Showing papers on "State of charge published in 2016"

Lithium-ion battery structure that self-heats at low temperatures

Abstract: Lithium-ion batteries suffer severe power loss at temperatures below zero degrees Celsius, limiting their use in applications such as electric cars in cold climates and high-altitude drones. The practical consequences of such power loss are the need for larger, more expensive battery packs to perform engine cold cranking, slow charging in cold weather, restricted regenerative braking, and reduction of vehicle cruise range by as much as 40 per cent. Previous attempts to improve the low-temperature performance of lithium-ion batteries have focused on developing additives to improve the low-temperature behaviour of electrolytes, and on externally heating and insulating the cells. Here we report a lithium-ion battery structure, the 'all-climate battery' cell, that heats itself up from below zero degrees Celsius without requiring external heating devices or electrolyte additives. The self-heating mechanism creates an electrochemical interface that is favourable for high discharge/charge power. We show that the internal warm-up of such a cell to zero degrees Celsius occurs within 20 seconds at minus 20 degrees Celsius and within 30 seconds at minus 30 degrees Celsius, consuming only 3.8 per cent and 5.5 per cent of cell capacity, respectively. The self-heated all-climate battery cell yields a discharge/regeneration power of 1,061/1,425 watts per kilogram at a 50 per cent state of charge and at minus 30 degrees Celsius, delivering 6.4-12.3 times the power of state-of-the-art lithium-ion cells. We expect the all-climate battery to enable engine stop-start technology capable of saving 5-10 per cent of the fuel for 80 million new vehicles manufactured every year. Given that only a small fraction of the battery energy is used for self-heating, we envisage that the all-climate battery cell may also prove useful for plug-in electric vehicles, robotics and space exploration applications.

Coordinated Control of Distributed Energy-Storage Systems for Voltage Regulation in Distribution Networks

Abstract: In this paper, distributed energy-storage systems (ESSs) are proposed to solve the voltage rise/drop issues in low-voltage (LV) distribution networks with a high penetration of rooftop photovoltaics (PVs). During the peak PV generation period, the voltages are mitigated by charging the ESSs, and the stored energy is discharged for voltage support during the peak load period. The impact of storage devices integrated with the PV source on feeder voltages is investigated in detail. A coordinated control method, which includes distributed and localized controls, is proposed for distributed ESSs. The distributed control using the consensus algorithm regulates the feeder voltages within the required limits, while the localized control regulates the state of charge (SoC) of each ESS within the desired SoC range. The entire control structure ensures voltage regulation while effectively utilizing storage capacity under various operation conditions. The proposed control method is evaluated in LV distribution networks, and the simulation results validate the effectiveness of this method.

Lithium Polymer Battery State-of-Charge Estimation Based on Adaptive Unscented Kalman Filter and Support Vector Machine

Abstract: An accurate algorithm for lithium polymer battery state-of-charge (SOC) estimation is proposed based on adaptive unscented Kalman filters (AUKF) and least-square support vector machines (LSSVM). A novel approach using the moving window method is applied, with AUKF and LSSVM to accurately establish the battery model with limited initial training samples. The effectiveness of the moving window modeling method is validated by both simulations and lithium polymer battery experimental results. The measurement equation of the proposed AUKF method is established by the LSSVM battery model and AUKF has the advantage of adaptively adjusting noise covariance during the estimation process. In addition, the developed LSSVM model is continuously updated online with new samples during the battery operation, in order to minimize the influence of the changes in battery internal characteristics on modeling accuracy and estimation results after a period of operation. Finally, a comparison of accuracy and performance between the AUKF and UKF is made. Simulation and experiment results indicate that the proposed algorithm is capable of predicting lithium battery SOC with a limited number of initial training samples.

High-performance flexible energy storage and harvesting system for wearable electronics.

