AU2017272188A1 - Electronic Monitoring of Battery Banks - Google Patents
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- AU2017272188A1 AU2017272188A1 AU2017272188A AU2017272188A AU2017272188A1 AU 2017272188 A1 AU2017272188 A1 AU 2017272188A1 AU 2017272188 A AU2017272188 A AU 2017272188A AU 2017272188 A AU2017272188 A AU 2017272188A AU 2017272188 A1 AU2017272188 A1 AU 2017272188A1
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Abstract
5 Abstract A battery monitoring system 10 comprises a battery measurement unit (BMU) 12 for measuring an operating parameter of one or more battery cells in a 10 battery bank 14. Typically a plurality of battery measurement units (BMUs) 12 are employed for monitoring a corresponding plurality of battery banks 14. The BMU 12 generates a measurement signal for each battery cell 16 in the battery bank 14, and is adapted to be networked with one or more similar battery measurement units (BMUs) 12', 12", etc. The system 10 further 15 comprises a central controller 20 networked with the BMU 12 for monitoring the condition of each cell 16 in the battery bank 14 based on the measurement signal. The central controller 20 processes the measurement signal and generates an alarm if necessary to alert an operator of a possible faulty battery cell. The central controller 20 is preferably connected to a 20 Human Machine Interface (HMI) 22 and to the Internet 24. By connecting the central controller 20 to the Internet the monitoring information for each of the battery cells in the battery bank 14 can be made available worldwide. Drawing suggested to accompany the Abstract: Figure 1 '10 2z PLC Internet cInter z Mobile System 1 CPhone Fiedbs Network Fieldbus Network @ i F Amplifier 1z~2 I 30 Batter Batr Battery Measrmn Measurement -- Measurement Unit 1 Unit 2 Unit n 16attery Bank 1 Bater Bank 2 Battery Bank n isolated Mtiico Network differential Buffer naaogel Controller Interface AmplfierDigital 16 Converter -ii
Description
COMPLETE SPECIFICATION
Invention title:
“ELECTRONIC MONITORING OF BATTERY BANKS”
Applicant:
Megatronic Power Systems Pty Ltd
Associated Provisional Application No.: 2017900058
The following statement is a full description of the invention, including the best method of performing it known to me:
2017272188 05 Dec 2017 “ELECTRONIC MONITORING OF BATTERY BANKS”
Field of the Invention
The present invention relates to a battery monitoring system and method and relates particularly, though not exclusively, to a battery monitoring system for monitoring a lithium-ion battery bank.
Background to the Invention
In Australia renewable energy sources are becoming more popular due to the high cost of electricity from the grid. Battery storage systems allow a household to store electricity from the grid when prices are lowest, (during off-peak periods) and to draw power from battery storage (rather than the grid) during the most expensive peak time, so as to minimise overall electricity costs. Battery storage of electrical power has also become more popular with the increased reliance on renewable energy sources, such as solar and wind power. Battery storage can overcome the problem of intermittency of supply. Batteries also permit excess renewable energy to be stored and returned to the gird. The market for reliable battery storage banks is also predicted to increase with the uptake of more electric cars.
Lithium-ion batteries are particularly popular for battery storage banks as an alternative to traditional lead-acid batteries, because of their improved lifetime (number of charge/discharge cycles); reduced footprint and weight; and low maintenance. The cost of lithium-ion batteries is expected to decrease as more suppliers come onto the market. When using lithium-ion batteries in a battery bank an effective battery monitoring system (BMS) is especially important as lithium-ion batteries are particularly sensitive to over/under voltage conditions. If a single cell in a lithium-ion battery bank fails, it can destroy the whole bank. External battery balancing is also required to maintain a consistent state of charge (SOC), as lithium-ion cells are not self-balancing and the cells do not maintain balance over many charge/discharge cycles.
2017272188 05 Dec 2017
Many prior art BMSs exist which are designed to address this requirement. However most of these prior art systems monitor the battery bank as a whole and do not provide information about individual cells in the bank. Other systems provide protection for each cell, but do not monitor the cell or provide information about each cell to a central controller in the BMS.
