Abstract:
A system for managing mobility of an electrically-powered vehicle. The system includes a monitoring module comprising a plurality of sensors. Each of the plurality of sensors is configured to sense the status of at least one feature of each of the electrically-powered vehicle, an environment in which the electrically-powered vehicle is residing, and a state of health of a battery of the electrically-powered vehicle. A mobility analysis module estimates mobility of the electric-powered vehicle based on the sensed status, and a telematics module displays the sensed statuses, the estimated mobility, or both. The telematics module resides on a cloud-based server.

Description:
[0001]    The present application claims the filing benefit of co-pending U.S. Provisional Patent Application No. 61/479,080, filed on Apr. 26, 2011, the disclosure of which is hereby incorporated by reference herein in its entirety. 
     
    
     FIELD OF THE INVENTION 
       [0002]    The present invention relates generally to vehicle power management systems and, more specifically, to electric vehicle power management systems as related to mobility. 
       BACKGROUND OF THE INVENTION 
       [0003]    Recent progress in rechargeable battery technologies, in combination with societal interests in decreasing greenhouse gas/carbon emissions, has accelerated innovations in electric vehicle (“EV”) and associated renewable energy storage devices, for example, batteries. Technological advances in reliability and dependability have been made, but there has not yet been much progress in the development of information systems that are configured to interact with these batteries. 
         [0004]    Currently, there is very little information available from monitoring of batteries in addition to unmet need for the flow of information during phases of the battery life-cycle. That is, data and information acquired in one phase is not applied to other phases in order to achieve a complete analysis of the battery life-cycle. 
         [0005]    The user&#39;s main concern when operating an EV is mobility rather than battery status, which is estimated using a Kalman filter or particle filter methods and is often reported as a State of Charge (“SOC”) or a State of Health (“SOH”). However, these reported states are only an indicator of the current health status of the battery. Because actual battery life is dynamic, in part due to actual load and individual usage, the current use of autoregressive moving average models and artificial neural network provide inaccurate results of remaining battery life and result in large deviations in the predicted battery life. 
         [0006]    Thus, there remains a need to close the information flow loop such that useful information with respect to mobility and battery-life may be shared and utilized by EV users as well as by manufacturers, designers, and material suppliers for improving battery life management and accurately predicting mobility. 
       SUMMARY OF THE INVENTION 
       [0007]    The present invention overcomes the foregoing problems and other shortcomings and drawbacks of the prior art. While the present invention will be described in connection with certain embodiments, it will be understood that the present invention is not limited to these embodiments. To the contrary, this invention includes all alternatives, modifications, and equivalents as may be included within the scope of the present invention 
         [0008]    According to one embodiment of the present invention, a system for managing mobility of an electrically-powered vehicle includes a monitoring module comprising a plurality of sensors. Each of the plurality of sensors is configured to sense the status of at least one feature from each of the electrically-powered vehicle, an environment in which the electrically-powered vehicle is residing, and a state of health of a battery of the electrically-powered vehicle. A mobility analysis module estimates mobility of the electric-powered vehicle based on the sensed statuses, and a telematics module displays the sensed status, the estimated mobility, or both. The telematics module resides on a cloud-based server. 
         [0009]    Another embodiment of the present invention includes a method of managing mobility of an electrically-powered vehicle. The method includes monitoring use of the electrically-powered vehicle and estimating the mobility from the monitored use. The monitored use, the estimated mobility, or both are displayed. 
         [0010]    These and other embodiments of the invention will be readily apparent from the following figures and detailed description of the present invention. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0011]    The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the present invention and, together with a general description of the invention given above, and the detailed description of the embodiments given below, serve to explain the principles of the present invention. 
           [0012]      FIG. 1  is a diagrammatic view of a power management system configured to evaluate the mobility of an electrically-powered vehicle in accordance with one embodiment of the present invention. 
           [0013]      FIG. 2  is a perspective view of an electrically-powered vehicle suitable for use with the system of  FIG. 1 . 
           [0014]      FIG. 3  is a bottom view of the electrically-powered vehicle of  FIG. 2 . 
           [0015]      FIG. 4  is a diagrammatic view of a controller for use in evaluating the mobility of the electrically-powered vehicle in accordance with one embodiment of the present invention. 
           [0016]      FIG. 5  is a side-elevational view of a user console within the electrically-powered vehicle of  FIG. 2  and incorporating a human machine interface for use with the system of  FIG. 1 . 
           [0017]      FIG. 6  is a diagrammatic view of sub-systems comprising the power management system of  FIG. 1 . 
