Claims:We claim:
1. A controller (110) to charge a battery pack (108), said controller (110) configured to:
retrieve information from a Battery Management System (BMS) of said battery pack (108) comprising capacity exchanged by said battery pack (108) and change in State of Health (SOH) of said battery pack (108), characterized in that,
calculate, before charging, cumulative capacity exchanged through said battery pack (108) and compute a deviation of said cumulative capacity exchanged with a reference cumulative capacity curve (120) at an actual SOH;
monitor, during charging, characteristic parameters of said battery pack (108) and determine stress on said battery pack (108) using stress weights as a function of said charging characteristics, said stress weights indicates stress caused on said battery pack (108) due to said charging;
determine, after charging, a difference between said cumulative capacity exchanged through said battery pack (108) with said reference cumulative capacity curve (120), and
determine a charging sequence and charging capacity, said charging sequence comprising at least one of a slow charging and a fast charging, for said battery pack (108) to meet said reference cumulative capacity curve (120) by using said stress weights and said difference, said charging sequence and charging capacity are selected in a manner to ensure no changes in a minimum throughput of said battery pack (108) over its lifecycle.

2. The controller (110) as claimed in claim 1, wherein said characteristic parameters comprises a charging profile, a voltage of said battery pack (108), and a temperature of said battery pack (108).

3. The controller (110) as claimed in claim 1, configured to

calculate equivalent capacity of said battery pack (108) based on said stress weights and an actual capacity used for charging said battery pack (108), and
calculate an equivalent SOH of said battery pack (108) based on said calculated equivalent capacity.

4. The controller (110) as claimed in claim 3, configured to
correct said stress weights using said difference in said equivalent SOH and said actual SOH, and
estimate charging capacity to be slow charged and fast charged for a next charging cycle of said battery pack (108).

5. The controller (110) as claimed in claim 1 is part of at least one of a charging station (104), a swapping station, a cloud server (102) and a vehicle (106).

6. A method for charging a battery pack (108), said method comprising the steps of:
retrieving information from a Battery Management System (BMS) of said battery pack (108) comprising capacity exchanged by said battery pack (108) and change in State of Health (SOH) of said battery pack (108), characterized by
calculating, before charging, cumulative capacity exchanged through said battery pack (108) and computing a deviation of said cumulative capacity exchanged with a reference cumulative capacity curve (120) at an actual SOH;
monitoring characteristic parameters of said battery pack (108) during charging and determining stress on said battery pack (108) using stress weights as a function of said charging characteristics, said stress weights indicates stress caused on said battery pack (108) due to charging of said battery pack (108);
determining, after charging, a difference between said cumulative capacity exchanged through said battery pack (108) with said reference cumulative capacity curve (120);
determining a charging sequence and charging capacity, said charging sequence comprising at least one of a slow charging and a fast charging, for said battery pack (108) to meet said reference cumulative capacity curve (120) by using said stress weights and said difference, said charging sequence and charging capacity are selected in a manner to ensure no changes in a minimum throughput of said battery pack (108) throughout its lifecycle.

7. The method as claimed in claim 6, wherein said characteristic parameters comprises a charging profile, a voltage of said battery pack (108), and a temperature of said battery pack (108).

8. The method as claimed in claim 6 comprises calculating equivalent capacity of said battery pack (108) based on said stress weights and an actual capacity used for charging said battery pack (108), followed by calculating an equivalent SOH of said battery pack (108) based on said equivalent capacity.

9. The method as claimed in claim 8 comprises revising said stress weights values using said difference in said equivalent SOH and said actual SOH, and estimating charging capacity to be slow charged and fast charged for a next charging cycle.

10. The method as claimed in claim 6 is executed by a controller (110) located in at least one of a charging station (104), a swapping station, a cloud server (102) and a vehicle (106).
, Description:Complete Specification:
The following specification describes and ascertains the nature of this invention and the manner in which it is to be performed:

Field of the invention:
[0001] The present invention relates to a controller and a method for charging a battery pack.

Background of the invention:
[0002] The existing battery ecosystem (swappable or plug-in or removable or combination) uses either one or multiple fixed charging profiles to charge the batteries in charging station in order to meet the battery demand. Each of the charging profile affects the batteries differently based on various factors like charge rate, temperature, present health of the battery and operating parameters. Due to these fixed charge profiles, batteries are stressed more than required which in turn leads to reduced energy throughput in the field before it reaches its end of life.

