Design Of Battery Management System Research Articles

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.

Battery swapping and management system design for electric trucks considering battery degradation

Battery swapping and management system design for electric trucks considering battery degradation

Design and Analysis of Battery Management System for Electrical Vehicles

Abstract: The quantity of pollution that is emitted into the atmosphere will only continue to climb as the number of people who drive automobiles powered by internal combustion engines continues to rise. To reduce emissions of greenhouse gases, academics and companies on every continent are now focusing their attention on the research and development of more sophisticated technologies for electric cars. During this investigation, we are going to model and create a two-way power converter for electric automobiles. Batteries, a dc-dc converter, and a dc motor are just a few of the many diverse parts that make up the power electronics module. It is advised that the starting battery be charged to about 90% of its capacity before using the motor mode, which has a discharge current of 44.5 A. The battery stack has a maximum capacity of 100 ampere-hours, and its nominal voltage is 350 volts. This is the greatest amount of energy that it can store. Direct current (DC) machines have the potential to generate up to 250 horsepower, provided that the armature voltage is 500 volts and the field voltage is 300 volts, respectively. The mode of operation for the power converter is determined by the readings of torque that are obtained by the DC machine when it is operating in the motor mode and the generator mode, respectively. The processes of charging and discharging batteries have been monitored and handled in relation to the wide variety of modes of operation that may be found in DC machines. This was done in order to make full use of the capabilities of these machines. The bidirectional dc-dc converter, which may operate in either direction, is managed by a fuzzy logic controller. This allows the converter to work in either direction. It's likely that the converter and controller that's been shown here are all that's needed for charge management and running the motor in electric automobiles.

Comparison and Analysis of SOC Estimation Based on First-Order and Second-Order Thevenin Battery Models Based on EKF

The accurate modeling and estimation of state-of-charge (SOC) of lithium-ion batteries are of great significance for their utilization efficiency, service life extension and battery management system (BMS) design. This paper aims to provide understanding and guidance for understanding and guidance on different battery models and their parameter selection by comparing and analyzing the performance differences of Extended Kalman Filter (EKF) algorithm based on the first-order Thevenin battery model and the second-order Thevenin battery model. The Battery module in Matlab/Simulink is used to simulate the HPPC (HybridPulse Power Characterization) test of real batteries, and the model is parameterically identified. By establishing Thevenin battery models of different orders and applying the EKF algorithm for SOC estimation, the differences in accuracy, robustness and computational complexity of the two models are compared and analyzed, and experimental verification is carried out under different working conditions.

Estimation of State of Charge of Battery Used In Electric Vehicles With Wireless Battery Management System

This research paper presents a comprehensive investigation into the development and analysis of a wireless battery management system (BMS) using MATLAB Simulink. The primary objective of this study is to create an efficient, reliable, and scalable BMS that caters to the demands of various applications, such as electric vehicles, grid energy storage, and portable electronics. Our methodology involves designing and simulating key BMS components, including state estimation algorithms, fault detection mechanisms, and communication protocols, within the MATLAB Simulink environment. The paper first elucidates the motivation for adopting wireless technology in BMS, emphasizing its advantages over traditional wired systems. Subsequently, we explore the intricacies of the proposed wireless BMS architecture, detailing the implementation of essential features such as state-of-charge estimation, fault diagnosis, and thermal management. We also address the challenges associated with wireless communication, including latency, security, and energy efficiency, by incorporating robust communication protocols and power management strategies. Through rigorous simulations, we demonstrate the efficacy of the proposed wireless BMS, showcasing its ability to ensure optimal performance, safety, and longevity of battery packs. The outcomes of this research not only contribute to the advancement of BMS technology but also pave the way for further improvements in battery-powered systems. In conclusion, this paper offers a holistic perspective on wireless BMS design, emphasizing its potential to revolutionize energy management and extend the applications of battery technology in various domains.