The present invention was developed with a view to providing a battery monitoring system and method that monitors each cell in a battery bank and provides detailed information about each cell to a central controller in the system.
References to prior art in this specification are provided for illustrative purposes only and are not to be taken as an admission that such prior art is part of the common general knowledge in Australia or elsewhere.
Summary of the Invention
According to one aspect of the present invention there is provided a battery measurement unit (BMU) for a battery monitoring system, the BMU comprising:
a plurality of isolated measurement channels, each channel being adapted to measure the applied voltage between -Vch to +Vch (where Vch is the peak voltage measurement for each channel) of a battery cell wherein, in use, the BMU measures an operating parameter of one or more battery cells in a battery bank and generates a measurement signal for each cell in the battery bank, the BMU being adapted to be networked with one or more similar battery measurement units (BMUs) in the battery monitoring system.
Preferably the BMU also provides the value of the applied voltage measurements as a combination of the channels.
Typically each channel of the BMU comprises an isolated differential amplifier and buffer, and is connected to a multichannel analog to digital
2017272188 05 Dec 2017 converter (ADC). Preferably the multichannel ADC is coupled to a microcontroller which is, in turn, connected to a network interface card.
According to another aspect of the present invention there is provided a battery monitoring system comprising:
a battery measurement unit (BMU) for measuring an operating parameter of one or more battery cells in a battery bank and generating a measurement signal for each cell in the battery bank, the BMU being adapted to be networked with one or more similar battery measurement units (BMUs); and, a central controller networked with the BMU for monitoring the condition of each cell in the battery bank based on the measurement signal, processing the measurement signal and generating an alarm if necessary to alert an operator of a possible faulty battery cell.
Preferably the BMU is provided with a plurality of isolated measurement channels, each channel being adapted to measure the applied voltage between -Vch to +Vch (where Vch is the peak voltage measurement for each channel).
Advantageously the BMU also provides the value of the measurements as a combination of the channels.
Typically each channel of the BMU comprises an isolated differential amplifier and buffer, and is connected to a multichannel analog to digital converter (ADC). Preferably the multichannel ADC is coupled to a microcontroller which is, in turn, connected to a network interface card.
Preferably the BMU is one of a plurality of BMUs employed in the system.
Preferably the central controller is programmed with an algorithm for calculating the state of charge (SOC) of each cell in the battery bank. Preferably the SOC algorithm comprises a charge accumulator or coulomb counter for each battery cell in the battery bank. Advantageously the SOC algorithm calculates an internal resistance of each battery cell, calculates a
2017272188 05 Dec 2017 charge stored in each battery cell, and calculates an internal battery cell voltage.
Preferably the central controller processes the measurement signal using the SOC algorithm in a timely manner in order to be able to correctly calculate the charge stored in each battery cell. Preferably the central controller processes the measurement signal using the SOC algorithm every second.
Advantageously the central controller processes the measurement signal using the SOC algorithm simultaneously for all battery cells in the battery bank.
Preferably the central controller measures the internal resistance of each battery cell using the SOC algorithm every time there is a spike detected in the current. The central controller will generate an alarm if the calculated resistance is higher than an expected resistance for any cell.
Preferably the central controller also uses SOC algorithm to calculate a standard deviation for the internal voltage of the battery cells and generates an alarm if the standard deviation for one or more of the cells is outside an expected range.
According to a still further aspect of the present invention there is provided a method of monitoring a plurality of battery cells in a battery bank, the method comprising:
measuring an operating parameter of one or more battery cells in the battery bank and generating a measurement signal for each cell;
monitoring the condition of each cell in the battery bank based on the measurement signal, by processing the measurement signal and generating an alarm if necessary to alert an operator of a possible faulty battery cell.
Preferably the step of processing the measurement signal employs an algorithm for calculating the state of charge (SOC) of each cell in the battery bank. Preferably the SOC algorithm comprises a charge accumulator or coulomb counter for each battery cell in the battery bank. Advantageously the
2017272188 05 Dec 2017
SOC algorithm calculates an internal resistance of each battery cell, calculates a charge stored in each battery cell, and calculates an internal battery cell voltage.
