Description:TITLE OF THE INVENTION: AN APPARATUS FOR TRIP-BASED AMELIORATED CHARGING OF A VEHICLE AND METHOD THEREOF
FIELD OF THE INVENTION
The present disclosure is generally related to charging of a vehicle. The present disclosure is particularly related to trip-based ameliorated charging of vehicle. The present disclosure is more particularly related to an apparatus, for trip-based ameliorated charging of vehicle and method thereof, in real time.
BACKGROUND OF THE INVENTION
The Lithium-ion based energy storage system has gained significant popularity in the realm of vehicles due to its prominent features, such as high energy density, power density, and the absence of a memory effect. However, the performance of such energy storage system is profoundly influenced by various operating conditions such as temperature, charging/discharging rate, depth of discharge range, and idle time.
The operation of said energy storage system is managed by a dedicated unit called a battery management system (BMS). The BMS plays a vital role in monitoring and controlling various aspects of the battery system, including charging/discharging processes, cell balancing, voltage regulation, and state of charge (SOC) estimation. It ensures that the battery operates within safe and efficient operating limits.
One critical aspect of optimizing the performance of the energy storage system is the selection of charging parameters such as charging rate (C-rate) and depth of discharge (DOD). These parameters have a significant impact on the performance of battery and cycle counts.
High charging rates can lead to issues like limited active material utilization, Li plating, and excessive heat generation, while slow charging prolongs charging time. Higher depth of discharge range increases stress and decreases cycle counts. Hence, precise control of these parameters is necessary to enhance the performance of battery in the vehicle. Furthermore, rate of charging can be control based on the distance of subsequent vehicle from charging depot.
Techniques are available for addressing the charging and discharging of energy storage systems of vehicles. For instance, US8138715B2 introduces a network-controlled charge transfer device that enables remote management of vehicle charging. This device comprises a control device, transceiver, and controller, allowing for efficient and automated control of the charging process (i.e., enable and disable the chargers to charge the vehicle at predefined charging rate) based on instructions received from a remote server.
Another invention, US8330415B2, presents a charge/discharge control apparatus that enhances the management and communication aspects of vehicle charging and discharging. This apparatus includes various units such as a reward information receiving unit, a computing unit for creating optimized charge/discharge plans, an instruction transmitting unit, a quantity monitoring unit, and a results transmitting unit. These units work together to facilitate efficient control and monitoring of charge/discharge operations while communicating with a central server. For charging of the vehicle, predefined charging rates are utilized to charge the EV.
In US9197091B2, an electric charging system for vehicle batteries is described. The system consists of a charging station with two operational modes: fast charging and slow charging. A data collection system acquires battery state information and optimization parameters, while a station control unit establishes a charging profile based on the station's energy transfer capabilities. The charging profile is designed to meet specific targets for state of charge and charge completion time, ensuring efficient and effective charging of the battery pack.
Despite of the above disclosed techniques, there exists a need to further develop techniques for effective charging the energy storage system of a vehicle with the control mechanism based on the past trip information and battery behaviour. There is, therefore, a need in the art, for an apparatus, for trip-based ameliorated charging of a vehicle and method thereof, which overcomes the aforementioned drawbacks and shortcomings.
SUMMARY OF THE INVENTION
An apparatus, for trip based ameliorated charging of a vehicle that is embedded on a processing member, is disclosed. Said apparatus broadly comprises: an at least an inclusive driving pattern recording member; an at least a weather monitoring member; an infrastructure service cloud; a trip-ahead data prognosticating member; an ameliorated charging parameters member; and a charging depot network.
Said at least one inclusive driving pattern recording member is configured to record a first plurality of parameters from vehicle, with the recorded data being transmitted to an infrastructure service cloud.
In an embodiment, the first plurality of parameters includes, but not limited to, current and past data of: longitude and latitude of locations, mileage, driving range, vehicle speed, acceleration and deceleration patterns, battery stem voltage, current, State-of-Charge (SOC), State-of-Health (SOH), and/or the like.
Said at least one weather monitoring member is configured to record weather parameters, with the recorded data being transmitted to an infrastructure service cloud.
In an embodiment, the weather parameters include, but not limited to, current and past data of temperature and humidity.
