Showing papers on "State of charge published in 2010"

State-of-Charge Estimation for Lithium-Ion Batteries Using Neural Networks and EKF

Abstract: This paper presents a method for modeling and estimation of the state of charge (SOC) of lithium-ion (Li-Ion) batteries using neural networks (NNs) and the extended Kalman filter (EKF). The NN is trained offline using the data collected from the battery-charging process. This network finds the model needed in the state-space equations of the EKF, where the state variables are the battery terminal voltage at the previous sample and the SOC at the present sample. Furthermore, the covariance matrix for the process noise in the EKF is estimated adaptively. The proposed method is implemented on a Li-Ion battery to estimate online the actual SOC of the battery. Experimental results show a good estimation of the SOC and fast convergence of the EKF state variables.

Rule-Based Control of Battery Energy Storage for Dispatching Intermittent Renewable Sources

Abstract: Integrating a battery energy storage system (BESS) with a solar photovoltaic (PV) system or a wind farm can make these intermittent renewable energy sources more dispatchable. This paper focuses on the development of a control strategy for optimal use of the BESS for this purpose. The paper considers a rule-based control scheme, which is the solution of the optimal control problem defined, to incorporate the operating constraints of the BESS, such as state of charge limits, charge/discharge current limits, and lifetime. The goal of the control is to have the BESS provide as much smoothing as possible so that the renewable resource can be dispatched on an hourly basis based on the forecasted solar/wind conditions. The effectiveness of this control strategy has been tested by using an actual PV system and wind farm data and it is shown that the BESS can indeed help to cope with variability in wind's and solar's generation.

Modeling Lithium Ion battery degradation in electric vehicles

Abstract: A new aging model for Lithium Ion batteries is proposed based on theoretical models of crack propagation. This provides an exponential dependence of aging on stress such as depth of discharge. A measure of stress is derived from arbitrary charge and discharge histories to include mixed use in vehicles or vehicle to grid operations. This aging model is combined with an empirical equivalent circuit model, to provide time and state of charge dependent charge and discharge characteristics at any rate and temperature. This choice of model results in a cycle life prediction with few parameters to be fitted to a particular cell.

Lithium-Ion Battery State of Charge and Critical Surface Charge Estimation Using an Electrochemical Model-Based Extended Kalman Filter

Abstract: This paper presents a numerical calculation of the evolution of the spatially resolved solid concentration in the two electrodes of a lithium-ion cell. The microscopic solid concentration is driven by the macroscopic Butler–Volmer current density distribution, which is consequently driven by the applied current through the boundary conditions. The resulting, mostly causal, implementation of the algebraic differential equations that describe the battery electrochemical principles, even after assuming fixed electrolyte concentration, is of high order and complexity and is denoted as the full order model. The full order model is compared with the results in the works of Smith and Wang (2006, “Solid-State Diffusion Limitations on Pulse Operation of a Lithium-Ion Cell for Hybrid Electric Vehicles,” J. Power Sources, 161, pp. 628–639) and Wang et al. (2007 “Control oriented 1D Electrochemical Model of Lithium Ion Battery,” Energy Convers. Manage., 48, pp. 2565–2578) and creates our baseline model, which will be further simplified for charge estimation. We then propose a low order extended Kalman filter for the estimation of the average-electrode charge similarly to the single-particle charge estimation in the work of White and Santhanagopalan (2006, “Online Estimation of the State of Charge of a Lithium Ion Cell,” J. Power Sources, 161, pp. 1346–1355) with the following two substantial enhancements. First, we estimate the average-electrode, or single-particle, solid-electrolyte surface concentration, called critical surface charge in addition to the more traditional bulk concentration called state of charge. Moreover, we avoid the weakly observable conditions associated with estimating both electrode concentrations by recognizing that the measured cell voltage depends on the difference, and not the absolute value, of the two electrode open circuit voltages. The estimation results of the reduced, single, averaged electrode model are compared with the full order model simulation. DOI: 10.1115/1.4002475

