WO2022229970A1

WO2022229970A1 - System and method for real time data extraction of vehicle operational impact on traction battery

Info

Publication number: WO2022229970A1
Application number: PCT/IN2022/050204
Authority: WO
Inventors: Ram Mohan Ramakrishnan
Original assignee: Ram Mohan Ramakrishnan
Priority date: 2021-04-25
Filing date: 2022-03-08
Publication date: 2022-11-03

Abstract

A system (100) and a method for a real time extraction of contextual data of operational impact on a traction battery is disclosed. A microprocessor based electronic device (131) is mounted on an electric vehicle (130) for extracting the contextual data of vehicle operating conditions that impact a traction battery. A data acquisition unit (106) is used for acquiring sensor data and CAN data from sensors (102) and CAN bus (104) respectively and storing in the data acquisition ring buffer (112) with timestamp. The impact detector (108) can determine an impact event from CAN data and sensor data based on an impact parameter and the impact criteria stored in the traction battery impact unit (118). The contextual data extracted by the contextual data extractor (116) from the data acquisition unit (106) based on the impact events detected by the impact detector (108) can be used by the analysis system for analysis.

Description

SYSTEM AND METHOD FOR REAL TIME DATA EXTRACTION OF VEHICLE OPERATIONAL IMPACT ON TRACTION BATTERY

TECHNICAL FIELD OF THE INVENTION

[0001] The present disclosure relates to analyzing the impact of vehicle operating conditions on a traction battery in a plurality of electrically propelled vehicle types employed for a plurality of end uses. More specifically, the present disclosure relates to a system and method for real time data extraction of contextual data of vehicle operating conditions that impact a traction battery.

BACKGROUND [0002]Growing concerns on global climate change have forced many leading economies to urgently announce timeline commitments for transitioning completely to electric vehicles. A few countries with a large consumer base but limited EV manufacturing capacity have adopted a phased and segment-specific-targets. For example, India had initially announced 2026 as an ambitious deadline for electrification of all 2-Wheelers, that accounts for over 75% of all vehicle sales in the country as of publication of the National Electric Mobility Mission Plan 2020 of the Government of India. However, this target was severely impacted by the Covid-19 pandemic and was later revised to a more realistic target of 30% by 2030. Similarly, Indonesia aims to sell only electric motorcycles by 2040. Similarly, Brazil, Vietnam, Mexico, Argentina, Malaysia, Iran, Thailand, Poland and Turkey are either doing likewise or expected to follow suit in the near future. The next few years therefore are expected to witness a dramatic increase globally in the number of electric vehicles.

[0003] In spite of incentive schemes implemented in countries like India in the EV manufacturing sector, for instance FAME-I and FAME-II for the Faster Adoption and Manufacturing of (Hybrid and) Electric Vehicles, as well as policy driven incentives from both the Union government and around 16 state governments who have announced or published their EV policies to fuel consumer demand, many hurdles have remained in the path of mass adoption of electric vehicles. Range anxiety and the total cost of ownership related to battery life have been the main concerns in the consumer’s mind. Other hurdles are poor density of charging infrastructure, the long duration required for charging EV battery and the means of safe disposal of batteries. [0004]Various approaches and solutions towards addressing range anxiety and battery life in electric vehicles have traditionally focused almost exclusively on the passenger car segment. This is incidentally the first mass consumer segment where global companies launched models in large markets like India where a few local manufacturers followed suit, although not necessarily with 100% indigenous technology.

[0005]As the electrification drive gains momentum in the aforementioned countries in the coming years towards legally prohibiting the manufacture, sale and use of fossil fuel powered vehicles completely, diverse segments of vehicles may witness radical changes. Consequently the term electric vehicle may gradually assume a broader scope in the consumer’s mind, comprising diverse types and diverse end uses, to include electric 2-Wheelers, 3-Wheelers (passenger), e-B ikes/e-Scooters, urban passenger transportation buses, light commercial vehicles, heavy goods vehicles, dump trucks, towing vehicles, short transit vehicles, rural cargo transportation 3- Wheelers, off-road vehicles such as agricultural machinery like tillers and combined harvesters, forklift trucks, golf carts and specialized vehicles employed in construction, material handling, mining, quarrying and heavy earth moving. To complete this landscape is the newly exploding segment of Drones encompassing Unmanned Aircraft Systems/UAS and Unmanned Aerial Vehicles/UAV for consumer, commercial, government and military use in many countries in the world.

[0006] Therefore, the well-known range anxiety in electric passenger cars could logically be generalized across the aforementioned segments, as a form of “energy anxiety” that is a phobic fear experienced by the consumer of a particular type of electric vehicle, employed for a particular end use, regarding the uncertainty of how long the charge in the traction battery would last in order to continue a particular operation or activity, before being forced to return to recharge. It is to be noted that in several end uses, the traction battery serves as the source of power for both the motive power of the vehicle itself as well as power for the specific work load. For example, in an electric tiller, the traction battery provides motive power for the tiller itself, as well as power for tilling the soil. Whereas in the case of a quadcopter drone employed for crop spraying the motive power to the drone as well as power for the spraying action are both supplied by the same battery pack. In Drones that operate beyond visual range, the energy anxiety could be exacerbated as it could potentially lead to the Drone crashing or getting lost or stolen.

[0007] Further, although numerous advances in battery technology are steadily driving down costs, the traction battery still represents one of the largest cost heads in the breakup of an electric vehicle’s cost. Therefore, fear of premature degradation of the traction battery capacity resulting in possible replacement cost, is a significant consumer concern. Particularly so in markets like India that is generally price sensitive, especially in segments like rural and agriculture, where consumer perception of the service lifetime of an agricultural vehicle (that is typically purchased with the help of an agricultural loan from a bank or other lender) with respect to the investment is relatively much longer. The concern regarding the total cost of ownership would be exacerbated in geographical areas that experience extremes of hot and cold climates annually, both of which are known to be detrimental to battery life, states in the northern latitudes of India being an example.

[0008]A significant lag is currently reported by the governments of the aforementioned countries in the industrial capability with reference to the leapfrogging nature of their electric mobility policies, and the regulatory deadlines that follow closely at the heels of policy announcements. An example is the decision of the Indian government to skip the hybrid electric vehicle stage entirely and leapfrog directly to fully battery-operated electric vehicles. Although this rapid momentum might be the need of the hour to save the environment, it denies indigenous electric vehicle manufacturers in these countries of valuable experience in perfecting electric vehicle design, mastering battery technology and scaling up their supply chains to the required levels of volume and quality. Ongoing government incentives both by the central government as well as a few state governments for boosting demand has been creating a huge gap in the supply of electric vehicles. [0009] Further, owing to the highly fragmented nature of many of these segments in the aforementioned countries, traditional automobile manufacturers are unlikely to monopolize the entire opportunity, thereby creating space for small and mid-sized players. Those sensing the opportunity currently include many small and mid-sized companies including startup ventures, a few of them backed by investments from large manufacturers, and even established non-automotive companies that have traditionally been in the traction motors and drives business. In greenfield segments like Drones, it seems to be a relatively level playing field, with many startups and mid- size companies in the fray, although some of them could be funded by larger players.

[0010] Most electric vehicle designs currently use an imported traction battery pack and this trend is not expected to change in the near future. Moreover, other major components of the electric vehicles like traction motor, battery management system and brake pads would be sourced from selected vendors, rendering the electric vehicle business more of a system integration challenge in many of these segments, as opposed to integrated manufacturing, as in passenger cars.

[0011] For these new entrants, in-house Research & Development (R&D) capability is expected to lag considerably in the initial years, as they scramble to roll out their first models to gain market entry. Their time-to-market pressures can be expected to set severe time and budget constraints for pre-production R&D and validation trials of electric vehicle designs before launch to attempt any design optimization for particular regions or end uses. Even with established electric vehicle manufacturers, the initial days are expected to see only the assembly of imported components and final testing done inhouse before launch.

[0012]Flowever, energy anxiety and battery life concerns would continue to pose adoption hurdles with consumers in all segments. Performance standards and approval procedures set by regulatory agencies such as the Automotive Research Association of India (ARAI) may generally help address some of these concerns to a certain extent. Nevertheless, consumer guidance regarding optimal vehicle handling for cost and economy, and operating conditions for minimizing battery drain and maximizing battery life for particular types of electric vehicles for particular end uses would still be almost non-existent at the time of the first series of launches.

[0013]Traditional manufacturers of fossil fuel powered vehicles have had several decades of design knowledge and sufficient field experience for optimizing the engine and vehicle design. They also got an opportunity to provide guidance to consumers regarding vehicle handling for cost and economy. A typical example is that consumers of fossil fuel powered passenger cars know well over the years that an optimum cruising speed between 40 & 50 kmph has to be maintained for the best mileage, that is also highlighted on the speedometer for maximum fuel economy. Similarly, vehicle owners in hilly areas know well that it is better to drive on a particular optimal gear ratio in order to avoid frequent change of gears to minimize wear and tear.

[0014] Unlike traditional vehicle manufacturers, it may take this new breed of electric vehicle businesses several months to years before being able to gain adequate insight and provide consumer guidance regarding cost and economy for specific electric vehicle types for specific end uses. For example, take the case of an electric agricultural vehicle that is to be employed in a mechanized farm, where the traction battery provides the power for both the vehicle’s motive power as well as power for the agricultural load, that may in turn depend on the soil conditions and various settings of the agricultural tool. Assuming the farm has a charging outlet, this consumer’s primary concern may be how often he needs to charge, meaning the overall cost of charging and the downtime in his agricultural productivity. Similar concern applies to the farm’s crop spraying drone. Under the liberalized Drone regulations announced in countries such as India, as more and more people qualify as licensed drone pilots in the coming years for diverse applications, the same concerns would be shared by a larger section of users in this segment. Take as another example an electric goods delivery van that plies short distances within a city to make deliveries to nearby destinations. This consumer’s primary concern may be the delays caused in the delivery operations due to the frequent need for recharging and the long time taken for charging. Take as a further example an electric transport mini-bus operating in a hilly terrain. This consumer’s primary concern may be the optimum number of passengers that should be allowed to board at the same time, given the steep gradients that abound in the area, so as to minimize the need for frequent recharging.

