Battery Management System

Problem Statement: There have been recent reports of multi-million dollar companies having to recall entire projects due to their BMS’s malfunctioning or operating incorrectly. The purpose of this project is to analyze the future of batteries, the Lithium-Ion cell, and to exercise a BMS to better understand its capabilities and possible cases for errors. Lithium ion batteries are intolerant of overcharge and overdischarge. Abuse of this kind can result in high temperatures, venting of gases, fire, or explosion. Therefore battery management systems have been devised to prevent such abuse. Recent events such as fires on the Boeing Dreamliner and the Tesla Model S have shown that these dangers are real. These products do have highly developed battery management systems, and the incidents were caused in spite of these systems. This study was undertaken to illustrate how one system from Texas Instruments, functions to monitor and control a simple battery pack. Part I: Battery Data Acquisition Preliminary battery tests were conducted to fully understand the operations of charging and discharging the battery. These tests were essential to gain a better understanding of typical battery behavior and to be able to perform calculations necessary in analyzing the characteristics of the batteries. Also, these tests were ran to ensure that the Lithium-Ion batteries being used correctly corresponded to the graphs and values provided by the datasheet. The knowledge gained from Part I was vital for a better comprehension of the functions needed to balance the cells for Part II as well as the importance of safety precautions necessary when dealing with multiple batteries. Part II: Exercising the BQ76PL536 & GUI The BQ76PL536 Evaluation Module (EVM) was tested using the BQ7PL536 BMS chips. An Aardvark adapter acts as a link between the EVM and PC allowing the user to read data from the BMS chips on a GUI. The BMS chips are validated by reading voltages of individual battery cells, pack voltage, and pack temperature. The GUI allows for enabling cell balancing between the cells but this feature is not automatic and must be engaged by the user. Part III: BMS Application A MSP430fr5969 microcontroller was implemented to create a BMS system that can read data from the BMS chips such as cell voltage, pack voltage, pack temperature, fault statuses, alert statuses, and a variety of other useful cell parameters. This data is displayed on a LCD screen through different menu options. The user scrolls through the menus using a capacitive touch slider on the microcontroller and selections a given option using the option select button. There is also a cell balance mode that will check the cells to see if they are out of balance and then enable cell balancing if the cells are unbalanced. This section is designed to remove the Aardvark adaptor and the GUI from the system

Online state of charge estimation for the aerial lithium-ion battery packs based on the improved extended Kalman filter method.

An effective method to estimate the integrated state of charge (SOC) value for the lithium-ion battery (LIB) pack is proposed, because of its capacity state estimation needs in the high-power energy supply applications, which is calculated by using the improved extended Kalman filter (EKF) method together with the one order equivalent circuit model (ECM) to evaluate its remaining available power state. It is realized by the comprehensive estimation together with the discharging and charging maintenance (DCM) process, implying an accurate remaining power estimation with low computational calculation demand. The battery maintenance and test system (BMTS) equipment for the aerial LIB pack is developed, which is based on the proposed SOC estimation method. Experimental results show that, it can estimate SOC value of the LIB pack effectively. The BMTS equipment has the advantages of high detection accuracy and stability and can guarantee its power-supply reliability. The SOC estimation method is realized on it, the results of which are compared with the conventional SOC estimation method. The estimation has been done with an accuracy rate of 95% and has an absolute root mean square error (RMSE) of 1.33% and an absolute maximum error of 4.95%. This novel method can provide reliable technical support for the LIB power supply application, which plays a core role in promoting its power supply applications

Zips Racing Electric Battery Management System

Zips Racing Electric currently uses a bulky, off-the-shelf battery management system to monitor and manage the voltage, temperature, and state-of-charge of an electric formula-style racecar battery pack (accumulator). The objective of this project is to research current battery management methodologies and apply said research to design and create a lightweight, compact, custom battery management system that is integrated with existing vehicle systems. This will allow for cleaner accumulator packaging and improved communication between the battery management system and the rest of the vehicle

Advances in Batteries, Battery Modeling, Battery Management System, Battery Thermal Management, SOC, SOH, and Charge/Discharge Characteristics in EV Applications