Preferably the step of processing the measurement signal using the SOC algorithm is performed in a timely manner in order to be able to correctly calculate the charge stored in each battery cell. Preferably the step of processing the measurement signal using the SOC algorithm is performed every second.
Advantageously the step of processing the measurement signal using the
SOC algorithm is performed simultaneously for all battery cells in the battery bank.
Preferably the SOC algorithm determines if the cell voltage is less than a low voltage setting or greater than a high voltage setting, and sets the charge accumulator to 0% or 100% accordingly. Typically the SOC algorithm then calculates a charge stored in each cell. Advantageously the SOC algorithm detects whether or not a current spike has occurred, and if so calculates the internal resistance of each battery cell. An alarm is preferably generated if the calculated resistance is higher than an expected resistance for any cell.
Preferably the SOC algorithm is also used to calculate a standard deviation for the internal voltage of the battery cells and an alarm is generated if the standard deviation for one or more of the cells is outside an expected range.
Throughout the specification, unless the context requires otherwise, the word “comprise” or variations such as “comprises” or “comprising”, will be understood to imply the inclusion of a stated integer or group of integers but not the exclusion of any other integer or group of integers. Likewise the word “preferably” or variations such as “preferred”, will be understood to imply that a stated integer or group of integers is desirable but not essential to the working of the invention.
2017272188 05 Dec 2017
Brief Description of the Drawings
The nature of the invention will be better understood from the following detailed description of several specific embodiments of battery monitoring system and method, given by way of example only, with reference to the accompanying drawings, in which:
Figure 1 is schematic diagram of a first embodiment of a battery monitoring system and method according to the present invention;
Figure 2 is functional block diagram of a preferred embodiment of a battery measurement unit (BMU) employed in the battery monitoring system and method of Figure 1; and,
Figure 3 is a flowchart of a preferred embodiment of a state of charge (SOC) algorithm employed in the battery monitoring system and method of Figure 1.
Detailed Description of Preferred Embodiments
A preferred embodiment of battery monitoring system 10 in accordance with the invention, as illustrated in Figures 1 to 3, comprises a battery measurement unit (BMU) 12 for measuring an operating parameter of one or more battery cells in a battery bank 14. In the illustrated embodiment the system 10 compromises a plurality of battery measurement units (BMUs) 12 for monitoring a corresponding plurality of battery banks 14. The BMU generates a measurement signal for each battery cell 16 in the battery bank 14, and is adapted to be networked with one or more similar battery measurement units (BMUs) 12’, 12”, etc.
The system 10 further comprises a central controller 20 networked with the BMU 12 for monitoring the condition of each cell 16 in the battery bank 14 based on the measurement signal. The central controller 20 processes the measurement signal and generates an alarm if necessary to alert an operator of a possible faulty battery cell. The central processor typically comprises an
2017272188 05 Dec 2017 industrial controller or Programmable Logic Controller (PLC) 20. The PLC 20 is preferably connected to a Human Machine Interface (HMI) 22 and to the Internet 24. By connecting the PLC 20 to the Internet the monitoring information for each of the battery cells in the battery bank 14 can be made available worldwide. The PLC 20 is thereby also able to send an SMS alert to a mobile phone 26 and emails to a personal computer 28, if required.
A wide range of industrial graphical user interfaces (GUIs) can be used to display important monitoring information about the system 10. This is only possible because a central controller or PLC 20 has been utilised in the proposed system. The monitoring information includes alarms, battery status, measurements, trends and diagnostic details. The HMI 22 also allows the use of mimics and animation rather than numerical display for ease of operation and maintenance. It is also possible to configure the type of the batteries and their manufacturer specific parameters (setpoints) like cut-off, cut-in voltages, overcharge alarm voltage, low voltage alarm setting, etc.
The system 10 is easily configurable and very flexible. It can be readily monitored using any supervisory control and data acquisition (SCADA) system 30 and software or HMI device without any requirement for uncommon drivers and therefore minimises the cost of integration and engineering time.
