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This paper proposes an analytical framework describing how initial capacity and resistance variability in parallel-connected battery cells may inflict additional variability or reduce variability while the cells age. We derive closed-form equations for current and SOC imbalance dynamics within a charge or discharge cycle. These dynamics are represented by a first-order equivalent circuit model and validated against experimental data. To demonstrate how current and state of charge (SOC) imbalance leads to cell degradation, we developed a successive update scheme in which the intercycle imbalance dynamics update the intracycle degradation dynamics, and vice versa. Using this framework, we demonstrate that current imbalance can cause convergent degradation trajectories, consistent with previous reports. However, we also demonstrate that different degradation assumptions, such as those associated with SOC imbalance, may cause divergent degradation. We finally highlight the role of different cell chemistries, including different OCV function nonlinearities, on system behavior, and derive analytical bounds on the SOC imbalance using Lyapunov analysis. [ABSTRACT FROM AUTHOR]

This paper introduces a new control method for web tension control on a complex roll-to-roll winding machine used in battery production. A traditional web tension control method cannot perform well enough under high winding speed: the parameter tuning process is time-consuming, the disturbance rejection performance is not satisfying, and the control performance is not stable. A hybrid control method is proposed, and it is easy to be implemented on common programming platform for commercial winding machines with an easy tuning process, while providing superior control performance to the traditional control method. The system modeling used in the control method is much simpler than the modeling in most of the tension control researches, providing better feasibility for industrial application. [ABSTRACT FROM AUTHOR]

Electric vehicle production is subjected to high manufacturing cost and environmental impact. Disassembling and remanufacturing the lithium-ion power packs can highly promote electric vehicle market penetration by procuring and regrouping reusable modules as stationary energy storage devices and cut life-cycle cost and environmental impact. Disassembly efficiency is crucial for battery remanufacturing companies in reverse supply chains. However, disassembly planning suffers from high computational complexity and inferior solutions. This paper developed a multi-objective mathematical model and presented a novel hybrid genetic-firework algorithm based on the precedence graph for obtaining solutions to disassemble the electric vehicle power pack into module levels in an efficient manner. The objectives for the model include not only smoothness of working stations, cycle time, and economic returns, but also consider operation safety and energy consumption. The proposed hybrid algorithm explored the performance of the novel solution searching mechanism of combining the firework and genetic algorithms. The proposed approach is compared with the commonly used multi-objective evolutionary algorithms in the literature, showing its feasibility and effectiveness. [ABSTRACT FROM AUTHOR]

Accurate prediction of the remaining useful life (RUL) of lithium-ion batteries can improve the durability, reliability, and maintainability of battery system operation in electric vehicles. To achieve high-accuracy RUL predictions, it is necessary to develop an effective method for long-term nonlinear degradation prediction and quantify the uncertainty of the prediction results. To this end, this paper proposes a hybrid approach for lithium-ion battery RUL prediction based on particle filter (PF) and long short-term memory (LSTM) neural network. First, based on the training set, the model parameters are iteratively updated using the PF algorithm. Second, the LSTM model parameters are obtained using the training set. The mean and standard deviation in the prediction stage are obtained through Monte Carlo (MC) dropout. Finally, the mean value predicted by MC-dropout is used as the measurement for the PF in the prediction phase, the standard deviation represents the uncertainty of the prediction result, and the mean and standard deviation are integrated into the measurement equation of the model. The experimental results show that the proposed hybrid approach has better prediction accuracy than the PF, LSTM algorithm, and two other types of hybrid approaches. The hybrid approach can obtain a narrower confidence interval. [ABSTRACT FROM AUTHOR]

