Design Of Battery Management System Research Articles

Design and Implementation of Battery Management System for Portable Solar Panel with Coulomb Counting Method

Secondary batteries are commonly used as the storage of energy produced by solar panels. However, the utilization of a battery without proper management can cause damage due to overcharging and over-discharging. This study aims to design a battery management system (BMS) on a Valve Regulated Lead-Acid (VRLA) battery. The method used was the battery State of Charge (SOC) estimation using Coulomb Counting (CC) method. The results showed that the BMS was successfully designed and implemented to automatically cut-off the current when the SOC value is 100% (charging limit) and 20% (discharging limit).

Analysis of performance improvement in energy storage system for electric vehicles: a review

The selection of energy storage system is very crucial for electric vehicles. It should have good energy density, considerable power density and also it must be light weight. So a battery with considerably high energy density must be used in electric vehicles. Lithium ion batteries are very much preferred as electric vehicle batteries. They have high energy density, high life cycle and smooth operation. But the problem related with lithium batteries is they have high temperature sensitivity, and their operation will be affected by over current charging and over current discharging beyond their maximum rated values and also influenced by driving conditions and performance of motors used. So battery management, control and optimisation system is essential in electric vehicle energy storage battery packs. This paper is a review of the design of a novel battery management and control system for lithium ion batteries for performance improvement in electric vehicles.

Analysis of performance improvement in energy storage system for electric vehicles: a review

The selection of energy storage system is very crucial for electric vehicles. It should have good energy density, considerable power density and also it must be light weight. So a battery with considerably high energy density must be used in electric vehicles. Lithium ion batteries are very much preferred as electric vehicle batteries. They have high energy density, high life cycle and smooth operation. But the problem related with lithium batteries is they have high temperature sensitivity, and their operation will be affected by over current charging and over current discharging beyond their maximum rated values and also influenced by driving conditions and performance of motors used. So battery management, control and optimisation system is essential in electric vehicle energy storage battery packs. This paper is a review of the design of a novel battery management and control system for lithium ion batteries for performance improvement in electric vehicles.

A novel fractional variable-order equivalent circuit model and parameter identification of electric vehicle Li-ion batteries

Accurate Li-ion battery modeling is integral to the design of effective battery management systems in electric vehicles. However, the voltage–current (U–I) characteristic of Li-ion batteries presents strong nonlinearity. The application of fractional-order models to create lower-order models to represent physical systems (e.g., the battery characteristics for the state of charge estimation) is interesting and timely. In this paper, a novel fractional variable-order equivalent circuit model (FVO-ECM) is proposed to represent the nonlinear U–I characteristic of Li-ion batteries; its parameter identification is achieved and verified by charge and discharge tests. Compared with the integral-order equivalent circuit model and the fractional constant-order model, the proposed FVO-ECM can identify battery nonlinear characteristics most accurately.

Design and Experimental Verification of Voltage Measurement Circuits Based on Linear Optocouplers with Galvanic Isolation for Battery Management Systems

A battery management system (BMS) design, based on linear optocouplers for Lithium-ion battery cells for automotive and stationary applications is proposed. The critical parts of a BMS are the input voltages and currents measurement circuits. In this design, they include linear optocouplers for galvanic isolation between the battery pack and the BMS. Optocouplers based on AlGaAs light emitted diodes (LED) and PIN photodiode with external operational amplifiers are used. The design features linear characteristics, to ensure the accuracy of the measurements. The suggested approach is based on graphical data digitalizing, which gives the precise values for the most sensitive parameters: photocurrent, normalized and transferred servo gain and helps the calculation procedure to be automated with MATLAB scripts. Several mathematical methods in the analysis are used in order for the necessary equations to be derived. The results are experimentally verified with prototypes.