           [0018]      FIG. 7  is a diagrammatic view illustrating details of the mobility management system depicted in  FIG. 6  and in accordance with one embodiment of the present invention. 
           [0019]      FIG. 8  is a diagrammatic view illustrating details of the remote monitoring system depicted in  FIG. 6  and in accordance with one embodiment of the present invention. 
           [0020]      FIG. 9  is a diagrammatic view illustrating details of the battery maintenance system depicted in  FIG. 6  and in accordance with one embodiment of the present invention. 
           [0021]      FIG. 10  is a diagrammatic view illustrating details of the suggestive service system depicted in  FIG. 6  and in accordance with one embodiment of the present invention. 
           [0022]      FIG. 11  is a diagrammatic view illustrating details of the intelligent analysis system depicted in  FIG. 6  and in accordance with one embodiment of the present invention. 
           [0023]      FIG. 12  is a flow chart illustrating a method of using the system of  FIG. 1  in accordance with one embodiment of the present invention. 
       
    
    
     DETAILED DESCRIPTION 
       [0024]    Turning now to the figures, and in particular to  FIG. 1 , a power management system  20  for evaluating the mobility of an electrically-powered vehicle (“EV”  22 ) is described in detail. The power management system  20  is configured to collect inputs from a variety of parameters with respect to the EV  22 , including, for example, charging and/or driving behavior  24 , environmental conditions  26 , and road conditions  28  and processes those parameters with a prognostic analytics  30  such that one or more outputs are generated. These outputs may include, for example, Global Positioning System (“GPS”) connectivity and monitoring  32 , customized visualizations  34  of power usage and terrain, and mobility, which are used herein as a measure of the distance, range, and/or time of operation for the EV  22 . 
         [0025]      FIGS. 2 and 3  illustrate details of one hybrid EV  22  suitable for use with the present invention. The hybrid EV  22  includes a body  38  positioned on a chassis  40 , which may be constructed of lightweight materials to reduce the overall weight of the EV  22 . Though not necessarily assembled in the illustrated manner, the EV  22  will typically include an internal combustion engine  42  (illustrated as being beneath a front hood  44 ), an electric motor  46 , and a radiator  48  to cool the engine  42  and/or electric motor  46 . While a true EV requires no fuel, the hybrid EV  22  will further include a fuel storage tank  50  along with a battery  52 . The battery  52  may be operably coupled to an external plug socket  56  via a charger  54  such that the hybrid EV  22  may be directly charged via the plug socket  56  and/or via regenerative braking, as is conventionally known. In use, the wheels  58  may be rotatively powered via the engine  42 , the electric motor  46 , or both. 
         [0026]    While other configurations of EVs may be used, the exemplary hybrid EV  22  of  FIGS. 2 and 3  is shown with some structural details for environment purposes. That is, the EV  22  may further include an inverter  60  to convert DC voltages to AC voltages, a steering pump  62  as part of the steering column, a clutch actuator  64  to convert operational force applied to a clutch to the engine  42 , and a DC/DC converter  66  for adjusting the DC voltage. Other features and/or assemblies may be included and should not be considered to be limited to those particularly illustrated herein. 
         [0027]    The EV  22  also includes a controller  70 , one embodiment of which is shown and described with reference to  FIG. 4 . The controller  70  may be considered to represent any type of computer, computer system, computing system, server, disk array, or programmable device such as multi-user computers, single-user computers, handheld devices, networked devices, or embedded devices, etc. The controller  70  may also be referred to as a “computer” for brevity&#39;s sake, although it should be appreciated that the term “computing system” may also include other suitable programmable electronic devices consistent with embodiments of the present invention. 
         [0028]    The controller  70  may be implemented with one or more networked computers  72  using one or more networks, e.g., in a cluster, a distributed computing system, or a cloud server  74  in which one or more cloud-based computing services are provided, through a network interface (illustrated as “NETWORK I/F”  76 ). The controller  70  may also be networked via satellite systems, such as a GPS (not shown) or other wired or wireless connection. 
         [0029]    The controller  70  typically includes at least one processing unit (illustrated as “CPU”  78 ) coupled to a memory  80  along with several different types of peripheral devices, e.g., a mass storage device  82  having one or more databases (not shown), an input/output interface (illustrated as “I/O I/F”  84 ), and the Network I/F  76 . 