In an embodiment, said at least one infrastructure service cloud broadly comprises: a charging station cloud and a vehicle operator cloud. Said charging station cloud performs data exchange with: the plurality of charging depots and the ameliorated charging parameters member, and the vehicle operator cloud performs data exchange with: at least one inclusive driving pattern recording member and the at least one weather monitoring member.
Said trip-ahead data prognosticating member broadly comprises: a trip-ahead weather prognosticating unit; a trip-ahead driving pattern prognosticating unit; a trip-ahead remaining discharge energy prognosticating unit; and a trip-ahead energy demand prognosticating unit.
The trip-ahead weather prognosticating unit is configured to prognosticate a weather condition, based on the weather parameters recorded by the at least one weather monitoring member.
In yet another embodiment, the trip-ahead weather prognosticating unit utilises a combination of Numerical Weather Prediction (NWP) technique, Machine Learning-Based Regression technique, and Kalman Filtering Technique.
The trip-ahead driving pattern prognosticating unit is configured to prognosticate a trip-ahead driving pattern, based on the first plurality of parameters recorded by the at least one inclusive driving pattern recording member.
In another embodiment, the trip-ahead driving pattern prognosticating unit utilises a combination of Long Short-Term Memory neural network technique and Auto-Regressive Integrated Moving Average (ARIMA) technique.
The trip-ahead remaining discharge energy prognosticating unit is configured to prognosticate remaining energy available in the energy storage system of the vehicle.
In yet another embodiment, the trip-ahead remaining discharge energy prognosticating unit utilises a combination of coulomb counting technique, Extended Kalman Filter (EKF) technique, Open Circuit Voltage (OCV) technique, and temperature and aging compensation technique, for prognosticating remaining energy available in the energy storage system of the vehicle.
The trip-ahead energy demand prognosticating unit is configured to prognosticate an energy demand for a trip-ahead, based on the prognosticated trip-ahead driving pattern and the prognosticated remaining energy available in the energy storage system.
The ameliorated charging parameters member is configured to prognosticate ameliorated charging parameters of the energy storage system, for charging the vehicle when connected to a charger among a plurality of chargers.
In an embodiment, said ameliorated charging parameters include ameliorate charging rate and depth of discharge.
A method of working of the apparatus is also disclosed.
The disclosed apparatus offers at least the following advantages: is simple in construction; is cost-effective; determines the trip ahead energy demand (kWh) of the vehicle; manage the predefined charging rate (fast and slow) to ameliorate charging rate and depth of the discharge range; improves the charging management in charging depots by reducing the required time for charging the vehicle.
BRIEF DESCRIPTION OF THE DRAWINGS
Figure 1 illustrates an apparatus for trip-based ameliorated charging of a vehicle, in accordance with an embodiment of the present disclosure;
Figure 1a illustrates a charging station cloud and a vehicle operator cloud, in accordance with an embodiment of the present disclosure;
Figure 1b illustrates various components of trip-ahead data prognosticating member, in accordance with an embodiment of the present disclosure;
Figure 1c illustrates a connection between the charging station cloud (represented as dotted line) and charging depot netwrok combines various charging depots avialble at different loocations, in accordance with an embodiment of the present disclosure;
Figure 1d illustrates connection between a plurality of charging depots and a plurality of chargers avialble for the charging, in accordance with an embodiment of the present disclosure;
Figure 2 illustrates different options avaliable for charging a vehcile in accordance with an embodiment of the present disclosure;
Figure 3 illustrates a flow chart describing a method for ameliorated charging of an energy storage system of a vehicle, in accordance with an embodiment of the present disclosure; and
Figure 4 is a flow chart that illustrates a process of evaluation of ameliorated charging parameters, of an energy storage system of a vehicle, in accordance with an embodiment of the present disclosure.
DETAILED DESCRIPTION OF THE INVENTION
Throughout this specification, the use of the words “comprise” and “include”, and variations, such as “comprises”, “comprising”, “includes”, and “including”, may imply the inclusion of an element (or elements) not specifically recited. Further, the disclosed embodiments may be embodied, in various other forms, as well.
Throughout this specification, the use of the word “apparatus” is to be construed as: “a set of technical components (also referred to as “members”) that are communicatively and/or operably associated with each other, and function together, as part of a mechanism, to achieve a desired technical result”.
Throughout this specification, the use of the words “communication”, “couple”, and their variations (such as communicatively), is to be construed as being inclusive of: one-way communication (or coupling); and two-way communication (or coupling), as the case may be, irrespective of the directions of arrows, in the drawings.
