GB2526816A

GB2526816A - Adaptive battery management system

Info

Publication number: GB2526816A
Application number: GB1409840.4A
Authority: GB
Inventors: Christopher Fredrick Baker-Brian; Ashley James Patrick Grealish
Original assignee: Bboxx Ltd
Current assignee: Bboxx Ltd
Priority date: 2014-06-03
Filing date: 2014-06-03
Publication date: 2015-12-09
Also published as: GB201409840D0; WO2015185890A1

Abstract

An adaptive battery management system comprises a renewable energy generator 1, a rechargeable battery 2 operatively connected to the renewable energy generator, a controller 3 operatively connected to the rechargeable battery and the renewable energy generator and a remote processing device 5 in communication with the controller. The controller is configured to send energy usage characteristics to the remote processing device. The energy usage characteristics are processed by the remote processing device to determine one or more optimisations of the adaptive battery management system e.g. in relation to a state of charge below which the battery is to be disabled. The remote processing device is configured to send said one or more optimisations to the controller. The controller is configured to implement said one or more optimisations. Methods of predicting failure of the battery are also disclosed.

Description

ADAPTIVE BATTERY MANAGEMENT SYSTEM

FIELD

The present invention relates to an adaptive battery management system, and more particularly to an adaptive battery management system for photovoltaic installations.

BACKGROUND

Large areas of parts of the world such as Africa, China and South America do not have access to on-grid electricity. The many millions of people of people who live in these areas rely on renewable energy generators such as photovoltaic panels and wind turbines to meet their energy needs.

Renewable energy generators can only provide energy when a renewable energy source is available.

For example, photovoltaic panels can only provide energy in daylight and wind turbines can only provide energy when the local wind speed is sufficient to turn the turbine.

The limitations of renewable energy generators are well recognised and a number of renewable energy systems include storage means for surplus energy. The stored energy can then be used when no renewable energy source is available. Typically such storage means will be in the form of a rechargeable battery connected to a renewable energy source.

Rechargeable batteries have a finite life which is affected depending on a number of use characteristics of the battery. Two common causes of failure of rechargeable batteries are overcharging and excessive discharge of the battery. Excessive discharge of the battery is typically caused by a poorly sized energy generator which is thus not able to produce sufficient energy to meet a user's needs. The battery is continuously cycled in a low state of charge which can lead to sulphation of the battery. In contrast, overcharging is typically caused by a poorly designed charge controller which continues to charge the battery even when at full capacity thus reducing its operational life.

The present invention seeks to limit a user's energy consumption to the amount of energy predicted to be generated by the renewable energy system.

While the life of a rechargeable battery can be optimised to some extent by an appropriate charge strategy, the battery will still fail at some point. In remote areas of Africa, China and South America, for example, it could take several weeks for a replacement battery to be sourced, delivered and installed into a renewable energy system. During that period the renewable energy system would not be able to store sufficient energy to meet a user's energy needs.

The present invention also seeks to predict when a rechargeable battery will fail so that it can be replaced before it fails to provide continuity of service.

SUMMARY

A first aspect of the invention provides an adaptive battery management system comprising: a renewable energy generator, a rechargeable battery operatively connected to the renewable energy generator, a controller operatively connected to the rechargeable battery and the renewable energy generator) a remote processing device configured to communicate with the controller, wherein the controller is configured to send energy usage characteristics to the remote processing device, wherein the remote processing device is configured to process the energy usage characteristics to determine one or more optimisations of the adaptive battery management system and is operable to send said one or more optimisations to the controller, and wherein the controller is configured to implement said one or more optimisations.

The battery management system of the present invention is particularly suited for adaptively managing a rechargeable battery that is part of a photovoltaic system.

Optimising the adaptive battery management system in response to energy use characteristics is advantageous in that the life of a battery can be maximised and the adaptive battery management system can be tailored to suit the energy requirements of an individual user. Different types of user will have different energy use requirements. For example, a business is likely to have a greater energy need during daylight hours when photovoltaic panels can provide for most, if not all, of their energy needs without relying on stored energy in a battery. The battery in such circumstances would only provide power when environmental conditions are such that the photovoltaic panel cannot produce sufficient energy. In contrast, a working family might have a greater energy need in the morning and evenings as the adults may be at work and children at school. In such circumstances, the battery would be charged by photovoltaic panels during the day and depleted through use in the mornings and evenings.

The present invention can provide for the controller of the adaptive battery management system to be programmed remotely by the remote processing device. Energy usage characteristics are sent to the remote processing device to limit the need for computation or processing by the controller. The controller can thus be kept simple to limit the cost of the controller part of the adaptive battery management system.

