Description:TITLE OF THE INVENTION: A SYSTEM AND METHOD FOR SHADOWING STATE OF X OF AN ENERGY STORAGE SYSTEM WITH LIMITED ONBOARD ENERGY
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 X, of energy storage systems. More particularly, the present disclosure is related to: a system and method, for shadowing of state of X, of a vehicle’s energy storage system with limited onboard energy, under real-time dynamic load conditions, to enhance overall life of the energy storage system.
BACKGROUND OF THE INVENTION
Energy storage systems (ESS) 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, to operate said energy storage systems, within safe operating limits, it is mandatory to accurately estimate various states, such as: state of charge (SOC); state of health (SOH); state of Energy (SOE); and state of function (SOF).
The SOC helps to protect the ESS, from overcharging and deep discharging, and controls charge rate and/or discharge rate. The SOE is a critical measure, for energy optimisation and management of the ESS, and for driving range prediction. The SOF helps to calculate continuous and instantaneous load capabilities of the ESS, while the SOH provides aging levels of the ESS. All the ESS states are highly correlated, with each other; a change, in one state, can significantly vary other states of the ESS. Therefore, it is required to envisage all the above states simultaneously, to improve overall performance of the ESS.
Techniques and methods, for estimating some of an ESS’s states, are known in the art, and have been disclosed in the following patent literature: CN105301509A; EP2963434B1; US20140350877A1; and CN106054080A.
However, the aforementioned disclosures are only capable of envisaging the SOC, SOH, SOE, and SOP, at a cell or module level. When envisaging the SOC, SOH, SOE, and SOF of a vehicle’s ESS, which uses a large number of connected cells in series and/or parallel configuration, it is necessary to take into account impact of inconsistencies, between the connected cells, modules, and/or packs.
Further, in the aforementioned disclosures, correlations that exist, between (or across) the different ESS states, over the course of an ESS's life, are ignored. In such a scenario, it is difficult to obtain effective vehicle performance, in real-time applications.
Furthermore, accuracy is one of the key concerns, in envisaging the ESS states, in real-time conditions, in environments that include large numbers of battery cells/modules/packs. Generally, it is required to simultaneously estimate module-level, pack-level, and ESS-level SOC, SOH, SOE, and SOF, at high accuracies.
There is, therefore, a need in the art, for: a system and method, for shadowing of state of X, of a vehicle’s energy storage system with limited onboard energy, under real-time dynamic load conditions, to enhance overall life and utilization of the energy storage system, which overcomes the aforementioned drawbacks and shortcomings.
SUMMARY OF THE INVENTION
A system, for shadowing of state of X, of a vehicle’s energy storage system with limited onboard energy, under real-time dynamic load conditions, is disclosed. Said system broadly comprises: an at least an ESS surveilling member; an at least 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. Said at least one ESS surveilling member is configured to surveil an at least a parameter, of said energy storage system, in real-time.
In an embodiment, said at least one parameter surveilled, by said at least one ESS surveilling member, includes, but is not limited to: terminal voltage of each battery module; voltage of each pack; voltage of cells that are connected in series, in said each battery module; voltage of said energy storage system; current across said each battery module; current across said energy storage system; temperature of said each battery module; temperature of said each pack; temperature of said energy storage system, and/or the like.
Said at least one parameter surveilled, by said at least one ESS surveilling member, is transmitted, to said at least one controlling member.
Said at least one ESS surveilling member broadly comprises: an at least a voltage sensing member; an at least a current sensing member; and an at least a temperature sensing member.
Said at least one controlling member is configured to track, monitor, and control operations of said system. Said at least one controlling member is embedded, with a SOX shadowing member. Said SOX shadowing member is configured to shadow said energy storage system’s state of X.
In an embodiment, said state of X is shadowed, after envisaging said energy storage system’s model parameters, in real-time.
In an embodiment, with a second-order Resistance and Capacitance (RC) equivalent ESS model, each cell’s behaviour is simulated, to envisage said energy storage system’s model parameters. Said energy storage system’s model parameters are envisaged, with adaptive filter techniques.
