Description:TITLE OF THE INVENTION: A SYSTEM AND METHOD FOR SHADOWING STATE OF HEALTH OF AN ENERGY STORAGE SYSTEM
FIELD OF THE INVENTION
The present disclosure is generally related to energy storage systems that power vehicles. Particularly, the present disclosure is related to shadowing of state of health, of energy storage systems, of vehicles. More particularly, the present disclosure is related to: a system and method, for shadowing of state of health, of an energy storage system, of a vehicle, to enhance performance of the energy storage system.
BACKGROUND OF THE INVENTION
Energy storage systems comprise a plurality of battery cells, battery modules, and/or battery packs, to fulfil energy demands (or power demands) of vehicles. Though their performance inevitably deteriorates, with time, there is a need, in respect of accurately shadowing their state of health.
Due to the extremely dynamic and non-linear nature of the energy storage systems, real-time shadowing of the state of health (SOH) is necessary, to safeguard them, against malfunctions. Further, shadowing the SOH of each battery module, and battery pack, connected within the energy storage systems, are also important.
With an accurate envisaging of the SOH, of every battery module, it would be possible to sort the battery modules, for second life applications. However, where large numbers of battery modules are involved, this envisaging is complex.
In addition, the state of health cannot be shadowed directly, with any measurement devices. Generally, reductions, in actual capacities of the energy storage systems, and increments, in ohmic internal resistance, are key indicators, based on applications.
Typically, capacity fade that is determined, from open circuit voltages (OCV) and state of current (SOC) curves, is used, to envisage the SOH. Nevertheless, as this method requires a lot of rest time, to obtain the OCV, it cannot be used, for real-time applications.
Alternatively, features of interest (FOI and/or FOIs), with high correlations with capacity degradations, are considered, to envisage the SOH. Examples include: reductions, in constant current periods, with aging; reductions, in areas of constant current duration, with aging; changes in surface temperatures, with aging; and/or increases in constant voltage periods, with aging, under charging, using constant current-constant voltage (CCCV) protocol. However, changes in C-rates and operating temperatures significantly affect most of the FOIs.
An alternative method involves evaluating capacity fade, using incremental capacity analysis (ICA). Several FOIs have been utilised, for envisaging the SOH, using the ICA, for example: peak heights; shifts in peak positions, with aging; and/or changes, in peak areas, with aging.
In WO2021064030A1, and US10393813B2, the FOIs based on peak positions of the IC ((dQ)⁄(dV)) curves are utilised. In KR20180099668A, the FOIs based on the IC ((dQ)⁄(dV)) curves and pressure data are utilised.
Nevertheless, there are a few challenges, in determination of the IC ((dQ)⁄(dV)) curves, in real-time conditions, such as: (i) high uncertainties, in extraction of peak positions, with aging; (ii) extraction of peak amplitudes may not be accurate, due to noise and uncertainties, in the IC ((dQ)⁄(dV)) curves, in real-time; and/or (iii) extraction of peak areas can introduce a lot of uncertainties, due to high subjective nature of starting and ending points.
Hence, it is required to reduce dependency on peak positions, to achieve accurate SOH envisaging. Furthermore, complexity is one of the key concerns, for envisaging the SOH, in real-time applications, in environments that include large numbers of battery modules. Furthermore, there is no system (and/or method), to shadow the SOH of each battery module, connected within the energy storage systems.
There is, therefore, a need in the art, for: a system and method, for shadowing state of health, of an energy storage system, of a vehicle, which overcomes the aforementioned drawbacks and shortcomings.
SUMMARY OF THE INVENTION
A system, for shadowing state of health, of an energy storage system, of a vehicle, to enhance performance of said energy storage system, is disclosed.
Said system broadly comprises: an at least an ESS surveilling member; an at least an envisaging member; a controlling member; and an at least an unveiling member.
Said at least one ESS surveilling member is communicatively associated with an energy storage system of a vehicle. Said at least one ESS surveilling member is configured to surveil an at least a parameter (of said energy storage system), in real-time. Said at least one parameter surveilled, by said at least one ESS surveilling member, during charging of said energy storage system, is transmitted, to said at least one envisaging member.