Power-Electronics-Based Solutions for Plug-in Hybrid Electric Vehicle Energy Storage and Management Systems

Abstract: Batteries, ultracapacitors (UCs), and fuel cells are widely being proposed for electric vehicles (EVs) and plug-in hybrid EVs (PHEVs) as an electric power source or an energy storage unit. In general, the design of an intelligent control strategy for coordinated power distribution is a critical issue for UC-supported PHEV power systems. Implementation of several control methods has been presented in the past, with the goal of improving battery life and overall vehicle efficiency. It is clear that the control objectives vary with respect to vehicle velocity, power demand, and state of charge of both the batteries and UCs. Hence, an optimal control strategy design is the most critical aspect of an all-electric/plug-in hybrid electric vehicle operational characteristic. Although much effort has been made to improve the life of PHEV energy storage systems (ESSs), including research on energy storage device chemistries, this paper, on the contrary, highlights the fact that the fundamental problem lies within the design of power-electronics-based energy-management converters and the development of smarter control algorithms. This paper initially discusses battery and UC characteristics and then goes on to provide a detailed comparison of various proposed control strategies and proposes the use of precise power electronic converter topologies. Finally, this paper summarizes the benefits of the various techniques and suggests the most viable solutions for on-board power management, more specific to PHEVs with multiple/hybrid ESSs.

Model-Based Electrochemical Estimation and Constraint Management for Pulse Operation of Lithium Ion Batteries

Abstract: High-power lithium ion batteries are often rated with multiple current and voltage limits depending on the duration of the pulse event. These variable control limits, however, are difficult to realize in practice. In this paper, a linear Kalman filter based on a reduced order electrochemical model is designed to estimate internal battery potentials, concentration gradients, and state-of-charge (SOC) from external current and voltage measurements. A reference current governor predicts the operating margin with respect to electrode side reactions and surface depletion/saturation conditions responsible for damage and sudden loss of power. The estimates are compared with results from an experimentally validated, 1-D, nonlinear finite volume model of a 6 Ah hybrid electric vehicle battery. The linear filter provides, to within ~ 2%, performance in the 30%-70% SOC range except in the case of severe current pulses that draw electrode surface concentrations to near saturation and depletion, although the estimates recover as concentration gradients relax. With 4 to 7 states, the filter has low-order comparable to empirical equivalent circuit models commonly employed and described in the literature. Accurate estimation of the battery's internal electrochemical state enables an expanded range of pulse operation.

Optimal Control of Battery Energy Storage for Wind Farm Dispatching

Abstract: Integrating a battery energy storage system (BESS) with a large wind farm can make a wind farm more dispatchable. This paper focuses on development of a control strategy for optimal use of the BESS for this purpose. The paper considers an open-loop optimal control scheme to incorporate the operating constraints of the BESS, such as state of charge limits, charge/discharge current limits, and lifetime. The goal of the control is to have the BESS to provide as much smoothing as possible, so that the wind farm can be dispatched on an hourly basis based on the forecasted wind conditions. The effectiveness of this control strategy has been tested by using an actual wind farm data. Furthermore, a real-time implementation strategy using model predictive control is also proposed. Finally, it is shown that the control strategy is very important in improving the BESS performance for this application.

Battery system and method for system state of charge determination

Abstract: A battery system and method of providing a state of charge of for the system in one embodiment includes at least one first cell, the at least one first cell having a first battery chemistry exhibiting a first open circuit potential, and at least one second cell in series connection with the at least one first cell, the at least one second cell having a second battery chemistry exhibiting a second open circuit potential, wherein the at least one first cell exhibits an open circuit potential with a center slope that is greater than the center slope of the open circuit potential exhibited by the at least one second cell.