In an embodiment, said energy storage system’s model parameters include, but are not limited to: internal ohmic resistance; electrochemical polarisation resistance and capacitance; electrochemical diffusion resistance and capacitance; and/or the like.
Said SOX shadowing member broadly comprises: a state of charge (SOC) envisaging member; a state of health (SOH) envisaging member; a state of energy (SOE) envisaging member; and a state of function (SOF) envisaging member.
Said SOC envisaging member is configured to envisage said energy storage system’s SOC, based on available ampere-hour counts (in Ah), related to said each pack (and/or said each module and/or each cell).
Said SOH envisaging member is configured to envisage said energy storage system’s blended SOH, based on SOH capacity fade and SOH power fade.
Said SOE envisaging member is configured to envisage said energy storage system’s SOE, based on available energy (in kWh), related to said energy storage system.
Said SOF envisaging member is configured to envisage said energy storage system’s SOF, based on real-time power capabilities (in kW).
In an embodiment, said SOX shadowing member is configured to shadow said SOX, locally, in a vehicle itself, without any network connectivity requirements.
Said shadowed SOX is transmitted, to said at least one unveiling member, for display, to a user, and to a vehicle control member.
Method of shadowing said SOX is also disclosed.
The disclosed system (and/or method) offers at least the following advantages: accurately shadows said state of X, under real-time dynamic load conditions; accurately shadows said state of X, at low computational costs; accurately envisages state of charge, through adaptive filter techniques; enhances overall life of an energy storage system; and/or keeps said energy storage system, within safe operating limits.
BRIEF DESCRIPTION OF THE DRAWINGS
Figure 1 illustrates a system, for shadowing of state of X, of a vehicle’s energy storage system with limited onboard energy, under real-time dynamic load conditions, in accordance with an embodiment of the present disclosure;
Figure 2 illustrates a battery pack, with “M” number of modules, connected in series, with each module comprising: “Np” number of cells, connected in parallel; and “Ns” number of cells, connected in series, said battery pack being part of a system, for shadowing of state of X, of a vehicle’s energy storage system with limited onboard energy, under real-time dynamic load conditions, in accordance with an embodiment of the present disclosure;
Figure 3 illustrates a process flow, during operation of a system, for shadowing of state of X, of a vehicle’s energy storage system with limited onboard energy, under real-time dynamic load conditions, in accordance with an embodiment of the present disclosure;
Figure 4 illustrates a second-order RC (Resistance and Capacitance) equivalent ESS, for simulating a vehicle’s ESS, during operation of a system, for shadowing of state of X, of a vehicle’s energy storage system with limited onboard energy, under real-time dynamic load conditions, in accordance with an embodiment of the present disclosure;
Figure 5 is a flow chart that illustrates an adaptive filter-based technique, for envisaging SOC, during operation of a system, for shadowing of state of X, of a vehicle’s energy storage system with limited onboard energy, under real-time dynamic load conditions, in accordance with an embodiment of the present disclosure;
Figure 6 is a flow chart that illustrates envisaging of blended SOH, during operation of a system, for shadowing of state of X, of a vehicle’s energy storage system with limited onboard energy, under real-time dynamic load conditions, in accordance with an embodiment of the present disclosure; and
Figure 7 is a flow chart that illustrates envisaging of SOE, during operation of a system, for shadowing of state of X, of a vehicle’s energy storage system with limited onboard energy, under real-time dynamic load conditions, 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, where applicable, the use of the phrase “at least one” is to be construed in association with a succeeding component name.
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 “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 be construed as being inclusive of: “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 phrase “state of X”, the acronym “SOX”, and their variations, is to be construed as: “combined information of different battery states, such as SOC, SOH, SOE and SOF, to: control a vehicle control member; and/or optimise performance of an ESS”.
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, for shadowing of state of X, of a vehicle’s energy storage system with limited onboard energy, under real-time dynamic load conditions”.
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; estimate; 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 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 of state of X, of a vehicle’s energy storage system with limited onboard energy, under real-time dynamic load conditions (also referred to as “system”), is disclosed. As illustrated, in Figure 1, said system broadly comprises: an at least an ESS surveilling member (200); an at least a controlling member (300; for example, a microcontroller); and an at least an unveiling member (500; for example, a display).