In an embodiment, said at least one parameter includes, includes, but is not limited to: terminal voltage of each battery module; current across each battery module; and/or the like.
Said energy storage system broadly comprises a plurality of battery packs. Each battery pack, among said plurality of battery packs, is connected with (or is associated with) other battery packs, among said plurality of battery packs, in parallel, to fulfil ampere-hour requirements of said vehicle’s load.
Said each battery pack broadly comprises a plurality of battery modules. Each battery module, among said plurality of battery modules, is connected with (or is associated with) other battery modules, among said plurality of battery modules, in series, to meet voltage requirements of said vehicle.
Said at least one ESS surveilling member broadly comprises: a plurality of voltage sensing members; and a plurality of current sensing members.
Each voltage sensing member, among said plurality of voltage sensing members, is associated with a respective battery module, among said plurality of battery modules, to surveil said terminal voltage of said respective battery module, in real-time.
Each current sensing member, among said plurality of current sensing members, is associated with a respective battery pack, among said plurality of battery packs, to surveil said current across said respective battery pack, in real-time.
Said at least one envisaging member is configured to envisage state of health (of said energy storage system). Said state of health is determined, based on: dQ⁄dV changes; and voltage changes, during a time period, during charging. Said changes in dQ⁄dV and voltage changes are determined, based on the peaks of a beginning of life incremental capacity curve and an aged incremental capacity curve, respectively.
This significantly reduces the requirement of a separate technique, for accurate extractions of IC curve peaks.
Said at least one envisaging member broadly comprises: an online envisaging member; and an offline envisaging member. Said online envisaging member resides on an external member. Said online envisaging member is configured to envisage said state of health, with network connectivity.
In an embodiment, said external member includes, but is not limited to: a remote server; a network cloud; and/or the like.
Said offline envisaging member is embedded within said controlling member. Said offline envisaging member is configured to envisage said state of health, locally, in said vehicle itself, without any network connectivity requirements.
Said controlling member is being configured to track, monitor, and control operations of said system.
Said at least one unveiling member is configured to display envisaged state of health, to a user.
Method of envisaging said state of health is also disclosed.
The disclosed system (and/or method) offers at least the following advantages: accurately envisages said state of health, under real-time dynamic load conditions; accurately envisages said state of health, using (dQ⁄dV) curves, at low computational costs; determines features of interest, from said dQ⁄dV curves; does not rely on a separate technique, for accurate peak determination; envisaging of said state of health is possible, under both online and offline modes; involves a minimum number of features of interest; and is configured to envisage said state of health, at various levels.
BRIEF DESCRIPTION OF THE DRAWINGS
Figure 1 illustrates a system, for shadowing state of health, of an energy storage system, of a vehicle, in accordance with an embodiment of the present disclosure;
Figure 2a illustrates an energy storage system, with “n” number of battery packs, connected in parallel, to fulfil Ah requirements of a vehicle load, said energy storage system being part of a system, for shadowing state of health, of an energy storage system, of a vehicle, in accordance with an embodiment of the present disclosure;
Figure 2b illustrates a battery pack, with “m” number of modules, connected in series, to fulfil voltage requirements of the vehicle load, said battery pack being part of a system, for shadowing state of health, of an energy storage system, of a vehicle, in accordance with an embodiment of the present disclosure;
Figure 3a illustrates V-Q (Voltage – Capacity) curves of a beginning of life (BOL) and aged battery module, said battery module being part of a system, for shadowing state of health, of an energy storage system, of a vehicle, in accordance with an embodiment of the present disclosure;
Figure 3b illustrates determined dQ⁄dV curves of the beginning of life (BOL) and aged battery module, said battery module being part of a system, for shadowing state of health, of an energy storage system, of a vehicle, in accordance with an embodiment of the present disclosure;
Figure 4 illustrates a process flow of envisaging state of health, of an energy storage system, of a vehicle, using Feature of Interests (FOIs) and linear regression models, in accordance with an embodiment of the present disclosure; and
Figure 5 is a flow chart that illustrates a method, for shadowing state of health, of an energy storage system, of a vehicle, in accordance with an embodiment of the present disclosure
DETAILED DESCRIPTION OF THE INVENTION
Throughout this specification, the use of the words “comprise” and “include”, and variations, such as “comprises”, “comprising”, “includes”, and “including”, may imply the inclusion of an element (or elements) not specifically recited. Further, the disclosed embodiments may be embodied, in various other forms, as well.
