A battery energy-saving control system for a high-strength impact-resistant scooter

Abstract

The application relates to the technical field of electric scooters, in particular to a vehicle battery energy-saving control system of a high-strength impact-resistant scooter, which comprises: an impact-resistant battery compartment comprising a multilayer composite shell, a vibration sensor and a filling layer; an energy-saving control module comprising an intelligent detection unit, a central controller and a power optimizer; the power optimizer is used for adjusting the PWM duty cycle of a motor drive circuit according to a power mapping table; an energy recovery module is used for receiving brake force, slope angle, motor speed and vehicle mass estimated value, calculating the optimal recovery current through a Kalman filter, and injecting the recovery current into a battery pack through a bidirectional Buck-Boost circuit; a safety cooperative controller executes a hierarchical protection strategy, a data bus connects each module, and data interaction is realized. Therefore, the problems of insufficient battery endurance, low energy recovery efficiency, weak impact resistance and poor cooperation of the electric scooter in the prior art are solved.

Description

Technical Field

[0001] This invention relates to the field of electric scooter technology, specifically to a high-strength, impact-resistant scooter battery energy-saving control system. Background Technology

[0002] With the acceleration of urbanization and the rapid growth in demand for short-distance travel, electric scooters, with their flexibility, portability, low carbon footprint, and environmental friendliness, have become an indispensable part of urban transportation systems. However, current electric scooter battery systems still face many technical bottlenecks: their range is insufficient for long-distance travel, and energy consumption fluctuates significantly under complex road conditions; energy recovery mechanisms are rudimentary, with recovery efficiency generally below 30%, resulting in a significant waste of braking energy; and the battery compartment's impact-resistant structure is poorly designed, making the battery pack susceptible to damage from vibration and compression on bumpy roads or in the event of an accidental collision, potentially even posing safety hazards.

[0003] The limitations of traditional battery management systems further exacerbate the above problems: protection mechanisms often rely on a single threshold trigger, making it impossible to dynamically adjust protection strategies based on impact intensity and riding scenario. For example, using the same protection logic in scenarios such as sudden braking on a steep slope and light collisions on flat ground can easily lead to over-protection or under-protection. The use of low-speed communication protocols between functional modules results in data interaction delays exceeding 100ms, leading to extremely poor coordination between energy-saving regulation, energy recovery, and safety protection. When abnormal battery temperature is detected, the energy recovery module often fails to stop working in time, and it is difficult to simultaneously cut off motor power output when safety protection is activated. This not only affects battery lifespan but also poses a serious threat to riding safety. Summary of the Invention

[0004] This application provides a high-strength, impact-resistant electric scooter battery energy-saving control system to solve the problems of insufficient battery life, low energy recovery efficiency, weak impact resistance, and poor coordination in the prior art.

[0005] The first aspect of this application provides a battery energy-saving control system for a high-strength, impact-resistant scooter, comprising: an impact-resistant battery compartment, an energy-saving control module, an energy recovery module, a safety coordination controller, and a data bus; wherein,

[0006] The impact-resistant battery compartment includes a multi-layer composite shell, a vibration sensor, and a filling layer;

[0007] The energy-saving control module includes an intelligent detection unit, a central controller, and a power optimizer. The intelligent detection unit is used to collect data on driving speed, motor current, battery temperature, slope angle, and real-time road condition images. The central controller is used to run an adaptive energy-saving algorithm to dynamically generate a motor power mapping table based on battery health status, road condition prediction, and user riding habits. The power optimizer is used to adjust the PWM duty cycle of the motor drive circuit according to the power mapping table.

[0008] The energy recovery module receives braking force, slope angle, motor speed and estimated vehicle weight, calculates the optimal recovery current through a Kalman filter, and injects the recovery current into the battery pack using a bidirectional Buck-Boost circuit.

[0009] The safety coordination controller executes a hierarchical protection strategy, wherein: when the longitudinal acceleration is greater than or equal to a first preset threshold, level one protection is activated; when the longitudinal acceleration is greater than or equal to a second preset threshold, level two protection is activated; and when the vibration sensor detects an impact frequency greater than a preset frequency, level three protection is triggered.

[0010] The data bus connects the impact-resistant battery compartment, the energy-saving control module, the energy recovery module, and the safety coordination controller to achieve real-time data interaction.

[0011] Preferably, the central controller is also equipped with a machine learning module, which generates a personalized power adjustment curve by continuously learning the user's riding parameters under different time periods and road conditions.

[0012] Preferably, the adaptive energy-saving algorithm includes a road condition recognition submodule and a dynamic efficiency optimization submodule, wherein,

[0013] The road condition recognition submodule is used to classify the road surface smoothness level based on real-time images;

[0014] The dynamic efficiency optimization submodule is used to adjust the optimal operating range of the motor efficiency according to the road surface grade and slope angle.

[0015] Preferably, the first-level protection is to limit the peak power of the motor to the rated value, the second-level protection is to cut off the motor drive and activate the maximum braking intensity of the energy recovery module, and the third-level protection is to disconnect the battery main relay and lock the hub motor.

[0016] Preferably, the outer layer of the multi-layer composite shell is a carbon fiber reinforced polymer, and the inner layer is an aluminum alloy honeycomb structure; the vibration sensor is embedded in the multi-layer composite shell to detect impact acceleration and transmit it to the safety coordination controller; the filling layer is a gradient density polyurethane foam located between the battery pack and the multi-layer composite shell.

[0017] Preferably, the data bus adopts the CAN FD protocol with a transmission rate of ≥5Mbps.

[0018] Preferably, the estimated vehicle mass is calculated using the starting acceleration and motor output torque collected by the intelligent detection unit, and the calculation formula is as follows:

[0019] Vehicle mass estimate = motor output torque ÷ (starting acceleration × wheel radius).