Throughout this specification, where applicable, the use of the phrase “at least” is to be construed in association with the suffix “one” i.e. it is to be read along with the suffix “one”, as “at least one”, which is used in the meaning of “one or more”. A person skilled in the art will appreciate the fact that the phrase “at least one” is a standard term that is used, in Patent Specifications, to denote any component of a disclosure, which may be present (or disposed) in a single quantity, or more than a single quantity.
Throughout this specification, where applicable, the use of the phrase “at least one” is to be construed in association with a succeeding component name.
Throughout this specification, the use of the word “plurality” is to be construed as being inclusive of: “at least one”.
Throughout this specification, the use of the word “vehicle”, and its variations, is to be construed as being inclusive of: “commercial electrical vehicles (CEV)”. A person skilled in the art will appreciate the fact that the use of the word “vehicle” may also include: “other electric vehicles; hybrid electric vehicles; and/or the like”.
Throughout this specification, where applicable, the use of the phrase “charging of a vehicle”, and its variations, is to be construed as being inclusive of “charging of an energy storage system of a vehicle”.
Throughout this specification, the use of the phrase “energy storage system”, the acronym “ESS”, and their variations, is to be construed as being inclusive of: “battery modules; battery packs; battery systems; and/or the like”.
Throughout this specification, where applicable, the phrase “energy storage system” and the word “battery” are used interchangeably.
Throughout this specification, the use of the word “battery”, and its variations, is to be construed as being inclusive of: “lithium-ion batteries”.
Throughout this specification, the use of the word “ameliorating”, and its variations, is to be construed as being inclusive of: “controlling; monitoring; regulating; adjusting; and/or the like”.
Throughout this specification, the use of the word “prognosticating”, and its variations, is to be construed as being inclusive of: “forecasting; estimating; predicting; and/or the like”.
Throughout this specification, the use of the word “trip-ahead”, and its variations, is to be construed as being inclusive of: “a trip or travel that is planned (or expected) to occur in the future or yet to happen”.
Throughout this specification the phrase “processing member”, and its variations, is to be construed as being inclusive of: central processing unit of a computing device; microcontroller; and/or the like.
Throughout this specification, the use of the phrase “computing device”, and its variations, is to be construed as being inclusive of: the cloud; remote servers; desktop computers; laptop computers; mobile phones; smart phones; tablets; phablets; smart watches; and/or the like.
Throughout this specification, the words “the” and “said” are used interchangeably.
Throughout this specification, the phrases “at least a”, “at least an”, and “at least one” are used interchangeably.
Throughout this specification, the word “sensor” and the phrase “sensing member” are used interchangeably. The disclosed sensing members may be of any suitable type known in the art.
Throughout this specification, the disclosure of a range is to be construed as being inclusive of: the lower limit of the range; and the upper limit of the range.
Also, it is to be noted that embodiments may be described as a method. Although the operations, in a method, are described as a sequential process, many of the operations may be performed in parallel, concurrently, or simultaneously. In addition, the order of the operations may be re-arranged. A method may be terminated, when its operations are completed, but may also have additional steps.
An apparatus (10), for trip based ameliorated charging of a vehicle (hereafter also referred to as “apparatus”), is disclosed. In an embodiment of the present disclosure, as illustrated, in Figure 1, said apparatus (10) broadly comprises: an at least an inclusive driving pattern recording member (100); an at least a weather monitoring member (200); an infrastructure service cloud (300); a trip-ahead data prognosticating member (500); and an ameliorated charging parameters member (600).
In another embodiment of the present disclosure, said apparatus (10) is embedded (or installed) on a processing member.
In yet another embodiment of the present disclosure, the at least one inclusive driving pattern recording member (100) records (or collects, or captures, or receives) a first plurality of parameters, from a vehicle (400). Said first plurality of parameters may comprises current and past (or historical) data.
Said first plurality of parameters may include, but is not limited to, longitude and latitude of locations, mileage, driving range, vehicle speed, acceleration and deceleration patterns, battery stem voltage, current, State-of-Charge (SOC), State-of-Health (SOH), and/or the like.
In yet another embodiment of the present disclosure, said inclusive driving pattern recording member (100) is communicatively associated with the at least one infrastructure service cloud (300).