Battery management system on electric bike using Lithium-Ion 18650

<span>Electric bike (E-Bike) is a bicycle driven using an electric motor and uses batteries as the energy source. It is environmentally friendly as no exhaust gas is resulted during its operation. More than one battery is normally required, being arranged in series or in parallel connection. Over limit or overloaded conditions of battery usage will reduce the lifecycle of battery, speed up its replacement and add to the maintenance cost of electric bike. This paper proposes the prevention of such degrading condition using a tool to manage the battery usage both during the charging and discharging process. The proposed electronic Battery Management System (BMS) serves to regulate, monitor, and maintain the condition of batteries to prevent any possible damage. The resulted BMS design could provide a well balancing action in a battery system consisting of 13 cells utilizing the cell-to-cell active balancing method. The test results showed that the proposed BMS could monitor the individual cell voltage with an average error of 0.032 V (0.824</span><span lang="IN">%</span><span>), while reading the charge and discharge current with an average error of 0.04 A (</span><span lang="IN">6.25%</span><span>), and the battery pack temperature with an average error of 1.21<sup>o</sup>C (</span><span lang="IN">2.9%</span><span>). Additionally, the BMS could offer a functional battery pack protection system from conditions such as undervoltage, overvoltage, overheat, and overcurrent.</span>

Nonlinear Model Predictive Control Strategies for Optimal Charging of a Lithium-Ion Battery

Lithium-ion (Li-ion) batteries, due to their high energy/power density, good temperature range and long battery life, can be used in a wide range of applications from electric vehicles to smart phones and smart grids1 2. For an optimal and safe operation of a Li-ion battery, Battery Management Systems (BMS) are pivotal. BMS monitors voltage, current and temperature, predicts State of Charge (SOC), State of Health (SOH) and estimates time before complete charge/discharge3. BMS depends on the mathematical models of Li-ion batteries for managing and regulating their performance. Most of the current BMS deploy empirical or semi-empirical models which may lead to inaccurate predictions of internal states3. Comparatively, using first principles models based on electrochemical and thermodynamic principles help in accurately predicting the dynamic nature of the internal states3. Therefore, BMS based on physics-based models is imperative for an enhanced and efficient performance of a Li-ion battery. Model Predictive Control (MPC) can be used in the design of BMS due its efficiency in handling constraints in an explicit manner and its ability for performing multivariable control.4–6 In MPC, the controller uses an explicit process model to generate a sequence of manipulated variable moves over a control horizon by optimizing an objective over a prediction horizon in a receding horizon approach.7–9 Classical MPC algorithms use linear process models to predict the outputs over the prediction horizon4,5. But, the main limitation in such classical MPC schemes is the usage of the linear prediction models restricting their application only to mildly nonlinear processes9. Though linear approximations of the first principles models have been used in the past for determining optimal charging profiles for Li-ion batteries,4–6 these versions might not be well placed to accurately capture the dynamics of the system. Traditionally, high computational cost of online calculations has been often cited as one of the main reasons for restricted use of nonlinear MPC formulations for battery models4–6. To the best of our knowledge, non-linear model predictive control (NMPC) has not yet been applied on physics-based battery models. In the current work, we present a strategy to formulate an NMPC framework for the reformulated pseudo-2D model in real-time. Such a strategy will be extremely useful for BMS applications. The performance objective in NMPC is defined to minimize the error between the deviation of the desired controlled variable from its set point. Optimal current densities (manipulated variables), satisfying a set of different bounds for a defined performance criteria, are reported. The performance of the proposed strategy is illustrated by (i) servo and regulatory control problems with Voltage, SOC as output variables (ii) maximizing SOC, (iii)minimizing the capacity fade, while simultaneously having constraints on state variables such as temperature and anode over-potential to mitigate lithium plating. Acknowledgements The work presented herein was funded in part by the Advanced Research Projects Agency –Energy (ARPA-E), U.S. Department of Energy, under Award Number DE-AR0000275 along with the Clean Energy Institute (CEI) at the University of Washington (UW) and the Washington Research Foundation.

