Battery state prediction system with battery voltage tracking mechanism

Abstract

The invention discloses a battery state prediction system with a battery voltage tracking mechanism. The battery state prediction system analyzes whether a battery voltage varies linearly with a change in battery state of health data when a battery remaining capacity reaches each of a plurality of battery capacity ratios to set a specified lower limit battery capacity ratio and a specified upper limit battery capacity ratio. The battery state prediction system analyzes the battery voltage variation along with the change of the health state of the battery when the residual capacity of the battery reaches the specified upper limit and lower limit battery capacity proportions respectively, so as to output a tracking rule. The battery state prediction system adjusts a sampled battery voltage range according to the tracking rule, and trains and establishes a prediction model based on battery charging time data corresponding to the adjusted battery voltage range.

Description

Technical Field

[0001] The present invention relates to a battery state prediction system, and in particular to a battery state prediction system with a battery voltage tracking mechanism. Background Art

[0002] Batteries are widely used in all aspects of people's daily lives. Whether it is for new energy vehicles using power batteries or backup power supplies for wireless communication base stations, battery life prediction has become a key factor in the development of various industries. In particular, rapid and accurate prediction of battery health status is the core of safety technology and also a difficult problem currently faced. Summary of the invention

[0003] In view of the deficiencies of the prior art, the present invention provides a battery state prediction system with a battery voltage tracking mechanism. The battery state prediction system includes a battery state detection module, a battery data linear analysis module, a data tracking rule analysis module and a prediction model training module. The battery state detection module is configured to detect the battery voltage and battery capacity of the battery after multiple charging and discharging. The battery data linear analysis module is connected to the battery state detection module. The battery data linear analysis module is configured to obtain the battery voltage when the remaining battery capacity reaches multiple battery capacity proportions. The battery data linear analysis module is configured to analyze whether the battery voltage when the remaining battery capacity reaches each of the battery capacity proportions changes linearly with the change of the battery health status data, and accordingly take out two of them from the multiple battery capacity proportions as a specified lower limit battery capacity proportion and a specified upper limit battery capacity proportion. The specified upper limit battery capacity proportion is greater than the specified lower limit battery capacity proportion. The data tracking rule analysis module is connected to the battery data linear analysis module. The data tracking rule analysis module is configured to analyze the battery voltage change when the battery health status changes from the previous battery health status data to the next battery health status data when the battery remaining capacity reaches the specified lower limit battery capacity percentage, and analyze the battery voltage change when the battery health status changes from the previous battery health status data to the next battery health status data when the battery remaining capacity reaches the specified upper limit battery capacity percentage, so as to output a tracking rule. The prediction model training module is connected to the data tracking rule analysis module. The prediction model training module is configured to adjust a sampled battery voltage range according to the tracking rule, collect battery charging time data corresponding to the adjusted battery voltage range to train and establish a prediction model. BRIEF DESCRIPTION OF THE DRAWINGS

[0004] Figure 1 FIG. 4 is a block diagram of a battery state prediction system with a battery voltage tracking mechanism according to a first embodiment of the present invention.

[0005] Figure 2FIG. 4 is a block diagram of a battery state prediction system with a battery voltage tracking mechanism according to a second embodiment of the present invention.

[0006] Figure 3 It is a schematic diagram of a curve and a tracking line constructed when the battery health state reaches the previous battery health state data of the battery state prediction system with a battery voltage tracking mechanism according to the second embodiment of the present invention.

[0007] Figure 4 It is a schematic diagram of a curve and a tracking line constructed when the battery health state reaches the next battery health state data of the battery state prediction system with a battery voltage tracking mechanism according to the second embodiment of the present invention.

[0008] Figure 5 It is a schematic diagram of a curve and a tracking line constructed by a battery state prediction system with a battery voltage tracking mechanism according to a second embodiment of the present invention when the battery health state changes from the previous battery health state data to the next battery health state data.

[0009] Figure 6 This is a graph of battery voltage changing with cycle times when the remaining battery capacity reaches a specified lower limit battery capacity percentage according to the battery state prediction system with a battery voltage tracking mechanism according to the second embodiment of the present invention.

[0010] Figure 7 This is a graph of battery voltage changing with cycle times when the battery remaining capacity reaches a specified upper limit battery capacity percentage according to the battery state prediction system with a battery voltage tracking mechanism according to the second embodiment of the present invention.

[0011] Figure 8 This is a graph of the battery voltage changing with the battery health state when the battery remaining capacity reaches a specified lower limit battery capacity ratio according to the battery status prediction system with a battery voltage tracking mechanism according to the second embodiment of the present invention.

