203035796
Cranking Capability Estimation Algorithm Based on Modeling and Online Update of Model Parameters for Li-Ion SLI Batteries
The terminal voltage of a starting–lighting–ignition (SLI) battery can decrease to a value lower than the allowable voltage range because of the high discharge current required to crank the engine of a vehicle. To avoid the safety problems generated by this voltage drop, this paper proposes a cranking capability estimation algorithm. The proposed algorithm includes an equivalent circuit model for describing the instantaneous voltage response to the cranking current profile. This algorithm predicts the minimum value of the terminal voltage for the cranking transient period by analyzing the polarization voltage and dynamic characteristic of the equivalent circuit model. The estimation accuracy is adjusted by an online update for the parameters of the equivalent circuit model, which varies with temperature, aging, and other factors. The proposed algorithm was validated by experiments with a 60Ah LiFePO4-type SLI battery.
111828834
State of health estimation algorithm of LiFePO4 battery packs based on differential voltage curves for battery management system application
This paper discusses a novel differential voltage curve capacity estimation to determine the state of health of LiFePO4 cells. Differential voltage curves are used because of their ability to detect and quantify degradation mechanisms. The estimation is carried out through partial charging or discharging tests, and is specifically designed for battery management systems, due to the trade off between accuracy and low computational effort. This means the method can be effectively executed online, in a real application. The technique is also able to accurately detect the end of life of the cells.
18715389
High-performance battery-pack power estimation using a dynamic cell model
In some battery applications, such as in hybrid electric vehicles or battery electric vehicles, it is necessary to be able to estimate, in real time, the present available power that may be sourced by the battery pack. Similarly, in rechargeable packs, it may be necessary to know how much charging power the pack can accept. These values must be carefully calculated in such a way that the pack will not be damaged by over/under charge or voltage or by exceeding a design current or power limit. This paper describes a method that uses a dynamic cell model and state-of-charge side information to very accurately predict the battery-pack available power.
108638937
A comparative study of state of charge estimation algorithms for LiFePO4 batteries used in electric vehicles
One of the most important aspects in battery management systems (BMS) in electric vehicles is the state of charge (SOC) estimation. SOC needs to be accurately determined for safety and performance reasons but cannot be measured directly due to the flatness and hysteresis of the open circuit voltage (OCV) curve of Lithium-ion chemistries as LiFePO4. The classical approach of current integration (Coulomb counting) cannot solve the problems of accumulative error and inaccurate initial values, thus advanced estimation algorithms are applied to determine the sate of charge. In this work, three model-based state observer designs including Luenberger observer, Extended Kalman Filter (EKF) and Sigma Point Kalman Filter (SPKF) are carried out and studied. These estimation approaches are verified using measurement data acquired from commercial LiFePO4 cells. In addition, computational tests analyze the systems performances in terms of tracking accuracy, estimation robustness against temperature uncertainty, sensor drift, and convergence behavior with an initial SOC offset.
23593858
Battery Health Prognosis for Electric Vehicles Using Sample Entropy and Sparse Bayesian Predictive Modeling
Battery health monitoring and management is of extreme importance for the performance and cost of electric vehicles. This paper is concerned with machine-learning-enabled battery state-of-health (SOH) indication and prognosis. The sample entropy of short voltage sequence is used as an effective signature of capacity loss. Advanced sparse Bayesian predictive modeling (SBPM) methodology is employed to capture the underlying correspondence between the capacity loss and sample entropy. The SBPM-based SOH monitor is compared with a polynomial model developed in our prior work. The proposed approach allows for an analytical integration of temperature effects such that an explicitly temperature-perspective SOH estimator is established, whose performance and complexity is contrasted to the support vector machine (SVM) scheme. The forecast of remaining useful life is also performed via a combination of SBPM and bootstrap sampling concepts. Large amounts of experimental data from multiple lithium-ion battery cells at three different temperatures are deployed for model construction, verification, and comparison. Such a multi-cell setting is more useful and valuable than only considering a single cell (a common scenario). This is the first known application of combined sample entropy and SBPM to battery health prognosis.
