![Energy density per unit mass and volume for common secondary cells [11]](https://www.researchgate.net/profile/Saeed-Sepasi/publication/280886489/figure/fig1/AS:669007095009286@1536515129563/Energy-density-per-unit-mass-and-volume-for-common-secondary-cells-11_Q320.jpg)
![Chemical reaction of a LiFePO4 cell [21]](https://www.researchgate.net/profile/Saeed-Sepasi/publication/280886489/figure/fig2/AS:669007095033870@1536515129588/Chemical-reaction-of-a-LiFePO4-cell-21_Q320.jpg)
Content uploaded by Saeed Sepasi
Author content
All content in this area was uploaded by Saeed Sepasi on Aug 12, 2015
Content may be subject to copyright.
A preview of the PDF is not available
Adaptive state of charge estimation for battery packs
Abstract and Figures
Rechargeable batteries as an energy source in electric vehicles (EVs), hybrid electric
vehicles (HEVs) and smart grids are receiving more attention with the worldwide demand for
greenhouse reduction. In all of these applications, the battery management system needs to have
an accurate online estimation of the state of charge (SOC) of the battery pack. This estimation is
difficult, especially after substantial aging of batteries. In order to overcome this problem, this
work addresses SOC estimation of Li-ion battery packs using fuzzy- improved extended Kalman
filter (fuzzy- IEKF) from new to aged cells. In the proposed approach, a fuzzy method with a new
class of membership function has been introduced and used to make the approximate initial value
to estimate SOC. Later on, the IEKF method, considering the unit single model for the battery
pack, is applied to estimate the SOC for the long working time of the pack. This approach uses a
model adaptive algorithm to update each single cell’s model in the battery pack. The algorithm’s
fast response and low computational burden, makes on-board estimation practical. A LiFePO4
single cell/battery pack consists of single/120 cells connected in series with a nominal voltage
3.6V/432 V is used to implement the experiments/simulations to verify the SOC estimation
method’s accuracy. The obtained results by the federal test procedure (FTP75) and the new
European driving cycle (NEDC) reveal that the proposed approach’s SOC and voltage estimation
error do not exceed 1.5%.
Figures - uploaded by Saeed Sepasi
Author content
All figure content in this area was uploaded by Saeed Sepasi
Content may be subject to copyright.
... The aging phenomenon is caused by two main mechanisms: loss (fracture) of active materials and Solid-Electrolyte Interface (SEI)-layer growth [24]. These factors account for capacity fading and an increase in impedance [25]. These effects cause changes in the battery model parameters used in SOC estimation methods. ...
... Vs and Vl are the short and long time transient voltage responses at RC branches. The equations for the electrical elements of the voltage response circuit of the model presented in Figure 1, including impedance elements are modeled as follows [25]: ...
... represents usable capacity, which indicates available capacity of the battery. This capacity decreases as the battery ages [25] and is a criterion for batteries SOH. In [35], authors discussed a method for updating this capacity based on the coulomb counting method. ...
As the world moves toward greenhouse gas reduction, there is increasingly active work around Li-ion chemistry-based batteries as an energy source for electric vehicles (EVs), hybrid electric vehicles (HEVs) and smart grids. In these applications, the battery management system (BMS) requires an accurate online estimation of the state of charge (SOC) in a battery pack. This estimation is difficult, especially after substantial battery aging. In order to address this problem, this paper utilizes SOC estimation of Li-ion battery packs using a fuzzy-improved extended Kalman filter (fuzzy-IEKF) for Li-ion cells, regardless of their age. The proposed approach introduces a fuzzy method with a new class and associated membership function that determines an approximate initial value applied to SOC estimation. Subsequently, the EKF method is used by considering the single unit model for the battery pack to estimate the SOC for following periods of battery use. This approach uses an adaptive model algorithm to update the model for each single cell in the battery pack. To verify the accuracy of the estimation method, tests are done on a LiFePO4 aged battery pack consisting of 120 cells connected in series with a nominal voltage of 432 V.