[0003] A patent literature TWM533218 discloses an equipment for determining the health status of a portable li-ion secondary battery pack on battery swap kiosk. A intelligent type charging module with "active type lithium ion two times battery charging health degrees estimates" technology of device, which including an exchange type make charging station, plural an intelligent type charging control module, complex number a can control type power supply device, complex number a within built battery module management system of lithium ion two times battery, complex number a lithium ion two times battery charging health degrees estimates implementation module, battery in charging during fast volume measuring and estimates battery of health degrees (SOH), auxiliary to corresponding of charging mode to reached protection battery, Avoid damage by improper charging battery aging.

Brief description of the accompanying drawings:
[0004] An embodiment of the disclosure is described with reference to the following accompanying drawing,
[0005] Fig. 1 illustrates a system block diagram of a controller to charge a battery pack of a vehicle, according to an embodiment of the present invention, and
[0006] Fig. 2 illustrates a method for charging the battery pack of the vehicle, according to the present invention.

Detailed description of the embodiments:
[0007] Fig. 1 illustrates a system block diagram of a controller to charge a battery pack of a vehicle, according to an embodiment of the present invention. The system 100 comprises a cloud server 102, a charging station 104 with the controller 110 and a vehicle 106 with at least one battery pack 108. The controller 110 configured to retrieve information from a Battery Management System (BMS) of the battery pack 108 comprising capacity exchanged by the battery pack 108 and change in State of Health (SOH) of the battery pack 108. The other information and process related to the BMS such as identification, authentication, voltage, and current measurement, etc. are also dealt with as per the requirement, but are not explained here for simplicity and for being state of the art. Further, the capacity corresponds to energy in Ampere Hour (Ah). The controller 110, characterized in that, further configured to calculate, before charging through the charging station 104, a cumulative capacity exchanged through the battery pack 108 and compute a deviation of the cumulative capacity exchanged with a reference cumulative capacity curve 120 at an actual SOH. The controller 110 monitors, during charging, characteristic parameters of the battery pack 108 and determines stress on the battery pack 108 using stress weights as a function of the characteristic parameters. The stress weights indicates stress caused on the battery pack 108 due to charging of the battery pack 108 by the charging station 104. The controller 110 determines, after charging, a difference between the cumulative capacity exchanged through the battery pack 108 with the reference cumulative capacity curve 120. The controller 110 determines a charging sequence and a charging capacity, where the charging sequence comprises at least one of a slow charging and a fast charging, for the battery pack 108 to meet the reference cumulative capacity curve 120 by using the stress weights and the calculated difference. The charging sequence and charging capacity are selected in a manner to ensure no changes in a minimum throughput of the battery pack 108 over its lifecycle.

[0008] In accordance to the present invention, the charging station 104 comprises necessary circuits such as current control unit, an AC-DC converter, and a DC-DC converter to enable charging of the battery pack 108. Further, the charging station 104 is connected with source of electrical energy such as AC supply from grid, or renewable source of energy such as Solar, Wind, Hydro, etc., or diesel generator sets, etc. The charging station 104 provides plug-in type charging or swapping based charging based on the need or type of the vehicle 106.

[0009] In the present invention, certain terms are/will be used such as a C-rate, reference cumulative capacity curve 120, stress weights and the actual SOH. These terms are now defined and described for simplicity in understanding of the present invention. The C-rate is defined as the measure at which the battery pack 108 is discharged/charged relative to the rated capacity of a cell. For example: In a 100 Ampere hour (Ah) battery pack, 1 Cis defined as 100 Ampere (A), 2C is 200 A, 0.5 C is 50 A and the like. The reference cumulative capacity curve 120 represents the energy delivery performance of the battery pack 108 over its lifetime i.e. the change in cumulative amount of capacity exchanged (both charge and discharge) by the battery pack 108 with respect to the actual SOH. The actual SOH corresponds to the latest/last SOH after charging or discharging phase/cycle.