The BMU 12 is typically an in house designed module with internal electronic functional blocks as illustrated in Figure 2. Preferably the BMU 12 is provided with a plurality of isolated measurement channels, each channel being adapted to measure the applied voltage between -Vch to +Vch (where Vch is the peak voltage measurement for each channel) of a battery cell 16.
Typically each channel of the BMU 12 comprises an isolated differential amplifier 32 and a buffer 34, and is connected to a multichannel analog to digital converter (ADC) 36. Preferably the multichannel ADC 36 is coupled to a microcontroller 38 which is, in turn, connected to a network interface card
40. Typically the BMUs 12 are networked together and to the PLC 20 via a fieldbus network as shown in Figure 1. Fieldbus refers to a family of industrial
2017272188 05 Dec 2017 computer network protocols used for real-time distributed control, which has been standardized by the IEEE as IEC 61158.
It will be seen therefore that the networking follows that of a typical complex automated industrial system that employs a distributed control system. In such a system there is usually a HMI at the top, which an operator can use to monitor or operate the system. The HMI is typically linked to a middle layer of PLCs via a communications system e.g. Ethernet, and at the bottom of the control chain is the fieldbus that links the PLCs to the components that actually do the work, in this case the BMUs 12.
In the illustrated embodiment, the fieldbus network use Modbus, a serial communications protocol originally published by Modicon (now Schneider Electric) in 1979 for use with its PLCs. Modbus has since become a de facto standard communications protocol, and it is now wisely used for connecting industrial electronic devices.
Advantageously each BMU 12 also provides the value of the measurements as a combination of the channels. In this way the same BMU can be used for higher voltage measurements. For example, considering the fieldbus network is using Modbus, the sum of channels 1 and 2 of a BMU 12 will be accessible in a memory area. Therefore by placing a physical link between the negative terminal of channel 1 and the positive terminal of channel 2 the combined channel is capable of measuring voltages between -2Vch and +2Vch. The measurement for all meaningful combinations of the channels can be presented in memory area, so that for a 12 channel BMU up to ±12Vch can be measured with a single combined channel. This provides the flexibility to use the same BMU for higher voltages, while keeping the accuracy for the battery measurements.
To avoid draining the batteries the BMU 12 preferably employs an external power source and will not be powered up through the batteries directly. If the system is being powered up from batteries it is recommended to install a contactor to turn off the system when a deep discharge situation occurs.
2017272188 05 Dec 2017
In an alternative embodiment, instead of the described BMU, a type of offthe-shelf analog IO with isolated channels can be used.
Preferably the central controller or PLC 20 is programmed with an algorithm 100 for calculating the state of charge (SOC) of each cell in the battery bank.
Preferably the SOC algorithm or software block comprises a charge accumulator or coulomb counter for each battery cell 16 in the battery bank 14. Advantageously the SOC algorithm 100 calculates an internal resistance of each battery cell, calculates a charge stored in each battery cell, and calculates an internal battery cell voltage.
Figure 3 is a flowchart for the SOC algorithm or software block 100 preferably employed in the PLC 20. The PLC 20 preferably employs this algorithm to determine the battery charge condition of each cell. To minimise the size of the code, it is preferred to use a processor that is able to use functional blocks, arrays of different types and constants.
To enhance the flexibility of the system a constant, NOC, is specified to represent the number of cells. Then the constant NOC can be used in the code to size the arrays. For example:
If NOC = 8 (this means that only 8 cells are connected to the system)
Voltage [1 ..NOC] of real (this defines an array of 8 floating point variables for the voltage, since NOC is defined to be 8)
This method of coding also ensures that the software can be customised to the number of the battery cells, before shipping the system to the customer, by changing the value of the NOC constant.
The SOC algorithm 100 is at the heart of the battery monitoring system. This software block is used to measure and analyse the state of each of the battery cells 16. Although every battery monitoring system has a SOC block of sorts the present SOC algorithm 100 has several unique features.
The SOC algorithm 100 preferably has a charge accumulator or coulomb counter for each battery cell. This is particularly important for lithium-ion
2017272188 05 Dec 2017 batteries where charge current is almost equal to discharge current. It is easier to have an array of charge accumulators of NOC dimensions (NOC is a constant representing the number of cells) as shown in Figure 3.