Estimation of State of Charge of a Lithium-Ion Battery Pack for Electric Vehicles Using an Adaptive Luenberger Observer

Abstract: In order to safely and efficiently use the power as well as to extend the lifetime of the traction battery pack, accurate estimation of State of Charge (SoC) is very important and necessary. This paper presents an adaptive observer-based technique for estimating SoC of a lithium-ion battery pack used in an electric vehicle (EV). The RC equivalent circuit model in ADVISOR is applied to simulate the lithium-ion battery pack. The parameters of the battery model as a function of SoC, are identified and optimized using the numerically nonlinear least squares algorithm, based on an experimental data set. By means of the optimized model, an adaptive Luenberger observer is built to estimate online the SoC of the lithium-ion battery pack. The observer gain is adaptively adjusted using a stochastic gradient approach so as to reduce the error between the estimated battery output voltage and the filtered battery terminal voltage measurement. Validation results show that the proposed technique can accurately estimate SoC of the lithium-ion battery pack without a heavy computational load.

Adaptive Equivalent Consumption Minimization Strategy for Hybrid Electric Vehicles

Abstract: This paper proposes a new method for solving the energy management problem for hybrid electric vehicles (HEVs) based on the equivalent consumption minimization strategy (ECMS). After discussing the main features of ECMS, an adaptation law of the equivalence factor used by ECMS is presented, which, using feedback of state of charge, ensures optimality of the strategy proposed. The performance of the A-ECMS is shown in simulation and compared to the optimal solution obtained with dynamic programming.Copyright © 2010 by ASME

State of charge estimation using an unscented filter for high power lithium ion cells

Abstract: High power lithium ion batteries are increasingly used in power tools, hybrid electric vehicles and military applications, as a transient power source capable of delivering instant energy, around a relatively fixed state of charge (SOC). Maintaining the battery within pre-specified limits for SOC is important, since lithium ion batteries are prone to safety and/or performance issues during overcharge or rapid discharge below the cut-off voltages. With an increase in the number of cells used in the battery, SOC has a crucial role in cell balancing and optimization of the pack performance. Several techniques have been proposed for the SOC estimation. Most of the existing literature supports an empirical model based on either an electric circuit, arbitrary pole placement or an analytical expression with an arbitrary set of parameters. In spite of their simplicity, the empirical battery models do not provide information on the physical cell limitations. Alternatively, a rigorous electrochemical cell model, aimed at incorporating transport, kinetic and thermodynamic limitations, can be used to estimate parameters that hold a physical significance and hence provide an accurate measure of the cell performance. However, the demand for onboard estimation devices requires estimation techniques that are computationally efficient. In this work, estimation of the SOC of a lithium ion cell using an unscented filtering algorithm is illustrated. The relative advantages and disadvantages of the proposed methodology are briefly discussed. Copyright © 2009 John Wiley & Sons, Ltd.

Comprehensive dynamic battery modeling for PHEV applications

Abstract: With the increasing demand in PHEV safety, performance, etc., the PHEV applications require a battery model which can accurately reflect and predict the battery performance under different dynamic loads, environmental conditions, and battery conditions. An accurate battery model is the basis of the precise battery state (state of charge, state of health and state of function) estimation. And the precise battery state information can be used to enable the optimal control over the battery's charging/discharging process, therefore to manage the battery to its optimal usage, prolong the battery life, and enable vehicle to grid and vehicle to home applications fitting into the future smart grid scenario. One of the challenges in constructing such a model is to accurately reflect the highly nonlinear battery I-V performance, such as the battery's relaxation effect and the hysteresis effect. This paper will mainly focus on the relaxation effect modeling. The relaxation effect will be modeled through series connected RC circuits. Accuracy analysis demonstrates that with more RC circuit the battery model gives better accuracy, yet increases the total computational time. Therefore, to select an appropriate battery model for a certain PHEV application is formulated as a multi-objective optimization problem balancing between the model accuracy and the computational complexity within the constraints set by the minimum accuracy required and the maximum computational time allowed. This multi-objective optimization problem is mapped into a weighted optimization problem to solve.

