Showing papers on "State of charge published in 2008"

Trip-Based Optimal Power Management of Plug-in Hybrid Electric Vehicles

Abstract: Hybrid electric vehicles (HEVs) have demonstrated the capability to improve fuel economy and emissions. The plug-in HEV (PHEV), utilizing more battery power, has become a more attractive upgrade of the HEV. The charge-depletion mode is more appropriate for the power management of PHEVs, i.e., the state of charge (SOC) is expected to drop to a low threshold when the vehicle reaches the trip destination. Trip information has so far been considered as future information for vehicle operation and is thus not available a priori. This situation can be changed by the recent advancement in intelligent transportation systems (ITSs) based on the use of on-board global positioning systems (GPSs), geographical information systems (GISs), and advanced traffic flow modeling techniques. In this paper, a new approach to optimal power management of PHEVs in the charge-depletion mode is proposed with driving cycle modeling based on the historic traffic information. A dynamic programming (DP) algorithm is applied to reinforce the charge-depletion control such that the SOC drops to a specific terminal value at the end of the driving cycle. The vehicle model was based on a hybrid electric sport utility vehicle (SUV). Only fuel consumption is considered for the current stage of the study. A simulation study was conducted for several standard driving cycles and two trip models using the proposed method, and the results showed significant improvement in fuel economy compared with a rule-based control and a depletion sustenance control for most cases. Furthermore, the results showed much better consistency in fuel economy compared with rule-based and depletion sustenance control.

Electrical battery model for use in dynamic electric vehicle simulations

Abstract: Simulation of electric vehicles, hybrid electric vehicles, and plug-in hybrid electric vehicles over driving schedules within a full dynamic hybrid and electric vehicle simulator requires battery models capable of predicting state-of-charge, I-V characteristics, and dynamic behavior of different battery types. A battery model capable of reproducing lithium-ion, nickel-metal hydride, and lead- acid I-V characteristics (with minimal model alterations) is proposed. A battery-testing apparatus was designed to measure the proposed parameters of the battery model for all three battery types and simulate driving schedules with a programmed source and load configuration. A multiple time-constant battery model was used for modeling lithium- ion batteries; verification of time constants in the seconds to minutes and hour ranges has been shown in numerous research articles and a time constant in the millisecond range is verified here with experiments. Lack of significant time constants in the millisecond range is validated through direct testing. A modeled capacity-rate effect within the state-of-charge determination portion of the proposed model is verified experimentally to ensure accurate prediction of battery state of charge after lengthy driving schedules. The battery model was programmed into a Matlab/Simulink environment and used as a power source for plug-in hybrid electric vehicle simulations. Results from simulations of lithium-ion battery packs show that the proposed battery model behaves well with the other subcomponents of the vehicle simulator; accuracy of the model and prediction of battery internal losses depends on the extent of tests performed on the battery used for the simulation. Extraction of model parameters and their dependence on temperature and cycle number is ongoing, as well as validation of the Simulink model with hardware- in-the-loop "driving schedule" cycling of real batteries.

Nonlinear State of Charge Estimator for Hybrid Electric Vehicle Battery

Abstract: A new method for battery state of charge estimation using a sliding mode observer has been developed. A nonlinear battery dynamic modeling technique is established and design methodology with the sliding mode observer is presented. Contrary to the conventional methods using complicated battery modeling, a simple resistor-capacitor battery model was used in this work. The modeling errors caused by the simple model are compensated by the sliding mode observer. The structure of the sliding mode observer is simple, but it shows robust control property against modeling errors and uncertainties. The convergence of the proposed observer has been proved by the equivalent control method. The performance of the system has been verified by the sequence of urban dynamometer driving schedule test. The test results of the proposed observer system shows robust tracking performance under real driving environments.

Lithium-ion battery state of charge estimation with a Kalman Filter based on a electrochemical model

Abstract: Lithium-ion battery is the core of new plug-in hybrid-electrical vehicles (PHEV) as well as considered in many 2nd generation hybrid electric vehicles (HEV). In most cases the lithium-ion battery performance plays an important role for the energy management of these vehicles as high-rate transient power source cycling around a relatively fixed state of charge (SOC). In this paper an averaged electrochemical Lithium-ion battery model suitable for estimation is presented. The model is based on an averaged approximated relationship between (i) the Butler-Volmer current and the solid concentration at the interface with the electrolyte and (ii) the battery current and voltage. A 4th order model based extended Kalman filter (EKF) is then designed and the estimation results are tested in simulation with the non-averaged model.