[0020] Preferably, the energy recovery module further includes a supercapacitor buffer unit, which is used to store the peak recovery current during braking and inject it into the battery pack at a constant current through a bidirectional Buck-Boost circuit.

[0021] Preferably, it also includes a wireless communication module, which supports Bluetooth 5.0 and Wi-Fi connectivity and can synchronize battery health status, power consumption data and fault information to the user's mobile terminal in real time.

[0022] The second aspect of this application provides a method for energy-saving control of the vehicle battery of a high-strength impact-resistant scooter, including: acquiring braking force, slope angle, motor speed, road condition data, impact signal and acceleration;

[0023] Based on the road condition data, slope angle, battery status, and user riding habits, a power strategy is dynamically generated, and the PWM duty cycle is adjusted according to the power strategy.

[0024] Based on the braking force, slope angle, and motor speed, combined with the estimated vehicle mass and the PWM duty cycle, the optimal recovery scheme is output through a Kalman filter, which uses a supercapacitor and bidirectional circuit for recharging, and is linked with the battery status data.

[0025] Based on the impact signal and acceleration, a progressive safety response is triggered, which is linked to the PWM duty cycle and energy recovery module.

[0026] Therefore, this application has the following beneficial effects:

[0027] In this embodiment, the energy-saving control module collects multi-dimensional data through an intelligent detection unit. The central controller runs an adaptive energy-saving algorithm and dynamically generates a motor power mapping table based on the user's riding habits. The power optimizer adjusts the PWM duty cycle accordingly, ensuring that the motor output power accurately matches the actual demand and reducing ineffective energy consumption. This dynamic adjustment based on real-time road conditions and user habits significantly reduces battery energy waste and effectively improves the range of the electric scooter.

[0028] The energy recovery module receives multiple parameters and calculates the optimal recovery current using a Kalman filter. The supercapacitor buffer unit effectively handles the peak recovery current during braking, while the bidirectional Buck-Boost circuit ensures that the recovery current is injected into the battery pack in an appropriate manner. This combination of multi-parameter collaborative calculation and efficient circuit design significantly improves energy recovery efficiency, enabling the effective utilization of previously wasted braking energy.

[0029] The impact-resistant battery compartment employs a multi-layered composite shell. An outer layer of carbon fiber reinforced polymer and an inner layer of aluminum alloy honeycomb structure provide robust structural support, while a gradient-density polyurethane foam filling layer further buffers impacts. Vibration sensors promptly detect impacts and transmit the data to a safety coordination controller. This multi-layered impact-resistant design significantly enhances the battery compartment's resistance to external impacts, effectively protecting the battery pack.

[0030] The data bus uses the CAN FD protocol to achieve real-time data interaction between modules, ensuring timely and efficient information transmission between the impact-resistant battery compartment, energy-saving control module, energy recovery module, and safety coordination controller. The hierarchical protection strategy implemented by the safety coordination controller can trigger other modules to take corresponding protection measures according to different impact conditions, realizing close coordination among modules in terms of safety protection and improving the overall system's coordination and reliability.

[0031] This solves the problems of insufficient battery life, low energy recovery efficiency, weak impact resistance, and poor coordination in existing electric scooter technologies.

[0032] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0033] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0034] Figure 1 This is a schematic diagram of the structure of a vehicle battery energy-saving control system for a high-strength impact-resistant scooter according to an embodiment of this application;

[0035] Figure 2 This is a structural diagram of the impact-resistant battery compartment provided according to an embodiment of this application;

[0036] Figure 3 This is a flowchart of a battery energy-saving control method for a high-strength impact-resistant scooter according to an embodiment of this application;

[0037] Figure 4 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0038] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0039] The following description, with reference to the accompanying drawings, illustrates an energy-saving control system for a high-strength, impact-resistant scooter's vehicle battery, according to an embodiment of this application. Addressing the issue of insufficient battery range mentioned in the background section, this application provides an energy-saving control system for a high-strength, impact-resistant scooter's vehicle battery. In this system, the energy-saving control module collects multi-dimensional data through an intelligent detection unit. The central controller runs an adaptive energy-saving algorithm and dynamically generates a motor power mapping table based on the user's riding habits. The power optimizer adjusts the PWM duty cycle accordingly, ensuring precise matching between the motor output power and actual demand, thus reducing ineffective energy consumption. This dynamic adjustment based on real-time road conditions and user habits significantly reduces battery energy waste and effectively improves the scooter's range. The energy recovery module receives multiple parameters and calculates the optimal recovery current using a Kalman filter. The supercapacitor buffer unit effectively handles the peak recovery current during braking, while the bidirectional Buck-Boost circuit ensures that the recovery current is injected into the battery pack in an appropriate manner. The combination of multi-parameter collaborative calculation and efficient circuit design significantly improves energy recovery efficiency, effectively utilizing previously wasted braking energy. The impact-resistant battery compartment employs a multi-layered composite shell, with an outer layer of carbon fiber reinforced polymer and an inner layer of aluminum alloy honeycomb structure providing robust structural support. A gradient-density polyurethane foam filling layer further buffers impacts, and vibration sensors promptly detect impacts and transmit data to the safety coordination controller. This multi-layered impact-resistant design greatly enhances the battery compartment's resistance to external impacts, effectively protecting the battery pack. The data bus uses the CAN FD protocol to achieve real-time data interaction between modules, ensuring timely and efficient information transmission between the impact-resistant battery compartment, energy-saving control module, energy recovery module, and safety coordination controller. The hierarchical protection strategy implemented by the safety coordination controller can trigger other modules to take corresponding protective measures based on different impact conditions, achieving close coordination among modules in safety protection and other aspects, improving the overall system's coordination and reliability. This solves the problems of insufficient battery life, low energy recovery efficiency, weak impact resistance, and poor coordination in existing electric scooter technologies.