The present invention also provides for the battery of the adaptive battery management system to be cycled in a way that meets a user's energy requirements and maximises the life of the battery. As described previously, overcharging and excessive discharge of a battery can drastically reduce its operational lifespan. By optimising the controller to cycle the battery between full capacity and an optimised depth of discharge, or disable state of charge, a sustained energy source is provided to the user and the life of the battery is maximised.

A second aspect of the invention provides a method of managing battery usage, the method comprising: i) providing a battery controller configured to communicate with a remote processing device; U) sending energy usage characteristics relating to a first predetermined time period to the remote processing device; Hi) receiving information relating to a disable state of charge from the remote processing device; iv) implementing a disable state of charge according to the information received from the remote processing device; v) comparing the disable state of charge to an actual state of charge of the battery; vi) disabling the outputs from the battery if the actual state of charge of the battery is less than or equal to the disable state of charge of the battery; A third aspect of the invention provides a method of managing battery usage, the method comprising: i) providing a remote processing device configured to communicate with a battery controller; U) receiving energy usage characteristics from the battery controller relating to a first predetermined time period; iii) collating predicted environmental variables for a second predetermined time period; iv) using said energy usage characteristics and said environmental variables to calculate predicted energy usage for said second predetermined time period; v) calculating a disable state of charge; vi) sending information relating to said disable state of charge to the battery controller.

Renewable energy sources are inherently sporadic in terms of the amount of energy that they can provide. For example, the amount of energy generated by a photovoltaic panel is dependent on the amount of sunlight. In certain areas of the world, no mains electricity is available hence all power is provided for by either batteries or renewable energy sources. Reliance on a non-constant energy supply can lead to periods of time where no energy is available to a user.

The second and third aspects of the invention use predicted environmental variables and historical user data to predict the amount of energy that will be generated by a renewable energy source and whether that amount of energy will be sufficient to meet a user's needs. If it is determined that the predicted environmental conditions will provide sufficient energy to recharge the battery, the user will be permitted to use a greater proportion of the stored energy in the battery than if it is determined that the predicted environmental conditions will not provide sufficient energy to recharge the battery.

A fourth aspect of the invention provides a method of predicting failure of a battery, the method comprising: i) providing a battery controller configured to communicate with a remote processing device; U) sending energy usage characteristics relating to a time period of predetermined length from the battery controller to the remote processing device; v) receiving a battery status from the remote processing device to the battery controller indicating if the battery is predicted to fail in less than a predetermined number of days; vi) visually or audibly indicating that the battery requires replacing if the battery is predicted to fail within said predetermined number of days.

A fifth aspect of the invention provides a method of predicting failure of a battery, the method comprising: i) providing a remote processing device configured to communicate with a battery controller; U) receiving energy usage characteristics relating to a time period of predetermined length from a battery controller; Ui) comparing said energy usage characteristics to a control data set; iv) identifying any differences between said energy usage characteristics and said control data set; v) predicting a date when the battery is expected to fail; vi) sending a battery status to the battery controller indicating if the battery is predicted to fail in less than a predetermined number of days.

Battery failure can havea significant impact on the efficiency of renewable energy sources. If the battery fails a user would only be able to use energy as it is generated. This would mean that no energy would be available to the user during periods where no energy is generated such as at night in the case of a photovoltaic panel. Additionally, in remote areas of the world it may take several weeks for a new battery to be delivered.

The fourth and fifth aspects of the invention use test data to identify when a battery is likely to fail before it fails. This is advantageous as a replacement battery can be sent to a user before the original battery fails. Alternatively, a user can order a replacement battery based on a warning output from the battery controller. This has the effect of reducing the risk of a break in service caused by battery failure.

BRIEF DESCRIPTION OF THE DRAWINGS

Certain embodiments will now be described with reference to the following drawings: Figure 1 illustrates a schematic of an adaptive battery management system according to the first aspect of the invention; Figure 2 illustrates a battery charging and usage algorithm according to the second and third aspects of the invention; Figure 3 illustrates usage characteristics of an adaptive battery management system comprising an appropriately sized photovoltaic panel as a renewable energy source; Figure 4 illustrates usage characteristics of an adaptive battery management system comprising a slightly undersized photovoltaic panel as a renewable energy source;

S

Figure 5 illustrates usage characteristics of an adaptive battery management system comprising a very undersized photovoltaic panel as a renewable energy source.

Figure 6 illustrates a battery failure algorithm according to the fourth and fifth aspects of the invention;

DETAILED DESCRIPTION OF THE CERTAIN EMBODIMENTS

Figure 1 illustrates a schematic of an adaptive battery management system. A solar panel land a battery 2 are each electrically connected to a controller 3. The controller 3 has at least one output 4 for connection to an electronic device and a means of communicating with a remote processing device 5, such as a computer. The means for the controllerS to communicate with the remote processing devices may be a GSM connection over a cellular network but any other suitable communication means may be used.

