Optimized System and Optimization Method

Abstract

The present invention provides an optimization system and method for optimizing the use of an electricity storage device. An optimization system includes a processor and an output unit, the processor being configured to: receive one or more criteria for optimizing the use of the electricity storage device in an application device, receive data including at least one of one or more user inputs, data from the cloud, and data about the application device, and based on the one or more optimization criteria, determine a charge / discharge curve for charging or discharging the electricity storage device according to the received data; the output unit is used to output the charge / discharge curve; wherein, the processor is further configured to: receive at least one of user feedback, machine learning data, big data, and changes in the optimization system and / or the application device regarding the charge / discharge curve, and update the charge / discharge curve based on at least one of the received user feedback, machine learning data, big data, and changes in the optimization system and / or the application device regarding the charge / discharge curve. According to this device, the use of an electricity storage device such as a battery can be dynamically optimized.

Description

Technical Field

[0001] The present invention generally relates to charging / discharging an electrical energy storage device, and particularly relates to an optimized system and method for the use of an electrical energy storage device. Background Art

[0002] Electrical energy storage devices such as batteries remain a key part of application devices including electric vehicles. Since the battery has limited power stored therein and cannot be replenished quickly, and the battery life in an electric vehicle may be relatively low (although its nominal life is long), (for example, in an electric motorcycle) the battery usually has to be replaced after 1 or 2 years, so the user acceptance of electric vehicles may still be low.

[0003] There are some optimization schemes for charging / discharging a battery. In one example, the optimization system only receives limited user input and optimizes the charging of the battery based on specific charging optimization criteria (such as battery life extension), or optimizes the charging of the battery without paying attention to optimization criteria such as battery life extension to meet the user's requirements. In another example, battery and power source information and user input are processed to calculate the optimal charging / discharging cycle. Specifically, the charging / discharging cycle for charging / discharging the battery can be determined based on user input indicating user preferences; the preferences include the desire to increase battery life or reduce the operating cost of the battery. Summary of the Invention

[0004] It is desirable to provide improved optimization systems and methods that can dynamically adjust the charging / discharging curve for optimizing the use of an electrical energy storage device (such as a battery) in an electric vehicle.

[0005] In one embodiment, an optimization system is provided. The optimization system includes a processor and an output unit. The processor is configured to: receive one or more criteria for optimizing the use of an electrical energy storage device in an application device, receive data including at least one of one or more user inputs, data from the cloud, and data about the application device, and based on the one or more optimization criteria, determine a charging / discharging curve for charging or discharging the electrical energy storage device according to the received data; the output unit is used to output the charging / discharging curve; wherein the processor is further configured to: receive at least one of user feedback, machine learning data, big data, and changes about the optimization system and / or the application device regarding the charging / discharging curve, and update the charging / discharging curve based on at least one of the received user feedback, machine learning data, big data, and changes about the optimization system and / or the application device regarding the charging / discharging curve.

[0006] In another embodiment, an optimization method is provided, the method comprising: receiving one or more criteria for optimizing the use of a power storage device in an application device; receiving data including at least one of one or more user inputs, data from the cloud, and data about the application device; determining a charge / discharge curve for charging or discharging the power storage device based on the received one or more optimization criteria and according to the received data; receiving at least one of user feedback, machine learning data, big data, and changes to the optimization system and / or the application device regarding the charge / discharge curve; and updating the charge / discharge curve based on at least one of the received user feedback, machine learning data, big data, and changes to the optimization system and / or the application device regarding the charge / discharge curve.

[0007] In yet another embodiment, a computer storage medium includes instructions for performing the steps of the optimization method when run by one or more processors.

[0008] One advantage is that the charge / discharge curve for optimizing the use of the power storage device can be dynamically updated.

[0009] Another advantage is that multiple criteria for optimizing the use of the power storage device can be considered simultaneously.

[0010] Yet another advantage is that more information is taken into account during optimization, including information about components of the application device other than the power storage device.

[0011] Aspects and features of the present disclosure are described in further detail below. Moreover, with reference to the description in conjunction with the drawings, other objects and advantages of the present invention will become more apparent and will be readily understood. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The present invention will be described and explained in more detail below in conjunction with embodiments and with reference to the drawings, where:

[0013] Figure 1 is a schematic block diagram of an optimization system 10 according to an embodiment of the present invention;

[0014] Figure 2 is a schematic block diagram of an optimization system 100 according to another embodiment of the present invention;

[0015] Figure 3 is a flowchart of an optimization method 300 according to an embodiment of the present invention;

[0016] Figure 4A and Figure 4B shows a schematic diagram of a charge curve determined / updated according to an embodiment of the present invention;

[0017] Figure 5A and Figure 5B respectively show schematic diagrams of efficiency graphs;

[0018] Figure 6 shows an example of a discharge curve according to an embodiment of the present invention.

[0019] In the drawings, the same reference numerals indicate similar or corresponding features and / or functions. Detailed Description of the Invention

[0020] The present invention will be described with respect to specific embodiments and with reference to certain drawings, but the present invention is not limited thereto and is only defined by the claims. The described drawings are merely illustrative and not restrictive. In the drawings, for illustrative purposes, the dimensions of some elements may be enlarged and not drawn to scale.