[0015] Further, these new electric vehicle businesses may be forced to postpone offering customized solutions to specific consumer segments to post launch, rather than at pre-production as was the traditional norm. For example, take the battery pack, which for most indigenous electric vehicles designed in India are known to be imported. At the time of integration into the electric vehicle, the battery pack may not have not been tested for local conditions like temperature, humidity, road/terrain, or traffic conditions. As a result, the manufacturer at the time of launch would probably not be fully aware of the exact rate of degradation of the battery under any of the above conditions. Another example is that for many of these electric vehicle businesses, an optimized battery pack for operation in extreme desert conditions would most probably be a post-launch afterthought, rather than a pre-production design decision. A further example is that for many of these electric vehicle businesses, customization of the vehicle control design for steep gradients found in mountainous regions that provide disproportionately high amounts of regenerative braking recovery while moving downhill, would probably be a post-launch optimization. [0016]The following prior arts are cited that disclose the nature of related work.

[0017] Referring to US Patent application, US Patent publication number: US20180118047A1 , filed by Flonda Motor Co Ltd, discloses detection of a connection of a charging link of a charging station to the electric vehicle and detecting an ignition status of the electric vehicle being OFF, and charge parameter data from the electric vehicle is transmitted to a remote server to provide feedback for the driver regarding charging energy source type, time of charging and temporal driving information. This disclosure aims at computing potential savings in charging energy cost and providing feedback generated by a remote server to the driver. It captures only charging parameter data while the electric vehicle is being charged. A few shortcomings observed in this disclosure are that it does not capture the charge utilization from the traction battery, nor captures real time vehicle operating parameters during the normal vehicle operation to provide the context of said utilization. [0018] Referring to Chinese Patent application, Chinese Patent publication number: CN106611884A, filed by XIANGTAN ZHILIAN TECH MATASTASIS PROMOTE CO LTD, discloses a Battery Management System consisting of collecting unit, judging unit, send single unit, control module, charge protection unit, processing unit, alarm unit, control unit and connection processing unit, with precision measurement of cells, for Battery State of Charge, total voltage, current, insulation resistance, battery case interior temperature for the purpose of accurate breakdown judgement from data analysis using advanced thresholding technique, in addition to fault diagnosis and warning system. A shortcoming observed in this disclosure is that it does not capture the real time vehicle operating parameters during the normal vehicle operation to analyze the impact on the traction battery.

[0019] Referring to US Patent application, US Patent publication number: US10310022B2, filed by Samsung Electronics Co Ltd, discloses a method to estimate state of a battery for the purpose of range estimation by checking the validity of a battery model configured based on a parameter related to battery state information, and if the battery model is found to be invalid being outside the estimation range of the battery model, transmit an update request to an external battery model provider, and updating an alternate battery model configured based on a different parameter. A shortcoming observed in this disclosure is that it does not capture the real time vehicle operating parameters during the normal vehicle operation to analyze the impact on the traction battery. [0020] Referring to Korean Patent application, Korean Patent publication number:

KR20160049950A, filed by Samsung Electronics Co Ltd and University of North Carolina State, discloses a range estimation method and apparatus for electric vehicles based on a range estimation model that is based on analyzed associations, correlates collected attribute data classified into predetermined categories based on statistics by calculating sensitivity and feeding it back into the system in order to improve estimation accuracy. A few shortcomings observed in this disclosure are that it does not address battery degradation, nor does it capture the real time vehicle operating parameters during the normal operation of the electric vehicle to analyze the impact on the traction battery, nor does it address a plurality of types of electric vehicles employed in a plurality of end uses.

[0021] Referring to “Enrichment and Context-based Analytics of Electric Vehicle Charging Transaction Data” published in the 6th IEEE International Electric Drives Production Conference (EDPC 2016) at Nuremburg, Germany in November 2016 by Moritz von Hoffen, discloses the analysis of charging transaction data to learn about patterns of utilization and charging behaviour from the existing charging stations over OCPP protocol messages from the charging operator's backend systems, and enriching the context of the data with background and environment data like weather, temperature, location and proximity to Point of Interests (POIs). A few shortcomings observed in this disclosure are that it does not capture the charge utilization from the traction battery, nor captures real time vehicle operating parameters during the normal vehicle operation to provide the context of said utilization.

[0022]Referring to “Towards electric mobility data mining” published in the IEEE International Electric Vehicle Conference at Greenville, SC, USA in March 2012 by Martin Schroer et al, discloses electric vehicles fitted with data loggers connected to Controller Area Network bus, that transmit data via GPRS to remote Server, wherein the parameters collected are GPS position, vehicle speed, battery current, battery voltage, battery temperature, battery State of Charge (SoC) and static vehicle information, and trip identification, trip distance, battery charge consumed per trip and charging intervals are calculated values, and uses Yahoo weather web service to provide road utilization statistics, trip statistics and charging interval identification. A few shortcomings observed in this disclosure are that it does not address battery degradation, nor does it capture the real time vehicle operating parameters during the normal operation of the electric vehicle to analyze the impact on the traction battery, nor does it address a plurality of types of electric vehicles employed in a plurality of end uses.

[0023] Referring to “Systems for delivering electric vehicle data analytics” published as Thesis Dissertation at Purdue University in 2014 by Vamshy Krishna Bolly, discloses cloud based unstructured big data related to electric vehicles and handles the problem of data analytics using Hadoop cluster-based architecture for parallel processing using techniques like MapReduce, to evaluate the effectiveness of the analytics and to ensure compatibility with visualization applications. A few shortcomings observed in this disclosure are that being a purely cloud based system it does not address data acquisition from the electric vehicle, nor does it eliminate the need for filtering out relevant traction battery date from large amounts of big data, no captures real time vehicle operating parameters during the normal vehicle operations to provide the context of charge utilization. [0024] Referring to “Smart Data Selection and Reduction for Electric Vehicle Service

Analytics”, presented at the 50th Hawaii International Conference on System Sciences in 2017 by Jennifer Schoch et al, discloses a battery degradation model that is built for the purpose of State of Health (SOH) prediction where the source of data is from German Mobility Panel and GPS logs from Uber taxi fleets in one particular city, with data reduction being achieved using algorithms like Principal Component Analysis. A few shortcomings observed in this disclosure are that it does not capture the charge utilization from the traction battery, nor captures real time vehicle operating parameters during the normal vehicle operation to provide the context of said utilization.

[0025] Referring to “Cloud-based Big Data Platform for Vehicle-to-Grid (V2G)” published in the World Electric Vehicle Journal in March 2020 by Florent Gree et al, discloses capturing vehicle speed, GPS, ambient temperature and date/time from the electric vehicle and other data from Google API and electricity tariff sites for the purpose of prediction of range and route and optimization of charging to address the problem of matching the requirement of overall electric vehicle charging to the capacity of the electricity grid and to optimize electric vehicle charging cost and battery degradation. A few shortcomings observed in this disclosure are that it does not capture the charge utilization from the traction battery, nor captures real time vehicle operating parameters during the normal vehicle operation to provide the context of said utilization.

[0026] Referring to “Cloud-connected Battery Management System Supporting e- Mobility” published in the Fujitsu Scientific & Technical Journal in October 2015 by Tetsu Tanizawa et al, discloses a cloud connected battery management system for the purpose of replacement of the traction battery or battery swap for use in e- motorcycles, e-bicycles and e-small vehicles where the owners themselves can replace the battery. For the purpose of battery sharing with least concerns on battery degradation, the cloud system monitors the state of charge of all shared batteries and changes in their characteristics along with location data. A few shortcomings observed in this disclosure are that it does not capture real time vehicle operating parameters during the normal vehicle operation, nor does it address a plurality of types of electric vehicles employed in a plurality of end uses.

[0027]A need, therefore exists over prior art for an improved system that would help the new brand of electric vehicle businesses to gain better insights into the influence of vehicle operating conditions of particular electric vehicle types for particular end uses on the state of charge and state of health of the traction battery, towards addressing consumer concerns of energy anxiety and battery life. Electric vehicle manufacturers, traction battery manufacturers, suppliers of sub-systems like battery management system, electric vehicle fleet owners and end-consumers are the different groups of expected beneficiaries.

OBJECT OF THE INVENTION

[0028] One object of the present invention is to provide a highly reduced and relevant dataset extracted from the electric vehicle in real time called contextual data for analysis. In the contextual data, every data point, defined by impact data parameters, represents relevant data from the point of view of impact of vehicle operating conditions on a traction battery.

[0029]Another object of the present invention is to eliminate the need for filtering data relevant to the impact on the traction battery from a large data set of general vehicle data, thereby greatly reducing data processing overhead and cost.

[0030]0ne another object of the present invention is to provide flexibility of applicability of contextual data analysis over a plurality of electric vehicle types and a plurality of end uses from the perspective of impact of vehicle operating conditions on the traction battery. A plurality of traction battery impact tables defined, each catering to the specific nature of the electric vehicle type and end use, renders the disclosure truly generic and not limited to any particular type of electric vehicle or end use.

[0031]One another object of the present invention is to provide an improved system that would help the new brand of electric vehicle businesses to gain better insights into the influence of vehicle operating conditions of particular electric vehicle types for particular end uses on the state of charge and state of health of the traction battery, towards addressing consumer concerns of energy anxiety and battery life.

SUMMARY

[0032]The following summary is provided to facilitate an understanding of some of the innovative features unique to the disclosed embodiment and is not intended to be a full description. A full appreciation of the various aspects of the embodiments disclosed herein can be gained by taking into consideration the entire specification, claims, drawings, and abstract as a whole.

[0033]The present disclosure relates to analyzing the impact of vehicle operating conditions on a traction battery in a plurality of electrically propelled vehicle types employed for a plurality of end uses. More specifically, the present disclosure relates to a system and method for real time data extraction of contextual data of vehicle operating conditions that impact a traction battery.

[0034]The present invention provides a system and a method for real time extraction of contextual data of operational impact on the traction battery is disclosed. A microprocessor based electronic device is mounted on an electric vehicle for extracting the contextual data of the vehicle operating conditions that impact the traction battery. A data acquisition unit is used for acquiring CAN data and sensor data from CAN bus and sensors and store in the data acquisition ring buffer with timestamp. The impact detector can determine an impact event from CAN data and sensor data based on an impact parameter and the impact criteria stored in the traction battery impact unit. The contextual data extracted by the contextual data extractor from the data acquisition unit based on the impact event detected by the impact detector can be used by analysis system for analysis.