Figure 2 illustrates an algorithm for limiting power usage to the power that a battery 2 is predicted to store based on historical power usage data and environmental variables. The controller 3 comprises a re-writable memory which stores battery and usage characteristics. Battery and usage characteristiscs stored in the re-writable memory are sent from the controller 3 to the remote processing devices via the GSM connection. The GSM connection is terminated after transmission of data to the remote processing device.

The remote processing device S also receives weather forecasts for the geographical location of the adaptive battery management system (step S.2). By considering the historical power usage data and environmental variables the remote processing device 5 estimates the power requirements of the user for the following day and determines whether the battery 2 will have sufficient charge to meet those requirements (steps 5.3 and S.4).

Based on the estimated power needs of the user for the following day, a state of charge (SOC) value at which outputs from the battery management system are disabled ("Disable SOC") can be determined (step 5.5). A SOC value is a percentage of the overall capacity of the battery 2 that it is permitted to discharge to before no further power is permitted to be drawn from the battery 2. It is recognised that a low SOC value leads to sulphation of the battery 2 thus reducing its battery life.

Conversely, a high SOC value would limit sulphation of the battery but would reduce the amount of power available to a user before no further power is permitted to be drawn from the battery 2.

The Disable SOC value is optimised to ensure that the battery 2 is not overcharged and is not discharged and cycled at a low SOC value. The Disable SOC value is sent from the remote processing device 5 to the controller 3 (step 5.6). The controller 3 compares the disable Soc to the battery SOC (step 5.7) and disables the outputs from the battery 2 (step 5.8) if the battery soc is equal to or less than the disable SOC. The process of the controller 3 comparing the disable SOC to the battery SOC is continuous. Once the outputs from the battery 2 have been disabled they will remain disabled until a) a pre-determined time period has passed, and/orb) the battery SOC is greater than the disable soc.

Power usage data is sent from the controller 3 to the remote processing device 5 at least once each day to ensure that the power usage can be accurately estimated for a future period of time (step 5.9). The controller creates a GSM connection between itself and the remote processing device in order to transmit data and terminates the GSM connection once data transmission is complete. The process begins again at the beginning of a new day (step 5.10) Referring to figures 3 to 5, usage patterns of adaptive battery management systems comprising various sized photovoltaic panels las a renewable energy source are shown. The top line 6 in each pattern signifies a low voltage disconnect level at which outputs from battery 2 are disabled. The bottom line 7 in each pattern signifies the state of charge of the battery 2 and the blocked out areas 8 in figures 4 and 5 signify time periods during which outputs from the battery 2 are disabled.

Figure 6 illustrates an algorithm for predicting failure of a battery 2 before it fails. The controller 3 stores battery usage characteristics such as number of charge cycles, how long the battery has been installed, operating temperature, average SOC, average daily SOC. minimum daily SOC and maximum daily Soc (step 5.11) in its re-writable memory. The remote processing device 5 stores a control data set (step 5.12). The stored battery usage characteristics are sent to the remote processing device 5 and compared against the control data set derived from extensive testing of specific battery types (steps 5.13 and 5.14). Use of the control data set assists a computer or operator to identify when batteries failed during testing and thus predict failure of a battery 2 (step 5.15) exhibiting similar characteristics to those in the control dataset.

The remote processing device S sends a signal to the controller 3 indicating whether the battery 2 is predicted to fail within a pre-determined number of days (step S.16). If the battery 2 is predicted to fail within said pre-determined number of days, an audible or visual indicator can be activated to indicate to a user that a battery 2 replacement is needed imminently (steps 5.17 and S.18). The user can then order a new battery 2 or the remote processing device 5 can request a service engineer to attend to install a new battery 2.

This algorithm may also be used to remotely instruct the adaptive management battery system to perform a maintenance cycle or send a maintenance technician to maintain the system and/or change the battery. Comparison of the battery usage characteristics with the control dataset provides an indication of when system maintenance is required.

The algorithms described with reference to figures 2 and 6 are by way of example only and are not intended to limit the scope of the invention. Many different configuration or optimisations could be sent to the controller 3 from the remote processing device 5.

The remote processing device 5 is able to compare battery usage information from a plurality of battery management systems from within a group to determine potential anomalies. In one particular example) the battery management system is part of a renewable energy source such as photovoltaic panels or wind turbines. Any significant variation between the plurality of battery management systems can be identified and investigated further by, if necessary, sending a maintenance technician to the site of the battery management system. Potential anomalies in a photovoltaic panel system could be caused by poor panel placement, dirty panels or mis-use of the system.

