WO2024177536A1 - Methods and apparatus for off-grid and semi-off-grid connectivity - Google Patents

Abstract

A base station system comprises base station circuitry (410), a battery system (420), battery-management controller circuitry (440) and one or more renewable electricity sources (430), where the battery-management controller circuitry (440) is configured to monitor and control the energy state and usage of the battery system and adapt one or more operational modes and/or operational parameters of the base station circuitry (410) to adjust power consumption of the base station circuitry (410), based on an energy-management model (442) that takes into account (i) an initial capacity of the battery system, (ii) a capacity degradation model of the battery system, and (iii) historical energy production data and/or an energy production model of seasonal energy production for each of the one or more renewable electricity sources (430).

Description

METHODS AND APPARATUS FOR OFF-GRID AND SEMI-OFF-GRID CONNECTIVITY

TECHNICAL FIELD

The present disclosure is generally related to base station systems using renewable energy sources and battery systems for power and is more particularly related to control of such systems for battery management.

BACKGROUND

Base stations in wireless communication systems are typically connected to the electrical power grid. In some areas, the power grids may be very unreliable (e.g., in India, down-times average about 20% per day). Even more generally, power grids are less reliable than might be imagined. For example, in the U.S., power grid reliability is only 98.6%.

One approach to addressing this problem is the adoption of distributed storage solutions, such as batteries close to each base station. However, this is rare.

A great deal of scientific research has focused on battery technology and renewable energy in general, e.g., on the modeling of lithium-ion battery degradation or on increasing the efficiency of photovoltaic panels. In addition, it has been previously suggested to complement access points with energy harvesting devices, e.g., photovoltaic panels. One example is International Patent Application Publication No. WO 2011/071425, filed 8 December 2009, which describes techniques for energy balancing among multiple base stations powered by any of a variety of power sources, including wind power, solar power, diesel-generation power, and the electrical grid.

Increased interest in off-grid operation or at least partial independence from the electrical grid for wireless base stations is anticipated. To address this, improved management systems are needed.

SUMMARY

Most renewable energy sources are intermittent, with their outputs and reliability being location dependent. Previously published literature does not address how this issue affects the operation of a base station that relies on such power sources, as it is usually assumed that base stations are connected to a reliable grid.

Renewable energy sources may be complemented with battery systems, to provide power when the renewable power is unavailable, and/or when a connected grid is out of service. Combining a battery system with renewable energy sources for powering a wireless network raises new issues regarding how to manage the battery usage, to optimize battery life without unduly sacrificing base station services or performance. The techniques described herein address these issues.

Embodiments of the techniques, apparatuses, and systems described herein include a base station system including base station circuitry, at least two electricity sources and battery-management controller circuitry. The base station circuitry includes transceiver circuitry and controller circuitry. The controller circuitry is operatively coupled to the transceiver circuitry and configured to control operation of the transceiver circuitry. The at least two electricity sources include a battery system and one or more renewable electricity sources. The battery-management controller circuitry is operatively coupled to the at least two electricity sources and to the base station circuitry and configured to monitor and control the energy state and usage of the battery system, as well as to adapt one or more operational modes and/or operational parameters of the base station circuitry to adjust power consumption of the base station circuitry, based on a battery-management model or, more generally, an energy-management model, that takes into account (i) an initial capacity of the battery system, (ii) a capacity degradation model of the battery system, and (iii) historical energy production data and/or an energy production model of seasonal energy production for each of the one or more renewable electricity sources.

Other embodiments include a corresponding method for operating a base station system that comprises base station circuitry, a battery system, battery-management controller circuitry, and one or more renewable electricity sources. This method comprises monitoring and controlling the energy state and usage of the battery system, and adapting one or more operational modes and/or operational parameters of the base station circuitry to adjust power consumption of the base station circuitry, again based on an energy-management model that takes into account (i) an initial capacity of the battery system, (ii) a capacity degradation model of the battery system, and (iii) historical energy production data and/or an energy production model of seasonal energy production for each of the one or more renewable electricity sources.

Still other embodiments include battery-management controller circuitry for use in or with a base station system described above, where the battery-management controller circuitry is configured to monitor and control the energy state and usage of the battery system and, once more, to adapt one or more operational modes and/or operational parameters of the base station circuitry to adjust power consumption of the base station circuitry, based on an energy-management model that takes into account (i) an initial capacity of the battery system, (ii) a capacity degradation model of the battery system, and (iii) historical energy production data and/or an energy production model of seasonal energy production for each of the one or more renewable electricity sources.

Variants of the above-summarized embodiments are detailed below, as are other objects, advantages, and novel features of the presently disclosed invention.

BRIEF DESCRIPTION OF THE FIGURES

Figure 1 is an example base station system according to some embodiments.

Figure 2 is another view of an example base station system.

Figure 3 is a process flow diagram illustrating example techniques according to some embodiments.