[0010] The stress weights are a measure of effect of charging C-rates on the health of the battery pack 108. For one particular C-rate, the stress weight is defined as the ratio of change in capacity of the battery pack 108 when charged at reference C-rate to the change in capacity of the battery pack 108 when charged at that particular C-rate over a fixed change in the SOH. The stress weight is “1” at the reference conditions. The stress weights for other C-rates and temperatures are calculated with respect to the reference. So, if stress weight is greater than “1”, then the charging is more stressful, while if stress weight is less than “1”, then the charging is less stressful than reference. The stress weight (denoted as “str_wt”) is a function of C-rate, temperature (T) and SOH of the battery pack 108.
str_wt = f (C-rate, T, SOH)

[0011] The stress weights are calculated dynamically by the controller 110 after every charge (or charging cycle/phase) to estimate the effect on health caused by that charging phase. The stress weights are unique for each battery pack 108 and are saved in a memory element 112 of the controller 110. The actual SOH is an output from already existing algorithm or sensors that gives the SOH of the battery pack 108 after every charge and discharge. The actual SOH value is considered as the actual state of the battery pack 108 with respect to the cumulative amount of capacity exchanged by the battery pack 108.

[0012] In accordance to the present invention, the controller 110 comprises the memory element 112 such as Random Access Memory (RAM) and/or Read Only Memory (ROM), Analog-to-Digital Converter (ADC) and a Digital-to-Analog Convertor (DAC), clocks, timers and at least one processor (capable of implementing machine learning) connected with each other and to other components through communication bus channels. The memory element 112 is pre-stored with logics or instructions or programs or applications or modules, which is/are accessed by the processor as per the defined routines. The internal components of the controller 110 are not explained for being state of the art, and the same must not be understood in a limiting manner. The controller 110 may also comprise communication units to communicate with the cloud server 102 through wireless or wired means such as Global System for Mobile Communications (GSM), 3G, 4G, 5G, Wi-Fi, Bluetooth, Ethernet, serial networks, and the like.

[0013] In accordance to an embodiment of the present invention, the characteristic parameters comprises a charging profile, a voltage of the battery pack 108, a temperature of the battery pack 108 and the actual SOH of the battery pack 108. As described above, the characteristic parameters are used for calculation of stress weights which are an indication of stress caused on the battery pack 108 after charging. The reference cumulative capacity curve 120 of the battery pack 108 shows the behavior through a plot where an X-axis is SOH and Y-axis is cumulative capacity in Ah.

[0014] In accordance to an embodiment of the present invention, the controller 110 configured to calculate equivalent capacity of the battery pack 108 based on the stress weights and an actual capacity used for charging the battery pack 108. The controller 110 then calculates an equivalent SOH of the battery pack 108 based on the calculated equivalent capacity. The controller 110 corrects/revises the stress weights using the difference in the equivalent SOH and the actual SOH, and estimates charging capacity to be slow charged and fast charged for a next charging cycle of the battery pack 108. Thus, the stress weights are regularly revised or corrected for the battery pack 108.

[0015] In accordance to an embodiment of the present invention, the controller 110 is part of the charging station 104 or swapping station. However, in alternate embodiment, the controller 110 is part of the vehicle 106 as being a Vehicle Control Unit (VCU) 114 or a Cluster Control Unit (CCU) or a Transmission Control Unit (TCU) or Body Control Unit (BCU) or BMS or any existing control unit of the vehicle 106 or a separate unit interfaced with the VCU 114 or the CCU or the existing control unit. In yet another alternative embodiment, the controller 110 is part of the cloud server 102. Hence, in simple words, the controller 110 is part of at least one of the charging station 104, the swapping station, the cloud server 102 and the vehicle 106.

[0016] According to an embodiment of the present invention, the charging station 104 with the controller 110 is provided. The charging station 104 either comprises slots 118 for swappable type battery pack 108 or connected with a cable 116 for plug-in type battery pack 108. The charging station 104 is connected with the cloud server 102 to access the specific information of the battery pack 108 and the performs the next steps. In another embodiment, the battery pack 108 with the BMS is provided. The BMS comprises the controller 110 and instructs the charging station 104 for the specific sequence and capacity to maintain the health of the battery pack 108. In yet another embodiment, the cloud server 102 for the battery pack 108 is provided. The battery pack 108 communicates with the cloud server 102 through telematics unit of the vehicle 106 or a built-in telematics unit or communication unit of the charging station 104. In still another embodiment, the system 100 comprising at least one of the cloud server 102, the charging station 104 and the vehicle 106 is provided connected with each other through necessary wired or wireless communications known in the art.

