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Rechargeable Batteries with Special Reference to Lithium-Ion Batteries
Chapter 11
Rechargeable Batteries with Special Reference to Lithium-Ion Batteries Matthias Vetter, Stephan Lux Fraunhofer Institute for Solar Energy Systems ISE, Freiburg, Germany
1 INTRODUCTION There are many different types of rechargeable batteries such as: lead–acid, alkaline, nickel–cadmium, nickel–hydrogen, nickel–metal hydride, nickel–zinc, lithium cobalt oxide, lithium-ion polymer, lithium iron phosphate, lithium sulfur, vanadium redox, lithium nickel manganese cobalt and sodium sulfur. Much has been written about the lead–acid battery and it is still the most popular of the rechargeable batteries. Lithium–ion and vanadium redox batteries offer specific characteristics, which are of special interest for use as stationary storage and possibly grid-level storage. The vanadium battery is discussed in chapter: Vanadium Redox Flow Batteries and the lithium-ion battery is the focus of this chapter. The main advantages of lithium-ion batteries are: high energy density, high cycle and calendar lifetimes, fast and efficient charging, with little energy wasted, low self-discharge rate, no need to be held upright, fairly maintenance free, and little voltage sag. Its main disadvantages are its relatively high price (at the moment but this is improving) and the possibility of thermal runaway. The latter is being solved by elaborate protective circuits such as battery management circuitry which is discussed in this chapter. Lithium-ion batteries are a relatively new invention and have only been around commercially since the 1980s. The chemistry and the technology are now reasonably well proven and this battery has edged out older rechargeable batteries such as the nickel–cadmium (NiCd) battery. In the 1990s one heard of lithium–ion batteries bursting into flames. The type of batteries used at this time were lithium-cobalt oxide (LiCoO2) batteries. These have been superseded by the lithium iron phosphate (LiFePO4) battery, and the lithium nickel manganese cobalt oxide (LiNMC) batteries which are very much safer than the LiCoO2 battery. Storing Energy. http://dx.doi.org/10.1016/B978-0-12-803440-8.00011-7 Copyright © 2016 Elsevier Inc. All rights reserved.
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TABLE 11.1 Comparison of Different Selected Battery Technologies [1,2] Lead acid
NiMh
Li NMC/ graphite
LiFePO4/ graphite
Vanadium redox-flow
Energy density/ (Wh kg–1)
40
75
160
110
45
Power density/ (W kg–1)
350
600
1 300
4 000
120
Cycle lifetime
600
900
2 500
5 000
12 000
Calendar lifetime/a
7
5
7
14
15
Efficiency/%
85
75
93
94
80
Monthly selfdischarge/%
8
20
3
3
5
Cost/€ (kW h)–1
60–300
400–600
200–2 000
200–2 000
150–800
Note: NiMh and LiNMC refer to nickel metal hydride and lithium nickel manganese cobalt batteries, respectively.
2 PHYSICAL FUNDAMENTALS OF BATTERY STORAGE Stationary battery storage is becoming more important with increasing shares in renewable energies in power supply systems and in grids, both off-grid and ongrid. In principle there exist a variety of different battery technologies suitable for these stationary applications. In Table 11.1 selected technologies are listed with their main parameters.
2.1 Lead–Acid Batteries The lead–acid battery was invented in 1859 by French physicist Gaston Planté and is the oldest type of rechargeable battery. Although it has a very low energyto-weight ratio and a low energy-to-volume ratio, it is able to supply high surges of current. Its low cost makes it attractive for many uses including in motor vehicles to provide a high starting current. The battery consists of lead plates in a solution of sulfuric acid. The basic cell reaction is given at the anode by: Pb (s) + HSO − (aq) → PbSO 4 (s) + H + (aq) + 2e − and at the cathode by: PbO 2 (s) + HSO − (aq) + 3H + (aq) + 2e − → PbSO 4 (s) + 2H 2 O(l)
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The total reaction can be written as: Pb (s) + PbO 2 (s) + 2H 2 SO 4 (aq) → 2PbSO 4 (s) + 2H 2 O (l) In the discharged state both the positive and negative plates become (PbSO4), and the electrolyte loses much of its dissolved sulfuric acid. The discharge process is driven by the conduction of electrons from the negative plate back into the cell at the positive plate in the external circuit. The liquid electrolyte did limit its application until the introduction of a gel electrolyte in the 1930s. This extended use of the lead–acid battery to be used in different positions without leakage. Further improvements in the 1970s led to the introduction of the valve-regulated lead–acid battery (often called “sealed”); this development made it possible to have the battery in any position.
2.2 Lithium-ion Batteries The lithium-ion battery is rapidly becoming the most important battery for both portable applications (computers and power tools) and stationary applications (storing grid-level energy). The term lithium-ion is used as there is no elemental lithium in the battery. As the lithium ions move from one host to another the process has been likened to a rocking chair. The basic half-cell reactions at each electrode (using the LiCoO2 battery as an example) are at the anode: xLi → xLi+ + xe − and at the cathode: xLi+ + xe− + LiCoO 2 → Li1+ x CoO 2 With the overall reaction: xLi + LiCoO 2 → Li1+ x CoO 2
3 DEVELOPMENT OF LITHIUM-ION BATTERY STORAGE SYSTEMS Lithium-ion battery systems are assembled from a number of modules interconnected parallel and/or serial, whereas a module typically consists of a number of cells which may be switched in parallel and/or in serial. The principle behind the design of lithium-ion battery systems will be discussed in the following section using as an example the battery used for photovoltaic (PV) applications. This fulfills the task of increasing self-consumption of generated PV electricity. In general, the development steps for a battery system are as follows: l l
Cell characterization and selection of appropriate cell technology Module and system design
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FIGURE 11.1 Concept of a 5.33 kW h lithium-ion battery system for residential photovoltaic applications consisting of three modules switched in parallel to be connected to a 48 V battery inverter. One module consists of 12 cells switched in serial [1]. The list of parameters is given in Table 11.2. l
l l l l
Cell interconnection l Electrical l Mechanical l Thermal Cooling system Safety concept Battery management Interfaces and integration in energy systems.
Justifiable storage sizes for residential PV applications are typically in the range (2–10) kW h. An example of such a storage system is the battery system based on lithium-ion pouch bag cells shown in Fig. 11.1; corresponding parameters are listed in Table 11.2. The system was designed for a battery inverter with a nominal input voltage of 48 V.
3.1 Design of Battery Modules and Systems for Stationary Applications For the construction of a battery module several aspects have to be considered. Among others, safety, reliability, efficiency, long lifetime, and reduced maintenance efforts play key roles. As temperatures have a huge impact not only on safe operation but also on aging mechanisms, the design of an appropriate cooling system is one of the key aspects. As a result certain temperature levels should not be exceeded, and furthermore a homogeneous temperature distribution within one cell, within a module, and also within the whole battery system is important. In the following section the simulation-based design of the battery module is described.