Abstract: This paper reports on the design and operation of a flexible power source integrating a lithium ion battery and amorphous silicon solar module, optimized to supply power to a wearable health monitoring device. The battery consists of printed anode and cathode layers based on graphite and lithium cobalt oxide, respectively, on thin flexible current collectors. It displays energy density of 6.98 mWh/cm(2) and demonstrates capacity retention of 90% at 3C discharge rate and ~99% under 100 charge/discharge cycles and 600 cycles of mechanical flexing. A solar module with appropriate voltage and dimensions is used to charge the battery under both full sun and indoor illumination conditions, and the addition of the solar module is shown to extend the battery lifetime between charging cycles while powering a load. Furthermore, we show that by selecting the appropriate load duty cycle, the average load current can be matched to the solar module current and the battery can be maintained at a constant state of charge. Finally, the battery is used to power a pulse oximeter, demonstrating its effectiveness as a power source for wearable medical devices.

A Practical Scheme to Involve Degradation Cost of Lithium-Ion Batteries in Vehicle-to-Grid Applications

Abstract: This paper concentrates on degradation of electric vehicle (EV) lithium-ion batteries in vehicle-to-grid (V2G) programs and proposes a practical wear cost model for EVs charge scheduling applications. As the first step, all the factors affecting the cycle life of lithium-ion batteries are identified and their impacts on degradation process are investigated. Subsequently, a general model for battery loss of cycle life is devised incorporating all the pertinent factors associated with charging and discharging activities in V2G applications. Modeling the battery wear cost as a series of equal-payments over the cycle life, a mechanism for calculating the cost incurred by EV users due to participation in V2G programs is developed. Taking into account the developed battery degradation cost model, EVs charge scheduling problem is revisited and it is formulated as a mixed integer linear programming problem. As the actual battery degradation cost and adopted charging strategy are mutually dependent, a novel iterative method is proposed to efficiently obtain the optimal solution to charge scheduling problem and calculate the associated wear price. Several case studies are presented to demonstrate the effectiveness and applicability of the proposed method in integrating the degradation cost of lithium-ion batteries into charge scheduling of V2G-capable EVs.

An Adaptive Fuzzy Logic-Based Energy Management Strategy on Battery/Ultracapacitor Hybrid Electric Vehicles

Abstract: One of the key issues for the development of electric vehicles (EVs) is the requirement of a supervisory energy management strategy, especially for those with hybrid energy storage systems. An adaptive fuzzy logic-based energy management strategy (AFEMS) is proposed in this paper to determine the power split between the battery pack and the ultracapacitor (UC) pack. A fuzzy logic controller is used due to the complex real-time control issue. Furthermore, it does not need the knowledge of the driving cycle ahead of time. The underlying principles of this adaptive fuzzy logic controller are to maximize the system efficiency, to minimize the battery current variation, and to minimize UC state of charge (SOC) difference. NetLogo is used to assess the performance of the proposed method. Compared with other three energy management strategies, the simulation and experimental results show that the proposed AFEMS promises a better comprehensive control performance in terms of the system efficiency, the battery current variation, and differences in the UC SOC, for both congested city driving and highway driving situations.

Electrochemical Model-Based State of Charge and Capacity Estimation for a Composite Electrode Lithium-Ion Battery

Abstract: Increased demand for hybrid and electric vehicles has motivated research to improve onboard state of charge (SOC) and state of health estimation (SOH). In particular, batteries with composite electrodes have become popular for automotive applications due to their ability to balance energy density, power density, and cost by adjusting the amount of each material within the electrode. SOH algorithms that do not use electrochemical-based models may have more difficulty maintaining an accurate battery model as the cell ages under varying degradation modes, such as lithium consumption at the solid-electrolyte interface or active material dissolution. Furthermore, efforts to validate electrochemical model-based state estimation algorithms with experimental aging data are limited, particularly for composite electrode cells. In this paper, we first present a reduced-order electrochemical model for a composite LiMn2O4-LiNi1/3Mn1/3Co1/3O2 electrode battery that predicts the surface and bulk lithium concentration of each material in the composite electrode, as well as the current split between each material. The model is then used in dual-nonlinear observers to estimate the cell SOC and loss of cyclable lithium over time. Three different observer types are compared: 1) the extended Kalman filter; 2) fixed interval Kalman smoother; and 3) particle filter. Finally, an experimental aging campaign is used to compare the estimated capacities for five different cells with the measured capacities over time.