[0040] Figure 1 This is a schematic diagram of the structure of a high-strength impact-resistant scooter's vehicle battery energy-saving control system, provided as an embodiment of this application.

[0041] This application provides a vehicle battery energy-saving control system for a high-strength impact-resistant scooter. The vehicle battery energy-saving control system 10 for the high-strength impact-resistant scooter includes: an impact-resistant battery compartment 100, an energy-saving control module 200, an energy recovery module 300, a safety coordination controller 400, and a data bus.

[0042] The impact-resistant battery compartment 100 includes a multi-layer composite shell, vibration sensors, and a filling layer; the energy-saving control module 200 includes an intelligent detection unit, a central controller, and a power optimizer. The intelligent detection unit collects data on driving speed, motor current, battery temperature, slope angle, and real-time road conditions. The central controller runs an adaptive energy-saving algorithm to dynamically generate a motor power mapping table based on battery health status, road condition prediction, and user riding habits. The power optimizer adjusts the PWM duty cycle of the motor drive circuit according to the power mapping table. The energy recovery module 300 receives data on braking force, slope angle, motor speed, and vehicle speed. The mass estimation value is used to calculate the optimal recovery current through a Kalman filter, and the recovery current is injected into the battery pack using a bidirectional Buck-Boost circuit. The safety coordination controller 400 executes a graded protection strategy, where first-level protection is activated when the longitudinal acceleration is greater than or equal to a first preset threshold; second-level protection is activated when the longitudinal acceleration is greater than or equal to a second preset threshold; and third-level protection is triggered when the vibration sensor detects an impact frequency greater than a preset frequency. The data bus connects the impact-resistant battery compartment 100, the energy-saving control module 200, the energy recovery module 300, and the safety coordination controller 400 to achieve real-time data interaction.

[0043] It is understood that in this embodiment, the impact-resistant battery compartment enhances the battery's impact resistance through a multi-layer composite shell, vibration sensors, and a filling layer; the energy-saving control module achieves energy saving and extends the range through intelligent detection and dynamic power regulation; the energy recovery module improves energy recovery efficiency through multi-parameter calculation and high-efficiency circuitry; the safety coordination controller ensures safety through a hierarchical protection strategy; and the data bus enables real-time interaction and collaborative work among the modules, effectively improving the safety, energy efficiency, range, and energy recovery efficiency of the electric scooter battery, and optimizing overall performance.

[0044] Specifically, when a user rides the electric scooter over a bumpy road, the multi-layered composite shell and filling layer of the impact-resistant battery compartment will buffer the impact caused by the road bumps. Vibration sensors detect the impact in real time and transmit the data to the safety coordination controller through the data bus. If the impact does not reach the threshold, special protection will not be triggered, ensuring the stability of the battery under complex road conditions.

[0045] When a user rides uphill on a steep slope, the intelligent detection unit of the energy-saving control module collects data such as the slope angle and motor current. The central controller combines the battery health status with the user's previous riding habits when going uphill to generate a motor power mapping table suitable for the current slope. The power optimizer adjusts the PWM duty cycle accordingly to make the motor output appropriate power, avoiding excessive power and rapid battery consumption, thus extending the range.

[0046] When a user brakes while riding on a flat road, the energy recovery module receives data such as braking force and motor speed. Combined with the estimated vehicle mass, it calculates the optimal recovery current using a Kalman filter. The supercapacitor buffer unit first stores the peak current at the moment of braking, and then injects the current into the battery pack through a bidirectional Buck-Boost circuit to improve energy recovery efficiency.

[0047] If an emergency occurs during riding and the vehicle's longitudinal acceleration reaches the first preset threshold, the safety coordination controller will activate level one protection, limiting the motor's peak power to the rated value. If the acceleration further increases and reaches the second preset threshold, level two protection will be activated, cutting off the motor drive and activating the energy recovery module to achieve maximum braking intensity. If the vehicle is subjected to a large impact and the impact frequency detected by the vibration sensor exceeds the preset frequency, level three protection will be triggered, disconnecting the battery main relay and locking the hub motor, thus comprehensively protecting the safety of the user and the vehicle.

[0048] It should be noted that the first preset threshold can be specifically defined, such as 1.5g, and the second preset threshold can be specifically defined, such as 3g.

[0049] In this embodiment, the central controller is also equipped with a machine learning module, which continuously learns the user's riding parameters under different time periods and road conditions to generate a personalized power adjustment curve.

[0050] It is understood that the machine learning module configured in the central controller of this application embodiment can continuously collect and analyze the user's riding parameters at different times (such as weekday morning rush hour and weekend afternoon) and under different road conditions (such as congested streets, open asphalt roads, and uphill sections), including commonly used speed ranges, acceleration habits (such as rapid acceleration or smooth acceleration), braking frequency, etc. Through continuous learning of this data, the module can accurately capture the user's riding preferences and habit patterns, and then generate a power adjustment curve that fits the user's personalized needs. The personalized power adjustment curve can make the motor output power highly matched with the user's riding habits, avoiding the power over- or under-power problems that may occur with general power strategies. For example, for users who are used to riding smoothly, the curve will make the motor power rise gradually during acceleration, reducing unnecessary energy consumption; while for users who prefer to start quickly, the curve will reasonably increase the power during the start-up phase to meet the needs, while avoiding power waste and effectively improving the utilization efficiency of battery energy. On the other hand, power adjustment based on user habits makes the riding process smoother and more comfortable. Users do not need to make frequent manual adjustments, and the system can "predict" their riding needs, improving the overall riding experience and making energy-saving control more intelligent and user-friendly.