Throughout this specification, the use of the word “system” is to be construed as: “a set of technical components (also referred to as “members”) that are communicatively and/or operably associated with each other, and function together, as part of a mechanism, to achieve a desired technical result”.
Throughout this specification, the use of the words “communication”, “couple”, and their variations (such as communicatively), is to be construed as being inclusive of: one-way communication (or coupling); and two-way communication (or coupling), as the case may be, irrespective of the directions of arrows, in the drawings.
Throughout this specification, where applicable, the use of the phrase “at least” is to be construed in association with the suffix “one” i.e. it is to be read along with the suffix “one”, as “at least one”, which is used in the meaning of “one or more”. A person skilled in the art will appreciate the fact that the phrase “at least one” is a standard term that is used, in Patent Specifications, to denote any component of a disclosure, which may be present (or disposed) in a single quantity, or more than a single quantity.
Throughout this specification, the use of the word “plurality” is to be construed as being inclusive of: “at least one”.
Throughout this specification, the use of the word “shadowing”, and its variations, is to be construed as being inclusive of: “tracking; monitoring; recording; analysing; controlling; alerting; and/or the like, by a system and method, for shadowing state of health, of an energy storage system”.
Throughout this specification, the use of the word “vehicle”, and its variations, is to be construed as being inclusive of: “commercial electrical vehicles (CEV)”. A person skilled in the art will appreciate the fact that the use of the word “vehicle” may also include: “other electric vehicles; hybrid electric vehicles; conventional internal combustion engine vehicles; and/or the like”.
Throughout this specification, the use of the phrase “energy storage system”, the acronym “ESS”, and their variations, is to be construed as being inclusive of: “battery modules; battery packs; battery systems; and/or the like”.
Throughout this specification, the use of the word “battery”, and its variations, is to be construed as being inclusive of: “lithium-ion batteries”.
Throughout this specification, the use of the word “surveilling”, and its variations, is to be construed as being inclusive of: “tracking; monitoring; recording; measuring; and/or the like”.
Throughout this specification, the use of the word “envisage”, and its variations, is to be construed as: “determine; calculate; compute; evaluate; and/or the like”.
Throughout this specification, the use of the word “unveil”, and its variations, is to be construed as: “display; and/or the like”.
Throughout this specification, the words “the” and “said” are used interchangeably.
Throughout this specification, the word “sensor” and the phrase “sensing member” are used interchangeably. The disclosed sensing members may be of any suitable type known in the art.
Throughout this specification, the phrases “at least a”, “at least an”, and “at least one” are used interchangeably.
Throughout this specification, where applicable, the phrase “energy storage system” and the word “battery” are used interchangeably.
Throughout this specification, where applicable, the words “sense” and “surveil” are used interchangeably.
Throughout this specification, the phrases “IC Curve” and “dQ⁄dV curve” are used interchangeably.
Throughout this specification, the disclosure of a range is to be construed as being inclusive of: the lower limit of the range; and the upper limit of the range.
Also, it is to be noted that embodiments may be described as a method. Although the operations, in a method, are described as a sequential process, many of the operations may be performed in parallel, concurrently, or simultaneously. In addition, the order of the operations may be re-arranged. A method may be terminated, when its operations are completed, but may also have additional steps.
A system, for shadowing state of health, of an energy storage system, of a vehicle (also referred to as “system”), is disclosed. As illustrated, in Figure 1, said system broadly comprises: an at least an ESS surveilling member (2000); an at least an envisaging member (3000); a controlling member (for example, a microcontroller); and an at least an unveiling member (4000; for example, a display).
Said at least one ESS surveilling member (2000) is communicatively associated with an energy storage system (1000) of a vehicle. The at least one ESS surveilling member (2000) is configured to surveil an at least a parameter (of the energy storage system (1000)), in real-time. The at least one parameter surveilled, by the at least one ESS surveilling member (2000), during charging of the energy storage system (1000), is transmitted, to the at least one envisaging member (3000), for example, through the at least one controlling member.