[0021] Figure 1 Shows an optimization system 10 according to an embodiment of the present invention. The optimization system 10 can be used to optimize the use of an electricity storage device 15 such as a battery, which includes charging the electricity storage device 15 from a power source 13 through a charging device 14 and / or discharging the electricity storage device 15. The charging device 14 can generate a charging current for charging the electricity storage device 15. The electricity storage device 15 is in an application device and is used to provide power to the application device. The application device can include an electric vehicle and any application in which an electricity storage device that can be charged or discharged is used. Electric vehicles include, but are not limited to, electric cars, electric two-wheelers, and motorcycles. The various embodiments will be explained below with reference to electric vehicles, but this does not mean that they cannot be used in other application devices to achieve similar effects.

[0022] The optimization system 10 includes a processor 11 and an output unit 12. The processor 11 receives data to be used for optimizing the charging / discharging of the electricity storage device in the electric vehicle. The data includes at least one of one or more user inputs, data from the cloud, and data about the electric vehicle, and optionally, when a charging curve is determined, the data further includes data about the power source 13 and / or data about the charging device 14. The data about the electric vehicle includes not only information about the electricity storage device in the electric vehicle, but also parameters of other components of the electric vehicle (e.g., power system components of the electric vehicle). In this way, more information can be taken into account during optimization. The received data can be real-time data or historical data stored in a memory. The historical data can be existing optimization schemes or heuristic data. The data received by the optimization system will be described in detail later.

[0023] The processor 11 also receives one or more criteria for optimizing the use of the electrical energy storage device 15 of the electric vehicle. These criteria may be predefined. Or in one example, these criteria may be input from a user (e.g., a driver), or determined based on the received data (e.g., the driver's previous charging / discharging behavior). The optimization criteria may include battery life extension, mileage improvement, performance, electricity cost, efficiency, and / or charging time, etc. In one embodiment, a table of selectable optimization criteria may be displayed to the driver, and then the driver may select one of the optimization criteria in the table to optimize the charging or discharging of the electrical energy storage device according to the selected optimization criterion. Since some of these criteria may be interdependent (e.g., mileage improvement also depends on the state of charge of the battery, which in turn affects the life), the driver may also select multiple optimization criteria to be considered in combination.

[0024] Based on the received one or more criteria and the received data, the processor 11 determines a charge / discharge curve for charging / discharging the electrical energy storage device 15. The charge curve may be used to generate a charging current for the electrical energy storage device. The discharge curve may be represented as, for example, a driving curve displayed to the driver to remind the driver of driving according to the driving curve. Examples of the charge curve and the discharge curve may be referred to Figure 4A 、 Figure 4B and Figure 6 to illustrate and explain. The charge / discharge curve may include various information for charging / discharging the electrical energy storage device, including but not limited to information about when charging / discharging starts and / or ends, the value of the charge / discharge current, the charge / discharge state, the charge / discharge voltage, and the driving speed for optimizing the discharge of the electrical energy storage device, etc. In the case of multiple optimization criteria, the processor 11 receives multiple optimization criteria and a weighting factor for each of the multiple optimization criteria, determines one or more optimization parameters for each of the multiple criteria based on the received data, and determines the charge / discharge curve based on the one or more optimization parameters and the weighting factor. The optimization parameters may include any information about the charge / discharge curve, e.g., the charge / discharge current, the charge / discharge state, or the charge / discharge voltage. The weighting factor may be received from user input or the cloud, or even pre-stored in the optimization system.

[0025] In one example, battery life and the efficiency of the inverter / motor are selected as criteria to be considered in combination. The most efficient input voltage of the inverter / motor may be different from the optimal charging voltage for battery life. For example, the optimal maximum charging voltage for battery life may be 55V, while the inverter has the highest operating efficiency at 58V. In this case, both the optimal maximum charging voltage for battery life and the optimal input voltage for the efficiency of the inverter / motor are determined in the processor 11. For example, both of them can form a Pareto front. If the weighting factor for battery life is 20% and the weighting factor for efficiency is 80%, the optimal voltage can be 57.4V.

[0026] In one embodiment, instead of setting the weighting factor, the processor 11 can also select one of the charging optimization parameters according to the user's preferences / priorities and / or default selections.

[0027] In one embodiment, the processor 11 determines the expected route and / or power consumption for the electric vehicle based on the driving behavior and / or charging behavior of the driver or a similar driver or for a similar vehicle, and determines a charge / discharge curve for charging or discharging the energy storage device based on the expected route and / or power consumption of the electric vehicle. The output unit 12 can be coupled to the processor 11 to output the determined charge / discharge curve. The output signal can be light, various displays, sound signals, or tactile signals. The output unit 12 can be a display, a lamp, a tablet device, a touch screen, or a sound generator. In one embodiment, the determined charge curve can be directly transmitted to the charging device via the output unit 12 to generate a charging current for the energy storage device. In another embodiment, the determined charge curve can be output to the user. The determined discharge curve can be output to a user such as the driver to remind him of his driving according to the discharge curve, or the determined discharge curve can be output to the electric vehicle to set limits for one or more parameters of the electric vehicle. These parameters include the driving speed and / or acceleration of the vehicle. If the current driving speed and / or acceleration exceed the limit, an alarm can be sent to the driver.

