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Abstract

The development of new algorithms for the management and state estimation of lithiumion batteries requires their verification and performance assessment using different approaches and tools. This paper aims at presenting an advanced hardware in the loop platform which uses an accurate model of the battery to test the functionalities of battery management systems (BMSs) in electric vehicles. The developed platform sends the simulated battery data directly to the BMS under test via a communication link, ensuring the safety of the tests. As a case study, the platform has been used to test two promising battery state estimators, the Adaptive Mix Algorithm and the Dual Extended Kalman Filter, implemented on a field-programmable gate array based BMS. Results show the importance of the assessment of these algorithms under different load profiles and conditions of the battery, thus highlighting the capabilities of the proposed platform to simulate many different situations in which the estimators will work in the target application.

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... Therefore, an early validation of the product's overall system behavior is useful. In the context of powertrain validation testing with mechanical Hardware-in-the-Loop (HiL) test benches is quite established [1][2][3][4][5][6][7][8][9][10]. The use of mechanical test benches is also well-established in the validation of power tools. ...
... In Equations (2) to (5), T is the current temperature of the battery cell, while Tref is the reference temperature, at which cell discharging starts. The current temperature T depends on the discharge current and will be calculated by the model. ...
... According to [26], both parameters have to be determined experimentally. The next part of the model calculates the temperature-sensitive parameters of the Shepherd equation by using Equations (2) to (5). For solving these equations, eight parameters x= ...
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This paper deals with the real-time simulation of a power tool battery pack on a mechatronic powertrain test bench. The ability of an easy-to-use model for quick and iterative test runs mainly depends on the effort of parameterization. For this purpose, an easily parameterizable battery model is required. The battery model used is based on the current state of research and simulates the battery’s behavior with an adequate precision. The suggested parameterization allows building the model without the necessity of experimental investigation. Three different procedures for model parameterization were used and compared with the real battery behavior. In conclusion, this paper shows a good tradeoff between precision and an easy way to handle a battery model for testing mechatronic powertrains.
... IEEE Access ` Besides, the extensive researches and developments are going on to decrease the production cost and enhance the longevity of LIBs [11]. The state-of-charge (SOC) describes the amount of energy left in BESS [12], [13]. SOC is not a physical quantity that can be measured directly. ...
... The proposed work has a quick execution time of 16.5μs and can run on low-cost hardware. Morello et al. [12] used HIL platform to test battery state estimators implemented on FPGA based BMS. ...
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Lithium-ion battery is an appropriate choice for electric vehicle (EV) due to its promising features of high voltage, high energy density, low self-discharge and long lifecycles. The successful operation of EV is highly dependent on the operation of battery management system (BMS). State of charge (SOC) is one of the vital paraments of BMS which signifies the amount of charge left in a battery. A good estimation of SOC leads to long battery life and prevention of catastrophe from battery failure. Besides, an accurate and robust SOC estimation has great significance towards an efficient EV operation. However, SOC estimation is a complex process due to its dependency on various factors such as battery age, ambient temperature, and many unknown factors. This review presents the recent SOC estimation methods highlighting the model-based and data-driven approaches. Model-based methods attempt to model the battery behavior incorporating various factors into complex mathematical equations in order to accurately estimate the SOC while the data-driven methods adopt an approach of learning the battery’s behavior by running complex algorithms with a large amount of measured battery data. The classifications of model-based and data-driven based SOC estimation are explained in terms of estimation model/algorithm, benefits, drawbacks, and estimation error. In addition, the review highlights many factors and challenges and delivers potential recommendations for the development of SOC estimation methods in EV applications. All the highlighted insights of this review will hopefully lead to increased efforts toward the enhancement of SOC estimation method of lithium-ion battery for the future high-tech EV applications.
... At the beginning, HIL was used to simulate the flight process of the aircraft, and then it was developed from nonreal-time simulation to real-time simulation [6]. After that, HIL is widely used in the automobile industry, and 90% of the traditional test drive faults are found through HIL simulation, which costs much less time and money than physical tests [7]. Over the past few decades, the application of HIL technology has been extended to other industrial related design, development, implementation and tests, such as power, robotics and aerospace, to enhance the quality, safety and verification testing of systems [8][9][10]. ...