Said at least one ESS surveilling member (200) is communicatively associated with an energy storage system (100) of a vehicle. The at least one ESS surveilling member (200) is configured to surveil an at least a parameter (of the energy storage system (100)), in real-time. The at least one parameter surveilled, by the at least one ESS surveilling member (200), is transmitted, to the at least one controlling member (300).
In another embodiment of the present disclosure, as illustrated, in Figure 2, the energy storage system (100) broadly comprises “N” number of packs that are connected in parallel (110; also referred to as “parallelly-connected packs”). Each pack, among the “N” number of packs, broadly comprises “M” number of modules that are connected in series (also referred to as “series-connected modules”). Each module, among the “M” number of modules, broadly comprises: Np + Ns number of cells (111), with: the Np number of cells being connected in parallel; and the Ns number of cells being connected in series.
In yet another embodiment of the present disclosure, the at least one ESS surveilling member (200) broadly comprises: an at least a voltage sensing member (210); an at least a current sensing member (220); and an at least a temperature sensing member (230).
In yet another embodiment of the present disclosure, the at least one voltage sensing member (210) is connected in parallel to the cells that are connected in series within a module.
In yet another embodiment of the present disclosure, the at least one current sensing member (220) is connected in series with the modules within a parallelly-connected packs.
In yet another embodiment of the present disclosure, the at least one temperature sensing member (230) is connected on the surface of the cells within a module.
In yet another embodiment of the present disclosure, the at least one parameter surveilled, by the at least one ESS surveilling member (200), includes, but is not limited to: terminal voltage of said each module; voltage of said each pack; voltage of said cells, connected in series (also referred to as “series-connected cells”); voltage of the ESS (100); current across said each module; current across the ESS (100); temperature of said each module; temperature of said each pack; temperature of the ESS (100); and/or the like.
In yet another embodiment of the present disclosure, as illustrated, in Figure 3, based on the real-time surveilling of said each module’s voltage (or voltages), current, and temperature (or temperatures), a state of X (SOX) of the ESS (100) is shadowed, after envisaging: a state of charge (SOC); a state of health (SOH); a state of energy (SOE), and a state of function (SOF), of the ESS (100).
In order to identify a representative module, in a pack, among the “N” number of packs (110), differences between maximum and minimum voltages of said series-connected cells, in said each module, are assessed. When shadowing the SOX, a sample module, among the “M” number of modules, is chosen, based on the highest difference, between the maximum and minimum cell voltages, across all modules, within a specific pack.
The shadowing of the SOX combines the envisaging of: the state of charge (SOC); the state of health (SOH); the state of energy (SOE); and the state of function (SOF), of the ESS (100). For example, the SOC, the SOH, the SOE, and the SOF may be envisaged, at various levels, including, but not limited to: module-level; ESS-level; pack-level; system-level; and/or the like.
All SOC values envisaged may be collectively referred to as SOCs. Likewise, all SOH values envisaged may be collectively referred to as SOHs. Along similar lines, all SOE values envisaged may be collectively referred to as SOEs. Further, all SOF values envisaged may be collectively referred to as SOFs.
Adaptive filter techniques (or adaptive filter technique) are (or is) employed, to envisage ESS model parameters (or said energy storage system’s model parameters), with the voltage, current, and temperature values, of the representative module (within a pack surveilled), in real-time. A person skilled in the art will appreciate the fact that the adaptive filter technique may be any suitable type known in the art, for example, Kalman filter, particle filter, H-infinity, and/or the like.
Along with the ESS model parameters, a max SOC and a min SOC of the representative module are envisaged, in real-time, using the adaptive filter techniques, for envisaging a pack SOC and a system SOC.
The envisaging of a respective representative module, a pack SOH, and a system SOH, are performed, in real-time, based on the envisaged SOCs. Further, power fade and capacity fade are incorporated, in the envisaging of the SOHs.