Battery Management System Based on Battery Nonlinear Dynamics Modeling

Abstract: This paper presents a method of determining electromotive force and battery internal resistance as time functions, which are depicted as functions of state of charge (SOC) because . The model is based on battery discharge and charge characteristics under different constant currents that are tested by a laboratory experiment. This paper further presents the method of determining the battery SOC according to a battery modeling result. The influence of temperature on battery performance is analyzed according to laboratory-tested data, and the theoretical background for calculating the SOC is obtained. The algorithm of battery SOC indication is depicted in detail. The algorithm of the battery SOC ldquoonlinerdquo indication considering the influence of temperature can be easily used in practice by a microprocessor. An NiMH battery is used in this paper to depict the modeling method. In fact, the method can also be used for different types of contemporary batteries, as well as Li-ion batteries, if the required test data are available.

Battery Management Systems: Accurate State-of-Charge Indication for Battery-Powered Applications (Philips Research Book Series) (Philips Research Book Series)

Abstract: Battery Management Systems: Accurate State-of-Charge Indication for Battery-Powered Applications describes the field of State-of-Charge (SoC) indication for rechargeable batteries. With the emergence of battery-powered devices accurately estimating the battery SoC, and even more important the remaining time of use, becomes more and more important. An overview of the state-of-the-art of SoC indication methods including available market solutions from leading semiconductor companies, e.g. Texas Instruments, Microchip, Maxim, is given in the first part of this book. Furthermore, a universal SoC indication system that enables 1% or better accuracy under all realistic user conditions is developed. A possible integration with a newly developed ultra-fast recharging algorithm is also described. The contents of this book builds further on the contents of the first volume in the Philips Research Book Series, Battery Management Systems - Design by Modelling. Since the subject of battery SoC indication requires a n umber of disciplines, this book covers all important disciplines starting from (electro)chemistry to understand battery behaviour, via mathematics to enable modelling of the observed battery behaviour and measurement science to enable accurate measurement of battery variables and assessment of the overall accuracy, to electrical engineering to enable an efficient implementation of the developed SoC indication system. It will therefore serve as an important source of information for any person working in engineering and involved in battery management.

An Improved Battery Characterization Method Using a Two-Pulse Load Test

Abstract: It is very important to have the ability to determine the available capacity, the state of charge (SoC), and the state of health (SoH) of a battery; this ensures that the battery has the available power for the system requirements. A battery is aged by charging and discharging cycles; this process degrades the chemical composition of the battery. An undercharged battery has sulphation and stratification effects that shorten the lifetime of the battery. Overcharging causes gassing and water loss. This paper describes a novel two-pulse test to determine the AHC, SoC, and SoH of a valve regulated lead acid (VRLA) and a lithium ion battery. These parameters are related to the voltage drop after each pulse of current discharge. The first pulse stabilizes the battery relative to its previous history, and the second pulse establishes the parameters. The new approach is fully validated by experiment.

Self-learning control system for plug-in hybrid vehicles

Abstract: A system is provided to instruct a plug-in hybrid electric vehicle how optimally to use electric propulsion from a rechargeable energy storage device to reach an electric recharging station, while maintaining as high a state of charge (SOC) as desired along the route prior to arriving at the recharging station at a minimum SOC. The system can include the step of calculating a straight-line distance and/or actual distance between an orientation point and the determined instant present location to determine when to initiate optimally a charge depleting phase. The system can limit extended driving on a deeply discharged rechargeable energy storage device and reduce the number of deep discharge cycles for the rechargeable energy storage device, thereby improving the effective lifetime of the rechargeable energy storage device. This “Just-in-Time strategy can be initiated automatically without operator input to accommodate the unsophisticated operator and without needing a navigation system/GPS input.

Battery Management Systems: Accurate State-Of-Charge Indication for Battery-Powered Applications

Abstract: This book describes the field of State-of-Charge (SoC) indication for rechargeable batteries An overview of the state-of-the-art of SoC indication methods including available market solutions from leading semiconductor companies is provided All disciplines are covered, from electrical, chemical, mathematical and measurement engineering to understanding battery behavior This book will therefore is for persons in engineering and involved in battery management

Method for operating an engine in a hybrid powertrain system

Abstract: An engine and an electric machine are operative to communicate tractive power with a transmission device to control output power to an output member. The electric machine is electrically coupled to an energy storage device. A method for controlling the engine and electric machine includes monitoring an operator request for power, monitoring a state of charge of the energy storage device, determining an operating cost for each of a plurality of candidate engine operating points based on the operator request for power and the state of charge of the energy storage device; and operating the engine at the candidate engine operating point having a preferred operating cost.