Comparative Analysis of Charging and Discharging Characteristics for Batteries in Aircraft Electric Power Systems

Abstract—This paper presents a comparative analysis of the charging and discharging characteristics of batteries used in aircraft electric power systems. The performance and efficiency of different battery technologies play a crucial role in determining the viability of electric propulsion and power systems in aircraft applications. In this study, we systematically evaluate the charg- ing and discharging behaviors of various battery types, including LiFePO4, Ni-Cd, and lead acid, using Simulink. The simulations encompass state of charge (SOC) dynamics, charging efficiency, discharging profiles, and passive cell balancing technique. By utilizing Simulink, we can accurately model the behavior of different battery technologies under various operating conditions. The results obtained from the simulations provide valuable insights into the performance, efficiency, and overall suitability of battery technologies for aircraft electric power systems. This comparative analysis, serves as a valuable resource for engineers and researchers involved in the design and optimization of battery management systems for aircraft applications, aiding in the informed selection and integration of batteries in aircraft electric power systems. Index Terms—Charging Characteristics, Discharging Charac- teristics, Simulink Simulation, State of Charge (SOC), Passive Cell Balancing

13S Battery Pack and Battery Management System Design and Implementation for Electric Bicycles

In this study, the battery pack and ESP32 microcontroller-based battery management system (BMS) design for the electric bicycle have been carried out. The software updates were realized when the battery pack was in the box, thanks to the built-in Wi-Fi feature of the ESP32. The voltage values were read precisely, and a stable BMS was created with a 12-bit ADC-based ESP32 controller. The battery had temperature control from 7 different points. The temperature data was received from DS18B20 digital temperature sensors, and the charging current was cut off when the threshold value was exceeded. At the same time, the ACS712 5A current sensor was used to determine and prevent the excessive current draw. The charging current was defined as 2A. BMS cuts off the charging current to protect the battery pack when the charging current exceeds 3A. The resistor used for passive balancing in BMS was isolated from the ESP module with the KPS2801 optocoupler. The voltage of the battery pack was charged from 3.7 V to 3.85 V in the real-time test. The charging process continued for one hour, and the voltage changes of each cell were obtained in 10-minute periods. It was observed that all cells were charged in a balanced way at the end of the charging process. Thus, the charging process of the designed battery pack was carried out successfully through the designed BMS.

Lithium-ion ferrous phosphate prismatic cell aging analysis and assessment for the development of battery management systems

Electric off-road vehicles are gaining popularity as an alternative to fossil fuel-driven vehicles due to their efficiency and eco-friendliness. A series of numerical modelling and experimentation analysis is required to evaluate the trade-off between battery accuracy and life span to identify optimal battery management system design. In this study, investigations were carried out on a lithium-ion ferrous phosphate (LFP) prismatic cell in an electric-powered farm tiller application using the single particle model (SPM) method. The SPM method was used to analyse the battery's behaviour during charge–discharge profiles at 0.5, 1, and 2C ratings. By applying SPM discharging profiles, the Newman, Tiedemann, Gu, and Kim (NTGK) empirical analysis carried out cascaded thermal investigations. The NTGK and experimental studies showed a difference in the error of 0.23 %, 0.38 %, and 0.65 % for the 0.5, 1, and 2C ratings, respectively. Model order reduction (MOR) studies were performed to compute the heat generation rate in a prismatic cell comprising electrode pairs, separators, and current collectors. A parallel study of electrochemical impedance spectroscopy (EIS) experimentation was performed to evaluate the charge transfer resistance analysis for 500, 1000, and 1500 cyclic operations under different depths of discharge (DOD) conditions. The EIS studies assessed charge transfer resistance at maximum DOD conditions for the mentioned cyclic operations were found to be 135, 140, and 164 mΩ.