Said at least one weather monitoring member (200) records (or collects, or captures, or receives) weather parameters, such as temperature and humidity. Said weather parameters may comprises current and past (or historical) data.
In yet another embodiment of the present disclosure, said at least one weather monitoring member (200) is communicatively associated with the at least one infrastructure service cloud (300).
In yet another embodiment of the present disclosure, said at least one infrastructure service cloud (300) broadly comprises: a charging station cloud (310); and a vehicle operator cloud (320).
In yet another embodiment of the present disclosure, as illustrated in Figure 1c, a charging depot network (700) broadly comprises: a plurality of charging depots (710; for example, “m” number of charging depots).
In yet another embodiment of the present disclosure, as illustrated in Figure 1d, each charging depot among the plurality of charging depots (710) broadly comprises: a plurality of charges (711; for example, “n” number of chargers).
In yet another embodiment of the present disclosure, each charging depot among the plurality of charging depots (710) is communicatively associated with the charging station cloud (310).
Said infrastructural cloud serves as a central hub for data exchange between the various components of the disclosed apparatus (10).
Said charging station cloud (310) is configured to perform data exchange with: the plurality of charging depots (710) and the ameliorated charging parameters member (600).
Said vehicle operator cloud (320) is configured perform data exchange with: at least one inclusive driving pattern recording member (100) and the at least one weather monitoring member (200).
As illustrated in Figure 1, said ameliorated charging parameters member (600) is associated with the vehicle (400). Said vehicle (400) broadly comprises: an energy storage system (410); a battery management unit (420); and a vehicle control unit (430).
The energy storage system (410) is monitored and controlled by the battery management unit (420). Said vehicle control unit (430) monitors and controls a drive train of the vehicle (400) continuously, in real-time.
Said trip-ahead data prognosticating member (500) is configured to prognosticate future energy requirements for a trip-ahead, based on the first plurality of parameters recorded by the at least one inclusive driving pattern recording member (100).
As illustrated in Figure 1b, said trip-ahead data prognosticating member (500) broadly comprises: a trip-ahead weather prognosticating unit (510); a trip-ahead driving pattern prognosticating unit (520); a trip-ahead remaining discharge energy prognosticating unit (530); and a trip-ahead energy demand prognosticating unit (540).
Said trip-ahead driving pattern prognosticating unit (520) prognosticates a trip-ahead driving pattern using a data forecasting technique, based on the first plurality of parameters recorded by the at least one inclusive driving pattern recording member (100).
Said trip-ahead driving pattern prognosticating unit (520) utilises a Long Short-Term Memory (LSTM) neural network technique for prognosticating the trip-ahead driving pattern. The LSTM neural network technique is effective in analysing sequential driving patterns and prognosticating the future driving patterns based on the past data.
Said LSTM neural network technique is trained using first plurality of parameters from the past data.
In yet another embodiment of the present disclosure, the apparatus (10) also employs an ARIMA (Auto-Regressive Integrated Moving Average) technique for time-series analysis to detect long-term driving patterns.
Combination of LSTM neural network technique and ARIMA technique ensures both short-term and long-term forecasting capabilities for improving the accuracy of trip-ahead prognostication.
Said trip-ahead weather prognosticating unit (510) prognosticates a weather condition using a weather forecasting technique, based on the weather parameters recorded by the at least one weather monitoring member (200).
In yet another embodiment of the present disclosure, the trip-ahead weather prognosticating unit (510) utilises a combination of Numerical Weather Prediction (NWP) technique, Machine Learning-Based Regression technique, and Kalman Filtering Technique. Said Numerical Weather Prediction technique, such as Weather Research and Forecasting (WRF) technique processes the real-time weather data from meteorological sources.
Techniques used in said machine learning-based regression technique may include, Gradient Boosting Regression Tree (GBRT) technique and Recurrent Neural Networks (RNNs) technique. Said gradient boosting regression trees technique and recurrent neural network technique analyses the past weather data and real-time weather data to prognosticate localized weather conditions that affecting the vehicle.
Said Kalman Filtering technique is used to continuously refine weather forecasting based on real-time data collected from the vehicle.
The above-mentioned techniques collectively improve the accuracy of forecasting temperature, humidity, and road conditions, thereby enabling optimized charging strategies.
Said trip-ahead remaining discharge energy prognosticating unit (530) is configured to prognosticate remaining energy available in the energy storage system (410) of the vehicle (400).