[0035] In a first aspect of the present disclosure, a system for real time extraction of at least one contextual data from a plurality of electric vehicles based on at least one vehicle operating condition that impacts a traction battery of the electric vehicle is disclosed. The system comprising: a microprocessor based electronic device mounted on the electric vehicle for extracting the contextual data of the vehicle operating conditions that impact the traction battery, wherein the microprocessor based electronic device comprises: at least one data acquisition unit for acquiring at least one CAN data from at least one CAN bus network of the electric vehicle and at least one sensor data from at least one sensor that measures a parameter of the electric vehicle or of the traction battery; a data acquisition ring buffer for persistently storing the CAN data and the sensor data with a timestamp; a traction battery impact unit for storing a plurality of traction battery impact tables, wherein each traction battery impact table comprises at least one impact parameter, at least one impact criteria corresponding to the impact parameter and at least one impact data parameter; an impact detector for autonomously determining a plurality of impact events from the CAN data or the sensor data in real time, based on the impact parameter and the impact criteria; a contextual data extractor for real time extraction of the contextual data comprising at least one impact data corresponding to the impact data parameter, directly from the data acquisition unit based on the impact events; and a contextual data ring buffer for persistently storing the contextual data extracted by the contextual data extractor; and an analysis system for performing analysis of the contextual data received from a plurality of microprocessor based electronic device, each contextual data based on at least one vehicle operating condition that impacts the traction battery of the electric vehicle.

[0036]According to an embodiment in conjunction to the first aspect of the present disclosure, the impact parameter acquired from the data acquisition unit is an electronic representation of the vehicle operating condition of the electric vehicle or of the traction battery that has a potential to impact the traction battery.

[0037]According to an embodiment in conjunction to the first aspect of the present disclosure, the impact parameter has a potential for impact on either the State of Charge or the State of Health of the traction battery as long as its instantaneous value satisfies a threshold condition represented by the impact criteria.

[0038]According to an embodiment in conjunction to the first aspect of the present disclosure, at least one of the traction battery impact tables are pre-configured as factory settings based on a plurality of electric vehicle types and a plurality of end uses of the electric vehicle.

[0039]According to an embodiment in conjunction to the first aspect of the present disclosure, further comprises at least one user system is connected to the traction battery impact unit through a wired or/and wireless means to modify the traction battery impact tables by one or more authorized users.

[0040]According to an embodiment in conjunction to the first aspect of the present disclosure, the traction battery impact table further comprises at least one pre-impact data parameter and at least one pre-impact time period corresponding to the pre impact data parameter.

[0041]According to an embodiment in conjunction to the first aspect of the present disclosure, the contextual data extractor performs retrospective extraction of pre impact data from the data acquisition ring buffer based on the pre-impact data parameter, the pre-impact time period and the timestamp corresponding to the impact start event. [0042]According to an embodiment in conjunction to the first aspect of the present disclosure, the contextual data comprising real time impact data and retrospective pre impact data stored in the contextual data ring buffer is accessed by one or more authorized users using at least one analysis system connected to a plurality of electric vehicles through a network.

[0043] In a second aspect of the present disclosure, a method for real time extraction of at least one contextual data from a plurality of electric vehicles based on at least one vehicle operating condition that impacts a traction battery of the electric vehicle is disclosed. The method comprising steps of: mounting a microprocessor based electronic device comprising at least one data acquisition unit, a data acquisition ring buffer, an impact detector, a contextual data ring buffer, a contextual data extractor and a traction battery impact unit having a plurality of traction battery impact tables; acquiring, by the data acquisition unit at least one CAN data from at least one CAN bus network of the electric vehicle; acquiring, by the data acquisition unit, at least one sensor data from at least one sensor that measures a parameter of the electric vehicle or of the traction battery; time stamping, by the data acquisition unit, the CAN data and sensor data on a common time base; persistently storing, by the data acquisition unit, the CAN data and the sensor data with the timestamp in a data acquisition ring buffer; configuring, by a user system, the traction battery impact tables, each traction battery impact table comprises at least one impact parameter, at least one impact criteria corresponding to the impact parameter and at least one impact data parameter; storing, by the user system, the impact parameter, the impact criteria and the impact data parameter in the traction battery impact tables of the traction battery impact unit; autonomously determining, by an impact detector, a plurality of impact events from the CAN data or the sensor data in real time, based on the impact parameter and the impact criteria; extracting in real time, by the contextual data extractor, the contextual data comprising at least one impact data corresponding to the impact data parameter, directly from the data acquisition unit based on the impact events; persistently storing, by the contextual data extractor, the contextual data in a contextual data ring buffer with the timestamp; and performing analysis, by an analysis system, the contextual data received from a plurality of electric vehicles, each contextual data based on at least one vehicle operating condition that impacts the traction battery of the electric vehicle.

[0044]According to an embodiment in conjunction to the second aspect of the present disclosure, the impact detector autonomously determines the impact events by: continuously monitoring the impact parameter in real time in the CAN data and the sensor data with reference to the corresponding impact criteria; autonomously determining an impact start event when the impact parameter satisfies the impact criteria; transmitting the impact start event to the contextual data extractor; autonomously determining an impact end event when the impact parameter no longer satisfies the impact criteria; and transmitting the impact end event to the contextual data extractor.

[0045]According to an embodiment in conjunction to the second aspect of the present disclosure, the contextual data extractor performs real time extraction of the contextual data by: extracting the impact data with the timestamp directly from the data acquisition unit starting from the impact start event till the impact end event.

[0046]According to an embodiment in conjunction to the second aspect of the present disclosure, further comprises: autonomously determining a pre-impact data start time for retrospective extraction from the data acquisition ring buffer by subtracting the pre impact time period from the impact start event time; and assigning the impact start event time to the pre-impact data end time for retrospective extraction from the data acquisition ring buffer.

[0047]According to an embodiment in conjunction to the second aspect of the present disclosure, further comprises: identifying the timestamp corresponding to the impact start event in the data acquisition ring buffer; and extracting retrospectively at least one pre-impact data with the timestamp from the data acquisition ring buffer based on the pre-impact data parameter starting from the pre-impact data start time till the pre impact data end time.

[0048]According to an embodiment in conjunction to the second aspect of the present disclosure, further comprises: accessing, by an analysis system, the contextual data from a plurality of electric vehicles; organizing the contextual data based on types of electric vehicles and their end uses, grouped by a plurality of factors such as the impact parameter; analyzing the organized contextual data, using statistical and correlational means; and presenting insights per vehicle type and end use, into the nature and degree of impact of specific vehicle operating conditions on the state of charge or/and state of health of the traction battery.

BRIEF DESCRIPTION OF THE DRAWINGS

[0049]The summary above, as well as the following detailed description of illustrative embodiments, is better understood when read in conjunction with the accompanying drawings. For the purpose of illustrating the present disclosure, exemplary embodiments of the disclosure are shown in the drawings. However, the disclosure is not limited to specific methods and instrumentalities disclosed herein. Moreover, those in the art will understand that the drawings are not to scale. Wherever possible, like elements have been indicated by identical numbers, and in which:

[0050] FIG. 1 is a block diagram of an example system 100 for real time extraction of at least one contextual data from a plurality of electric vehicles 130 based on at least one vehicle operating condition that impacts a traction battery 101 of the electric vehicle, in accordance with the disclosed embodiment.

[0051] FIG. 2 is a block diagram of an example traction battery impact unit 118 of the system 100 of FIG. 1 comprising a plurality of traction battery impact tables 150 180 and 190, in accordance with the disclosed embodiment.

[0052] FIG. 3 is a schematic diagram of an example traction battery impact unit 118 of the system 100 of FIG. 1 configured using a user system 202, in accordance with the disclosed embodiment.

[0053] FIG. 4 is a schematic diagram of an example traction battery impact table 150 in the traction battery impact unit 118 of FIG. 3, in accordance with the disclosed embodiment.

[0054] FIG. 5 is a schematic diagram of an example system 100 of FIG. 1 with the data acquisition unit 106, the impact detector 108 and the data acquisition ring buffer 112, in accordance with the disclosed embodiment. [0055] FIG. 6 is a schematic diagram of an example system 100 of FIG. 1 with the contextual data extractor 116, the data acquisition ring buffer 112 and the contextual data ring buffer 114, in accordance with the disclosed embodiment.

[0056] FIG. 7 is a schematic diagram of an example analysis system 400 for analyzing the contextual data 412, 414 and 416 extracted using one or more device 131 of FIG. 1, in accordance with the disclosed embodiment. [0057] FIG. 8 is a flowchart pertaining to an example method 500 for real time extraction of at least one contextual data from a plurality of electric vehicles based on at least one vehicle operating condition that impacts the traction battery of the electric vehicle, in accordance with the disclosed embodiment. [0058] FIG. 9 is a flowchart pertaining to an example method 520 for configuration of traction battery impact tables in the traction battery impact unit, in accordance with the disclosed embodiment.

[0059]FIG. 10 is a flowchart pertaining to an example method 530 for real time extraction of at least one impact data from a plurality of electric vehicles based on at least one vehicle operating condition that impacts the traction battery of the electric vehicle, in accordance with the disclosed embodiment.

[0060] FIG. 11 is a flowchart pertaining to an example method 550 for retrospective extraction of at least one pre-impact data from a plurality of electric vehicles based on at least one vehicle operating condition that impacts the traction battery of the electric vehicle, in accordance with the disclosed embodiment.

[0061] FIG. 12 is a flowchart pertaining to an example method 600 for analysis of the contextual data received from a plurality of electric vehicles by the analysis system, in accordance with the disclosed embodiment. [0062] Further, persons skilled in the art to which this disclosure belongs may appreciate that elements in the figures are illustrated for simplicity and may not have been necessarily been drawn to scale. Furthermore, in terms of the construction of the system and, one or more components of the system may have been represented in the figures by conventional symbols, and the figures may show only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the figures with details that are readily apparent to those of ordinary skill in the art having benefit of the description herein.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

[0063]These particular configurations discussed in the following description are non limiting examples that can be varied and are cited merely to illustrate at least one embodiment and are not intended to limit the scope thereof.

[0064] For the purpose of this application, the following definitions may be employed. For the purpose of this disclosure, the following definitions of the terms should be taken in a larger scope in a non-limiting manner.