In another embodiment of the present disclosure, the at least one parameter includes, but is not limited to: terminal voltage of each battery module; current across each battery module; and/or the like.
In yet another embodiment of the present disclosure, the at least one ESS surveilling member (2000) broadly comprises: a plurality of voltage sensing members (V or 2100; Figure 2b); and a plurality of current sensing members (I or 2200; Figure 2b).
The at least one envisaging member (3000) is configured to envisage state of health (SOH), of the ESS (1000), with said envisaged SOH being transmitted, to the at least one unveiling member (4000), for example, through the at least one controlling member, for subsequent display, to a user.
In yet another embodiment of the present disclosure, the at least one envisaging member (3000) broadly comprises: an online envisaging member (3100); and an offline envisaging member (3200). Said online envisaging member (3100) resides on an external member, while said offline envisaging member (3200) is embedded within the controlling member.
In yet another embodiment of the present disclosure, the offline envisaging member (3200) is configured to envisage the SOH of the ESS (1000), locally, in the vehicle itself, without any network connectivity requirements.
In yet another embodiment of the present disclosure, the external member includes, but is not limited to: a remote server; a network cloud; and/or the like. For envisaging the SOH of the ESS (1000), with the online envisaging member (3100), network connectivity (for example, the internet) is required.
In yet another embodiment of the present disclosure, the controlling member is configured to track, monitor, and control operations of the system. Said controlling member is communicatively associated with at least: the at least one ESS surveilling member (2000); and the at least one unveiling member (4000).
In yet another embodiment of the present disclosure, as illustrated, in Figure 2a, the ESS (1000) broadly comprises a plurality of battery packs (1100; for example, “n” number of battery packs). Each battery pack, among the plurality of battery packs (1100), is connected with (or is associated with) other battery packs, among the plurality of battery packs (1100), in parallel, to fulfil ampere-hour (Ah) requirements of the vehicle’s load.
In yet another embodiment of the present disclosure, as illustrated, in Figure 2b, said each battery pack broadly comprises a plurality of battery modules (1110; for example, “M” number of battery modules). Each battery module, among the plurality of battery modules (1110), is connected with (or is associated with) other battery modules, among the plurality of battery modules (1110), in series, to meet voltage requirements of the vehicle.
Each voltage sensing member (V-1, V-2, V-3, … V-M), among the plurality of voltage sensing members (V), is associated with a respective battery module, among the plurality of battery modules (1110), to surveil said terminal voltage of said respective battery module, in real-time.
Each current sensing member, among the plurality of current sensing members (I), is associated with a respective battery pack, among the plurality of battery packs (1100), to surveil flow of current within (or across) said respective battery pack, in real-time.
Method of envisaging the SOH shall now be explained.
Voltage plateaus (6000; for example, V-Q curves), in a beginning of life (BOL) and aged battery module, during charging, are illustrated, in Figure 3a. Incremental capacity (IC) curves (7000; also referred to as BOL IC curve and aged IC curve) are illustrated, in Figure 3b.
Features of interests (FOI), for envisaging the SOH, are determined, from the IC curves (7000), using charging fragments, such as FOIs based on: changes in dQ⁄dV; and changes in voltages, during a time period.
Changes in dQ⁄dV are determined, based on peaks of the IC curves (7000), and intersection of the IC curves (7000). This significantly reduces the requirement of a separate technique (for example, an algorithm), for accurate extractions of IC curve peaks.
The FOIs (〖FOI〗_1 (M,n) and 〖FOI〗_2 (M,n)), for said each battery module, and said each battery pack, are determined, based on the peaks.
〖FOI〗_1 (M,n) is evaluated, from differences, in dQ⁄dV values, between P1 and P2, while 〖FOI〗_2 (M,n) refers to differences, between: area of the BOL IC curve, from P1 to P2; and area of the aged IC curve, from P1 to P2, as illustrated, in Figure 3b.
P1 is the point, corresponding to the BOL IC curve’s peak, and P2 is the point, on the aged IC curve, derived from the first point of intersection, of the aged IC curve, with the BOL IC curve, after the peak position of the BOL IC curve. The values of P1 and P2 may be different, for said each battery module.