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... A specific interface connects the battery emulator to a PC that controls the entire system. The final goal is to enable the emulation of the voltage and current of each cell according to specific equivalent models of the cells, for example the equivalent electrical circuit model with 2 RC branches provided with an electrical equivalent thermal model as shown in [17]. ...
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Battery Management Systems are fundamental components of the present battery generation. The development and characterization phases of a BMS often require an emulator of the battery cells with which the Battery Management System functions can be assessed with no safety risks as it would instead happen using a real battery. This work describes the design and characterization of a modular cell emulator circuit to be used as platform for the Hardware-in-the-loop test of a Battery Management System. The design constraints and choices are first described. Then, the experimental characterization of the cell emulator is shown and discussed. The proposed circuit shows a voltage resolution of 76 μV, an accuracy of 2.17 mV, and a setting time of 340 μs. Its cost is around 40 USD. The circuit results to be a very good trade-off between performance and cost. The Project is available to the scientific community as open hardware platform freely downloadable. It could be useful to small-size laboratories to self-produce a low-cost battery emulator with good performance for the development and the functional test of custom Battery Management Systems.
... In some implementations, SoC evaluation combines ECM and different methods, such as Kalman Filters (KF) or Proportional-Integral (PI) Observer [14], to improve accuracy despite the increase in computational demand [11]. Different implementations were presented in research based on microcontroller systems [15][16][17], but complex models and algorithms also led to solutions exploiting the intrinsic parallelism of FPGA devices [18][19][20][21]. For instance, in [21], the model-based design of a Mixed Algorithm (MA) combining ECM and Coulomb Counting (CC) for SoC estimation was proposed. ...
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Monitoring the State of Charge (SoC) in battery cells is necessary to avoid damage and to extend battery life. Support Vector Machine (SVM) algorithms and Machine Learning techniques in general can provide real-time SoC estimation without the need to design a cell model. In this work, an SVM was trained by applying an Ant Colony Optimization method. The obtained trained model was 10-fold cross-validated and then designed in Hardware Description Language to be run on FPGA devices, enabling the design of low-cost and compact hardware. Thanks to the choice of a linear SVM kernel, the implemented architecture resulted in low resource usage (about 1.4% of Xilinx Artix7 XC7A100TFPGAG324C FPGA), allowing multiple instances of the SVM SoC estimator model to monitor multiple battery cells or modules, if needed. The ability of the model to maintain its good performance was further verified when applied to a dataset acquired from different driving cycles to the cycle used in the training phase, achieving a Root Mean Square Error of about 1.4%.
... The diagnostic tests should include both the check of the BMS functionalities and the characterization of the lithium-ion cells that compose the battery. Many solutions are proposed to verify the BMS capabilities, mainly by using the hardware-in-the-loop concept [15][16][17][18][19]. Instead, the development of low-cost instrumentation to diagnose the aging of the lithium-ion cells is still lacking. ...
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The large increment expected in the diffusion of light-electric-vehicles will raise several issues that must be addressed to cope with this trend, including battery diagnostic and maintenance services. The battery system is the most expensive part in the majority of the e-mobility devices. Therefore, battery manufacturers tend to reduce the battery cost by using simple battery management systems that provide only basic safety features. Possible advanced functionalities are not implemented and the battery may lose performanceduring its use. Widely spread maintenance centers are thus required to support the mobility electrification process, but their diffusion is limited by the high cost ofprofessional battery characterization instruments. This work proposes an open-hardware low-cost battery maintenance tool architecture that can be used with common laboratory instruments. The tool is based on a relay-matrix and a battery monitor integrated circuit. It is able to completely characterize and optimize the state of a battery independently of the battery management system and also gives a figure of the individual aging of the battery cells. The work shows the architecture and the experimental validation of a 16-cells battery maintenance tool prototype. The results demonstrate that utilizing the tool brings the battery in the best possible state and identifies the degradation of the cells in terms of capacity and resistance.
... The status of charge explains the quantity of energy stored in the battery [76,77]. SoC is not a physical property which can be directly measured. ...