Deep-Discharging Li-Ion Battery State of Charge Estimation Using a Partial Adaptive Forgetting Factors Least Square Method

State of charge (SOC) estimation of deep-discharging Li-ion batteries under complicated working conditions at different temperatures is still challenging. Nowadays, the depth of discharge (DOD) of batteries in electric vehicles (EVs) is generally low, resulting in the insufficient use of battery energy. This paper proposes a SOC estimation method using a novel partial adaptive forgetting factors recursive least square (PAFFRLS), which adjusts the forgetting factors based on the own physical properties of each parameter in equivalent circuit models (ECMs) to accommodate to greatly changing under deep-discharging range and high dynamic working conditions. The gain matrix in the proposed method is split to update independently according to each parameter, which solves the issue of mutual influence between parameters vary with different rates. In addition, four typical test profiles, including DST, UDDS, US06, and EUDC, are employed to simulate different working conditions of EVs. Eventually, numerous simulations and experiments results at different temperatures are employed to verify the validity of the proposed method. All average errors of the SOC estimation under four different kinds of working conditions are less than 1.3% as well as all peak errors are less than 5%. All peak errors are less than 3% while DOD is larger than 90%, which illustrates the effectiveness of the proposed method in the case of deep-discharging and provides better guidance to the design of battery management system (BMS) in EVs.

Modeling the Impedance Characterization of Prismatic Lithium-Ion Batteries

Abstract The internal battery resistance can be measured using any one of the three available methods; conductance, impedance, or resistance measurements. An experimentally validated modeling technique using COMSOL multiphysics software has been developed to explain the importance of electrochemical impedance spectroscopy (EIS) modeling. The influence of the design parameters on the cell impedance in a wide frequency range has been investigated. Some of the defined properties studied in this work may have a significant role in the design of battery management system (BMS) for EVs and HEVs. The verification results have shown the dependency of the impedance calculation on active material particle radius and the current rate, especially at the positive electrode.

Power battery parameter online identification for electric vehicle using a decoupling multiple forgetting factors recursive least squares method

Li-ion batteries are widely used in electric vehicles (EVs). However, the accuracy of online SOC estimation is still challenging due to the time-varying parameters in batteries. This paper proposes a decoupling multiple forgetting factors recursive least squares method (DMFFRLS) for EV battery parameter identification. The errors caused by the different parameters are separated and each parameter is tracked independently taking into account the different physical characteristics of the battery parameters. The Thevenin equivalent circuit model (ECM) is employed considering the complexity of battery management system (BMS) on the basis of comparative analysis of several common battery ECMs. In addition, decoupling multiple forgetting factors are used to update the covariance due to different degrees of error of each parameter in the identification process. Numerous experiments are employed to verify the proposed DMFFRLS method. The parameters for commonly used LiFePO 4 (LFP), Li (NiCoMn)O 2 (NCM) battery cells and battery packs are identified based on the proposed DMFFRLS method and three conventional methods. The experimental results show that the error of the DMFFRLS method is less than 15 mV, which is significantly lower than the conventional methods. The proposed DMFFRLS shows good performance for parameter identification on different kind of batteries, and provides a basis for state of charge (SOC) estimation and BMS design of EVs.

Performance and Life Degradation Characteristics Analysis of NCM LIB for BESS

The battery energy storage system (BESS) market is growing rapidly around the world. Lithium Nickel Cobalt Manganese Oxide (LiNiCoMnO2) is attracting attention due to its excellent energy density, high output power, and fast response characteristics. It is being extensively researched and is finding use in many applications, such as in electric vehicles (EV) and energy storage systems (ESS). The performance and lifetime characteristics of a battery change for varying Ni contents. The consideration of these characteristics of a battery allow for a more reliable battery management system (BMS) design. In this study, various experiments and analyses were carried out using a lithium-ion battery (NCM LIB) with differing Ni contents. In particular, the following two combinations were studied: LiNi0.5Co0.2Mn0.3O2(NCM523) and LiNi0.6Co0.2Mn0.2O2 (NCM622). Various analyses were performed, such as C-rate (C-rate is the charge-discharge rate of a battery relative to nominal capacity) performance tests, hybrid pulse power characterization (HPPC), accelerated deterioration experiments, electrochemical impedance spectroscopy (EIS), parameter estimations of battery equivalent circuits through alternating current (AC) and direct current (DC) impedance, and comparative analyses of battery modeling.

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 20.68 mΩ. The average internal resistance is 1.36 mΩ, 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.