... The ECM can be broadly divided into two categories: the impedance model and the Thevenin equivalent circuit model [15]. The impedance-based ECM model of a battery is developed by exciting the battery with a small voltage/current signal over a range of frequencies (from kHz to Hz) and analyzing the impedance spectrum of the battery over that frequency range. ...
... This method of determining impedance model of a battery is called electrochemical impedance spectroscopy (EIS) [10]. Though the EIS based impedance model of a battery can accurately represent battery characteristics under a wide range of operating conditions, the online implementation of this method is regarded expensive, as it requires additional and complex circuitry [15]. ...
... A battery is a voltage source [12] that depends on the generated current and on the state of charge (SOC) of the battery itself [13][14]. For battery a dynamic, rechargeable model presented in [15][16] has been used. ...
In this paper, the simultaneous optimization of transmission and distribution grid has been proposed. The dynamic model of the grid consists of turbine governors (TG), automatic voltage regulators (AVR) as well as wind turbines, solar power units and energy storage units. Grid stability is obtained by line stability and voltage stability indices and also by conducting eigenvalue analysis of the complete power system grid. Particle Swarm Optimization (PSO) algorithm adjusts the grid control settings (active and reactive power management) to maximize the renewable penetration at all layers of the grid. IEEE 14 bus system in conjunction with a micro grid is used as the test system. The results demonstrate that simultaneous reactive power management of transmission and distribution grid can maximize the renewable penetration at transmission and distribution layers of the grid. However different case studies with different renewable power pro-files at each layer of the grid are remained to be optimized, this study recommends that real-time management of reactive power at all layers of the grid can be a good candidate to deal with intermittency of renewable sources.
... Accurate SOC estimation of single cells and the battery packs of Li-ion batteries have been reported recently [19]. In the case of single cells, the most common method used for SOC estimation is the coulomb counting method [20]. ...
Battery powered mobile machines for underground mining have just been introduced at the market by major suppliers such as Epiroc and Sandvik. Capabilities and properties of these machines are still insufficiently communicated for their better understanding. This paper provides some general information of battery powered machines for underground mining, and overall properties of power packs i.e. their batteries as a general information of new parameters which influence the overall performance of mobile machines.
Batteries are a popular and important item that are utilized as energy sources in a variety of applications. The rise of electric vehicles in the twenty-first century has increased its importance. A battery's state of charge (SoC) is critical information. It is vital to estimate SoC with a reasonable degree of precision. During charge and discharge cycles, lithium-ion batteries change their internal state. The level of charge of a lithium-ion battery changes throughout the charging cycle and is dependent on the internal structure of the components, which can degrade over time. Battery management systems that depend on charge counting and cell voltage monitoring frequently estimate the status of charge. It's difficult to tell what state the battery components are in physically. This work will focus on different methods of estimating SoC. Basically, this study will cover terminal Voltage method, Open Circuit Voltage Method, Coulomb Counting Method and a dynamic method based on Unscented Kalman Filter (EKF). The study will be based on simulation on MATLAB and it will also cover a practical experiment by charging and discharging a battery multiple time. A comparative study of the advantages and drawbacks of the existing methods will be discussed in this work.
In this paper, a nonlinear control of a tilting rotor quadcopter
is presented. The overall control architecture is divided
into two sub-controllers. The first controller is based on the feedback
linearization control derived from the dynamic model of the
tilting quadcopter. This controls the pitch, roll, and yaw motions
required for movement along an arbitrary trajectory in space.
The second controller is based on two PD controllers which are
used to control the tilting of the quadcopter independently along
the pitch and the yaw directions respectively. The overall control
enables the quadcopter to combine tilting and movement along
a desired trajectory simultaneously. Simulation studies are presented
based on the developed nonlinear dynamic model of the
tilting rotor quadcopter to demonstrate the validity and effectiveness
of the overall control system for an arbitrary trajectory
tracking.