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TABLE 11.2 Parameters of the Lithium-Ion Battery System, Shown in Fig. 11.1 Parameter battery details
Value and unit
Parameter battery module Cell chemistry
Graphite/NMC
Number of cells per module
12 (switched in serial)
Rated voltage/V
44.4
L × W × H/cm
30 × 24 × 15
Gravimetric energy density/(Wh kg–1)
105.3
Volumetric energy density/(Wh L–1)
164.8 l
Energy content/(kW h)
1.78
Efficiency/%
95 (0.5C), 97 (0.2C)
Parameter battery system Number of modules per system
3 (switched in parallel)
L × W × H/cm
82 × 25.5 × 57.5
Gravimetric energy density/(Wh kg–1) –1
82.2
Volumetric energy density/(Wh L )
44.4
Energy content/(kW h)
5.34
Cooling
2 fans
Rated current/A
100
Battery management
1 central unit and 3 balancing boards
NMC refers to nickel manganese cobalt (the use of the abbreviation 0.5C and 0.2C will be described in the next section).
For the design and construction of the modules and the battery system, thermal simulations were carried out using the simulation tool Dymola [3] and the model description language Modelica [4]. The approach is based on partition of the cells and cooling plates into segments. Each cell was divided into 49 segments and the cooling plates between the cells were divided into 81 segments (Fig. 11.2). For every segment an electrical and thermal model was developed. The electric model is based on an inner resistance model of a used NMC cell, describing the dependence on state of charge (SOC), temperature, and C rate. The C rate is a measure of the charge and discharge current of the battery and a discharge of 1C draws a current equal to the rated capacity. For example, a battery rated at 1 000 mA h provides 1 000 mA for 1 h if discharged at the 1C rate. Based on the results of the thermal simulation the cooling area and the thickness of the cooling plates can be calculated. The challenge was to reduce the temperature difference between the single battery cells to a minimum. The lowtemperature difference should avoid too much cell balancing and allows homogeneous aging of single cells.
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FIGURE 11.2 Segmentation of lithium-ion cells and aluminum cooling plates for simulation of thermal behavior [5].
To assure a homogeneous air flow over each cell and the three modules the system was simulated within a computational fluid dynamics (CFD) simulation program. By using this CFD simulation an optimized angle of skewness could be identified (Fig. 11.3). The skewness enables a uniform air flow over every module of the system. To verify the goal of a maximum temperature difference of 2 K, tests in a climate chamber were carried out with one battery module. Using the flow channel the air distribution over the cells could be investigated. Tests in the climate chamber showed that the temperature difference between the cells of
FIGURE 11.3 Principle of the air cooling system showing built-in skewness [6].
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FIGURE 11.4 Temperature profile of three lithium-ion cells of a battery module for a 1 C charge/discharge test in the laboratory [7].
one module is nearly constant and almost below 1 K for a charge/discharge Crate of 1 (Fig. 11.4). For the analysis of critical temperature segments, module tests were carried out and a thermal imaging camera was used to identify the hotspots. The results (Fig. 11.5) show that the module warmed up mostly at the cells and the cooling plates between the cells. Within these tests no critical sectors could be detected.
FIGURE 11.5 Analysis of a thermal camera with 1C in a climate chamber [7].
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FIGURE 11.6 Efficiencies of a lithium-ion battery module, consisting of 12 cells switched in serial for different C rates [1].
3.1.1 Consideration of Efficiencies Compared with all other battery technologies, lithium-ion batteries offer very high efficiencies. In Fig. 11.6, achieved efficiencies of the developed battery module (Fig. 11.2) are shown. Due to internal losses, within the cells, and also at the cell interconnectors the efficiencies decrease with higher C-rates. But even at a C-rate of 1, which is not a common operation mode for storages in residential PV applications, the results show values clearly above 90%.
3.2 Battery Management Systems Battery systems, for example, those using lithium-ion technology, need to be managed. Battery cells have to be monitored and controlled. Challenges in terms of safety, electrical isolation, and energy efficiency have to be considered [11]. Within this section the principles of battery management systems are explained and different concepts introduced. There are several functions to be handled by a battery management system (BMS). Fig. 11.7 provides an overview of these functions. Every BMS needs a safety layer to prevent the batteries from being overcharged or deep discharged. Furthermore, the temperature of the cells has to be controlled. Therefore, monitoring of system temperature, load current, and cell voltages is required. Functions such as switches and coolers or fluid pumps have to be triggered by the BMS. Advanced BMS possess sophisticated and precise state estimation algorithms for the state of charge and state of health (SOH) as well as end-of-life predictions and model-based thermal control. To secure high overall system efficiencies, optimization algorithms for smart cell balancing, load, and thermal management are necessary. Furthermore, the BMS needs a communication interface to internal and to external components like a power electronics or supervisory energy management device to secure safe and reliable system integration. To handle these functions, there exist several types of BMSs with their specific advantages and disadvantages. One may classify the types into modular, central, and single-cell BMS approaches [11].
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FIGURE 11.7 Overview of the management functions of a battery management system.
3.2.1 Modular Concept In a modular approach the BMS contains a central control unit and module management systems (MMSs). The latter are responsible for the measurement and control of each lithium-ion battery module, which contains several battery cells, mostly connected in series to reach higher voltages. The number of cells connected in series strongly depends on the application [10]. Especially for storage applications with higher capacities a higher number of battery cells per module helps to reduce the costs of electronics. Typically, the MMS electronic system carries out the measurement and control on battery cell level and communicates with the central management system (CMS), which collects the measurement and control data of single modules. Furthermore, it controls switches and a cooling system and establishes communication with external components. A schematic representation of the modular approach is shown in Fig. 11.8. The particular description of an MMS and a CMS is based on a system developed at Fraunhofer ISE. The concept is an example for a modular approach. The main parts of the MMS are the battery front end unit and a controller unit. The front end unit is responsible for measuring the voltages of the battery cells and for controlling the temperature of the printed circuit board (PCB). Furthermore, it provides a hardware interface for cell balancing, which is needed to balance the SOC of the cells within a battery module. The controller unit consists of a low-power reduced instruction set computer (RISC) microcontroller. The microcontroller controls the battery front end unit by communicating via a bus system. In addition, there is a bus interface to send measured data and commands to a CMS or a host PC for further handling. By
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FIGURE 11.8 Concept of a modular battery management system with a MMS and a CMS system with internal and external communication [11]. (Source: SMA)
means of an integrated analog-to-digital (AD) converter, temperature measurement at the cell level [12] is obtained. Fig. 11.9 shows an photo of such an MMS. It is a prototype developed at Fraunhofer ISE that has been tested in both laboratory and in prototype applications. The module management system is scalable up to 1000 V of battery voltage. This is insured by integration of a high isolation barrier [11]. Especially in electric vehicle applications, it is important to minimize the self-discharge rate of the battery to prevent deep discharge after extended times without operation. To insure a very low self-discharge rate the MMS system has low current consumption. The module management control unit contains flexible software modules. Filter algorithms based on Kalman and particle filter approaches for SOC and SOH estimations at the cell level with high accuracy are implemented. Error
FIGURE 11.9 Photo of a module management system unit with a passive resistive cell balancing, voltage and temperature control and a controller unit [11].