[0012] Fig. 9 The data of battery health status predicted by the battery status prediction system of the present invention and the traditional battery status prediction system as a function of cycle number as well as the curve chart of actual data are shown. DETAILED DESCRIPTION

[0013] The following is an explanation of the embodiments of the present invention through specific embodiments. Those skilled in the art can understand the advantages and effects of the present invention from the contents disclosed in this specification. The present invention can be implemented or applied through other different specific embodiments, and the details in this specification can also be modified and changed in various ways based on different viewpoints and applications without departing from the concept of the present invention. In addition, the drawings of the present invention are only simple schematic illustrations and are not depicted according to actual dimensions. It is stated in advance. The following embodiments will further explain the relevant technical contents of the present invention in detail, but the disclosed contents are not intended to limit the scope of protection of the present invention. In addition, the term "or" used herein may include any one or more combinations of the associated listed items depending on the actual situation.

[0014] See also Figure 1 , which is a block diagram of a battery state prediction system with a battery voltage tracking mechanism according to a first embodiment of the present invention.

[0015] like Figure 1 As shown, in the first embodiment of the present invention, the battery state prediction system with battery voltage tracking mechanism of the present invention may include a battery state detection module 20, a battery data linear analysis module 40, a data tracking rule analysis module 50 and a prediction model training module 60.

[0016] The battery status detection module 20 is connected to, in contact with or adjacent to the battery to sense and detect data such as the battery voltage and battery capacity after the battery has been charged and discharged for multiple times.

[0017] The battery health status data is expressed in the following equation:

[0018] SOH=(Q max / Q n )·100%,

[0019] Among them, SOH battery health status data, Qmax refers to the maximum amount of electricity that can be discharged when the battery is fully charged, and Qn is the rated capacity of the battery.

[0020] The battery data linear analysis module 40 is connected to the battery status detection module 20 to obtain the battery voltage when the remaining battery capacity reaches each of a plurality of battery capacity ratios (for example but not limited to a plurality of battery capacity ratios including 0% to 100%) from the battery status detection module 20 .

[0021] The battery data linear analysis module 40 analyzes whether the battery voltage changes linearly with the change of the battery health status data when the remaining battery capacity reaches each of the multiple battery capacity ratios, and accordingly selects two of the multiple battery capacity ratios as a specified lower limit battery capacity ratio and a specified upper limit battery capacity ratio, respectively. The specified upper limit battery capacity ratio is greater than the specified lower limit battery capacity ratio.

[0022] The data tracking rule analysis module 50 is connected to the battery data linear analysis module 40. The data tracking rule analysis module 50 analyzes the battery voltage change when the battery health status changes from the previous battery health status data to the next battery health status data when the battery remaining capacity reaches a specified lower limit battery capacity percentage, and analyzes the battery voltage change when the battery health status changes from the previous battery health status data to the next battery health status data when the battery remaining capacity reaches a specified upper limit battery capacity percentage, so as to output a tracking rule.

[0023] The prediction model training module 60 is connected to the data tracking rule analysis module 50. The prediction model training module 60 adjusts a sampled battery voltage range according to a tracking rule obtained from the data tracking rule analysis module 50, collects battery charging time data corresponding to the adjusted battery voltage range to train and establish a prediction model.

[0024] See also Figures 2 to 9 ,in Figure 2 FIG. 4 is a block diagram of a battery state prediction system with a battery voltage tracking mechanism according to a second embodiment of the present invention.

[0025] like Figure 2 As shown, in the second embodiment of the present invention, the battery state prediction system with a battery voltage tracking mechanism of the present invention may include a battery state curve construction module 70 and a battery state tracking construction module 80 in addition to including a battery state detection module 20, a battery data linear analysis module 40, a data tracking rule analysis module 50 and a prediction model training module 60.

[0026] The battery state curve construction module 70 is connected to the battery state detection module 20 and the battery data linear analysis module 40 . The battery state tracking construction module 80 is connected to the battery data linear analysis module 40 and the data tracking rule analysis module 50 .

[0027] The battery status detection module 20 can detect the battery voltage during multiple charging processes (for example Figures 3 to 5 The battery voltage on the horizontal axis of the graph) and the battery capacity per unit battery voltage (e.g. Figures 3 to 5 (battery capacity per unit battery voltage on the vertical axis of the graph).

[0028] The battery state curve building module 70 can be based on the previous battery health state data detected by the battery state detection module 20, such as Figure 3 and Figure 5 As shown, the first battery health status curve CU1 is established in a curve graph, wherein the horizontal axis data of the curve graph is the battery voltage, and the vertical axis data of the curve graph is the battery capacity per unit battery voltage.

[0029] The battery state curve building module 70 can be based on the next battery health state data detected by the battery state detection module 20, such as Figure 4 and Figure 5 A second battery health state curve CU2 is shown established in the graph.