[0065] Electric Vehicle: The term electric vehicle has popularly been used to refer to electrically propelled passenger cars - Hybrid Electric Vehicles (FIEV), Pluggable Hybrid Electric Vehicles (PFIEV) or fully Battery-Operated Vehicles (BEV). This disclosure defines the term electric vehicle in a larger scope that, in addition to the abovementioned vehicles, comprises electric 2-Wheelers, passenger 3-Wheelers, e- Bikes/e-Scooters, urban passenger transportation buses, and encompasses all types of electric vehicles for diverse end uses, like light commercial vehicles, heavy goods vehicles, dump trucks, towing vehicles, short transit vehicles, rural cargo transportation 3-Wheelers, off-road vehicles such as agricultural machinery/harvesting vehicles like tillers and combined harvesters, forklift trucks, golf carts and specialized vehicles employed in construction, material handling, mining, quarrying and heavy earth moving. Also included in the definition of the term electric vehicle for the purpose of this disclosure are battery-operated Drones encompassing all possible categories of Unmanned Aircraft Systems/UAS and Unmanned Aerial Vehicles/UAV for consumer, commercial, government and military use.

[0066]Type of electric vehicles: This disclosure covers all possible types of electric vehicle, for example, electric 2-Wheelers, and electric 3-Wheelers. Also included are all possible categories of battery-operated Drones like quadcopters and all possible types of unmanned battery-operated rovers, like for underwater exploration.

[0067] End use of electric vehicle: The end uses of electric vehicle in this disclosure, can be not limited to for example, cargo transportation and agricultural vehicles. Also included are all possible end uses of battery-operated Drones, for agricultural spraying, aerial survey, mapping, disaster management, inspection/monitoring of infrastructure, aerial photography, intelligence, surveillance and reconnaissance etc. and battery-operated unmanned rovers for underwater exploration, mining etc.

[0068] Traction battery: The term traction battery is defined as one or more units of chemical energy storage that serve the prime motive power to the traction motor in an electrically propelled vehicle. The term traction battery is used in a larger scope to encompass all technologies, battery chemistries and configurations, as long as it is used for said purpose. It is to be noted that auxiliary batteries, also known as Starting, Lighting and Ignition (SLI) batteries, that supply electric power to the auxiliary loads, such as headlamps, computer controls, infotainment systems and accessory systems, are excluded from the scope of traction battery. Examples of traction battery types are Lithium Ion and Sodium Nickel Chloride.

[0069]Vehicle operating condition: The term vehicle operating condition is defined as a dynamic state of the electric vehicle while in use for the intended operation. The term operating condition in a non-limiting manner to encompass all dynamic states of the electric vehicle or a traction battery that lend themselves to being detected or measured by electronic means and is available either as CAN data or as sensor data or optionally computed data from either thereof. Examples of vehicle operating conditions are over speeding, battery charging and negotiating a descent where the regenerative braking force is used for energy recovery by recharging the traction battery. For a Drone, vehicle operating conditions could be take-off, cruising, hovering or descent. [0070] Impact: The term impact is defined to refer to an influence of vehicle operating conditions on the traction battery. The term impact encompasses all expected as well as suspected influences of vehicle operating conditions on the State of Charge (SoC) or State of Health (SoH) of the traction battery. Examples of vehicle operating conditions that impact the traction battery are vehicle acceleration, climbing an uphill gradient, high ambient temperature and DC fast charging of the traction battery.

[0071] Interface: In a microprocessor based electronic device mounted on the electric vehicle, an interface can be defined as a wired or/and wireless electronic connection with a peripheral component, such as CAN bus, sensor, user system and analysis system. Examples of interfaces are USB, Bluetooth, Wi-Fi and cellular.

[0072]CAN bus: The term CAN refers to Controller Area Network, a popular industry standard for vehicle networking not limited to electric vehicles. In an electric vehicle the CAN bus typically comprises battery management system, traction motor controller, charging controller, supervisory controller and dashboard/cluster. These are only illustrative examples of CAN nodes in an electric vehicle and by no means limiting the diversity of CAN bus network design or topology found in electric vehicles. Further, for the purpose of this disclosure, CAN bus operates at any of the standard baud rates of 250 kbps, 500 kbps and 1 Mbps or Flexible data rate, with different data field lengths like but not limited to 8 bytes or 64 bytes. CAN bus is fast becoming popular in Drone designs as a robust communication alternative to supersede legacy systems like Pulse Width Modulation (PWM) owing to the superior immunity of CAN to withstand electromagnetic interference. This is a critical reliability requirement in applications such as defense and law enforcement. Although the CAN messaging format for Drone applications is not yet standardized, 1 Mbps baud rate is seen commonly adopted in Drone designs. [0073] CAN data: The CAN bus supports a plurality of CAN messaging protocols like, but not limited to, SAE J1939, SAE J1979/OBD-II and CAN Open. Further, CAN data may comprise electric vehicle parameters like, but not limited to, vehicle speed, accelerator pedal position and traction motor current. CAN data may additionally comprise traction battery parameters like, but not limited to, battery voltage, battery current and battery temperature. These are only illustrative examples of CAN data found in an electric vehicle and by no means limiting the scope for this disclosure.

[0074] Sensor: The term sensor is defined to refer to an instrument that is employed to measure at least one parameter of the vehicle. For the purpose of this disclosure, the term sensor is used in a larger scope in a non-limiting manner to encompass all electrical, electromechanical, electronic, solid state and other technologies, wired or wireless, passive or active, as long as it is used to measure at least one parameter that represents a vehicle operating condition of the electric vehicle or of the traction battery. Accelerometer, gyroscope, humidity sensor, temperature sensor and GPS are a few examples of sensors. Certain components of the electric vehicle like the auxiliary contacts of AC switch and headlamp switch are included in the definition of sensor. These are only illustrative examples of sensors and by no means limiting the diverse types and applications of sensors in an electric vehicle.

[0075] Sensor data: The term sensor data is defined to refer to the data that is generated by a sensor. Sensor data is defined to comprise parameters like, but not limited to, gradient, acceleration, GPS, humidity, temperature, and the states of various switches in the electric vehicle. The instantaneous value, or optionally computed value thereof, of each sensor data is representative of at least one vehicle operating condition. As an example, gradient sensor data represents the pitch angle of the vehicle that is determined by the slope of the terrain it currently stands on. As other examples, accelerometer data, after appropriate filtering and processing, represents an indicative road surface type, GPS sensor provides location of the vehicle, humidity and temperature sensor and air conditioner status switch indicate weather conditions, accelerator pedal, brake pedal and steering wheel angle, along with optionally computed data thereof, represent the driving pattern, headlamp switch indicates the time of driving whether it is day or night or bad weather, and clock provides exact time. Sensor data may additionally comprise traction battery parameters like, but not limited to, battery temperature and battery charging events. These are only illustrative examples of sensor data and by no means limiting the diverse sensor signals encountered in an electric vehicle. [0076] Data acquisition unit: Data acquisition unit is defined as responsible for acquiring CAN data and sensor data in real time, performing timestamping on both CAN data and sensor data on a common time base and optionally performing real time computations on the acquired parameters for deriving calculated parameters. For the purpose of this disclosure, data acquisition unit may implement all the hardware interfaces to CAN bus and sensor and include the corresponding interfacing firmware drivers. Data acquisition unit may employ a suitable CAN transceiver for interfacing with the CAN bus providing a matching impedance for error free CAN bus communication at the appropriate baud rate. Optionally galvanic or optical isolation may be provided at the CAN transceiver for better protection. Data acquisition unit may employ a programmable CAN Controller that implements message filters and masks as required for CAN communication. Data acquisition unit provides appropriate interface firmware drivers for the specific messaging protocol employed, like, but not limited to, SAE J1939. Data acquisition unit may include appropriate front-end circuitry for interfacing with the sensor, including any powering requirements of the sensor. Signal conditioning, non-linearity correction, filtering, scaling, bias and offset may be implemented at the hardware level or driver level based on the sensor characteristics. Data acquisition unit may optionally perform computations on the input data.

[0077] Data acquisition ring buffer: The data acquisition ring buffer is a data structure implemented in persistent storage for persistently storing the CAN data and the sensor data from the data acquisition unit with a timestamp. The data acquisition buffer is implemented as a circular First-In-First-Out (FIFO) buffer, where the earliest data starts getting erased as soon as the ring buffer becomes full. [0078] Traction battery impact unit: The traction battery impact unit is an element implemented in persistent memory in which a plurality of traction battery impact tables is implemented as data structures. The traction battery impact unit can be configured by an authorized user of the vehicle using the user system for modifying the various parameters and criteria preset or set in the traction battery impact tables.

[0079] Traction battery impact table: The traction battery impact table is a data structure implemented in the traction battery impact unit that can store impact parameter, impact criteria, and impact data parameter that can be used to extract at least one impact data in real time from the CAN data or the sensor data from a plurality of electric vehicles based on at least one vehicle operating condition that impacts the traction battery of the electric vehicle. The traction battery impact tables can optionally store pre-impact data parameter and pre-impact time period that can be used by the system to extract at least one pre-impact data retrospectively from the data stored in the data acquisition ring buffer based on timestamp. Traction battery impact tables are the elements that make this disclosure generic and applicable for a plurality of electric vehicle types for a plurality of end uses.

[0080] Impact parameter: Impact parameter is a parameter acquired by the data acquisition unit from CAN data or Sensor data or optionally from computed data that is an electronic representation of at least one vehicle operating condition of the electric vehicle or of the traction battery that has a potential to impact either the State of Charge or the State of Health of the traction battery. In the case of a land vehicle, the speed of the vehicle, gradient of the slope etc. could be considered examples for impact parameter, since these have the potential to impact the traction battery. Whereas for a Drone, the flight speed, load (weight including the payload) etc. that could cause a larger overall motor current of the propellors, could be considered examples of impact parameter.

[0081] Impact criteria: Impact criteria represents the threshold condition for the corresponding impact parameter, which when satisfied by the instantaneous value of the impact parameter, has a potential to impact the traction battery. Here satisfying the threshold includes the movement of the instantaneous value in both directions - either rising higher than the impact criteria in which case it exceeds, or falling lower than the impact criteria in which case it falls below, depending upon the nature of the impact parameter in its impact on the traction battery. The threshold may be the same in both directions or different in which case a hysteresis is defined optionally. The threshold may be static defined by an absolute value within the operating range of the impact parameter. For example, for an impact parameter of ambient temperature, the impact criteria may be defined as 40 degree Celsius. Alternatively, the threshold may be a dynamic value that is computed by the data acquisition unit either based on the impact parameter only, or based on a combination of analogously varying input data from CAN or/and sensor, or based on logical combinations of digital input data from CAN or/and sensor. Examples are rate of change or differential of an analog input and combination of the ON/OFF states of a plurality of vehicle switches.