[0051] In this embodiment, the adaptive energy-saving algorithm includes a road condition recognition submodule and a dynamic efficiency optimization submodule. The road condition recognition submodule is used to classify the road surface smoothness level according to the real-time image; the dynamic efficiency optimization submodule is used to adjust the optimal working range of the motor efficiency according to the road surface level and slope angle.

[0052] It is understood that in this embodiment of the adaptive energy-saving algorithm, the road condition recognition submodule classifies the road surface smoothness level through real-time image classification, providing basic road condition information for the dynamic efficiency optimization submodule; the dynamic efficiency optimization submodule combines the road surface level and slope angle to adjust the optimal working range of the motor efficiency, ensuring that the motor can operate efficiently under different road conditions, effectively reducing battery energy consumption and improving the scooter's range.

[0053] Specifically, when the electric scooter travels on a smooth urban asphalt road, the road condition recognition submodule analyzes the real-time images collected by the intelligent detection unit and identifies the road surface smoothness level as "excellent." Subsequently, the dynamic efficiency optimization submodule receives this road surface level information and, upon obtaining that the current road segment's slope angle is 0° (flat road), adjusts the motor's optimal efficiency operating range to the low-to-medium power range (e.g., PWM duty cycle 40%-50%). At this point, the motor's output power is moderate, ensuring normal travel speed without excessive energy consumption, achieving high efficiency and energy saving.

[0054] If the vehicle enters a section of concrete road with a few potholes, the road condition recognition submodule updates the road surface smoothness level to "medium" through image recognition. The dynamic efficiency optimization submodule, taking into account this level and the still 0° slope angle, fine-tunes the motor's optimal operating range to the medium power range (PWM duty cycle 50%-60%) to avoid increased energy consumption due to frequent power fluctuations caused by road bumps. This ensures the motor outputs sufficient power to maintain driving stability while keeping energy consumption within a reasonable range.

[0055] When the vehicle travels uphill on an 8° slope with a medium-level smoothness gravel road surface, the road condition recognition submodule determines the road surface level. The dynamic efficiency optimization submodule then integrates the road surface level and slope information to increase the motor's optimal operating range to the medium-high power range (PWM duty cycle 60%-75%). Within this range, the motor can output sufficient power to overcome the slope and road resistance, ensuring smooth uphill driving. Furthermore, compared to blindly increasing power, this range allows the motor to maintain high efficiency under high load, reducing unnecessary energy waste.

[0056] In this embodiment, the first-level protection limits the motor's peak power to the rated value; the second-level protection cuts off the motor drive and activates the energy recovery module to achieve maximum braking intensity; and the third-level protection disconnects the battery main relay and locks the hub motor.

[0057] In the embodiments of this application, such as Figure 2 As shown, the outer layer of the multi-layer composite shell is made of carbon fiber reinforced polymer, and the inner layer is made of aluminum alloy honeycomb structure. Vibration sensors are embedded in the multi-layer composite shell to detect impact acceleration and transmit it to the safety co-controller. The filling layer is a gradient density polyurethane foam located between the battery pack and the multi-layer composite shell.

[0058] It is understood that in the embodiments of this application, the carbon fiber reinforced polymer of the outer layer of the multi-layer composite shell and the aluminum alloy honeycomb structure of the inner layer form a robust protective barrier that can effectively resist external impact forces; the vibration sensor detects the impact acceleration in real time and transmits it to the safety coordination controller, providing a basis for safety protection; the gradient density polyurethane foam filling layer plays a buffering role between the battery pack and the shell.

[0059] It should be noted that the inner layer of the gradient density polyurethane foam is 300 kg / m³. 3 Middle layer 200kg / m 3 Outer layer 80kg / m 3 .

[0060] In this embodiment, the data bus adopts the CAN FD protocol with a transmission rate of ≥5Mbps.

[0061] In this embodiment, the estimated vehicle mass is calculated using the starting acceleration and motor output torque collected by the intelligent detection unit. The calculation formula is as follows:

[0062] Vehicle mass estimate = motor output torque ÷ (starting acceleration × wheel radius).

[0063] Specifically, when a user starts the electric scooter, the intelligent detection unit collects two key parameters in real time: one is the acceleration at the moment of start-up (measured by an acceleration sensor, for example, 2 m / s²). 2 Secondly, the output torque of the motor at the same time (obtained through the motor controller, for example, 15 N·m). Meanwhile, the wheel radius of the scooter is a known fixed parameter (for example, 0.15 m).

[0064] Substituting these data into the formula "Estimated vehicle mass = Motor output torque ÷ (Starting acceleration × Wheel radius)", we get: Estimated vehicle mass = 15 N·m ÷ (2 m / s²) 2 (×0.15m) = 15 ÷ 0.3 = 50kg. This result comprehensively reflects the sum of the scooter's own weight and the user's weight.

[0065] In the energy recovery phase, the energy recovery module adjusts its recovery strategy based on this value—for example, when the estimated mass is large, the Kalman filter calculates a larger recovery current to match the inertial kinetic energy. In the power regulation phase, the central controller also refers to this value to optimize power output, ensuring that the motor drive force matches the actual load and avoiding power waste or insufficient power. Through dynamically updated vehicle mass estimates, the system can more accurately adapt to different users (weight differences) and load changes (such as carrying items), improving overall control precision.

[0066] In this embodiment of the application, the energy recovery module further includes a supercapacitor buffer unit, which is used to store the peak recovery current at the moment of braking and inject it into the battery pack at a constant current through a bidirectional Buck-Boost circuit.

[0067] It is understood that the supercapacitor buffer unit of the energy recovery module in this application embodiment quickly stores the peak recovery current at the moment of braking, avoiding the direct impact of large current on the battery pack. Then, the current is converted into a constant value and injected into the battery through the bidirectional Buck-Boost circuit, which not only protects the battery from damage, but also improves the stability and efficiency of energy recovery, and realizes the efficient utilization of recovered energy.