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Lithium-ion batteries are the most used these days for charging electric vehicles (EV). It is important to study the aging of batteries because the deterioration of their characteristics largely determines the cost, efficiency, and environmental impact of electric vehicles, especially full-electric ones. The estimation of batteries’ state-condition is also very important for improving energy efficiency, lengthening the life cycle, minimizing costs and ensuring safe implementation of batteries in electric vehicles. However, batteries with large temporal variables and non-linear characteristics are often affected by random factors affecting the equivalent internal resistance (EIR), battery state of charge (SoC), and state of health (SoH) in EV applications. The estimation of batteries’ parameters is a complex process, due to its dependence on various factors such as batteries age and ambient temperature, among others. A good estimate of SoC and internal resistance leads to long battery life and disaster prevention in the event of a battery failure. The classification of estimation methodologies for internal parameters and the charging status of batteries will be very helpful in choosing the appropriate method for the development of a reliable and secure battery management system (BMS) and an energy management strategy for electric vehicles.
... It is a measure of execution time-constrained imposed by the SW system on the development computer. The C1 includes cell state and electrical quantity computation, algorithm execution, and data retrieved from the estimation algorithms [49]. Generally, the estimation methods have unpredictable execution time requirements, such as impedance estimation, which increases the computational burden [48]. ...
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... For accurate prototyping, the HIL established in [113] has assumed a prior knowledge of the error bounds in parameters. Similarly, the HIL performances accuracy was enhanced in [118] via proceeding filtering/signal calibration for noisy data acquisition systems. The HIL has recently become a tool to ensure vehicle automation. ...
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... Other two important areas of industry in which HIL simulations are vastly used and are considered effective methodologies for testing control systems are automotive and power control applications. Regarding the first one, there are many studies available in literature using HIL simulations for validating automotive control systems under more realistic scenarios: experimental validation of the propulsion system of a GM Chevrolet Volt 2nd Generation electric car [9], lateral stability and rollover prevention via active braking [10], design and statistical validation of spark ignition engine electronic control units [11], fault injection strategy for realtime simulation in traction control systems (TCS) [12], hydraulic pressure control in automotive braking systems [13], estimation of battery in electric vehicles [14], among many others. ...
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... HIL is industrially significant. It avoids crashing the costly part of the system and guarantees safety and at the same time, it offers a large coverage of test conditions and traceable replicas of the same tests [11], [12]. More importantly, it is independent from the long-term waiting of the construction of real The main purpose of this article is to offer a guided methodology for the realization of a hardware in the loop system aimed at the development of the control algorithm. ...
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In this paper, a robust model-based battery state of charge (SOC) estimating algorithm is proposed with a novel approach based on multi-models data fusion technique and particle filter (PF). The proposed method is particularly adapted for SOC estimation under conditions of sharp current variations and presence of measurement noise. In this innovative approach, multiple battery models have been used in order to accurately estimate a battery SOC. The measured battery terminal voltage is compared with the multiple battery models output to generate a residual, which is then used to calculate the weight of estimated value from each battery model. This weight, which represents the accuracy of observation equation of each battery model, is inversely proportional to the residual. The estimated SOC values from different models are then fused and the weights of estimated values from each battery model are adjusted dynamically using particle filter and weighted average methodology, in order to calculate the final SOC estimation of the battery. In addition to the simulation, the proposed method has been validated by experimental results. The results demonstrate that the proposed multi-models based algorithm can achieve better accuracy than single model-based methods.
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A Battery Management System (BMS) is needed to ensure a safe and effective operation of a Lithium-ion battery, especially in electric vehicle applications. An important function of a BMS is the reliable estimation of the battery state in a wide range of operating conditions. To this end, a BMS often uses an equivalent electrical model of the battery. Such a model is computationally affordable and can reproduce the battery behaviour in an accurate way, assuming that the model parameters are updated with the actual operating condition of the battery, namely its state-of-charge, temperature and ageing state. This paper compares the performance of two battery state and parameter estimation techniques, i.e., the Extended Kalman Filter and the classic Least Squares method in combination with the Mix algorithm. Compared to previous ones, this work focuses on the concurrent estimation of battery state and parameters using experimental data, measured on a Lithium-ion cell subject to a current profile significant for an electric vehicle application.
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Developers and manufacturers of Battery Management Systems (BMSs) require extensive testing of controller HW and SW, such as analog front-end (AFE) and performance of generated control code. In comparison with tests conducted on real batteries, tests conducted on a state-of-the-art hardware-in-the-loop (HIL) simulator can be more cost and time effective, easier to reproduce and safer beyond the normal range of operation, especially at early stages in the development process or during fault insertion. In this paper a HIL simulation battery model is developed for purposes of BMS testing on a commercial HIL simulator. A multi-cell electro-thermal Li-ion battery (LIB) model is integrated in a system-level simulation. Then, the LIB system model is converted to C code and run in real-time with the HIL simulator. Finally, in order to demonstrate the capabilities of the setup, experimental results of BMS tests over a certain set of exemplary cases are shown.