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FIGURE 11.10 Photo of a central management system with an embedded system as central controller and several interfaces [11].
management handling of events such as overcharging, deep discharging, or too high temperatures insures reliable operation of the batteries. The central management unit retrieves all management and measurement data from the modules. Furthermore, it communicates with the modules via a bus system. For communication with external components a second bus interface is implemented. The central management unit controls the battery switches and the integrated cooling system. Signals are integrated for the control of external switches, cooling pumps, and other components. Air cooling, using fluid cooling or a combination of both, is possible. Additionally, temperature, high-current, and high-voltage measurement are integrated to measure the parameters at the system level via a controller area network (CAN) bus. Important parameters for system safety are monitored. These comprise measurement of the insulation resistance for an electrically isolated system and redundant control of battery switches. The central controller consists of an embedded system involving thermal management, a central error-handling system, and a charge and discharge management system. With thermal and electric battery models, precise central battery management is needed. Furthermore, with integrated battery models and filter algorithms, lifetime prediction of the battery system can be made available. Fig. 11.10 shows a photo of the central management unit.
3.2.2 Single Central Concept The single central BMS approach (Fig. 11.11) is based on only one PCB, which controls all the battery cells of a storage system, and which can be switched in serial or parallel. The advantage over the modular system is the lower cost of the electronics. All functions regarding safety, control, and measurement are integrated. Furthermore, energy efficiency is higher because there is only one central controller. Battery systems based on such a
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FIGURE 11.11 Single central BMS concept of a battery management system with one PCB [11].
BMS are not easily expandable, but this approach enables significant cost reductions in many applications. The decreased system complexity of this solution is often an advantage, too. A disadvantage is the decreased flexibility in comparison with the modular approach. In the modular approach another module is easily integrated on the bus system, which enables easy integration of modules in a spatially distributed system since only a bus cable must be connected and no cable for cell voltage measurement and cell balancing for each single cell are needed. The battery front end unit and the controller unit are integrated on one PCB (see Section 3.2.1). Several front end units control and measure all cells in the battery pack. The controller unit could contain an advanced RISC machine (ARM) controller with low current consumption.
3.2.3 Single-Cell Concept A third approach is placing a single-cell control unit (SCU) in each single battery cell. The SCU is a rather simple integrated circuit, measuring cell temperature and voltage. It is also possible to integrate cell balancing. Communication with a CMS is established through a bus interface. Normally, the management functions of the CMS are more complex than for the modular approach. SOC and SOH estimation plus error management have to be executed in this unit. The SCU can only carry out basic safety and measurement functions. Fig. 11.12 shows the architecture of such a system.
FIGURE 11.12 Single-cell BMS concept of a BMS with several single-cell control units (SCU) and a CMS [11].
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3.2.4 State-of-Charge Estimation Since accurate knowledge of a battery’s SOC is unavoidable for proper usage of a battery system many different approaches have been investigated. Today, in many sophisticated battery management systems it is state of the art to use Kalman filters to estimate actual SOC. But since the Kalman filter uses some particular assumptions (like Gaussian distributions), its correctness and applicability are limited. A new approach is the so-called “particle filter” which is derived from the same family (Bayesian filters) as the Kalman filter [13]. By employing Monte Carlo sampling methods the particle filter offers the possibility to deal with any distribution. As depicted in Fig. 11.13 a Markov chain is assumed for a particle filter: l l l
ut, input which will change the system’s state over time (it can be measured); xt, state of the system at time t; zt, output is in some correlation with the system’s state enabling rough estimation (quantity can be measured).
So one assumes only the quantities of input u t −1 and output zt and not the former values have an effect on the calculated probability of the current state. From Bayes theorem one gets the following equation: P ( xt ) = η −1 ( P( zt | xt ) ∫ P ( xt | xt −1 , ut −1 ) P ( xt −1 ) dxt −1 The particle filter algorithm (see Fig. 11.14) runs in three steps [14]: 1. State transition—the influence of input ut–1 on each sample (particle) stk is calculated. By adding a different random value to the input value for each sample measurement error the uncertainty of this step is taken into account. 2. Weighting—samples are weighted according to the observed measurement zt and a probability density function P( zt | xt ). Then the sum of all weights ( wtk ) is normalized to 1.
FIGURE 11.13 Assumption of a Markov chain for SOC determination.
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FIGURE 11.14 Illustration of the particle filter algorithm. For initialization (init.) all samples (or particles) are distributed uniformly over the possible value range of state x. In Step 1 of the algorithm the influence of input u on every sample is calculated. Noise ε, which is taken from a suitable probability distribution, is added to the value of u. Step 2 gives every sample a weight according to probability taken from the measurement value z and the measurement model. Step 3 resamples the weighted particles to gain an unweighted particle set. The low-variance resampling method is depicted [14].
3. Resampling—in this step the samples are resampled according to their weights. After that all samples have the same weight again. This approach uses the low-variance resampling method for low computational afford. In Step 1 the so-called “process model” is used, which describes the influence of input ut–1 on state xt. For SOC estimation the following model equation is used: k k sSOC, t = sSOC, t −1 +
( I batt + ε k ) ∆t SOH ⋅ Cn
where ε k represents the random value which is added to the input value. It can be sampled from any distribution suitable for the application. For this application a Cauchy–Lorentz distribution is used.
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FIGURE 11.15 Open-circuit voltage versus state of charge curve of a lithium iron phosphate battery with a graphite anode. One can see the hysteresis between charging and discharging voltage and the very flat voltage in the medium state-of-charge range [13].
By adding this noise to that of the particles, particle diffusion is increased. This diffusion models—in case of SOC estimation—the increasing uncertainty of ampere-hour counting. Step 2 decreases this diffusion by weighting particles according to their probability. Therefore, the so-called “measurement model” is used, which calculates estimated terminal voltage at the actual SOC of the particle. For phosphate-based lithium-ion batteries like lithium iron phosphate (LFP) cells the open-circuit voltage (OCV) vs. SOC curve is very flat (see Fig. 11.15) and shows a hysteresis between charging and discharging. For that, two voltages are calculated, one for charging and one for discharging. The following equations show the measurement model for an LFP battery:
(
)
(
)
k k k Vdischarge, t = OCVdischarge sSOC, t + Ri sSOC, t , T , I batt ⋅ I batt
(
)
(
)
k k k Vcharge, t = OCVcharge sSOC, t + Ri sSOC, t , T , I batt ⋅ I batt
These two voltages are compared with the measured terminal voltage. So two probabilities are determined with the following formula (using a Cauchy– Lorentz distribution): k wSOC, t =
γ 1 ⋅ π γ 2 + V k discharge, t − Vmeas
(
)
2
+
(
γ
k γ 2 + Vcharge, t − Vmeas
)
2
After that, in Step 3 these weighted particles are resampled to regain an unweighted set of particles. In this approach the low-variance resampling method is used as depicted in Fig. 11.15. Low-variance resampling puts all particles in
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PART | C Electrochemical
a row, each particle has a length according to its weight. After that, this row is sampled as many times as the number of particles using a fixed sampling width. The sampled particles now represent an unweighted set of particles and the filter goes back to Step 1. The estimated value for the SOC is the mean value of all particle values.