For the envisaging of the SOEs, along with the voltage, current and temperature, the envisaged SOCs and the envisaged SOHs are also included. Real-time envisaging of different components of energy, including maximum remaining energy, maximum charge energy, and maximum available energy, is performed.
For the envisaging of the SOFs, fully charged system maximum power, load power demand, and instantaneous power, envisaged in real-time, are included.
The at least one controlling member (300) 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 (200); the at least one unveiling member (500); and a vehicle control member (400).
In yet another embodiment of the present disclosure, the at least controlling member (300) is embedded, with a SOX shadowing member (310). Said SOX shadowing member is configured to shadow the ESS’s SOX, with said shadowed SOX being transmitted, to: the at least one unveiling member (500), for subsequent display, to a user; and the vehicle control member (400).
In yet another embodiment of the present disclosure, the SOX shadowing member (310) broadly comprises: a SOC envisaging member (311); a SOH envisaging member (312); a SOE envisaging member (313); and a SOF envisaging member (314).
In yet another embodiment of the present disclosure, the SOX shadowing member (310) is configured to shadow the ESS’s SOX locally, in the vehicle itself, without any network connectivity requirements.
In yet another embodiment of the present disclosure, the SOC envisaging member (311) is configured to envisage the ESS’s SOC, based on available ampere-hour counts (in Ah), related to said each pack (and/or said each module and/or each cell, among the Np + Ns number of cells (111)).
In yet another embodiment of the present disclosure, the SOH envisaging member (312) is configured to envisage the ESS’s SOH, based on said capacity fade and said power fade.
In yet another embodiment of the present disclosure, the SOE envisaging member (313) is configured to envisage the ESS’s SOE, based on available energy (in kWh) that is related to the ESS (100).
In yet another embodiment of the present disclosure, the SOF envisaging member (314) is configured to envisage the ESS’s SOF, based on real-time power capabilities (in kW), depending on the ESS’s SOC, the ESS’s SOH, and load conditions.
To achieve high SOX shadowing accuracy, it is crucial to accurately envisage the second-order RC (Resistance and Capacitance) equivalent ESS model parameters (also referred to as “model parameters”). Due to time variant and dynamic nature of the model parameters, under different operating conditions (like temperature, C-rate, battery aging, and/or the like), it is required to envisage the ESS model parameters, in real-time, with the adaptive filter techniques.
In yet another embodiment of the present disclosure, the ESS model parameters include, but are not limited to: internal ohmic resistance; electrochemical polarisation resistance and capacitance; electrochemical diffusion resistance and capacitance; and/or the like.
Figure 4 illustrates a second-order RC equivalent ESS model (111A), for simulating behaviour of said each cell. With said second-order RC equivalent ESS model (111A), the ESS model parameters are envisaged.
Said second-order RC equivalent ESS model (111A) broadly comprises: an internal ohmic resistance (R0); a pair of electrochemical polarisation/diffusion resistances (R1 and R2), connected in parallel; a pair of polarisation/diffusion capacitances (C1 and C2), connected in parallel, to form a resistance-capacitor branch; and a SOC controller voltage source (OCV) function.
The envisaging of the ESS’s SOC is illustrated, in Figure 5. The first step is to identify the representative module, based on voltage differences (∆V), between a max cell voltage (V_max) and a min cell voltage (V_min) of parallel branches that are connected in series, within series-connected modules. Then, at step S101, the second-order RC equivalent ESS model parameters (e.g., R0(t), R1(t), C1(t), R2(t), and C2(t)), along with a max capacity (Q_(mod_max) (t)) and a min capacity (Q_(mod_min) (t)) of the representative module, are identified, with adaptive filter techniques.
At step S102, with the identified model parameters, the max envisaged SOC value of the representative module (〖Mod〗_(i,j) ), at time instant “t” (〖SOC〗_(max,〖Mod〗_(i,j) ) (t)), and the min estimated SOC value of the representative module (〖Mod〗_(i,j) ), at the time instant “t”, are estimated, with adaptive filter techniques. 〖Mod〗_(i,j) denotes a j^th module, in an i^th pack, in the ESS (100).