Parameter sensitivity analysis of an electrochemical-thermal model for energy-storage lithium-ion batteries

The lithium-ion batteries used for energy storage have the characteristics of large volume, high capacity, and long cycle life. Understanding the influence of physical parameters on electric potential and temperature is of critical importance for the design and operation of battery management systems. Here we developed an electrochemical-thermal coupled model that is composed of lithium-ion battery Pseudo-2D (P2D) model and heat transfer equations. A sensitivity analysis is carried out to illuminate the influence of various electrochemical parameters on battery capacity, terminal voltage, temperature curve and other technical indicators for a LiFePO4 battery with the capacity of 117Ah. The sensitivity matrices of the parameters under different C-rates and depth of discharge regions are organized, and a new five segments sensitivity analysis method is proposed in this study, which is helpful to reduce the sensitivity variance caused by the change of capacity during the discharging process. The parameters which have the greatest impact on battery capacity, voltage, and temperature are sorted out and illustrated. Furthermore, the influence rules of electrochemical parameters on the capacity and temperature-related technical indicators have also been summarized.

Design of Lithium Battery Management System of Hydrogen Emergency Vehicle

The hydrogen emergency power supply vehicle is mainly powered by a pure lithium battery power supply. Therefore, the reliable operation of the power supply and the analysis of charging characteristics play an important role. A battery management system for emergency vehicle power supply is designed in this paper. Through the battery management system, battery information collection, battery information analysis, and control management are carried out to realize the reliable operation of the power supply. The battery management system adopts the active equalization method for balance management, which improves the balance efficiency, improves the health status of the power supply, and extends the life of the power battery pack.

Multi-State Online Estimation of Lithium-Ion Batteries Based on Multi-Task Learning

Deep learning-based state estimation of lithium batteries is widely used in battery management system (BMS) design. However, due to the limitation of on-board computing resources, multiple single-state estimation models are more difficult to deploy in practice. Therefore, this paper proposes a multi-task learning network (MTL) combining a multi-layer feature extraction structure with separated expert layers for the joint estimation of the state of charge (SOC) and state of energy (SOE) of Li-ion batteries. MTL uses a multi-layer network to extract features, separating task sharing from task-specific parameters. The underlying LSTM initially extracts time-series features. The separated expert layer, consisting of task-specific and shared experts, extracts features specific to different tasks and shared features for multiple tasks. The information extracted by different experts is fused through a gate structure. Tasks are processed based on specific and shared information. Multiple tasks are trained simultaneously to improve performance by sharing the learned knowledge with each other. SOC and SOE are estimated on the Panasonic dataset, and the model is tested for generalization performance on the LG dataset. The Mean Absolute Error (MAE) values for the two tasks are 1.01% and 0.59%, and the Root Mean Square Error (RMSE) values are 1.29% and 0.77%, respectively. For SOE estimation tasks, the MAE and RMSE values are reduced by 0.096% and 0.087%, respectively, when compared with single-task learning models. The MTL model also achieves reductions of up to 0.818% and 0.938% in MAE and RMSE values, respectively, compared to other multi-task learning models. For SOC estimation tasks, the MAE and RMSE values are reduced by 0.051% and 0.078%, respectively, compared to single-task learning models. The MTL model also outperforms other multi-task learning models, achieving reductions of up to 0.398% and 0.578% in MAE and RMSE values, respectively. In the process of simulating online prediction, the MTL model consumes 4.93 ms, which is less than the combined time of multiple single-task learning models and almost the same as that of other multi-task learning models. The results show the effectiveness and superiority of this method.

Development and Experimental Validation of Novel Thevenin-Based Hysteretic Models for Li-Po Battery Packs Employed in Fixed-Wing UAVs