Figure 4 is still another view of an example base station system.

Figure 5 is a process flow diagram illustrating an example method, according to some embodiments.

DETAILED DESCRIPTION

The present solutions are described herein in terms of methods and arrangements in a communication system, which may be put into practice in the embodiments described below. The present solutions may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the present solution. It should be understood that there is no intent to limit the present methods, and/or arrangements to any of the particular forms disclosed, but on the contrary, the present methods and arrangements are to cover all modifications, equivalents, and alternatives falling within the scope of the present solution as defined by the claims.

The present solutions may, of course, be carried out in other ways than those specifically set forth herein without departing from essential characteristics of the solution. The present embodiments are to be considered in all respects as illustrative and not restrictive, and all changes coming within the meaning and equivalency range of the appended claims are intended to be embraced therein.

As noted above, most renewable energy or electricity sources are intermittent and location dependent. In the context of supplying power to a base station, this problem can be addressed, at least in part, by providing the base station with a battery system, e.g., an arrangement of lithium-ion cells configured to store surplus power generated by the renewable energy sources and to power the base station when the power generated by the renewable energy sources is inadequate, or completely unavailable.

However, one problem with such a solution is how to properly manage the battery system to maximize its lifetime. As is well-known, the storage capacity of a battery system will degrade with use. With lithium-ion-based systems, for example, the storage capacity of the cells will gradually decrease, as a function of charge-discharge cycles, until they reach a critical point, after which the degradation in capacity becomes severe enough that the battery system may be considered unusable. This problem becomes particularly acute when a base station has no connection at all to the electrical power grid, or when the electrical grid is unreliable or only intermittently available.

The techniques described herein address these problems by equipping a base station, which may or may not be connected to a power grid, with an energy storage device, e.g., a lithium-ion battery system, and one or more renewable energy harvesting devices, e.g., a solar panel and a wind turbine, together with an algorithm or algorithms to facilitate managing the charging process in real time.

Such a system could be completely off-grid or semi-off-grid, in various embodiments, with the latter including a situation where a power grid is available but where the system draws energy from the grid only in limited circumstances, e.g., to replenish the battery at night, or to draw energy in the event of unexpected low renewable energy supply. Such a system might also supply energy to the grid in certain circumstances, e.g., to sell excess energy obtained from a solar system during the day.

It will be appreciated that at least some renewable energy sources, such as solar power systems or wind energy systems, may be considered to be intermittent or unreliable sources of power since, for example, solar power is unavailable at night and prevailing winds may vary over time. In some cases, the base station may be connected to an electrical grid that is considered to be an intermittent or unreliable source of power, as well. The battery management systems and techniques described herein can take any or all of this into account, when determining how to best manage the use of the battery system.

Figure 1 thus illustrates an example system in which some or all of the techniques described herein may be employed. The illustrated system includes a base station 110, as well as several power sources or power generators, including solar panels 120, wind turbine 130, and power grid 140. While Figure 1 illustrates base station 110 separately, the entire system might together be considered a "base station system." It will be understood that other power sources might be available as well, and that various systems may only contain one or a few of these sources.

The system shown in Figure 1 also includes a battery system 150, which may be, for example, a bank of lithium-ion battery cells with appropriate charging and power conversion circuitry. A batterymanagement system 160, which includes battery-management controller circuitry that will be described in more detail below, monitors and controls the energy state and usage of the battery system 150 and, as will be discussed below, may also adapt one or more operational modes and/or operational parameters of the base station 110, based upon, inter alia, the state of the battery system 150, which includes its energy state , or charge level, as well as its current capacity, current demand for or supply of energy from/to the rest of the system, etc.

Implementations of the battery management system 160 include an algorithm that defines the charging and discharging processes for the battery system 150, based on the demand of the base station and the supply from the renewable electricity sources. The battery management system 160 may take into account all or some of the following parameters, models, etc., which may be stored in database 180:

— Initial battery capacity;

— A capacity degradation model for the battery system;

— Historical prices of power per kWh depending on day and time if an open energy market is available and a variable energy contract is adopted, otherwise fixed price per kWh;

— Current prices of power;

— Historical data traffic patterns;

— Historical seasonal patterns of energy supplied by the chosen renewable electricity sources;

— Daily weather forecast.