As an example of the software code for each coulomb counter in the SOC algorithm 100 is as follows:
ACCUM[1 ..NOC] of Dint
Void SOC(void){
For i=1 to NOC DO //do this for all cells
ACCUM[i]= ACCUM[i]+CURRENT[i]; //add scaled current to the accumulator // calculate the internal resistance // calculate the charge in percentage // calculate the internal battery cell voltage
End_for; //end of for loop }
While(1) { // infinity loop
Call SOC();
} //go back to start of the infinity loop
Referring to Figure 3, the SOC algorithm 100 determines if the cell voltage is less than the low voltage setting or greater than high voltage setting at steps
102 and 106 respectively, and sets the charge accumulator to 0% or 100% accordingly at steps 104 and 108 respectively. It then calculates the charge stored in each cell at step 110. At step 112 it detects whether or not a current spike has occurred, and if so calculates the internal resistance of each battery cell at step 114. For example, a spike in the current will typically occur if the battery cell switches from charging state to discharging or the battery
2017272188 05 Dec 2017 load changes suddenly. The central controller will generate an alarm if the calculated resistance is higher than an expected resistance for any cell.
The charge accumulator is updated at step 116, by adding or subtracting the calculated charge from the previous charge state, and the current values for cell currents and voltages are saved at step 118.
At step 120 the SOC algorithm also calculates the standard deviation for the internal voltage of the battery cells and generates an alarm if the standard deviation for one or more of the cells is outside an expected range. This alarm will be used to identify the probable faulty cell to be replaced in the next maintenance service schedule, and thereby avoid costly replacement of the whole battery bank.
To find the fault module the standard deviation needs to be calculated as:
i (Voltage[l] - μ) 2 + (Voltage[2] - μ) 2 +
J-meWhere:
Voltage[l] + 7oita5e[2] -I— μ= NOC
If the voltage of any cell is detected at step 122 to fall outside the μ ±1.55 range, that means that particular cell is acting oddly and it will raise an alarm for that cell at step 124.
The SOC algorithm also raises an alarm for each cell if the cell voltage is higher or lower than the appropriate set points.
At step 126 the counter ‘i’ is updated, and the SOC algorithm 100 restarts at step 102. Preferably the central controller processes the measurement signals using the SOC algorithm in a timely manner in order to be able to correctly calculate the charge stored in each battery cell. Preferably the central controller processes the measurement signals using the SOC algorithm once every second.
2017272188 05 Dec 2017
Advantageously the central controller processes the measurement signals for all battery cells in the battery bank simultaneously using the SOC algorithm. In another words, there is no need to have multiple blocks for each cell. This improves the efficiency of the coding for the SOC software block 100.
Now that a preferred embodiment of the battery monitoring system and method has been described in detail, it will be apparent that the described embodiment provides a number of advantages over the prior art, including the following:
1) It provides a battery monitoring solution that is fully compatible with almost all industrial sites and SCADA systems.
2) It eliminates the need for unnecessary site visits, increases the reliability of the system and decreases the cost of maintenance
3) The use of a PLC as the central controller enables it to deploy highly complex algorithms to detect the faults or possible issues within the battery bank.
4) The system is easily configurable and very flexible. It can be readily monitored using any SCADA software or HMI device without any requirement for uncommon drivers, and therefore minimises the cost of integration and engineering time.
5) The BMU can measure the voltage for combined channels and thus the same BMU can be used for higher voltages.
6) High degree of compatibility with existing monitoring systems on almost all industrial sites.
7) It employs smarter algorithms to detect issues and provides more information for maintenance and diagnosis.
8) More efficient installation due to use of fieldbus network.
It will be readily apparent to persons skilled in the relevant arts that various 30 modifications and improvements may be made to the foregoing embodiments, in addition to those already described, without departing from
2017272188 05 Dec 2017 the basic inventive concepts of the present invention. Therefore, it will be appreciated that the scope of the invention is not limited to the specific embodiments described.