[0017] In accordance to an embodiment of the present invention, the vehicle 106 is any one of a two-wheeler such as a motorcycle, an electric bicycle, a scooter, a moped, a three-wheelers such as auto-rickshaws, four wheelers such as cars and the other existing vehicles 106 such as buses, trucks, van, and new vehicles (even snow mobiles). The present invention is also applicable for battery packs which are used in different domain such as power tools, and those areas where ecosystem of regular swapping or plug-in type charging is needed

[0018] In accordance to an embodiment of the present invention, the controller 110 is configured/enabled through a health managing module 122. The health managing module 122 is stored in the memory element 112 and comprises various sub-modules to complete the processing. The various sub-modules are a first module 124, a second module 126, a third module 128, a fourth module 130 and a fifth module 132. The working of the health managing module 122 or the controller 110 is elaborated through description of each sub-module as per below scenario and the same must not be understood in limiting manner. Consider a network of charging stations 104 in a geographical area, where each charging station 104 comprises multiple battery packs 108 to be charged in swappable manner. A vehicle 106, such as a hybrid vehicle or an Electric Vehicle (EV), is registered with the network of charging station 104 and uses the swappable battery pack 108. The battery pack 108 currently in use in the vehicle 106 has been used multiple times. After knowing the status of battery pack 108 being depleted to certain level, a driver of the vehicle 106 visits one charging station 104 in the network for swapping. On reaching the venue, the battery pack 108 is removed from the vehicle 106 and inserted in the charging station 104. After necessary authentication or verification, the controller 110 of the charging station 104 retrieves the information from the BMS of the inserted battery pack 108 and calculates the capacity change (dAh) caused by previous discharge through the first module 124.

[0019] In accordance to an embodiment of the present invention, the first module 124 is configured to process the retrieved information in following manner,
a) Calculates change (or delta) in SOH caused by the discharge of the battery pack 108 in the vehicle 106 represented by del_SoH_dis(N-1), where N denotes the charging phase in the charging station 104.
b) Calculates the actual cumulative capacity exchanged including the discharge which is denoted by Tot_cum_cap_dis(N-1) and represented by
Tot_cum_cap_dis(N-1) = tot_cum_cap_ch(N-1) +Ah_dis(N-1),
where,
tot_cum_cap_ch(N-1) denotes total cumulative capacity charged till N-1 phase, and
Ah_dis(N-1) denotes capacity discharged
c) Finds the difference in actual cumulative capacity (tot_cum_cap_dis(N-1)) and reference cumulative capacity curve 120 (ref_cum_cap_dis(N-1)) at the actual SOH value to get total capacity change (-dAh) of the battery pack 108, which is denoted by tot_del_Ah_dis(N-1) and represented by
tot_del_Ah_dis(N-1) = ref_cum_cap_dis(N-1) – tot_cum_cap_dis(N-1),
d) Subtracts the total capacity change (dAh) of the actual discharge phase (N-1) (tot_del_Ah_ch(N-1)) from total change in capacity of a previous discharge phase tot_del_Ah_dis(N-1) to get capacity change (dAh) of the battery pack 108 caused only due to last discharge in the vehicle 106 (del_Ah_dis(N-1)), represented as below:
del_Ah_dis(N-1) = tot_del_Ah_dis(N-1) – tot_del_Ah_ch(N-1)
e) Add the individual capacity changes (dAh) caused by the discharge to quantify the stress caused by the discharge on the battery pack 108, represented by
cum_del_Ah_dis(N-1) = cum_del_Ah_dis(N-2) + del_Ah_dis(N-1)

[0020] The controller 110 initiates the charging of the battery pack 108, and simultaneously monitors the characteristic parameters of the charging to calculate stress weights and to arrive at the equivalent capacity of the battery pack 108 through the second module 126. The second module 126 takes the characteristics parameter as input and calculates stress weight to arrive at the equivalent capacity (Ah). The second module 126 processes the input in below manner.
a) Segregates the charge profile used for charging the battery pack 108 based on C-rates and temperatures as below. The below table is provided as an example and for ease of understanding, and the same must not be understood in limiting manner.
Sr.No C-rate Temperature Amount of Ah charged
1 1C 25-30 deg C 15
2 1.8C 25-30 deg C 5
3 3C 30-35 eg C 5

b) Calculates the stress weights for each segregation considering the actual SOH value before charging as a function of the characteristic parameters, and calculates equivalent capacity (Ah) which is the actual capacity (Ah) multiplied by the respective stress weight. Later, all the equivalent capacities (Ah) are added to determine a total equivalent capacity (eqv_Ah_chN).
Sl.
No. C-rate Temperature Amount of Capacity charged (Ah) str_wt Calculated Equivalent Capacity
1 1C 25-30 deg C 15 a 15*a
2 1.8C 25-30 deg C 5 b 5*b
3 3C 30-36 eg C 5 c 5*c

eqv_Ah_chN = sigma (Actual_capacity *str_wt calculated)

