Design Of Battery Management System Research Articles

Research on the Remaining Useful Life Prediction Method of Energy Storage Battery Based on Multimodel Integration.

The remaining useful life (RUL) of lithium-ion batteries (LIBs) needs to be accurately predicted to enhance equipment safety and battery management system design. Currently, a single machine learning approach (including an improved machine learning approach) has poor generalization performance due to stochasticity, and the combined prediction approach lacks sufficient theoretical support at the same time. In this paper, we first analyze the prediction principles and applicability of models such as long and short-term memory networks and random forests, and then propose a method for predicting the RUL of batteries based on the integration of multiple-model, and finally validate the proposed model by using experimental data. The experimental results show that (1) for the proposed model, in the best case, the root-mean-square error (RMSE) does not exceed 0.14%, which has a stronger generalization; (2) for the comparison with the single model used, the average RMSE is reduced by 46.2%, 43.7%, and 80.6%, which has a better fitting performance. These results show that the model has good prediction accuracy and application prospects for predicting the RUL of energy storage batteries.

Dynamic Fault Tree Generation and Quantitative Analysis of System Reliability for Embedded Systems Based on SysML Models.

As embedded systems become increasingly complex, traditional reliability analysis methods based on text alone are no longer adequate for meeting the requirements of rapid and accurate quantitative analysis of system reliability. This article proposes a method for automatically generating and quantitatively analyzing dynamic fault trees based on an improved system model with consideration for temporal characteristics and redundancy. Firstly, an "anti-semantic" approach is employed to automatically explore the generation of fault modes and effects analysis (FMEA) from SysML models. The evaluation results are used to promptly modify the system design to meet requirements. Secondly, the Profile extension mechanism is used to expand the SysML block definition diagram, enabling it to describe fault semantics. This is combined with SysML activity diagrams to generate dynamic fault trees using traversal algorithms. Subsequently, parametric diagrams are employed to represent the operational rules of logic gates in the fault tree. The quantitative analysis of dynamic fault trees based on probabilistic models is conducted within the internal block diagram of SysML. Finally, through the design and simulation of the power battery management system, the failure probability of the top event was obtained to be 0.11981. This verifies that the design of the battery management system meets safety requirements and demonstrates the feasibility of the method.

Charge and discharge strategies of lithium-ion battery based on electrochemical-mechanical-thermal coupling aging model

The capacity degradation is unfavorable to the electrochemical performance and cycle life of lithium-ion batteries, but the systematic and comprehensive analysis of capacity loss mechanism, and the related improvement measures are still lacking. Considering the aging mechanism of solid electrolyte interphases (SEI) growth, lithium plating, active material loss, and electrolyte oxidation, an electrochemical-mechanical-thermal coupling aging model is developed to investigate the lithium-ion battery capacity degradation. The results show that as the charge and discharge rates increase, all degradation losses of the battery get serious. The loss of positive active material is more sensitive to the discharge rate. The lithium plating loss is more susceptible to the charging rate. The increased charge cut-off voltage and the reduced discharge cut-off voltage both accelerate the battery aging. The charge cut-off voltage plays great roles in the electrolyte oxidation, loss of negative active material, and loss of lithium plating, while the discharge cut-off voltage greatly influences the loss of positive active material. Finally, the battery charging and discharging process is optimized and analyzed to obtain better anti-aging and safety performance. By clarifying the degradation mechanism and proposing effective measures, it is of great benefit to the design and operation of battery management system.

Redundant series cell voltage measurement circuit design of battery management system for functional safety

In most battery management system (BMS) circuit designs, the analog front end (AFE) chip is used to get the series cell voltage of the battery pack. However, it can lead to incorrect calculations by the BMS, causing errors in battery information, failures in protection control, and triggering a battery system fire accident if the AFE chip is abnormal. Unfortunately, the BMS will never know the correct of measured results if it is measured by a single AFE chip. This paper proposes a redundant series voltage measurement system (RSVMS) for the series cell voltage of the battery pack. The original AFE is referred to as the main AFE (m-AFE). The RSVMS can be regarded as redundant AFE (r-AFE). In this paper, the r-AFE consists of a series cell selector and an analog-to-digital (ADC) converter with an isolated communication function to transmit measurement results to the microcontroller unit (MCU) of the BMS. Due to the hardware requirements of functional safety, it does not allow using two identically designed hardware as redundant systems. To satisfy the hardware level of functional safety and to minimize the hardware cost and size, the series cell selector of r-AFE shares the same hardware circuit as the Active Hybrid Equalizer Circuit (A-HEC). The cell selector of A-HEC is composed of the back-to-back MOSFET switch array with a simple ON/OFF function. The MCU of BMS will identify the abnormal of voltage measurement results when the difference of m-AFE and r-AFE is over the threshold. In summary, r-AFE can ensure the accuracy of cell voltage measurement for m-AFE to avoid significant errors when estimating battery information and avoid disaster caused by failure or malfunction of protection functions. In addition, the m-AFE and r-AFE are two independent designs to avoid similar errors caused by identical circuit configurations.