Battery management systems (BMSs), which monitor and optimize performance while ensuring safety, require control-oriented models, i.e., models tailored to the design and implementation of estimation and control algorithms. Physics-based electrochemical models describe detailed battery phenomena, but are too computationally intensive for use in estimation and control applications. Single particle models (SPMs), which retain some of the physics of electrochemical models, are often used for control-oriented battery modeling since they are computationally efficient; however, they are only valid over very low frequency ranges and C-rates. Empirical equivalent circuit models (ECMs) are also used for control-oriented battery modeling since they are computationally efficient and can describe battery behavior over wide frequency ranges; however, they provide no physical understanding of the battery and, therefore, have limited applicability. Further, fractional order terms (e.g., Warburg impedances) are often employed, making the models unwieldy for use in the time domain. This work provides a control-oriented battery model that combines the benefits of SPM and ECM models, while overcoming their limitations. The proposed model incorporates some of the battery physics found in electrochemical models, can easily be used in both the time and frequency domains, and describes battery behavior over its entire frequency range. A linearized single particle model, which incorporates key electrochemical parameters, is used for modeling battery physics at very low frequencies. For low frequencies, integer-order linear systems are used to approximate diffusion physics described by Warburg impedances, and high frequency behavior is modeled by the double layer capacitance effect. The proposed battery model is more computationally efficient than full electrochemical models since it does not require the solution of PDEs, is accurate for a wider frequency range than the SPM considered in this paper, and does not suffer from the unwieldiness and limited applicability of empirical ECMs. The model is validated in the time and frequency domains via a comparison to pseudo-two-dimensional (P2D) model simulations and experimental data. [ABSTRACT FROM AUTHOR]

This paper examines the problem of controlling the exchange of current in photovoltaic-plus-storage systems to achieve photovoltaic (PV) maximum power point tracking (MPPT). This work is motivated by the need for MPPT algorithms that are less costly and complex to implement in PV farms with integrated battery energy storage. We study the online optimal control of a "hybrid" PV/lithium (Li)-ion battery integration topology that is self-balancing in nature. The self-balancing behavior ensures that the state of charge (SOC) across different cells balances to the same stable equilibrium value without needing any balancing power electronics, thereby significantly reducing the integration cost. The DC-DC converters in this hybrid system are controlled to achieve PV MPPT that maximizes energy generation and storage. However, sensing needs for traditional MPPT controllers can render the hybrid system unnecessarily complex and costly. We surmount this problem by: (i) developing a novel model-based PV power estimation algorithm that only requires voltage measurement, and (ii) using this algorithm together with extremum-seeking (ES) control to achieve closed-loop, estimation-based PV MPPT. Simulation case studies show that this estimation-based MPPT controller is able to harness more than 99% of the maximum available solar energy under different irradiation profiles. [ABSTRACT FROM AUTHOR]

The increasing significance on the development of high-performance lithium-ion (Li-ion) batteries is calling for new battery materials, theoretical models, and simulation tools. Lithiation-induced deformation in electrodes calls attention to study the multiphysics coupling between mechanics and electrochemistry. In this paper, a simultaneous multiscale and multiphysics model to study the coupled electrochemistry and mechanics in the continuum battery cell level and the microscale particle level was developed and implemented in comsolmultiphysics. In the continuum scale, the porous electrode theory and the classical mechanics model were applied. In the microscale, the specific particle structure has been incorporated into the model. This model was demonstrated to study the effects of mechanical constraints, charging rate, and silicon/C ratio, on the electrochemical performance. This model provides a powerful tool to perform simultaneous multiscale and multiphysics design on Li-ion batteries, from the particle level to full-cell level. [ABSTRACT FROM AUTHOR]

This paper presents a novel design for a test platform to determine the state of health (SOH) of lithium-ion batteries (LIBs). The SOH is a key parameter of a battery energy storage system and its estimation remains a challenging issue. The batteries that have been tested are 18,650 Li-ion cells as they are the most commonly used batteries on the market. The test platform design is detailed from the building of the charging and discharging circuitry to the software. Data acquired from the testing circuitry are stored and displayed in LabVIEW to obtain the charging and discharging curves. The resulting graphs are compared to the outcome predicted by the battery datasheets, to verify that the platform delivers coherent values. The SOH of the battery is then calculated using a Coulomb counting method in LabVIEW. The batteries will be discharged through various types of resistive circuits, and the differences in the resulting curves will be discussed. A single battery cell will also be tested over 30 cycles and the decrease in the SOH will be clearly identified. [ABSTRACT FROM AUTHOR]