[0068] In this embodiment, a wireless communication module is also included.

[0069] The wireless communication module supports Bluetooth 5.0 and Wi-Fi connectivity, and can synchronize battery health status, power consumption data and fault information to the user's mobile terminal in real time.

[0070] It is understood that the wireless communication module in this application embodiment supports Bluetooth 5.0 and Wi-Fi connection, and synchronizes the battery health status, power consumption data and fault information to the user's mobile terminal in real time, so that the user can keep track of the device status at any time, charge, maintain or deal with faults in a timely manner, improve the convenience and safety of use, and enhance the user's sense of control over the device.

[0071] This application proposes a high-strength, impact-resistant scooter battery energy-saving control system. The energy-saving control module collects multi-dimensional data through an intelligent detection unit. The central controller runs an adaptive energy-saving algorithm and dynamically generates a motor power mapping table based on user riding habits. The power optimizer adjusts the PWM duty cycle accordingly, ensuring precise matching of motor output power with actual demand and reducing ineffective energy consumption. This dynamic adjustment based on real-time road conditions and user habits significantly reduces battery energy waste and effectively improves the scooter's range. The energy recovery module receives multiple parameters and calculates the optimal recovery current using a Kalman filter. A supercapacitor buffer unit properly handles peak recovery current during braking, while a bidirectional Buck-Boost circuit ensures the recovery current is injected into the battery pack in an appropriate manner. The combination of multi-parameter collaborative calculation and efficient circuit design significantly improves energy recovery efficiency, effectively utilizing previously wasted braking energy. The impact-resistant battery compartment uses a multi-layer composite shell. The outer carbon fiber reinforced polymer and inner aluminum alloy honeycomb structure provide robust structural support, while a gradient-density polyurethane foam filling layer further buffers impacts. Vibration sensors promptly detect impacts and transmit them to the safety collaborative controller. This multi-layered impact-resistant design significantly enhances the battery compartment's resistance to external impacts, effectively protecting the battery pack. The data bus utilizes the CAN FD protocol to enable real-time data exchange between modules, ensuring timely and efficient information transmission between the impact-resistant battery compartment, energy-saving control module, energy recovery module, and safety coordination controller. The hierarchical protection strategy implemented by the safety coordination controller can trigger corresponding protective measures from other modules based on different impact conditions, achieving close coordination among modules in safety protection and other aspects, thus improving the overall system's coordination and reliability. This solves the problems of insufficient battery life, low energy recovery efficiency, weak impact resistance, and poor coordination in existing electric scooter technologies.

[0072] The following will illustrate a specific embodiment of an energy-saving control system for a high-strength, impact-resistant scooter's battery, including:

[0073] User Xiao Wang uses this high-strength, impact-resistant scooter to commute. His journey from home to work involves traversing a main urban road, an uphill section, and a bumpy road after construction.

[0074] At 7 a.m., Xiao Wang started his scooter, and the intelligent detection unit immediately recorded the acceleration at the moment of start as 2.5 m / s². 2 The motor output torque is 18 N·m. Given a wheel radius of 0.16 m, substituting these values ​​into the formula, the estimated vehicle weight is 18 ÷ (2.5 × 0.16) = 45 kg (including Xiao Wang's weight and the scooter's own weight). The central controller's machine learning module records Xiao Wang's morning rush hour starting habits, which will be used to optimize power adjustment later.

[0075] When riding on smooth asphalt roads of urban main roads, the road condition recognition submodule classifies the road surface smoothness level as "excellent" based on real-time images. With the current slope angle at 0°, the dynamic efficiency optimization submodule adjusts the optimal operating range of the motor efficiency to a PWM duty cycle of 40%-50%. The intelligent detection unit collects data showing a riding speed of 18km / h, a motor current of 7A, and a battery temperature of 28℃. The central controller, combining the battery health status (90% charge) and Xiao Wang's riding habits on flat roads (maintaining a moderate and constant speed), generates a power mapping table. The power optimizer stabilizes the PWM duty cycle at 45%, resulting in moderate motor output power and stable energy consumption.

[0076] Upon reaching an uphill section with a 6° gradient, the intelligent detection unit transmits the gradient data to the central controller. The central controller then uses the machine learning module to record Xiao Wang's uphill habits (a preference for faster speeds with slightly higher power). Combining this with the battery status, a corresponding power mapping table is generated. The power optimizer adjusts the PWM duty cycle to 65%, ensuring the motor outputs appropriate power to maintain uphill momentum while preventing excessive energy consumption due to excessive power. At this point, the energy recovery module is in standby mode and not activated.

[0077] When traversing the bumpy road after construction, the impact-resistant battery compartment comes into play. The outer carbon fiber reinforced polymer layer resists the impact of roadside gravel, while the inner aluminum alloy honeycomb structure and gradient density polyurethane foam filling layer buffer the impact of road bumps. The vibration sensor detected an impact frequency of 50Hz (preset frequency is 100Hz), which did not reach the threshold. After the data was transmitted to the safety coordination controller via the CAN FD protocol data bus (transmission rate 5Mbps), no special protection was triggered, and the battery pack remained stable during the bumpy ride.

[0078] As Xiao Wang approached the company, he braked and slowed down on a flat road. The energy recovery module received information on the braking force (medium), motor speed (25 revolutions per second), slope angle (0°), and estimated vehicle weight (45kg). Using a Kalman filter, it calculated the optimal recovery current to be 4A. The 8A peak current generated during braking was stored in the supercapacitor buffer unit and then injected into the battery pack at a constant current of 4A through a bidirectional Buck-Boost circuit, thus achieving energy recovery.