Life-Prediction Methods for Lithium-Ion Batteries Derived from a Fatigue Approach I. Introduction: Capacity-Loss Prediction Based on Damage Accumulation

Abstract: The possibility of using mechanical-fatigue life-prognostic methods for the life prediction of Li-ion batteries is evaluated. To this end, a physics-based model of a battery experiencing a single source of aging, i.e., the growth of a solid electrolyte interphase at the carbonaceous anode material, is employed as a surrogate battery. Dummy aging data sets, consisting of current microcycles at a nearly constant state of charge, are readily generated and used to evaluate the prediction capability of damage-accumulation relationships, among which is the Palmgren-Miner (PM) rule, which is well known in the field of mechanical fatigue. The PM rule is slightly more accurate than the regular damage accumulation over time. A complete methodology for predicting the aging of a battery experiencing a complex current profile is proposed in a second paper.

Estimation of the SOC and the SOH of li-ion batteries, by combining impedance measurements with the fuzzy logic inference

Abstract: This paper describes experimental results aiming at analyzing lithiumion batteries performances with aging, for different state of charge and temperatures. The results are provided from electrochemical impedance spectroscopy (EIS) measurements on new and aged cells. A climatic test chamber is used during experiments to simulate the conditions of the temperature. Experimental data are plotted over a broad spectrum of frequencies on Nyquist diagrams. We will also inspect the impact of the presence of a DC current during the EIS measurement. Finally, obtained results will be processed in a way to be used in a fuzzy logic system (FLS), to assess either the state of charge (SOC) or the state of health (SOH) of cells.

Load Frequency Control in power system using Vehicle-to-Grid system considering the customer convenience of Electric Vehicles

Abstract: In recent years, photovoltaic and wind power have generations become the most popular renewable energy based generations. However, these power generations cannot supply constant electric power output and sometimes cause the imbalance between supply and demand. To alleviate the mentioned problem, Battery Energy Storage System (BESS), which is significantly expensive, is indispensable. A new idea of Vehicle-to-Grid (V2G) is that a battery of Electric-Vehicle (EV) can be used as an equivalent large-scale BESS in a power system. Thus, the deployment of EVs can reduce the required capacity of BESS. This paper proposes a V2G model considering EV customers' convenience and the State Of Charge (SOC) control method by the local control centers which enables the SOCs of the EVs to be synchronized. The simulation results show that the EVs equipped with the V2G controller can help the conventional thermal power plants suppress the system frequency fluctuation.

Grid-Tied PV system energy smoothing

Abstract: Grid-tied PV energy smoothing was implemented by using a valve regulated lead-acid (VRLA) battery as a temporary energy storage device to both charge and discharge as required to smooth the inverter energy output from the PV array. Inverter output was controlled by the average solar irradiance over the previous 1h time interval. On a clear day the solar irradiance power curve is offset by about 1h, while on a variable cloudy day the inverter output power curve will be smoothed based on the average solar irradiance. Test results demonstrate that this smoothing algorithm works very well. Battery state of charge was more difficult to manage because of the variable system inefficiencies. Testing continued for 30-days and established consistent operational performance for extended periods of time under a wide variety of resource conditions. Both battery technologies from Exide (Absolyte) and East Penn (Advanced Valve Regulated Lead-Acid) proved to cycle well at a partial state of charge over the time interval tested.

Energy efficient and fast charge modes of a rechargeable battery

Abstract: A method of providing power to an electronic device in an energy-efficient manner includes transitioning between power states corresponding to charging and discharging a battery. The state of charge of the battery is detected. Upon detecting a high threshold state of charge, an external power source such as an AC-to-DC adapter is disabled, and the battery to provides primary power to the electronic device. Upon a low threshold state of charge, the AC-to-DC adapter is controlled to provide a high current output to charge the battery and provide primary power to the electronic device. The power states, when cycled over time based on the state of the battery, provide for an energy-efficient method of powering the electronic device.