Impact of Pretension and Cycling Window on Degradation of Graphite/Silicon Composite Anodes

Challenges such as mechanical degradation and limited cycle life persist for high energy density lithium-ion batteries with silicon/graphite composite anodes. In this research work, the patterns of degradation of cells with silicon/graphite composite and NMC622 cathode are examined at varied cycling conditions and applied external pressure or pretension. The most notable outcome of this analysis is that cells cycled between 0 and 100 State-of-Charge (SoC) exhibit the most accelerated aging process. Increasing the pretension force effectively restrains the irreversible expansion of the cells, and has a positive effect on capacity retention.In this research, a comprehensive experiment was conducted involving 46 cells subjected to diverse cycling conditions, voltage windows, pretension forces, and temperatures. Reference performance tests (e.g. HPPC and 1/20 C-rate charge tests) are conducted regularly for analysis of degradation mechanisms. The capacity fade, resistance growth, and thickness increase are correspondingly shown in Figures 1, 2, and 3 with ampere hour throughput as the x-axis. The legend columns indicate test conditions, including C-rate, SoC window during cycling, temperature (in degrees Celsius), and pretension force (in psi). As is shown in all figures, there is a substantial dependence on the SoC window for cycling. To be specific, a rapid rate of capacity loss, resistance increase, and thickness increase occurs in the cell group cycled over the full SoC window (plotted in blue). Cells cycled under full SoC windows also exhibit an early accelerated fading (knee [1]) of capacity and accelerated increase (elbows [2]) of resistance and thickness. According to [3], this accelerated aging could be a consequence of side reactions and increased mechanical stress within the silicon particle when operating across a broad potential range.Meanwhile, for the cell group with restrained cycle windows (plotted in gray) the cells have not yet reached any knee, and have a relatively linear capacity loss. It should be noted that, within the range of partial cycling windows examined, cells subjected to a cycling range of 50-100 exhibit the most rapid degradation, which aligns with the conclusions presented in [4] due to the time at elevated potential. In Figure 2, the elevated temperature (depicted in red) exhibits a significant influence on the increase in resistance, potentially attributed to the growth of the solid electrolyte interface (SEI), but minimal impact on capacity loss. Maintaining other conditions constant and comparing cells under 25 psi and 15 psi (marked with hollow circles and filled circles, respectively), it is evident that a higher pretension force has a positive effect on cell capacity loss. Simultaneously, in Figure 3, the pretension force at 25 psi (marked with hollow circles) effectively restrains the irreversible expansion of the cells. The results are similar to the degradation pattern outlined in [5], it is observed that employing a high pretension force facilitates the mitigation of both degradation and expansion. As highlighted in [6], heightened temperatures lead to accelerated resistance growth. Nevertheless, the impact of various C-rates on degradation remains inconclusive [6].This research systematically analyzed cell-level degradation through an extensive array of experiments, providing valuable insights into the intricate dynamics of capacity fade, resistance increase, and thickness growth. The study's revelation that the cycle window exerts a pronounced impact on battery health could offer crucial guidance for the design of Battery Management Systems (BMS). Moreover, the work establishes a foundational basis for future research, particularly in exploring electrode-level degradation patterns. These contributions collectively enhance the understanding of energy storage systems, offering practical implications for optimizing battery performance and longevity in various applications.[1]Attia, Peter M., et al. "“Knees” in lithium-ion battery aging trajectories." Journal of The Electrochemical Society 169.6 (2022): 060517.[2] Strange, Calum, et al. "Elbows of internal resistance rise curves in Li-ion cells." Energies 14.4 (2021): 1206.[3] Verbrugge, Mark, et al. "Fabrication and characterization of lithium-silicon thick-film electrodes for high-energy-density batteries." Journal of The Electrochemical Society 164.2 (2016): A156.[4] Xu, Bolun, et al. "Modeling of lithium-ion battery degradation for cell life assessment." IEEE Transactions on Smart Grid 9.2 (2016): 1131-1140.[5] Mohtat, Peyman, et al. "Reversible and irreversible expansion of lithium-ion batteries under a wide range of stress factors." Journal of The Electrochemical Society 168.10 (2021): 100520.[6] Pannala, Sravan, et al. "An Experimental Correlation of Degradation with Cell Reversible and Irreversible Expansion Measurement in Pouch Cells." Electrochemical Society Meeting s 243. No. 2. The Electrochemical Society, Inc., 2023. Figure 1

