A control method and device compatible with charging of multiple batteries

Abstract

A control method and device compatible with charging of multiple batteries, comprising the steps of: an STM chip detecting in real time whether a charging pile charging head is electrically connected to a battery pack to be charged, the charging head electrically connecting to the battery pack to be charged, the STM chip selecting a charging type according to the type of battery to be charged, inputting the acquired real-time data of the battery pack into a trained convolutional neural network, the neural network adjusting the error parameter in real time according to the real-time data of the battery pack, thereby adjusting the voltage or current at the input end of the battery pack, and the STM chip controlling the electrical connection of the charging head according to the output result of the convolutional neural network. The present invention automatically and flexibly switches different charging modes according to different needs, and taking into account different battery parameters, significantly improves the charging efficiency and saves resources.

Description

Technical Field

[0001] The present invention relates to the technical field of battery charging, and in particular to a control method and device compatible with charging of multiple batteries. Background Art

[0002] At present, with the continuous popularization and expansion of the scale of the electric vehicle industry, batteries and charging technologies have been rapidly and widely used, and higher requirements have been placed on charging piles and rechargeable batteries. Fast charging allows users to use their cars in real time anytime and anywhere. Therefore, the smoothness of charging is closely related to the widespread application of charging piles and battery performance. Especially in an environment of high oil prices and continuous depletion of energy, electric vehicles have been widely used in charging, which has played an important role in the development of human society.

[0003] However, the traditional charging efficiency is low, and the charging interface standards of various countries and even domestically are inconsistent. Each company develops its own charging connector separately, and the charging current and voltage are different, resulting in high charging costs, a great waste of resources, and cannot meet people's needs for fast and automatic charging. A fast, convenient, and efficient charging method that is compatible with multiple battery charging is currently very necessary. In addition, traditional battery charging is directly charged, which cannot effectively utilize the structural characteristics of the battery, does not consider the differences in different battery parameters, and does not use artificial intelligence neural network charging selection methods. Therefore, a real-time, fast, compatible and efficient charging method has become an urgent need to improve the user experience. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a control method and device compatible with charging of multiple batteries. According to different charging methods, the present invention adopts wiring heads made by routes, automatically and flexibly switches different charging methods according to different needs, and considers different battery parameters, significantly improves charging efficiency, and saves resources; A control method compatible with charging of multiple batteries, comprising the steps of:

[0005] S1: The STM chip detects in real time whether the charging head of the charging pile is electrically connected to the battery pack to be charged. The conductive wire of the charging head interface is made of aluminum wire. The STM chip can automatically switch to the adapter charging head according to the interface of the battery pack to be charged;

[0006] S2: The charging head is electrically connected to the battery pack to be charged, and the STM chip selects the charging type according to the type of the battery to be charged. The charging types include constant current charging, constant voltage charging, AC charging, pulse charging, and reflection charging;

[0007] S21: When constant current charging or constant voltage charging is selected, the STM chip acquires the real-time data of the battery pack in real time. The real-time data of the battery pack includes the battery ohmic internal resistance, polarization internal resistance, battery pack voltage, battery capacity, and temperature data. The acquired real-time data of the battery pack is input into the trained convolutional neural network. The neural network adjusts the error parameters in real time according to the real-time data of the battery pack, thereby adjusting the voltage or current of the input terminal of the battery pack.

[0008] S22: When AC charging, pulse charging or reflection charging is selected, the STM chip acquires the real-time data of the battery pack in real time. The real-time data of the battery pack includes the battery ohmic internal resistance, polarization internal resistance, capacitance value, inductance value, battery pack capacity, temperature data, and conductivity coefficient, and calculates the conductivity coefficient G(s);

[0009]

[0010] Where L is the battery inductance, C is the capacitance, R is the resistance, s is the transfer variable, δ is the battery type charging parameter, which is determined by different battery types or battery pack sizes, and τ is the battery life correlation value, which changes with the battery usage time; the battery resistance will increase with the charging time, and the transfer function will change accordingly;

[0011] S3: The STM chip controls the electrical connection of the charging head based on the output results of the convolutional neural network.

[0012] Preferably, the trained convolutional neural network is trained using historical charging data and usage data of different types of batteries to obtain a convolutional neural network model.

[0013] Preferably, the STM chip controls the electrical connection of the charging head according to the output result of the convolutional neural network, specifically including controlling the charging head to disconnect the electrical connection if the output result of the convolutional neural network is to pause, and continuing to keep the charging head electrically connected if the output result is to continue charging or adjust the error parameters.