[0028] The user can view the charge / discharge curve and request an update of the charge / discharge curve by inputting feedback information. In this case, the optimization system should include an input unit for the user to input their feedback. The feedback can be provided by the user as auditory feedback and / or tactile feedback or input data. The feedback can be provided to the optimization system 10 via the user input unit, which is optionally included in the optimization system 10. The user input unit can be any type of input device through which the user can input information, such as a keyboard, a mouse, a touch screen, etc. The user can input information via buttons (e.g., switch between settings and turn on / off a certain function), via a slider bar (e.g., input the departure time or the desired state of charge), or via gestures and voice recognition.

[0029] When the feedback from the user is received, the processor 11 can update the charge / discharge curve based on the user's feedback. For example, the driver is accustomed to driving the vehicle at 7 AM. Therefore, the charge curve determined according to the driver's previous driving behavior shows that the charging of the energy storage device will end at 6:45 (leaving 15 minutes as a margin to avoid the driver using the vehicle prematurely before 7 AM) to ensure that the driver can use the vehicle at 7 AM. However, the driver changes their schedule for tomorrow and will drive the vehicle at 6:30 AM. When the determined charge curve is displayed to the driver, the driver can give feedback indicating that they hope to drive the vehicle at 6:30 AM tomorrow. Then, the processor 11 can update the charge curve based on the feedback indicating the changed schedule from the driver. According to the updated charge curve, the charging can end at, for example, 6:15 AM. The user can also directly change the determined charge curve via the feedback.

[0030] Although it is described above that the processor 11 updates the charge / discharge curve only based on the user's feedback, during the update, the processor can reuse the received data to update the charge / discharge curve according to the user's feedback.

[0031] In addition to updating the charge / discharge curve in response to user feedback, the processor 11 can also update the charge / discharge curve in response to machine learning data, big data, or changes in any of the optimization system and / or the electric vehicle including the electricity storage device 15. More specifically, the charge curve can also be updated in response to changes in the charging device 14 and / or the power supply 13. Such changes include any recorded or exported data and setting changes as well as software updates. For example, the determined charge / discharge curve can be compared with machine learning data and / or big data from the cloud to see if the determined charge / discharge curve can, for example, conform to the driving behavior of the driver, and if not, the charge / discharge curve can be updated. The machine learning data and big data can be derived from data of similar drivers (e.g., drivers with similar charging / driving behaviors) and / or similar vehicles. Alternatively, if the input from the sensor indicates a change in the temperature of the vehicle, the charge / discharge curve can be updated to make corresponding changes because temperature affects the charge / discharge of the electricity storage device. In addition, the charge curve can be updated in response to, for example, setting changes in the electric vehicle, the power supply 13, or the charging device 14 and / or any software updates in the optimization system (e.g., software updates for the processor 11 or the charging device 14).

[0032] The above examples are not restrictive and exhaustive, and other parameters can also be envisioned to trigger the update of the charge / discharge curve, such as changes in the external temperature, the power supply temperature, the electricity storage device temperature, changes in the expected or known driving behavior, etc. These parameters can be detected by sensors.

[0033] In one embodiment, the processor 11 can detect the received machine learning data, big data, or changes in the above parameters, and determine that an update is necessary when detecting the newly received machine learning data, big data, or changes in the above parameters. In another embodiment, the processor 11 can notify the user of the update via the output unit 12, and the user can determine whether he wishes to perform the update.

[0034] In the case of updating the charge / discharge curve in response to machine learning data, big data, or changes in the optimization system and / or the electric vehicle including the electricity storage device 15 and optionally changes in the charging device 14 and / or the power supply 13, the output unit 12 may not output the initially determined charge / discharge curve to the user; or even the output unit 12 may not output any charge / discharge curve to the user at all, including the initially determined charge / discharge curve and the updated charge / discharge curve. Instead, the output unit 12 outputs the charge / discharge curve to the charging device 14 / application device to generate an appropriate charging current for the electricity storage device or to set vehicle settings such as speed limits.

[0035] According to the Figure 1The optimization system 10 shown in [description] can only include a processor 11 and an output unit 12, and is configured as an independent device that can be used in combination with a charging device 14 and a power supply 13 to charge a power storage device 15 in an electric vehicle. In this way, the optimization device 10 can be located at the electric vehicle, the charging device, or a remote device such as a cloud computing device. However, this is not restrictive, and the optimization system 10 can also include other components such as the charging device 14. In [description] Figure 1 The optimization system 10 is shown in dashed lines in [description]. The optimization system can be embodied by computer software, hardware, circuits, processors, controllers, etc. Alternatively, the optimization system can be embodied in a computer storage medium including instructions for implementing corresponding functions when run by one or more processors.

[0036] Figure 2 An optimization system 100 according to another embodiment of the present invention is shown. Different from the optimization system 10 shown in [description] Figure 1 The optimization system 100 can include: a charging device 1000 for generating a charging current for a power storage device such as a battery; an application device 2000 including, for example, an electric vehicle or a motorcycle; and at least one external device 3000. The external device 3000 can be a smart phone, a computer, a tablet device, a smart watch, a cloud computing device, and any other device that can implement computing, storage, display, or input / output and can be coupled to either the charging device or the application device.