2017272188 05 Dec 2017
Claims (10)
1. A battery measurement unit (BMU) for a battery monitoring system, the BMU comprising:
10 a plurality of isolated measurement channels, each channel being adapted to measure the applied voltage between -Vch to +Vch (where Vch is the peak voltage measurement for each channel) of a battery cell wherein, in use, the BMU measures an operating parameter of one or more battery cells in a battery bank and generates a measurement signal for each cell in the battery
15 bank, the BMU being adapted to be networked with one or more similar battery measurement units (BMUs) in the battery monitoring system.
2. A BMU as defined in claim 1, wherein the BMU also provides the value of the applied voltage measurements as a combination of the channels.
3. A BMU as defined in claim 1 or claim 2, wherein each channel of the BMU
20 comprises an isolated differential amplifier and buffer, and is connected to a multichannel analog to digital converter (ADC).
4. A BMU as defined in claim 3, wherein the multichannel ADC is coupled to a microcontroller which is, in turn, connected to a network interface card.
5 15. A battery monitoring system as defined in claim 14, wherein the central controller processes the measurement signal using the SOC algorithm every second.
16. A battery monitoring system as defined in claim 14, wherein the central controller processes the measurement signal using the SOC algorithm
10 simultaneously for all battery cells in the battery bank.
17. A battery monitoring system as defined in claim 13, wherein the central controller measures the internal resistance of each battery cell using the SOC algorithm every time there is a spike detected in the current.
18. A battery monitoring system as defined in claim 17, wherein the central
15 controller will generate an alarm if the calculated resistance is higher than an expected resistance for any cell.
19. A battery monitoring system as defined in claim 18, wherein the central controller also uses the SOC algorithm to calculate a standard deviation for the internal voltage of the battery cells and generates an alarm if the
20 standard deviation for one or more of the cells is outside an expected range.
20. A method of monitoring a plurality of battery cells in a battery bank, the method comprising:
measuring an operating parameter of one or more battery cells in the battery bank and generating a measurement signal for each cell; and,
25 monitoring the condition of each cell in the battery bank based on the measurement signal, by processing the measurement signal and generating an alarm if necessary to alert an operator of a possible faulty battery cell.
2017272188 05 Dec 2017
21. A method of monitoring a plurality of battery cells as defined in claim 20, wherein the step of processing the measurement signal employs an algorithm for calculating the state of charge (SOC) of each cell in the battery bank.
5
22. A method of monitoring a plurality of battery cells as defined in claim 21, wherein the SOC algorithm comprises a charge accumulator or coulomb counter for each battery cell in the battery bank.
23. A method of monitoring a plurality of battery cells as defined in claim 20, wherein the SOC algorithm calculates an internal resistance of each battery
10 cell, calculates a charge stored in each battery cell, and calculates an internal battery cell voltage.
24. A method of monitoring a plurality of battery cells as defined in claim 20, wherein the step of processing the measurement signal using the SOC algorithm is performed in a timely manner in order to be able to correctly
15 calculate the charge stored in each battery cell.
25. A method of monitoring a plurality of battery cells as defined in claim 24, wherein the step of processing the measurement signal using the SOC algorithm is performed every second.
26. A method of monitoring a plurality of battery cells as defined in claim 24,
20 wherein the step of processing the measurement signal using the SOC algorithm is performed simultaneously for all battery cells in the battery bank.
27. A method of monitoring a plurality of battery cells as defined in claim 22, wherein the SOC algorithm determines if the cell voltage is less than a low voltage setting or greater than a high voltage setting, and sets the charge
25 accumulator to 0% or 100% accordingly.
28. A method of monitoring a plurality of battery cells as defined in claim 27, wherein the SOC algorithm then calculates a charge stored in each cell.
2017272188 05 Dec 2017
29. A method of monitoring a plurality of battery cells as defined in claim 28, wherein the SOC algorithm detects whether or not a current spike has occurred, and if so calculates the internal resistance of each battery cell.
30. A method of monitoring a plurality of battery cells as defined in claim 29, 5 wherein an alarm is generated if the calculated resistance is higher than an expected resistance for any cell.
31. A method of monitoring a plurality of battery cells as defined in claim 30, wherein the SOC algorithm is also used to calculate a standard deviation for the internal voltage of the battery cells and an alarm is generated if the
5 being adapted to measure the applied voltage between -Vch to +Vch (where Vch is the peak voltage measurement for each channel).