Method for controlling power flow within a powertrain system

Abstract: A method for operating a powertrain system includes monitoring a state of charge of an energy storage device and determining an effective state of charge based upon the monitored state of charge of the energy storage device and a range of available power from the energy storage device. A preferred output power to an output member is determined. A preferred charge state for operating the powertrain is concurrently selected with operating an engine in one of a cylinder deactivation state and an all-cylinder state based upon the effective state of charge and the preferred output power to the output member.

Design of a Conceptual Framework for the V2G Implementation

Abstract: The major increases in oil prices and the rising environmental concerns are key drivers in the growing popularity of electric and plug-in hybrid vehicles. Car manufacturers understand this trend quite well and are developing new models. For the 90% of Americans who use their cars to get to work every day, the average daily commute distance is 45 km and the average daily time that cars remain parked is 22 hours. A salient feature that these vehicles have in common is the batteries, which provide good storage capacity that can be effectively integrated into the grid. We focus on the design of a conceptual framework needs to integrate the electric vehicles into the grid the so-called V2G concept. The basic premise we use is to treat the battery vehicles as distributed energy resources that can act both as supply and demand resources. We assess the deployment of an aggregation of battery vehicles for the provision of frequency regulation requiring very fast response times and energy supply for peak shaving. We also investigate the impacts of the aggregated battery vehicle charging load on the low load generation schedules and on regulation requirements. The assessment of these impacts takes into consideration the explicit representation of uncertainty and the importance of the state of charge as a key variable in the use of the batteries for the supply and demand roles. For the framework completeness, we also explore the role of the energy services provider in the V2G integration.

Automotive battery management systems

Abstract: Battery management system (BMS) is an integral part of an automobile. It protects the battery from damage, predicts battery life and maintains the battery in an operational condition. The BMS performs these tasks by integrating one or more of the functions, such as protecting the cell, controlling the charge, determining the state of charge (SOC), the state of health (SOH), and the remaining useful life (RUL) of the battery, cell balancing, as well as monitoring and storing historical data. In this paper, we propose a BMS that estimates three critical characteristics of the battery (SOC, SOH, and RUL) using a data-driven approach. Our estimation procedure is based on an equivalent circuit battery model consisting of resistors, capacitor, and Warburg impedance. The resistors usually characterize the self-discharge and internal resistance of the battery, the capacitor generally represents the charge stored in the battery, and the Warburg impedance represents the diffusion phenomenon. We investigate the use of support vector machines to predict the capacity fade and power fade, which characterize the SOH of a battery, as well as estimate the SOC of the battery. The circuit parameters are estimated from electrochemical impedance spectroscopy (EIS) test data using nonlinear least squares estimation techniques. Predictions of remaining useful life (RUL) of the battery are obtained by support vector regression of the power fade and capacity fade estimates.

Model-based electrochemical estimation of lithium-ion batteries

Abstract: 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. The estimates are compared with results from an experimentally validated one-dimensional nonlinear finite volume model of a 6 Ah hybrid electric vehicle battery. The linear filter gives, 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; however, the estimates recover as concentration gradients relax. With 4 to 7 states, the filter has low order comparable to empirical equivalent circuit models but provides estimates of the batterypsilas internal electrochemical state.

Plugin hybrid electric vehicle with V2G optimization system

Abstract: In one aspect of the present invention, a vehicle comprises: a consumable fuel powered engine, a battery and an electric motor powered by the battery. The battery is rechargeable both from an external electric power source (such as an electric power grid) and from the consumable fuel powered engine. A computer receives data as inputs and providing outputs, wherein the input data includes an expected state of the electric power source at a time when the vehicle is expected to be coupled to the electric power source. The outputs include control signals to control the state of charge of the battery during the time the vehicle is expected to be coupled to the electric power source.

Comparison of battery charging algorithms for stand alone photovoltaic systems

Abstract: The battery is the most common method of energy storage in stand alone solar systems; the most popular being the valve regulated lead acid battery (VRLA) due to its low cost and ease of availability. Photovoltaics are not an ideal source for charging batteries as their output is heavily dependent on weather conditions. Therefore, when batteries are used in photovoltaic systems, the performance characteristics differ significantly from batteries used in more traditional applications and the battery life is usually shortened. In conditions of varying solar radiation and load profile the battery may experience a low state of charge (SOC). A low SOC for extended periods of time will cause increased sulphation, which severely reduces the life of the battery. Typically, steps are carried out to protect the battery and to charge the battery more effectively. Such methods include intermittent charging (IC), three stage charging (TSC) and interrupted charge control (ICC), among others. This paper quantifies the effectiveness of these three battery charging algorithms and evaluates their ability to maintain the battery at a high state of charge. The measurement setup is comprised of a solar simulator, which replicates the output of a large 50 W photovoltaic panel using a low power cell. Repeatable load and solar radiation profiles and temperature control are implemented using LabView so that identical operating conditions can be set up to compare the three battery charging systems.