Reconfigurable Battery Techniques and Systems: A Survey

Abstract: Battery packs with a large number of battery cells are becoming more and more widely adopted in electronic systems, such as robotics, renewable energy systems, energy storage in smart grids, and electronic vehicles. Therefore, a well-designed battery pack is essential for battery applications. In the literature, the majority of research in battery pack design focuses on battery management system, safety circuit, and cell-balancing strategies. Recently, the reconfigurable battery pack design has gained increasing attentions as a promising solution to solve the problems existing in the conventional battery packs and associated battery management systems, such as low energy efficiency, short pack lifespan, safety issues, and low reliability. One of the most prominent features of reconfigurable battery packs is that the battery cell topology can be dynamically reconfigured in the real-time fashion based on the current condition (in terms of the state of charge and the state of health) of battery cells. So far, there are several reconfigurable battery schemes having been proposed and validated in the literature, all sharing the advantage of cell topology reconfiguration that ensures balanced cell states during charging and discharging, meanwhile providing strong fault tolerance ability. This survey is undertaken with the intent of identifying the state-of-the-art technologies of reconfigurable battery as well as providing review on related technologies and insight on future research in this emerging area.

Robust Adaptive Sliding-Mode Observer Using RBF Neural Network for Lithium-Ion Battery State of Charge Estimation in Electric Vehicles

Abstract: This paper presents a robust sliding-mode observer (RSMO) for state-of-charge (SOC) estimation of a lithium-polymer battery (LiPB) in electric vehicles (EVs). A radial basis function (RBF) neural network (NN) is employed to adaptively learn an upper bound of system uncertainty. The switching gain of the RSMO is adjusted based on the learned upper bound to achieve asymptotic error convergence of the SOC estimation. A battery equivalent circuit model (BECM) is constructed for battery modeling, and its BECM is identified in real time by using a forgetting-factor recursive least squares (FFRLS) algorithm. The experiments under the discharge current profiles based on EV driving cycles are conducted on the LiPB to validate the effectiveness and accuracy of the proposed framework for the SOC estimation.

A symmetric organic-based nonaqueous redox flow battery and its state of charge diagnostics by FTIR

Abstract: Redox flow batteries have shown outstanding promise for grid-scale energy storage to promote utilization of renewable energy and improve grid stability. Nonaqueous battery systems can potentially achieve high energy density because of their broad voltage window. In this paper, we report a new organic redox-active material for use in a nonaqueous redox flow battery, 2-phenyl-4,4,5,5-tetramethylimidazoline-1-oxyl-3-oxide (PTIO) that has high solubility (>2.6 M) in organic solvents. PTIO exhibits electrochemically reversible disproportionation reactions and thus can serve as both anolyte and catholyte redox materials in a symmetric flow cell. The PTIO flow battery has a moderate cell voltage of ∼1.7 V and shows good cyclability under both cyclic voltammetry and flow cell conditions. Moreover, we demonstrate that FTIR can offer accurate estimation of the PTIO concentration in electrolytes and determine the state of charge of the PTIO flow cell, suggesting FTIR as a powerful online battery status sensor. This study is expected to inspire more insights in this under-addressed area of state of charge analysis aiming at operational safety and reliability of flow batteries.

Design and Control of Modular Multilevel Converters for Battery Electric Vehicles

Abstract: New advanced power conversion systems play an essential role in the extension of range and life of batteries. This paper proposes a new modular multilevel converter with embedded electrochemical cells that achieves very low cell unbalancing without traditional balancing circuits and a negligible harmonic content of the output currents. In this new topology, the cells are connected in series by means of half-bridge converters, allowing high flexibility for the discharge and recharge of the battery. The converter features a cell balancing control that operates on each individual arm of the converter to equalize the state of charge of the cells. The paper shows that the proposed control does not affect the symmetry of the three-phase voltage output, even for significantly unbalanced cells. The viability of the proposed converter for battery electric vehicles and the effectiveness of the cell balancing control are confirmed by numerical simulations and experiments on a kilowatt-size prototype.