[0082] Impact data parameter: The impact data parameter is the scope of real time data to be extracted by the contextual data extractor from the CAN data or/and sensor data directly from the data acquisition unit upon receiving impact events from the impact detector. A plurality of impact data parameters can be defined as the scope of real time data extraction.

[0083] Impact data: The impact data is a real time data extracted by the contextual data extractor from the CAN data or/and sensor data directly from the data acquisition unit upon receiving impact events from the impact detector, the scope of real time extraction being defined by the impact data parameter.

[0084] Pre-impact data parameter: The pre-impact data parameter is defined as the scope of past data to be extracted retrospectively by the contextual data extractor from the data stored in the data acquisition ring buffer upon receiving impact events from the impact detector. A plurality of pre-impact data parameters can be defined as the scope of retrospective data extraction.

[0085] Pre-impact time period: The pre-impact time period is defined as the temporal scope of past data to be extracted retrospectively by the contextual data extractor from the data stored in the data acquisition ring buffer upon receiving impact events from the impact detector. The temporal scope of retrospective extraction being determined using timestamps of data stored in the data acquisition ring buffer. [0086] Pre impact data: The pre-impact data is defined as retrospective data extracted by the contextual data extractor from the data stored in the data acquisition ring buffer upon receiving impact events from the impact detector, the scope of retrospective extraction being defined by the pre-impact data parameter and the pre-impact time period.

[0087] Pre-impact data start time: The pre-impact data start time is the timestamp of data stored in the data acquisition ring buffer starting from which retrospective extraction is performed by the contextual data extractor upon receiving impact events from the impact detector. Pre-impact data start time is autonomously determined by subtracting the pre-impact time period from the impact start event time.

[0088] Pre-impact data end time: The pre-impact data end time is the timestamp of data stored in the data acquisition ring buffer ending up to which retrospective extraction is performed by the contextual data extractor upon receiving impact events from the impact detector. Pre-impact data end time is autonomously determined by assigning the impact start event time.

[0089] User system: For the purpose of this disclosure, the term user system is defined as an element connected to the traction battery impact unit through a wired or/and wireless means and used by one or more authorized users to modify the traction battery impact tables in the traction battery impact unit. For example, the user system may be connected to the traction battery impact unit using a USB cable, or over a Wi Fi connection.

[0090]Authorized user: For the purpose of this disclosure, the term authorized user is defined as a person with appropriate authorization of a vehicle, after it is shipped from the manufacturer’s factory, to modify the traction battery impact tables. A vehicle supervisor at a cargo transportation fleet headquarters acting in the role of an administrator is an example of an authorized user who has the required permission to modify the traction battery impact tables as per the requirements of the fleet. [0091]Wired or wireless means: For the purpose of this disclosure, the term wired or wireless means is used in a larger scope in a non-limiting manner to encompass a plurality of wired technologies, including but not limited to serial and USB, and a plurality of wireless technologies, including but not limited to Bluetooth, Wi-Fi and cellular.

[0092] Impact detector: The impact detector is an element that can autonomously determine a plurality of impact events from the CAN data and/or the sensor data in real time, based on the impact parameter and the impact criteria set in one or more traction battery impact tables in the traction battery impact unit.

[0093] Impact event: For the purpose of this disclosure, impact event is defined as an event generated by the impact detector when the instantaneous value of the impact parameter satisfies the impact criteria, either exceeds or falls below the threshold, as the case may be.

[0094] Impact start event: For the purpose of this disclosure, impact start event is defined as the event when the instantaneous value of the impact parameter satisfies the impact criteria, either exceeds or falls below the threshold, as the case may be.

[0095] Impact end event: For the purpose of this disclosure, impact end event is defined as the event corresponding to an impact start event, when the instantaneous value of the said impact parameter, moving in the opposite direction, no longer satisfies the impact criteria, either falls below or exceeds the threshold, as the case may be.

[0096] Contextual data extractor: The contextual data extractor is an element that performs extraction of contextual data based on the impact event received from impact detector. Contextual data comprises both real time extraction of impact data from the CAN data or the sensor data obtained directly from the data acquisition unit as well as retrospective extraction of pre-impact data from the data stored in the data acquisition ring buffer. [0097] Contextual data ring buffer: The contextual data ring buffer is an element implemented in persistent storage for persistently storing the contextual data extracted by the contextual data extractor. Contextual data comprises both real time impact data as well as retrospective pre-impact data. The contextual data ring buffer is implemented as a circular First-In-First-Out (FIFO) buffer, where the earliest data starts getting erased as soon as the ring buffer becomes full.

[0098] Contextual data: The term contextual data refers to a temporal subset of electronic data of a pre-defined subset scope that is extracted from general vehicle data every time, and only if, there is an impact of vehicle operating conditions on the traction battery. Contextual data is a highly filtered data obtained from the electric vehicle by means of this disclosure, specific in scope and time, that contains the context of the impact of vehicle operating conditions on the State of Charge or State of Health of the traction battery. Contextual data is a superset of both impact data extracted in real time and pre-impact data extracted retrospectively by the contextual data extractor and stored in the contextual data ring buffer. Contextual data greatly reduces the volume of data to a relevant subset, forming the basis of meaningful analysis. [0099] Analysis system: The term analysis system is an element that performs analysis on the contextual data received from a plurality of electric vehicles to provide better insights into the nature and degree of impact of specific vehicle operating conditions on the State of Charge and State of Health of the traction battery per type of the electric vehicle per end-use.

[00100] Analysis unit: The analysis unit is defined as a part of the analysis system that helps to organize the contextual data based on types and end uses of electric vehicles. The analysis unit receives the contextual data from a plurality of electric vehicles. The analysis unit provides graphical user interfaces to enable setting the grouping of contextual data based on various factors such as vehicle make/model, ownership, region and impact parameter. The analysis unit also uses statistical and correlational means to analyze and plot the contextual data using interactive graphs and charts to enable the discovery of accurate correlations between impact parameters that represent various vehicle operating conditions and their impact on the traction battery.

[00101] Contextual analysis: For the purpose of this disclosure, the term contextual analysis is defined as the analysis performed by the analysis system on the contextual data received from a plurality of electric vehicles, using statistical and correlational means. Contextual data received from a plurality of electric vehicles may be suitably grouped based on vehicle type, make/model, ownership, region and end use for performing meaningful contextual analysis.

[00102] Network: The term network is used in a larger scope in a non-limiting manner to encompass all possible means and technologies employed to interconnect a microprocessor based electronic device mounted on a plurality of electric vehicles to a central server hosted on the internet. Examples of network are cellular data communication network and Wi-Fi network with internet connectivity provided through a Wi-Fi router.

[00103] The subject matter described in the present disclosure relates to analyzing the impact of vehicle operating conditions on a traction battery in a plurality of electrically propelled vehicle types employed in a plurality of end uses. More specifically, the present disclosure relates to the real time extraction of the contextual data of said impact for analysis for better insights into the influence of vehicle operating conditions on the traction battery parameters of State of Charge and State of Health, for each and every type and end use of the electric vehicle.

[00104] Embodiments of the present disclosure are described below in detail with reference to the accompanying figures.

[00105] Referring to the system 100 of FIG. 1, a preferred embodiment of the present disclosure includes a microprocessor based electronic device 131 that is mounted on the electric vehicle 130 and powered from it, comprising of interfaces to: at least one CAN bus network 104 of the electric vehicle 130; at least one sensor 102 that measures a parameter of the electric vehicle 130 or of a traction battery 101; at least one user system 202 connected to the electric vehicle 130 either locally or remotely through a wired or/and wireless means 204 for configuration purposes by authorized users; and at least one analysis system 400 connected remotely to a plurality of electric vehicles 130, 132 and 134 through a network 420 for analysis purposes by authorized users.

[00106] The system 100 for real time extraction of at least one contextual data from a plurality of electric vehicles 130 based on at least one vehicle operating condition that impacts a traction battery 101 of the electric vehicle 130 is disclosed. The system 100 has the microprocessor based electronic device 131 that is mounted on the electric vehicle 130. The microprocessor based electronic device 131 comprises a data acquisition unit 106, a traction battery impact unit 118, an impact detector 108, a contextual data extractor 116, a data acquisition ring buffer 112 and a contextual data ring buffer 114.

[00107] For example, a supplier of Battery Management System, while performing integration tests of a traction battery pack with the electric vehicle before the commencement of production, could employ the present disclosure to collect contextual data related to the impact of specific vehicle operating conditions, like but not limited to, vehicle speed and acceleration on the traction battery. Additional impact data parameters like accelerator pedal position, degree of gradient and air-conditioner switch ON/OFF status could be extracted in order to accurately assess the said impact. [00108] The data acquisition unit 106 is used for acquiring in real time at least one CAN data from at least one CAN bus network 104 of the electric vehicle 130 and at least one sensor data from at least one sensor 102 that measures a parameter of the electric vehicle 130 or of the traction battery 101. The system 100 also has a data acquisition ring buffer 112 for persistently storing the CAN data and the sensor data with a timestamp.

[00109] The device 131 is employed as a CAN node in the CAN bus network of the electric vehicle 130, providing a matching impedance on the CAN bus for error- free communication in real time with other CAN nodes like, but not limited to, Battery Management System (BMS). For example, CAN data acquired from the CAN bus of the electric vehicle may comprise vehicle speed, motor speed, traction motor current, forward/reverse mode, accelerator pedal position and the charging status of the traction battery. These are only illustrative examples of CAN data in an electric vehicle and by no means limiting the scope for the purpose of this disclosure.

[00110] For example, sensor data acquired from a suitable temperature sensor may represent the ambient temperature. As another example, latitude/longitude data acquired through a GPS receiver may represent the vehicle’s current location. These are only illustrative examples of sensor data in an electric vehicle and by no means limiting the scope for the purpose of this disclosure.

[00111] The device 131 is equipped with persistent memory for non-volatile storage of data, like Flash memory or other persistent memory technologies. The data acquisition ring buffer 112 is implemented in persistent memory. Owing to the limitation of storage size and the continuous nature of data arrival from the data acquisition unit 106, the data acquisition buffer is implemented as a circular First-In-First-Out (FIFO) buffer, where the earliest data starts getting erased as soon as the ring buffer becomes full.