[0079] Suddenly, a pedestrian rushed out from the intersection ahead. Xiao Wang braked suddenly, and the vehicle's longitudinal acceleration instantly reached 3g (the second preset threshold is 3g). The safety coordination controller immediately activated the secondary protection, cut off the PWM output of the motor drive, and activated the maximum braking intensity of the energy recovery module. Emergency braking was achieved with the help of the recharge circuit to avoid a collision.

[0080] Throughout the commute, the wireless communication module synchronizes the battery health status (75% remaining power) and energy consumption data (average energy consumption 8Wh / km) to Xiao Wang's mobile app in real time, which Xiao Wang can check at any time. Through the coordinated work of its various modules, the system ensures riding safety while effectively saving energy and recovering energy, demonstrating excellent overall performance.

[0081] Next, referring to the accompanying drawings, a method for energy-saving control of vehicle batteries for high-strength impact-resistant scooters according to embodiments of this application is described.

[0082] like Figure 3 As shown, the energy-saving control of the vehicle battery of this high-strength impact-resistant scooter includes the following steps:

[0083] In step S101, braking force, slope angle, motor speed, road condition data, impact signal and acceleration are acquired.

[0084] It is understood that the embodiments of this application obtain braking force, slope angle, motor speed, road condition data, impact signal and acceleration to provide accurate basis for the energy recovery module to calculate the optimal recovery current, the energy-saving control module to adjust the motor power, and the safety coordination controller to execute the hierarchical protection strategy.

[0085] In step S102, a power strategy is dynamically generated based on road condition data, slope angle, battery status, and user riding habits, and the PWM duty cycle is adjusted according to the power strategy.

[0086] In PWM, the duty cycle refers to the ratio of the time the pulse signal is at a high level within one cycle to the total cycle time in pulse width modulation technology, and is usually expressed as a percentage.

[0087] It is understood that the embodiments of this application dynamically generate a power strategy by combining road condition data, slope angle, battery status and user riding habits, and then adjust the PWM duty cycle according to the strategy, so that the motor output power can be accurately matched with the actual riding demand. This avoids energy waste caused by excess power and prevents insufficient power from affecting the riding experience, thereby improving battery energy utilization efficiency, extending the range, and enhancing the smoothness and adaptability of riding.

[0088] The power optimizer controls the output voltage and current of the motor drive circuit by adjusting the PWM duty cycle, thereby changing the motor's output power. For example, when the PWM duty cycle is 45%, it means that the motor drive circuit is in the conducting state for 45% of the time in one cycle, outputting a corresponding proportion of voltage to enable the motor to obtain the corresponding power. The higher the duty cycle, the higher the average voltage obtained by the motor and the greater the output power, and vice versa. By dynamically adjusting the PWM duty cycle, precise control of motor power can be achieved to adapt to different road conditions and riding needs, achieving a balance between energy saving and efficient drive.

[0089] In step S103, based on braking force, slope angle and motor speed, combined with vehicle mass estimation and PWM duty cycle, the optimal recovery scheme is output through Kalman filter, and recharge is achieved with the help of supercapacitor and bidirectional circuit, linked with battery status data.

[0090] It is understood that the embodiments of this application achieve precise and efficient energy recovery by using multi-parameter fusion and Kalman filter output to optimize the recovery scheme, and by using supercapacitors and bidirectional circuits to recharge and link with battery status data. This not only avoids damage to the battery from high current, but also allows the recovered energy to be matched with the battery status, thereby improving energy utilization and battery safety, and enhancing the overall synergy of the system.

[0091] Specifically, the state equation is:

[0092]

[0093] in, Here is the state transition matrix. To control the input matrix, For process noise, Let be the state vector at time k. In order to be in The state vector at time t, For control input;

[0094] The observation equation is:

[0095]

[0096] in, Let H be the observation vector and H be the observation matrix. To observe the noise,

[0097] Prediction based on Kalman filter:

[0098]

[0099]

[0100] in, For prior state estimation, To estimate the covariance a priori, This is a posterior estimate from the previous time step. To estimate the covariance of the previous time step, The process noise covariance matrix;

[0101] The updated formula is:

[0102]

[0103]

[0104]

[0105] in, Here is the Kalman gain matrix. To estimate the covariance in the posterior time, For posterior state estimation, To observe the noise covariance matrix.

[0106] In step S104, a progressive safety response is triggered based on the impact signal and acceleration, which is linked to the PWM duty cycle and energy recovery module.

[0107] It is understood that, based on the impact signal and acceleration, the embodiments of this application trigger a progressive safety response linked to the PWM duty cycle and the energy recovery module. Depending on the severity of the impact and acceleration, the motor power can be limited or the drive can be cut off by adjusting the PWM duty cycle, and the energy recovery module can be linked to enhance braking, thereby achieving graded protection from minor protection to emergency braking, comprehensively protecting the safety of users and vehicles, and improving the safety and reliability of the system.

[0108] This application proposes a battery energy-saving control method for a high-strength, impact-resistant scooter. The energy-saving control module collects multi-dimensional data through an intelligent detection unit. The central controller runs an adaptive energy-saving algorithm and dynamically generates a motor power mapping table based on user riding habits. The power optimizer adjusts the PWM duty cycle accordingly, ensuring precise matching of motor output power with actual demand and reducing ineffective energy consumption. This dynamic adjustment based on real-time road conditions and user habits significantly reduces battery energy waste and effectively improves the scooter's range. The energy recovery module receives multiple parameters and calculates the optimal recovery current using a Kalman filter. A supercapacitor buffer unit effectively handles peak recovery current during braking, while a bidirectional Buck-Boost circuit ensures the recovery current is injected into the battery pack in an appropriate manner. The combination of multi-parameter collaborative calculation and efficient circuit design significantly improves energy recovery efficiency, effectively utilizing previously wasted braking energy. The impact-resistant battery compartment uses a multi-layer composite shell. The outer carbon fiber reinforced polymer and inner aluminum alloy honeycomb structure provide robust structural support, while a gradient-density polyurethane foam filling layer further buffers impacts. Vibration sensors promptly detect impacts and transmit them to the safety collaborative controller. This multi-layered impact-resistant design significantly enhances the battery compartment's resistance to external impacts, effectively protecting the battery pack. The data bus utilizes the CAN FD protocol to enable real-time data exchange between modules, ensuring timely and efficient information transmission between the impact-resistant battery compartment, energy-saving control module, energy recovery module, and safety coordination controller. The hierarchical protection strategy implemented by the safety coordination controller can trigger corresponding protective measures from other modules based on different impact conditions, achieving close coordination among modules in safety protection and other aspects, thus improving the overall system's coordination and reliability. This solves the problems of insufficient battery life, low energy recovery efficiency, weak impact resistance, and poor coordination in existing electric scooter technologies.