18593854
Online Algorithms for Automotive Idling Reduction With Effective Statistics
Idling, or running the engine when the vehicle is not moving, accounts for 13%-23% of vehicle driving time and costs billions of gallons of fuel each year. In this paper, we consider the problem of idling reduction under the uncertainty of vehicle stop time. We abstract it as a classic ski rental problem, and propose a constrained version with two statistics μB- and qB+, the expected length of short stops and the probability of long stops. We develop two online algorithms, a suboptimal closed-form algorithm and an optimal numerical solution, that combine the best of the well-known deterministic and randomized schemes to minimize the worst case competitive ratio. We demonstrate the algorithms perform better than existing solutions in terms of both worst case guarantee and average case performance using simulation and real-world driving data.
110006697
Idling-stop vehicle road tests of advanced valve-regulated lead-acid (VRLA) battery
The results of road tests on valve-regulated lead-acid (VRLA) batteries in an idling-stop (stop and go) vehicle are reported. Idling-stop systems are simple systems to improve fuel economy of automobiles. They are expected to spread widely from an environmental perspective. Performances of a conventional flooded battery, a conventional VRLA battery, and an improved VRLA battery were compared in road tests with an idling-stop vehicle. It was found that the improved VRLA battery was suited to idling-stop applications because it had a smaller capacity loss than the conventional flooded battery during partial-state-of-charge (PSoC) operation. The positive grid was corroded in layers, unlike the usual grain boundary corrosion of SLI battery grid. It is because the corrosion proceeded mainly under PSoC conditions. The corrosion rate could be controlled by potential control of positive plates.
28398557
A Generic Electrothermal Li-ion Battery Model for Rapid Evaluation of Cell Temperature Temporal Evolution
This paper presents a generic electrothermal model for Li-ion battery. The model is developed with the objective to simplify the parameter identification procedure, while representing adequately the thermal effects on the battery performance. Most of the well-accepted electrothermal Li-ion battery models require in-depth and proprietary battery data or dedicated test environments for parameter identification. The dedicated test bench usually involves expensive thermal test chambers, calorimeters, and temperatures sensors, and challenges associated with their installations. This makes the electrical and thermal simulation of Li-ion batteries difficult to achieve. This paper proposes a generic electrothermal model with a simpler parameter identification process. The parameters identification process is solely based on datasheet discharge curves and simple experiments at room temperature. The model is validated experimentally using a 12 V 40 Ah LiFePO4 battery module. The performance of the model is tested with constant current discharges, constant current-constant voltage charges, as well as with a Simplified Federal Urban Driving Schedule dynamic driving cycle, at different operating temperatures. As expected, the simulation results show an error within ±1% and ±1.3% compared to experimental results, for both steady and dynamic states, respectively.
104554442
An online method to simultaneously identify the parameters and estimate states for lithium ion batteries
Abstract Currently, state of charge (SOC) estimation on the basis of Kalman filter (KF) is realized to be applied online, but the parameters of the battery model that is implanted in KF are commonly identified offline. The offline identification is not only a time-consumed process but also provides the inaccurate results. Considering the complex and changeable operating conditions of batteries employed in electric vehicles, the parameters are also varied in fact and need to be identified online, by which the real state of the battery is reflected. In this study, the online identification of parameters and estimation of SOC are fulfilled simultaneously by using a novel algorithm named dual unscented Kalman filter (DUKF). Results show that the parameter identification on the basis of the DUKF accurately simulates the dynamic performance of the terminal voltage. By using the proposed algorithm and under three different operating conditions, the state of the battery is effectively estimated. The maximum error of SOC estimation is less than 3%, which is better than the results by using the extend Kalman filter (EKF) and unscented Kalman filter (UKF). Furthermore, the online identification of parameters that are changeable and related to the fading state of the battery, enables the state of health (SOH) to be estimated online.
105569824
Towards a smarter battery management system: A critical review on battery state of health monitoring methods
Abstract To ensure the driving safety and avoid potential failures for electric vehicles, evaluating the health state of the battery properly is of significant importance. This study aims to serve as a useful support for researchers and practitioners by systematically reviewing the available literature on state of health estimation methods. These methods can be divided into two types: experimental and model-based estimation methods. Experimental methods are conducted in a laboratory environment to analyze battery aging process and provide theoretical support for model-based methods. Based on a battery model, model-based estimation methods identify the parameters, which have certain relationships with battery aging level, to realize state of health estimation. On the basis of reading extensive literature, methods for determining the health state of the battery are explained in a deeper way, while their corresponding strengths and weaknesses of these methods are analyzed in this paper. At the end of the paper, conclusions for these methods and prospects for the development trend of health state estimation are made.