Charge-discharge management apparatus and computer readable medium comprising instructions for achieving the apparatus

Abstract: In charge-discharge control for a battery in a hybrid vehicle, when a difference between a target SOC (State Of Charge) and a present SOC becomes greater than or equal to a reference range in an estimated route to a destination, a charge schedule is drawn up again as a charge re-schedule. However, if the number of times of the re-schedule becomes greater than or equal to predetermined N times or if a remaining distance to the destination becomes less than a predetermined reference distance, a hybrid control based on the charge schedule is stopped without the charge re-schedule drawn up.

State-of-charge estimation for lead-acid batteries based on dynamic open-circuit voltage

Abstract: The charging and discharging characteristics of lead-acid batteries are investigated to learn the relationship between the state-of-charge (SOC) and the dynamically changed open-circuit voltage Experimental results indicate that the open-circuit voltage of the lead-acid battery varies methodically with the charging or discharging rates and the duration since they have been disconnected from the load or charger Accordingly, a dynamic open-circuit voltage method by taking account of the open-circuit time and the previous charging/discharging rate is capable of more precisely estimating the SOC in a shorter time Experiments that emulate practical operations are carried out to demonstrate the effectiveness and accuracy of the proposed method

Method for battery capacity estimation

Abstract: An embodiment contemplates a method for estimating a capacity of a battery. A state of charge is determined at a first instant of time and at a second instant of time. A difference in the state of charge is determined between the first instant of time and the second instant of time. A net coulomb flow is calculated between the first instant of time and the second instant of time. The battery capacity is determined as a function of the change in the state of charge and the net coulomb flow.

Vehicle, method of estimating state of charge of secondary battery, and method of controlling vehicle

Abstract: A vehicle comprising a chargeable/dischargeable battery, a current sensor for detecting the current from the battery, and a control unit for estimating the charged state of the battery and controlling the charging/discharging of the battery according to the charged state. In a first operation mode in which charging and discharging cycles are repeated, the control unit estimates the open-circuit voltage of the battery, and determines the charged state on the basis of the value which is obtained by correcting the open-circuit voltage according to the polarization. In a second operation mode in which either one of charging and discharging is continued, the control unit determines the charged state on the basis of the result of integration of the current detected by the current sensor. Hence, the charged and discharged amperehours are large, thereby providing a vehicle which can effectively utilize the secondary battery.

Apparatus and Methods of Determination of State of Charge in a Redox Flow Battery

Abstract: Apparatus and methods for determining the individual states of charge of electrolytes in a redox battery with mixed or unmixed reactants by optical absorption spectrophotometry are disclosed. The state of charge thus obtained may serve as a gauge for the amount of electro-chemical energy left in the system. Further, the information on anolyte and catholyte charge states may be used for any rebalancing mechanism if the states are different.

State of charge equalizing device and assembled battery system including same

Abstract: A state-of-charge equalizing device equalizes the state of charge of each of cells connected in series to form an assembled battery, and comprises charging/discharging circuits connected in parallel to the respective cells to discharge and/or charge the respective cells, voltage measurement circuits connected to the respective charging/discharging circuits to measure the voltages across the respective cells, and a control circuit. The control circuit comprises a unit calculating an equalization target voltage based on the measured voltages before the charge or discharge starts, a unit starting discharging/charging the cells having a voltage thereacross different from the target voltage, a unit correcting the target voltage to thereby calculate a corrected equalization target voltage for each of those cells based on the difference between the measured voltages before and at a certain time period after the charging/discharging, and a unit ending the discharging/charging when the measured voltage reaches the corrected target voltage.

Estimation of battery state-of-charge using ν-support vector regression algorithm

Abstract: Accurately estimating the SOC of a battery during the electric vehicle drive cycle is a vital issue that currently remains unresolved. A support vector regression algorithm (SVR), which has good nonlinear approximation ability, a quick convergence rate and global optimal solution, is proposed to estimate the battery SOC. First, the training data and the test data required in the estimation operation are collected using the ADVISOR software, followed by normalization of the data above. Then, cross validation and grid search methodologies are used to determine the parameters in the ν-SVR model. Finally, simulation experiments have been carried out in the LIBSVM simulator. The simulation results show that, compared to the BP neural network algorithm, the ν-Support Vector Regression algorithm performs better in estimating the battery SOC.