Data-Driven Design of a Cascaded Observer for Battery State of Health Estimation

The design of a combined observer for battery state of charge (SoC) and state of health (SoH) using data-driven models is addressed. Knowledge of both SoC and SoH is an important topic in battery management system design for (hybrid) electrical vehicles. The proposed cascaded observer is designed for estimating SoC and SoH during normal vehicle operation based on measured signals, which are directly available in the car. For an SoC estimation, that is accurate in the entire battery lifespan, the underlying battery model has to be adapted based on the estimated SoH. For that purpose, ageing data analysis using data-driven models and the associated model adaptation is discussed. A cascaded observer structure for combined estimation of SoC and SoH is presented and the performance of the proposed concepts is demonstrated by means of battery ageing data covering the entire lifetime of a lithium-ion cell.

Mechanisms for the evolution of cell variations within a LiNixCoyMnzO2/graphite lithium-ion battery pack caused by temperature non-uniformity

Cell variations always undermine the efficiency of energy utilization of battery pack. How does the temperature non-uniformity affect the status of cell variations remains unclear. This paper investigates the underlying mechanisms of the status change of cell variations caused by temperature non-uniformity in a battery pack. First, the status of cell variations has been defined using a graphical model, in which each cell has a coordinate of (capacity, electric quantity). Second, the status transformation caused by temperature non-uniformity has been clarified. Dynamics that describes the thermal-electrochemical effect on the movement of the coordinate for individual cells are firstly established by transformation functions. The movement of the cell locations in the graphical model can vividly interpret the evolution of cell variations caused by temperature non-uniformity with the help of the built transformation functions. Modelling analysis points out that 5 °C increase in the maximum temperature difference among the battery pack can lead to 1.5%–2% capacity loss of the battery pack, therefore the capacity loss caused by cell variations due to temperature non-uniformity should be considered in battery system design. The methodology can be further used to analyze the status change of cell variations in a battery pack caused by all kinds of temperature non-uniformities, benefiting the design of battery management system with high efficiency of energy utilization.

Reliability design of battery management system for power battery

This paper proposes a distributed battery management system (BMS) to meet the reliability design requirements. The proposed BMS consist of two parts that is the main control module and the sampling module. The reliability and electromagnetic compatibility design of these two modules are presented. In addition, the state of health (SOH) estimation of the two modules is analysed. A reliability test platform is developed and the test results show that the proposed BMS has high reliability.

Electrochemical and Thermal Model of a 21700 Cylindrical Lithium Ion Battery with Integrated Solid Electrolyte Interphase

An exact prediction of temperature inside cylindrical lithium ion (Li-ion) batteries is important for their safe operation, extended life expectancy and development of next generation battery management systems. Research attempts have been made to place temperature sensors inside cylindrical Li-ion batteries [1, 2], but such arrangements are not yet commercially available. Alternatively, a computational fluid dynamics (CFD) approach can be used to accurately predict internal temperature profiles. This computational technique couples the multiphysics phenomena occurring inside cylindrical Li-ion batteries [3, 4]. In this work we present a coupled one dimensional electrochemical and three dimensional thermal CFD model, to predict internal and surface temperature profiles of a commercially available 21700 cylindrical Li-ion battery. Since the operating temperature has an important effect on Li-ion battery ageing, the CFD model is coupled with the solid electrolyte interphase (SEI) formation on the negative electrode. The SEI formation and growth is one of the main reasons that shortens the life expectancy of Li-ion batteries with liquid electrolytes and needs to be addressed in computational models [5]. The developed CFD model will be used to predict temperature profiles and SEI growth response to charge, discharge and rest duty-cycle periods of the 21700 cylindrical Li-ion battery. Surface temperature and voltage profiles acquired from the CFD model will be compared against experimental results to confirm the validity of the computational model. Such an extended model will represent a computational framework to potentially decrease the cost and number of 21700 cylindrical Li-ion battery experiments with different duty-cycle scenarios. It will also support the design of more advanced battery management systems and optimise the cooling and heating system of battery modules and packs.

Design and development of battery management system (BMS) using hybrid multilevel converter

ABSTRACTBatteries that are integrated to the renewable system have to bear a wide range of operational conditions such as varying rates of charge/discharge, depth of discharges, temperature fluctuations and so on. When these batteries are connected in series, after several charging and discharging cycles, the voltage and state-of-charge (SOC) imbalance will occur between different batteries. In the proposed topology, the hybrid multilevel converter is used along with auxiliary battery to get constant output voltage during discharging and store excess power during charging. As a result, the voltage and SOC get balanced and also overcharging and discharging can be avoided. The multilevel output is used to improve the performance of the load and battery lifetime.