[0037] Since the processing capabilities of the charging device 1000 and the application device 2000 may be limited, it is preferable to use the external device 3000 to calculate complex tasks. It is conceivable that computationally intensive processes such as comparisons with big data or machine learning data are executed at the external device 3000 such as a cloud computing device, while less intensive processes are executed at the application device 2000 such as an electric vehicle or the charging device 1000.

[0038] In one aspect, the optimization system 100 can provide online services (e.g., receiving and processing real-time data, online determining a charge / discharge curve, or online updating a charge / discharge curve), or even the optimization system 100 can perform the entire online optimization and update with sufficient computing power.

[0039] However, one or more of the online services in the online service may be unavailable. For example, the local processor (e.g., the processor of the charging device or the application device) has insufficient computing power, and / or is not connected to the external device 3000, or the connection is interrupted. In this case, one or more of the unavailable online services can be pre-executed offline. Heuristic data or characteristic data determined based on the offline service and / or the previously available optimization can be provided for the simpler online optimization used to determine / update the optimization curve. The heuristic data or characteristic data can be a heuristic or characteristic table or a heuristic or characteristic graph.

[0040] In a specific embodiment, the heuristic data can be data based on the previously available optimization results. For example, if the current battery life may not be received for some reason, the heuristic data indicating the battery life after the previously available optimization can be used for the current optimization.

[0041] The optimization system can include an electric vehicle as the application device, a smart phone as the external device, and a charging device, both the smart phone and the charging device being coupled to the electric vehicle. In one embodiment, the smart phone can include a graphical user interface such as an application program, which can be used to request detailed settings and parameters input from the user. Other embodiments are also conceivable.

[0042] In Figure 2In the embodiments shown, each of the charging device 1000, the application device 2000, and the external device 3000 may include a corresponding storage device, a display, a user input unit, and a processor. Specifically, the charging device 1000 includes a processor 1100, a display 1200, a storage device 1300, and a user input unit 1400. The charging device 1000 may further include connections 1010 and 1020 to a power source and a power storage device. The processor 1100 of the charging device 1000 may include an input unit 1110 and an output unit 1120, which are respectively configured to receive data from the external device 3000 and / or the application device 2000 and transmit data to the external device 3000 and / or the application device 2000. The application device 2000 includes a processor 2100, a display 2200, a storage device 2300, and a user input unit 2400. The application device 2000 may further include a power storage device 2900 such as a battery. In the case of an electric vehicle, the processor 2100 of the electric vehicle may be located in the motor controller and / or the battery management system of the electric vehicle, and the display 2200 may be located at the speedometer of the electric vehicle. The external device 3000 includes a processor 3100, a display 3200, a storage device 3300, and a user input unit 3400. Both the application device 2000 and the external device 300 may have an input interface / output interface coupled to the charging device 1000 (specifically, the input unit 1110 and the output unit 1120 of the charging device 1000). The external device 3000 may be a device in the cloud that can store or process the received data.

[0043] Figure 2 The input unit 1110 and the output unit 1120 in the processor 1100 of the charging device 1000 are shown. It is also conceivable to arrange the connections as separate components. The charging device 1000 may be coupled to a power source 4000 via the connection 1010 and to the power storage device 2900 via the connection 1020.

[0044] In this embodiment, any one or any combination of the processors 1100, 2100, and 3100 may implement the functions of the processor 11 as Figure 1 shown, that is, to determine and update the charging curve. The user may interact with the optimization system via any one of the user input units and displays of the charging device 1000, the application device 2000, and the external device 3000. In one example, the user may set and reset system settings or change system parameters via interaction with the optimization system. In another example, the user may reject, accept, or change the charging / discharging curve / parameters via interaction with the optimization system. In additional embodiments, the user may input parameters to the optimization system via any one of the user input units and obtain feedback from any one of the displays.

[0045] In Figure 2 In the optimization system 100 shown in, one or more processors, displays, user input units, and storage devices of the charging device, application device, and external device can be omitted. For example, the charge / discharge curve can be determined and / or updated in the external device 3000 and / or the application device 2000, and then the charge / discharge curve can be transmitted to the charging device to generate a charging current based on the charge curve, or the charge / discharge curve can be transmitted to the application device to set driving restrictions or give driver prompts based on the discharge curve. In this case, the processor 1100, the display 1200, and the user input unit 1400 can be omitted. Or, the charge / discharge curve can be determined and / or updated in the charging device 1000 and the charge / discharge curve can be displayed to the user. The application device 2000 and the external device 3000 only provide at least part of the data and parameters to be used during the determination and / or update of the charge / discharge curve, so that, for example, the displays 2200, 3200 and the processors 2100, 3100 can be omitted from the optimization system. The external device 3000 can even refer to a temperature sensor. In fact, the entire external device 3000 can also be omitted. In addition, the application device can also be omitted from the optimization system. In this way, the charging device can implement the determination and update of the charge / discharge curve by itself. Or, the external device can implement the determination and update of the charge / discharge curve by itself.

[0046] The data received or derived by the optimization system can be stored at any one of the charging device 1000, the application device 2000, or the external device 3000, or even the data can be stored at a remote location (e.g., a cloud device). In view of charging the energy storage device, the received data can include one or more user inputs, data from the cloud, and at least one of the data about the application device, the charging device, the external device, and / or the power supply.