[0014] Preferably, the STM chip can automatically switch to adapt the charging head according to the interface of the battery pack to be charged, including a 7-terminal structure of the AC output interface, including an AC power interface, a first spare contact, a second spare contact, a neutral line, a protective grounding head, a charging confirmation head, and a control confirmation head; it also includes a DC output interface plug, that is, a standard 9-hole plug, containing 2 communication interfaces, 2 DC output interfaces, 2 spare power interfaces, 2 gun insertion judgment interfaces, and 1 PE grounding port; it is made of aluminum wire, and the contact heads of the DC output interface and the AC output interface share multiple interfaces to achieve automatic switching in different charging modes.

[0015] The present invention also discloses a control device compatible with charging of multiple batteries, comprising:

[0016] The charging head detection module, the STM chip detects in real time whether the charging head of the charging pile is electrically connected to the battery pack to be charged. The conductive wire of the charging head interface is made of aluminum wire. The STM chip can automatically switch the adapter charging head according to the interface of the battery pack to be charged;

[0017] Charging type selection module: the charging head is electrically connected to the battery pack to be charged, and the STM chip selects the charging type according to the type of the battery to be charged, and the charging type includes constant current charging, constant voltage charging, AC charging, pulse charging, and reflection charging;

[0018] When constant current charging or constant voltage charging is selected, the STM chip acquires the real-time data of the battery pack in real time. The real-time data of the battery pack includes the battery ohmic internal resistance, polarization internal resistance, battery pack voltage, battery capacity, and temperature data. The acquired real-time data of the battery pack is input into the trained convolutional neural network. The neural network adjusts the error parameters in real time according to the real-time data of the battery pack, thereby adjusting the voltage or current of the input terminal of the battery pack.

[0019] When AC charging, pulse charging or reflection charging is selected, the STM chip obtains the real-time data of the battery pack in real time. The real-time data of the battery pack includes the battery ohmic internal resistance, polarization internal resistance, capacitance value, inductance value, battery pack capacity, temperature data, and conductivity coefficient, and calculates the conductivity coefficient G(s);

[0020]

[0021] Where L is the battery inductance, C is the capacitance, R is the resistance, s is the transfer variable, δ is the battery type charging parameter, which is determined by different battery types or battery pack sizes, and τ is the battery life correlation value, which changes with the battery usage time; the battery resistance will increase with the charging time, and the transfer function will change accordingly;

[0022] Charging completion judgment module: The STM chip controls the electrical connection of the charging head based on the output results of the convolutional neural network.

[0023] Preferably, the trained convolutional neural network is trained using historical charging data and usage data of different types of batteries to obtain a convolutional neural network model.

[0024] Preferably, the STM chip controls the electrical connection of the charging head according to the output result of the convolutional neural network, specifically including controlling the charging head to disconnect the electrical connection if the output result of the convolutional neural network is to pause, and continuing to keep the charging head electrically connected if the output result is to continue charging or adjust the error parameters.

[0025] Preferably, the STM chip can automatically switch to adapt the charging head according to the interface of the battery pack to be charged, including a 7-terminal structure of the AC output interface, including an AC power interface, a first spare contact, a second spare contact, a neutral line, a protective grounding head, a charging confirmation head, and a control confirmation head; it also includes a DC output interface plug, that is, a standard 9-hole plug, containing 2 communication interfaces, 2 DC output interfaces, 2 spare power interfaces, 2 gun insertion judgment interfaces, and 1 PE grounding port; it is made of aluminum wire, and the contact heads of the DC output interface and the AC output interface share multiple interfaces to achieve automatic switching in different charging modes.

[0026] Compared with the prior art, the technical solution of the present invention has the following beneficial effects:

[0027] The present invention provides a control method and device compatible with charging of multiple batteries. The wiring head is made of aluminum wire. The contact heads of the DC output interface and the AC output interface share multiple interfaces, and automatic switching is achieved in different charging modes. According to different charging methods, the wiring head made of the route is used to automatically and flexibly switch different charging methods according to different needs, and give different functions to the interface; and considering different battery parameters, the charging efficiency is significantly improved and resources are saved; the application adopts the method of inputting the acquired real-time data of the battery pack into the trained convolutional neural network, and the neural network adjusts the error parameter in real time according to the real-time data of the battery pack, thereby realizing the adjustment of the voltage or current at the input end of the battery pack, δ is the charging parameter of the battery type, and considering the different conduction coefficients of different batteries, the charging efficiency is greatly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 This is a flow chart of a control method compatible with charging of multiple batteries according to the present invention; DETAILED DESCRIPTION