Propensity of 21700 cylindrical cells to thermal runaway under slight overcharging cycling – A numerical study

Slight overcharge of lithium-ion batteries (LIBs) could occur due to inadequate design of battery management system or unexpected malfunction of charger. In comparison with other abuse conditions, the evolution of thermal runaway (TR) under slight overcharging often requires a lengthy incubation period, necessitating relatively long periods for experiments. Numerical simulations with validated models offer a viable alternative to evaluate such risks in modules/packs and associated mitigation measures. Since a current-cutting device is usually installed inside the LIB module/packs, the current will be cut off once the overcharge reaches a certain level, therefore, slight overcharging is of more potential relevance. However, previous simulations mainly focused on TR process induced by continuous overcharging, the conditions associated with slight overcharging have been overlooked. As slight overcharging is difficult to detect, the generated heat could easily accumulate as the cycle process prevails, result in unwanted cell temperature increases with increasing propensity for TR. A three-dimensional (3-D) predictive tool for the transition of cylindrical 21700 cell from slight overcharging cycle to TR has been developed by implementing the published models for heat generation from various thermal decomposition reactions and ISC into in-house version of the opensource computational fluid dynamics (CFD) code OpenFOAM. The code is validated with newly conducted experiments involving both normal and slight overcharging cycles. The validated model has then been used to conduct further parametric studies to investigate the effects of the overcharge voltage, current-rates and convective cooling on the propensity and run up time to TR as well as the maximum cell temperature during TR for cells with different cathode materials under different ambient temperatures. This work forms a sound basis for evaluating TR propagation in modules/packs under slight overcharging/discharging cycles to aid the development of mitigative measures to improve LIB process safety in practical applications.

Real driving cycle based SoC and battery temperature prediction for electric vehicle using AI models

The increase in electric vehicles has surpassed expectations leading to the eventual replacement of traditional IC (internal combustion) engine vehicles. However, to achieve this, it is crucial to research and develop more efficient and reliable electric batteries to create a sustainable transportation system. The performance of the battery directly impacts the power and range of the vehicle making battery management research imperative. Accurate estimation of battery state of charge (SoC) and temperature is vital for the overall performance, drivability and safety of the vehicle. This paper proposes a comprehensive approach to create an AI-based model to estimate the battery SoC and temperature that matches the performance of conventional vehicles. Various regression models are used as prediction models and the results are presented. These insights offer valuable understandings of battery thermal behavior, aiding in the design of an effective battery management system.

Estimation of Li-ion battery SOH based on factors combination of FBG wavelength shift and charge-discharge process

For the purpose of daily battery maintenance, safety forecasting, and energy ladder use, it is crucial to accurately estimate the state of health (SOH) of Li-ion batteries. The external fiber Bragg grating (FBG) sensor is used in this work to collect the signal fluctuations of the wavelength shift in different areas during the Li-ion battery's charge-discharge cycle using a combination of feature analysis and data-driven method, and the characteristics of the wavelength shift and the charge- discharge process in different regions under 125 cycles are statistically analyzed. The reaction degree of Li-ion battery in different areas was compared by wavelength shift value. The wavelength shift factor and charge-discharge process factor related to battery SOH were extracted and trained in the NGO-BP regression model. The experimental results show that the combination of fiber Bragg grating wavelength shift factor and charge-discharge process factor can accurately estimate the SOH of Li-ion battery, and the prediction effect of three areas combination is the best, the goodness of fit R2 is 0.99958, the mean square error MSE is 2.71 × 10−4, the mean absolute error MAE is 1.3373 × 10−2, the root mean square error RMSE is 1.6462 × 10−2, and the mean absolute percentage error MAPE is 1.3679 × 10−2%.The combination of negative electrode and middle area uses fewer sensors to obtain higher prediction accuracy, and its cost performance is the highest. The utilization of fiber Bragg grating wavelength shift factor and charge-discharge process factor has essentially led to the accurate calculation of Li-ion battery SOH, which holds significance for battery health monitoring as well as the design and development of battery management systems.

