Description:Field of the invention

[0001] The present invention relates to a method for estimating the battery stress. More specifically, the present invention relates to a method for estimating the stress of a battery by analysing various operational parameters.

Background of the invention

[0002] Batteries are essential to modern technology, powering a wide range of applications from electric vehicles (EVs) to large-scale energy storage systems. As dependence on battery technology grows, so does the need for advanced management systems capable of ensuring optimal performance and longevity. The ability to accurately predict and manage battery degradation and stress not only enhances performance but also reduces operational costs and environmental impact. Despite these advancements, existing battery management systems often fall short of providing a comprehensive and accurate assessment of battery health.

[0003] Traditional methods for monitoring the health and performance of batteries typically involve basic diagnostic techniques that measure voltage, current, and temperature. While these parameters are useful, they do not provide a comprehensive understanding of a battery's long-term performance or its degradation path. These systems frequently rely on static threshold values for triggering maintenance actions, which do not account for the dynamic conditions under which batteries operate. This can lead to premature battery failures or inefficient use of the battery's capacity, as the actual stress experienced by the battery under varied operational conditions is not accurately captured.

[0004] Furthermore, prior art in battery management systems generally lacks a means of integrating multiple types of stress data such as electrical, thermal, and mechanical stresses in a unified model that predicts battery life and performance degradation over time. Such limitations might prevent the effective prediction of battery lifespan and the proactive management of battery replacements or maintenance, resulting in increased costs and reduced reliability in applications.

[0005] Additionally, most of the existing systems do not provide a mechanism to utilize degradation data to enhance the operational efficiency of battery swapping stations. The absence of a reliable method to determine the real-time health of swapped batteries leads to inefficiencies in the management of battery inventories and reduces the overall effectiveness of swapping systems, which are increasingly becoming integral to the operation of EVs.

[0006] Therefore, there is a need for method for estimating the degradation and stress of a battery which overcomes one or more drawbacks of the above-mentioned prior art.
Objects of the invention

[0007] The object of the present invention is to provide a method for estimating the stress of a battery.

[0008] Another object of the present invention is to provide a method for estimating the stress of a battery by analysing various operational parameters, such as electrical, thermal, and mechanical stress.

[0009] Another one object of the present invention is to provide a method for estimating the stress of a battery that provides actionable insights for users, enabling informed decisions regarding battery swapping, continued use, or repurposing.

[0010] Further object of the present invention is to a method for estimating the stress of a battery that assess the suitability of batteries for automotive or second-life applications.

[0011] One more object of the present invention is to a method for estimating the stress of a battery that reduces operational costs and increase battery reliability by enabling proactive maintenance, optimizing battery usage, and preventing premature battery failures through continuous stress analysis.

Summary of the invention

[0012] According to the present invention, a method for estimating the stress of a battery is provided. Initially the electrical, thermal, and mechanical stress parameters to which the battery has been subjected is measured by a battery management system (BMS). The measured stress parameters are then analysed by comparing with a predefined stress threshold (T1). The stress threshold (T1) monitors non-critical stress levels that leads to degradation of the battery and a fault threshold (T2) monitors the critical stress levels indicating the safety hazard point of the battery. An integrated analysis is then performed in a connected platform to generate a stress profile. A fault response is triggered when the magnitude of the measured stress parameters exceeds the fault threshold (T2). Finally, a Battery Degradation Index (BDI) is computed based on the generated stress profile. The Battery Degradation Index (BDI) is calculated by integrated analysis/cumulative analysis of the measured stress parameters, quantifying the overall degradation of the battery.

[0013] The Battery Degradation Index (BDI) assess the suitability of using the battery for continued automotive use or for second-life battery applications. Initially a Battery Degradation Index (BDI) rating is provided to each battery at a battery swapping station based on the Battery Degradation Index (BDI) and by calculating the number of charge/ discharges cycles the battery has undergone. A user application module integrated with the connected platform is utilized to monitor the health and performance of the battery in real time during driving mode, wherein the user application provides feedback to the user based on data processed by the connected platform. Similarly, a master application module integrated with the connected platform is utilized to assess the suitability of the battery for continued use or for second-life battery applications at the battery swapping station. A swapping station operator is enabled to access the BDI rating through a master application module integrated with the connected platform. The master application module is configured to assess the suitability of the battery for continued use in automotive applications or for second-life applications based on the BDI rating. The swapping station operator then adjust operational parameters based on the BDI rating, including applying variable pricing that reflects the degradation level of the battery thereby allowing pricing adjustments according to the battery’s condition. The Battery Degradation Index (BDI) is configured to provide an indicator of the condition of the battery for multiple applications within a swappable electric vehicle (EV) ecosystem. The applications include enabling the adjustment of operational parameters by a swapping station operator. The operational parameters include pricing adjustments based on the degradation level of the battery (80) as indicated by the BDI.