Steps involved in envisaging the SOH with 〖FOI〗_1 (M,n) and 〖FOI〗_2 (M,n) are illustrated, in Figure 4 and Figure 5, and are as follows.
At S101, a moving average filter, with a fixed window size, is chosen, to remove field data noise (related to voltage and current signals), and obtain filtered field data. Further, this helps to remove abnormalities, from the field data, to get smoothened voltage and current signals, of said each module.
At S102, based on past charging profiles, a single charging fragment, per day, with a minimum charging state of charge range, is stored, for said each module, from the filtered current and voltage signals obtained, in the previous step (S101), to obtain sorted voltage and current data.
At S103, the (dQ⁄(dV)) values (and/or the IC curves), for the beginning of life (BOL) module as well as the aged module, are evaluated (and/or constructed), as follows:
dQ/dV=ΔQ/ΔV≈(Q_k-Q_(k-1))/(V_k-V_(k-1) )≈IΔt/ΔV
Where: Q is acquired, by ampere-hour integration of current, within the sorted charge fragment of said each module; k is discrete time instant; ΔQ is charged capacity, in a time Δt; and ΔV is increment of terminal voltage, in the time Δt.
Selection of correct ΔV is crucial, in the derivation of the (dQ⁄(dV)) values (and/or the IC curves). With a high value of ΔV , the (dQ⁄(dV)) values (and/or the IC curves) could be smoothened and flattened, leading to false identification of the peaks. In order to preserve adequate information, the value of ΔV is varied, between about 0.1 V and about 0.5 V, based on the number of cells that are connected (within a module).
At S104, the derived IC (dQ⁄(dV)) values (and/or the derived IC curves) of said each module are further smoothened, with said moving average filter, so that available noise is further removed, to obtain smoothened values (and/or smoothened curves).
Based on the smoothened dQ⁄dV values (and/or smoothened curves), for the BOL and aged battery modules, for “N” number of charge cycles, the values of points P1 and P2 are identified, for evaluation of 〖FOI〗_1 and 〖FOI〗_2, for “M” number of battery modules, available in the “n” number of battery packs, at S105, as follows:
[[■(〖FOI〗_1 (1,1),〖FOI〗_2 (1,1) &⋯&〖FOI〗_1 (M,1),〖FOI〗_2 (M,1) @⋮&⋱&⋮@〖FOI〗_1 (1,n),〖FOI〗_2 (1,n) &⋯&〖FOI〗_1 (M,n),〖FOI〗_2 (M,n) )]]
To choose the best suitable FOI (for 〖FOI〗_1 and 〖FOI〗_2), for development of linear regression model (LRM), using least squares, Pearson correlation coefficients (R) are evaluated, for the FOIs, at S106, as follows:
R_(〖FOI〗_1 (M,n))=(E[(〖FOI〗_1 (M,n)-mean〖(FOI〗_1 (M,n)).(SOH(M,n)-mean(SOH(M,n))])/(mean〖(FOI〗_1 (M,n))×mean(SOH(M,n)) )
R_(〖FOI〗_2 (M,n))=(E[(〖FOI〗_2 (M,n)-mean(〖FOI〗_2 (M,n))(SOH(M,n)-mean(SOH(M,n))])/(mean(〖FOI〗_2 (M,n)×mean(SOH(M,n)) )
E is expectation of all past sample values. FOIs with highest R values are chosen.
With the selected FOIs as training input, the LRM model is determined, at S108, as follows:
Y=β_1+β_2 X+u
where: β_1 is intercept of Y; β_2 refers to slope; X is the independent variable; and Y is the dependent variable.
