# US 2026/0094884 A1 · Battery pack, electronic combination, battery management system, power tool system, and control method for a power tool

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- Number: US 2026/0094884 A1
- Document ID: US20260094884A1
- Status: Published application
- Kind: A1
- Application number: 19/388,443
- Filed: 2025-11-13
- Published: 2026-04-02
- Earliest claimed: 2023-06-15
- Assignees: Nanjing Chervon Industry Co., Ltd.
- Applicants: Nanjing Chervon Industry Co., Ltd.
- Inventors: Shijiao Yuwen; Dezhong Yang; Zhen Xu; Qiang Xu; Dong Yang; Yongchao Ji; Di Wu
- Web page: https://patentgrep.com/patent/US20260094884A1

## CPC

- G06N 20/00: Machine learning
- H01M 10/425: {Structural combination with electronic components, e.g. electronic circuits integrated to the outside of the casing (printed circuits H05K1/00)}
- G06N 3/044: Recurrent networks, e.g. Hopfield networks
- G06N 3/0442: characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
- G06N 3/045: Combinations of networks
- G06N 3/0455: Auto-encoder networks; Encoder-decoder networks
- G06N 3/0464: Convolutional networks [CNN, ConvNet]
- G06N 3/048: Activation functions
- G06N 3/08: Learning methods
- G06N 3/084: Backpropagation, e.g. using gradient descent
- G06N 3/09: Supervised learning
- H01M 10/441: {for several batteries or cells simultaneously or sequentially}
- H01M 10/486: {for measuring temperature}
- H01M 50/247: specially adapted for portable devices, e.g. mobile phones, computers, hand tools or pacemakers
- H02J 7/44: between battery management systems and power sources
- H02J 7/855: {with circuits adapted for supplying loads from the battery}
- H01M 2010/4271: {Battery management systems including electronic circuits, e.g. control of current or voltage to keep battery in healthy state, cell balancing}
- H01M 2010/4278: {Systems for data transfer from batteries, e.g. transfer of battery parameters to a controller, data transferred between battery controller and main controller}
- H02J 7/62: against overcurrent
- H02J 7/64: against overvoltage
- H02J 7/65: against overtemperature
- H02J 7/82: Control of state of charge [SOC]
- H02J 7/84: Control of state of health [SOH]
- H02J 7/933: {the cycle being controlled or terminated in response to electric parameters}
- H02J 7/977: {of the battery}
- Y02E 60/10: Energy storage using batteries
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- H01M: PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H02J: ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- Y02E: REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION

## Abstract

A battery pack includes: a sensor that generates sensor data indicating an operating parameter of the battery pack; and an electronic controller including a machine learning program. The electronic controller is configured to: receive the sensor data; process the sensor data using the machine learning program, where the machine learning program includes a trained neural network model; and use the machine learning program to generate an output based on the sensor data, where the output indicates at least one of a state of charge (SOC), a state of temperature (SOT), a state of health (SOH), and a state of power (SOP) of a cell. The effective utilization of a battery is improved.

## Claims

### Claim 1

A battery pack for powering a power tool, comprising:
a housing;
a cell accommodated in the housing;
a sensor electrically connected to the cell, the sensor generating a sensor data indicating an operating parameter of the battery pack; and
an electronic controller comprising an electronic processor and a memory storing a machine learning program, the electronic controller being electrically connected to the sensor and configured to:
receive the sensor data; and
process the sensor data using the machine learning program to generate an output based on the sensor data;
wherein the machine learning program comprises a trained neural network model and the output indicates at least one of a state of charge (SOC), a state of temperature (SOT), a state of health (SOH), and a state of power (SOP) of the cell.

### Claim 2 (depends on 1)

The battery pack for powering a power tool according to claim 1, wherein the neural network model is a physics-informed neural network (PINN) model.

### Claim 3 (depends on 2)

The battery pack for powering a power tool according to claim 2, wherein the physics-informed neural network (PINN) model uses a heat transfer partial differential equation as a constraint of a neural network.

### Claim 4 (depends on 1)

The battery pack for powering a power tool according to claim 1, wherein the sensor data comprises at least one of a current data, a voltage data, a temperature data, and a time data.

### Claim 5 (depends on 1)

The battery pack for powering a power tool according to claim 1, wherein the neural network model is a deep neural network.

### Claim 6 (depends on 5)

The battery pack for powering a power tool according to claim 5, wherein the electronic controller resamples a preset raw data set to create an augmented sample and trains a series of extreme learning machines based on the sample.

### Claim 7 (depends on 1)

The battery pack for powering a power tool according to claim 1, wherein the neural network model is a recurrent neural network.

### Claim 8 (depends on 1)

The battery pack for powering a power tool according to claim 1, further comprising a communication module, wherein a machine learning control program is generated outside the battery pack by being trained on preset sensor data and a related output, and is received by the communication module.

### Claim 9 (depends on 1)

The battery pack for powering a power tool according to claim 1, wherein the battery pack is a detachable battery pack.

### Claim 10 (depends on 1)

The battery pack for powering a power tool according to claim 1, wherein the battery pack is a rechargeable battery pack.

### Claim 11

A battery pack for powering a power tool, comprising:
a housing;
a cell accommodated in the housing;
a power tool interface disposed on the housing and couplable to the power tool;
a discharging circuit electrically connected to the cell and the power tool interface separately, the discharging circuit transferring electrical energy to the power tool via the power tool interface;
a sensor electrically connected to the cell, the sensor generating a sensor data indicating an operating parameter of the battery pack; and
a first electronic controller comprising an electronic processor and a memory storing a machine learning program, the first electronic controller at least controlling the discharging circuit and being configured to:
receive the sensor data;
process the sensor data using the machine learning program to generate an output based on the sensor data; and
adjust a protection threshold of the battery pack based on the generated output.

### Claim 12 (depends on 11)

The battery pack for powering a power tool according to claim 11, wherein the first electronic controller further comprises a second machine learning program stored in the memory, and the second machine learning program controls the discharging circuit based on the protection threshold of the battery pack.

### Claim 13 (depends on 11)

The battery pack for powering a power tool according to claim 11, wherein the sensor data comprises a current data, a voltage data, a temperature data, and a time data.

### Claim 14 (depends on 11)

The battery pack for powering a power tool according to claim 11, wherein the protection threshold comprises at least one of an over-temperature protection threshold, an under-temperature protection threshold, an overvoltage protection threshold, an undervoltage protection threshold, and an overcurrent protection threshold.

### Claim 15 (depends on 11)

The battery pack for powering a power tool according to claim 11, wherein the first electronic controller controls a power supply from the battery pack to the power tool based on the protection threshold.

### Claim 16 (depends on 11)

The battery pack for powering a power tool according to claim 11, wherein the protection threshold is sent to the power tool via the power tool interface, and the power tool controls power consumption of the power tool based on the protection threshold.

### Claim 17 (depends on 11)

The battery pack for powering a power tool according to claim 11, further comprising a second electronic controller, wherein the second electronic controller at least controls the discharging circuit, and the second electronic controller controls the discharging circuit based on the protection threshold of the battery pack.

### Claim 18 (depends on 17)

The battery pack for powering a power tool according to claim 17, wherein the second electronic controller comprises a machine learning program.

### Claim 19 (depends on 17)

The battery pack for powering a power tool according to claim 17, wherein the second electronic controller and the first electronic controller are disposed on a same circuit board.

### Claim 20 (depends on 17)

The battery pack for powering a power tool according to claim 17, wherein the second electronic controller and the first electronic controller are disposed on different circuit boards.