At step S103, max and min SOC, of a pack’s modules, are envisaged, as follows:
〖SOC〗_(mod_max,i) (t)=〖SOC〗_(max,〖Mod〗_(i,j) ) (t)); j=1,2,3,….M and i=1,2,3,….N
〖SOC〗_(mod_min,i) (t)=〖SOC〗_(min,〖Mod〗_(i,j) ) (t))); j=1,2,3,….M and i=1,2,3,….N
〖SOC〗_(mod_max,i) (t) denotes max SOC value (of series-connected modules, within the i^th pack, at the time instant “t”) and 〖SOC〗_(mod_min,i) (t) denotes min SOC value (of the series-connected modules, within the i^th pack, at the time instant “t”). M denotes the number of series-connected modules, within a pack.
At step S104, said each pack’s SOC is envisaged, as follows:
〖SOC〗_(〖pack〗_i ) (t)={■(〖SOC〗_(mod_max) (t)&:〖SOC〗_(〖pack〗_i ) (t) ≥80 %@(〖SOC〗_(mod_max) (t)+〖SOC〗_(mod_min) (t) )/2&: 〖80 % >SOC〗_(〖pack〗_i ) (t) ≥20 %@〖SOC〗_(mod_min) (t)&: 〖0% >SOC〗_(〖pack〗_i ) (t) >20 %)┤ ; i=1,2,…,N
At step S105, the ESS’s SOC is determined, as follows:
〖SOC〗_system (t)=1/N ∑_(i=1)^N▒〖〖SOC〗_(〖pack〗_i ) (t)〗
N denotes the number of packs, connected in parallel, within the ESS (100).
To maintain the SOC envisaging accuracy, with battery aging, the envisaged Q_(〖Mod〗_(i,j) ) (t) value of the j^th module, in the i^th pack, at the time instant “t”, is considered, to update an actual capacity value, in the SOC envisaging member (311), after every 10th full equivalent cycle, as follows:
Q_(〖Mod〗_(i,j) ) (t)=(Q_(max,〖Mod〗_(i,j) ) (t) +Q_(min,〖Mod〗_(i,j) ) (t) )/2
Q_(max,〖Mod〗_(i,j) ) (t) and Q_(max,〖Mod〗_(i,j) ) (t) are the max and min identified Q of the representative module (〖Mod〗_(i,j)).
The envisaging of the ESS’s SOH is illustrated, in Figure 6. At step S201, a battery SOH power fade (SOH_(P_〖Mod〗_(i,j) )) and SOH capacity fade (SOH_(C_〖Mod〗_(i,j) )), for the j^th module, in the i^th pack, at the time instant “t” are envisaged, as follows:
SOH_(P_〖Mod〗_(i,j) ) (t)=(R_(〖Mod〗_(i,j) ) (t)-R_(〖Mod〗_(i,j) ) (BOL))/(R_(〖Mod〗_(i,j) ) (EOL)-R_(〖Mod〗_(i,j) ) (BOL))×100 %
SOH_(C_〖Mod〗_(i,j) ) (t)=(Q_(〖Mod〗_j ) (t)-Q_(〖Mod〗_j ) (BOL))/(Q_(〖Mod〗_j ) (EOL)-Q_(〖Mod〗_j ) (BOL))×100 %
R_(〖Mod〗_(i,j) ) (EOL) denotes end of life ohmic resistance and R_(〖Mod〗_(i,j) ) (BOL) denotes beginning of life ohmic resistance. Generally, R_(〖Mod〗_(i,j) ) (EOL) is equal to about 200 % of the R_(〖Mod〗_(i,j) ) (BOL). Q_(〖Mod〗_(i,j) ) (EOL) denotes actual capacity, at the end of life, and Q_(〖Mod〗_(i,j) ) (BOL) denotes beginning of life actual capacity. Generally, Q_(〖Mod〗_(i,j) ) (EOL) is equal to about 80 % of the Q_(〖Mod〗_(i,j) ) (BOL).