30390933
Characterization and Modeling of a Hybrid-Electric-Vehicle Lithium-Ion Battery Pack at Low Temperatures
Although lithium-ion batteries have penetrated hybrid electric vehicles (HEVs) and pure electric vehicles (EVs), they suffer from significant power capability losses and reduced energy at low temperatures. To evaluate those losses and to make an efficient design, good models are required for system simulation. Subzero battery operation involves nonclassical thermal behavior. Consequently, simple electrical models are not sufficient to predict bad performance or damage to systems involving batteries at subzero temperatures. This paper presents the development of an electrical and thermal model of an HEV lithium-ion battery pack. This model has been developed with MATLAB/Simulink to investigate the output characteristics of lithium-ion batteries over the selected operating range of currents and battery capacities. In addition, a thermal modeling method has been developed for this model so that it can predict the battery core and crust temperature by including the effect of internal resistance. First, various discharge tests on one cell are carried out, and then, cell's parameters and thermal characteristics are obtained. The single-cell model proposed is shown to be accurate by analyzing the simulation data and test results. Next, real working conditions tests are performed, and simulation calculations on one cell are presented. In the end, the simulation results of a battery pack under HEV driving cycle conditions show that the characteristics of the proposed model allow a good comparison with data from an actual lithium-ion battery pack used in an HEV.
4566381
A Practical Lithium-Ion Battery Model for State of Energy and Voltage Responses Prediction Incorporating Temperature and Ageing Effects
The state of energy (SOE) is a key indicator for the energy optimization and management of lithium-ion (Li-ion) battery-based energy storage systems in smart grid applications. To improve the SOE estimation accuracy, a Li-ion battery model is presented in this study against dynamic loads and battery ageing effects. First, an electrical battery model is combined with an analytical model in order to take advantages of both models for accurate prediction of battery terminal voltage characteristics, SOE, and remnant runtime. Second, a novel method to separate the fast and slow dynamics of the electrical battery model is developed, and its superior performance is presented. Third, the effects of the battery initial SOC, load current rate and direction, operating temperature, and ageing level are systematically scrutinized and involved in the proposed model for robust SOE and terminal voltage prediction. Commercial Li-ion batteries are then tested under dynamic loads and at an arbitrary battery ageing level to validate the effectiveness and robustness of the proposed model. The laboratory-scale experimental test results show superb accuracy and reliability of the proposed battery model for estimating battery SOE and terminal voltage under dynamic loads and battery ageing conditions.
24723324
Temperature-Compensated Model for Lithium-Ion Polymer Batteries With Extended Kalman Filter State-of-Charge Estimation for an Implantable Charger
As implantable devices become more sophisticated and their extended functionalities impact their energy requirements, they not only rely on charging for the extra energy but also become ever more sensitive to battery deep discharge or overcharge. Accurate state-of-charge (SOC) estimation plays a fundamental role in ensuring the operation safety of implantable medical devices. Temperature variation can impact the battery model parameters and directly affect the accuracy of SOC estimation. This study investigates a temperature-compensated model for lithium-ion polymer batteries that incorporates an extended Kalman filter method to estimate the state of the dynamic nonlinear system and its parameters, from 37 °C to 40 °C at intervals of 1 °C. Both simulation and experimental results indicate that the estimation error can be effectively limited to within ±3%. Through the accurate SOC estimation, the conventional constant current to constant voltage charging strategy is guided in order to reduce the charging time and increase the charging capacity.
22645698
Adaptive State-of-Charge Estimation Based on a Split Battery Model for Electric Vehicle Applications
The conventional state-of-charge (SoC) estimation methods based on the equivalent circuit model (ECM) integrate all state variables into one augmented state vector. However, the correlations between RC voltages and SoC degrade the stability and accuracy of the estimates. To address this problem, this paper presents an adaptive SoC estimation method based on the split battery model, which divides the conventional augmented battery model into two submodels: the RC voltage submodel and the SoC submodel. Hence, the cross interference between RC voltages and SoC is reduced, which effectively reduces the oscillation in the estimation and improves the estimation accuracy. In addition, the adaptive algorithm is applied on the SoC submodel to improve the system robustness to noise disturbances. A case of a second-order ECM is analyzed and two types of Lithium-ion batteries are employed to verify the universality of the proposed method. Experimental results show that the undesired oscillation is eliminated during the convergence stage and the maximum SoC error is within 1% over a wide SoC range.