[0047] Although the optimization system is described with reference to the embodiments shown in Figure 1 and Figure 2 respectively, it is conceivable to configure the optimization system in other ways as long as its function can be realized to determine and update the charge / discharge curve for the energy storage device. In Figure 1 and Figure 2 The components shown can be combined, split, or even deleted respectively as long as the charge / discharge curve for the energy storage device can be determined and updated. If necessary, some components can be added to the optimization system to realize its function.

[0048] The terms "make a connection", "connect", "make a coupling", and "couple" in the present disclosure include various manners, for example, wireless (such as induction, Bluetooth, or WIFI) or wired (such as via a cable). Data can be received or transmitted in a wireless manner or a wired manner according to a specific application. The term "processor" can refer to any kind of device having the ability to process data, including but not limited to logic devices, microcontrollers, computers, cores, cloud devices, or CPUs, etc.

[0049] Data received by the optimization system

[0050] As mentioned above, the data received by the (one or more) processors of the optimization systems 10, 100 includes at least one of (one or more) user inputs, data from the cloud, data about the application device, and optionally data about the power source and data about the charging device.

[0051] User inputs can include the desired departure time, for example, when to drive to work in the morning, route / distance / destination information, or the user's preferences or choices for driving the next day, temperature, etc. For example, the user can select fast charging or give a time window for the time to start driving the next day. Or the user can be requested to input data related to the experience, based on which the processor can obtain the driving style, for example, through machine learning.

[0052] Data from the cloud includes, for example, machine learning data, big data, or any real-time data or historical data recorded, stored, or exported at the cloud, such as temperature obtained from weather forecasts, various driving data including routes, speeds, and accelerations, GPS data, etc. Machine learning data and big data about similar energy storage devices, similar vehicles, similar drivers, etc. can be stored. Big data can be obtained through fleet data in the cloud. Machine learning data, big data, and driving data can also be stored in the application device and can be part of the data about the application data.

[0053] Data about the application device includes one or more parameters of the application device and data recorded or exported at the application device. Specifically, data about an application device such as an electric vehicle can be the vehicle type, the types of vehicle components (such as the types of electric motors, controllers, gearboxes, and tires), and any vehicle parameters. Vehicle parameters include maximum / average speed, tire pressure, throttle data, tire size, coefficient of friction, wind resistance, weight, driver weight, power, voltage, current requirements, torque characteristics, control schemes used, inductance, magnetic force, and efficiency maps, etc. In Figure 5A and Figure 5BAn example of an efficiency map is shown and will be described hereinafter. Vehicle parameters include parameters regarding the electricity storage device. Parameters regarding the electricity storage device include, but are not limited to, battery type, battery size, number of battery cells and connection type, battery cell chemistry, capacity and internal resistance, vehicle type, aging behavior, type of battery management system, and battery temperature. The vehicle parameters can also be input by the user. Data regarding the power source can include the type of power source, electricity cost, etc. If data regarding the charging device is stored in the charging device, the data regarding the charging device can include previous charge / discharge curves. Any one of the charging device, the application device, and the external device can include sensors.

[0054] Data received by the optimization system can include sensors for sensing one or more environmental parameters, including the temperature for the charging device, the power source, and / or the electricity storage device. The sensors can be located at the charging device, the application device, and the power source, or can be an external device.

[0055] The received data includes real-time data and historical data. The real-time data can be directly collected by sensors, which includes temperature, current value, tire pressure, etc. The historical data includes various driving data, such as previous speed, acceleration, and distance / route, charge / discharge curves, charging behavior and driving behavior of a specific driver, and data of similar drivers, similar routes, or similar application devices, etc.

[0056] Although specific examples of the received data are described above, they are not restrictive and exhaustive, and it is conceivable to use any data that may be beneficial to optimizing the use of the battery.

[0057] Data output by the optimization system

[0058] The optimization system can output data to the user or exchange data with the user. Light, various types of displays, and sound signals or tactile signals are used to display data to the user. Depending on the system settings, the data can be displayed on the application device, the charging device, and / or an external device such as a smart phone.

[0059] The output data may include system parameters for, e.g., a charging device and an application device. The system parameters may be detected by sensors or obtained from the system, including speed, acceleration, GPS-derived position, slope, battery temperature and motor temperature, as well as current, voltage and throttle in an electric vehicle. An external device can add certain information for prediction, e.g., weather forecast. The system parameters may also include driving, charging and storage times, and average, maximum and minimum charging currents. It is also possible to record and show past values of all these sensor and parameter data. Generally, all system parameters known to the optimization system can be displayed. This can be component information (e.g., tire, motor, battery size and nominal power) and component key parameters (e.g., motor resistance, inductance and flux, battery capacity and resistance), as well as all aging-related parameters (e.g., lithium-ion transport and solid electrolyte interface layer growth factor or fitted average parameters).

[0060] The output data may also include derived data (e.g., obtained through optimization). This includes information about the possible ranges of current charging and driving styles, estimated remaining battery life, expected charging times to reach full charge or a specified state of charge, as well as optimal charging times, expected energy consumption, component status and aging, state of charge, and other effects known to the system. The derived data may also include the current state of charge, notifications or alerts when some predefined or calculated thresholds are exceeded. Additionally, the derived data may include information about expected performance, e.g., possible and desired acceleration and top speed.