The algorithm may optimize one, several, or a combination of the following objectives, e.g., weighted together in one objective referred to as reward:

— Maximum remaining battery lifetime, e.g., defined as the number of days before the capacity of the battery is a fraction of the initial capacity. As an example, this might be defined as the time before the fully-charged capacity of the battery is expected to reach 70% of the initial capacity, due to repeated charges and discharges); — Minimum downtime for the base station (e.g., in the case the system is completely off-grid or where there is a known likelihood of grid outages, the system might at least sometimes be completely dependent on the battery system, which may be unable to maintain 100% availability - in such a scenario, the energy state of the battery may be optimized at least in part to minimize downtime, in view of predicted grid outages); one of the objective functions could be the downtime of the base station (due to lack of power) and the optimization problem could be to minimize such downtime. This impacts the choices for energy storage and potentially battery lifetime because the algorithm may 1) require a larger battery, and 2) use the battery at levels that degrade it faster than at other levels (e.g., use the battery when the charge is 10% is worse than using it when the charge is at 50%, but the algorithm may be forced to use the battery when the level is 10% because otherwise the base station would be turned off).

— Maximum revenues in terms of price paid for electricity pushed into the power grid minus cost of electricity drawn from the power grid. This applies in cases where the system is connected to the power grid. Without renewable energy or electricity sources, such a revenue would be negative. With renewables and battery, such a revenue may be positive or negative.

Some of the above objectives can be merged, with each other or with other objectives/costs. For example, the revenue streams during the battery lifetime and the capital costs (often referred to as capital expenditures, or CAPEX) of the system can be all merged to provide a complete picture of the cost/benefit of building and running the system.

The operation of the algorithm for managing the battery system 150 may be characterized as utilizing feedback from various components of the overall system. A detailed example follows.

For simplicity, assume that time is discretized: t = 0, 1, 2, ... . Let the initial capacity of the battery be C_o, which is measured in joules, or kWh (thousand watts x hour). The energy supply and demand at any given discrete time step can be normalized to C_o, in which case these quantities are dimensionless. For example, if the energy supplied at time 0 is S_o = 0.05, it means that, if there is no demand and all energy supplied is stored (ideal case), then at time 1 the energy stored is 5% higher than at time 0 (unless, of course, the battery stored more than 95% of its capacity, in which case it would reach full capacity and some of the available energy would be wasted, unless it can be supplied to the grid).

Next, the computation that is needed to end up with dimensionless energy supply and demand in a realistic scenario is detailed.

Suppose, for example, that a wind turbine generates 10 kWh with wind blowing at 3 m/s (10.8 km/h), and suppose that the time step in the algorithm is 1 minute, during which interval is assumed that the wind speed is almost constant, with average 3 m/s. Thus, the energy supplied in that minute is 10 kWh/60 ~ 166 Wh.

Suppose further that the battery capacity is 6 kWh. Then S_o 0.0276 or 2.76% of the initial battery capacity. If this energy is actually stored in the battery, the battery charge level goes up by 2.76%. But, the battery capacity for time step 1 is reduced, over that interval, due to battery degradation, by a small but nonzero amount, say AC₀, that is,

= C_o — AC₀. Note that the specifics of this degradation are technology-specific, and also depend on the usage patterns and state of the battery system - all of these may be captured in a capacity degradation model of the battery system, which may be, for example, empirically derived by the manufacturer or supplier of the battery system and which uses, as inputs, the energy state and usage of the battery over time.

In general, at time t, the typical operation of the system may proceed as follows:

1. The renewable energy sources (e.g., wind and solar) supply some energy, say S_t (whether or not this energy is stored, in the case it exceeds demand, is a choice that will be made by the algorithm described here).

2. The base station demands some power, say D_t (the base station requires a baseline level of power even when idle, and additional power is needed depending on traffic and other parameters).

3. If connected to a properly functioning power grid, let the price of one normalized unit of power (equal to C_o joules or equivalent in kWh) be P_t.

4. The battery management system takes as inputs S_t, D_t, P_t, and all the historical patterns, including possibly the energy price and weather forecasts, to come up with a choice on i) whether or not to store the excess energy X_t = S_t — D_t if it is positive, and ii) whether or not to fully or partially satisfy the demand. Note that if demand is not satisfied, then the base station may go down, or services may be curtailed in some way. In extreme situations, e.g., in scenarios where there is no grid power available, it may be a desirable outcome for the base station to become fully inoperative, but in general it may be assumed that it is preferable for the base station's demand to be always satisfied.

The two choices may be represented by two variables, <J_t E {0,1}, which represents the action of not storing (cr_t = 0) or storing (cr_t = 1) excess energy, and 6_t E {0,1}, which represents the action of not satisfying (<5_t = 0) or satisfying (<5_t = 1) the base station's demand. Note that limiting the choices for <5_tto 0 and 1 suggests that the latter choice is limited to either fully satisfying the base station's demand or not satisfying it at all. In some embodiments, it may be possible to partially satisfy the base station's demand by altering or adapting an operational mode or operational parameter, e.g., limiting transmission power, turning off one or more services, etc. This may be modeled by either allowing D_t to take on a variety of values, depending on the operational modes and/or parameters applicable to a given interval or by allowing 8_t to take on intermediate values between 0 and 1. In either case, the energy available to be stored for the interval in which the base station's demand is satisfied (if any) is limited to the difference between the total energy supplied by all sources and the energy consumed by the base station,.