5. A battery monitoring system comprising:
25 a battery measurement unit (BMU) for measuring an operating parameter of one or more battery cells in a battery bank and generating a measurement signal for each cell in the battery bank, the BMU being adapted to be networked with one or more similar battery measurement units (BMUs); and, a central controller networked with the BMU for monitoring the condition of
30 each cell in the battery bank based on the measurement signal, processing
2017272188 05 Dec 2017 the measurement signal and generating an alarm if necessary to alert an operator of a possible faulty battery cell.
6. A battery monitoring system as defined in claim 5, wherein the BMU is provided with a plurality of isolated measurement channels, each channel
7. A battery monitoring system as defined in claim 6, wherein the BMU also provides the value of the applied voltage measurements as a combination of the channels.
10
8. A battery monitoring system as defined in claim 6 or claim 7, wherein each channel of the BMU comprises an isolated differential amplifier and buffer, and is connected to a multichannel analog to digital converter (ADC).
9. A battery monitoring system as defined in claim 8, wherein the multichannel ADC is coupled to a microcontroller which is, in turn, connected
15 to a network interface card.
10. A battery monitoring system as defined in any one of claims 5 to 9, wherein the BMU is one of a plurality of BMUs employed in the system.
11. A battery monitoring system as defined in any one of claims 5 to 10, wherein the central controller is programmed with an algorithm for calculating
20 the state of charge (SOC) of each battery cell in the battery bank.
12. A battery monitoring system as defined in claim 11, wherein the SOC algorithm comprises a charge accumulator or coulomb counter for each battery cell in the battery bank.
13. A battery monitoring system as defined in claim 12, wherein the SOC
25 algorithm calculates an internal resistance of each battery cell, calculates a charge stored in each battery cell, and calculates an internal battery cell voltage.
2017272188 05 Dec 2017
14. A battery monitoring system as defined in any one of claims 11 to 13, wherein the central controller processes the measurement signal using the SOC algorithm in a timely manner in order to be able to correctly calculate the charge stored in each battery cell.
10 standard deviation for one or more of the cells is outside an expected range.
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| AU2017900058 | 2017-01-10 | ||
| AU2017900058A AU2017900058A0 (en) | 2017-01-10 | Electronic Monitoring of Battery Banks |
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| AU2017272188A1 true AU2017272188A1 (en) | 2018-07-26 |
| AU2017272188B2 AU2017272188B2 (en) | 2020-07-09 |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP3627170A1 (en) | 2018-09-18 | 2020-03-25 | KNORR-BREMSE Systeme für Nutzfahrzeuge GmbH | A sensor arrangement and a method for monitoring a storage system |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP4490926B2 (en) * | 2006-01-20 | 2010-06-30 | 矢崎総業株式会社 | Voltage detector |
| ES2712935T3 (en) * | 2010-11-25 | 2019-05-16 | Belenos Clean Power Holding Ag | Measuring system of the cells of a fuel cell |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP3627170A1 (en) | 2018-09-18 | 2020-03-25 | KNORR-BREMSE Systeme für Nutzfahrzeuge GmbH | A sensor arrangement and a method for monitoring a storage system |
| WO2020057978A1 (en) | 2018-09-18 | 2020-03-26 | Knorr-Bremse Systeme für Nutzfahrzeuge GmbH | A sensor arrangement and a method for monitoring a storage system |
| JP2022500676A (en) * | 2018-09-18 | 2022-01-04 | クノル−ブレムゼ ジステーメ フューア ヌッツファールツォイゲ ゲゼルシャフト ミット ベシュレンクテル ハフツングKnorr−Bremse Systeme fuer Nutzfahrzeuge GmbH | How to monitor sensor devices and storage systems |
| JP7331109B2 (en) | 2018-09-18 | 2023-08-22 | クノル－ブレムゼ ジステーメ フューア ヌッツファールツォイゲ ゲゼルシャフト ミット ベシュレンクテル ハフツング | Method for monitoring sensor devices and storage systems |
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