[0029] Those skilled in the art understand that, as mentioned in the background art, the traditional charging efficiency is low, the charging interface standards of various countries and even domestically are inconsistent, each company develops its own charging connector separately, and the charging current and voltage are different, resulting in high charging costs, a great waste of resources, and cannot meet people's needs for fast and automatic charging; and a fast, convenient, and efficient charging method that is compatible with a variety of battery charging is currently very necessary. In addition, traditional battery charging is directly charged, which cannot effectively utilize the structural characteristics of the battery, does not consider the differences in different battery parameters, and does not adopt an artificial intelligence neural network charging selection method; therefore, a real-time, fast, and highly compatible charging method has become an urgent need to improve the user experience. In order to make the above-mentioned purposes, features, and beneficial effects of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings.

[0030] Embodiment 1:

[0031] like Figure 1 As shown, the present invention provides a control method compatible with charging of multiple batteries, including the following steps: S1: an STM chip detects in real time whether a charging head of a charging pile is electrically connected to a battery pack to be charged, the conductive wire of the charging head interface is made of aluminum wire, and the STM chip can automatically switch to an adapter charging head according to the interface of the battery pack to be charged;

[0032] S2: The charging head is electrically connected to the battery pack to be charged, and the STM chip selects the charging type according to the type of the battery to be charged. The charging types include constant current charging, constant voltage charging, AC charging, pulse charging, and reflection charging;

[0033] S21: When constant current charging or constant voltage charging is selected, the STM chip acquires the real-time data of the battery pack in real time. The real-time data of the battery pack includes the battery ohmic internal resistance, polarization internal resistance, battery pack voltage, battery capacity, and temperature data. The acquired real-time data of the battery pack is input into the trained convolutional neural network. The neural network adjusts the error parameters in real time according to the real-time data of the battery pack, thereby adjusting the voltage or current of the input terminal of the battery pack.

[0034] S22: When AC charging, pulse charging or reflection charging is selected, the STM chip acquires the real-time data of the battery pack in real time. The real-time data of the battery pack includes the battery ohmic internal resistance, polarization internal resistance, capacitance value, inductance value, battery pack capacity, temperature data, and conductivity coefficient, and calculates the conductivity coefficient G(s);

[0035]

[0036] Where L is the battery inductance, C is the capacitance, R is the resistance, s is the transfer variable, δ is the battery type charging parameter, which is determined by different battery types or battery pack sizes, and τ is the battery life correlation value, which changes with the battery usage time; the battery resistance will increase with the charging time, and the transfer function will change accordingly;

[0037] S3: The STM chip controls the electrical connection of the charging head based on the output results of the convolutional neural network.

[0038] In some embodiments, the trained convolutional neural network is trained using historical charging data and usage data of different types of batteries to obtain a convolutional neural network model.

[0039] In some embodiments, the STM chip controls the electrical connection of the charging head according to the output result of the convolutional neural network, specifically including controlling the charging head to disconnect the electrical connection if the output result of the convolutional neural network is to pause, and continuing to keep the charging head electrically connected if the output result is to continue charging or adjust the error parameters.

[0040] In some embodiments, the STM chip can automatically switch to adapt the charging head according to the interface of the battery pack to be charged, including a 7-terminal structure of an AC output interface, including an AC power interface, a first spare contact, a second spare contact, a neutral line, a protective grounding head, a charging confirmation head, and a control confirmation head; it also includes a DC output interface plug, that is, a standard 9-hole plug, containing 2 communication interfaces, 2 DC output interfaces, 2 spare power interfaces, 2 gun plug judgment interfaces, and 1 PE grounding port; it is made of aluminum wire, and the contact heads of the DC output interface and the AC output interface share multiple interfaces to achieve automatic switching in different charging modes.