Battery state of charge estimation using a load-classifying neural network

Abstract: Battery state-of-charge estimation is an important component in battery management system design. Many known issues with lithium ion batteries such as performance decay, accelerated aging and even hazardous incidents were associated with faulty state-of-charge estimation. Different estimation algorithms can be summarized in a nutshell as: 1) modeless approaches, i.e. columbic counting; 2). model based observers, i.e. extended Kalman filter; and 3). data driven nonlinear models, i.e. neural networks, and learning machines. This paper adopts the third approach, and proposes a new architecture for SoC estimation using a load-classifying neural network. This approach pre-processes battery inputs and categorizes battery operation modes as idle, charge and discharge, with three neural networks trained in parallel. Using a vehicle drive cycle load profile for model training and a pulse test duty cycle for validation, the proposed method yields a 3.8% average estimation error. This result demonstrates that data driven machine learning approach can deliver estimation performance comparable with other advanced observer designs. The neural network however has a simpler model training procedure, boarder choice of training data, and smaller computational cost. In addition, with simple filtering and output constraints, estimation error spikes associated with ‘uncharted’ inputs can be effectively suppressed.

Cooperative Multi-Agent Control of Heterogeneous Storage Devices Distributed in a DC Microgrid

Abstract: This paper proposes a multi-agent control strategy to coordinate power sharing between heterogeneous energy storage devices distributed throughout a DC microgrid. Without requiring a central controller, the proposed control strategy extends the benefits offered by hybrid energy storage systems to DC microgrids with batteries and ultracapacitors spatially distributed at different levels of the power distribution hierarchy. The proposed control strategy has the following advantages. 1) The high frequency microgrid load is provided by the ultracapacitors. 2) The low frequency load is provided by batteries used for bulk energy storage during islanded mode, and the main grid during grid connected operation. 3) The ultracapacitor voltages are regulated at a desired reference. 4) State of charge balancing is provided between the batteries. 5) The energy storage systems cooperate based on neighbor-to-neighbor output feedback over a sparse communication network. The only communication requirement is a spanning tree from the ultracapacitor leaders and battery leaders to their respective followers. Simulations are presented demonstrating the performance of the proposed control strategy for a 380 VDC datacenter during grid connected and islanded operation.

Lithium-Ion Battery Aging Experiments at Subzero Temperatures and Model Development for Capacity Fade Estimation

Abstract: Lithium-ion (Li-ion) batteries widely used in electric vehicles (EVs) and hybrid EVs (HEVs) are insufficient for vehicle use after they have degraded to 70% to 80% of their original capacity. Battery lifespan is a large consideration when designing battery packs for EVs/HEVs. Aging mechanisms, such as metal dissolution, growth of the passivated surface film layer on the electrodes, and loss of both recyclable lithium ions, affect the longevity of the Li-ion battery at high-temperature operations. Even vehicle maneuvers at low temperatures $(T contribute to battery lifetime degradation, owing to the anode electrode vulnerability to other degradation mechanisms such as lithium plating. Nowadays, only a few battery thermal management schemes have properly considered low-temperature degradation. This is due to the lack of studies on aging of Li-ion batteries at subzero temperature. This paper investigates how load cycle and calendar life properties affect the lifetime and aging processes of Li-ion cells at low temperatures. Accelerated aging tests were used to determine the effect of the ambient temperature on the performance of three 100-Ah LiFeMnP04 Li-ion cells. Two of them were aged through a normalized driving cycle at two temperature tests ( $-\mbox{20}\ ^\circ\mbox{C}$ and 25 °C). The calendar test was carried out on one single battery at –20 °C and mid-range of state of charge (50%). Their capacities were continuously measured every two or three days. An aging model is developed and added to a preliminary single-cell electrothermal model to establish, in future works, a thermal strategy capable of predicting how the cell ages. This aging model was then validated by comparing its predictions with the aging data obtained from a cycling test at 0 °C.

State-of-Charge and State-of-Health Lithium-Ion Batteries’ Diagnosis According to Surface Temperature Variation

Abstract: This paper presents a hybrid state-of-charge (SOC) and state-of-health (SOH) estimation technique for lithium-ion batteries according to surface temperature variation (STV). The hybrid approach uses an adaptive observer to estimate the SOH while an extended Kalman filter (EKF) is used to predict the SOC. Unlike other estimation methods, the closed-loop estimation strategy takes into account the STV and its stability is guaranteed by Lyapunov direct method. In order to validate the proposed method, experiments have been carried out under different operating temperature conditions and various discharge currents. Results highlight the effectiveness of the approach in estimating SOC and SOH for different aging conditions.