[0021] Once the equivalent capacity (Ah) is calculated, the second module 126 calculates equivalent SOH followed by difference (error) between the equivalent SOH and the actual SOH, and uses the difference to correct the stress weights as below.
a) From the calculated equivalent capacity (Ah), the second module 126 calculates the cumulative equivalent capacity (Ah) (eqv_cum_cap_chN) followed by equivalent SOH from the non-linear SOH model derived for the reference cumulative capacity curve 120 as below.
eqv_cum_cap_chN = ref_cum_cap_dis(N-1) + eqv_Ah_chN
b) The actual SOH is provided/calculated by an already existing algorithm or sensors.
c) The difference/error between the actual SOH and equivalent SOH is given as feedback to recalibrate parameters for the stress weights.
del_SoH_chN = SoH_chN - eqv_SoH_chN

[0022] The third module 128 calculates the capacity change (dAh) caused by the current charging through the charging station 104. The third module 128 processes in the following manner.
a) Calculates the actual cumulative capacity (Ah) exchanged including discharging (tot_cum_cap_dis(N-1)) and the charging (Ah_ch(N)) represented by
tot_cum_cap_ch(N) = tot_cum_cap_dis(N-1) +Ah_ch(N)
b) Finds the difference in actual cumulative capacity (tot_cum_cap_ch(N)) and the reference cumulative capacity (ref_cum_cap_ch(N)) at the actual SOH value to get total capacity change (tot_del_Ah_ch(N)), represented as:
tot_del_Ah_ch(N) = ref_cum_cap_ch(N) – tot_cum_cap_ch(N)
c) Subtracts the total capacity change (dAh) (tot_del_Ah_dis(N-1)) of this cycle from total capacity change (tot_del_Ah_ch(N)) of the last cycle to get capacity change (del_Ah_ch(N)) caused only due to charging.
del_Ah_ch(N) = tot_del_Ah_ch(N) – tot_del_Ah_dis(N-1)
d) Adds the individual capacity changes caused by the charging to quantify the stress, caused by charging, on the battery pack 108.
cum_del_Ah_ch(N) = cum_del_Ah_ch(N-1) + del_Ah_ch(N)

[0023] The controller 110 now estimates the amount of capacity to be slow charged (Est_slow_ch_Ah) to meet the reference cumulative capacity curve 120 using the fourth module 130. The basis of the fourth module 130 is that at the point where a deviated health line of the battery pack 108 meets the reference cumulative capacity curve 120, the actual cumulative capacity and the reference capacity are equal. The fourth module 130 use of below equation to estimate the amount of capacity to be slow charged.
tot_cum_cap_ch(N) + Est_slow_ch_Ah + Est_dis_Ah = Ref_cum_cap_ch(N) + Est_eqv_slow_ch_Ah + Est_eqv_dis_Ah
where,
Est_slow_ch_Ah is the amount of capacity (Ah) that will be pumped in by slow charging
Est_dis_Ah is the estimated amount of capacity (Ah) that will be discharged
tot_cum_cap_ch(N) is the actual cumulative capacity (Ah) exchanged till the actual cycle
Ref_cum_cap_ch(N) is the reference cumulative capacity (Ah) exchanged till the actual cycle
Est_eqv_slow_ch_Ah is the amount of equivalent capacity (Ah) that will be pumped in by slow charge, for example:
[Est_eqv_slow_ch_Ah = = Est_slow_ch_Ah * str_wt(0.3 C, 25)]
here, str_wt (0.3 C, 25) is the stress weight for the slowest C-rate possible which is assumed being used for slow charging, changes for every cycle as it is a function of SOH
Est_eqv_dis_Ah is the estimated amount of equivalent capacity (Ah) that will be discharged
Est_eqv_dis_Ah = Est_dis_Ah