3.2.5 State-of-Health Estimation The estimation of SOH is also done using a particle filter [13]. Both filters run in parallel and share their results. As the particle filter for SOC estimation needs the SOH as a parameter, the filter for SOH estimation needs the SOC difference between two calculation steps as a parameter. Therefore, the approach is called “parallel particle filter” for SOC and SOH estimation. SOH in this section is defined as the ratio between actual battery capacity and nominal battery capacity: Cact Cn
SOH =
In general, the particle filter for SOH estimation works similar to that for SOC estimation. as described in the previous section. Due to the very slow change in SOH, it is assumed in the process model that no input value u influences the state. Only the noise value ε is added to every particle: k k sSOH, t = sSOH, t −1 + ε
k
The weighting step is done by using the SOC change of the SOC filter during the last step (∆ SOCmeas) as the measurement value z and following equation as the measurement model: ∆ SOCtk =
Q
step k SOH,t
s
⋅ Cn
Variable Qstep represents integrated charge flowing into and out of the battery. If using a Cauchy–Lorentz distribution the weight is calculated as follows: k wSOH =
γ 1 ⋅ 2 k π γ + ∆ SOCt − ∆ SOCmeas
(
)
2
Thereafter, the low-variance resampling step is performed as described earlier. The SOH estimated value is gained by taking the mean value of all particle values.
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FIGURE 11.16 Depicted is the state of charge during a PV current profile. The battery used is a lithium iron phosphate battery with a graphite anode [14].
3.2.6 Validation The dual-particle filter approach is validated for different types of lithium-ion batteries (LFP and NMC, both with graphite anode) and different types of current profiles including electric vehicle (EV) cycles and PV applications [14]. The EV profile is characterized by large currents, high dynamics, but also relatively long pauses with no current. The PV profile, on the other hand, shows lower currents, but there are virtually no phases without any current. In Fig. 11.16 a validation sequence for LFP-graphite lithium-ion cells is shown by using a PV profile [14]. Estimation of LFP is much more complicated still, as a result of flat OCV and hysteresis; however, exact SOC is found to be fast and estimated reliably. SOH estimation is very accurate as well, as depicted in Fig. 11.17.
4 SYSTEM INTEGRATION 4.1 Configuration Battery systems and DC power sources like PV generators can be coupled via power electronics on a DC bus bar or on the AC side. Exemplarily, an AC coupled system is introduced in the following (Fig. 11.18), which allows the integration of lithium-ion battery systems in PV systems by using a marketavailable battery inverter.
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FIGURE 11.17 SOH of a lithium iron phosphate/graphite battery during a PV current profile. The reference value was determined with a standard charge–discharge regime [14].
FIGURE 11.18 Integration of the developed lithium-ion battery system (Fig. 11.1) in a residential PV system using a market-available battery inverter [7].
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In these AC coupled system configurations the PV generator and the battery system are connected to the AC grid via two separate inverters. The conventional PV system, consisting of PV modules and a PV inverter, is in principle not affected by integration of a battery. Therefore, installed PV systems can easily be complemented with storages later without any adaptation. Due to the modular concept, sizing of the battery system is almost independent of the size of PV system components like the PV inverter. The disadvantages of this topology are limited cost reduction potential, as two full inverters are needed, as well as the limited voltage levels of marketavailable battery inverters for residential applications, which are in the range (24–48) V. Therefore, the inverters possess transformers and offer only relatively low efficiencies (∼94%) at the nominal operating point and efficiencies much below this value across a wide range of the typical operating window.
4.2 Communication Infrastructure Lithium-ion batteries are a very promising storage technology especially for decentralized grid-connected PV battery systems. Due to several reasons, for example, safety aspects, battery management is part of the lithium-ion battery system itself and is not integrated into the battery inverter or the charge controller as is usual for lead–acid and nickel-based batteries. This battery management system has to control the battery system itself and the connected power electronics. Furthermore, it has to exchange all relevant data with the supervisory energy management system. Field bus communication is necessary for both tasks (Fig. 11.19), but market-available products offer only proprietary solutions. Therefore, system integrators are not free to choose different system components for specific solutions. Furthermore, it is predefined which battery systems can be operated with which inverters or charge controllers avoiding the need for a huge adaptation effort for the communication system. To enable higher degrees of freedom in system assembly, a standard for communication at the field bus level among battery systems, power electronics, and energy management systems is necessary. Such an approach is encapsulated currently by the so-called “EnergyBus” [17], which was initially developed for simplified connection of system components of light electric vehicles. This standard defines the communication protocol as well as the power connectors. The CANopen user profile CiA 454 “energy management systems” is used as the communication protocol [16]. This protocol specifies data exchange between single components such as storages, generators, loads, and energy management systems and enables implementation of optimized operating control strategies. Based on this field bus communication standard, components of different manufacturers can be assembled by system integrators. The power connectors are defined especially for light electric vehicles, whereas the communication protocol was extended for stationary applications like PV battery systems. New specification parts for generators and loads have been designed
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FIGURE 11.19 Field bus communication based on the so-called EnergyBus/CiA 454 protocol [16–18] for setting up a network of different generators, storages, power electronics, and energy management systems.
in such an abstract way that general manageability by a supervisory control system is enabled. For example, a PV generator and a CHP (combined heat and power) unit can be described by the same specification part. Furthermore, smart-metering components can be easily integrated.
5 CONCLUSIONS In conclusion, in the relatively short time of the development and application of the lithium-ion battery, its main advantages such as high energy density, long lifecycle, fast and efficient charging, little maintenance, and little voltage sag have been undercut by the possibility of thermal runaway. This has resulted in much research and development resulting in elaborate battery management protective circuits and programs which have been the focus of this chapter.
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[5] Lux S, Dennenmoser M, Becker M, Lang N, Jung M, Vetter M. Entwicklung und thermische Modellierung eines innovativen Lithium-Ionen-Speichers für den stationären dezentralen Einsatz. Entwicklerforum 2013, Aschaffenburg; August 25, 2013. [6] Fraunhofer Institute Project Urban Hybrid Energy Storage; 2014. [7] Vetter M. Development of an optimized battery system for residential PV applications. Intersolar Conference North America; 2014. [8] Goodenough JB. Journal of Power Sources 2007;174:996–1000. [9] Wang J, Liu P, Hicks-Garner J, Sherman E, Soukiazian S, Verbrugge M, Tataria H, Musser J, Finamore P. J Power Sources 2011;196:3942–8. [10] Stuart TA, F, Wei Zhou. Journal of Power Sources 2011;196:458–64. [11] Jung M, Schwunk S. High end battery management systems for renewable energy and EV applications. Green 2013;3(1):19–26. [12] Rao L, Newman J. J Electrochem Soc 1997;144:2697–704. [13] Schwunk S, Ambruster N, Straub S, Kehl J, Vetter M. J Power Sources 2013;239:705–10. [14] Armbruster N et al. Particle filter for state of charge and health estimation for both NMC and LFP based batteries. Advanced Automotive Battery Conference, Strasbourg; 24–28, 2013. [15] http://dx.doi.org/10.1016/j.jpowsour.2012.10.058 [16] www.can-cia.org [17] www.energybus.info [18] Vetter M. Dezentrale netzgekoppelte PV-Batteriesysteme. Intersolar—PV Energy World, Munich; June 8, 2011.