[0014] In an aspect of the invention, a system for managing battery health and facilitating battery swapping decisions is provided. The system includes a Battery Management System (BMS), communicatively coupled with batteries. The system utilizes connected platform to perform battery diagnostics and management tasks.

[0015] In an aspect of the invention, the data regarding the number of charge/ discharges cycles the battery has undergone is provided by the battery management system (BMS).

[0016] In an aspect of the invention, the user application module and the master application module include a user interface for notifying the user or system operator about the battery swapping or replacement information.

[0017] In an aspect of the invention, the connected uses/hosts include Machine Learning (ML) and Artificial Intelligence (AI) based models to evaluate the measured parameters and predicts the performance and the life of the battery. Specifically, the measured parameters are input into Machine Learning (ML) and Artificial Intelligence (AI) models to predict the performance and degradation of the battery.

[0018] In an aspect of the invention, the Battery Management System (BMS) includes a memory integrated for storing the Battery Degradation Index (BDI) rating associated with the battery.

[0019] In an aspect of the invention, a method for establishing a battery swapping model based on a Battery Degradation Index (BDI) is provided. The method is described in conjunction with the method and system. Initially the BDI of a battery is accessed through a master application module integrated with a connected platform. The master application module is configured to determine operational parameters for the battery swapping model based on the BDI. The BDI is then displayed to an end user through a user application module The user application module enables the end user to make a battery swapping decision based on the BDI. The operational parameters determined by the master application module include commercial parameters. The commercial parameters include at least cost, charging patterns, maintenance schedules, and battery recycling based on the BDI. The swapping station operators determine commercial parameters of the battery swapping model based on the BDI. The Battery Degradation Index (BDI) is processed by the connected platform for access through the user application module and master application module.

Brief Description of drawings

[0020] The advantages and features of the present invention will be understood better with reference to the following detailed description and claims taken in conjunction with the accompanying drawings, wherein like elements are identified with like symbols, and in which:

[0021] Figure 1 illustrates a method for estimating the degradation and stress of a battery in accordance with the present invention;

[0022] Figure 2 illustrates a system for estimating the degradation and stress of a battery in accordance with the present invention;

[0023] Figure 3 illustrates a flow diagram for estimating the degradation and stress of a battery in accordance with the present invention; and

[0024] Figure 4 illustrates a method (300) for establishing a battery swapping model based on a Battery Degradation Index (BDI) in accordance with the present invention.

Detailed description of the invention

[0025] An embodiment of this invention, illustrating its features, will now be described in detail. The words "comprising," "having," "containing," and "including," and other forms thereof, are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items.

[0026] The present invention relates to a method for estimating the battery stress. More specifically, the present invention relates to a method for estimating the stress of a battery by analysing various operational parameters of a battery.
[0027] The terms “first,” “second,” and the like, herein do not denote any order, quantity, or importance, but rather are used to distinguish one element from another, and the terms “a” and “an” herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced items.

[0028] The disclosed embodiments are merely exemplary of the invention, which may be embodied in various forms.

[0029] Referring now to Figure 1, a method (100) for estimating the stress of a battery (80) in accordance with the present invention is illustrated. The method (100) enables real-time monitoring of battery health and supports informed decision-making at battery swapping stations. The method (100) is applicable to electric vehicle (EV) batteries and other energy storage systems for battery management and extended lifespan.

[0030] The method (100) includes a series of analytical steps to assess the stress levels, degradation rate, and overall health of the battery (80). The method (100) helps in determining when a battery (80) should be swapped or reused for second-life applications based on performance indicators such as the Battery Degradation Index (BDI) and a Battery Degradation Index (BDI) rating (50). In an embodiment, within a swappable electric vehicle (EV) ecosystem, the BDI provides a quantified measure of the battery’s condition, accessible to an end user to allow evaluation of the battery’s state for continued use.
[0031] The BDI further allows a swapping station operator to modify operational parameters, including pricing, based on the battery’s degradation level. For instance, a battery exhibiting a higher degradation level, as indicated by the BDI, may be assigned a lower usage rate or other operational adjustments that reflect its condition.