Lithium batteries employed in lightweight fixed-wing UAVs are required to operate with large temperature variations and, especially for the emerging applications in hybrid propulsion systems, with relevant transient loads. The detailed dynamic modelling of battery packs is thus of paramount importance to verify the feasibility of innovative hybrid systems, as well as to support the design of battery management systems for safety/reliability enhancement. This paper deals with the development of a generalised approach for the dynamic modelling of battery packs via Thevenin circuits with modular hysteretic elements (open circuit voltage, internal resistance, RC grids). The model takes into account the parameters’ dependency on the state of charge, temperature, and both the amplitude and sign of the current load. As a relevant case study, the modelling approach is here applied to the Li-Po battery pack (1850 mAh, 6 cells, 22.2 V) employed in the lightweight fixed-wing UAV Rapier X-25 developed by Sky Eye Systems (Cascina, Italy). The procedure for parameter identification with experimental measurements, obtained at different temperatures and current loads, is firstly presented, and then the battery model is verified by simulating an entire Hybrid Pulse Power Characterisation test campaign. Finally, the model is used to evaluate the battery performance within the altitude (i.e., temperature) envelope of the reference UAV. The experiments demonstrate the relevant hysteretic behaviour of the characteristic relaxation times, and this phenomenon is here modelled by inserting Bouc–Wen hysteresis models on RC grid capacitances. The maximum relative error in the terminal output voltage of the battery is smaller than 1% for any value of state of charge greater than 10%.

Development of Passive Battery Management System at TRL 4

Nowadays, the usage of the electric vehicle (EV) is exponentially increasing. Therefore, a battery management system (BMS) is required to properly operate the Li-ion battery used in electric vehicles for extending the battery life. The main function of BMS is to sense the voltage, current, and temperature of the battery and cells independently. Further, it evaluates different parameters from the data fetched by the BMS. Finally, based on the evaluation, it controls the cell balancing. Presently, BMS is implemented using different microcontrollers and is under improvement with the advancement in existing technology. Passive balancing is commonly used in BMS, since, it is inexpensive and straightforward to implement. The passive resistor uses the passive balancing method to discharge the battery’s excess charge. For small battery capacities, this resistor is very useful. This paper analyses BMS design which combines a power resistor and transistor as a balancing resistor. The proposed analyses were applied to a battery pack consisting of 13 lithium-ion battery cells which enabled a fast-charging scheme. The most significant features of the passive balancing system are based on the results of this experiment, taking into account the impact on battery performance and energy loss. The aim of this paper is to make a battery pack that is with high energy carrying capability and proper thermal runaway. Thus, extensive monitoring is needed to operate the battery within specified operating limits to avoid fire hazards and explosions. In order to achieve this, the proposed design creates a demand for a Sophisticated management system which not only optimize the power drawn from battery but also maintain the battery operation within specified limits.

Investigation on lithium-ion battery degradation induced by combined effect of current rate and operating temperature during fast charging

Operating temperature and current rate are the main parameters that induce lithium-ion battery (LIB) degradation during the fast-charging process. In this study, fast-charging degradation was investigated using a commercial 18650 Nickel-Manganese-Cobalt battery at different charging current rates (C-rates) and operating temperatures. The degradation process was evaluated using electrochemical impedance spectroscopy and incremental capacity analysis. The electrode morphology change was investigated via a multiscale post-mortem analysis. The battery degradation mechanism at different C-rates and temperatures was characterized, and the coupled effects of the C-rate and operating temperature on the battery capacity fade were determined. With increasing operating temperatures, the most significant factor influencing the battery capacity fade changed gradually from lithium plating to the growth of the solid-electrolyte interface (SEI). An experimental analysis revealed that the battery capacity fade at the higher C-rates can be alleviated to some extent by employing higher operating temperatures. In comparison, the capacity fade at lower C-rates accelerated with increasing operating temperature. The changing and position shifting of the peaks in the incremental capacity curves were analyzed. It was found in the post-mortem analysis that the fast charging degradation was embodied in the macroscopic detachment of electrode material, the microscopic cracking of electrode particle, the SEI growth, the lithium plating, and the structural change of layered material crystal. The results of this study provide insight into the development of fast-charging strategies and the design of battery management systems.