Energy management for plug-in hybrid electric vehicles using equivalent consumption minimisation strategy

Abstract: One strategy to minimise petroleum fuel consumption of a Plug-in Hybrid Electric Vehicle (PHEV) is to attain the lowest admissible battery State of Charge (SOC) at the end of driving cycle while following an optimal SOC profile. The challenge of an optimisation algorithm is to find this optimal profile by using least future information about the power demand. An application of Equivalent Consumption Minimisation Strategy (ECMS) for PHEV is presented in this paper and benchmarked against the dynamic programming (DP) for information requirement and optimality. The optimality is assessed in simulation by considering petroleum fuel economy and deviation of the optimal SOC profile from a reference profile for different driving scenarios and battery sizes. Results show that for longer distances and larger battery sizes, ECMS and DP provide similar fuel economy and SOC profiles. A sensitivity analysis with respect to driving distance is presented at the end of the paper.

Load model for prediction of electric vehicle charging demand

Abstract: Increasing environmental concerns, the decarbonisation of future auto industry, the consequent regulatory requirements and the depletion of oil have made the fuel independent battery electric vehicle (EV), with zero emission increasingly more attractive as practical and economical alternative to the gasoline fuelled car. The expected increasing number of EV connected to power systems for charging will have significant impact on power systems, such as generation capacity, transformer loading level, line congestion level and load profile, among which, the impact of EV charging load on the system load profile claims most attention. This paper develops a methodology to determine the EV battery charging load on the power system load profile. Three scenarios were simulated, comprising uncontrolled charging, controlled off-peak charging and smart charging. The proposed method in this paper takes into account the initial state of charge and start time of EV battery charging. Results show that uncontrolled charging will impose a new peak to the system and may cause congestion issues to local network. Controlled off-peak charging can shift EVs charging load to an off-peak time, however, EV can also introduce a new peak or near peak in early off-peak time. Smart charging method which optimises the start time of EVs charging is the most beneficial charging method to both distribution network operator and EV users.

Cell equalization in battery stacks through State Of Charge estimation polling

Abstract: Differences in chemical characteristics, operating temperature or different internal resistance can cause differences in cell remaining capacity, leading to overcharging or overdischarging of respectively most charged and most discharged cells, decreasing the total stack storage capacity, shortening the battery lifetime and, eventually, permanently damaging the cells. The system proposed in this paper uses the switching capacitor method for cell equalization during bidirectional operations and it relies on a State Of Charge (SOC) estimator in order to select the target cell for charge distribution. The SOC estimator, based on an Extended Kalman Filter (EKF) [14], polls cells sequentially and updates simple coulomb-counting SOC estimators. The polling limits the speed of equalization but it reduces the computational load of multiple EKF for every cell in the stack.

Prediction of state of charge of Lithium-ion rechargeable battery with electrochemical impedance spectroscopy theory

Abstract: For the purpose of predicting the state of charge of Lithium-ion rechargeable battery accurately, we introduced the method of electrochemical impedance spectroscopy to solve the problem. The experiment data of impedance spectroscopy is comprised of an inductive arc in the high-frequency region and two capacitive arcs in the low-frequency region, and by which the reasonable equivalent circuit of battery was established. The component parameters obtained at several state of charge values of the battery had been analyzed by a non-linear least-squares fitting procedure and some electrochemical knowledge. Through researching the changing regulation of parameters with the different States of charge, the frequency of maximum of the semicircle (f max ), the phase angle o, the equivalent series capacitance (C s ) had been substantiated to be the suitable parameters for analyzing and predicting the state of charge values of the lithium-ion battery.

Research on Li-ion Battery Management System

Abstract: Battery management system (BMS) is an integral part of the electric vehicle (EV) and the hybrid electric vehicle (HEV). The BMS performs the tasks by integrating one or more of the functions, such as sampling the voltages of the battery cells and the temperatures in the battery module, sampling the voltage of the battery, sampling the current of the battery, as well as cells balancing and determining the state of charge (SOC). In this paper the research of the cells voltage sampling, battery temperature sampling, battery voltage sampling and battery current sampling will be discussed. The method of sampling cells voltage, battery temperature and cells balancing using special integrate circuit (IC) will be presented. This method resolves the problems of sampling cells voltage in Li-ion battery, which has hundreds of cells. We discuss two methods about the result of battery current integral, and pick out the better via the data of experiment.