[0109] The following will illustrate a specific embodiment of an energy-saving control method for a high-strength, impact-resistant scooter's vehicle battery, including:

[0110] User Xiao Li rode the scooter home from get off work, a distance of 2.5 kilometers, which included urban auxiliary roads, short uphill sections, and a section of potholed road under temporary repair.

[0111] Step S101: Data Acquisition

[0112] The intelligent detection unit collects various data in real time throughout the entire process: When driving on urban auxiliary roads, it updates the driving speed (20km / h), motor current (6.8A), and battery temperature (30℃) every second. It captures road condition data on smooth asphalt surfaces via a camera, and the slope sensor displays a slope angle of 0°. When reaching an uphill section with a 5° slope, the speed drops to 16km / h, the motor current rises to 9.2A, and the road condition image shows a concrete uphill road. After entering a potholed section, the speed drops to 12km / h, the motor current fluctuates between 7.5-8.3A, the vibration sensor detects a 35Hz impact signal, and the acceleration sensor displays a longitudinal acceleration of 0.3g. Simultaneously, it collects braking force (20% of pedal travel for light braking, 60% for heavy braking) and motor speed (28 rpm for braking on flat roads, 22 rpm for braking uphill).

[0113] Step S102: Power Strategy Generation and Adjustment

[0114] After receiving the data, the central controller dynamically generates a power strategy based on the battery status (82% remaining power) and Xiao Li's riding habits (preferring a slightly faster speed after get off work and continuous acceleration when going uphill): On urban auxiliary roads, due to the good road conditions and 0° gradient, a "medium-low power to maintain high speed" strategy is generated, and the power optimizer adjusts the PWM duty cycle to 48%, with the motor output power stabilizing at 800W; When going uphill, based on a 5° gradient and acceleration habits, the strategy is updated to "medium-high power climbing", the PWM duty cycle increases to 62%, and the power increases to 1200W; When entering bumpy road sections, based on the bumpy road condition data, the strategy is adjusted to "stable power to resist bumps", the PWM duty cycle is set to 55%, and the power is maintained at 1000W, which avoids both insufficient power causing stuttering and excessive power increasing energy consumption.

[0115] Step S103: Energy Recovery

[0116] When riding to the flat road at the entrance of the residential area, Xiao Li lightly applied the brakes to slow down, and the system entered the energy recovery process: it obtained the braking force of 20%, the slope angle of 0°, and the motor speed of 28 rpm. Combining this with the estimated vehicle weight (Xiao Li's weight is 65kg + the vehicle weight is 15kg = 80kg, calculated using the formula) and the current PWM duty cycle of 48%, the Kalman filter calculated the optimal recovery current of 3.5A. The 7A peak current generated at the moment of braking was absorbed by the supercapacitor buffer unit, and then the bidirectional Buck-Boost circuit stabilized the current at 3.5A and injected it into the battery for 1.5 seconds, increasing the battery charge from 75% to 75.3%.

[0117] Step S104: Security Response

[0118] While driving through a bumpy section of road, a piece of gravel struck the vehicle. The vibration sensor detected an impact signal frequency of 60Hz (preset frequency threshold 80Hz) and a longitudinal acceleration of 0.5g (first preset threshold 1.5g), which did not reach the protection threshold, so the system did not trigger a special response. When the vehicle was almost home, a bicycle suddenly crossed the road. The driver braked suddenly, and the longitudinal acceleration instantly reached 2g (exceeding the first preset threshold 1.5g). The safety coordination controller immediately triggered a progressive safety response: first, the PWM duty cycle was suddenly reduced from 48% to 0% to cut off the motor drive; at the same time, the energy recovery module was activated to start the maximum braking intensity, and the recovery current instantly increased to 5A. The recovery resistance was used to achieve rapid braking and avoid a collision.

[0119] After the entire process, the battery had 73% charge remaining, and the app showed an energy recovery of 0.08 kWh. This represents a reduction of approximately 15% in energy consumption compared to a regular scooter on the same route. Furthermore, no battery malfunctions or safety hazards were observed throughout the process, demonstrating the energy efficiency and safety of this control method.

[0120] In summary, in this embodiment, when user Xiao Li rides a scooter home from get off work, the control method operates in an orderly manner step by step: Step S101 collects data such as speed, current, and gradient of each road segment in real time to provide a basis for subsequent control; Step S102 generates a power strategy adapted to different road segments based on battery status and riding habits, and achieves precise power control by adjusting the PWM duty cycle; Step S103 calculates the optimal recovery current based on multiple parameters during braking, and efficiently recharges energy with the help of relevant components; Step S104 triggers a progressive safety response based on impact and acceleration conditions. The overall energy consumption is reduced by approximately 15%, while ensuring safety, fully demonstrating the advantages of this control method in terms of energy saving, safety, and adaptability.

[0121] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include:

[0122] The memory 401, the processor 402, and the computer program stored on the memory 401 and capable of running on the processor 402.