The second-generation hybrid and Electric Vehicles are currently leading the paradigm shift in the automobile industry, replacing conventional diesel and gasoline-powered vehicles. The Battery Management System is crucial in these electric vehicles and also essential for renewable energy storage systems. This review paper focuses on batteries and addresses concerns, difficulties, and solutions associated with them. It explores key technologies of Battery Management System, including battery modeling, state estimation, and battery charging. A thorough analysis of numerous battery models, including electric, thermal, and electro-thermal models, is provided in the article. Additionally, it surveys battery state estimations for a charge and health. Furthermore, the different battery charging approaches and optimization methods are discussed. The Battery Management System performs a wide range of tasks, including as monitoring voltage and current, estimating charge and discharge, equalizing and protecting the battery, managing temperature conditions, and managing battery data. It also looks at various cell balancing circuit types, current and voltage stressors, control reliability, power loss, efficiency, as well as their advantages and disadvantages. The paper also discusses research gaps in battery management systems.publishedVersio

Universal Programmable Battery Charger with Optional Battery Management System

This report demonstrates improvements made in battery charging and battery management technology through the design of a universal programmable battery charger with optional battery management system attachment. This charger offers improvements in charge efficiency and unique battery charging algorithms to charge a variety of battery chemistries with variety of power requirements. Improvements in efficiency result from a synchronous Buck Controller topology as compared to previous universal chargers that use asynchronous Buck-Boost Converter topologies. This battery charger also surpasses current universal battery chargers by offering different charge modes for different battery chemistries. Charge modes provide the user an option between extending the life of the battery by selecting a mode with a slower, less stressful charge rate or a shorter charge time with a fast, more stressful charging mode. The user can also choose a charge mode in which the battery charges to full capacity, resulting in maximum runtime or a less than full capacity, which puts less stress on the battery thus extending the lifetime. Ultimately, this system permits weighing the performance tradeoff of battery lifetime and charge time. The optional battery management system attachment offers more precise monitoring of each cell and cell balancing for Li-Ion batteries. This further enhances the performance of the charger when integrated, but is not necessary for charger operation. The battery charger consists of three subcircuits: A microcontroller unit, a power stage, and a current sensing circuit. A C2000 Piccolo F28069 microcontroller controls a LM5117 Buck Controller by injecting a pulse-width modulated signal into the feedback node controlling the output of the buck to set a constant current or constant voltage thus creating a programmable battery charger. The pulse-width modulated signal changes according to charge algorithms created in software for specific battery chemistries and charge requirements. An analog-to-digital converter on the microcontroller monitors battery voltage by using a voltage divider and an INA169 current shunt monitor, which outputs a voltage corresponding to the charge current to another analog-to-digital converter on the microcontroller, monitors the charge current. This allows the charger program to maintain correct and safe charging conditions for each charge mode in addition to measuring output power. Lights on the microcontroller display a real-time status to the user of which portion of the charge profile the charger is in. A solid red light means the charger is in the constant current portion of the charge profile. A blinking red light means the charger is in the constant voltage portion. No red light means the battery charger finished and the battery is currently charged above nominal voltage. The battery charger works with the battery management system in the next section to provide ultimate battery charging and managing capabilities. The battery management system consists of two subcircuits: A microcontroller and a battery monitoring circuit. The MSP430FR5969 microcontroller unit communicates with BQ76PL536 battery management integrated circuits to create a battery management system that monitors data such as cell voltage, pack voltage, pack temperature, state of charge, fault statuses, alert statuses, and a variety of other useful cell parameters. This data displays on a liquid crystal display screen through different menu options. The user scrolls through the menus using a capacitive touch slider on the microcontroller unit and selects a given option using the option select button. A cell balance mode allows the user to check the balance of the cells and allows cell balancing if the cells differ by more than a set threshold

Study and modelling of lithium ion cell with accurate soc measurement algorithm using Kalman filter for electric vehicles

Lithium Ion cells are preferred over lead acid cells for electric vehicles due to their energy density, higher discharge current and size. The cost of lithium ion cells is scaling down compared to ten years earlier, but as their performance characteristics increase, the need for safety and accurate modelling also increases. The absence of a generic cell model is associated to the different makes of cells and different chemistries of Lithium ion cells behave differently under the testing conditions required for every unique application. The focus of this thesis will be on how to provide intelligence to the battery management system for calculating the state of charge of a cell so that the depth of discharge of the pack can be controlled, and to balance the voltage levels of all modules in a battery pack. This will involve cycling of the chosen type of cell, modelling it for its parameters, analyzing the cycling data and choosing the perfect depth of discharge required for the application from the energy or capacity vs open circuit voltage (OCV) graph. The lithium ion model will be evaluated from the transient response of the battery pack. This will then be made as a working prototype on an electric vehicle car and its behavior studied practically