         [0030]    The I/O I/F  84  may further comprise a customized, user-friendly human machine interface (“HMI”  86 ), one embodiment of which is shown in  FIG. 5 . The HMI  86  may be incorporated into the user consol  88  within the EV  22  ( FIG. 1 ), which conventionally includes air vents  90 , climate control knobs  92 , etc. The HMI  86 , which is specifically shown herein as a touch screen LCD monitor  94 , is configured to provide and receive information, as appropriate or desired, to and from the user (which may be the driver or a passenger). Such information may include details related to health and risk of the EV  22  ( FIG. 1 ). The health and risk details may be presented in a visual display. Some exemplary displays may include visualization charts  96  indicating single battery performance monitoring, radar charts  98  for multiple battery performance monitoring or single battery fault diagnosis, and so forth. Other information may also be displayed, as determined by the user, such as maps  100 , with or without GPS tracking, calendars  102 , displayable keypad for data entry  104 , searchable databases for entertainment  106 , etc. The user may also interact with the cloud server  74  ( FIG. 4 ), such as synchronization of data  108 , open a help menu  110 , or return to a home menu  112 . Still other functions and operations may be incorporated into the HMI  86  and should not be limited to the particular illustrated features of  FIG. 5 . Alternatively still, the HMI may be incorporated into, or be operable with, an intelligence device (e.g., a tablet (for example, an iPad), a smart phone (iPhone®, Android®, Blackberry®), a laptop computer, or other like device) or a server (such as the cloud server  74  ( FIG. 4 )). The HMI according to these embodiments may include one or more apps  118  ( FIG. 4 ) for interfacing with the controller  70 . 
         [0031]    With reference again to  FIG. 4 , the memory  80  of the controller  70  may include dynamic random access memory (“DRAM”), static random access memory (“SRAM”), non-volatile random access memory (“NVRAM”), persistent memory, flash memory, at least one hard disk drive, and/or another digital storage medium. The mass storage device  82  is typically at least one hard disk drive and may be located externally to the controller  70 , such as in a separate enclosure or in one or more networked computers  72 , one or more networked storage devices  116  (including, for example, a tape or optical drive), and/or one or more other networked devices (including, for example, the cloud server  74 ). 
         [0032]    The CPU  78  may be, in various embodiments, a single-thread, multi-threaded, multi-core, and/or multi-element processing unit (not shown) as is well known in the art. In alternative embodiments, the controller  70  may include a plurality of processing units that may include single-thread processing units, multi-threaded processing units, multi-core processing units, multi-element processing units, and/or combinations thereof as is well known in the art. Similarly, the memory  80  may include one or more levels of data, instruction, and/or combination caches, with caches serving the individual processing unit or multiple processing units (not shown) as is well known in the art. 
         [0033]    The memory  80  of the controller  70  may include one or more applications (illustrated as “Program Code”  118 , or otherwise referred to as “apps”), or other software program, which are configured to execute in combination with the Operating System (illustrated as “OS”  120 ) and operating in accordance with one or more embodiments of the present invention, with or without accessing further information or data from the database(s) of the mass storage device  82  or via the cloud server  74 . 
         [0034]    Those skilled in the art will recognize that the environment illustrated in  FIG. 4  is not intended to limit the present invention. Indeed, those skilled in the art will recognize that other alternative hardware and/or software environments may be used without departing from the scope of the invention. 
         [0035]    With reference now to  FIG. 6 , as well as continued reference to  FIGS. 1 and 2 , the prognostic analytics  30  associated with the power management system  20 , in conjunction with the controller  70  and the cloud server  74 , may be used in accordance with one embodiment of the present invention to determine the health and performance of the battery  52 , diagnose the root-cause of a particular problem associated with the battery  52 , estimate the remaining usefulness of the battery  52 , and/or predict future risks or problems as a result of user-specific habits. In that regard, the power management system  20  includes a plurality of sub-systems (referenced herein as “systems”  130 ,  132 ,  134 ,  136 ), spanning the controller  70  of the EV  22 , the cloud server  74 , and/or other networked computers  72  and/or devices  116 , as necessary, to effectuate the prognostic analytics  30 . 
         [0036]    In that regard, and with reference now to  FIGS. 6 and 7 , a mobility management telematics system  130  of the power management system  20  is described and is generally configured to receive, transmit, and display mobility information and/or otherwise interact with authorized users via the cloud server  74 . More particularly, the mobility management telematics system  130  includes a cloud-based mobility analysis module  150 , a mobility management module  152 , a feature extraction module  154 , and a data receiving and transmitting module (illustrated as “Rx/Tx”  156 ). 