Multilayered ultrasonic welding (USW) is widely used in joining of electrodes or tabs in lithium-ion batteries. To achieve quality joints and enhance the welding process robustness, an improved understanding of the joint formation is highly desirable. In this paper, USW of four-layered Ni-coated Cu is studied to investigate the joint formation at a single interface and joint propagation from interface to interface under both ambient and preheated conditions. The results indicate that joint formation involves three major mechanisms: Ni-Ni bonding with minimal mechanical interlocking, Ni-Ni bonding with moderate mechanical interlocking, and a combination of Ni-Ni bonding, Cu-Cu bonding, and severe mechanical interlocking. Results also show that joints propagate from the interface close to the sonotrode side to that close to the anvil side. It is further observed that the joint formation can be accelerated and the joint strength can be improved with process preheating, especially at the interface closest to the anvil. The effect of preheating is most significant during the early stage of the process, and diminishes as process progresses. The favorable effects of preheating improve the robustness of multilayered USW. [ABSTRACT FROM AUTHOR]

This paper introduces a new method to monitor battery state of health (SOH). In particular, the side reaction current density is estimated as a direct SOH indicator for the first time and its estimation is formulated as an inaccessible subsystem identification problem, where the battery health subsystem is treated as an inaccessible subsystem with the side reaction current density as the output. Inaccessibility in this context refers to the fact that the inputs and outputs of the subsystem are not measurable in situ. This subsystem is identified using retrospective-cost subsystem identification (RCSI) algorithm, and the output of the identified battery health subsystem provides an estimate for the side reaction current density. Using an example parameter set for a LiFePO4 battery, simulations are performed to obtain estimates under various current profiles. These simulations show promising results in identifying the battery health subsystem and estimating the side reaction current density with RCSI under ideal conditions. Robustness of the algorithm under nonideal conditions is analyzed. Estimation of the side reaction current density using RCSI is shown to be sensitive to nonideal conditions that cause errors in the measurement or estimation of the battery voltage. A method for quantitatively assessing the impact of nonideal conditions on the side reaction current estimation accuracy is provided. The proposed estimation technique, including the method for estimating the side reaction current density using RCSI and the framework analyzing its robustness, can also be applied to other parameter sets and other battery chemistries to monitor the SOH change resulting from any electrochemical-based degradation mechanism that consumes cyclable Li-ions. [ABSTRACT FROM AUTHOR]

Ultrasonic metal welding has been used to join multiple layers of battery tabs with the bus bar in lithium-ion battery assembly operations. This paper describes joint performance models for ultrasonic metal welds of multiple layers of dissimilar battery tab materials, i.e., aluminum and copper. Finite element (FE) models are developed to predict the mechanical performance of the ultrasonically welded joints. The models predict peak shear load, energy absorption capability, and failure modes, which are necessary for modeling product performance and defining process requirements for the welds. The models can be adjusted to represent different quality of welds created in conditions of underweld (UW), normal-weld (NW), or overweld (OW) using physical attributes observed through microscopic analysis. The models are validated through lap shear tests, which demonstrate excellent agreement for the maximum force in the NW condition and good agreement for the UW and OW conditions. The models provide in-depth understanding of the relationship among welding process parameters, physical weld attributes, and the weld performance. The models also provide significant insight for further development of ultrasonic welding process for battery tabs and help optimize welding process for more than four-layered joints. [ABSTRACT FROM AUTHOR]

This paper presents a tool wear monitoring framework for ultrasonic metal welding which has been used for lithium-ion battery manufacturing. Tool wear has a significant impact on joining quality. In addition, tool replacement, including horns and anvils, constitutes an important part of production costs. Therefore, a tool condition monitoring (TCM) system is highly desirable for ultrasonic metal welding. However, it is very challenging to develop a TCM system due to the complexity of tool surface geometry and a lack of thorough understanding on the wear mechanism. Here, we first characterize tool wear progression by comparing surface measurements obtained at different stages of tool wear, and then develop a monitoring algorithm using a quadratic classifier and features that are extracted from space and frequency domains of cross-sectional profiles on tool surfaces. The developed algorithm is validated using tool measurement data from a battery plant. [ABSTRACT FROM AUTHOR]