The trip-ahead remaining discharge energy prognosticating unit (530) evaluates the remaining energy available in the energy storage system (410) as explained below.
The charge inflow and/or outflow is tracked by integrating current over time using Coulomb counting technique.
State of Charge (SOC) of the energy storage system (410) is determined by refining voltage, current, and temperature readings using Extended Kalman Filter (EKF) technique.
SOC under specific condition is inferred based on the measure terminal voltage using Open Circuit Voltage (OCV) technique.
The apparatus (10) applies State of Health (SOH) correction factors to account degradation and temperature effects on the energy storage system (410) using Temperature and aging compensation strategy.
The aforementioned technique collectively provides a precise evaluation of remaining discharge energy, ensuring accurate trip planning and charging strategies.
Said trip-ahead energy demand prognosticating unit (540) is configured to prognosticates an energy demand for the trip-ahead, based on the trip-ahead driving pattern prognosticated by the trip-ahead driving pattern prognosticating unit (520) and the remaining energy available in the energy storage system (410) prognosticated by the trip-ahead remaining discharge energy prognosticating unit (530).
The trip-ahead energy demand prognosticating unit (540) prognosticates the trip-ahead energy demand by based on the following parameters.
Power demand at different route segments determined based on past driving data using LSTM technique;
Energy consumption of the vehicle determined using Energy Consumption Model;

Energy recovery potential determined based on topography and braking patterns using Regenerative braking estimation technique; and
Wind resistance, road friction, and temperature effects adjusted using Weather impact adjustment technique.
Aforementioned parameters ensures that an accurate prognostication of the energy required for the upcoming trip (or trip-ahead).
The ameliorated charging parameters member (600) is configured to prognosticate ameliorated charging parameters such as; ameliorate charging rate and depth of discharge (DOD trip ahead) of the energy storage system (410), for charging the vehicle (400) when connected to a charger among the plurality of chargers (711).
In yet another embodiment of the present disclosure, as illustrated in Figure 2, a charging rate (or charging option) of the energy storage system (410) can be: a predefined fast charging, a predefined slow charging, or an ameliorated charging, according to the choice of a vehicle operator and/or a charger operator, as per requirement.
Method of working of the disclosed apparatus (10) shall now be explained with the help of Figure 3 and Figure 4.
As illustrated in Figure 3, in step S101, the first plurality of parameters is recorded by the at least one inclusive driving pattern recording member (100). Likewise, the weather condition including temperature and humidity are recorded by the at least one weather monitoring member (200). Then, the recorded information from the at least one inclusive driving pattern recording member (100) and at least one weather monitoring member (200) is transmitted (or forwarded, or sent) to the vehicle operator cloud (320).
Said first plurality of parameters and weather condition from the vehicle operator cloud (320) is transmitted (or forwarded, or sent) to the trip-ahead driving pattern prognosticating unit (520), in step S102.
In step S103, the trip-ahead driving pattern prognosticating unit (520) prognosticates the trip-ahead driving pattern using the Long Short-Term Memory neural network technique and the ARIMA technique, based on the first plurality of parameters. Said data forecasting techniques utilises the first plurality of parameters in order to train various parameters.
Likewise, in step S103, the trip-ahead weather prognosticating unit (510) prognosticates the weather condition using a combination of Numerical Weather Prediction technique, Machine Learning-Based Regression technique, and Kalman Filtering technique based on the weather parameters recorded by the at least one weather monitoring member (200).
Based on the output of the trip-ahead driving pattern prognosticating unit (520) and the trip-ahead weather prognosticating unit (510), battery parameters, such as State-of-Charge (SOC), voltage, and current of the energy storage system (410) is evaluated.
Now, in step S104, the trip-ahead remaining discharge energy prognosticating unit (530) prognosticates remaining energy (or discharge energy) available in an energy storage system (410). The value of remaining discharge energy (〖Ener〗_Rm) is prognosticated from the following equation.
〖Ener〗_Rm=f(I_System,V_System,T_weather,〖SOC〗_System)
Where, I_System,V_System,〖 SOC〗_System refers to the current, terminal voltage, and SoC of the energy storage system (410), respectively, and 〖 T〗_weather refers to atmospheric temperature.