Design and Analysis of Battery Management System for Electric Vehicle Applications

Design and Analysis of Battery Management System for Electric Vehicle Applications

Review on Design and Analysis of Battery Management System for Electric Vehicles

Abstract: In order to ensure effective functioning, the Batteries Management System (BMS) plays a crucial role in supervising and controlling the charging and discharge procedures of rechargeable battery packs within electric cars. Protection of the battery, preserving its dependability, and increasing its whole life are its fundamental goals. A range of monitoring methods, including as ambient temperature, voltage, and current monitoring, are used to maintain ideal battery conditions in order to do this. The use of microcontrollers in combination with analogue and digital sensors allows for efficient monitoring. In addition, this study investigates variables including battery maximum capacity, health condition, and charge state in an effort to offer thorough understandings of BMS features. Through the examination of these techniques, the research aims to anticipate and prepare for future difficulties related to BMS, while also addressing current concerns. An essential component of BMS functioning is evaluating the battery's state, involving factors like charge status, condition, and overall life status. The study provides a comprehensive analysis of state-of-the-art battery condition evaluation approaches, highlighting impending issues for BMS and offering workable solutions

MONITORING AND MANAGEMENT OF ELECTRIC MOTORCYCLE BATTERY SYSTEMS

Some problems that often occur with electric motorbike batteries include overcharge, overdischarge and overheat. To avoid these problems, it is necessary to regulate or monitor the battery, which is called a Battery Management System (BMS). The BMS in this research is focused on the use of PSDKU Kediri electric motors. With this BMS design, it is hoped that it can be used as a reference in battery system management so that the battery lasts longer. In this study, 112 batteries were used, consisting of 14 series and 8 parallel. The voltage sensor circuit is made to determine the battery cell voltage so that overvoltage and undervoltage protection can be carried out. Measurements can be made using a multimeter by adjusting the reading position on the voltage. The maximum voltage produced is around 57.7 volts with a charging time of 7 hours

Design and optimization of battery and thermal management system for AC photovoltaic energy module

The utilization of renewable energy sources has increased due to concerns about climate change. However, injecting the power from renewable energy sources into grid-tied systems is challenging. The techno-economic analysis a photovoltaic (PV) energy systems is investigated. As a result, this paper presents AC-PV module for Grid-Tied and Off-Grid Scenarios via optimization, modeling, and test results. Even though the output of a PV panel is DC voltage, a three-port inverter and a lithium-ion battery pack are integrated with the back of the PV panel. They are packaged as a PV system module that makes the module output have AC voltage. Therefore, an optimized AC-PV module can be a solution for residential and commercial use, which are grid-tied systems; it can be very efficient for those without access to electricity, which is an off-grid system. An integrated battery and thermal management strategy is crucial for this AC-PV module. In the article, the battery capacity optimization, the electrical and the thermal model of the battery pack, battery heat generation model are discussed by using stochastic analysis techniques; the battery test results are also obtained to identify the models’ parameters and a control algorithm is proposed to extract the battery information such as temperature, current, voltage, SoC and SoH of the battery pack.

Design and analysis of battery management system in electric vehicle

The usage of electric vehicles is gaining momentum in recent time’s thus providing support to the growth in sales of electric vehicles. The Battery management system is the most important aspect to ensure the smooth functioning of an electric vehicle. This research highlights some key statements on the background of electric vehicles. The increase in the overall growing importance of electric vehicles has also been explained in this work. Battery management system has an importance in the functioning of electric vehicles, thus presenting the key highlights of this article. The finding presents the importance of batteries and their type used in EVs. The simulation results of the Lithium battery cell – 1 RC, 2 RC equivalent circuit parameters such as charging current, terminal voltage, state of charge, and battery current have been simulated and analysed in Matlab. The future scope of BMS and its development has been discussed.