Low-order, explicit models of lithium ion cells are critical for real-time battery management system (BMS) applications. This paper presents a seventh-order, electrolyte enhanced single particle model (ESPM) with electrolyte diffusion and temperature dependent parameters (ESPM-T). The impedance transfer function coefficients are explicit in terms of the model parameters, simplifying the implementation of temperature dependence. The ESPM-T model is compared with a commercially available finite volume based model and results show accurate matching of pulse responses over a wide range of temperature (T) and C-rates (I). The voltage response to 30 s pulse charge-discharge current inputs is within 5% of the commercial code for 25°C < T < 50°C at I ≤ 12.5C and -- 10°C < T < 50°C at I ≤ IC for a graphite!nickel cobalt manganese (NCM) lithium ion cell. [ABSTRACT FROM AUTHOR]

This paper develops reduced order, linear models of lithium ion batteries that can be used for model-based power train simulation, design, estimation, and controlling in hybrid and electric vehicles (HEV). First, a reduced order model is derived from the fundamental governing electrochemical charge and Li+ conservation equations that are linearized at the operating state of charge and low current density. The equations are solved using analytical and numerical techniques to produce the transcendental impedance or transfer function from input current to output voltage. This model is then reduced to a low order state space model using a system identification technique based on least squares optimization. Given the prescribed current, the model predicts voltage and other variables such as electrolyte and electrode surface concentration distributions. The second model is developed by neglecting electrolyte diffusion and modeling each electrode with a single active material particle. The transcendental particle transfer functions are discretized using a Padé Approximation. The explicit form of the single particle model impedance can be realized by an equivalent circuit with resistances and capacitances related to the cell parameters. Both models are then tuned to match experimental electrochemical impedance spectroscopy (EIS) and pulse current-voltage data. [ABSTRACT FROM AUTHOR]

This paper details the fabrication and testing of a combined temperature and expansion sensor to improve state of charge (SOC) and state of health (SOH) estimation for Li-ion batteries. These sensors enable the characterization of periodic stress and strain changes in the electrode materials of Lithium-ion batteries during the charge and discharge process. These ultrathin sensors are built on a polyimide substrate which can enable direct integration between cells without compromising safety or cell cooling design. Leveraging the sensor design and fabrication process used to create inductive coil eddy current (EC) sensors for crack detection, these sensors were characterized on three Panasonic 5 A-h cells showing the capability to measure expansion of Li-ion batteries. By sensing the intercalation effects, which cause cell expansion, improvements in estimation of SOH and SOC can be enabled through the use of physics-based battery models, which combine the thermal, mechanical, and electrochemical aspects of its operation. [ABSTRACT FROM AUTHOR]

Portable bandsaws are gaining in popularity for their use on remote jobsites to efficiently cut structural materials such as bar, pipe, and channel. Some of their increased popularity is due to the recent introduction of high watt-hour lithium ion batteries, which has further improved the portability of bandsaws by making them cordless. However, with cordless bandsaws, knowledge of cutting rates becomes more important as battery runtime limits productivity. Unlike industrial cutoff bandsaws that typically have feed rate control, the cutting rate of portable bandsaws is determined by operator applied pressure and gravity. While some research has highlighted the cutting mechanics of bandsaws and related wear processes, there is a lack of progress in the area of predicting cutoff time as a function of sawing parameters, such as applied thrust force, blade speed, workpiece material properties, and geometry of the cross section. Research was conducted to develop and experimentally verify a mechanistic model to predict cutting rates of various cross sectional geometries with a gravity fed portable bandsaw. The analytical model relies upon experimental determination of a cutting constant equation, which was developed for a low carbon steel workpiece cut with an 18 teeth per inch (TPI) blade. The model was employed to predict crosscutting times for steel rounds, squares, and tubes for several conditions of thrust force and blade speed. Model predictions of cutting time were in close agreement with experimental results. [ABSTRACT FROM AUTHOR]