105833751
A novel framework for Lithium-ion battery modeling considering uncertainties of temperature and aging
Abstract Temperature and cell aging are two major factors that influence the reliability and safety of Li-ion batteries. A general battery model considering both temperature and degradation is often difficult to develop, given the fact that there are many different types of cells with different shapes and/or internal chemical components. In response, a migration-based framework is proposed in this paper for battery modeling, in which the effects of temperature and aging are treated as uncertainties. An accurate model for a fresh cell is established first and then migrated to the degraded batteries through a Bayes Monte Carlo method. Experiments are carried out on both LiFePO4 batteries and Li(Ni1/3Co1/3Mn1/3) O2 batteries under various ambient temperatures and aging levels. The results indicate that the typical voltage prediction error can be limited within ± 20 mV, for the cases of temperature change up to 40  ° C, and capacity degradation up to 20%. The proposed method paves ways to an effective battery management and energy control for electric vehicles or micro grid applications.
109074956
Comparison study on the battery models used for the energy management of batteries in electric vehicles
Abstract Battery model plays an important role in the simulation of electric vehicles (EVs) and states estimation of the batteries in the development of the model-based battery management system. To build a battery model with enough precision and suitable complexity, firstly this paper summarizes the seven representative battery models, which belong to the simplified electrochemical models or the equivalent circuit models. Then the model equations are built and the model parameters are identified with an online parameter identification method. The battery test bench is built and the experiment schedule is designed. Finally an evaluation is performed on the seven battery models by an experiment approach from the aspects of the estimation accuracy of the terminal voltages. To evaluate the effect of the number of RC networks on the model’s precision, the battery general equivalent circuit models (GECMs) with different RC networks are also discussed further. The results indicate the equivalent circuit model with two RC networks, the DP model, has an optimal performance.
115846121
Comparative study of reduced order equivalent circuit models for on-board state-of-available-power prediction of lithium-ion batteries in electric vehicles
Battery management systems (BMS) are responsible for the reliable and safe operation of lithium-ion battery packs in electric vehicles (EVs). State-of-Charge (SoC), State-of-Health (SoH) and State-of-Available-Power (SoAP) are the major battery states that must be determined by means of so-called monitoring algorithms. In this study, a comparative study of a wide range of impedance-based equivalent circuit models (ECMs) for on-board SoAP prediction is carried out. In total, seven dynamic ECMs including ohmic resistance, RC-elements, ZARC-elements connected in series with a voltage source are implemented. The investigated ECMs are verified under varying conditions (different temperatures and wide SoC range) in a model-in-the-loop (MiL) environment using real vehicle data obtained in an EV prototype and current pulse tests. In this context, LIBs at different aging states using various active materials (NMC/C, NMC/LTO, LFP/C) are investigated. Furthermore, the current dependence of the charge transfer resistance is considered by applying the Butler-Volmer equation. The dependence of voltage estimation and SoAP prediction accuracy for different prediction time horizons on SoC, temperature and applied current rate is examined comprehensively.
113998213
Optimal charging for general equivalent electrical battery model, and battery life management
Abstract The contributions of this paper are threefold. First, we present the linear quadratic solution to optimally charge a Li-ion battery in a general form. Although the battery model considered here is a circuit comprised of an open-circuit voltage (OCV), a series resistance, and an RC circuit, the methodology is applicable to any electrical circuit model of a battery. A combination of different cost functions is considered including: time-to-charge, energy loss, and temperature rise index. We discuss the effect of different weightings in the cost functions on the current and voltage profiles. Second, we present two models for normalized battery capacity as a function of the number of cycles and two charge control parameters, viz., maximum terminal voltage of the battery and maximum charge current. These models are compared to a bi-exponential capacity model. The effectiveness of the proposed models for forecasting the battery capacity is validated using the experimental data. Third, these models are used for battery life management by developing an optimal charging parameter selection method which provides the best setpoint values for the control variables to achieve a pre-specified desired “useful cycle life” while attaining the fastest possible time-to-charge. The proposed optimal charging parameter selection method is illustrated via numerical results.