In this chapter, we discuss set operations on fuzzy sets by beginning with early fuzzy set operations and continuing with their generalization, interpretations, formal requirements and realizations. We emphasize that complement and negations, triangular norms and triangular co-norms are unifying, general realizations of the set-theoretic operations. Combinations of fuzzy sets through aggregation processes are also essential by delivering an additional level of flexibility of processing fuzzy sets.
h i g h l i g h t s Build a method for battery pack SOC estimation. Analyze the effect of the uneven cells problems to the pack SOC. The SOC is estimated with consideration of different balance control strategies. The UPF method is used to estimate the SOC to improve the accuracy. Inconsistency analysis Unscented particle filter Li-ion battery pack Battery model a b s t r a c t The state-of-charge (SOC) is a critical parameter of a Li-ion battery pack. Differences among in-pack cells are inevitable and can change the total capacity of a pack and the remaining available capacity. Because the traditional methods for the estimation of the SOC of a pack did not consider the difference among the cells and the impact of balance control, we developed a new method that accounts for these problems. To accurately estimate the pack SOC, we establish the relationship between the parameters of the pack and those of in-pack cells under different balance control strategies. This paper also studies the two different types of connections of a battery pack: in series and in parallel. Based on the model of the first overcharged cell and that of the first over-discharged cell, the estimation of the SOC of a battery pack is realized by the Unscented Particle Filter (UPF) algorithm. A simulation experiment verified the method for the estimation of the SOC for a battery pack based on actual data and proved that an accurate estimation value can be obtained by the method.
A Renewable Energy Vehicle Simulator (REVS) that enables to simulate renewable energy vehicles using combination of propulsion system and fuels by adapting library modules to suit particular applications has been developed. The simulation software predicts the energy use of the vehicle, taking into account the duty cycle and driver habits. Library modules have been developed to simulate the Plug-In Hybrid Electric Vehicle (PHEV) architecture as this platform offers energy scenario for cars and buses allowing combination of energy sources that include renewable electricity and renewable biofuels. This paper will discuss the methodology for designing system level vehicles using the REVS package. A series parallel HEV with a conventional ICE and power split transmission system has been designed using the simulation package. A fuzzy controller has been developed to simulate the driver and to command the acceleration and brake pedal. Another fuzzy controller has been employed to manage the power flow in hybrid vehicle. The energy management strategy has been applied through a rule-based fuzzy approach. Several rules are used to determine, based on the power demand value, how much power to get from electric motor if the power demand is positive. Simulation results of the vehicle will be presented for high and low capacity battery in various driving schedules.
This thesis considers the drivetrain and battery system requirements of Hybrid Electric Vehicles. The data herein proves that a series hybrid electric drivetrain with Lithium-ion batteries and plug-in recharge promises to be viable and sustainable. However, for mass production of series HEVs comprehensive performance characteristics and prediction of ageing behaviour of Lithium-ion batteries is essential but currently not available.
The main part of the thesis, following a graphical comparison of different energy storage solutions, is a detailed treatise on large Li-ion batteries. Construction and Li-ion working principles are summarised, together with several effects such as Peukert and memory effects, ageing of Li-ion cells, their temperature dependence and safety, and limits of charging/discharging.
Preliminary performance tests on 50 and 100 Ah Li-ion cells showed the necessity for a careful investigation of suitable reference conditions in order to achieve reproducibly precise results from repeated discharge/charge cycles. Then the main tests result in detailed graphs and tables of the discharge and charge characteristics. These main tests include effects of rate of discharge, energy-efficiency, temperature, resting time between test-cycles, hysteresis, ageing, and degradation. A new testing method that is based on the step response technique is suggested and investigated to whether it gives a meaningful but rapid measure of open circuit voltage and equivalent circuit models of the battery. Statistically significant theoretical models, equations and graphs are included.
The Appendix gives summaries of the author's seven main publications and presentations dealing with Systems Approach and five publications on Large Li-ion batteries, followed by most of these in full.