[0061] The output data may also include optimization results to show the user the improvements and benefits he has achieved. This can be, e.g., information about battery life extension (either in a qualitative or quantitative manner). Tips can show in a qualitative way which behaviors are beneficial to the expected goal, or in a quantitative way how the behaviors are beneficial to the expected goal. For example, the current charging and driving behaviors can be compared with the user's previous charging and driving behaviors, or, e.g., using cloud fleet data to compare with other users, and information such as "Your current charging and driving behaviors are more beneficial to your battery compared to a few days ago", "You have extended your battery life by 1 day compared to your previous charging style" can be output to the driver. If the charge / discharge curve is optimized for range and charging time, information about the normal range and the range achieved through the optimized charge / discharge, charge / discharge times and driving tips, and efficiency improvement can be displayed.

[0062] The output data may also include tips for the driver to encourage the user to use the described charging strategy and to make him understand how he can increase the lifespan and mileage. The tips may include, for example, "The user should not always fully charge his battery. Charge only when the vehicle is not too hot and drive more slowly at high temperatures." The user can, for example, receive a tip each time before starting, which may be directly related to the optimization criterion he has selected. All these tips can depend on the information entered by the user and the data available to the system.

[0063] The output data may also include maintenance information. For example, depending on the calculated battery lifespan, the user can be notified when the battery must be replaced. Historical data can be used to detect faults or possible future faults in the battery. This can be, for example, a sudden increase in the battery resistance during charging / driving and a corresponding increase in the battery temperature.

[0064] The output data may also include possible software updates made by the user and reward information. The optimization system can compare the user's daily itinerary, mileage, and battery lifespan extension, as well as the charge / discharge curve, with other users in the cloud. The criteria for rewards can be, for example, economical or ecological driving, route and driving distance, high-speed or stable driving. The reward information can be "driving awards" or reward points for vehicle maintenance.

[0065] Although specific examples of output data have been described above, they are not restrictive and exhaustive. It is conceivable to use any data that may be beneficial for optimizing the use of the battery.

[0066] Figure 3 An optimization method 300 according to an embodiment of the present invention is shown. First, at step 301, data including at least one of (one or more) user inputs, data from the cloud, data about an application device including an electrical storage device to be charged / discharged, etc. is received, and at step 302, criteria for discharging or charging the electrical storage device are received. For example, the criteria can be selected by the user or determined automatically. At step 303, based on the determined criteria, a charge / discharge curve is determined according to the received data. At step 304, user feedback on the charge / discharge curve, machine learning data, big data, and at least one of changes in the optimization system and / or the application device are received in order to determine whether an update is needed. For example, the user can determine that an update is needed by giving feedback. If an update is confirmed by the user rejecting the determined charge / discharge curve, then at step 305, the charge / discharge curve can be updated based on at least one of the received user feedback on the charge / discharge curve, machine learning data, big data, and changes in the optimization system and / or the application device. If it is determined that no update is needed, then at step 306, the determined charge / discharge curve can be output.

[0067] In one embodiment, method 300 includes: at step 301, determining an expected route and / or power consumption for an electric vehicle based on a user's driving behavior and / or charging behavior; and at step 303, determining a charge / discharge curve for charging or discharging an energy storage device based on the expected route and / or power consumption for the electric vehicle.

[0068] In another embodiment, method 300 includes: at step 301, receiving at least one of data about a charging device, data about a power source, and data from an external device configured to be coupled to an application device and / or the charging device; at step 303, determining a charging curve based on the received data and the at least one received of data about the charging device, data about the power source, and data from the external device, wherein the data about the charging device and the data about the power source each include one or more parameters of the charging device and the power source and data recorded or derived at the charging device and the power source.

[0069] In Figure 2 The method 300 shown in

[0070] Figure 4A and Figure 4B shows a schematic diagram of a charging curve determined / updated according to criteria for extending the life of at least an energy storage device according to an embodiment of a system or method of the present invention. Figure 4A shows the relationship of the state of charge (SOC) (Y-axis) of the charging curve with respect to time (X-axis), and Figure 4B shows the relationship of the charging current (Y-axis) of the charging curve with respect to time (X-axis). At 20:00, the previous driving of the electric vehicle ends, and the energy storage device of the electric vehicle is connected to a charging device for charging, and then the energy storage device can be charged according to the charging curve determined by the optimization system / method mentioned above.

[0071] As Figure 4A and Figure 4B shown, the charging curve is determined to have a first charging stage I, a second charging stage II, and a third charging stage III. In Figure 4AIn this case, first, the energy storage device is charged to the optimal state of charge A during the first charging stage I. During the second charging stage II, a wait is performed. And before the expected end of charging, the energy storage device is charged to the desired state of charge or the determined optimal state of charge B for the energy storage device during the third charging stage III, where the state of charge B is lower than the fully charged state C. The first charging stage I can start from the connection of the electric vehicle to the charging device, and the third charging stage III can end at 6:45 AM, shortly before the scheduled start time of driving (i.e., 7:00 AM), in order to provide a margin (e.g., 15 minutes from 6:45 AM to 7:00 AM), thus preventing the driver from starting to drive earlier than the scheduled start time of driving. Figure 4B Shows the corresponding charging current of the charging stage of the charging curve.