Modeling and On-Road Testing of an Electric Two-Wheeler towards Range Prediction and BMS Integration

The automotive sector is currently shifting its focus from traditional fossil fuels to electrification. The deployment of a Battery Management System (BMS) unit is the key point to oversee the battery state of the electric vehicle (EV) to ensure safety and performances. The development and assessment of electric vehicle models in turn lays the groundwork of the BMS design as it provides a quick and cheap solution to test battery optimal control logics in a Software-in-the-Loop environment. Despite the various contribution to the literature in battery and vehicle modeling, electric scooters are mostly disregarded together with a reliable estimation of their performance and electric range. The present paper hence aims at filling the gap of knowledge through the development of a numerical model for considering a two-wheeler. The latter model relies on the conservation energy based-longitudinal dynamic approach and is coupled to a Li-Ion Battery second-order RC equivalent circuit model for the electric range prediction. More specifically, the presented work assesses the performance and electric range of a two-wheeler pure electric scooter in a real-world driving cycle. The e-powertrain system embeds an Electrical Energy Storage System (EESS) Li-Ion Battery pack. On-road tests were initially conducted to retrieve the main model parameters and to perform its validation. A global battery-to-wheels efficiency was also calibrated to account for the percentual amount of available net power for the vehicle onset. The model proved to properly match the experimental data in terms of total distance traveled over a validation driving mission.

Influences of multi factors on thermal runaway induced by overcharging of lithium-ion battery

Thermal runaway caused by overcharging results in catastrophic disasters. The influences of charging rate, ambient temperature and aging on thermal runaway caused by overcharging are studied qualitatively and quantitatively in this manuscript. The results of overcharging tests indicate that high charging rate and ambient temperature increase thermal runaway risk. Aging in 40 °C decreases thermal runaway risk. The risk increase of battery with high overcharging rate and in high ambient temperature is due to fast lithium plating reaction and accelerated SEI decomposition, respectively. The risk decrease of aged battery is due to the occurrence of SEI before overcharging tests. SEI suppresses the side reactions between lithium plating and electrolyte. The results of orthogonal tests indicate that the rank of effect is: discharging rate > ambient temperature > aging. The heat generation is calculated based on the results of overcharging tests. The calculation results indicate that heat generated by side reactions contributes more to the total heat generation. Although thermal runaway does not occur during overcharging with low current, the heat dissipation of the lithium-ion battery is the most and deserves focus. The results are important to the design of battery management system and thermal management system to prevent thermal runaway induced by overcharging in total lifespan of battery.

Design of Modular BMS and Real-Time Practical Implementation for Electric Motorcycle Application

Battery management systems (BMSs) are widely used in different battery applications to maximize the system operating efficiency. Due to different applications of BMS, different topologies are developed and used for implementation. In this letter, we propose a modular BMS circuit for electric motorcycles battery voltage applications and present a BMS platform for testing. We conduct a real-time evaluation of the BMS in the designed BMS platform considering a varying total input load voltage ranging from 51.2–67.2 V. The real-time evaluation is conducted using four local modules and one central module. The results are verified using the BMS platform designed for this specific BMS. The BMS voltage measurement accuracy is evaluated. Actual input load voltage values are used to verify the voltage values measured by the BMS, and significantly high accuracy is achieved with a mean RMSE of 0.0018895. In terms of relative error, a mean value of 0.03575 is achieved for the four-cell modular BMS application.

Design of Battery management system for Residential applications

Battery management system plays an important role for modern battery-powered application such as Electric vehicles, portable electronic equipment and storage for renewable energy sources. It also increases the life-cycle of the battery, battery state and efficiency. Monitoring the state of charge of the battery is a crucial factor for battery management system. This paper deals with monitoring the state of charge of the battery along with temperature, current for Solar panel fitted with battery for residential application. Microcontroller is used for controlling purpose, analog sensors are used for sensing the parameters of voltage, current. The information of the battery is given with tabular form and shown in photograph. Battery parameters are displayed with the LCD screen.

Design of battery management system based on improved ampere-hour integration method

Design of battery management system based on improved ampere-hour integration method

Design of battery management system based on improved ampere-hour integration method

Design of battery management system based on improved ampere-hour integration method