         [0037]    The mobility management module  150  is configured to interface and manage information flow between the cloud server  74 , remote users (not shown), the EV  22 , and the user via the HMI  86  ( FIG. 5 ). A feature extraction module  154  is configured to receive signals representing sensory information with respect to the battery and EV components from the battery maintenance system  134 , encodes, and transforms those signals into a format suitable for use by the mobility analysis module  150 , and delivers the encoded and process data to the mobility analysis module  150 . The mobility analysis module  150  also receives data from the intelligent analysis system  138 , as described in detail below, and, with the data from the feature extraction module  154 , analyzes and predicts a mobility of the EV  22 . 
         [0038]    Although not specifically shown, the mobility analysis module  150  may further comprise a storage module (not shown) that is configured to save data indicative of a position of the EV  22 , such data operable to be displayed on a geographic information system (“GIS”). The results of the analysis by the mobility analysis module  150  may then be sent back to the HMI  86  ( FIG. 5 ), for example, via a wireless network. While not required, it is preferred that the mobility analysis module  150  be configured to calculate and estimate mobility of the EV  22  in real time. 
         [0039]    With reference now to  FIG. 8 , a remote monitoring system  132  is described in greater detail with respect to one embodiment of the present invention and may include a position information module  160  and a GSI module  162 . Generally, the remote monitoring system  132  is configured to determine, store, and interactively display data with respect to the EV  22 . More specifically, the position information storage module  160  receives and stores data with respect to the global position of the EV  22  and transmits the global position to web-based GIS module  162  for interactively displaying information for the user. The suggestive service system  136  may also receive global position data for us in the manner described in greater detail below. 
         [0040]    Turning now to  FIG. 9 , the details of a battery maintenance system  134  in accordance with one embodiment of the present invention are shown. The battery maintenance system  134  includes a plurality of modules  166 ,  168 ,  170 ,  172 ,  174 ,  176 ,  178 ,  180 ,  182 ,  194 , each operably coupled to a sensor associated with the EV  22 . Each sensor evaluates, or receives, a signal representative of a functioning component of the EV  22 . Thus, a portion of the modules may be configured to evaluate a bias voltage (module  172 ), an electrical current (module  174 ), a temperature (module  176 ), or an electrochemical impedance (module  178 ) with respect to the status and performance of the EV battery  52 . Other modules receive sensory information with respect to the environment in which the EV  22  is being operated (for example, an ambient temperature (module  180 ), an ambient moisture (module  182 ), and global positioning (module  184 )). Still other modules may be directed to the EV&#39;s overall function and use, including data flow (module  166 ), load (module  168 ), and three-axis acceleration (module  170 ). Each module may be operably coupled to an electric control unit (“ECU”), which is configured to record and compile the generated signals as user driving behavior. 
         [0041]    Details of a suggestive services system  136  according to one embodiment of the present invention are provided with reference to  FIG. 10 . The suggestive service system  136 , which is operably coupled to the intelligent analysis system  138 , is configured to suggest vehicular service and maintenance schedules based on user driving behavior. In accordance with one embodiment, the suggestive service system  136  may provide a schedule for charging the battery  52  or provide information with respect to the locations of charging stations during extended travel destinations. The suggestive services system  136  includes a user information share and evaluation storage module  190  that is configured to store data received from networked EVs and various users of EVs. The compiled data from the various users of EVs may then be recalled for use in calculating and estimating a driving range and/or mobility of a particular EV based, in part, on the collective experiences of the EV users. 
         [0042]    The suggestive service systems  136  may also be configured such that users may share experiences with respect to EV function and performance, lifestyle and entertainment (for example, ratings of hotels, restaurants, charging stations, etc.), or travel and route (frequency of use, construction, etc.). 
         [0043]    A statistic and analysis module  192  may include various statistical, analysis, models, and evaluation modalities for calculating and estimating the driving range and/or mobility. For example, signal processing may include one or more of a time domain analysis, a frequency domain analysis, a time-frequency analysis, a wavelet packet analysis, and a Principal Component Analysis (“PCA”); performance prediction may include one or more of AutoRegressive Moving Average (“ARMA”), Elman recurrent neural network, fuzzy logic, and match matrix; health assessment may include one or more of logistic regression, statistical pattern recognition, feature map pattern matching (self-organizing maps), neural networks, and Gaussian Mixture Models (“GMM”); and health diagnosis may include one or more of a Support Vector Machine (“SVM”), feature map pattern matching (self-organizing maps), Bayesian Belief Network (“BBN”), and Hidden Marker Models (“HMM”). Use of the suggestive service system  136  is described with greater detail below. 