[0041] Embodiment 2:

[0042] The present invention also discloses a control device compatible with charging of multiple batteries, including:

[0043] The charging head detection module, the STM chip detects in real time whether the charging head of the charging pile is electrically connected to the battery pack to be charged. The conductive wire of the charging head interface is made of aluminum wire. The STM chip can automatically switch the adapter charging head according to the interface of the battery pack to be charged;

[0044] Charging type selection module: the charging head is electrically connected to the battery pack to be charged, and the STM chip selects the charging type according to the type of the battery to be charged, and the charging type includes constant current charging, constant voltage charging, AC charging, pulse charging, and reflection charging;

[0045] When constant current charging or constant voltage charging is selected, the STM chip acquires the real-time data of the battery pack in real time. The real-time data of the battery pack includes the battery ohmic internal resistance, polarization internal resistance, battery pack voltage, battery capacity, and temperature data. The acquired real-time data of the battery pack is input into the trained convolutional neural network. The neural network adjusts the error parameters in real time according to the real-time data of the battery pack, thereby adjusting the voltage or current of the input terminal of the battery pack.

[0046] When AC charging, pulse charging or reflection charging is selected, the STM chip obtains the real-time data of the battery pack in real time. The real-time data of the battery pack includes the battery ohmic internal resistance, polarization internal resistance, capacitance value, inductance value, battery pack capacity, temperature data, and conductivity coefficient, and calculates the conductivity coefficient G(s);

[0047]

[0048] Where L is the battery inductance, C is the capacitance, R is the resistance, s is the transfer variable, δ is the battery type charging parameter, which is determined by different battery types or battery pack sizes, and τ is the battery life correlation value, which changes with the battery usage time; the battery resistance will increase with the charging time, and the transfer function will change accordingly;

[0049] Charging completion judgment module: The STM chip controls the electrical connection of the charging head based on the output results of the convolutional neural network.

[0050] In some embodiments, the trained convolutional neural network is trained using historical charging data and usage data of different types of batteries to obtain a convolutional neural network model.

[0051] In some embodiments, the STM chip controls the electrical connection of the charging head according to the output result of the convolutional neural network, specifically including controlling the charging head to disconnect the electrical connection if the output result of the convolutional neural network is to pause, and continuing to keep the charging head electrically connected if the output result is to continue charging or adjust the error parameters.

[0052] In some embodiments, the STM chip can automatically switch to adapt the charging head according to the interface of the battery pack to be charged, including a 7-terminal structure of an AC output interface, including an AC power interface, a first spare contact, a second spare contact, a neutral line, a protective grounding head, a charging confirmation head, and a control confirmation head; it also includes a DC output interface plug, that is, a standard 9-hole plug, containing 2 communication interfaces, 2 DC output interfaces, 2 spare power interfaces, 2 gun plug judgment interfaces, and 1 PE grounding port; it is made of aluminum wire, and the contact heads of the DC output interface and the AC output interface share multiple interfaces to achieve automatic switching in different charging modes.

[0053] Compared with the prior art, the technical solution of the present invention has the following beneficial effects:

[0054] The present invention provides a control method and device compatible with charging of multiple batteries. The wiring head is made of aluminum wire. The contact heads of the DC output interface and the AC output interface share multiple interfaces, and automatic switching is achieved in different charging modes. According to different charging methods, the wiring head made of the route is used to automatically and flexibly switch different charging methods according to different needs, and give different functions to the interface; and considering different battery parameters, the charging efficiency is significantly improved and resources are saved; the application adopts the method of inputting the acquired real-time data of the battery pack into the trained convolutional neural network, and the neural network adjusts the error parameter in real time according to the real-time data of the battery pack, thereby realizing the adjustment of the voltage or current at the input end of the battery pack, δ is the charging parameter of the battery type, and considering the different conduction coefficients of different batteries, the charging efficiency is greatly improved.

[0055] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products, and thus the present application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects.

[0056] Although the present invention is disclosed as above, the present invention is not limited thereto. Any person skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the scope defined by the claims.