Rechargeable Batteries with Special Reference to Lithium-Ion Batteries Matthias Vetter, Stephan Lux Fraunhofer Institute for Solar Energy Systems ISE, Freiburg, Germany
1 INTRODUCTION There are many different types of rechargeable batteries such as: lead–acid, alkaline, nickel–cadmium, nickel–hydrogen, nickel–metal hydride, nickel–zinc, lithium cobalt oxide, lithium-ion polymer, lithium iron phosphate, lithium sulfur, vanadium redox, lithium nickel manganese cobalt and sodium sulfur. Much has been written about the lead–acid battery and it is still the most popular of the rechargeable batteries. Lithium–ion and vanadium redox batteries offer specific characteristics, which are of special interest for use as stationary storage and possibly grid-level storage. The vanadium battery is discussed in chapter: Vanadium Redox Flow Batteries and the lithium-ion battery is the focus of this chapter. The main advantages of lithium-ion batteries are: high energy density, high cycle and calendar lifetimes, fast and efficient charging, with little energy wasted, low self-discharge rate, no need to be held upright, fairly maintenance free, and little voltage sag. Its main disadvantages are its relatively high price (at the moment but this is improving) and the possibility of thermal runaway. The latter is being solved by elaborate protective circuits such as battery management circuitry which is discussed in this chapter. Lithium-ion batteries are a relatively new invention and have only been around commercially since the 1980s. The chemistry and the technology are now reasonably well proven and this battery has edged out older rechargeable batteries such as the nickel–cadmium (NiCd) battery. In the 1990s one heard of lithium–ion batteries bursting into flames. The type of batteries used at this time were lithium-cobalt oxide (LiCoO2) batteries. These have been superseded by the lithium iron phosphate (LiFePO4) battery, and the lithium nickel manganese cobalt oxide (LiNMC) batteries which are very much safer than the LiCoO2 battery. Storing Energy. http://dx.doi.org/10.1016/B978-0-12-803440-8.00011-7 Copyright © 2016 Elsevier Inc. All rights reserved.
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TABLE 11.1 Comparison of Different Selected Battery Technologies [1,2] Lead acid
NiMh
Li NMC/ graphite
LiFePO4/ graphite
Vanadium redox-flow
Energy density/ (Wh kg–1)
40
75
160
110
45
Power density/ (W kg–1)
350
600
1 300
4 000
120
Cycle lifetime
600
900
2 500
5 000
12 000
Calendar lifetime/a
7
5
7
14
15
Efficiency/%
85
75
93
94
80
Monthly selfdischarge/%
8
20
3
3
5
Cost/€ (kW h)–1
60–300
400–600
200–2 000
200–2 000
150–800
Note: NiMh and LiNMC refer to nickel metal hydride and lithium nickel manganese cobalt batteries, respectively.
2 PHYSICAL FUNDAMENTALS OF BATTERY STORAGE Stationary battery storage is becoming more important with increasing shares in renewable energies in power supply systems and in grids, both off-grid and ongrid. In principle there exist a variety of different battery technologies suitable for these stationary applications. In Table 11.1 selected technologies are listed with their main parameters.
2.1 Lead–Acid Batteries The lead–acid battery was invented in 1859 by French physicist Gaston Planté and is the oldest type of rechargeable battery. Although it has a very low energyto-weight ratio and a low energy-to-volume ratio, it is able to supply high surges of current. Its low cost makes it attractive for many uses including in motor vehicles to provide a high starting current. The battery consists of lead plates in a solution of sulfuric acid. The basic cell reaction is given at the anode by: Pb (s) + HSO − (aq) → PbSO 4 (s) + H + (aq) + 2e − and at the cathode by: PbO 2 (s) + HSO − (aq) + 3H + (aq) + 2e − → PbSO 4 (s) + 2H 2 O(l)
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The total reaction can be written as: Pb (s) + PbO 2 (s) + 2H 2 SO 4 (aq) → 2PbSO 4 (s) + 2H 2 O (l) In the discharged state both the positive and negative plates become (PbSO4), and the electrolyte loses much of its dissolved sulfuric acid. The discharge process is driven by the conduction of electrons from the negative plate back into the cell at the positive plate in the external circuit. The liquid electrolyte did limit its application until the introduction of a gel electrolyte in the 1930s. This extended use of the lead–acid battery to be used in different positions without leakage. Further improvements in the 1970s led to the introduction of the valve-regulated lead–acid battery (often called “sealed”); this development made it possible to have the battery in any position.
2.2 Lithium-ion Batteries The lithium-ion battery is rapidly becoming the most important battery for both portable applications (computers and power tools) and stationary applications (storing grid-level energy). The term lithium-ion is used as there is no elemental lithium in the battery. As the lithium ions move from one host to another the process has been likened to a rocking chair. The basic half-cell reactions at each electrode (using the LiCoO2 battery as an example) are at the anode: xLi → xLi+ + xe − and at the cathode: xLi+ + xe− + LiCoO 2 → Li1+ x CoO 2 With the overall reaction: xLi + LiCoO 2 → Li1+ x CoO 2
3 DEVELOPMENT OF LITHIUM-ION BATTERY STORAGE SYSTEMS Lithium-ion battery systems are assembled from a number of modules interconnected parallel and/or serial, whereas a module typically consists of a number of cells which may be switched in parallel and/or in serial. The principle behind the design of lithium-ion battery systems will be discussed in the following section using as an example the battery used for photovoltaic (PV) applications. This fulfills the task of increasing self-consumption of generated PV electricity. In general, the development steps for a battery system are as follows: l l
Cell characterization and selection of appropriate cell technology Module and system design
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FIGURE 11.1 Concept of a 5.33 kW h lithium-ion battery system for residential photovoltaic applications consisting of three modules switched in parallel to be connected to a 48 V battery inverter. One module consists of 12 cells switched in serial [1]. The list of parameters is given in Table 11.2. l
l l l l
Cell interconnection l Electrical l Mechanical l Thermal Cooling system Safety concept Battery management Interfaces and integration in energy systems.
Justifiable storage sizes for residential PV applications are typically in the range (2–10) kW h. An example of such a storage system is the battery system based on lithium-ion pouch bag cells shown in Fig. 11.1; corresponding parameters are listed in Table 11.2. The system was designed for a battery inverter with a nominal input voltage of 48 V.