The LRM works on the principle of minimum sum of squared residuals. For example, a sample regression model is defined as:
Y_i=β_1+β_2 X_i+e_i
Therefore:
e_i=Y_i-β ̂_1-β ̂_2 X_i
Residual Sum of Squares:
Q=∑_(i=1)^n▒〖e_i〗^2 =∑_(i=1)^n▒〖(Y_i-β ̂_1-β ̂_2 X_i)〗^2
Upon Solving:
β ̂_2=(n∑▒〖X_i Y_i-∑▒〖X_i ∑▒Y_i 〗〗)/(n∑▒〖X_i〗^2 -〖(∑▒X_i )〗^2 )
β ̂_1=(∑▒〖〖X_i〗^2 ∑▒Y_i -∑▒〖X_i ∑▒〖X_i Y_i 〗〗〗)/(n∑▒〖X_i〗^2 -〖(∑▒X_i )〗^2 )
To get final β_1 and β_2, for the LRM model, it is considered that e_i is the random error that follows a Gaussian distribution. Therefore, samples with an error of more than about 5% are considered as outliers, and removed (from the training data), to get the final LRM model parameters.
The “M” number of battery modules, connected within “n” number of battery packs, may have different values of β_1 and β_2, for the LRM model, such as:
[[■(β_1 (1,1),β_2 (1,1) &⋯&β_1 (M,1),β_2 (M,1)@⋮&⋱&⋮@β_1 (1,n),β_2 (1,n)&⋯&β_1 (M,n),β_2 (M,n))]]
Envisaged SOH of said each module, for “N+x” number of cycles, is evaluated at S109, as follows:
[[■(SOH(1,1)&⋯&SOH(M,1)@⋮&⋱&⋮@SOH(1,n)&⋯&SOH(M,n))]]
At S110, based on the envisaged SOH of said each battery module, the battery pack level SOH, for “N+x” number of cycles, is determined, as follows:
[■(SOH_(〖Pack〗_1 )@SOH_(〖Pack〗_2 )@■(⋮@SOH_(〖Pack〗_n ) ))]=[■(Min⁡[SOH(1,1),SOH(1,1)……SOH(M,1)]@Min⁡[SOH(1,2),SOH(1,2)……SOH(M,3)]@■(⋮@Min⁡[SOH(1,n),SOH(1,n)……SOH(M,n)]))]
Lowest value of the envisaged SOH, within the plurality of battery modules (1110), available within a battery pack, represents the SOH of the respective battery pack.
At S110, the system level SOH, for "N+x" number of cycles, is evaluated, as follows:
SOH_(B_System )=Min[■(SOH_(〖Pack〗_1 )@SOH_(〖Pack〗_2 )@■(⋮@SOH_(〖Pack〗_n ) ))]
Lowest value of the envisaged SOH of the plurality of battery packs (1100), within the energy storage system (1000), represents the envisaged SOH of the energy storage system (1000).
To improve the SOH envisaging accuracy, the training of the model may be repeated, after "T" number of charge/discharge cycles, to get a new set of β_1 and β_2 values.
For envisaging the SOH, with the offline envisaging member (3200), the determined values of β_1 and β_2 are embedded within (or into) the controlling member.
A person skilled in the art will appreciate the fact that the configurations of the system and its various components may be varied, based on requirements.
The disclosed system (and/or method) offers at least the following advantages: accurately envisages the SOH, under real-time dynamic load conditions; accurately envisages the SOH, using the (dQ⁄dV) curves, at low computational costs; determines the FOIs, from the dQ⁄dV curves; does not rely on a separate technique, for accurate peak determination; envisaging of SOH is possible, under both online and offline modes; involves a minimum number of FOIs; and is configured to envisage the SOH, at various levels.
Implementation of the disclosure can involve performing or completing selected tasks manually, automatically, or a combination thereof. Further, according to actual instrumentation of the disclosure, several selected tasks could be implemented, by hardware, by software, by firmware, or by a combination thereof, using an operating system.
For example, as software, selected tasks according to the disclosure could be implemented, as a plurality of software instructions being executed, by a computing device, using any suitable operating system.
In yet another embodiment of the disclosure, one or more tasks, according to embodiments of the disclosure, is (or are) performed, by a data processor, such as a computing platform, for executing a plurality of instructions. Further, the data processor includes a processor, and/or non-transitory computer-readable medium, for storing instructions and/or data, and/or a non-volatile storage, for storing instructions and/or data. A network connection, a display, and/or a user input device, such as a keyboard (or mouse), are also provided.