         [0044]      FIG. 11  illustrates an intelligent analysis system  138  according to one embodiment of the present invention and that is configured to analyze the signals received from the Rx/Tx  156  of the mobility management telematics system  130  with the user information, statistics, and analyses methods from the suggestive service system  136 . In that regard, the intelligent analysis system  138  includes a data mining module  194  that receives data from an energy consumption storage module data based on road conditions (including, for example, whether the surface is wet or dry; smooth or rough; inclined, flat, or declined; the elevation, and so forth)  196  data, a storage module for the battery data  198 , and a storage module for driving characteristics data  200  such that the data mining module  194  may learn the user&#39;s driving behavior via online learning algorithms. The data mining module  194  discovers patterns within and across the types of stored data, and to generate an analysis parameter that is transmitted to an analysis parameter storage module  202 . The data mining module  194  obtains fusion information from a plurality of EV users and so as to build a database of information that may lead to more accurate model parameters. Continually updating the stored information within the database permits the most recent and complete evaluation by the data mining module  194  for all users. The analysis parameter may feed back into the battery maintenance system  134  for interaction with the EV  22  or into the suggestive service system  136  for use in evaluating and comparing driving characteristics, battery function, preferences, and so forth of other users. 
         [0045]    Although not specifically shown, non-dynamic data may also be stored in one or more modules of the intelligent analysis system  138 , such as an EV make and model, type or physical characteristics of the battery (such as lithium ion battery or nickel cadmium battery), physical characteristics of the EV make and model, manufacturer specifications of the battery, engine specifications, charger specifications, and so forth. 
         [0046]    With the detail of the power management system  20  described according to one embodiment and with reference to  FIG. 12 , a flow chart  208  illustrating a method of using the power management system  22  ( FIG. 1 ) to determine the mobility of the EV  22  ( FIG. 1 ) is described in detail with respect to a destination or other user input and in accordance with one embodiment of the present invention. Before or after starting the EV  22  ( FIG. 1 ), the global positioning information module  184  ( FIG. 9 ) of the battery maintenance system  134  determines the current location of the EV  22  ( FIG. 1 ) (Block  210 ). The location may be determined using one more of a global positioning system, cellular-phone towers, or other global information mapping system. 
         [0047]    At some point, the user selects a destination, a preference, or otherwise provides information to the power management system  20  ( FIG. 1 ) via the HMI  86  ( FIG. 5 ) (Block  214 ). In this way, the power management system  20  may provide direction-driven services or content-driven services. Direction-driven services improve the estimation of mobility to direct the user to the selected destination via the route that utilizes the least battery life while content-driven services utilize mobility to direct the user to the nearest, selected or desired content provider (entertainment, food services, lodging, etc.) while using the least battery life. For example, and with brief reference again to  FIG. 5 , the user may select a location such as by selecting maps  100 , entering an address via the keyboard  104 , select a destination from the database of entertainment  106 , including hotels, restaurants, and entertainment establishments, or from a listing of favorite locations and destinations. Other information input by the user may include a desired time of arrival, preferred route (for example, highway versus side streets), and so forth. 
         [0048]    With such information now input, the mobility analysis module  150  ( FIG. 7 ) of the mobility management telematics system  130  ( FIG. 7 ) may identify at least one route from a current location of the EV to the selected destination (Block  212 ). Although not shown, the one or more routes may be determined in accordance with certain predetermined criteria, including, for example, length of route, traffic patterns, traffic incident reports, and so forth. Conventional determinations of routes are improved by incorporating the estimated mobility, which is determined in accordance with one embodiment of the present invention and as described herein. 
         [0049]    The identified routes (Block  212 ) with other information inputs (Block  218 ), such user behavior characteristics (loaded from the intelligent analysis system  138 ) and shared user information (loaded from the suggestive service system  136 ) may be provided to the mobility analysis module  150 , with the necessary and appropriate statistics and analysis tools (loaded from the suggestive service system  136 ) to determine a required mobility for each of the identified routes (Block  216 ). In other words, the identified routes will generally vary in distance, terrain, traffic (highway versus city street), etc., which affects a level of mobility necessary to reach the destination via that route. Because remaining battery power is a dynamic parameter, varying routing decisions, multiple measures, internal as well as external, are necessary to fully evaluate, in real time, remaining battery power and mobility. In fact, a selected route will be considered as a regime with specific parameters that influence the battery&#39;s state of charge; therefore an appropriate intelligent classification tool is required to recognize the regime of operation and then predict the battery remaining power and mobility. For instance, a route having more and/or steeper hills as compared to another route will require a larger mobility to complete that route. Furthermore, whether the EV is carrying one person or a plurality, with or without luggage, the current, voltage, temperature of the battery will change over time and affects the health of the battery and eventually, the battery life cycle, as well as mobility. 