[0032] In a further embodiment, when the BDI reaches or exceeds a specified threshold, the system (200) categorizes the battery (80) as unsuitable for continued automotive operation. At this point, the battery (80) may be designated for second-life applications, such as stationary energy storage, where it can continue to perform within the requirements of applications with reduced performance demands.

[0033] The method (100) involves a Battery Management System (BMS) (90). The BMS (90) is configured to communicate wirelessly with a connected platform (20), which is a cloud- based platform. The BMS (90) collects real-time data regarding the usage of the battery (80) and transmits the data to the cloud or a local server or alternatively, processes the data on the BMS itself for further analysis and stress profiling. The BMS (90) includes charging and discharging profiles to evaluate electrical stress, thermal stress, mechanical stress, cycle life, health degradation, and performance of the battery (80).
[0034] The method (100) starts at step 110.

[0035] At step 120, an electrical, thermal, and mechanical stress parameters to which the battery (80) has been subjected is measured by the BMS (90). The BMS (90) tracks electrical stress, thermal stress, and mechanical stress experienced by the battery (80). The electrical stress includes fluctuations in voltage and current that occur during the charging and discharging cycles of the battery (80), as well as those caused by overcharging, irregular power demands, or excessive current draw. Further, the BMS (90) monitors thermal stress, which involves temperature variations, including extreme or prolonged high temperatures that degrade the internal components of the battery (80) and low temperatures that hinder the battery’s ability to function efficiently. Furthermore, the BMS (90) measures mechanical stress by detecting physical forces such as vibrations, shocks, or pressure, which may result from vehicle movement or external environmental conditions.

[0036] At step 130, the measured stress parameters are analysed by comparing with a predefined stress threshold (T1). The stress threshold (T1) monitors non-critical stress levels that leads to degradation of the battery (80). The fault threshold (T2) is configured to monitor critical stress levels for electrical, thermal, and mechanical parameters independently. Exceeding the fault threshold (T2) indicates that the battery (80) is operating under conditions that may lead to irreversible damage or pose thermal runaway and combustion risks.

[0037] Each stress parameter including electrical, thermal, and mechanical is independently monitored against its respective threshold to evaluate the impact of each parameter on the condition of the battery (80).

[0038] By tracking the stress levels above T1 and below T2, the predictions including when the battery (80) begin to lose efficiency or capacity due to prolonged exposure to these sub-critical stresses can be calculated. The predictions provide early warnings of degradation that can extend the useful life of the battery (80).

[0039] At step 140, an integrated analysis is performed in a connected platform (20) to generate a stress profile. Referring now to Figure 3, a flow diagram represents the stress over time, marked by peaks and troughs indicating varying levels of stress experienced by the battery (80). The flow diagram is the visual representation of the method (100) for estimating stress and degradation of a battery (80) using two critical thresholds including a stress threshold (T1) and a fault threshold (T2). The horizontal lines marked T1 and T2 represent the predefined thresholds that trigger specific actions when exceeded by the stress magnitude. Exceeding T2 indicates a severe condition that could lead to failure or substantial harm to the safety of the battery (80).

[0040] The method (100) is initialized with T2 configured to detect fault-level stress and T1 set to monitor less severe electrical, thermal or mechanical stress. The stress values are monitored, with any non-zero stress value triggering the further analysis. As the battery (80) operates, the BMS (90) continuously measures stress levels of the battery (80). The graph specifically tracks electrical, thermal or mechanical stress as it fluctuates over time. Each measured stress value (referred as VAL in Figure 3) is compared against T1 and T2. The system (200) detects each instance where the stress value is measured. For each instance where the stress value exceeds T1, the stress count (referred as (VAL_STR) in Figure 3) is incremented, and the corresponding stress value is added to an integrated stress profile. The stress profile helps in tracking and analysing the accumulated stress in the battery (80) over time.