State estimation of a reduced electrochemical model of a lithium-ion battery

Abstract: Batteries are the key technology for enabling further mobile electrification and energy storage. Accurate prediction of the state of the battery is needed not only for safety reasons, but also for better utilization of the battery. In this work we present a state estimation strategy for a detailed electrochemical model of a lithium-ion battery. The benefit of using a detailed model is the additional information obtained about the battery, such as accurate estimates of the state of charge within the individual electrodes, overpotential, concentration and current distribution across the electrodes, which can be utilized for safety and optimal operation. We propose an observer based on a reduced set of partial differential-algebraic equations, which are solved on a coarse spatial grid. The reduced model has a less complex structure, while still capturing the main dynamics. The observer is tested in experiments for actual electric-vehicle drive cycles. Experimental results show the observer to be robust with respect to unmodeled dynamics, as well as to noisy and biased voltage and current measurements. The available state estimates can be used for monitoring purposes, or incorporated into a model based controller to improve the performance of the battery while guaranteeing safe operation.

State of charge range

Abstract: A management system for a battery cell pack, the management system including a controller determining an adjustable charge profile for the battery cell pack wherein the adjustable charge profile includes an operational parameter identifying a next operation drive range mode from a set of drive range modes for the battery cell pack wherein each the drive range mode includes a state of charge (SOC) window between a charge SOC and a discharge SOC, with the set of drive range modes including a first drive range mode having a first SOC window and including a second drive range mode having a second SOC window less than the first SOC window; and one or more energy transfer stages to produce the charge SOC of the next operation drive range mode in the battery cell pack.

A universal state-of-charge algorithm for batteries

Abstract: State-of-charge (SOC) measures energy left in a battery, and it is critical for modeling and managing batteries. Developing efficient yet accurate SOC algorithms remains a challenging task. Most existing work uses regression based on a time-variant circuit model, which may be hard to converge and often does not apply to different types of batteries. Knowing open-circuit voltage (OCV) leads to SOC due to the well known mapping between OCV and SOC. In this paper, we propose an efficient yet accurate OCV algorithm that applies to all types of batteries. Using linear system analysis but without a circuit model, we calculate OCV based on the sampled terminal voltage and discharge current of the battery. Experiments show that our algorithm is numerically stable, robust to history dependent error, and obtains SOC with less than 4% error compared to a detailed battery simulation for a variety of batteries. Our OCV algorithm is also efficient, and can be used as a real-time electro-analytical tool revealing what is going on inside the battery.

On Conversion of Hybrid Electric Vehicles to Plug-In

Abstract: The retrofit conversion of currently available hybrid electric vehicles (HEVs) to plug-in HEVs (PHEVs) is studied in this paper through experiments and simulations using the powertrain system analysis toolkit (PSAT). First, a rule-based fuzzy controller of the battery energy-management unit is developed to simulate different energy-management policies. Second, by modifying the energy-control strategy, the model of the conversion PHEV (C-PHEV) is verified with experiments. Finally, the C-PHEV model is used to simulate different battery energy-management control strategies. The results show improvement in fuel economy, whereas the energy-management controller discharges the power through the plug-in battery pack only when the state of charge of the base vehicle battery is close to its minimum value. This method keeps the advantage of driving in electric mode using a combination of two batteries and optimizing the use of regenerative braking capabilities, which is the main advantage of HEVs. It is also found that increasing the power threshold of the internal combustion engine (ICE) improves the performance of C-PHEV. Increasing the ICE power threshold increases the engine efficiency by running the engine in its efficient points. It also drives the vehicle in electric mode in higher power demands.