[0030] The battery data linear analysis module 40 obtains the battery voltage when the battery remaining capacity reaches multiple battery capacity percentages from the battery status detection module 20, analyzes whether the battery voltage when the battery remaining capacity reaches each battery capacity percentage changes linearly with the change of the battery health status data, and accordingly selects two of the multiple battery capacity percentages as a specified lower limit battery capacity percentage and a specified upper limit battery capacity percentage, respectively, and the specified upper limit battery capacity percentage is greater than the specified lower limit battery capacity percentage.

[0031] The battery status tracking construction module 80 can be based on the battery remaining capacity reaching a specified lower limit battery capacity ratio (for example but not limited to Figure 3 and Figure 5 20% as shown) and the battery health status reaches the previous battery health status data (such as but not limited to Figure 3 and Figure 5 99.12%) to establish a first lower limit battery data tracking line (such as but not limited to Figure 3 and Figure 5 The first lower limit battery data tracking line SC11 is shown in the graph, perpendicular to the horizontal axis of the graph and intersecting with the first battery health state curve CU1.

[0032] The battery status tracking module 80 can be configured to detect the battery status of the battery when the remaining battery capacity reaches a specified upper limit battery capacity ratio (for example, but not limited to Figure 3 and Figure 5 60% as shown) and the battery health status reaches the previous battery health status data (such as but not limited to Figure 3 and Figure 5 99.12%) to establish a first upper limit battery data tracking line (such as but not limited to Figure 3 and Figure 5 The first upper limit battery data tracking line SC12 is shown in the graph, perpendicular to the horizontal axis of the graph and intersecting with the first battery health state curve CU1.

[0033] The battery status tracking construction module 80 may be configured to generate a battery status tracking module 80 based on the battery remaining capacity reaching a specified lower limit battery capacity (for example, but not limited to Figure 4 and Figure 5 20% as shown) and the battery health status reaches the next battery health status data (such as but not limited to Figure 4 and Figure 5 The battery voltage at 87.29% is as shown in Figure 4 and Figure 5 The second lower limit battery data tracking line is established as shown (for example but not limited to Figure 4 and Figure 5 The second lower limit battery data tracking line SC21) shown in the graph is perpendicular to the horizontal axis of the graph and intersects with the second battery health status curve CU2.

[0034] The battery status tracking module 80 can be configured to detect the battery status of the battery when the remaining battery capacity reaches a specified upper limit battery capacity ratio (for example, but not limited to Figure 4 and Figure 5 60% as shown) and the battery health status reaches the next battery health status data (such as but not limited to Figure 4 and Figure 5 The battery voltage at 87.29% is as shown in Figure 4 and Figure 5 The second upper limit battery data tracking line is established as shown (for example but not limited to Figure 4 and Figure 5 The second upper limit battery data tracking line SC22 is shown in the graph, perpendicular to the horizontal axis of the graph and intersecting with the second battery health status curve CU2.

[0035] The data tracking rule analysis module 50 can calculate the movement vector (including the voltage change, for example) from the first lower limit battery data tracking line to the second lower limit battery data tracking line. Figure 5 and Figure 6 The change in voltage from 3.59V of the battery voltage aligned with the first lower limit battery data tracking line SC11 on the graph to 3.66V of the battery voltage aligned with the second lower limit battery data tracking line SC21 on the graph) is shown as a lower limit battery data tracking coefficient.

[0036] The data tracking rule analysis module 50 calculates the moving vector (including the voltage change, e.g. Figure 5 and Figure 7 The change in voltage from 3.94V of the horizontal axis battery voltage aligned with the first upper limit battery data tracking line SC12 on the graph to 3.99V of the horizontal axis battery voltage aligned with the second upper limit battery data tracking line SC22 on the graph) is shown as an upper limit battery data tracking coefficient.

[0037] The data tracking rule analysis module 50 outputs a tracking rule to the prediction model training module 60 according to a lower limit battery data tracking coefficient and an upper limit battery data tracking coefficient. The prediction model training module 60 adjusts a sampled battery voltage range according to a tracking rule obtained from the data tracking rule analysis module 50, collects battery charging time data corresponding to the adjusted battery voltage range to train and establish a prediction model.

[0038] like Fig. 9 As shown, compared with the traditional battery status prediction system that adopts a fixed voltage range to predict the battery health status data, the battery status prediction system of the present invention (the first and second embodiments) tracks the battery charging time data corresponding to the battery voltage range to predict the battery health status data, which is closer to the actual data.

[0039] In summary, the present invention provides a battery status prediction system with a battery voltage tracking mechanism. The battery status prediction system with a battery voltage tracking mechanism of the present invention tracks a battery voltage range that changes with the battery health state under a specified battery capacity ratio (including a specified lower limit battery capacity ratio and a specified upper limit battery capacity ratio), and establishes a prediction model based on the battery charging time data corresponding to this battery voltage range, which can be used to accurately predict the battery health state data that changes with the battery charging time data after the battery has undergone a specified number of charge and discharge cycles.