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With the research object of LiFePO4 battery, this paper aims to correctly estimate the battery state of charge (SOC) by constructing a comprehensive SOC estimation strategy. Firstly, recursive least square (RLS) algorithm is adopted to realize online parameter identification of the equivalent battery model; and then an elaborate combination of RLS and Unscented Kalman Filter (UKF) is established, thus the battery model parameters used in UKF are actually obtained recursively by RLS; finally, SOC can be estimated by UKF. This strategy has an obvious adaptability due to the adoption of online parameter identification, so it is also called adaptive SOC estimation technique. Experimental results show that sometimes battery model parameters of different cells can be much different even though terminal voltages of these cells are very close or same when they are under resting state, and this inconsistency among LiFePO4 batteries is captured by the RLS-UKF strategy presented in this paper; and of course battery SOC can also be correctly estimated by using the continuously updated model parameters.
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This paper discusses the simultaneous state of charge (SOC) and parameter estimation of the battery for electric vehicles (EVs) and hybrid electric vehicles (HEVs). Although it is important to know the SOC and parameters of the battery to maximize its longevity, performance and reliability, there are still some difficulties in estimating them. The estimation often suffers from the battery model complexity, the poor numerical stability, and the constraints of the physical parameters of the battery. To address such issues, this paper proposes a simultaneous SOC and parameter estimation method using log-normalized UKF (LnUKF) cooperated with the battery model considering diffusion phenomena. This approach is verified by performing a series of simulations using experimental data with an EV.
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A battery fuel gauge (BFG) helps to extend battery life by tracking the state of charge (SOC) and many other diagnostic features. In this paper, we present an approach to validate the SOC and time-to-shutdown (TTS) estimates of a BFG. Hardware-in-the-loop (HIL) testing under realistic usage scenarios provides a means for BFG algorithm evaluation and provides insights into practical implementation and testing of BFG algorithms in battery management systems. We report the details of a HIL system that was designed to validate the SOC and TTS estimation capability of BFG algorithms; different current load profiles were synthesized to replicate typical battery usage in portable electronic applications; the HIL system is automated with the help of programmable current profiles and is designed to operate at various controlled temperatures; three performance validation metrics are formulated for an objective assessment of SOC and TTS tracking algorithms. The HIL setup and the performance validation metrics are used to evaluate a BFG developed by the authors using three different batteries at temperatures ranging from -20 °C to 40 °C.
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Battery management system (BMS) plays a critical role in the development of hybrid electric vehicles (HEVs), plug-in hybrid vehicles (PHEVs) and battery electric vehicles (BEVs). The cell-BMS is the lower-level part of the BMS, which generally takes care of the individual cells directly, with functions mainly including voltage detection and cell balancing. In this paper, a configurable battery cell emulating system is developed to implement the hardware-in-the-loop (HIL) validation of the cell-BMS. The battery cell dynamics is simulated with a parameter-configurable equivalent circuit model consisting of three resistors, two capacitors and a SOC-controlled voltage source. The HIL system emulates battery cell dynamics to validate the function of voltage monitoring. With the bi-directional and power-amplified outputs, the system can also evaluate the performance of both active and passive cell balancing module. Meanwhile the emulated cells can be connected in series, and can be adapted to simulate some faults, e.g., over-charge and over-discharge as well. Initial testing cases using a cell-BMS prototype for the LiMnO2 based battery cells show a good performance of the system. The system standardizes function validation of the cell-BMS before the design finalization and thereby accelerates the BMS development and reduces the development costs.
Conference Paper
An effective management of the onboard energy storage system is a key point for the development of electric vehicles. This requires the accurate estimation of the battery state over time and in a wide range of operating conditions. The battery state is usually expressed as state-of-charge and state-of-health. Its estimation demands an accurate model to represent the static and dynamic behaviour of the battery. Developing such a model requires the online identification of the battery parameters. This paper aims at comparing the performance of two popular system identification techniques, i.e., the Extended Kalman Filter and the classic Least Squares method. A significant contribution of this work is the definition of a benchmark which is representative of the real use of the battery in an electric vehicle. Simulation results show the peculiarities of both methods and their effectiveness.
