JPWO2022071228A5 - - Google Patents
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- JPWO2022071228A5 JPWO2022071228A5 JP2022553955A JP2022553955A JPWO2022071228A5 JP WO2022071228 A5 JPWO2022071228 A5 JP WO2022071228A5 JP 2022553955 A JP2022553955 A JP 2022553955A JP 2022553955 A JP2022553955 A JP 2022553955A JP WO2022071228 A5 JPWO2022071228 A5 JP WO2022071228A5
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- vehicle body
- driving force
- rule base
- state
- sensor
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- 230000002159 abnormal effect Effects 0.000 description 2
- 238000001514 detection method Methods 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
Description
モータユニット42は、補助駆動力を出力することで、人力駆動力である踏力に補助駆動力を加えて、チェーン19を介して後輪13に伝達する。 By outputting the auxiliary driving force, the motor unit 42 adds the auxiliary driving force to the pedaling force, which is the human-powered driving force, and transmits the auxiliary driving force to the rear wheels 13 via the chain 19 .
まず、図4に示すように、電動自転車2が走行面を走行する際、複数のセンサは、電動自転車2の車体走行情報を取得する(S11)。本実施の形態では、電動自転車2には、複数のセンサとして、クランク回転センサ31、速度センサ32、トルクセンサ33、ジャイロセンサ34、傾斜センサ35、及び、バッテリ状態検知センサ36等が搭載されている。 First, as shown in FIG. 4, when the electric bicycle 2 runs on the running surface, the plurality of sensors acquire vehicle body running information of the electric bicycle 2 (S 11 ). In this embodiment, the electric bicycle 2 is equipped with a plurality of sensors such as a crank rotation sensor 31, a speed sensor 32, a torque sensor 33, a gyro sensor 34, an inclination sensor 35, and a battery state detection sensor 36. there is
また、解析部41は、ルールベース、及び、機械学習の少なくともいずれかに基づいて、車体走行情報を解析することで、車体走行情報と間接的に関連する車体10の状態を推定することができる。例えば予め構築されたルールベースを用いて、解析部41は、予め設定した条件(閾値判断)に基づいてそれぞれの車体走行情報を解析し、車体10に関連する車体10の状態を推定する。つまり、車体に異常のある状態を推定できるルールベース、車体が正常な状態を推定できるルールベースを予め構築することで、解析部41は、車体10の状態が異常か正常かを推定する。例えば、このルールベースは、速度を示す情報、人力駆動力を示す情報、電動モータ43の単位時間当たりの回転数を示す情報、タイヤ12a、13aの径を示す情報等の車体走行情報のそれぞれに基づいて閾値判定されることで、タイヤ12a、13aの空気圧の推定に用いられる。 Further, the analysis unit 41 can estimate the state of the vehicle body 10 indirectly related to the vehicle body running information by analyzing the vehicle body running information based on at least one of rule base and machine learning. . For example, using a rule base constructed in advance, the analysis unit 41 analyzes each piece of vehicle travel information based on preset conditions (threshold determination), and estimates the state of the vehicle body 10 related to the vehicle body 10 . In other words, the analysis unit 41 estimates whether the state of the vehicle body 10 is abnormal or normal by building in advance a rule base capable of estimating an abnormal state of the vehicle body and a rule base capable of estimating a normal state of the vehicle body. For example, this rule base includes information indicating speed, information indicating manpower driving force, information indicating the number of revolutions per unit time of the electric motor 43, information indicating the diameter of the tires 12a and 13a, and other vehicle body traveling information. Based on the above, threshold value determination is performed and used for estimating the air pressure of the tires 12a and 13a.
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
JP2020164794 | 2020-09-30 | ||
PCT/JP2021/035399 WO2022071228A1 (en) | 2020-09-30 | 2021-09-27 | Vehicle body state detection system, electric bicycle, vehicle body state detection method, and program |
Publications (2)
Publication Number | Publication Date |
---|---|
JPWO2022071228A1 JPWO2022071228A1 (en) | 2022-04-07 |
JPWO2022071228A5 true JPWO2022071228A5 (en) | 2023-06-16 |
Family
ID=80951650
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
JP2022553955A Pending JPWO2022071228A1 (en) | 2020-09-30 | 2021-09-27 |
Country Status (3)
Country | Link |
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JP (1) | JPWO2022071228A1 (en) |
DE (1) | DE112021005122T5 (en) |
WO (1) | WO2022071228A1 (en) |
Families Citing this family (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN115112387A (en) * | 2022-06-13 | 2022-09-27 | 杭州雷风新能源科技有限公司 | Electric bicycle brake fault detection method, terminal equipment and electric bicycle |
Family Cites Families (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP2001180238A (en) * | 1999-12-27 | 2001-07-03 | Kawasaki Heavy Ind Ltd | Device for detecting lowering of tire pneumatics |
JP2009064226A (en) | 2007-09-06 | 2009-03-26 | Hitachi Communication Technologies Ltd | Vehicle abnormality distribution device |
JP2019196920A (en) * | 2018-05-07 | 2019-11-14 | トヨタ自動車株式会社 | Diagnostic device |
JP7249540B2 (en) * | 2018-12-28 | 2023-03-31 | パナソニックIpマネジメント株式会社 | FAILURE DETECTION DEVICE, ELECTRIC BICYCLE, AND FAILURE DETECTION METHOD |
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2021
- 2021-09-27 WO PCT/JP2021/035399 patent/WO2022071228A1/en active Application Filing
- 2021-09-27 JP JP2022553955A patent/JPWO2022071228A1/ja active Pending
- 2021-09-27 DE DE112021005122.9T patent/DE112021005122T5/en active Pending
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