         [0050]    With the EV in motion, the sensor modules  166 - 184  of the battery maintenance system  134  may generate signals (Block  221 ) representing the internal and external measures. Real time measurements may include, apart from those described previously, a condition of the road based on a set of acceleration signals, turn information, road bumps, and a three-axis acceleration sensor configured to detect vehicular vibration, and so forth. 
         [0051]    Signals representing the internal and external measures are transmitted from the battery maintenance system  134  to the mobility analysis module  150  of the mobility management telematics system  130 . The sensor signals, along with historic driving characteristics (user decision/preferences, frequency of brake use, applied braking forces, frequency of lane changes, and so forth from module  200 ), battery performance and maintenance (module  198 ), and energy consumption patterns (module  196 ), with or without other user information provided via the suggestive service system  136 , are used in calculating an estimated remaining mobility of the EV (Block  220 ). 
         [0052]    If the user has not previously designated as preference with respect to routes, the power management system  20  may then make an inquiry (Block  222 ) as to whether the remaining mobility of the EV  22  is greater than or equal to at least one of the identified routes. In that regard, improved estimates of mobility, as determined in accordance with embodiments of the present invention, in turn improve the accuracy of this determination. Accordingly, and if the determination is that EV  22  lacks the mobility to arrive at the selected destination (“No” branch of decision block  222 ), then the power management system  20  may determine whether a battery charging (or changing) station exists within the remaining mobility (Block  224 ). In that regard, the EV  22  may determine the location providing battery services within the geographical area attainable by the mobility and show the driver the closest battery surface locations. If the remaining mobility is such that it is not likely the EV  22  could arrive at the destination or a charging/changing station (“No” branch of decision block  224 ), then an error may be returned to the user (Block  226 ), for example, “Change battery pack.” Otherwise, (“Yes” branch of decision block  224 ), the power management system  20  may enter the charging/changing station as the destination and notify the user that the selected destination has been overridden (Block  228 ). Although not shown, the user may be presented with an option of overriding the change in selected destination or other alternative response. 
         [0053]    Returning again to the inquiry as to whether remaining mobility is sufficient for at least one route (Block  222 ) and if there is at least one suitable route (“Yes” branch of decision block  230 ), then a route is selected. Selection of the route may depend on various factors, including which route has smallest required mobility or is in accordance with a user-defined preference (Block  232 ), such as is shown according to the present embodiment. Still other factors may be considered, including, user driving habits, battery maintenance service, route optimization service, charging schedule service, driving behavior analysis service, and power-oriented route optimization path suggestion services. 
         [0054]    Once determined, the power management system  20  may display the route information, such as a turn-by-turn description or on a map (Block  234 ). Otherwise, and if only one route was appropriate (“No” branch of decision block  230 ), the one route is selected and the route information displays (Block  234 ). The power management system  20  may then update the stored information (Block  236 ), such as those storage modules  196 ,  198 ,  200  of the intelligent analysis system  138  for future use and/or transmitting information to the user information share and evaluation storage module  190  of the suggestive service system  136  for use by EV users, at large. 
         [0055]    As provided in detail herein, a cloud-based, mobility management system configured to provide dynamic mobility management service, for use and exchange by those of the EV user community is described. The enabling features of the present invention include the flexible instrumentation of the battery; the prognostic analytics capabilities; and the customized visualizations. The flexible instrumentation enables online data acquisition from the field and the automated testing procedures for fast acquisition of battery data in a variety of operating regimes. Additionally, battery observational data are captured by sensory devices under each operating regime. The prognostic analytics capabilities digest the large amount of data and convert it to useful health and risk information representing the state of health and performance of the battery, the diagnostic information of the root-cause of the problems, remaining-useful life of the battery, and battery risk based on different user specified performance criteria. 
         [0056]    While the present invention has been illustrated by description of various embodiments and while those embodiments have been described in considerable detail, those skilled in the art will readily appreciate that many modifications are possible in the exemplary embodiments without materially departing from the novel teachings and advantages of this invention. The invention in its broader aspects is therefore not limited to the specific details and illustrative examples shown and described. Accordingly, departures may be made from such details without departing from the scope of the present invention.