[0041] At step 150, a fault response is triggered when the magnitude of the measured stress parameters exceeds the fault threshold (T2). Specifically, when the battery (80) experiences critical stress, exceeding T2, the system (200) responds by activating a fault response. The fault response includes actions such as reducing the load on the battery (80), stopping the operation, or issuing a warning to the user or system operator thereby preventing sudden battery failure.

[0042] At step 160, a Battery Degradation Index (BDI) is determined based on the generated stress profile. The BDI is calculated by integrating the measured stress parameters, quantifying the overall degradation of the battery (80), thereby providing an indicator of the current health status. The Battery Degradation Index (BDI) assess the suitability of using the battery for continued automotive use or for second-life battery applications based on the degradation level of the battery (80),

[0043] The method (200) ends at step 170.

[0044] In an aspect of the invention, a Battery Degradation Index (BDI) rating (50) is provided to each battery (80) at the swappable electric vehicle (EV) ecosystem/battery swapping station based on the Battery Degradation Index (BDI) and by calculating the number of charge/ discharges cycles the battery (80) has undergone. In the present embodiment, the data regarding the number of charge/ discharges cycles the battery (80) has undergone is provided by the BMS (90). The BDI rating (50) is displayed on a user interface during both driving mode and at the battery swapping station. The user interface can be a mobile phone or a laptop or any web enabled electronic device. The BDI rating (50) ranges from 0 to 10, with higher values indicating better health and lower values indicating increased degradation. A swapping station operator is enabled to access the BDI rating (50) through a master application module (40) integrated with the connected platform (20). The master application module (40) is configured to assess the suitability of the battery (80) for continued use in automotive applications or for second-life applications based on the BDI rating (50). The swapping station operator then adjust operational parameters based on the BDI rating (50), including applying variable pricing that reflects the degradation level of the battery (80), thereby allowing pricing adjustments according to the battery’s condition.

[0045] As an illustrative example, let’s consider a battery with a BDI rating (50) of 8/10, as shown in the Figure 2. The BDI rating (50) indicates that the battery (80) is still in good condition, having retained a large portion of its original capacity and performance and likely to be suitable for continued use in its current automotive application. Now, consider a scenario where the system (200) sets a BDI rating (50) as 5. If a battery's (80) BDI rating (50) falls to 5 or below, the system (200) triggers an alert or recommendation for the battery (80) to be swapped or repurposed for second-life applications. The Batteries with a BDI of 5 or less are approaching the end of their useful life in automotive applications and may no longer deliver the performance required for continued use in vehicles. The BDI rating (50) associated with the battery (80) is stored in a memory in the BMS (90).

[0046] During driving mode, a user application module (30) integrated with the connected platform (20) is utilized to monitor the health and performance of the battery (80) in real time. The user application (30) provides feedback to the user based on data processed by the connected platform (20). For example, a smart phone/integrated display unit inside the vehicle having the user application module (30) can be used to monitor the health and performance of the battery (80) in real time during the driving mode.
[0047] Similarly, the master application module (40) integrated with the connected platform (20) is utilized to assess the suitability of the battery (80) for continued use or for second-life battery applications at the battery swapping station. For example, a smart phone/Laptop/workstation having the master application module can be used to assess the suitability of the battery (80) at the battery swapping station.

[0048] The user application module (30) and the master application module (40) include a user interface for notifying the user or system operator about the battery swapping or replacement information. Specifically, the user interface for the user application (30) is configured to provide real-time feedback on a mobile device/smart phone or in-vehicle display for notifying the user about battery health, battery swapping recommendations, or replacement information based on data processed by the connected platform (20). Similarly, the user interface for the master application (40) is configured for use at battery swapping stations, allowing operators to assess the battery’s (80) suitability for continued automotive use or second-life applications based on the BDI, and to manage battery swapping operations. The user interface displays both the BDI and BDI rating (50) thereby giving users real-time information on the battery’s health.

[0049] In an aspect of the invention, the method (100) involves training Machine Learning (ML) and Artificial Intelligence (AI) based models to evaluate the measured parameters and predicts the performance and the life of the battery (80). Specifically, the measured parameters are input into Machine Learning (ML) and Artificial Intelligence (AI) models to predict the performance and degradation of the battery. The AI/ML models are trained on historical data to recognize patterns in battery degradation and predict future performance. By continuously evaluating real-time data, the connected platform (20) predicts the remaining lifespan of the battery (80), predict capacity loss, and provide information on when the battery may need replacement. The AI models improve over time through continuous learning, thereby providing efficient battery management.