[0072] Combined with the above description of the embodiments of the present invention, the charging curve can be determined based on, for example, the received start time of driving, end time of driving, driving destination / route, driver behavior, and the received criteria. The above parameters can be received from the user input unit, the cloud or the application device, or even an external device (e.g., a smart phone). After charging from 20:00 to 6:45 using the determined charging curve, the driver can drive the electric vehicle from 7:00 to 7:45 according to the received start time of driving, end time of driving, driving destination / route, and driver behavior for meeting the received criteria (e.g., fast driving, power saving, or extending the life of the energy storage device). The current driving data can be collected and used to determine subsequent charging curves.

[0073] Figure 4A and Figure 4B Only shows an example of the charging curve. The charging curve can also be shown in other ways. For example, in addition to the time, charging current, and state of charge as shown in Figure 4A and Figure 4B The charging curve can also be shown with respect to different factors that may affect the charging curve. These factors include but are not limited to temperature (e.g., external temperature, motor temperature, power supply temperature, and energy storage temperature, etc.), depth of discharge, driving behavior on the next day, etc.

[0074] Figure 5A and Figure 5B Show schematic diagrams of the efficiency map for a given driving route, respectively. When selecting mileage improvement as the criterion for optimizing the use of the energy storage device, the efficiency map for a given driving route can be used to optimize the driving mileage of the electric vehicle by considering efficiency. Alternatively, efficiency can be used as a separate criterion for charging / discharging, and can be combined with other criteria (e.g., battery life) instead.

[0075] Figure 5AShows the relationship between the state of charge (X-axis) and the efficiency (Y-axis) of the energy storage device for a given route. According to Figure 5A , it can be determined that for a given route, the optimal efficiency is not proportional to the state of charge. In fact, the optimal efficiency can be obtained when the voltage and current are both in the optimal region for both the vehicle's electric motor and power electronics. To achieve better efficiency, the required state of charge can be determined to be within the range D for charging the energy storage device as shown in FIG. 5.

[0076] Figure 5B Shows the relationship between the driving speed (X-axis) and the efficiency (Y-axis) of the driver for different (e.g., low or high) states of charge. It can be seen that the driving speed is not directly proportional to the driving speed. It is possible that, as described regarding the above optimization system and method, the energy storage device can be first charged to a sufficient state of charge for the next journey, for example, according to the received criteria, and then the driving speed can be determined according to the efficiency map for obtaining a preferred speed for the driver to drive the vehicle as shown in Figure 5B .

[0077] In Figure 6 , an example of a driving curve is shown, which shows the relationship between time (X-axis) and the optimal efficiency driving speed (Y-axis). The driving curve can be an embodiment of the determined / updated discharge curve. As shown in Figure 6 , the previous driving ended and the energy storage device was connected to the charging system at 12:00. A charging process was performed between 12:00 and 12:25. The charging curve used during the charging process can be obtained, for example, by the above-described optimization system and / or method. Five minutes are left as a margin before the scheduled driving start time (i.e., 12:30) to prevent the driver from starting to drive early. Between 12:30 and 13:30, the driver can drive the vehicle by referring to the driving curve shown in the figure to achieve the optimal efficiency. The current driving data can be collected and used to determine the subsequent charging curve. The driving curve shown in Figure 6 is only an example of the determined / updated discharge curve, and other forms of discharge curves can also be envisioned.

[0078] It should be noted that the above embodiments illustrate rather than limit the present invention, and those skilled in the art will be able to design alternative embodiments without departing from the scope of the claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in the claim or the specification. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. In a product claim enumerating several units, several of these units may be embodied by the same item of software and / or hardware. The use of the words first, second, third, etc. does not denote any order. These words should be construed as names.

Claims

1. An optimization system, comprising: a processor configured to: receive one or more criteria for optimizing the use of a power storage device in an application device, receive data including at least one of one or more user inputs, data from the cloud, and data about the application device, and based on the received one or more criteria, determine a charge / discharge curve for charging or discharging the power storage device according to the received data, the charge / discharge curve at least describing the relationship of at least one of the charge / discharge current, state of charge, and driving speed for optimizing the discharge of the power storage device during the charging or discharging process with respect to time; and an output unit for outputting the charge / discharge curve to an electric vehicle to set limits for one or more parameters of the electric vehicle, the one or more parameters including driving speed and / or acceleration; wherein the processor is further configured to: receive at least one of user feedback, machine learning data, big data, and changes to the optimization system and / or the application device regarding the charge / discharge curve, and update the charge / discharge curve based on at least one of the received user feedback, machine learning data, big data, and changes to the optimization system and / or the application device regarding the charge / discharge curve.

2. The optimization system according to claim 1, wherein the data about the application device includes one or more parameters of the application device and data recorded or exported at the application device, and the one or more parameters of the application device include one or more parameters of the power storage device and power system components.

3. The optimization system according to claim 2, wherein the data includes real-time data and historical data.

4. The optimization system according to claim 1, wherein the application device includes an electric vehicle, and the processor is configured to: determine an expected route and / or power consumption for the electric vehicle according to the user's driving behavior and charging behavior, and determine the charge / discharge curve for charging or discharging the power storage device based on the expected route and / or power consumption for the electric vehicle.