113998213
Optimal charging for general equivalent electrical battery model, and battery life management
Abstract The contributions of this paper are threefold. First, we present the linear quadratic solution to optimally charge a Li-ion battery in a general form. Although the battery model considered here is a circuit comprised of an open-circuit voltage (OCV), a series resistance, and an RC circuit, the methodology is applicable to any electrical circuit model of a battery. A combination of different cost functions is considered including: time-to-charge, energy loss, and temperature rise index. We discuss the effect of different weightings in the cost functions on the current and voltage profiles. Second, we present two models for normalized battery capacity as a function of the number of cycles and two charge control parameters, viz., maximum terminal voltage of the battery and maximum charge current. These models are compared to a bi-exponential capacity model. The effectiveness of the proposed models for forecasting the battery capacity is validated using the experimental data. Third, these models are used for battery life management by developing an optimal charging parameter selection method which provides the best setpoint values for the control variables to achieve a pre-specified desired “useful cycle life” while attaining the fastest possible time-to-charge. The proposed optimal charging parameter selection method is illustrated via numerical results.
55689004
A survey of mathematics-based equivalent-circuit and electrochemical battery models for hybrid and electric vehicle simulation
Abstract In this paper, we survey two kinds of mathematics-based battery models intended for use in hybrid and electric vehicle simulation. The first is circuit-based, which is founded upon the electrical behaviour of the battery, and abstracts away the electrochemistry into equivalent electrical components. The second is chemistry-based, which is founded upon the electrochemical equations of the battery chemistry.
94937937
Modeling of the cranking and charging processes of conventional valve regulated lead acid (VRLA) batteries in micro-hybrid applications
Abstract The cranking and charging processes of a VRLA battery during stop–start cycling in micro-hybrid applications were simulated by one dimensional mathematical modeling, to study the formation and distribution of lead sulfate across the cell and analyze the resulting effect on battery aging. The battery focused on in this study represents a conventional VRLA battery without any carbon additives in the electrodes or carbon-based electrodes. The modeling results were validated against experimental data and used to analyze the “sulfation” of negative electrodes – the common failure mode of lead acid batteries under high-rate partial state of charge (HRPSoC) cycling. The analyses were based on two aging mechanisms proposed in previous studies and the predictions showed consistency with the previous teardown observations that the sulfate formed at the negative interface is more difficult to be converted back than anywhere else in the electrodes. The impact of cranking pulses during stop–start cycling on current density and the corresponding sulfate layer production was estimated. The effects of some critical design parameters on sulfate formation, distribution and aging over cycling were investigated, which provided guidelines for developing models and designing of VRLA batteries in micro-hybrid applications.
111289611
Parity-relation-based state-of-health monitoring of lead acid batteries for automotive applications
Effective vehicular power management requires accurate knowledge of battery state, including state-of-charge (SOC) and state-of-health (SOH). This paper presents an integrated algorithm for reliable battery SOH monitoring. The dynamics of lead acid batteries during engine cranking is investigated, and a new battery model is presented. Moreover, a parity-relation-based integrated method for battery SOH monitoring is proposed. It is shown that the diagnostic residual combines the SOH information provided by both battery resistance and voltage loss during engine cranking, hence enhancing diagnostic performance. Extensive evaluation results using real vehicle cranking data have verified the effectiveness of the proposed method.
20905092
Automotive lead-acid battery state-of-health monitoring system
In this paper, we present a system to estimate the health of a lead-acid car battery and warn the driver of upcoming battery failure in the near future. Most existing lead-acid battery state of health (SOH) estimation systems measure the battery impedance by sensing the voltage and current of a battery. However, current sensing is costly for parts and/or labor. A method to estimate SOH by sensing the battery voltage during a cranking event was proposed. Our system intends to improve a previous method. It measures the battery voltage during a cranking event, processes critical voltage points to estimate the SOH of the battery, and warns the driver of a potential upcoming failure.
109514843
Impedance measurements on lead–acid batteries for state-of-charge, state-of-health and cranking capability prognosis in electric and hybrid electric vehicles
Abstract Various attempts have been made to use impedance measurements for online analysis and offline modelling of lead–acid batteries. This presentation gives an overview on the latest and successful approaches based on impedance measurements to assess state-of-charge (SoC), state-of-health (SoH) and cranking capability of lead–acid batteries. Furthermore, it is shown that impedance data can serve as a basis for dynamic battery models for the simulation of vehicle power-supply systems. The methods and procedures aim for a reliable prediction of battery performance in electric vehicles, hybrid cars and classical automotive applications. Although, it will become obvious that impedance measurements give valuable information on the battery state, typically the information needs to be combined with other conventional algorithms or self-learning tools to achieve reliable and stable results for real-world applications.