[00112] The device 131 is equipped with a real-time clock (RTC) with backup battery for determining the timestamp of CAN data and sensor data arriving at the data acquisition unit 106 and timestamping them on a common time base at a resolution of 1 millisecond. This time resolution will take care of fast varying CAN data like motor rpm that typically gets updated once in every 10 milliseconds, and fast varying data from sensors like accelerometer. Slow varying parameters from temperature sensor and rarely varying CAN data like Vehicle Identification Number (VIN) are also easily handled. Optionally, synchronization of time with Cloud based time server may be employed for improving the accuracy of the timestamps over prolonged operation.

[00113] Data acquisition unit may optionally perform computations on the input data. An example is the calculation of the rate of change of the steering angle to determine the rate of turn of the vehicle. Another example is if battery charging status information is not available in CAN data or sensor data, then the same can be computed using the rate of increase in battery charge indicating that the vehicle is plugged into the charging station, combined with zero vehicle speed to ensure that the vehicle is stationary and to rule out the possibility of increase in battery charge due to regenerative braking. A further example is the logical operations on multiple binary input data like switch ON/OFF status to determine specific vehicle operating conditions. [00114] The traction battery impact unit 118 comprises a plurality of traction battery impact tables 150, 180 and 190. The traction battery impact tables store impact parameter, impact criteria, and impact data parameter that are used by the system to extract at least one impact data in real time from the CAN data or the sensor data based on at least one vehicle operating condition that impacts the traction battery of the electric vehicle. The traction battery impact tables optionally store pre-impact data parameter and pre-impact time period that can be used by the system to extract at least one pre-impact data retrospectively from the data stored in the data acquisition ring buffer based on timestamp. [00115] The traction battery impact unit 118 is implemented in persistent memory. The traction battery impact tables are implemented as data structures within the traction battery impact unit.

[00116] The system 100 has an impact detector 108 for autonomously determining a plurality of impact events 110 from the CAN data or the sensor data in real time, based on the impact parameter 152 and the impact criteria 154 set in one or more traction battery impact tables 150 in the traction battery impact unit 118.

[00117] The impact detector 108 detects an impact when the instantaneous value of an impact parameter satisfies the impact criteria, either exceeds or falls below the threshold, as the case may be. This point in time defined as the impact start event with a corresponding impact start event time. Correspondingly, the impact is no longer valid when the impact parameter, moving in the opposite direction, no longer satisfies the impact criteria, either falls below or exceeds the threshold, as the case may be. This point in time defined as the impact end event with a corresponding impact end event time. [00118] Suitable hysteresis/lag may be optionally provided between rising and falling patterns of the impact parameter in order to avoid triggering spurious impact events in situations where the instantaneous value of the impact parameter hovers around the threshold value defined by the impact criteria. For example, for an impact parameter of ambient temperature, if the impact criteria for the rising pattern is defined as 40 degree Celsius, the corresponding impact criteria for the falling pattern may be defined as 35 degree Celsius.

[00119] The contextual data extractor 116 is used to perform real time extraction of the contextual data from the CAN data or the sensor data obtained directly from the data acquisition unit 106 based on the impact events 110 received from impact detector 108.

[00120] Impact data is extracted in real time directly from the data acquisition unit 106. Real time extraction starts at the impact start event and continues till the impact end event and provides a temporal subset of real time electronic data of a pre defined subset scope that is valid throughout the entire period of said impact.

[00121] For example, a battery manufacturer could employ the present disclosure to collect contextual data related to battery degradation across regions with high extremes in temperature, for providing guidance to consumers regarding seasonal best practices for battery charging for prolonging battery life. Flere ambient temperature measured by a suitable temperature sensor is the impact parameter along with suitable impact criteria that is defined based on the battery characteristics, to yield impact data in real time.

[00122] In case if pre-impact data of an impact event is required, the contextual data extractor 116 can optionally extract the pre-impact data retrospectively from the data acquisition ring buffer 112 based on the pre-impact data parameter 170 the pre- impact time period 156 by comparison of timestamps of data stored in the data acquisition ring buffer 112 corresponding to the impact start event.

[00123] Pre-impact data is extracted retrospectively from the data acquisition ring buffer 112 to provide a window into the recent past data before the impact occurred. Retrospective extraction starts at pre-impact time period time prior to the impact start event time till the impact start event time as identified by the timestamp of the data stored in the data acquisition ring buffer 112. Pre-impact data helps better understand the conditions that were existing immediately preceding one or more of the vehicle operating conditions actually impacting the traction battery.

[00124] In the aforementioned example of battery manufacturer analyzing battery degradation across regions with high extremes in temperature, the state of charge of the battery can be defined as the pre-impact data parameter for the required pre-impact time period. This will help understand the state of charge of the battery and its correlation at the time when the said extreme temperature impacted battery degradation. Further, the pre-impact data also helps to fine tune the values set for the impact criteria for future contextual data extraction. [00125] The contextual data may comprise at least one impact data corresponding to the impact data parameter 160, extracted in real time directly from the data acquisition unit 106 based on the impact events 110. The contextual data extracted by the contextual data extractor 116 is persistently stored in the contextual data ring buffer 114.

[00126] The contextual data may optionally comprise at least one pre-impact data corresponding to the pre-impact data parameter 170 and the pre-impact time period 156, extracted retrospectively from the data acquisition ring buffer 112 based on the impact events 110. The contextual data extracted by the contextual data extractor 116 is persistently stored in the contextual data ring buffer 114.

[00127] For example, a manufacturer of cargo electric vehicles performing on road trials, could employ the present disclosure to check the effect of simultaneous vehicle loading and gradient slope on the discharge pattern of the traction battery in order to optimize the vehicle’s control design. Here vehicle load and degree of gradient can be defined as separate impact parameters, each with specific impact criteria to provide relevant contextual data.

[00128] The contextual data ring buffer 114 is implemented in persistent memory. Owing to the limitation of storage size and the continuously increasing size of real time impact data and retrospective pre-impact data extracted by the contextual data extractor 116 over time, the data acquisition ring buffer is implemented as a circular First-In-First-Out (FIFO) buffer, where the earliest data starts getting erased as soon as the ring buffer becomes full.

[00129] Referring to FIG. 2, an example traction battery impact unit 118 of the system 100 of FIG. 1 comprising a plurality of traction battery impact tables 150, 180 and 190. Referring to FIG. 3 a schematic diagram of an example traction battery impact unit 118 of the device 131 of FIG. 1 configured using the user system 202. After installing the microprocessor based electronic device 131 in vehicle for real time extraction of at least one contextual data based on at least one vehicle operating condition that impacts the traction battery of the electric vehicle, subsequently the authorized user of the vehicle can modify 180 and 190 if required. This allows the device 131 to modify the required contextual data to be extracted based on a plurality of vehicle operating conditions, each of which is defined by at least one impact parameter 152 and at least one impact criteria 154 corresponding to the impact parameter 152 set in the traction battery impact tables 150, 180 and 190, that impacts the traction battery of the electric vehicle.

[00130] FIG. 4 is a schematic diagram of an example traction battery impact table 150 in the impact data parameter unit 118 of FIG. 3, in accordance with the disclosed embodiment. The traction battery impact table 150 comprises at least one impact parameter 152, at least one impact criteria 154 corresponding to the impact parameter 152 and at least one impact data parameter 160. In one embodiment of the invention, the impact data parameter 160 may comprise various impact data parameters 162, 164 and 166. In another embodiment of the invention, the traction battery impact table 150 optionally comprises at least one pre-impact data parameter 170. In yet another embodiment of the invention, the pre-impact data parameter 170 may comprise various pre-impact data parameters 172, 174 and 176 and a pre-impact time period

156

[00131] The traction battery impact tables 150, 180 or 190 are pre-configured as factory settings based on a plurality of electric vehicle 130 types and a plurality of end uses of the electric vehicle 130. The traction battery impact table 150, 180 or 190 can be modified later after installing in the vehicle, by one or more authorized users, using at least one user system 202 connected to the traction battery impact unit 118 through a wired or/and wireless means 204.

[00132] One or more traction battery impact tables are pre-configured at the factory into the traction battery impact unit with the impact parameter, impact criteria and impact data parameter defined for the particular type of electric vehicle and considering its end use. Said initial configuration is based on specific vehicle operating conditions, that are both expected and suspected, to have a potential to impact the traction battery. [00133] For example, in one of the traction battery impact tables of a heavy earth moving electric vehicle, the vehicle load may be defined as the impact parameter with a specific threshold limit of load in metric tons defined as the impact criteria, and may further define impact data parameter as comprising of traction motor current, battery state of charge and battery temperature. In this example, the impact will occur when the load of the vehicle exceeds the threshold limit set, at which time real time extraction of traction motor current, battery state of charge and battery temperature will start and continue till the time the vehicle load falls below the threshold limit.

[00134] Optionally, the pre-impact parameter may be defined as comprising of battery state of charge and battery temperature with a pre-impact time period of 60 seconds. When the impact occurs, the battery state of charge and battery temperature will optionally be retrospectively extracted from the data acquisition ring buffer for a time period of 60 seconds prior to the occurrence of the impact. [00135] It may be noted that the configurable nature of the traction battery impact tables is what makes the disclosure generic for a plurality of electric vehicle types and a plurality of end uses from the perspective of impact on the traction battery. Since the vehicle operating conditions are chosen for a particular electric vehicle type for a particular end use, the impact parameter, the impact criteria and impact data parameter are configured accordingly in one or more traction battery impact tables. For example, the contents and values of the traction battery impact tables would vary widely across a heavy earth moving electric vehicle, an agricultural electric vehicle and a goods carrier electric 3-Wheeler. However, the contextual data of the impact of vehicle operating conditions on the traction battery are extracted in a uniform and consistent manner across diverse types and diverse end uses of electric vehicles.

[00136] Said traction battery impact tables may be modified later after installing in the vehicle at the customer’s premises using a user system connected to the traction battery impact unit locally through a wired means like USB, or wireless means like Bluetooth or Wi-Fi, said modification being restricted to authorized users only. Further, said traction battery impact tables may be modified remotely during road trials through wireless means like Wi-Fi or Cellular, said modification being restricted to authorized users only.