3.1 Design of Battery Modules and Systems for Stationary Applications For the construction of a battery module several aspects have to be considered. Among others, safety, reliability, efficiency, long lifetime, and reduced maintenance efforts play key roles. As temperatures have a huge impact not only on safe operation but also on aging mechanisms, the design of an appropriate cooling system is one of the key aspects. As a result certain temperature levels should not be exceeded, and furthermore a homogeneous temperature distribution within one cell, within a module, and also within the whole battery system is important. In the following section the simulation-based design of the battery module is described.
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TABLE 11.2 Parameters of the Lithium-Ion Battery System, Shown in Fig. 11.1 Parameter battery details
Value and unit
Parameter battery module Cell chemistry
Graphite/NMC
Number of cells per module
12 (switched in serial)
Rated voltage/V
44.4
L × W × H/cm
30 × 24 × 15
Gravimetric energy density/(Wh kg–1)
105.3
Volumetric energy density/(Wh L–1)
164.8 l
Energy content/(kW h)
1.78
Efficiency/%
95 (0.5C), 97 (0.2C)
Parameter battery system Number of modules per system
3 (switched in parallel)
L × W × H/cm
82 × 25.5 × 57.5
Gravimetric energy density/(Wh kg–1) –1
82.2
Volumetric energy density/(Wh L )
44.4
Energy content/(kW h)
5.34
Cooling
2 fans
Rated current/A
100
Battery management
1 central unit and 3 balancing boards
NMC refers to nickel manganese cobalt (the use of the abbreviation 0.5C and 0.2C will be described in the next section).
For the design and construction of the modules and the battery system, thermal simulations were carried out using the simulation tool Dymola [3] and the model description language Modelica [4]. The approach is based on partition of the cells and cooling plates into segments. Each cell was divided into 49 segments and the cooling plates between the cells were divided into 81 segments (Fig. 11.2). For every segment an electrical and thermal model was developed. The electric model is based on an inner resistance model of a used NMC cell, describing the dependence on state of charge (SOC), temperature, and C rate. The C rate is a measure of the charge and discharge current of the battery and a discharge of 1C draws a current equal to the rated capacity. For example, a battery rated at 1 000 mA h provides 1 000 mA for 1 h if discharged at the 1C rate. Based on the results of the thermal simulation the cooling area and the thickness of the cooling plates can be calculated. The challenge was to reduce the temperature difference between the single battery cells to a minimum. The lowtemperature difference should avoid too much cell balancing and allows homogeneous aging of single cells.
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FIGURE 11.2 Segmentation of lithium-ion cells and aluminum cooling plates for simulation of thermal behavior [5].
To assure a homogeneous air flow over each cell and the three modules the system was simulated within a computational fluid dynamics (CFD) simulation program. By using this CFD simulation an optimized angle of skewness could be identified (Fig. 11.3). The skewness enables a uniform air flow over every module of the system. To verify the goal of a maximum temperature difference of 2 K, tests in a climate chamber were carried out with one battery module. Using the flow channel the air distribution over the cells could be investigated. Tests in the climate chamber showed that the temperature difference between the cells of
FIGURE 11.3 Principle of the air cooling system showing built-in skewness [6].
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FIGURE 11.4 Temperature profile of three lithium-ion cells of a battery module for a 1 C charge/discharge test in the laboratory [7].
one module is nearly constant and almost below 1 K for a charge/discharge Crate of 1 (Fig. 11.4). For the analysis of critical temperature segments, module tests were carried out and a thermal imaging camera was used to identify the hotspots. The results (Fig. 11.5) show that the module warmed up mostly at the cells and the cooling plates between the cells. Within these tests no critical sectors could be detected.
FIGURE 11.5 Analysis of a thermal camera with 1C in a climate chamber [7].
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FIGURE 11.6 Efficiencies of a lithium-ion battery module, consisting of 12 cells switched in serial for different C rates [1].
3.1.1 Consideration of Efficiencies Compared with all other battery technologies, lithium-ion batteries offer very high efficiencies. In Fig. 11.6, achieved efficiencies of the developed battery module (Fig. 11.2) are shown. Due to internal losses, within the cells, and also at the cell interconnectors the efficiencies decrease with higher C-rates. But even at a C-rate of 1, which is not a common operation mode for storages in residential PV applications, the results show values clearly above 90%.
3.2 Battery Management Systems Battery systems, for example, those using lithium-ion technology, need to be managed. Battery cells have to be monitored and controlled. Challenges in terms of safety, electrical isolation, and energy efficiency have to be considered [11]. Within this section the principles of battery management systems are explained and different concepts introduced. There are several functions to be handled by a battery management system (BMS). Fig. 11.7 provides an overview of these functions. Every BMS needs a safety layer to prevent the batteries from being overcharged or deep discharged. Furthermore, the temperature of the cells has to be controlled. Therefore, monitoring of system temperature, load current, and cell voltages is required. Functions such as switches and coolers or fluid pumps have to be triggered by the BMS. Advanced BMS possess sophisticated and precise state estimation algorithms for the state of charge and state of health (SOH) as well as end-of-life predictions and model-based thermal control. To secure high overall system efficiencies, optimization algorithms for smart cell balancing, load, and thermal management are necessary. Furthermore, the BMS needs a communication interface to internal and to external components like a power electronics or supervisory energy management device to secure safe and reliable system integration. To handle these functions, there exist several types of BMSs with their specific advantages and disadvantages. One may classify the types into modular, central, and single-cell BMS approaches [11].
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FIGURE 11.7 Overview of the management functions of a battery management system.
3.2.1 Modular Concept In a modular approach the BMS contains a central control unit and module management systems (MMSs). The latter are responsible for the measurement and control of each lithium-ion battery module, which contains several battery cells, mostly connected in series to reach higher voltages. The number of cells connected in series strongly depends on the application [10]. Especially for storage applications with higher capacities a higher number of battery cells per module helps to reduce the costs of electronics. Typically, the MMS electronic system carries out the measurement and control on battery cell level and communicates with the central management system (CMS), which collects the measurement and control data of single modules. Furthermore, it controls switches and a cooling system and establishes communication with external components. A schematic representation of the modular approach is shown in Fig. 11.8. The particular description of an MMS and a CMS is based on a system developed at Fraunhofer ISE. The concept is an example for a modular approach. The main parts of the MMS are the battery front end unit and a controller unit. The front end unit is responsible for measuring the voltages of the battery cells and for controlling the temperature of the printed circuit board (PCB). Furthermore, it provides a hardware interface for cell balancing, which is needed to balance the SOC of the cells within a battery module. The controller unit consists of a low-power reduced instruction set computer (RISC) microcontroller. The microcontroller controls the battery front end unit by communicating via a bus system. In addition, there is a bus interface to send measured data and commands to a CMS or a host PC for further handling. By
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FIGURE 11.8 Concept of a modular battery management system with a MMS and a CMS system with internal and external communication [11]. (Source: SMA)
means of an integrated analog-to-digital (AD) converter, temperature measurement at the cell level [12] is obtained. Fig. 11.9 shows an photo of such an MMS. It is a prototype developed at Fraunhofer ISE that has been tested in both laboratory and in prototype applications. The module management system is scalable up to 1000 V of battery voltage. This is insured by integration of a high isolation barrier [11]. Especially in electric vehicle applications, it is important to minimize the self-discharge rate of the battery to prevent deep discharge after extended times without operation. To insure a very low self-discharge rate the MMS system has low current consumption. The module management control unit contains flexible software modules. Filter algorithms based on Kalman and particle filter approaches for SOC and SOH estimations at the cell level with high accuracy are implemented. Error
FIGURE 11.9 Photo of a module management system unit with a passive resistive cell balancing, voltage and temperature control and a controller unit [11].