At step S202, the SOH, for the j^th module, in the i^th pack, at the time instant “t”, is envisaged, as follows:
SOH_(B_〖Mod〗_(i,j) ) (t)=α ×SOH_(P__〖Mod〗_(i,j) ) (t)-(1-α)×SOH_(C__〖Mod〗_(i,j) ) (t)
α is degree of blending that ranges between about 0.3 and about 0.7, based on load profile.
As said each pack comprises of said “M” number of series-connected modules, the SOH, of said each pack is evaluated, at step S203, as follows:
SOH_(B_Pack ) (t)=SOH_(B_〖Mod〗_(i,j) ) (t) ; j=1,2,3,….M and i=1,2,3,….N
At step S204, based on the SOH_(B_Pack ) (t), a system-blended SOH, with said “N” number of packs, connected in parallel, is envisaged, as follows:
SOH_(B_System ) (t)=Min(SOH_(B_(〖Pack〗_1 ) ) (t),SOH_(B_(〖Pack〗_2 ) ) (t),…,SOH_(B_(〖Pack〗_i ) ) (t)) ; i=1,2,3,….N
Envisaging of the ESS’s SOE is illustrated, in Figure 7. At step S301, with the estimated Q_(max,〖Mod〗_(i,j) ) (t), Q_(min,〖Mod〗_(i,j) ) (t), and SOH_(B_〖Mod〗_(i,j) ) (t), the maximum remaining energy (〖Energy 〗_(rem〖_max〗_(〖Mod〗_(i,j) ) ) (t)), the maximum charge energy (〖Energy 〗_(chr〖_max〗_(〖Mod〗_(i,j) ) ) (t)), and the maximum available energy (〖Energy 〗_(av〖l_max〗_(〖Mod〗_(i,j) ) ) (t)), of the battery module “j”, are envisaged, as follows:
〖Energy 〗_(rem〖_max〗_(〖Mod〗_(i,j) ) ) (t)=∫_(SOC_lower)^(〖SOC〗_(max,〖Mod〗_(i,j) ) (t))▒〖Q_(max,〖Mod〗_(i,j) ) (t)×SOH_(B_〖Mod〗_(i,j) ) (t)×Uocv(SOC)dSOC〗
〖Energy 〗_(chr〖_max〗_(〖Mod〗_(i,j) ) ) (t)=∫_(〖SOC〗_(max,〖Mod〗_(i,j) ) (t))^(SOC_upper)▒〖Q_(max,〖Mod〗_(i,j) ) (t)×SOH_(B_〖Mod〗_(i,j) ) (t)×Uocv(SOC)dSOC〗
〖Energy 〗_(av〖l_max〗_(〖Mod〗_(i,j) ) ) (t)=〖Energy 〗_(rem〖_max〗_(〖Mod〗_(i,j) ) ) (t)+〖Energy 〗_(chr〖_max〗_(〖Mod〗_(i,j) ) ) (t)
〖SOE〗_(max_(〖Mod〗_(i,j) ) ) (t)=(〖Energy 〗_(rem_max_(〖Mod〗_(i,j) ) ) (t))/(〖Energy〗_(avl_max_(〖Mod〗_(i,j) ) ) (t))×100 %
Similarly, a minimum remaining energy (〖Energy 〗_(rem_(min_j ) ) (t)), a minimum charge energy (〖Energy 〗_(chr_(min_j ) ) (t)), and a minimum available energy (〖Energy 〗_(avl_(min_j ) ) (t)), are envisaged, as follows:
〖Energy 〗_(rem〖_min〗_(〖Mod〗_(i,j) ) ) (t)=∫_(SOC_lower)^(〖SOC〗_(min,〖Mod〗_(i,j) ) (t))▒〖Q_(min,〖Mod〗_(i,j) ) (t)×SOH_(B_〖Mod〗_(i,j) ) (t)×Uocv(SOC)dSOC〗
〖Energy 〗_(chr〖_min〗_(〖Mod〗_(i,j) ) ) (t)=∫_(〖SOC〗_(min,〖Mod〗_(i,j) ) (t))^(SOC_upper)▒〖Q_(min,〖Mod〗_(i,j) ) (t)×SOH_(B_〖Mod〗_(i,j) ) (t)×Uocv(SOC)dSOC〗
〖Energy 〗_(av〖l_min〗_(〖Mod〗_(i,j) ) ) (t)=〖Energy 〗_(rem〖_min〗_(〖Mod〗_(i,j) ) ) (t)+〖Energy 〗_(chr〖_min〗_(〖Mod〗_(i,j) ) ) (t)
〖SOE〗_(min_(〖Mod〗_(i,j) ) ) (t)=(〖Energy 〗_(rem_min_(〖Mod〗_(i,j) ) ) (t))/(〖Energy〗_(avl_min_(〖Mod〗_(i,j) ) ) (t))×100 %
At step S302, SOE envisaging, for the “M” number of modules, is done, by envisaging maximum and minimum SOE, as follows:
〖SOE〗_(mod_max) (t)=〖SOE〗_(max_(〖Mod〗_(i,j) ) ) (t); j=1,2,3,….M and i=1,2,3,….N