[0050] In an aspect of the invention a system (200) for managing battery health and facilitating battery swapping decisions is provided. The system (200) is described in conjunction with the method (100). Referring now to Figure 2, the system (200) includes a Battery Management System (BMS) (90), integrated/communicatively coupled with the battery (80). In the present embodiment, the BMS is disposed within the housing of the battery, providing direct monitoring and control of the battery.

[0051] The system (200) utilizes connected platform (20) to perform battery diagnostics and management tasks. The connected platform (20) is a cloud platform. The connected platform (20) receives and analyse the data collected from the Battery Management System (BMS) (90) which includes charging and discharging profiles to calculate electrical thermal or mechanical stress, cycle life, health degradation, and performance of the battery (80). The connected platform (20) is configured to compare the measured stress parameters against a stress threshold (T1) to generate a stress profile and to calculate a Battery Degradation Index (BDI) based on the stress profile. The Battery Degradation Index (BDI) is calculated by integrating the measured electrical, thermal, and mechanical stresses, quantifying the overall degradation of the battery, and providing an indicator of the current health status and remaining lifespan of the battery (80).

[0052] The Battery Degradation Index (BDI) assess the suitability of using the battery (80) for continued automotive use or for second-life battery applications based on the degradation level of the battery (80), The BDI rating (50) is provided to each battery (80) at a battery swapping station based on the Battery Degradation Index (BDI) and by calculating the number of charge/ discharges cycles the battery has undergone. The BMS (90) includes a memory integrated for storing the Battery BDI rating (50) associated with the battery.

[0053] The connected platform (20) is a cloud-based system that communicates with a user application module (30) and a master application module (40). The user application module (30) is configured to monitor health and performance of the batteries in real time during a driving mode. The user application module (30) provides feedback based on the data processed by the connected platform (20). The connected platform (20) communicates with the master application module (40) that utilizes the Battery Degradation Index (BDI) to assess the suitability of using the battery (80) for continued automotive use or for second-life battery applications. The user application module (30) and the master application module (40) include a user interface for notifying the user or system operator about the battery swapping or replacement information. The Battery Degradation Index (BDI) is configured to provide an indicator of the condition of the battery (80) for multiple applications within a swappable electric vehicle (EV) ecosystem. The applications include enabling the adjustment of operational parameters by a swapping station operator. The operational parameters include pricing adjustments based on the degradation level of the battery (80) as indicated by the BDI.

[0054] The connected platform (20) is configured to evaluate the measured parameters and predicts the performance and the life of the battery (80). In an aspect, the connected platform use/hosts Machine Learning (ML) and Artificial Intelligence (AI) based models to evaluate the measured parameters and predicts the performance and the life of the battery (80). Specifically, the measured parameters are input into Machine Learning (ML) and Artificial Intelligence (AI) models to predict the performance and degradation of the battery. The models are trained on historical stress data to recognize patterns in battery degradation and predict future performance. By continuously evaluating real-time data, the platform connected platform (20) predicts the battery's remaining lifespan, predict capacity loss, and provide information on when the battery (80) need replacement. The AI models improve over time through continuous learning, thereby providing efficient battery management.

[0055] In an alternate embodiment, the connected platform can perform the evaluation and prediction through algorithmic models or rule-based analysis without using ML or AI.

[0056] In an aspect of the invention, a method (300) for establishing a battery swapping model based on a Battery Degradation Index (BDI) is provided. The method (300) is described in conjunction with the method (100) and system (200).

[0057] The method (300) starts at step 310.

[0058] At step 320, the BDI of a battery (80) is accessed through a master application module (40) integrated with a connected platform (20). The master application module (40) is configured to determine operational parameters for the battery swapping model based on the BDI. The master application module (40) is configured to determine operational parameters for a battery swapping model based on the BDI, where the BDI provides a quantified measure of the battery’s condition, including its degradation level. This allows the master application module (40) to configure the battery swapping model according to the specific state of each battery.