Conference Paper
The major tasks of Battery Management Systems (BMS) are to guaranty safe operation conditions and to maintain every single cell of the hole battery pack. These tasks require measurements and balancing processes for each cell. Modern BMS take advantage of the wealth of information to estimate hardly measurable conditions like State of charge (SoC) or State of Health (SoH). This paper describes a method to estimate the State of Charge for every single cell in a battery pack using an Unscented Kalman Filter (UKF) running on an electronic control unit of the BMS. The verification of the developed algorithms on the control unit takes a long time on a real battery system. For that purpose a real-time Hardware-in-the-Loop (HiL) test bench is developed. In this test bench a LiFePO4 cell model was implemented by Matlab/Simulink®. So the developed and embedded algorithm can be verified by means of various test cases. In this paper the results are presented on signal level. Future work will include a HiL test bench on power level together with an cell emulator. Beside this the test bench offers the opportunity to develop models and sophisticated algorithms for further beneficial state variables like the SoH or the inner temperature of each single cell of the battery pack.
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With the rapidly evolving technology of the smart grid and electric vehicles (EVs), the battery has emerged as the most prominent energy storage device, attracting a significant amount of attention. The very recent discussions about the performance of lithium-ion (Li-ion) batteries in the Boeing 787 have confirmed so far that, while battery technology is growing very quickly, developing cells with higher power and energy densities, it is equally important to improve the performance of the battery management system (BMS) to make the battery a safe, reliable, and cost-efficient solution. The specific characteristics and needs of the smart grid and EVs, such as deep charge/discharge protection and accurate state-of-charge (SOC) and state-of-health (SOH) estimation, intensify the need for a more efficient BMS. The BMS should contain accurate algorithms to measure and estimate the functional status of the battery and, at the same time, be equipped with state-of-the-art mechanisms to protect the battery from hazardous and inefficient operating conditions.
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Real-time estimation of the state of charge (SOC) of the battery is a crucial need in the growing fields of plug-in hybrid electric vehicles and smart grid applications. The accuracy of the estimation algorithm directly depends on the accuracy of the model used to describe the characteristics of the battery. Considering a resistance-capacitance (RC)-equivalent circuit to model the battery dynamics, we use a piecewise linear approximation with varying coefficients to describe the inherently nonlinear relationship between the open-circuit voltage (VOC) and the SOC of the battery. Several experimental test results on lithium (Li)-polymer batteries show that not only do the VOC-SOC relationship coefficients vary with the SOC and charging/discharging rates but also the RC parameters vary with them as well. The moving window least squares parameter-identification technique was validated by both data obtained from a simulated battery model and experimental data. The necessity of updating the parameters is evaluated using observers with updating and nonupdating parameters. Finally, the SOC coestimation method is compared with the existing well-known SOC estimation approaches in terms of performance and accuracy of estimation.
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The charge stored in series-connected lithium batteries needs to be well equalized between the elements of the series. We present here an innovative lithium-battery cell-to-cell active equalizer capable of moving charge between series-connected cells using a super-capacitor as an energy tank. The system temporarily stores the charge drawn from a cell in the super-capacitor, then the charge is moved into another cell without wasting energy as it happens in passive equalization. The architecture of the system which employs a digitally-controlled switching converter is compared with the state of the art, then fully investigated, together with the methodology used in its design. The performance of the system is described by presenting and discussing the experimental results of laboratory tests. The most innovative and attractive aspect of the proposed system is its very high efficiency, which is over 90%.
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This paper describes the use of Hardware-in-Loop (HIL) simulation and Rapid Control Prototyping (RCP) tools for the accelerated design and optimization of battery management systems (BMS) typically found in hybrid/electric vehicles. The BMS is an electronic system that manages a rechargeable battery pack. Its functions include monitoring the cell/pack voltage, current, temperature, state-of-charge, depth-of-discharge, and state-of-health. Besides reporting this data to a supervisory (powertrain) controller, the BMS protects the battery by preventing it from operating outside its safe operating range and balancing the individual cells. Programming, testing and validation of the BMS with real batteries is a time-consuming, expensive and potentially dangerous operation since physical batteries needs to be discharged and re-charged for every development iteration. With the help of virtual batteries models as part of a HIL simulation, the BMS algorithm can be developed, calibrated and validated in a very secure and time-efficient manner resulting in a significant product development time reduction.