[0024] Similarly, the controller 110 estimates the maximum capacity that can be fast charged possible without considering operational constraints using the fifth module 132. The assumption for this estimation is that after this maximum number of fast charges is done, the battery pack 108 is then restricted to be charged only by slowest possible charge.
The fifth module 132 uses below equation to calculate the maximum capacity that can be fast charged (Est_fast_ch_Ah):

tot_cum_cap_ch(N) + Est_slow_ch_Ah + Est_fast_ch_Ah + Est_dis_Ah = Ref_cum_cap_ch(N) + Est_eqv_slow_ch_Ah + Est_eqv_fast_ch_Ah + Est_eqv_dis_Ah
where,
Rem_cum_cap is the capacity that can be exchanged from the battery pack 108 before End of Line (EOL)
Rem_cum_cap = M - tot_cum_cap_ch(N)
Est_slow_ch_Ah is the amount of capacity (Ah) that will be pumped in by slow charging
Est_slow_ch_Ah = Rem_cum_cap * 0.5 - Est_fast_ch_Ah
Est_fast_ch_Ah is the amount of capacity that will be pumped in by fast charging
Est_dis_Ah is the estimated amount of capacity that will be discharged in the next Nr cycles
[Est_dis_Ah = Rem_cum_cap * 0.5]
tot_cum_cap_ch(N) is the actual cumulative capacity exchanged till the actual cycle
Ref_cum_cap_ch(N) is the reference cumulative capacity exchanged till the actual cycle
Est_eqv_fast_ch_Ah is the amount of equivalent capacity that will be pumped by fast charging
[Est_eqv_fast_ch_Ah = Est_fast_ch_Ah * str_wt(2C, 35)]
Here, str_wt(2C, 35) is the stress weight for the fastest C-rate possible which is assumed being used for charging. str_wt(2C, 35) changes for every cycle as it is a function of SOH
Est_eqv_dis_Ah is the estimated amount of equivalent capacity (Ah) that will be discharged.

[0025] In accordance to an embodiment of the present invention, the controller 110 uses the previously determined/estimated stress weights determined for the battery pack 108 and uses specific charging sequence and charging capacity for charging through the charging station 104. The stress caused by the charging in the charging station 104 is then determined by revaluating or correcting the previously determined and then stored for the next charging cycle.

[0026] Fig. 2 illustrates a method for charging the battery pack of the vehicle, according to the present invention. The method comprises plurality of steps, of which a step 202 comprises receiving the battery pack 108 in the charging station 104 and retrieving information from the Battery Management System (BMS) of the battery pack 108 comprising capacity exchanged by the battery pack 108 and change in State of Health (SOH) of the battery pack 108. The method is characterized by a step 204 which comprises calculating, before charging, cumulative capacity exchanged through the battery pack 108 and computing the deviation of the cumulative capacity exchanged with the reference cumulative capacity curve 120 at the actual SOH. A step 206 comprises monitoring characteristic parameters of the battery pack 108 during charging and determining stress on the battery pack 108 using stress weights as the function of the charging characteristics. The stress weights indicates stress caused on the battery pack 108 due to charging of the battery pack 108. The characteristic parameters comprises the charging profile, the voltage of the battery pack 108, the temperature of the battery pack 108, and the actual SOH. The charging profile comprises slow charging and fast charging details as well.

[0027] A step 208 comprises determining, after charging, the difference between the cumulative capacity exchanged through the battery pack 108 with the reference cumulative capacity curve 120. In other words, coulomb counting is done to calculate the actual capacity charged in the actual charging phase. A step 210 comprises determining the charging sequence and charging capacity, where the charging sequence comprises at least one of the slow charging and the fast charging, for the battery pack 108 to meet the reference cumulative capacity curve 120 by using the stress weights and the difference. The charging sequence and charging capacity are selected in a manner to ensure no changes in the minimum throughput of the battery pack 108 throughout its lifecycle.