[0059] At step 330, the BDI is displayed to an end user through a user application module (30), wherein the user application module (30) enables the end user to make a battery swapping decision based on the BDI. The operational parameters determined by the master application module (40) include commercial parameters. The commercial parameters include at least cost, charging patterns, maintenance schedules, and battery recycling based on the BDI. The swapping station operators determine commercial parameters of the battery swapping model based on the BDI and allows the operator to tailor pricing and maintenance practices to the specific condition of each battery. For one example, if the BDI indicates a high level of degradation, the operator may set lower costs for that battery, adjust its charging cycle, or schedule it for immediate recycling once it reaches a threshold level.

[0060] The Battery Degradation Index (BDI) is processed by the connected platform (20) for access through the user application module (30) and master application module (40).

[0061] The method (300) ends at step 340.

[0062] Thus, the present invention has the advantage of providing a method for estimating the stress of a battery by analysing various operational parameters such as electrical, thermal, and mechanical stress. This method offers actionable insights to users, enabling them to make informed decisions regarding battery swapping, continued use, or repurposing. Additionally, the invention assesses the suitability of batteries for both automotive applications and second-life uses, ensuring optimal performance throughout the battery's lifecycle. Furthermore, the invention reduces operational costs and enhances battery reliability by enabling proactive maintenance, optimizing battery usage, and preventing premature failures through continuous stress analysis.

[0063] The foregoing descriptions of specific embodiments of the present invention have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the present invention to the precise forms disclosed, and obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the present invention and its practical application, and to thereby enable others skilled in the art to best utilize the present invention and various embodiments with various modifications as are suited to the particular use contemplated. It is understood that various omissions and substitutions of equivalents are contemplated as circumstances may suggest or render expedient, but such omissions and substitutions are intended to cover the application or implementation without departing from the spirit or scope of the claims of the present invention.
, C , C , C , Claims:We Claim:

1. A method (100) for estimating the stress of a battery (80), the method (100) comprising the steps of:
measuring electrical, thermal, and mechanical stress parameters to which the battery (80) has been subjected by a battery management system (BMS) (90);
analysing the measured stress parameters by comparing with a predefined stress threshold (T1), the stress threshold (T1) monitors non-critical stress levels that leads to degradation of the battery (80);
performing an integrated analysis in a connected platform (20) to generate a stress profile;
triggering a fault response when the magnitude of the measured stress parameters exceeds a fault threshold (T2); and
determining a Battery Degradation Index (BDI) based on the generated stress profile, wherein the Battery Degradation Index (BDI) is calculated by integrating the measured stress parameters, quantifying the overall degradation of the battery (80), thereby providing an indicator of the current health status and remaining lifespan of the battery (80).
2. The method (100) for estimating the stress of a battery (80) as claimed in claim 1, further comprising:
assigning a Battery Degradation Index (BDI) rating (50) to each battery (80) at a battery swapping station based on the calculated Battery Degradation Index (BDI) and the number of charge/ discharges cycles the battery (80) has undergone;
providing the BDI rating (50) to an end user through a user application module (30) integrated with a connected platform (20), the user application module (30) being configured to monitor battery health and performance in real time during driving mode and to display feedback to the user based on data processed by the connected platform (20), thereby enabling the user to assess the condition of the battery (80);
enabling a swapping station operator to access the BDI rating (50) through a master application module (40) integrated with the connected platform (20), the master application module (40) being configured to assess the suitability of the battery (80) for continued use in automotive applications or for second-life applications based on the BDI rating (50); and
enabling the swapping station operator to adjust operational parameters based on the BDI rating (50), including applying variable pricing that reflects the degradation level of the battery (80), thereby allowing pricing adjustments according to the battery’s condition.
3. The method (100) for estimating the stress of a battery (80) as claimed in claim 1, wherein the connected platform (20) receives and analyse the data collected from the Battery Management System (BMS) (90) which includes charging and discharging profiles to evaluate cumulative stress (electrical thermal or mechanical stress), cycle life, health degradation, and performance of the battery (80).
4. The method (100) for estimating the stress of a battery as claimed in claim 2, wherein the user application module (30) and the master application module (40) includes a user interface for notifying the user or system operator about the battery swapping or replacement information.
5. The method (100) for estimating the stress of a battery (80) as claimed in claim 1, wherein the connected platform (20) is configured to evaluate the measured parameters and predicts the performance of the battery (80).
6. The method (100) for estimating the stress of a battery (80) as claimed in claim 1, wherein the Battery Management System (BMS) (90) includes a memory integrated for storing the Battery Degradation Index (BDI) rating (50) associated with the battery (80).
7. A system (200) for estimating stress of a battery (80), the system (200) comprising:
a Battery Management System (BMS) (90) for measuring electrical, thermal, and mechanical stress parameters to which the battery (80) has been subjected; and
a connected platform (20) configured to compare the measured stress parameters against a stress threshold (T1) to generate a stress profile and to calculate a Battery Degradation Index (BDI) based on the stress profile, wherein the Battery Degradation Index (BDI) is calculated by integrating the measured electrical, thermal, and mechanical stresses, quantifying the overall degradation of the battery (80), and providing an indicator of the current health status and remaining lifespan of the battery (80).
8. The system (200) for estimating the stress of a battery (80) as claimed in claim 7, wherein the Battery Degradation Index (BDI) is configured to provide an indicator of the condition of the battery (80) for multiple applications within a swappable electric vehicle (EV) ecosystem, the applications comprising:
assessing the suitability of the battery (80) for continued automotive use or for repurposing in second-life battery applications; and
enabling the adjustment of operational parameters by a swapping station operator, wherein the operational parameters include pricing adjustments based on the degradation level of the battery (80) as indicated by the BDI.
9. The system (200) for estimating the stress of the battery (80) as claimed in claim 7, wherein a BDI rating is generated for each battery at a battery swapping station based on the Battery Degradation Index (BDI) and by calculating the number of charge/ discharges cycles the battery (80) has undergone.
10. The system (200) for estimating the stress of the battery (80) as claimed in claim 7, wherein the connected platform (20) is adapted to communicate with a user application module (30) configured to monitor health and performance of the batteries in real time during a driving mode, wherein the user application module (30) provides feedback based on the data processed by the connected platform (20).
11. The system (200) for estimating the degradation and stress of the battery (80) as claimed in claim 7, wherein the connected platform (20) is adapted to communicate with a master application module (40) configured to integrate with the connected platform (20), wherein the master application module (40) utilizes the Battery Degradation Index (BDI) to determine the suitability of the battery (80) for continued automotive use or for second-life battery applications based on the degradation level of the battery (80), output the BDI to a user interface of the master application module (40) to provide an indication of the condition of the battery (80) to an end user; and enable the adjustment of pricing parameters by a swapping station operator based on the degradation level of the battery (80) as indicated by the BDI.
12. The system (200) for estimating the stress of the battery (80) as claimed in claim 7, wherein the connected platform (20) receives and analyse the data collected from the Battery Management System (BMS) (90) which includes charging and discharging profiles to evaluate cumulative stress including electrical thermal or mechanical stress, cycle life, health degradation, and performance of the battery (80).
13. The system (200) for estimating the stress of the battery (80) as claimed in claim 7, wherein the user application module (30) and the master application module (40) includes a user interface to display information to the user or system operator regarding battery swapping or replacement based on the Battery Degradation Index (BDI).
14. The system (200) for estimating the stress of the battery (80) as claimed in claim 7, wherein the connected platform (20) is configured to evaluate the measured parameters and predicts the performance and the life of the battery (80).
15. The system (200) for estimating stress of the battery (80) as claimed in claim 8, wherein the Battery Management System (BMS) (90) includes a memory integrated for storing the Battery Degradation Index (BDI) rating associated with the battery (80).
16. A method (300) for establishing a battery swapping model based on a Battery Degradation Index (BDI), the method (300) comprising the steps of
accessing the BDI of a battery (80) through a master application module (40) integrated with a connected platform (20), the master application module (40) being configured to determine operational parameters for the battery swapping model based on the BDI; and
displaying the BDI to an end user through a user application module (30), wherein the user application module (30) enables the end user to make a battery swapping decision based on the BDI.
17. The method (300) as claimed in claim 16, wherein the operational parameters determined by the master application module (40) include commercial parameters, the commercial parameters comprising at least cost, charging patterns, maintenance schedules, and battery recycling based on the BDI, wherein the swapping station operators determine commercial parameters of the battery swapping model based on the BDI.
18. The method (100) as claimed in claim 16, wherein the Battery Degradation Index (BDI) is processed by the connected platform (20) for access through the user application module (30) and master application module (40).