〖SOE〗_(mod_min) (t)=〖SOE〗_(min_(〖Mod〗_(i,j) ) ) (t); j=1,2,3,….M and i=1,2,3,….N
〖SOE〗_(mod_max) (t) is the maximum value of the envisaged SOE, for the “M" number of series-connected modules, within a pack, at the time instant “t”, and 〖SOE〗_(mod_min) (t) is the min value of the envisaged SOE, for the “M" number of series-connected modules, within a pack, at the time instant “t”.
At Step S303, SOE of said each pack is envisaged, as follows:
〖SOE〗_(〖pack〗_i ) (t)=(〖SOE〗_(mod_max) (t)+〖SOE〗_(mod_min) (t) )/2 ; i=1,2,…,N
At step S304, SOE of the ESS (100) is envisaged, as follows:
〖SOE〗_system (t)=1/N ∑_(m=1)^N▒〖〖SOE〗_(〖pack〗_m ) (t)〗
Finally, the SOF of the ESS (100) is envisaged, as follows:
SOF_System=(P_System (t)-P_(Demand(t)))/(P_(System_max) 〖- P〗_(Demand(t)) )
P_(System_max) is the fully charged system’s maximum power, at SOH_(B_System ), P_Demand (t) denotes the load power demand, at the time instant “t”, and P_System (t) is the instantaneous power provided, by the ESS (100), at the time instant “t”.
P_System (t) is evaluated, as follows:
P_System (t)=P_(System_max)×〖SOC〗_system (t)×SOH_(B_System )(t)
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.
With the accurate shadowing of SOX of ESS (100), the performance of the ESS (100) can be optimized in the following ways: the charging/discharging rate and operating window in terms of SOC range can be optimally adjusted and controlled; the remaining useful life of the ESS (100) can be determined; accurate envisaging of driving range can be performed; energy utilization determination and control of energy/power flow can be performed; valuable to control starting, speeding up, climbing, and the required charge power for regenerative braking and quick charge without over-discharge and overcharge, helps to optimize the power limits in accordance with the application expectations considering safety, power performance and longevity.
With accurate shadowed SOX, the vehicle control member (400) can regulate and optimize the power flow between the ESS (100) and a motor more efficiently. Further, with shadowed SOX, monitoring and controlling the other systems by vehicle control member (400), such as the regenerative braking system and the charging system can be improved.
The disclosed system (and/or method) offers at least the following advantages: accurately shadows the SOX, of the energy storage system with limited onboard energy, under real-time dynamic load conditions; accurately shadows the SOX, of the energy storage system with limited onboard energy, under real-time dynamic load conditions, at low computational costs; accurately envisages the SOC, through adaptive filter techniques; enhances overall life of the energy storage system; and/or keeps the energy storage system, within safe operating limits.