121291774
A fractional order model for lead-acid battery crankability estimation
Abstract With EV and HEV developments, battery monitoring systems have to meet the new requirements of car industry. This paper deals with one of them, the battery ability to start a vehicle, also called battery crankability. A fractional order model obtained by system identification is used to estimate the crankability of lead-acid batteries. Fractional order modelling permits an accurate simulation of the battery electrical behaviour with a low number of parameters. It is demonstrated that battery available power is correlated to the battery crankability and its resistance. Moreover, the high-frequency gain of the fractional model can be used to evaluate the battery resistance. Then, a battery crankability estimator using the battery resistance is proposed. Finally, this technique is validated with various battery experimental data measured on test rigs and vehicles.
19851152
On Lead-Acid-Battery Resistance and Cranking-Capability Estimation
With hybrid and electric vehicle developments, battery-monitoring systems have to meet the new requirements of the automobile industry. This paper deals with one of them, the battery's ability to start a vehicle, also called battery crankability, through battery-resistance estimation. A fractional-order model obtained by system identification is used to estimate the internal resistance of lead-acid batteries. Fractional-order modeling permits an accurate simulation of the battery electrical behavior with a low number of parameters. Moreover, the high-frequency gain of the fractional model is directly linked to the battery resistance. A resistance-estimation method based on a frequency-invalidation method is, thus, proposed. It is demonstrated that the battery's available power that defines battery crankability is correlated to the battery resistance. Thus, a battery-crankability estimator using the battery resistance is suggested. Validation tests are carried out with various batteries. This estimator cannot be embedded in a microcontroller due to the linear-matrix-inequality-based optimization algorithm in the invalidation-model method used. A simplified algorithm is finally proposed, and its efficiency is proved.
21845237
State of charge, state of health, and state of function monitoring for EV BMS
The computation and monitoring of three key indices, namely, state of charge (SOC), state of health (SOH), and state of function (SOF) for EV (electrical vehicle) BMS (battery management system) are proposed in this work. Because most of SOC definitions are directly related to nominal capacity, the accuracy of residual capacity is doubtful. Therefore, the SOC is redefined by present maximum capacity to reduce the error of the SOC estimation. The measurement of the proposed SOC demonstrates that the maximum error is 0.334 %. Moreover, this paper also proposes SOF based on SOC and SOH to reveal the driving power of the system.
31824773
The Co-estimation of State of Charge, State of Health, and State of Function for Lithium-Ion Batteries in Electric Vehicles
This paper proposes a co-estimation scheme of state of charge (SOC), state of health (SOH), and state of function (SOF) for lithium-ion batteries in electric vehicles. The co-estimation denotes that the SOC, SOH, and SOF are estimated simultaneously in real-time application. The model-based SOC estimation is fulfilled by the extended Kalman filter. The battery parameters related with the battery SOH and SOF are online identified using the recursive least square algorithm with a forgetting factor. The capacity and the maximum available output power are then estimated based on the identified parameters. The online update of the capacity and correlated parameters help improve the accuracy of the state estimation but with limited increase in the computation load, by making good use of the correlations among the states. The co-estimation scheme is validated in a real battery management system with good real-time performance and convincible estimation accuracy.
21667175
Model-Based Lithium-Ion Battery Resistance Estimation From Electric Vehicle Operating Data
State-of-health estimates of batteries are essential for onboard electric vehicles in order to provide safe, reliable, and cost-effective battery operation. This paper suggests a method to estimate the 10-s discharge resistance, which is an established battery figure of merit from laboratory testing, without performing the laboratory test. Instead, a state-of-health estimate of batteries is obtained using data directly from their operational use, e.g., onboard electric vehicles. It is shown that simple dynamical battery models, based on a current input and a voltage output, with model parameters dependent on temperature and state of charge, can be derived using AutoRegressive with eXogenous input models, whose order can be adjusted to describe the complex battery behavior. Then, the 10-s discharge resistance can be conveniently computed from the identified model parameters. Moreover, the uncertainty of the estimated resistance values is provided by the method. The suggested method is validated with usage data from emulated electric vehicle operation of an automotive lithium-ion battery cell. The resistance values are estimated accurately for a state-of-charge and temperature range spanning typical electric vehicle operating conditions. The identification of the model parameters and the resistance computation are very fast, rendering the method suitable for onboard application.