The remote processing device Scan be used to compare differing configurations between groups of battery management systems in order to determine comparative efficiency of slightly differing controller configurations or charging algorithms. The controller 3 of each battery management system sends battery usage information to the remote processor 5. The remote processor collates the battery usage information from each battery management system and indicates which controller configuration or algorithm is most efficient.

Claims

Claims 1. An adaptive battery management system comprising: a renewable energy generator; a rechargeable battery operatively connected to the renewable energy generator; a controller operatively connected to the rechargeable battery and the renewable energy generator; a remote processing device configured to communicate with the controller, wherein the controller is configured to send energy usage characteristics to the remote processing device, wherein the remote processing device is configured to process the energy usage characteristics to determine one or more optimisations of the adaptive battery management system and is operable to is operable to send said one or more optimisations to the controller, and wherein the controller is configured to implement said one or more optimisations.
2. An adaptive battery management system according to claim 1, wherein the renewable energy generator is a photovoltaic panel.
3. An adaptive battery management system according to claim 1 or 2, wherein the rechargeable battery is a sealed lead acid battery.
4. An adaptive battery management system according to any preceding claim) wherein the energy usage characteristics are one or more of battery terminal voltage, current, operating temperature, low voltage disconnect level and state of charge.
5. An adaptive battery management system according to any preceding claim, wherein the remote processing means is a computer located remotely geographically from the adaptive battery management system.
6. An adaptive battery management system according to any of claims ito 5, wherein the remote processing means is cloud based.
7. An adaptive battery management system according to any preceding claim, wherein the controller comprises a communication means.
8. An adaptive battery management system according to any preceding claim, wherein the communication means comprises a mobile or cellular network and preferably a global system for mobile communications (GSM).
9. An adaptive battery management system according to any preceding claim, wherein the controller comprises a non-volatile, re-writeable memory.
10. An adaptive battery management system substantially as described herein.
11. A method of managing battery usage, the method comprising: i) providing a battery controller configured to communicate with a remote processing device; U) sending energy usage characteristics relating to a first time period of predetermined length to the remote processing device; Ui) receiving information relating to a disable state of charge from the remote processing device; iv) implementing a disable state of charge according to the information received from the remote processing device; v) comparing the disable state of charge to an actual state of charge of the battery; vi) disabling the outputs from the battery if the actual state of charge of the battery is less than or equal to the disable state of charge of the battery;
12. A method of managing battery usage comprising: i) providing a remote processing device configured to communicate with a battery controller; ii) receiving energy usage characteristics from the battery controller relating to a first time period of predetermined length; Hi) collating predicted environmental variables for a second time period of predetermined length; iv) using said energy usage characteristics and said environmental variables to calculate predicted energy usage for said second time period; v) calculating a disable state of charge; vi) sending said disable state of charge to the battery controller.
13. A method according to claim 11 or 12, wherein the energy usage characteristics are one or more of battery terminal voltage, current, operating temperature, low voltage disconnect level and state of charge.
14. A method according to claim 11 or 12, wherein the first time period of predetermined length is about a day.
15. A method according to any of claim 12, wherein the predicted environmental variables include one or more of temperature, cloud coverage, hours of sunlight, sunrise time, sunset time and geographical location.
16. A method according to any of claim 12 or 15, wherein the second time period of predetermined length is about a day.
17. A method according to any of claims 11 to 16, wherein the remote processing system is cloud based.
18. A method of managing battery usage substantially as described herein.
19. A method of predicting failure of a battery, the method comprising: i) providing a battery controller configured to communicate with a remote processing device; H) sending energy usage characteristics relating to a time period of predetermined length to the remote processing device; v) receiving a battery status from the remote processing device indicating if the battery is predicted to fail in less than a predetermined number of days; vi) visually or audibly indicating that the battery requires replacing if the battery is predicted to fail within said predetermined number of days.
20. A method of predicting failure of a battery, the method comprising: i) providing a remote processing device configured to communicate with a battery controller; H) receiving energy usage characteristics relating to a time period of predetermined length from the battery controller; iii) comparing said energy usage characteristics to a control data set; iv) identifying any differences between said energy usage characteristics and said control data set; v) predicting a date when the battery is expected to fail; vi) sending a battery status to the battery controller indicating if the battery is predicted to fail in less than a predetermined number of days.
21. A method according to claim 19 or 20, wherein the energy usage characteristics are one or more of battery terminal voltage, current, operating temperature, low voltage disconnect level and state of charge.
22. A method according to any of claims 19 to 21, wherein the time period of predetermined length is about a day.
23. A method according to any of claims 19 to 22, wherein the remote processing device is cloud based.
24. A method of predicting failure of a battery substantially as described herein.