[00137] For example, a fleet has procured a particular model of good carriers electric 3-Wheelers from a particular manufacturer. At the time of shipment of the vehicles from the factory, the traction battery impact tables of all the vehicles have been pre-configured to a factory setting, applicable for this type and end use of electric vehicle. However, the fleet administrator who is an authorized user could modify the existing traction battery impact tables, or create new tables as per the requirements of the entire fleet. This can be accomplished by connecting a user system to the traction battery impact unit locally through a wired or/and wireless means.

[00138] As another example, during field trials of the aforementioned example, the fleet administrator finds that vehicles plying through a given route particularly abounding in steep gradients need to have additional contextual data defined, extracted and analyzed. In this situation the authorized user could modify the existing traction battery impact tables, or create new tables as per the requirements of specific vehicles only. This can be accomplished by connecting a user system to the traction battery impact unit locally through a wired or/and wireless means when the vehicle is present in the fleet headquarters, or alternatively when the vehicle is out on a trip by connecting the user system remotely over a cellular connection.

[00139] FIG. 5 is a schematic diagram of an example system 100 of FIG. 1 with the data acquisition unit 106 and the impact detector 108, in accordance with the disclosed embodiment. The data acquisition unit 106, acquires CAN data from the CAN bus 104 and sensor data from the sensor 102 of the electric vehicle that measures a parameter of the electric vehicle 130 or of the traction battery 101. The impact detector 108 autonomously determines a plurality of impact events 110 from the CAN data and/or the sensor data in real time, based on the impact parameter 152 and the impact criteria 154 set in one or more traction battery impact tables 150 in the traction battery impact unit 118. The impact detector 108 determines impact events in real time before the CAN data and the sensor data are persistently stored in the data acquisition ring buffer 112 with a timestamp. [00140] Optionally, the CAN data and the sensor data in the data acquisition ring buffer 112 with timestamp can be used by the device 131 for retrospective extraction of the pre-impact data from the electric vehicle based on the vehicle operating condition that impacts the traction battery of the electric vehicle. [00141] FIG. 6 is a schematic diagram of an example system 100 of FIG. 1 with the contextual data extractor 116, the data acquisition ring buffer 112 and the contextual data ring buffer 114, in accordance with the disclosed embodiment.

[00142] The contextual data extractor 116 extracts the contextual data in real time based on the impact events 110 determined by the impact detector 108 and the CAN data or the sensor data that is acquired by the data acquisition unit 106. The contextual data may comprise at least one impact data corresponding to the impact data parameter 160, extracted directly from the data acquisition unit 106 based on the impact events 110. The contextual data extracted by the contextual data extractor 116 is persistently stored in the contextual data ring buffer 114. Thus, the contextual data that are relevant to the vehicle operating condition that impacts the traction battery of the electric vehicle are extracted and the same can be used for contextual analysis.

[00143] In one embodiment of the invention, the contextual data extractor 116 optionally extracts the pre-impact data retrospectively from the data acquisition ring buffer 112 based on the pre-impact data parameter 170, the pre-impact time period 156 and the timestamp of data stored in the data acquisition ring buffer 112 corresponding to the impact start event. It should be noted that contextual data extractor 116 can refer to the traction battery impact unit 118 for obtaining the pre impact data parameter 170 and the pre-impact time period 156 to be used for retrospectively extracting the pre-impact data. [00144] FIG. 7 is a schematic diagram of an example analysis system 400 for analyzing the contextual data 412, 414 and 416 extracted using one or more device 131 of FIG. 1, in accordance with the disclosed embodiment. For example, the device

131 can be installed in the electric vehicles, for example electric vehicles 130, 132 or 134 for extracting the contextual data, for example the contextual data 412, 414, 416, respectively. The contextual data 412, 414, 416 is transferred remotely to analysis unit 430 using a network 420 for performing contextual analysis. It should be noted that it is not only impact data extracted in real time by the contextual data extractor of the preferred embodiment of the present disclosure, the pre-impact data is also extracted retrospectively by the contextual data extractor. Hence the contextual data 412, 414, 416 made available by the device 131 for contextual analysis by the analysis system

400 comprises impact data as well as pre-impact data from the electric vehicles 130,

132 or 134 respectively.

[00145] For example, a manufacturer of an electric vehicle employed for agriculture, in which both the motive power for the vehicle and the power to drive the agricultural load are both supplied by the traction battery, could employ the present invention during initial trials to collect contextual data. Insights gained after grouping and analysis of the contextual data could be used to provide guidance to consumers on the optimum vehicle speed to be maintained while performing a certain agricultural activity like tilling, in order to minimize rapid drain of the traction battery.

[00146] FIG. 8 is a flowchart pertaining to an example method 500 for real time extraction of at least one contextual data from a plurality of electric vehicles based on at least one vehicle operating condition that impacts the traction battery of the electric vehicle, in accordance with the disclosed embodiment. As at step 502, the data acquisition unit can be used to acquire at least one sensor data from at least one sensor that measures a parameter of the electric vehicle or of the traction battery. As at step 504, at least one data acquisition unit can also be used to acquire at least one CAN data from at least one CAN bus network of the electric vehicle. The CAN data and sensor data acquired at steps 504 and 502 are time stamped on a common time base, as at step 506. As at step 508, the CAN data and the sensor data with the timestamp are persistently stored by the data acquisition unit in the data acquisition ring buffer.

[00147] As at step 510, the impact detector can be used to autonomously determine a plurality of impact events from the CAN data or the sensor data in real time, based on the impact parameter and the impact criteria set in one or more traction battery impact tables in the traction battery impact unit.

[00148] As at step 512, the contextual data extractor can extract in real time the contextual data comprising at least one impact data corresponding to the impact data parameter, extracted directly in real time from the data acquisition unit based on the impact event detected by the impact detector. As at step 514, the extracted contextual data can be persistently stored by the contextual data extractor with the timestamp in the contextual data ring buffer. The contextual data can then be transferred to the analysis system for contextual analysis. [00149] FIG. 9 is a flowchart pertaining to an example method 520 for configuration of traction battery impact tables in the traction battery impact unit. Using the user system, the preconfigured traction battery impact tables which are set from the factory in the traction battery impact unit can be further modified by authorized user with a better understanding of the vehicle operating conditions of the particular type and end use of the electric vehicle. The traction battery impact unit has a plurality of traction battery impact tables, each traction battery impact table comprises at least one impact parameter, at least one impact criteria corresponding to the impact parameter and at least one impact data parameter. Each traction battery impact table optionally comprises at least one pre-impact data parameter and one pre-impact time period. The user system can be used by the authorized user to configure or modify and store the impact parameter, impact criteria and impact data parameter in one or more traction battery impact tables of the traction battery impact unit, as at steps 522 and 524. The user system can be optionally used to configure or modify and store the pre-impact data parameter and the pre-impact time period, as at steps 522 and 524.

[00150] FIG. 10 is a flowchart pertaining to an example method 530 for real time extraction performed by the contextual data extractor of at least one impact data based on at least one vehicle operating condition that impacts the traction battery of the electric vehicle, in accordance with the disclosed embodiment. The impact detector can autonomously determine the impact events using the steps in the method 530. As at step 532, the impact parameters in the real time CAN data and the sensor data are continuously monitored with reference to the corresponding impact criteria 154.

[00151] As at step 534, impact start events are autonomously determined when the impact parameter satisfies the impact criteria, either exceeds or falls below the threshold, as the case may be. Then as at step 536, the impact start events are transmitted to the contextual data extractor.

[00152] As at step 538, impact end events are autonomously determined when the impact parameter, moving in the opposite direction, no longer satisfies the impact criteria, either falls below or exceeds the threshold, as the case may be. Then as at step 540, the impact end events are transmitted to the contextual data extractor.

[00153] The contextual data extractor can use the impact start event and impact end event for extracting the contextual data based on the vehicle operating conditions that impact the traction battery of the electric vehicle. As at step 542, the contextual data extractor can perform real time extraction of the contextual data by extracting the impact data with the timestamp directly from the data acquisition unit starting from the impact start event till the impact end event. [00154] For example, if the impact start event occurred at 16:20:22:435

(hh:mm:ss:ms) and the impact end event occurred at 16:20:23:546 (hh:mm:ss:ms), then the real time extraction of impact data directly from the data acquisition unit, as per the defined impact data parameters, happens for a duration of 1 second and 111 milliseconds. The impact data is persistently stored in the contextual data ring buffer.

[00155] FIG. 11 is a flowchart pertaining to an example method 550 for retrospective extraction performed by the contextual data extractor of at least one pre impact data from a plurality of electric vehicles based on at least one vehicle operating condition that impacts the traction battery of the electric vehicle, in accordance with the disclosed embodiment. As at step 552, a pre-impact data start time is autonomously determined by subtracting the pre-impact time period from the impact start event time. Then as at step 554, the impact start event time is assigned to a pre impact data end time. [00156] As at step 556, the timestamp corresponding to the impact start event in the data stored in the data acquisition ring buffer is determined. Then as at step 558, at least one pre-impact data with the timestamp is extracted from the data acquisition ring buffer based on the pre-impact data parameter starting from the pre-impact data start time till the pre-impact data end time.

[00157] For the aforementioned example, if the impact start event occurred at 16:20:22:435 (hh:mm:ss:ms) and the pre-impact time period is defined as 15 seconds, then the retrospective extraction of pre-impact data from the data acquisition ring buffer, as per the defined pre-impact data parameters, happens for a duration of 15 seconds, starting from 16:20:07:435 (hh:mm:ss:ms) to 16:20:22:435 (hh:mm:ss:ms) which is the impact start event time. The pre-impact data is persistently stored in the contextual data ring buffer. [00158] FIG. 12 is a flowchart pertaining to an example method 600 for analysis of the contextual data received from a plurality of electric vehicles by the analysis system 400, in accordance with the disclosed embodiment. As at step 610 the contextual data from a plurality of electric vehicles is received remotely using a network 420.

[00159] As at step 612, the contextual data is organized based on types and end uses of electric vehicles and grouped based on a plurality of factors such as vehicle make/model, ownership, region and impact parameter, by the authorized user using the analysis unit 430.

[00160] As at step 614, the organized contextual data is analyzed by the authorized user by statistical and correlational means, by using the analysis unit 430. The analysis unit plots the contextual data from a plurality of vehicles, organized and grouped as mentioned above, in the form of interactive graphs and charts to enable the discovery of correlations between impact parameters that represent various vehicle operating conditions and their impact on the traction battery.