Note also that it may be undesirable to store or satisfy demand at every interval possible. The reason for this is that every time the battery is used, its capacity degrades. Consequently, here is thus an optimal strategy that can be adopted. One way to find this optimal strategy is by using a learning algorithm, e.g., a deep Q-learning. However, this is not the only way to proceed - it is simply one possible implementation. Other machine-learning algorithms such as a trained random forest might be used, as might a decision tree or even simpler set of rules. The energy stored in the battery is updated accordingly. Let E_t be the energy stored at time t normalized as usual to C_o. Then, at time t = 1, the energy will be equal to

^Et+i = E_t + X_t , provided that demand is satisfied (as it usually is), the excess energy X_t is stored, and 0 <

E_t+1 < The last inequality indicates that the energy in the battery is always nonnegative Co and at most it is equal to the (degraded) capacity (normalized to C_o). In fact, as operations go on, C_t decreases because the energy storage capability of the battery deteriorates. In total generality, the energy at the following time step is given by:

Note that excess energy may be sold, rather than being stored. In this case, of course, the equations above should be modified to account for excess generated energy that is sold and not used to charge the battery system. Likewise, in a grid-connected system, it is also possible to pull energy from the grid and add it to the battery - this likewise will require straightforward modifications to the above equations.

6. The battery is degraded by a quantity equal to AC_t > 0 and thus C_t+1 = C_t — AC_t.

7. All quantities above are recorded in the database and become part of historical data used in step 4.

8. The time step is updated, t «- t + 1, and the process continues (go to step 1).

This process continues until the capacity drops below a certain pre-determined level. For example, for lithium-ion batteries, there is a fairly linear degradation for a few thousand cycles, until the capacity degrades to about 70% of the initial capacity. Beyond the 70% level or thereabouts, the degradation accelerates. Therefore, the process detailed above should continue until the capacity approaches a point close to the nonlinear part of the degradation. At that point, further use of the battery system may be regarded as infeasible, and maintenance actions may be taken.

There are multiple choices for the algorithm to be used in step 4. One such choice is to base the algorithm on deep Q-learning. Other reinforcement learning algorithms would work, and also nonlearning algorithms may be suited in case there is no database available. A benefit of a learning algorithm though is that it becomes tailored to the specific location and specific products used, i.e., the specific battery, solar panels, and wind turbines. Such a learning algorithm can run locally, on one or more processors co-located with the base station, or in the cloud, if internet access is available. It does not require special hardware accelerators (like GPUs), although if present they can be leveraged.

The block diagram of Figure 2 illustrates basic components of a system. Arrows represent messages about power requested or supplied. As seen in the figure, several energy sources 210 supply power for the operation of a base station 220. These energy sources 210 include at least one renewable energy generator (e.g., solar panels and/or wind turbine), such that some or all of these sources have available energy outputs that vary with time. The hardware also includes a battery system, illustrated as energy storage unit 230. The battery management system 240, which includes a compute unit 245, which may be regarded as an example implementation of the battery-management controller circuitry described elsewhere herein, takes as inputs energy measurements, historical data, and current data, including possibly weather forecasts, to determine when and how to serve the demand of the base station and when to store energy in the battery system.

The dashed boxes correspond to storage of a historical database 250, e.g., for historical data regarding the outputs of the energy sources and historical operation of the battery management system 240. This historical data may be stored in and/or retrieved from the cloud, in some instances. While the availability of this historical data is not essential for the operation of the system, if present, it offers the possibility of more accurate prediction and better overall decisions.

Figure 3 is a process flow diagram illustrating the main loop of operation of an example algorithm carried out by, for example, battery-management control circuitry 245 in battery management system 245. Note that this example assumes a connection to the electrical grid, as well as access to a database of historical information. In various embodiments, one or both of these might not be present.

300: Time is initialized to zero when the energy storage system or battery system, which might be referred to as simply a battery, is new and the full initial capacity is available. Each time step thereafter corresponds conceptually to a time interval, which can be measured for the sake of simplicity in seconds or minutes.

310-320: As shown at block 310, the current energy storage capacity of the battery is checked, to determine whether it is less than an end-of-life value. This may be done, for example, by computing the ratio of the current capacity of the battery to its initial, or nominal, capacity, e.g., to obtain a percentage value, which can be compared to a threshold value. If the current capacity of the battery has degraded below a certain threshold, then maintenance or replacement of the battery must be performed, as shown at block 320, after which the battery management algorithm may be re-started. 330-360: As shown at block 330, at the current time step, the battery management system receives a measurement of the average supply of energy during the interval from all the renewable sources. As shown at block 340, for systems with a connection to the electrical grid, a measurement of the current price of energy from the power grid is obtained/received. Note that this may a historical average price, in some cases, or a contracted price or dynamic price offered by the grid in others. As shown at block 350, the system may check to confirm that the grid is available to supply power, or "up." Finally, as shown at block 360, the system may receive a weather forecast, or an indication of current weather conditions, or both.