[0028] The method comprises a step 212 and a step 214. The step 212 comprises calculating equivalent capacity of the battery pack 108 based on the stress weights and the actual capacity used for charging the battery pack 108, followed by calculating the equivalent SOH of the battery pack 108 based on the equivalent capacity. The equivalent capacity is calculated as the sum of all the products of actual capacity charged and the stress weight corresponding to the charge rate and the temperature. The step 214 comprises correcting/revising the stress weights values using the difference in the equivalent SOH and the actual SOH, and estimating charging capacity to be slow charged and fast charged for a next charging cycle. The step 212 and the step 214 are executed either after the step 206 and then followed by steps 208 and 210, or the steps 212 and the step 214 are executed separately and used in the steps 208 and the step 210. The method is executed/performed by the controller 110 located in at least one of the charging station 104, the swapping station, the cloud server 102 and the vehicle 106. The corrected/revised stress weights are used during next charging cycle of the battery pack 108, while the previously calculated stress weights are used for deciding the charging sequence and the charging capacity.

[0029] According to the present invention, the method is described below in few steps for ease of understanding and the same is simplification of steps from the step 202 through the step 210 including the step 212 and the step 214. A first step (by first module 124) comprises quantifying the affect due to previous discharge on the health of the battery pack 108, i.e. capacity change caused with respect to the reference cumulative capacity curve 120 is calculated. The input for the first step is capacity discharged in (N-1)th drive and change in SOH in (N-1)th drive, and the output of the first step is total capacity deviated at the end of N-1th drive, capacity deviated due to N-1th drive and cumulative capacity deviated due to N-1 drives.

[0030] A second step (by second module 126) comprises determining stress caused because of charging at different C-rates and temperatures. The stress weights are corrected dynamically by looping in the error/difference (as feedback) between the equivalent SOH and the actual SOH. The input for the second step is charge profile used in Nth cycle, and stress weights at C-rates and temperature at which the battery pack was charged. The output of the second step is error between actual SOH and equivalent SOH and corrected stress weights.

[0031] A third step (by third module 128) comprises quantifying affect due to charging on health of the battery pack 108. The capacity change caused with respect to the reference cumulative capacity curve 120 is calculated. The input for the third step is capacity pumped in Nth charging cycle and change in SOH caused because of the Nth charge. The output of the third step is total capacity deviated at the end of the Nth charge, capacity deviated due to Nth charge and cumulative capacity deviated due to N charges.

[0032] A fourth step (by fourth module 130) comprises estimating the amount of capacity to be slow charged in order to meet the reference capacity curve 206. The input of the fourth step are stress weights, the cumulative capacity exchanged till that point of time and reference cumulative capacity at that point of time. The output of the fourth step is estimated amount of capacity to be slow charged.

[0033] A fifth step (by fifth module 132) comprises estimating the maximum amount of capacity that can be fast charged. The assumption is for the remaining amount of capacity till EOL, the battery pack 108 will be slow charged. The input of the fifth step are stress weights, cumulative capacity exchanged till that point of time and reference cumulative capacity at that point of time. The output of the fifth step is estimated maximum amount of capacity that can be fast charged.

[0034] According to the present invention, the battery packs 108 are charged in a dynamic way to meet a demand for battery pack 108 (in swappable ecosystem) as well as to ensure a minimum energy throughput in its life. The controller 110 monitors the deviation of the health of the battery pack 108 from a reference characteristics. The controller 110 controls the charge sequence required to keep the battery characteristics at least on the reference level. The controller 110 controls the quantum of the capacity that should be charged at different charge rates for ensuring minimum energy throughput. The controller 110 also ensures the operation point is not breached, at which the battery characteristics are in irrecoverable zone, i.e. whatever may be the charge protocol and sequence, the battery pack 108 can no longer deliver the minimum energy throughput. The present invention is also usable in plug-in type charging infrastructure.

[0035] In an embodiment, the controller 110 is implemented for swappable battery packs 108 which will be charged in public charging stations 104. The present invention provides controller 110 and method to determine the charge sequence of swappable battery packs 108 to deliver intended throughput and ensures minimum energy throughput of the swappable battery pack 108 as well as meeting the demand in the field. The controller 110 quantifies the quantum of capacity that can be charged at different charge rates and thereby ensures a particular battery pack 108 is not always selected for higher charge rates in the field to meet the demand. The controller 110 provides input for intelligent selection of battery pack 108 for higher charging rate thereby ensuring a set of battery pack 108 are not constantly stressed.

[0036] It should be understood that embodiments explained in the description above are only illustrative and do not limit the scope of this invention. Many such embodiments and other modifications and changes in the embodiment explained in the description are envisaged. The scope of the invention is only limited by the scope of the claims.