Claims

1. A control method compatible with charging of multiple batteries, characterized in that: Includes steps: S1: The STM chip detects in real time whether the charging head of the charging pile is electrically connected to the battery pack to be charged. The conductive wire of the charging head interface is made of aluminum wire. The STM chip can automatically switch to the adapter charging head according to the interface of the battery pack to be charged; S2: The charging head is electrically connected to the battery pack to be charged, and the STM chip selects the charging type according to the type of the battery to be charged, and the charging type includes constant current charging, constant voltage charging, AC charging, pulse charging, and reflection charging; when constant current charging or constant voltage charging is selected, the STM chip obtains the real-time data of the battery pack in real time, and the real-time data of the battery pack includes the battery ohmic internal resistance, polarization internal resistance, battery pack voltage, battery capacity, and temperature data; when AC charging or pulse charging or reflection charging is selected, the STM chip obtains the real-time data of the battery pack in real time, and the real-time data of the battery pack includes the battery ohmic internal resistance, polarization internal resistance, capacitance value, inductance value, battery pack capacity, temperature data, and conduction coefficient, and calculates the conduction coefficient G(s); Wherein, L is the battery inductance, C is the capacitance, R is the resistance, s is the transfer variable, δ is the battery type charging parameter, which is determined by different battery types or battery pack sizes, and τ is the battery life correlation value, which changes with the battery usage time; the battery resistance will increase with the charging time, and the transfer function will change accordingly; the acquired real-time data of the battery pack is input into the trained convolutional neural network, and the neural network adjusts the error parameters in real time according to the real-time data of the battery pack, thereby realizing the adjustment of the voltage or current at the input end of the battery pack; S3: The STM chip controls the electrical connection of the charging head according to the output of the convolutional neural network; The trained convolutional neural network is trained using historical charging data and usage data of different types of batteries to obtain a convolutional neural network model; The STM chip controls the electrical connection of the charging head according to the output result of the convolutional neural network, specifically, if the output result of the convolutional neural network is to pause, then control the charging head to disconnect the electrical connection, and if the output result is to continue charging or adjust the error parameter, then continue to keep the charging head electrically connected; The STM chip can automatically switch to adapt the charging head according to the interface of the battery pack to be charged, including an AC output interface 7-terminal structure, including an AC power interface, a first spare contact, a second spare contact, a neutral line, a protective grounding head, a charging confirmation head, and a control confirmation head; it also includes a DC output interface plug, that is, a standard 9-hole plug, containing 2 communication interfaces, 2 DC output interfaces, 2 spare power interfaces, 2 gun plug judgment interfaces, and 1 PE grounding port.

2. A control device compatible with charging of multiple batteries, characterized in that: include: The charging head detection module, the STM chip detects in real time whether the charging head of the charging pile is electrically connected to the battery pack to be charged. The conductive wire of the charging head interface is made of aluminum wire. The STM chip can automatically switch to the adapter charging head according to the interface of the battery pack to be charged; Charging type selection module: the charging head is electrically connected to the battery pack to be charged, and the STM chip selects the charging type according to the type of the battery to be charged, and the charging type includes constant current charging, constant voltage charging, AC charging, pulse charging, and reflection charging; When constant current charging or constant voltage charging is selected, the STM chip acquires the real-time data of the battery pack in real time, including the battery ohmic internal resistance, polarization internal resistance, battery pack voltage, battery capacity, and temperature data; when AC charging, pulse charging, or reflection charging is selected, the STM chip acquires the real-time data of the battery pack in real time, including the battery ohmic internal resistance, polarization internal resistance, capacitance value, inductance value, battery pack capacity, temperature data, and conduction coefficient, and calculates the conduction coefficient G(s); Wherein, L is the battery inductance, C is the capacitance, R is the resistance, s is the transfer variable, δ is the battery type charging parameter, which is determined by different battery types or battery pack sizes, and τ is the battery life correlation value, which changes with the battery usage time; the battery resistance will increase with the charging time, and the transfer function will change accordingly; the acquired real-time data of the battery pack is input into the trained convolutional neural network, and the neural network adjusts the error parameters in real time according to the real-time data of the battery pack, thereby realizing the adjustment of the voltage or current at the input end of the battery pack; Charging completion judgment module: The STM chip controls the electrical connection of the charging head according to the output results of the convolutional neural network; The trained convolutional neural network is trained using historical charging data and usage data of different types of batteries to obtain a convolutional neural network model; The STM chip controls the electrical connection of the charging head according to the output result of the convolutional neural network, specifically, if the output result of the convolutional neural network is to pause, then control the charging head to disconnect the electrical connection, and if the output result is to continue charging or adjust the error parameter, then continue to keep the charging head electrically connected; The STM chip can automatically switch to adapt the charging head according to the interface of the battery pack to be charged, including an AC output interface 7-terminal structure, including an AC power interface, a first spare contact, a second spare contact, a neutral line, a protective grounding head, a charging confirmation head, and a control confirmation head; it also includes a DC output interface plug, that is, a standard 9-hole plug, containing 2 communication interfaces, 2 DC output interfaces, 2 spare power interfaces, 2 gun plug judgment interfaces, and 1 PE grounding port.

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