370: As shown at block 370, the battery management system then applies an algorithm on the basis of information in a historical database, shown at block 365, and measurements from blocks 330, 340, 350, 360, using a battery-management model or, more generally, an energy-management model, which is discussed in more detail below. A decision on satisfying demand (5) and storing energy (a) is taken here.

380: The decision taken at block 370 is satisfied at block 380. This decision may be to satisfy the power demand of the base station, i.e., to provide energy to the base station from one or more of the resources and/or the battery system, and/or to store energy in the battery system. Note that this satisfying of the base station's energy demands may be full or partial, in various situations and/or embodiments, and may comprise, for any given interval, supply all or a portion of the energy needed by the base station from the battery, when inadequate energy is available from the other energy sources. As discussed above, it may be possible to partially satisfy the base station's demand by altering or adapting an operational mode or operational parameter of the base station, e.g., limiting transmission power, turning off one or more services, etc. This may be modeled, for example, by allowing 6_t to take on intermediate values between 0 and 1.

The decision taken at block 370 may instead or also be to store energy. In some cases, this may be a decision to store energy supplied by the energy resources over and above the demand of the base station. In other, more extreme cases, this may be a decision that complements a decision to turn off the base station, i.e., to store whatever energy is supplied by the energy resources instead of satisfying base station demand.

To the extent that any of the demand is supplied from the battery or any energy is stored in the battery, the impact of that supply on the battery's energy storage status, or energy state, is updated. The battery's energy storage status may be tracked in units of energy, or as a percentage of nominal or current capacity, for example. Note that managing a battery system that requires multiple cells or multiple sets of cells may include selecting, from among the multiple cells or sets of cells, one or more cells or sets of cells to charge or discharge at a given time, e.g., to level out the degradation or to otherwise optimize the battery system's life and/or performance.

390: As shown at block 390, the battery's capacity is updated, using a capacity degradation model for the battery system that uses, as input, the battery's energy state and usage over time. Other factors, such as age and temperature might also be included in the capacity degradation model.

395: Finally, as shown at block 395, updates to the battery capacity are stored. Usage information for the battery, whether per-interval usage or usage statistics, may also be stored, for use by the energymanagement model in making future decisions. Likewise, any or all of the most recent data for energy available from the energy resources, grid pricing, weather conditions, etc., may be stored, again for use by the energy-management model in subsequent decisions. The time step is updated, and control passes back to the beginning of the algorithmic loop.

Figure 4 is another view of an example base station system, including an example of the battery management system described herein. The base station system comprises base station circuitry 410, which in turn comprises transceiver circuitry 414 and controller circuitry 418 operatively coupled to the transceiver circuitry 414 and configured to control operation of the transceiver circuitry 414. The base station system further comprises at least two electricity sources, the at least two electricity sources comprising a battery system 420 and one or more renewable electricity source(s) 430, such as a solar power generation system or a wind power generation system.

The base station system further comprises battery-management controller circuitry 440, operatively coupled to the at least two electricity sources and to the base station circuitry 410. The batterymanagement controller circuitry 440, which may comprise, in various embodiments, one or more processors, memory storing program instructions by the one or more processors, digital hardware/logic, analog switches and other analog circuitry, such as current/voltage sensors, etc., is configured to carry out one or more of the techniques described above. Namely, the batterymanagement controller circuitry 440 is configured to monitor and control the energy state and usage of the battery system 420 and to adapt one or more operational modes and/or operational parameters of the base station circuitry 410 to adjust power consumption of the base station circuitry 410. Adapting an operational mode of the base station circuitry 410 may comprise selectively activating and/ or deactivating all or portions of the base station's functionality, in various embodiments or instances. Adapting operational parameters may comprise adjusting or setting/resetting any parameters that impact the energy consumption of the base station circuitry 410. Examples include, for instance, a transmit power parameter, or a parameter controlling a number of transmit beams.

The adapting of the operational mode(s) and/or operational parameter(s) of the base station circuitry 410 by battery-management controller circuitry 440 is based on an energy-management model 442 that takes into account (i) an initial capacity of the battery system, (ii) a capacity degradation model 444 of the battery system 420, and (iii) historical energy production data and/or an energy production model of seasonal energy production for each of the one or more renewable energy or electricity sources 430. In some embodiments, the energy-management model 442 may further take into account a historical data traffic pattern for traffic data flowing through the base station system. Note that the term "energy-management model" is used here to reflect that the model utilizes input from and is used to control energy usage throughout the system, even as its most basic function may be to control charge and discharge of the battery system 420. This model might alternatively be referred to as, for instance, a battery-management model.