Further Cost Reduction of Battery Manufacturing

The demand for batteries for energy storage is growing with the rapid increase in photovoltaics (PV) and wind energy installation as well as electric vehicle (EV), hybrid electric vehicle (HEV) and plug-in hybrid electric vehicle (PHEV). Electrochemical batteries have emerged as the preferred choice for most of the consumer product applications. Cost reduction of batteries will accelerate the growth in all of these sectors. Lithium-ion (Li-ion) and solid-state batteries are showing promise through their downward price and upward performance trends. We may achieve further performance improvement and cost reduction for Li-ion and solid-state batteries through reduction of the variation in physical and electrical properties. These properties can be improved and made uniform by considering the electrical model of batteries and adopting novel manufacturing approaches. Using quantum-photo effect, the incorporation of ultra-violet (UV) assisted photo-thermal processing can reduce metal surface roughness. Using in-situ measurements, advanced process control (APC) can help ensure uniformity among the constituent electrochemical cells. Industrial internet of things (IIoT) can streamline the production flow. In this article, we have examined the issue of electrochemical battery manufacturing of Li-ion and solid-state type from cell-level to battery-level process variability, and proposed potential areas where improvements in the manufacturing process can be made. By incorporating these practices in the manufacturing process we expect reduced cost of energy management system, improved reliability and yield gain with the net saving of manufacturing cost being at least 20%

Smart battery pack for electric vehicles based on active balancing with wireless communication feedback

In this paper, the concept of smart battery pack is introduced. The smart battery pack is based on wireless feedback from individual battery cells and is capable to be applied to electric vehicle applications. The proposed solution increases the usable capacity and prolongs the life cycle of the batteries by directly integrating the battery management system in the battery pack. The battery cells are connected through half-bridge chopper circuits, which allow either the insertion or the bypass of a single cell depending on the current states of charge. This consequently leads to the balancing of the whole pack during both the typical charging and discharging time of an electric vehicle and enables the fault-tolerant operation of the pack. A wireless feedback for implementing the balancing method is proposed. This solution reduces the need for cabling and simplifies the assembling of the battery pack, making also possible a direct off-board diagnosis. The paper validates the proposed smart battery pack and the wireless feedback through simulations and experimental results by adopting a battery cell emulator

Simulation, Set-Up, and Thermal Characterization of a Water-Cooled Li-Ion Battery System

A constant and homogenous temperature control of Li-ion batteries is essential for a good performance, a safe operation, and a low aging rate. Especially when operating a battery with high loads in dense battery systems, a cooling system is required to keep the cell in a controlled temperature range. Therefore, an existing battery module is set up with a water-based liquid cooling system with aluminum cooling plates. A finite-element simulation is used to optimize the design and arrangement of the cooling plates regarding power consumption, cooling efficiency, and temperature homogeneity. The heat generation of an operating Li-ion battery is described by the lumped battery model, which is integrated into COMSOL Multiphysics. As the results show, a small set of non-destructively determined parameters of the lumped battery model is sufficient to estimate heat generation. The simulated temperature distribution within the battery pack confirmed adequate cooling and good temperature homogeneity as measured by an integrated temperature sensor array. Furthermore, the simulation reveals sufficient cooling of the batteries by using only one cooling plate per two pouch cells while continuously discharging at up to 3 C

A novel endurance prediction method of series connected lithium-ion batteries based on the voltage change rate and iterative calculation.

High-power lithium-ion battery packs are widely used in large and medium-sized unmanned aerial vehicles and other fields, but there is a safety hazard problem with the application that needs to be solved. The generation mechanism and prevention measurement research is carried out on the battery management system for the unmanned aerial vehicles and the lithium-ion battery state monitoring. According to the group equivalent modeling demand of the battery packs, a new idea of compound equivalent circuit modeling is proposed and the model constructed to realize the accurate description of the working characteristics. In order to realize the high-precision state prediction, the improved unscented Kalman feedback correction mechanism is introduced, in which the simplified particle transforming is introduced and the voltage change rate is calculated to construct a new endurance prediction model. Considering the influence of the consistency difference between battery cells, a novel equilibrium state evaluation idea is applied, the calculation results of which are embedded in the equivalent modeling and iterative calculation to improve the prediction accuracy. The model parameters are identified by the Hybrid Pulse Power Characteristic test, in which the conclusion is that the mean value of the ohm internal resistance is 20.68mΩ. The average internal resistance is 1.36mΩ, and the mean capacitance value is 47747.9F. The state of charge prediction error is less than 2%, which provides a feasible way for the equivalent modeling, battery management system design and practical application of pack working lithium-ion batteries