In step S105, said trip-ahead energy demand prognosticating unit (540) prognosticates the energy demand (〖Ener〗_Dm ) for the trip-ahead, based on the trip-ahead driving pattern (D.P_Trip-ahead ) prognosticated by the trip-ahead driving pattern prognosticating unit (520) and remaining energy available (〖Ener〗_Rm) in the energy storage system (410) prognosticated by the trip-ahead remaining discharge energy prognosticating unit (530). Equation for prognosticating the trip-ahead energy demand (〖Ener〗_Dm) is represented as,
〖Ener〗_Dm=f(〖Ener〗_Rm,D.P_Trip-ahead )
In step 106, the ameliorated charging parameters member (600) prognosticates the ameliorated charging parameters such as; ameliorate charging rate and depth of discharge (DOD trip ahead) of the energy storage system (410). Steps involved in prognosticating of the ameliorated charging parameters using evaluated trip ahead energy demand (〖Ener〗_Dm), remaining discharge energy (〖Ener〗_Rm), state of charge (〖SOC〗_System), are as follows, as illustrated in Figure 4.
In step S201, the values of trip ahead energy demand (〖Ener〗_Dm), remaining discharge energy (〖Ener〗_Rm), State-of-Charge (〖SOC〗_System), State-of-Health (〖SOH〗_System) of the energy storage system (410) are reordered and transfer to next step.
Depth of discharge (DOD) of a fully charged energy storage system (410) is defined as,
DOD=100-〖SOC〗_System
Where, 〖SOC〗_System is the state-of-charge of the energy storage system (410).
The DOD range for charging is defined as the addition of 〖DOD〗_Buffer with the difference in DOD at start of charging and end of charging. This can be represented by the following equation.
DOD_range=〖DOD〗_strt-〖DOD〗_end+〖DOD〗_Buffer
The value of 〖DOD〗_Buffer is set in a range between about 5% and about 10 %.
In step 202, the value of trip ahead DOD_range is prognosticated based on: trip ahead energy demand (〖Ener〗_Dm), remaining discharge energy (〖Ener〗_Rm), and State-of-Charge (〖SOC〗_System), using the following formula:
〖DOD〗_(Trip_Ahead)=f(〖Ener〗_Dm, 〖Ener〗_Rm, 〖SOC〗_System,dSOH⁄(dDOD_range))
Where, the DOD range with less effect on the health of the energy storage system (410), the relation between health degradation rate under different DOD_range (dSOH⁄(dDOD_range)) is determined from the battery data and past recorded driving pattern data.
In step S203, the value of ameliorated charging rate (〖Crate〗_Ameliorated) is prognosticated based on the 〖DOD〗_(Trip_Ahead ), distance between a nearest charging depot among the plurality of charging depots (710) from the vehicle (400) coming for charging (𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒), state-of-health of the energy storage system (〖SOH〗_System), and trip-ahead prognosticated energy demand (〖Ener〗_Dm). This is represented by the following equation.
〖Crate〗_Ameliorated=f(〖DOD〗_(Trip_Ahead ),Distance,〖Ener〗_Dm,〖SOH〗_System)
After prognosticating the ameliorated charging parameters by the ameliorated charging parameters member (600), said ameliorated charging parameters are transmitted to: a charging depot among the plurality of charging depots (710) in the charging depot network (700) through the charging station could (310) from the battery management unit (420), and then to a charger among the plurality of charges (711) while charging the vehicle (400).
Based on the demand of the vehicle (400) or load on each charging depot among the plurality of charging depots (710) or availability of charger among the plurality of charges (711), vehicle operators or charging station operators can choose the charging option available for charging the vehicle (400), such as predefined fast charging, predefined slow charging, or ameliorated charging on 〖Crate〗_Ameliorated.
The disclosed apparatus offers at least the following advantages: is simple in construction; is cost-effective; determines the trip ahead energy demand of the vehicle; manages the predefined charging rate (fast and slow) to ameliorate charging rate and depth of the discharge range; improves the charging management in charging depots by reducing the required time for charging the vehicle.
A person skilled in the art will appreciate the fact that the apparatus, and its various components, may be made of any suitable materials known in the art. Likewise, a person skilled in the art will also appreciate the fact that the configurations of the apparatus, and its various components, may be varied, based on requirements.
Implementation of the disclosure can involve performing or completing selected tasks manually, automatically, or a combination thereof. Further, according to actual instrumentation of the disclosure, several selected tasks could be implemented, by hardware, by software, by firmware, or by a combination thereof, using an operating system. For example, as software, selected tasks according to the disclosure could be implemented, as a plurality of software instructions being executed, by a computing device, using any suitable operating system.