[00161] As at step 616, insights into the nature and degree of impact of specific vehicle operating conditions represented by specific impact parameters or combinations thereof, on the State of Charge and State of Health of the traction battery are presented by the analysis system 400 per type and end use of the electric vehicle.

[00162] For example, a fleet owner of electric vehicles employed as heavy goods carriers could employ the present disclosure during routine fleet operations to analyze the contextual data related to the impact of route and terrain on the discharge patterns of the traction battery and correlate to the overall charging costs of the fleet. Guidance to fleet drivers could include the optimum speed to be maintained on level ground, optimum vehicle loading for economy, acceleration guidelines while starting from rest in a laden condition, braking guidelines both general and specific while descending a slope for maximum regenerative recovery, battery charging guidelines before long trips and maximum speed limit. [00163] Another example is a manufacturer of passenger 3-Wheelers who could employ the present disclosure during road trials to analyze the contextual data related to city driving conditions, to enable providing guidance to consumers on bumper-to- bumper driving and stop-and-go traffic conditions, charging guidelines during peak summer & winter and maximum limits of speed and acceleration for best economy and cost.

[00164] A further example involving the end consumers directly, especially in newly emerging end use segments such as rural cargo transportation and agriculture, is to employ the present disclosure to study and analyze their own past data on battery charge utilization with reference to their particular operation or activity. With the aid of a convenient mobile app, they could develop insights autonomously into cost and economy of their vehicle operations and optimize accordingly. [00165] The following examples illustrate the possibility of extending the present disclosure beyond its key objectives of addressing energy anxiety and battery degradation.

[00166] The present disclosure could be employed with suitable adaptations to improve the accuracy of in-vehicle range estimation systems to render it more reliable through the actual measurement of both the current battery state of charge as well as other discharge related parameters such as agricultural load, gradient slope and driving patterns. [00167] The present disclosure could be combined with data on time-of-day tariffs offered by power grids to provide advice to consumers on optimal charging times with reference to their current activity in order to minimize energy anxiety while simultaneously minimizing charging costs. [00168] The term “impact” that applies to state of charge and state of health of the traction battery as defined by the present disclosure could be generalized to a wider scope, and the embodiments of the disclosure suitably modified and adapted, to cover the impact on any vehicle parameter to offer a preventive maintenance solution. For example, maintenance of traction motor, battery or wear and tear of moving parts, by suitably redefining the impact parameters, impact criteria and impact data parameters accordingly. [00169] The present disclosure could be suitably modified and adapted for fleets to monitor driver behavior and provide feedback to fleet drivers, or to address the impact on various parameters of interest in trip operations, like overloading and idling time. [00170] The present disclosure could be suitably modified and adapted for monitoring driving patterns of vehicle owners and to assess their impact on the safety scores, to offer Usage-based Insurance solutions where users are incentivized by calibrating the insurance premium based on their safety scores and suitably addressing their privacy concerns. For example, to detect unsafe maneuvers like throttle flooring, sudden braking, wild swerving and tight cornering patterns are deemed to adversely impact the driver’s safety scores.

[00171] It will be appreciated that variations of the above disclosed and other features and functions, or alternatives thereof, may be desirably combined into many other different systems or applications. Also, various presently unforeseen or unanticipated alternatives, modifications, variations, or improvements therein may be subsequently made by those skilled in the art which are also intended to be encompassed by the following claims. [00172] Although embodiments of the current disclosure have been described comprehensively in considerable detail to cover the possible aspects, those skilled in the art would recognize that other versions of the disclosure are also possible.

Claims

1. A system (100) for real time extraction of at least one contextual data from a plurality of electric vehicles (130) based on at least one vehicle operating condition that impacts a traction battery (101) of the electric vehicle (130), the system (100) characterized in that comprising: a microprocessor based electronic device (131) mounted on the electric vehicle (130) for extracting the contextual data of the vehicle, wherein the microprocessor based electronic device (131) comprises: at least one data acquisition unit (106) for acquiring at least one CAN data from at least one CAN bus network (104) of the electric vehicle (130) and at least one sensor data from at least one sensor (102) that measures a parameter of the electric vehicle (130) or of the traction battery (101); a data acquisition ring buffer (112) for persistently storing the CAN data and the sensor data with a timestamp; a traction battery impact unit (118) for storing a plurality of traction battery impact tables (150, 180 or 190), wherein each traction battery impact table comprises at least one impact parameter (152), at least one impact criteria (154) corresponding to the impact parameter (152) and at least one impact data parameter (160); an impact detector (108) for autonomously determining a plurality of impact events (110) from the CAN data or the sensor data in real time, based on the impact parameter (152) and the impact criteria (154); a contextual data extractor (116) for real time extraction of the contextual data comprising at least one impact data corresponding to the impact data parameter (160), directly from the data acquisition unit (106) based on the impact events (110); and a contextual data ring buffer (114) for persistently storing the contextual data extracted by the contextual data extractor (116); and an analysis system (400) for performing analysis of the contextual data received from a plurality of microprocessor based electronic device (131), each contextual data based on at least one vehicle operating condition that impacts the traction battery (101) of the electric vehicle (130).

2. The system (100) of claim 1, wherein the impact parameter acquired from the data acquisition unit (106) is an electronic representation of the vehicle operating condition of the electric vehicle (130) or of the traction battery (101) that has a potential to impact the traction battery (101).

3. The system (100) of claim 1 , wherein the impact parameter has a potential for impact on either the State of Charge or the State of Health of the traction battery (101) as long as its instantaneous value satisfies a threshold condition represented by the impact criteria (101).

4. The system (100) of claim 1 , wherein at least one of the traction battery impact tables (150, 180 or 190) are pre-configured as factory settings based on a plurality of electric vehicle (130) types and a plurality of end uses of the electric vehicle (130).

5. The system (100) of claim 1, further comprises at least one user system (202) is connected to the traction battery impact unit (118) through a wired or/and wireless means (204) to modify the traction battery impact tables (150, 180 or 190) by one or more authorized users.

6. The system (100) of claim 5, wherein the traction battery impact table (150, 180 or 190) further comprises at least one pre-impact data parameter (170) and at least one pre-impact time period (156) corresponding to the pre-impact data parameter.

7. The system (100) of claim 6, wherein the contextual data extractor (116) performs retrospective extraction of pre-impact data from the data acquisition ring buffer (112) based on the pre-impact data parameter (170), the pre-impact time period (156) and the timestamp corresponding to the impact start event.

8. The system of claim 7, wherein the contextual data comprising real time impact data and retrospective pre-impact data stored in the contextual data ring buffer (114) is accessed by one or more authorized users using at least one analysis system (400) connected to a plurality of electric vehicles through a network (420).

9. A method (500, 520, 530, 550, 600) for real time extraction of at least one contextual data from a plurality of electric vehicles (130) based on at least one vehicle operating condition that impacts a traction battery (101) of the electric vehicle, using system (100) of claim 1, the method characterized in that comprising steps of: mounting a microprocessor based electronic device (131) comprising at least one data acquisition unit (106), a data acquisition ring buffer (112), an impact detector (108), a contextual data ring buffer (114), a contextual data extractor (116) and a traction battery impact unit (118) having a plurality of traction battery impact tables

(150, 180 or 190); acquiring, by the data acquisition unit (106) at least one CAN data from at least one CAN bus network (104) of the electric vehicle (130); acquiring, by the data acquisition unit (106), at least one sensor data from at least one sensor (102) that measures a parameter of the electric vehicle (130) or of the traction battery (101); time stamping, by the data acquisition unit (106), the CAN data and sensor data on a common time base; persistently storing, by the data acquisition unit (106), the CAN data and the sensor data with the timestamp in a data acquisition ring buffer (112); configuring, by a user system (202), the traction battery impact tables (150, 180 or 190), each traction battery impact table comprises at least one impact parameter (152), at least one impact criteria (154) corresponding to the impact parameter (152) and at least one impact data parameter (160); storing, by the user system (202), the impact parameter (152), the impact criteria (154) and the impact data parameter (160) in the traction battery impact tables (150, 180 or 190) of the traction battery impact unit (118); autonomously determining, by an impact detector (108), a plurality of impact events (110) from the CAN data or the sensor data in real time, based on the impact parameter (152) and the impact criteria (154); extracting in real time, by the contextual data extractor (116), the contextual data comprising at least one impact data corresponding to the impact data parameter (160), directly from the data acquisition unit (106) based on the impact events (110); persistently storing, by the contextual data extractor (116), the contextual data in a contextual data ring buffer (114) with the timestamp; and performing analysis, by an analysis system (400), the contextual data received from a plurality of electric vehicles, each contextual data based on at least one vehicle operating condition that impacts the traction battery (101) of the electric vehicle (130).

10. The method (530) of claim 9, wherein the impact detector (108) autonomously determines the impact events (110) by: continuously monitoring the impact parameter (152) in real time in the CAN data and the sensor data with reference to the corresponding impact criteria (154); autonomously determining an impact start event when the impact parameter (152) satisfies the impact criteria (154); transmitting the impact start event to the contextual data extractor (116); autonomously determining an impact end event when the impact parameter (152) no longer satisfies the impact criteria (154); and transmitting the impact end event to the contextual data extractor (116).

11. The method (530) of claim 10, wherein the contextual data extractor (116) performs real time extraction of the contextual data by: extracting the impact data with the timestamp directly from the data acquisition unit (106) starting from the impact start event till the impact end event.

12. The method (550) of claim 11 , further comprises: autonomously determining a pre-impact data start time for retrospective extraction from the data acquisition ring buffer (112) by subtracting the pre-impact time period from the impact start event time; and assigning the impact start event time to the pre-impact data end time for retrospective extraction from the data acquisition ring buffer (112).

13. The method (550) of claim 12, further comprises: identifying the timestamp corresponding to the impact start event in the data acquisition ring buffer (112); and extracting retrospectively at least one pre-impact data with the timestamp from the data acquisition ring buffer based on the pre-impact data parameter starting from the pre-impact data start time till the pre-impact data end time.

14. The method (600) of claim 13, further comprises: accessing, by an analysis system (400), the contextual data from a plurality of electric vehicles; organizing the contextual data based on types of electric vehicles and their end uses, grouped by a plurality of factors such as the impact parameter; analyzing the organized contextual data, using statistical and correlational means; and presenting insights per vehicle type and end use, into the nature and degree of impact of specific vehicle operating conditions on the state of charge or/and state of health of the traction battery.