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Electric propulsion becomes more and more important. Despite the fact that appropriate design tools tailored for the use with electric propulsion systems have to be developed, test benches for verification and hardware-in-the-loop simulations are needed. The battery system, as it is a very important component, is in the focus of this article. Facing the costs of real hardware components a realtime emulation with programmable power supplies and electric loads seems reasonable. This article introduces an approaches in battery modeling and figures out the main influences of the model parameters. Because of a very strong impact of the thermal behavior on the parameters a dedicated thermal model is proposed. One major point is the modularized modeling and the coupling of the different physical domains. The advantage of this approach is that the granularity of the single modules can be adjusted separately to fit the dedicated needs of a given task. For battery emulation the entire model has to be implemented on a realtime platform. The article gives a detailed overview and demonstrates the feasibility of the entire system in practical experiments.
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The error sources of state of charge (SOC) estimation algorithm on the basis of Kalman filter is analyzed in this paper. Aiming at the influence of the measuring precision of voltage and current in battery management system (BMS) to SOC estimation, a simulation analysis is performed independently in Simulink on the assumption that other factors are under ideal conditions, in which the effects of Gaussian white noise and the offset error of measurement of BMS are discussed respectively to simulate the actual vehicle condition. The principle to the precision index design of BMS is proposed according to the simulation result. At last, a high-precision data acquisition system is developed as a precise calibration benchmark device for BMS.
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Battery management systems in hybrid-electric-vehicle battery packs must estimate values descriptive of the pack’s present operating condition. These include: battery state-of-charge, power fade, capacity fade, and instantaneous available power. The estimation mechanism must adapt to changing cell characteristics as cells age and therefore provide accurate estimates over the lifetime of the pack.In a series of three papers, we propose methods, based on extended Kalman filtering (EKF), that are able to accomplish these goals for a lithium ion polymer battery pack. We expect that they will also work well on other battery chemistries. These papers cover the required mathematical background, cell modeling and system identification requirements, and the final solution, together with results.This third paper concludes the series by presenting five additional applications where either an EKF or results from EKF may be used in typical BMS algorithms: initializing state estimates after the vehicle has been idle for some time; estimating state-of-charge with dynamic error bounds on the estimate; estimating pack available dis/charge power; tracking changing pack parameters (including power fade and capacity fade) as the pack ages, and therefore providing a quantitative estimate of state-of-health; and determining which cells must be equalized. Results from pack tests are presented.
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Battery algorithms play a vital role in hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), extended-range electric vehicles (EREVs), and electric vehicles (EVs). The energy management of hybrid and electric propulsion systems needs to rely on accurate information on the state of the battery in order to determine the optimal electric drive without abusing the battery.In this study, a cell-level hardware-in-the-loop (HIL) system is used to verify and develop state of charge (SOC) and power capability predictions of embedded battery algorithms for various vehicle applications. Two different batteries were selected as representative examples to illustrate the battery algorithm verification and development procedure. One is a lithium-ion battery with a conventional metal oxide cathode, which is a power battery for HEV applications. The other is a lithium-ion battery with an iron phosphate (LiFePO4) cathode, which is an energy battery for applications in PHEVs, EREVs, and EVs.The battery cell HIL testing provided valuable data and critical guidance to evaluate the accuracy of the developed battery algorithms, to accelerate battery algorithm future development and improvement, and to reduce hybrid/electric vehicle system development time and costs.
Conference Paper
An accurate model of the elementary accumulation device is fundamental for sizing and controlling the battery pack to be used in electric and hybrid vehicles. Indeed, the implementation of such a model within the Battery Management System makes it possible to evaluate the status and the behavior of the battery pack in every condition and to apply a correct control strategy. This work shows the characterization and modeling of a commercial Lithium-Polymer cell, which properly considers thermal effects on cell behavior. The specific designed thermostatic chamber is described and the experimental results are presented and compared to those simulated with the developed model.
On battery State of Charge estimation: A new mixed algorithm
  • F Codeca
  • S M Savaresi
  • G Rizzoni
F. Codeca, S. M. Savaresi, and G. Rizzoni, "On battery State of Charge estimation: A new mixed algorithm," in 2008 IEEE Int. Conf. Control Appl. IEEE, 2008, pp. 102-107.