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FIGURE 11.10 Photo of a central management system with an embedded system as central controller and several interfaces [11].
management handling of events such as overcharging, deep discharging, or too high temperatures insures reliable operation of the batteries. The central management unit retrieves all management and measurement data from the modules. Furthermore, it communicates with the modules via a bus system. For communication with external components a second bus interface is implemented. The central management unit controls the battery switches and the integrated cooling system. Signals are integrated for the control of external switches, cooling pumps, and other components. Air cooling, using fluid cooling or a combination of both, is possible. Additionally, temperature, high-current, and high-voltage measurement are integrated to measure the parameters at the system level via a controller area network (CAN) bus. Important parameters for system safety are monitored. These comprise measurement of the insulation resistance for an electrically isolated system and redundant control of battery switches. The central controller consists of an embedded system involving thermal management, a central error-handling system, and a charge and discharge management system. With thermal and electric battery models, precise central battery management is needed. Furthermore, with integrated battery models and filter algorithms, lifetime prediction of the battery system can be made available. Fig. 11.10 shows a photo of the central management unit.
3.2.2 Single Central Concept The single central BMS approach (Fig. 11.11) is based on only one PCB, which controls all the battery cells of a storage system, and which can be switched in serial or parallel. The advantage over the modular system is the lower cost of the electronics. All functions regarding safety, control, and measurement are integrated. Furthermore, energy efficiency is higher because there is only one central controller. Battery systems based on such a
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FIGURE 11.11 Single central BMS concept of a battery management system with one PCB [11].
BMS are not easily expandable, but this approach enables significant cost reductions in many applications. The decreased system complexity of this solution is often an advantage, too. A disadvantage is the decreased flexibility in comparison with the modular approach. In the modular approach another module is easily integrated on the bus system, which enables easy integration of modules in a spatially distributed system since only a bus cable must be connected and no cable for cell voltage measurement and cell balancing for each single cell are needed. The battery front end unit and the controller unit are integrated on one PCB (see Section 3.2.1). Several front end units control and measure all cells in the battery pack. The controller unit could contain an advanced RISC machine (ARM) controller with low current consumption.
3.2.3 Single-Cell Concept A third approach is placing a single-cell control unit (SCU) in each single battery cell. The SCU is a rather simple integrated circuit, measuring cell temperature and voltage. It is also possible to integrate cell balancing. Communication with a CMS is established through a bus interface. Normally, the management functions of the CMS are more complex than for the modular approach. SOC and SOH estimation plus error management have to be executed in this unit. The SCU can only carry out basic safety and measurement functions. Fig. 11.12 shows the architecture of such a system.
FIGURE 11.12 Single-cell BMS concept of a BMS with several single-cell control units (SCU) and a CMS [11].
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3.2.4 State-of-Charge Estimation Since accurate knowledge of a battery’s SOC is unavoidable for proper usage of a battery system many different approaches have been investigated. Today, in many sophisticated battery management systems it is state of the art to use Kalman filters to estimate actual SOC. But since the Kalman filter uses some particular assumptions (like Gaussian distributions), its correctness and applicability are limited. A new approach is the so-called “particle filter” which is derived from the same family (Bayesian filters) as the Kalman filter [13]. By employing Monte Carlo sampling methods the particle filter offers the possibility to deal with any distribution. As depicted in Fig. 11.13 a Markov chain is assumed for a particle filter: l l l
ut, input which will change the system’s state over time (it can be measured); xt, state of the system at time t; zt, output is in some correlation with the system’s state enabling rough estimation (quantity can be measured).
So one assumes only the quantities of input u t −1 and output zt and not the former values have an effect on the calculated probability of the current state. From Bayes theorem one gets the following equation: P ( xt ) = η −1 ( P( zt | xt ) ∫ P ( xt | xt −1 , ut −1 ) P ( xt −1 ) dxt −1 The particle filter algorithm (see Fig. 11.14) runs in three steps [14]: 1. State transition—the influence of input ut–1 on each sample (particle) stk is calculated. By adding a different random value to the input value for each sample measurement error the uncertainty of this step is taken into account. 2. Weighting—samples are weighted according to the observed measurement zt and a probability density function P( zt | xt ). Then the sum of all weights ( wtk ) is normalized to 1.
FIGURE 11.13 Assumption of a Markov chain for SOC determination.
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FIGURE 11.14 Illustration of the particle filter algorithm. For initialization (init.) all samples (or particles) are distributed uniformly over the possible value range of state x. In Step 1 of the algorithm the influence of input u on every sample is calculated. Noise ε, which is taken from a suitable probability distribution, is added to the value of u. Step 2 gives every sample a weight according to probability taken from the measurement value z and the measurement model. Step 3 resamples the weighted particles to gain an unweighted particle set. The low-variance resampling method is depicted [14].
3. Resampling—in this step the samples are resampled according to their weights. After that all samples have the same weight again. This approach uses the low-variance resampling method for low computational afford. In Step 1 the so-called “process model” is used, which describes the influence of input ut–1 on state xt. For SOC estimation the following model equation is used: k k sSOC, t = sSOC, t −1 +
( I batt + ε k ) ∆t SOH ⋅ Cn
where ε k represents the random value which is added to the input value. It can be sampled from any distribution suitable for the application. For this application a Cauchy–Lorentz distribution is used.
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FIGURE 11.15 Open-circuit voltage versus state of charge curve of a lithium iron phosphate battery with a graphite anode. One can see the hysteresis between charging and discharging voltage and the very flat voltage in the medium state-of-charge range [13].