In some embodiments, the base station system further comprises an electrical interface for connection to an electrical grid, in which case the energy-management model may further take into account one or more of: a current cost for power supplied by the electrical grid, a current price paid for power supplied to the electrical grid from renewable sources, historical data or a model representing cost over time for energy supplied by the electrical grid, and historical data or a model representing price paid for power supplied to the electrical grid from renewable sources.

In various embodiments or instances, the battery-management controller circuitry 440 is configured to adapt the one or more operational modes and/or operational parameters of the base station circuitry 410 using an algorithm that optimizes a weighted combination of at least (iv) a remaining useful lifetime of the battery system and (v) on-time for each of one or more base station operational modes or for the base station circuitry 410 as a whole. In embodiments where the base station system has an electrical interface for connection to an electrical grid, the weighted combination optimized by the algorithm may further include (vi) a parameter corresponding to a net cost of energy supplied to and received from the grid. The algorithm may be a reinforcement-learning algorithm, in some embodiments, such as a deep-Q. learning algorithm.

In various embodiments or instances, the battery-management controller circuitry 440 may be configured to adapt the one or more operational modes and/or operational parameters of the base station circuitry to adjust power consumption of the base station circuitry by selectively turning on and/or off one or more communication services provided to users by the base station system. In some embodiments or instances, the battery-management controller circuitry 440 may be configured to adapt the one or more operational modes and/or operational parameters of the base station circuitry 410 to adjust power consumption of the base station circuitry 410, for example by adjusting transmission power of one or more signals transmitted by the base station circuitry 410.

Portions of the battery-management controller circuitry 440 executing software may be implemented using processing circuitry local to the base station system or remotely, e.g., in the "cloud," or some combination thereof, in various embodiments.

Figure 5 is a process flow diagram illustrating an example method for operating a base station system that comprises base station circuitry, a battery system, battery-management controller circuitry, and one or more renewable electricity sources. This method is intended to be a generalization of and to include the techniques described above - thus, where terminology here differs from similar or related terminology used above, the terminology used to describe Figure 5 should be understood to at least encompass the related terms used above.

As shown at block 510, the method comprises monitoring and controlling the energy state and usage of the battery system. This comprises measuring and tracking energy supplied from and supplied to the battery system. The method further comprises, as shown at block 520, adapting one or more operational modes and/or operational parameters of the base station circuitry to adjust power consumption of the base station circuitry, based on an energy-management model that takes into account (i) an initial capacity of the battery system, (ii) a capacity degradation model of the battery system, and (iii) historical energy production data and/or an energy production model of seasonal energy production for each of the one or more renewable energy or electricity sources. In some embodiments or instances, the energy-management model further takes into account a historical data traffic pattern for traffic data flowing through the base station system. In some embodiments, the base station system may comprise an electrical interface for connection to an electrical grid, in which case the energy-management model may further take into account one or more of: a current cost for power supplied by the electrical grid, a current price paid for power supplied to the electrical grid from renewable sources, historical data or a model representing cost over time for energy supplied by the electrical grid, and historical data or a model representing price paid for power supplied to the electrical grid from renewable sources.

In various embodiments or instances, adapting the one or more operational modes and/or operational parameters of the base station circuitry may comprise using an algorithm that optimizes a weighted combination of at least (iv) a remaining useful lifetime of the battery system and (v) on-time for each of one or more base station operational modes or for the base station circuitry as a whole. In some embodiments where the base station system comprises an electrical interface for connection to an electrical grid, the weighted combination optimized by the algorithm may further comprise a parameter corresponding to a net cost of energy supplied to and received from the grid.

In various embodiments, the algorithm may be a reinforcement-learning algorithm, such as a deep-Q. learning algorithm.

In some embodiments or instances, adapting the one or more operational modes and/or operational parameters of the base station circuitry comprises selectively turning on and/or off one or more communication services provided to users by the base station system. In some of these and in other embodiments or instances, adapting the one or more operational modes and/or operational parameters of the base station circuitry may comprise adjusting transmission power of one or more signals transmitted by the base station circuitry.

Thus, the techniques, circuits, and systems described above comprise a tool that operates a base station system and determines when the energy storage should be increased or used and by what amount, and when the power demand needs to be satisfied. This management tool increases or decreases energy storage in order to maximize a battery's lifetime or to minimize the downtime of the equipment, or to optimize a cost function comprising a weighted combination of several parameters, such as battery lifetime, service downtime, etc.

This tool may utilize a simple theoretical model underpinning the battery capacity degradation in a reinforcement learning framework (e.g., using deep Q-learning techniques) to optimize battery life while trying to satisfy the traffic demand. The tool may employ an algorithm that also takes into account the energy price when optimizing the battery charging and discharging processes.

