequently, the EV business is one example where cloud-to-vehicle data connection saves total costs. A digital twin for an EV battery would aid in the estimate of battery parameters for predictive maintenance. This paper presents a Digital Twin paradigm for EV battery management in which SoH is predicted in the cloud and SoC is estimated on-vehicle. A continuous learning method is also proposed for forecasting SoH, whereas the Kalman filter is used to estimate SoC. The proposed framework predicts the SoH with a mean square error of 0.022.

IoT Based Electric Vehicle Battery Management System for Enhance Battery Life

This abstract describes the concept of an IoT-based electric vehicle battery management system that can enhance the battery life of electric vehicles.By utilizing IoT technologies such as cloud computing, big data analytics, and machine learning algorithms, this system can monitor and analyze data in real-time, including charging patterns, driving habits, and battery performance. This enables the system to optimize the charging and discharging process, which in turn prolongs the battery life. In this project, we propose an Internet of Things (IoT) based BMS for EVs that integrates real-time data monitoring, analysis, and control to enhance battery life.The proposed system includes a range of sensors, such as temperature sensors, current sensors, and voltage sensors, that collect real-time data from the EV battery. This data is transmitted wirelessly to an IoT gateway, which then forwards it to a cloud-based platform for analysis and storage. The platform uses machine learning algorithms to identify patterns and anomalies in the data, and provides insights to improve battery performance and extend battery life.

Lithium-ion battery state of health estimation using a hybrid model based on a convolutional neural network and bidirectional gated recurrent unit

This paper proposes a real-time state of health (SOH) estimation model based on a deep learning (DL) framework. The proposed model is a combination of two different architectures; a one-dimensional convolutional neural network (1D-CNN) and a bidirectional gated recurrent unit (BiGRU). The hybrid CNN-BiGRU uses the 1D CNN layers to extract pertinent features from input data and then relies on the BiGRU layers for sequence learning in both directions. To account for all SOH indicators, the proposed approach uses the current, voltage, and temperature measurements, which are readily obtainable from the electric vehicle's battery management system (BMS). This prevents the complex and time-consuming feature extraction used in most related papers. Since the hyperparameters have a significant impact on the performance of neural network models, a Bayesian Optimization (BO) technique based on the Gaussian Process (GP) was considered to tune the CNN-BiGRU model hyperparameters. Accordingly, the objective function was able to converge to a low Mean Squared Error (MSE) of 1.2×10−5 in just 19 iterations. Afterward, to verify the accuracy of the optimized model, a Lithium-ion battery dataset with several discharge profiles provided by the National Aeronautics and Space Administration (NASA) was used. The obtained results demonstrated the accuracy and robustness of the proposed method compared to other commonly used models. The CNN-BiGRU model yielded a Mean Absolute Error (MAE) of 2.080% and a root-mean-square error (RMSE) of 2.516% in the case of the battery set #C, referring to a set with 70 cycles already used at 24 °C. Additionally, the End of life (EOL) indicator error of zero cycles for the same data.

Design of Electric Vehicle Battery Management System

The Battery Management System (BMS) is a fundamental component of electric vehicles, primarily utilized to ensure battery safety and enhance battery lifespan. This article presents a design for both the hardware and software components of the BMS, enabling battery monitoring and management. The hardware component encompasses the design of voltage acquisition circuitry, second-order filtering circuitry, sampling and holding circuitry, CAN bus communication circuitry, and other relevant features. The software section comprises subroutines for battery information collection, equalization circuitry, SOC estimation, and other relevant features. The BMS developed in this study successfully collects voltage, temperature, current, and other relevant information, and accurately estimates SOC and other crucial parameters. Testing confirmed that the battery management system precisely collects battery voltage, current, and temperature information, while the SOC estimation achieves a relatively high degree of accuracy.

VLSI design and FPGA implementation of state-of-charge and state-of-health estimation for electric vehicle battery management systems

State of charge (SOC) and state of health (SOH) estimation is an important function in electric-vehicle (EV) battery-management systems (BMSs). SOC and SOH are the principal parameters for judging a battery’s state and critical for ensuring its safety and performance. In a conventional EV BMS, SOC and SOH estimation is performed on the master. However, owing to the increasing number of battery cells and computational cost of estimation algorithms, hardware-oriented processing for each single-battery module on the slave is being researched to reduce the master’s burden. Accordingly, this paper describes the hardware-optimized algorithm, VLSI design, and field-programmable gate array (FPGA) implementation verification results of an application-specific integrated circuit in a slave module to estimate SOC and SOH for multiple cells. We introduce an SOC and SOH estimation algorithm with 70% complexity reduction within a 2% error margin based on each cell’s voltage, current, and temperature and design a hardware block and logic circuit to implement the algorithm. An FPGA was utilized to verify the designed logic circuit, revealing a performance time of 0.65μs at 100 MHz. Finally, an FPGA testbed connected to a real battery and analog-front-end board was built to check the real-time performance through a graphical user interface.

Lithium-Ion Battery Modeling and State of Charge Prediction Based on Fractional-Order Calculus

Predicting lithium-ion batteries’ state of charge (SOC) is essential to electric vehicle battery management systems. Traditional lithium-ion battery models mainly include equivalent circuit models (ECMs) and electrochemical models (EMs). ECMs are based on integer-order component modeling, which cannot characterize the internal electrochemical reaction mechanism of the battery, resulting in lower SOC prediction accuracy. In contrast, due to their complex structure, EMs are limited in their application. This study takes lithium batteries as the research object and proposes a fractional-order impedance model (FOIM) that characterizes the dynamic properties of the internal behavior of lithium-ion batteries using fractional-order elements. Considering the highly nonlinear characteristics of lithium-ion batteries, this study introduces the theory of fractional-order calculus into the extended Kalman filter (EKF) algorithm, and proposes the fractional-order extended Kalman filter (FEKF) algorithm applied to the prediction of battery charge state. Comparative analysis of simulation and experimental results shows that the accuracy of the FOIM, compared to ECMs, is significantly improved. The FEKF algorithm has good robustness in estimating the SOC, and the SOC prediction accuracy achieved with the algorithm is also improved compared with that obtained using the EKF algorithm of the integer-order model.