By adding this noise to that of the particles, particle diffusion is increased. This diffusion models—in case of SOC estimation—the increasing uncertainty of ampere-hour counting. Step 2 decreases this diffusion by weighting particles according to their probability. Therefore, the so-called “measurement model” is used, which calculates estimated terminal voltage at the actual SOC of the particle. For phosphate-based lithium-ion batteries like lithium iron phosphate (LFP) cells the open-circuit voltage (OCV) vs. SOC curve is very flat (see Fig. 11.15) and shows a hysteresis between charging and discharging. For that, two voltages are calculated, one for charging and one for discharging. The following equations show the measurement model for an LFP battery:
(
)
(
)
k k k Vdischarge, t = OCVdischarge sSOC, t + Ri sSOC, t , T , I batt ⋅ I batt
(
)
(
)
k k k Vcharge, t = OCVcharge sSOC, t + Ri sSOC, t , T , I batt ⋅ I batt
These two voltages are compared with the measured terminal voltage. So two probabilities are determined with the following formula (using a Cauchy– Lorentz distribution): k wSOC, t =
γ 1 ⋅ π γ 2 + V k discharge, t − Vmeas
(
)
2
+
(
γ
k γ 2 + Vcharge, t − Vmeas
)
2
After that, in Step 3 these weighted particles are resampled to regain an unweighted set of particles. In this approach the low-variance resampling method is used as depicted in Fig. 11.15. Low-variance resampling puts all particles in
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PART | C Electrochemical
a row, each particle has a length according to its weight. After that, this row is sampled as many times as the number of particles using a fixed sampling width. The sampled particles now represent an unweighted set of particles and the filter goes back to Step 1. The estimated value for the SOC is the mean value of all particle values.
3.2.5 State-of-Health Estimation The estimation of SOH is also done using a particle filter [13]. Both filters run in parallel and share their results. As the particle filter for SOC estimation needs the SOH as a parameter, the filter for SOH estimation needs the SOC difference between two calculation steps as a parameter. Therefore, the approach is called “parallel particle filter” for SOC and SOH estimation. SOH in this section is defined as the ratio between actual battery capacity and nominal battery capacity: Cact Cn
SOH =
In general, the particle filter for SOH estimation works similar to that for SOC estimation. as described in the previous section. Due to the very slow change in SOH, it is assumed in the process model that no input value u influences the state. Only the noise value ε is added to every particle: k k sSOH, t = sSOH, t −1 + ε
k
The weighting step is done by using the SOC change of the SOC filter during the last step (∆ SOCmeas) as the measurement value z and following equation as the measurement model: ∆ SOCtk =
Q
step k SOH,t
s
⋅ Cn
Variable Qstep represents integrated charge flowing into and out of the battery. If using a Cauchy–Lorentz distribution the weight is calculated as follows: k wSOH =
γ 1 ⋅ 2 k π γ + ∆ SOCt − ∆ SOCmeas
(
)
2
Thereafter, the low-variance resampling step is performed as described earlier. The SOH estimated value is gained by taking the mean value of all particle values.
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FIGURE 11.16 Depicted is the state of charge during a PV current profile. The battery used is a lithium iron phosphate battery with a graphite anode [14].
3.2.6 Validation The dual-particle filter approach is validated for different types of lithium-ion batteries (LFP and NMC, both with graphite anode) and different types of current profiles including electric vehicle (EV) cycles and PV applications [14]. The EV profile is characterized by large currents, high dynamics, but also relatively long pauses with no current. The PV profile, on the other hand, shows lower currents, but there are virtually no phases without any current. In Fig. 11.16 a validation sequence for LFP-graphite lithium-ion cells is shown by using a PV profile [14]. Estimation of LFP is much more complicated still, as a result of flat OCV and hysteresis; however, exact SOC is found to be fast and estimated reliably. SOH estimation is very accurate as well, as depicted in Fig. 11.17.
4 SYSTEM INTEGRATION 4.1 Configuration Battery systems and DC power sources like PV generators can be coupled via power electronics on a DC bus bar or on the AC side. Exemplarily, an AC coupled system is introduced in the following (Fig. 11.18), which allows the integration of lithium-ion battery systems in PV systems by using a marketavailable battery inverter.
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FIGURE 11.17 SOH of a lithium iron phosphate/graphite battery during a PV current profile. The reference value was determined with a standard charge–discharge regime [14].
FIGURE 11.18 Integration of the developed lithium-ion battery system (Fig. 11.1) in a residential PV system using a market-available battery inverter [7].
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In these AC coupled system configurations the PV generator and the battery system are connected to the AC grid via two separate inverters. The conventional PV system, consisting of PV modules and a PV inverter, is in principle not affected by integration of a battery. Therefore, installed PV systems can easily be complemented with storages later without any adaptation. Due to the modular concept, sizing of the battery system is almost independent of the size of PV system components like the PV inverter. The disadvantages of this topology are limited cost reduction potential, as two full inverters are needed, as well as the limited voltage levels of marketavailable battery inverters for residential applications, which are in the range (24–48) V. Therefore, the inverters possess transformers and offer only relatively low efficiencies (∼94%) at the nominal operating point and efficiencies much below this value across a wide range of the typical operating window.
4.2 Communication Infrastructure Lithium-ion batteries are a very promising storage technology especially for decentralized grid-connected PV battery systems. Due to several reasons, for example, safety aspects, battery management is part of the lithium-ion battery system itself and is not integrated into the battery inverter or the charge controller as is usual for lead–acid and nickel-based batteries. This battery management system has to control the battery system itself and the connected power electronics. Furthermore, it has to exchange all relevant data with the supervisory energy management system. Field bus communication is necessary for both tasks (Fig. 11.19), but market-available products offer only proprietary solutions. Therefore, system integrators are not free to choose different system components for specific solutions. Furthermore, it is predefined which battery systems can be operated with which inverters or charge controllers avoiding the need for a huge adaptation effort for the communication system. To enable higher degrees of freedom in system assembly, a standard for communication at the field bus level among battery systems, power electronics, and energy management systems is necessary. Such an approach is encapsulated currently by the so-called “EnergyBus” [17], which was initially developed for simplified connection of system components of light electric vehicles. This standard defines the communication protocol as well as the power connectors. The CANopen user profile CiA 454 “energy management systems” is used as the communication protocol [16]. This protocol specifies data exchange between single components such as storages, generators, loads, and energy management systems and enables implementation of optimized operating control strategies. Based on this field bus communication standard, components of different manufacturers can be assembled by system integrators. The power connectors are defined especially for light electric vehicles, whereas the communication protocol was extended for stationary applications like PV battery systems. New specification parts for generators and loads have been designed
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FIGURE 11.19 Field bus communication based on the so-called EnergyBus/CiA 454 protocol [16–18] for setting up a network of different generators, storages, power electronics, and energy management systems.
in such an abstract way that general manageability by a supervisory control system is enabled. For example, a PV generator and a CHP (combined heat and power) unit can be described by the same specification part. Furthermore, smart-metering components can be easily integrated.
5 CONCLUSIONS In conclusion, in the relatively short time of the development and application of the lithium-ion battery, its main advantages such as high energy density, long lifecycle, fast and efficient charging, little maintenance, and little voltage sag have been undercut by the possibility of thermal runaway. This has resulted in much research and development resulting in elaborate battery management protective circuits and programs which have been the focus of this chapter.
REFERENCES [1] Vetter M. Energy storage—renewable energy’s key “blade” for grid integration. Canada Energy Storage Summit, Toronto; November 12, 2014. [2] Vetter M. Decentralized PV battery storage systems—system design, integration and optimization. Intersolar Conference North America; 2014. [3] www.3ds.com/products/catia/portfolio/dymola [4] www.modelica.org
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