While the techniques, circuits, and systems described herein may be advantageously employed in base station systems that have a connection to the electrical grid, a completely off-grid base station is also possible, providing the potential for increased coverage in rural areas since the location of the base station is mostly free. With such an off-grid solution, a mast or pole can be erected almost everywhere.

Other non-technical advantages include 1) a reduced reliance on the power grid, which implies a potential reduction of energy to be supplied, and 2) reduced operational expenditures (OPEX) for operators that quickly compensate the increased CAPEX incurred to equip base stations with the necessary hardware and software to make them independent or semi-independent of the power grid.

Claims

CLAIMS What is claimed is:

1. A base station system, comprising: base station circuitry (410) comprising transceiver circuitry (414) and controller circuitry (418) operatively coupled to the transceiver circuitry (414) and configured to control operation of the transceiver circuitry (414); at least two electricity sources, the at least two electricity sources comprising a battery system (420) and one or more renewable electricity sources (430); and battery-management controller circuitry (440) operatively coupled to the at least two electricity sources and to the base station circuitry (410), the battery-management controller circuitry (440) being configured to: monitor and control the energy state and usage of the battery system (420); and adapt one or more operational modes and/or operational parameters of the base station circuitry (410) to adjust power consumption of the base station circuitry (410), based on an energy-management model (442) that takes into account (i) an initial capacity of the battery system (420), (ii) a capacity degradation model (444) of the battery system (420), and (iii) historical energy production data and/or an energy production model of seasonal energy production for each of the one or more renewable electricity sources (430).

2. The base station system of claim 1, wherein the one or more renewable electricity sources (430) comprises one or more of: a solar power generation system; and a wind power generation system.

3. The base station system of claim 1 or 2, wherein the energy-management model (442) further takes into account a historical data traffic pattern for traffic data flowing through the base station system.

4. The base station system of any one of claims 1-3, wherein the base station system further comprises an electrical interface for connection to an electrical grid and wherein the energy-management model (442) further takes into account one or more of: a current cost for power supplied by the electrical grid, a current price paid for power supplied to the electrical grid from renewable sources, historical data or a model representing cost over time for energy supplied by the electrical grid; and historical data or a model representing price paid for power supplied to the electrical grid from renewable sources.

5. The base station system of any one of claims 1-4, wherein the battery-management controller circuitry (440) is configured to adapt the one or more operational modes and/or operational parameters of the base station circuitry (410) using an algorithm that optimizes a weighted combination of at least (iv) a remaining useful lifetime of the battery system and (v) on-time for each of one or more base station operational modes or for the base station circuitry (410) as a whole.

6. The base station system of any one of claims 1-3, wherein the base station system further comprises an electrical interface for connection to an electrical grid, and wherein the battery-management controller circuitry (440) is configured to adapt the one or more operational modes and/or operational parameters of the base station circuitry (410) using an algorithm that optimizes a weighted combination of at least (iv) a remaining useful lifetime of the battery system and (v) on-time for each of one or more base station operational modes or for the base station circuitry (410) as a whole and (vi) a parameter corresponding to a net cost of energy supplied to and received from the grid.

7. The base station system of claim 5 or 6, wherein the algorithm is a reinforcement-learning algorithm.

8. The base station system of claim 7, wherein the algorithm is a deep-Q. learning algorithm.

9. The base station system of any one of claims 1-8, wherein the battery-management controller circuitry (440) is configured to adapt the one or more operational modes and/or operational parameters of the base station circuitry (410) to adjust power consumption of the base station circuitry (410) by selectively turning on and off one or more communication services provided to users by the base station system.

10. The base station system of any one of claims 1-8, wherein the battery-management controller circuitry (440) is configured to adapt the one or more operational modes and/or operational parameters of the base station circuitry (410) to adjust power consumption of the base station circuitry (410) by adjusting transmission power of one or more signals transmitted by the base station circuitry (410).

11. A method for operating a base station system that comprises base station circuitry, a battery system, battery-management controller circuitry, and one or more renewable electricity sources, wherein the method comprises: monitoring and controlling (510) the energy state and usage of the battery system; and adapting (520) one or more operational modes and/or operational parameters of the base station circuitry to adjust power consumption of the base station circuitry, based on an energy-management model that takes into account (i) an initial capacity of the battery system, (ii) a capacity degradation model of the battery system, and (iii) historical energy production data and/or an energy production model of seasonal energy production for each of the one or more renewable electricity sources.

12. The method of claim 11, wherein the energy-management model further takes into account a historical data traffic pattern for traffic data flowing through the base station system.

13. The method of claim 11 or 12, wherein the base station system further comprises an electrical interface for connection to an electrical grid and wherein the energy-management model further takes into account one or more of: a current cost for power supplied by the electrical grid, a current price paid for power supplied to the electrical grid from renewable sources, historical data or a model representing cost over time for energy supplied by the electrical grid; and historical data or a model representing price paid for power supplied to the electrical grid from renewable sources.