It will be apparent to a person skilled in the art that the above description is for illustrative purposes only and should not be considered as limiting. Various modifications, additions, alterations, and improvements, without deviating from the spirit and the scope of the disclosure, may be made, by a person skilled in the art. Such modifications, additions, alterations, and improvements should be construed as being within the scope of this disclosure.  
LIST OF REFERENCE NUMERALS
1000 - Energy Storage System
1100 - Plurality of Battery Packs
1110 - Plurality of Battery Modules
2000 - At Least One ESS Surveilling Member
V or 2100 - Plurality of Voltage Sensing Members
V-1, V-2, V-3, … V-M - Individual Voltage Sensing Members
I or 2200 - Plurality of Current Sensing Members
3000 - At Least One Envisaging Member
3100 - Online Envisaging Member
3200 - Offline Envisaging Member
4000 - At Least One Unveiling Member
6000 - Voltage Plateaus
7000 - IC Curves , Claims:1. A system, for shadowing state of health, of an energy storage system, of a vehicle, under real-time dynamic load conditions, said system comprising:
an at least an ESS surveilling member (2000) that is communicatively associated with an energy storage system (1000) of a vehicle, said at least one ESS surveilling member (2000) being configured to surveil an at least a parameter, of said energy storage system (1000), in real-time, with:
said energy storage system (1000) comprising a plurality of battery packs (1100), with: each battery pack, among said plurality of battery packs (1100), being connected with other battery packs, among said plurality of battery packs (1100), in parallel, to fulfil ampere-hour requirements of said vehicle’s load;
said each battery pack comprising a plurality of battery modules (1110), with: each battery module, among said plurality of battery modules (1110), being connected with other battery modules, among said plurality of battery modules (1110), in series, to meet voltage requirements of said vehicle;
said surveilled at least one parameter being transmitted, to an at least an envisaging member (3000); and
said at least one ESS surveilling member (2000) comprising: a plurality of voltage sensing members (V or 2100); and a plurality of current sensing members (I or 2200);
said at least one envisaging member (3000) that is configured to envisage state of health, of said energy storage system (1000), with:
said state of health being determined, based on: changes, in dQ⁄dV; and voltage changes, during a time period, during charging, said changes in dQ⁄dV and voltage changes being determined, based on the peaks of a beginning of life incremental capacity curve and an aged incremental capacity curve, respectively;
a controlling member that is configured to track, monitor, and control operations of said system; and
an at least an unveiling member (4000) that is configured to display envisaged state of health, to a user.
2. The system, for shadowing state of health, of an energy storage system, of a vehicle, under real-time dynamic load conditions, as claimed in claim 1, wherein:
said at least one parameter includes: terminal voltage of said each battery module; and current across said each battery module.
3. The system, for shadowing state of health, of an energy storage system, of a vehicle, under real-time dynamic load conditions, as claimed in claim 1, wherein: said at least one envisaging member (3000) comprises:
an online envisaging member (3100) that: resides on an external member; and is configured to envisage said state of health, with network connectivity; and
an offline envisaging member (3200) that is embedded within said controlling member, said offline envisaging member (3200) being configured to envisage said state of health, locally, in said vehicle itself, without any network connectivity requirements.
4. The system, for shadowing state of health, of an energy storage system, of a vehicle, under real-time dynamic load conditions, as claimed in claim 3, wherein: said external member is a remote server or a network cloud.
5. The system, for shadowing state of health, of an energy storage system, of a vehicle, under real-time dynamic load conditions, as claimed in claim 1 or claim 2, wherein:
each voltage sensing member (V-1, V-2, V-3, … V-M), among said plurality of voltage sensing members (V), is associated with a respective battery module, among said plurality of battery modules (1110), to surveil said terminal voltage of said respective battery module, in real-time.
6. The system, for shadowing state of health, of an energy storage system, of a vehicle, under real-time dynamic load conditions, as claimed in claim 1 or claim 2, wherein:
each current sensing member, among said plurality of current sensing members (I), is associated with a respective battery pack, among said plurality of battery packs (1100), to surveil said current across said respective battery pack, in real-time.
7. The system, for shadowing state of health, of an energy storage system, of a vehicle, under real-time dynamic load conditions, as claimed in claim 1, wherein: said peaks are different, for said each battery module.