Battery health management for small-size rotary-wing electric unmanned aerial vehicles: An efficient approach for constrained computing platforms

This article presents a holistic framework for the design, implementation and experimental validation of Battery Management Systems (BMS) in rotatory-wing Unmanned Aerial Vehicles (UAVs) that allows to accurately (i) estimate the State of Charge (SOC), and (ii) predict the End of Discharge (EOD) time of lithium-polymer batteries in small-size multirotors by using a model-based prognosis architecture that is efficient and feasible to implement in low-cost hardware. The proposed framework includes a simplified battery model that incorporates the electric load dependence, temperature dependence and SOC dependence by using the concept of Artificial Evolution to estimate some of its parameters, along with a novel Outer Feedback Correction Loop (OFCL) during the estimation stage which adjusts the variance of the process noise to diminish bias in Bayesian state estimation and helps to compensate problems associated with incorrect initial conditions in a non-observable dynamic system. Also, it provides an aerodynamic-based characterization of future power consumption profiles. A quadrotor has been used as validation platform. The results of this work will allow making decisions about the flight plan and having enough confidence in those decisions so that the mission objectives can be optimally achieved.

Experimental Identification using Equivalent Circuit Model for Lithium-Ion Battery

Abstract The modelling of Lithium-ion batteries is considered as a powerful tool for the introduction and testing of this technology in energy storage applications. In fact, new application domains for the battery technology have recently placed greater emphasis on their energy management, monitoring, and control strategies. Battery models have become an essential tool for the design of battery-powered systems; their usage includes battery state-of-charge (SoC), state-of-health (SoH) estimations, and battery management system design and battery characterization. This paper presents a method on how to estimate Lithium-Ion battery equivalent circuit model (ECM) parameters based on experimental characteristic measurements by charging and discharging the battery at different modes. The experiment is realized with a computer that realizes the control of charge and discharge via LabVIEWTM software. In this paper, tests are conducted on Lithium-Ion battery 18650 (nominal voltage of 3.7 V and nominal capacity of 2900 mAh) with the proposed method to evaluate the battery model parameters. The proposed method has the best dynamic performance and gives accurate parameter identification which enables the use of models to simulate the battery system performance.

FPGA-based design of advanced BMS implementing SoC/SoH estimators

Energy storage system, usually a battery, become essential part for all electric drive vehicles such as hybrid electric vehicle (HEV), plug-in hybrid electric vehicle (PHEV) and electric vehicle (EV) in the coming decades. These energy storage systems include Li-ion batteries, Ni-MH batteries, lead-acid batteries and ultra-capacitors. An accurate Battery Management System (BMS) is highly demanded integrated system in all electric derive vehicles to ensure the optimum use of an energy storage system. The battery's state monitoring & evaluation, charge control and cell balancing are the important features of any BMS. However, due to unavailability of inaccurate battery's state-of-charge (SoC)/state-of-health (SoH) estimators and uncertainty of battery's performance, new approaches of BMS design are under development to control batteries optimally and hence, the vehicle performance. In addition, most of the existing BMSs either do not provide SoH at all or provide it as a function of capacity degradation over the battery usage. This research paper presents the field-programmable gate array (FPGA) - based Advanced BMS design using MATLAB-to-FPGA design flow. The Advanced BMS design provides the combined estimation of both SoC and SoH of a rechargeable battery. This research paper also summarizes the Neuro-Fuzzy & statistical models implemented in Advanced BMS for accurate estimation of battery's SoC & SoH respectively. Further, this research paper presents the selection of suitable FPGA and its hardware realization implementing Advanced BMS. Finally, the experimental results are confirmed by simulation and synthesis of its register transfer level (RTL) design. FPGA-based Advanced BMS would provide the best chip solution for a generalized BMS with benefits of low Non-recurring engineering (NRE) cost, low power consumption, high speed of operation, large reconfigurable logic and large data storage capacity.