14. The method of any one of claims 11-13, wherein said adapting the one or more operational modes and/or operational parameters of the base station circuitry comprises using an algorithm that optimizes a weighted combination of at least (iv) a remaining useful lifetime of the battery system and (v) on-time for each of one or more base station operational modes or for the base station circuitry as a whole.

15. The method of claim 11 or 12, wherein the base station system further comprises an electrical interface for connection to an electrical grid, and wherein said adapting the one or more operational modes and/or operational parameters of the base station circuitry comprises using an algorithm that optimizes a weighted combination of at least (iv) a remaining useful lifetime of the battery system and (v) on-time for each of one or more base station operational modes or for the base station circuitry as a whole and (vi) a parameter corresponding to a net cost of energy supplied to and received from the grid.

16. The method of claim 14 or 15, wherein the algorithm is a reinforcement-learning algorithm.

17. The method of claim 16, wherein the algorithm is a deep-Q. learning algorithm.

18. The method of any one of claims 11-17, wherein said adapting the one or more operational modes and/or operational parameters of the base station circuitry comprises selectively turning on and off one or more communication services provided to users by the base station system.

19. The method of any one of claims 11-17, wherein said adapting the one or more operational modes and/or operational parameters of the base station circuitry comprises adjusting transmission power of one or more signals transmitted by the base station circuitry.

20. Battery-management controller circuitry (440) for use in or with a base station system that comprises base station circuitry (410), a battery system (420), and one or more renewable electricity sources (430), wherein the battery-management controller circuitry (440) is configured to: monitor and control the energy state and usage of the battery system (420); and adapt one or more operational modes and/or operational parameters of the base station circuitry (410) to adjust power consumption of the base station circuitry (410), based on an energy-management model (442) that takes into account (i) an initial capacity of the battery system (420), (ii) a capacity degradation model of the battery system (420), and (iii) historical energy production data and/or an energy production model of seasonal energy production for each of the one or more renewable electricity sources (430).

21. The battery-management controller circuitry (440) of claim 20, wherein the energy-management model further (442) takes into account a historical data traffic pattern for traffic data flowing through the base station system.

22. The battery-management controller circuitry (440) of claim 20 or 21, wherein the base station system further comprises an electrical interface for connection to an electrical grid and wherein the energy-management model (442) further takes into account one or more of: a current cost for power supplied by the electrical grid, a current price paid for power supplied to the electrical grid from renewable sources, historical data or a model representing cost over time for energy supplied by the electrical grid; and historical data or a model representing price paid for power supplied to the electrical grid from renewable sources.

23. The battery-management controller circuitry (440) of any one of claims 20-22, wherein the batterymanagement controller circuitry (440) is configured to adapt the one or more operational modes and/or operational parameters of the base station circuitry (410) using an algorithm that optimizes a weighted combination of at least (iv) a remaining useful lifetime of the battery system and (v) on-time for each of one or more base station operational modes or for the base station circuitry (410) as a whole.

24. The battery-management controller circuitry (440) of claim 20 or 21, wherein the base station system further comprises an electrical interface for connection to an electrical grid, and wherein the battery-management controller circuitry (440) is configured to adapt the one or more operational modes and/or operational parameters of the base station circuitry (410) using an algorithm that optimizes a weighted combination of at least (iv) a remaining useful lifetime of the battery system and (v) on-time for each of one or more base station operational modes or for the base station circuitry (410) as a whole and (vi) a parameter corresponding to a net cost of energy supplied to and received from the grid.

25. The battery-management controller circuitry (440) of claim 23 or 24, wherein the algorithm is a reinforcement-learning algorithm.

26. The battery-management controller circuitry (440) of claim 25, wherein the algorithm is a deep-Q. learning algorithm.

27. The battery-management controller circuitry (440) of any one of claims 20-26, wherein the batterymanagement controller circuitry (440) is configured to adapt the one or more operational modes and/or operational parameters of the base station circuitry (410) to adjust power consumption of the base station circuitry (410) by selectively turning on and off one or more communication services provided to users by the base station system.

28. The battery-management controller circuitry (440) of any one of claims 20-26, wherein the batterymanagement controller circuitry (440) is configured to adapt the one or more operational modes and/or operational parameters of the base station circuitry (410) to adjust power consumption of the base station circuitry (410) by adjusting transmission power of one or more signals transmitted by the base station circuitry (410).

29. The battery-management controller circuitry (440) of any one of claims 20-28, comprising processing circuitry and memory storing program instructions for execution by the processing circuitry, the program instructions being configured to cause the battery-management controller circuitry (440) to monitor and control the energy state and usage of the battery system and to adapt the one or more operational modes and/or operational parameters of the base station circuitry (410).

30. A computer-program product comprising program instructions configured to cause processing circuitry to carry out a method according to any one of claims 11-19.