18769831
Online Parameter Identification of Ultracapacitor Models Using the Extended Kalman Filter
Ultracapacitors (UCs) are the focus of increasing attention in electric vehicle and renewable energy system applications due to their excellent performance in terms of power density, efficiency, and lifespan. Modeling and parameterization of UCs play an important role in model-based regulation and management for a reliable and safe operation. In this paper, an equivalent circuit model template composed of a bulk capacitor, a second-order capacitance-resistance network, and a series resistance, is employed to represent the dynamics of UCs. The extended Kalman Filter is then used to recursively estimate the model parameters in the Dynamic Stress Test (DST) on a specially established test rig. The DST loading profile is able to emulate the practical power sinking and sourcing of UCs in electric vehicles. In order to examine the accuracy of the identified model, a Hybrid Pulse Power Characterization test is carried out. The validation result demonstrates that the recursively calibrated model can precisely delineate the dynamic voltage behavior of UCs under the discrepant loading condition, and the online identification approach is thus capable of extracting the model parameters in a credible and robust manner.
3256711
An Adaptive Unscented Kalman Filtering Approach for Online Estimation of Model Parameters and State-of-Charge of Lithium-Ion Batteries for Autonomous Mobile Robots
In this brief, to get a more accurate and robust state of charge (SoC) estimation, the lithium-ion battery model parameters are identified using an adaptive unscented Kalman filtering method, and based on the updated model, the battery SoC is estimated consequently. An adaptive adjustment of the noise covariances in the estimation process is implemented using a technique of covariance matching in the unscented Kalman filter (UKF) context. The effectiveness of the proposed method is evaluated through experiments under different power duties in the laboratory environment. The obtained results are compared with that of the adaptive extended Kalman filter, extended Kalman filter, and unscented Kalman filter-based algorithms. The comparison shows that the proposed method provides better accuracy both in battery model parameters estimation and the battery SoC estimation.
11288963
Recurrent Neural Network-Based Modeling and Simulation of Lead-Acid Batteries Charge–Discharge
This paper presents the main experiences and results obtained about the problem of the lead-acid battery modeling and simulation. A nonlinear mathematical model is presented as well as results of neuroprocessing of the charge-discharge experimental and simulated data. Recurrent neural networks were used to provide a state-of-charge observer and model parameter estimation and tuning. The simulation results are compared with those obtained by extensive lab tests performed on different batteries used for electric vehicle and photovoltaic application.
98733082
ANN modeling of cold cranking test for sealed lead-acid batteries
Abstract A cold cranking test for 17 sealed lead-acid batteries with grids of lead–calcium alloy at −18 °C was performed at different discharge currents. Time–voltage behavior of the batteries during 10 s discharge, voltage values at discharge times of 30, 60 and 90 s, and time of discharge to reach a final voltage of 6 V are critical points in the cold cranking test. These were modeled by artificial neural networks in MATLAB 7 media. Nine discharge currents were used for the training set, five discharge currents for the prediction set and three discharge currents for the validation set. Maximum prediction errors in the modeling of the time-voltage behavior during a 10 s discharge (model 1), the voltage of critical points of 30, 60, 90 s (model 2) and the time to reach a final voltage of 6 V (model 3) were under 3.1%, 3.3%, and 3.5%, respectively for each model. The results obtained showed that the models can be used in the battery industry for the prediction of the cold cranking behavior of lead-acid batteries at high discharge currents based on experimental cold cranking data at low discharge currents without the use of expensive and complex instruments. A file (EXE file) based on the model obtained by WinNN 32 was prepared to enable inexpert operators in the lead-acid battery industry to use the method.
10063711
Improvement on the Cold Cranking Capacity of Commercial Vehicle by Using Supercapacitor and Lead-Acid Battery Hybrid
Paralleling a supercapacitor with a lead-acid battery decreases the size and the capacity of the starting battery that is required for cold cranking. A simple circuit model for the hybrid power source is introduced and analyzed, showing that lower effective series resistance (ESR) gives greater effective discharge capacity. Laboratory experiments and road tests on reduced-volume batteries that are ganged with aqueous carbon-carbon (C/C) supercapacitors demonstrate practical and economic advantages. Applications to engineering are discussed.