Design and Analysis of Battery Management System for Electric Vehicles

Abstract: This article presents a design for both the hardware and software components of the BMS, enabling battery monitoring and management. The Battery Management System (BMS) is a fundamental component of electric vehicles, primarily utilized to ensure battery safety and enhance battery lifespan. 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.

Accelerating the transition to cobalt-free batteries: a hybrid model for LiFePO4/graphite chemistry

The increased adoption of lithium-iron-phosphate batteries, in response to the need to reduce the battery manufacturing process’s dependence on scarce minerals and create a resilient and ethical supply chain, comes with many challenges. The design of an effective and high-performing battery management system (BMS) for such technology is one of those challenges. In this work, a physics-based model describing the two-phase transition operation of an iron-phosphate positive electrode—in a graphite anode battery—is integrated with a machine-learning model to capture the hysteresis and path-dependent behavior during transient operation. The machine-learning component of the proposed “hybrid” model is built upon the knowledge of the electrochemical internal states of the battery during charge and discharge operation over several driving profiles. The hybrid model is experimentally validated over 15 h of driving, and it is shown that the machine-learning component is responsible for a small percentage of the total battery behavior (i.e., it compensates for voltage hysteresis). The proposed modeling strategy can be used for battery performance analysis, synthetic data generation, and the development of reduced-order models for BMS design.

A Review on Design and Development of Battery Management System for Electric Vehicle

A Review on Design and Development of Battery Management System for Electric Vehicle

A novel MIMO BMS design for user-friendly replacement of batteries

The proposed design introduces an innovative Multiple-Input Multiple-Output (MIMO) based Battery Management System (BMS) design that facilitates convenient battery replacement in battery packs. The technology allows for real-time monitoring of battery health through separate communication channels, making it easier to identify and replace batteries without the need for complicated disassembly. Granular control of charging/discharging and cell balancing allows for the achievement of enhanced safety and increased pack performance.

SOC estimation of lithium battery based on the combination of electrical parameters and FBG non-electrical parameters and using NGO-BP model

Accurately estimating of the state of charge (SOC) of lithium batteries is of great significance for improving the service life and utilization efficiency of lithium batteries. In this paper, fiber Bragg grating (FBG) sensors are used to capture the offset of the FBG central wavelength caused by strain and temperature during the charging and discharging process of lithium batteries, and the current state of charge of lithium batteries is obtained through the combination of electrical parameters and FBG non-electrical parameters.A battery monitoring experimental system was established, and the comparison of wavelength offset collected by PDMS packaged FBG and bare FBG during the charging and discharging process of lithium batteries was discussed. The prediction results were compared by temperature compensating the central wavelength offset collected during the charging and discharging of lithium batteries, then introduced BP neural network and optimized NGO- BP neural network regression models for training to compare the prediction effects, the results of which point to the fact that in terms of regression model performance and improvement of FBG non-electrical parameters on model performance, the various indicators predicted by NGO-BP are stronger than those predicted by BP. After adding FBG non-electrical parameters, both the BP regression model and the NGO-BP regression model showed remarkable improvement in RMSE, MAE, and R2 compared to the purely electrical parameters, and the best prediction method for the NGO-BP non-purely electrical parameters, with RMSE of 0.0201 and MAE of 0.0154, which decreased by 77.94% and 73.12% respectively compared to the BP pure electrical parameter prediction method, and R2 of 0.9988, which increased by 5.88%, thus the joint accurate estimation of lithium battery SOC by electrical measurement characteristics and FBG non-electrical parameters is basically realized, which brings values for the design and development of battery management system.

Design and Application of Station Power Supply System for Lithium Iron Phosphate Battery Based on Power Exchange Operation

Based on the engineering application design and development of the power supply system of lithium iron phosphate battery pack in the operation and maintenance mode, this paper conducts the application research from four aspects of battery quantity selection, capacity calculation selection, battery management system design, battery pack modular design, etc. The design scheme of the lithium iron phosphate power supply system is formulated, and the matching battery management system is designed. A universal lithium iron phosphate battery module with an “N+1” redundant configuration is developed to improve the maintenance efficiency of the battery pack. It reliably supports the substation power changing operation and maintenance mode and ensures the reliability and operation safety of the substation power supply system.

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.