In yet another embodiment of the disclosure, one or more tasks, according to embodiments of the disclosure, is (or are) performed, by a data processor, such as a computing platform, for executing a plurality of instructions. Further, the data processor includes a processor, and/or non-transitory computer-readable medium, for storing instructions and/or data, and/or a non-volatile storage, for storing instructions and/or data. A network connection, a display, and/or a user input device, such as a keyboard (or mouse), are also provided.
It will be apparent to a person skilled in the art that the above description is for illustrative purposes only and should not be considered as limiting. Various modifications, additions, alterations, and improvements, without deviating from the spirit and the scope of the disclosure, may be made, by a person skilled in the art. Such modifications, additions, alterations, and improvements should be construed as being within the scope of this disclosure.
LIST OF REFERENCE NUMERALS
10 – Apparatus for Trip-Based Ameliorated Charging of a Vehicle
100 – At Least one Inclusive Driving Pattern Recording Member
200 – At Least one Weather Monitoring Member
300 – Infrastructure Service Cloud
310 – Charging Station Cloud
320 – Vehicle Operator Cloud
400 – Vehicle
410- Energy Storage System
420 – Battery Management Unit
430 – Vehicle Control Unit
500 – Trip-Ahead Data Prognosticating Member
510 – Trip-Ahead Weather Prognosticating Unit
520 – Trip-Ahead Driving Pattern Prognosticating Unit
530 – Trip-Ahead Remaining Discharge Energy Prognosticating Unit
540 – Trip-Ahead Energy Demand Prognosticating Unit
600 – Ameliorated Charging Parameters Member
700 – Charging Depot Network
710 – Plurality of Charging Depots
711 – Plurality of Chargers , Claims:1. An apparatus (10), for trip based ameliorated charging of a vehicle that is embedded on a processing member, comprising:
an at least an inclusive driving pattern recording member (100) that recording a first plurality of parameters from a vehicle (400), with the recorded data being transmitted to an infrastructure service cloud (300);
an at least a weather monitoring member (200) that recording weather parameters, with the recorded data being transmitted to an infrastructure service cloud (300);
a trip-ahead data prognosticating member (500) that comprising;
a trip-ahead weather prognosticating unit (510) that prognosticating a weather condition, based on the weather parameters recorded by the at least one weather monitoring member (200);
a trip-ahead driving pattern prognosticating unit (520) that prognosticating a trip-ahead driving pattern, based on the first plurality of parameters recorded by the at least one inclusive driving pattern recording member (100);
a trip-ahead remaining discharge energy prognosticating unit (530) that prognosticating remaining energy available in the energy storage system (410) of the vehicle (400); and
a trip-ahead energy demand prognosticating unit (540) that prognosticating an energy demand for a trip-ahead, based on the prognosticated trip-ahead driving pattern and the prognosticated remaining energy available in the energy storage system (410); and
an ameliorated charging parameters member (600) that prognosticating ameliorated charging parameters of the energy storage system (410), for charging the vehicle (400) when connected to a charger among a plurality of chargers (711).
2. The apparatus (10), for trip based ameliorated charging of a vehicle that is embedded on a processing member, as claimed in claim 1, wherein: the first plurality of parameters includes current and past data of: longitude and latitude of locations, mileage, driving range, vehicle speed, acceleration and deceleration patterns, battery stem voltage, current, State-of-Charge, and State-of-Health.
3. The apparatus (10), for trip based ameliorated charging of a vehicle that is embedded on a processing member, as claimed in claim 1, wherein: the weather parameters include current and past data of: temperature and humidity.
4. The apparatus (10), for trip based ameliorated charging of a vehicle that is embedded on a processing member, as claimed in claim 1, wherein: said at least one infrastructure service cloud (300) comprising:
a charging station cloud (310) that performing data exchange with: the plurality of charging depots (710) and the ameliorated charging parameters member (600); and
a vehicle operator cloud (320) that performing data exchange with: at least one inclusive driving pattern recording member (100) and the at least one weather monitoring member (200).
5. The apparatus (10), for trip based ameliorated charging of a vehicle that is embedded on a processing member, as claimed in claim 1, wherein: said ameliorated charging parameters include ameliorate charging rate and depth of discharge.