[0040] The contents disclosed above are only preferred feasible embodiments of the present invention, and are not intended to limit the claims of the present invention. Therefore, all equivalent technical changes made using the description and drawings of the present invention are included in the claims of the present invention.

Claims

1. A battery status prediction system with a battery voltage tracking mechanism, characterized in that: The battery state prediction system with battery voltage tracking mechanism includes: A battery status detection module configured to detect the battery voltage and battery capacity after the battery has been charged and discharged multiple times; a battery data linear analysis module, connected to the battery status detection module, configured to obtain the battery voltage when the remaining battery capacity reaches a plurality of battery capacity ratios, analyze whether the battery voltage when the remaining battery capacity reaches each of the battery capacity ratios changes linearly with the change of the battery health status data, and accordingly take two of the plurality of battery capacity ratios as a specified lower limit battery capacity ratio and a specified upper limit battery capacity ratio, respectively, the specified upper limit battery capacity ratio being greater than the specified lower limit battery capacity ratio; a data tracking rule analysis module, connected to the battery data linear analysis module, configured to analyze the battery voltage change when the battery health status changes from the previous battery health status data to the next battery health status data when the battery remaining capacity reaches the specified lower limit battery capacity percentage, and to analyze the battery voltage change when the battery health status changes from the previous battery health status data to the next battery health status data when the battery remaining capacity reaches the specified upper limit battery capacity percentage, so as to output a tracking rule; and The prediction model training module is connected to the data tracking rule analysis module and is configured to adjust a sampled battery voltage range according to the tracking rule, collect battery charging time data corresponding to the adjusted battery voltage range to train and establish a prediction model.

2. The battery status prediction system with battery voltage tracking mechanism according to claim 1, characterized in that: The battery state prediction system with battery voltage tracking mechanism further comprises: A battery status curve construction module is connected to the battery status detection module and is configured to establish a first battery health status curve in a curve graph based on the previous battery health status data detected by the battery status detection module. The horizontal axis data of the curve graph is the battery voltage and the vertical axis data is the battery capacity per unit battery voltage.

3. The battery status prediction system with battery voltage tracking mechanism according to claim 2, characterized in that: The battery state curve building module is configured to establish a second battery health state curve in the curve graph according to the next battery health state data detected by the battery state detection module.

4. The battery status prediction system with battery voltage tracking mechanism according to claim 3, characterized in that: The battery state prediction system with battery voltage tracking mechanism further comprises: A battery status tracking construction module is connected to the battery data linear analysis module and is configured to establish a first lower limit battery data tracking line in the curve graph, perpendicular to the horizontal axis of the curve graph and intersecting with the first battery health status curve, based on the battery voltage when the battery remaining capacity reaches the specified lower limit battery capacity percentage and the battery health status reaches the previous battery health status data.

5. The battery status prediction system with battery voltage tracking mechanism according to claim 4, characterized in that: The battery status tracking construction module establishes a first upper limit battery data tracking line in the curve graph based on the battery voltage when the remaining battery capacity reaches the specified upper limit battery capacity percentage and the battery health status reaches the previous battery health status data, which is perpendicular to the horizontal axis of the curve graph and intersects with the first battery health status curve.

6. The battery status prediction system with battery voltage tracking mechanism according to claim 5, characterized in that: The battery status tracking construction module establishes a second lower limit battery data tracking line in the curve graph based on the battery voltage when the battery remaining capacity reaches the specified lower limit battery capacity percentage and the battery health status reaches the next battery health status data, which is perpendicular to the horizontal axis of the curve graph and intersects with the second battery health status curve.

7. The battery status prediction system with battery voltage tracking mechanism according to claim 6, characterized in that: The battery status tracking construction module establishes a second upper limit battery data tracking line in the curve graph based on the battery voltage when the remaining battery capacity reaches the specified upper limit battery capacity percentage and the battery health status reaches the next battery health status data, which is perpendicular to the horizontal axis of the curve graph and intersects with the second battery health status curve.

8. The battery status prediction system with battery voltage tracking mechanism according to claim 7, characterized in that: The data tracing rule analysis module calculates a movement vector from the first lower limit battery data tracing line to the second lower limit battery data tracing line as a lower limit battery data tracing coefficient. The tracing rule output by the data tracing rule analysis module includes the lower limit battery data tracing coefficient.

9. The battery status prediction system with battery voltage tracking mechanism according to claim 8, characterized in that: The data tracking rule analysis module calculates a movement vector from the first upper limit battery data tracking line to the second upper limit battery data tracking line as an upper limit battery data tracking coefficient. The tracking rule output by the data tracking rule analysis module further includes the upper limit battery data tracking coefficient.

Images