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
100 - Energy Storage System
110 - Parallelly Connected Packs
111 - Np + Ns Number of Cells
111A - Second-Order RC Equivalent ESS Model
R0 - Internal Ohmic Resistance
R1 and R2 - Pair of Electrochemical Polarisation/Diffusion Resistances
C1 and C2 - Pair of Polarisation/Diffusion Capacitances
200 - At Least One ESS Surveilling Member
210 - At Least One Voltage Sensing Member
220 - At Least One Current Sensing Member
230 - At Least One Temperature Sensing Member
300 - At Least One Controlling Member
310 - SOX Shadowing Member
311 - SOC Envisaging Member
312 - SOH Envisaging Member
313 - SOE Envisaging Member
314 - SOF Envisaging Member
400 - Vehicle Control Member
500 - At Least One Unveiling Member , Claims:1. A system, for shadowing of state of X, of a vehicle’s energy storage system with limited onboard energy, under real-time dynamic load conditions, said system comprising:
an at least an ESS surveilling member (200) that is communicatively associated with a vehicle’s energy storage system (100), said at least one ESS surveilling member (200) being configured to surveil an at least a parameter, of said energy storage system (100), in real-time, with:
said at least one parameter that is surveilled, by said at least one ESS surveilling member (200), being transmitted, to an at least a controlling member (300); and
said at least one ESS surveilling member (200) comprising: an at least a voltage sensing member (210); an at least a current sensing member (220); and an at least a temperature sensing member (230);
said at least one controlling member (300) that is configured to track, monitor, and control operations of said system, said at least one controlling member (300) being embedded, with a SOX shadowing member (310), said SOX shadowing member (310) being configured to shadow said energy storage system’s state of X, with:
said SOX shadowing member (310) being configured to shadow said energy storage system’s state of X, locally, in said vehicle, without any network connectivity requirements;
said state of X being shadowed, after envisaging said energy storage system’s model parameters, in real-time; and
said shadowed SOX being transmitted, to an at least an unveiling member (500), for display, to a user, and to a vehicle control member (400); and
said SOX shadowing member (310) that comprises:
a state of charge envisaging member (311) that is configured to envisage said energy storage system’s state of charge, based on available ampere-hour counts;
a state of health envisaging member (312) that is configured to envisage said energy storage system’s state of health, based on state of health capacity fade and state of health power fade;
a state of energy envisaging member (313) that is configured to envisage said energy storage system’s state of energy, based on available energy; and
a state of function envisaging member (314) that is configured to envisage said energy storage system’s state of function, based on real-time power capabilities.
2. The system, for shadowing of state of X, of a vehicle’s energy storage system with limited onboard energy, under real-time dynamic load conditions, as claimed in claim 1, wherein:
said at least one voltage sensing member (210) being connected in parallel to the cells that are connected in series within a module;
said at least one current sensing member (220) being connected in series with the modules within a parallelly-connected packs; and
said at least one temperature sensing member (230) being connected on the surface of the cells within a module.
3. The system, for shadowing of state of X, of a vehicle’s energy storage system with limited onboard energy, under real-time dynamic load conditions, as claimed in claim 1, wherein:
said at least one parameter surveilled, by said at least one ESS surveilling member (200), includes: terminal voltage of each battery module; voltage of each pack; voltage of cells, connected in series, in said each battery module; voltage of said energy storage system (100); current across said each battery module; current across said energy storage system (100); temperature of said each battery module; temperature of said each pack; and temperature of said energy storage system (100).
4. The system, for shadowing of state of X, of a vehicle’s energy storage system with limited onboard energy, under real-time dynamic load conditions, as claimed in claim 1, wherein:
said energy storage system’s model parameters include: internal ohmic resistance (R0); electrochemical polarisation resistance and capacitance (R1 and R2); and electrochemical diffusion resistance and capacitance (C1 and C2).
5. The system, for shadowing of state of X, of a vehicle’s energy storage system with limited onboard energy, under real-time dynamic load conditions, as claimed in claim 1 or claim 3, wherein: said energy storage system’s model parameters are envisaged, with a second-order Resistance and Capacitance equivalent ESS model (111A).
6. The system, for shadowing of state of X, of a vehicle’s energy storage system with limited onboard energy, under real-time dynamic load conditions, as claimed in claim 1, claim 3, or claim 4, wherein: said energy storage system’s model parameters are envisaged, with adaptive filter techniques.