5. The optimization system according to claim 1, wherein the processor is configured to: receive a plurality of criteria for optimizing the use of the power storage device in the application device; receive a weighting factor for each of the plurality of criteria; determine one or more optimization parameters for each of the plurality of criteria based on the received data; and determine the charge / discharge curve based on the one or more optimization parameters and the weighting factor for each of the plurality of criteria.

6. The optimization system according to any one of claims 1-5, wherein the one or more criteria include battery life, mileage improvement, performance, electricity cost, efficiency, and / or charging time.

7. The optimization system according to any one of claims 1-5, further comprising: The application device includes the energy storage device to be charged. A charging device configured to be coupled to a power source and generate a charging current from the power source to the energy storage device, and At least one external device configured to be coupled to the application device and / or the charging device, Wherein the processor is located at least at one of the application device, the charging device, and the at least one external device.

8. The optimization system according to claim 7, Wherein, The processor is further configured to: receive at least one of data about the charging device, data about the power source, and data from the external device, and determine the charging curve based on the received data and at least one of the received data about the charging device, data about the power source, and data from the external device. The data about the charging device and the data about the power source respectively include one or more parameters of the charging device and the power source and data recorded or derived at the charging device and the power source.

9. The optimization system according to claim 1, Wherein, The one or more criteria for charging at least include battery life, and the processor is configured to: determine the charging curve to have a first charging stage, a second charging stage, and a third charging stage. The energy storage device is charged to an optimal storage state of charge in the first charging stage, waits in the second charging stage, and is charged to a desired state of charge in the third charging stage before the expected end of charging.

10. The optimization system according to claim 1, Wherein, The application device includes an electric vehicle, The one or more received criteria include: an increase in the mileage of the electric vehicle, and An efficiency map for a given driving route is used to determine charging / discharging to optimize the driving mileage of the electric vehicle by considering efficiency.

11. The optimization system according to claim 1, Wherein, The application device includes an electric vehicle and at least one of the following: The efficiency for a given driving route is used as a separate criterion for charging / discharging, and the efficiency is combined with at least one other criterion into the received multiple criteria.

12. An optimization method, Comprising: Receiving one or more criteria for optimizing the use of an energy storage device in an application device; Receiving data including at least one of one or more user inputs, data from the cloud, and data about the application device; Based on the received one or more optimization criteria, determining a charging / discharging curve for charging or discharging the energy storage device according to the received data. The charging / discharging curve at least describes the relationship of at least one of the charging / discharging current, the state of charge, and the driving speed for optimizing the discharging of the energy storage device during the charging or discharging process with respect to time; Outputting the charging / discharging curve to an electric vehicle to set limits for one or more parameters of the electric vehicle, the one or more parameters including driving speed and / or acceleration; Receive at least one of user feedback, machine learning data, big data, and changes regarding the optimization system and / or the application device with respect to the charge / discharge curve; and Update the charge / discharge curve based on at least one of the received user feedback, machine learning data, big data, and changes regarding the optimization system and / or the application device with respect to the charge / discharge curve.

13. The optimization method according to claim 12, wherein, the data regarding the application device includes one or more parameters of the application device and data recorded or derived at the application device, and the one or more parameters of the application device include one or more parameters of the energy storage device and the power system components.

14. The optimization method according to claim 13, wherein, the data includes real-time data and historical data.

15. The optimization method according to claim 14, wherein, the application device includes an electric vehicle, and the optimization method further includes: determining an expected route and / or power consumption for the electric vehicle based on the driving behavior and / or charging behavior of the user, and determining the charge / discharge curve for charging or discharging the energy storage device based on the expected route and / or power consumption for the electric vehicle.

16. The optimization method according to claim 12, wherein, a plurality of criteria for optimizing the use of the energy storage device are received, and the optimization method includes: receiving a weighting factor for each of the plurality of criteria; determining one or more optimization parameters for each of the plurality of criteria based on the received data; and determining the charge / discharge curve based on the one or more optimization parameters and the weighting factor for each of the plurality of criteria.

17. The optimization method according to any one of claims 12-16, wherein, the one or more criteria include battery life, mileage improvement, performance, power cost, efficiency, and / or charging time.

18. The optimization method according to claim 12, wherein, the one or more criteria for charging include at least battery life, and the optimization method includes: determining the charging curve to have a first charging stage, a second charging stage, and a third charging stage, where the energy storage device is charged to an optimal stored state of charge in the first charging stage, waits in the second charging stage, and is charged to a desired state of charge in the third charging stage before the expected end of charging.

19. The optimization method according to claim 12, further includes: receiving at least one of data regarding a charging device, data regarding a power source, and data from an external device, the charging device being configured to be coupled to the power source and generate a charging current from the power source to the energy storage device; determining the charging curve based on the received data and at least one of the data regarding the charging device, the data regarding the power source, and the data from the external device; Wherein, the data regarding the charging device and the data regarding the power supply respectively include one or more parameters of the charging device and the power supply, as well as data recorded or derived at the charging device and the power supply.

20. A computer storage medium comprising instructions for performing the steps of the optimization method according to any one of claims 12-19 when run by one or more processors.

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