[0123] When the processor 402 executes the program, it implements the energy-saving control method for the vehicle battery of a high-strength impact-resistant scooter provided in the above embodiments.

[0124] Furthermore, electronic devices also include:

[0125] Communication interface 403 is used for communication between memory 401 and processor 402.

[0126] The memory 401 is used to store computer programs that can run on the processor 402.

[0127] The memory 401 may include high-speed RAM (Random Access Memory) memory, and may also include non-volatile memory, such as at least one disk storage.

[0128] If the memory 401, processor 402, and communication interface 403 are implemented independently, then the communication interface 403, memory 401, and processor 402 can be interconnected via a bus to complete communication between them. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 4 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0129] Optionally, in a specific implementation, if the memory 401, processor 402, and communication interface 403 are integrated on a single chip, then the memory 401, processor 402, and communication interface 403 can communicate with each other through an internal interface.

[0130] Processor 402 may be a CPU (Central Processing Unit), an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of this application.

[0131] In the description of this specification, the references to "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0132] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0133] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0134] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any of the following techniques known in the art, or a combination thereof: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0135] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it includes one or a combination of the steps of the method embodiments.

[0136] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A battery energy-saving control system for a high-strength, impact-resistant scooter, characterized in that, include: The system includes an impact-resistant battery compartment, an energy-saving control module, an energy recovery module, a safety coordination controller, and a data bus; among these components, The impact-resistant battery compartment includes a multi-layer composite shell, a vibration sensor, and a filling layer; The energy-saving control module includes an intelligent detection unit, a central controller, and a power optimizer. The intelligent detection unit is used to collect data on driving speed, motor current, battery temperature, slope angle, and real-time road condition images. The central controller is used to run an adaptive energy-saving algorithm to dynamically generate a motor power mapping table based on battery health status, road condition prediction, and user riding habits. The power optimizer is used to adjust the PWM duty cycle of the motor drive circuit according to the power mapping table. The energy recovery module receives braking force, slope angle, motor speed and estimated vehicle weight, calculates the optimal recovery current through a Kalman filter, and injects the recovery current into the battery pack using a bidirectional Buck-Boost circuit. The safety coordination controller executes a hierarchical protection strategy, wherein: when the longitudinal acceleration is greater than or equal to a first preset threshold, level one protection is activated; when the longitudinal acceleration is greater than or equal to a second preset threshold, level two protection is activated; and when the vibration sensor detects an impact frequency greater than a preset frequency, level three protection is triggered. The data bus connects the impact-resistant battery compartment, the energy-saving control module, the energy recovery module, and the safety coordination controller to achieve real-time data interaction.

2. The energy-saving control system for the vehicle battery of the high-strength impact-resistant scooter according to claim 1, characterized in that, The central controller is also equipped with a machine learning module, which continuously learns the user's riding parameters under different time periods and road conditions to generate personalized power adjustment curves.

3. The energy-saving control system for the vehicle battery of the high-strength impact-resistant scooter according to claim 1, characterized in that, The adaptive energy-saving algorithm includes a road condition recognition submodule and a dynamic efficiency optimization submodule, wherein... The road condition recognition submodule is used to classify the road surface smoothness level based on real-time images; The dynamic efficiency optimization submodule is used to adjust the optimal operating range of the motor efficiency according to the road surface grade and slope angle.

4. The energy-saving control system for the vehicle battery of the high-strength impact-resistant scooter according to claim 1, characterized in that, The first-level protection limits the motor's peak power to the rated value; the second-level protection cuts off the motor drive and activates the energy recovery module to achieve maximum braking intensity; and the third-level protection disconnects the battery main relay and locks the hub motor.

5. The energy-saving control system for the vehicle battery of the high-strength impact-resistant scooter according to claim 1, characterized in that, The outer layer of the multi-layer composite shell is made of carbon fiber reinforced polymer, and the inner layer is made of aluminum alloy honeycomb structure. The vibration sensor is embedded in the multi-layer composite shell to detect impact acceleration and transmit it to the safety coordination controller. The filling layer is a gradient density polyurethane foam located between the battery pack and the multi-layer composite shell.

6. The energy-saving control system for the vehicle battery of the high-strength impact-resistant scooter according to claim 1, characterized in that, The data bus uses the CAN FD protocol with a transmission rate of ≥5Mbps.

7. The energy-saving control system for the vehicle battery of the high-strength impact-resistant scooter according to claim 1, characterized in that, The estimated vehicle mass is calculated using the starting acceleration and motor output torque collected by the intelligent detection unit. The calculation formula is as follows: Vehicle mass estimate = motor output torque ÷ (starting acceleration × wheel radius).

8. The energy-saving control system for the vehicle battery of the high-strength impact-resistant scooter according to claim 1, characterized in that, The energy recovery module also includes a supercapacitor buffer unit, which is used to store the peak recovery current during braking and inject it into the battery pack at a constant current through a bidirectional Buck-Boost circuit.

9. The energy-saving control system for the vehicle battery of the high-strength impact-resistant scooter according to claim 1, characterized in that, It also includes a wireless communication module that supports Bluetooth 5.0 and Wi-Fi connectivity, and can synchronize battery health status, power consumption data and fault information to the user's mobile terminal in real time.

10. A method for an energy-saving control system for a vehicle battery of a high-strength, impact-resistant scooter according to any one of claims 1-9, characterized in that, The method includes: It acquires braking force, slope angle, motor speed, road condition data, impact signal, and acceleration; Based on the road condition data, slope angle, battery status, and user riding habits, a power strategy is dynamically generated, and the PWM duty cycle is adjusted according to the power strategy. Based on the braking force, slope angle, and motor speed, combined with the estimated vehicle mass and the PWM duty cycle, the optimal recovery scheme is output through a Kalman filter, which uses a supercapacitor and bidirectional circuit for recharging, and is linked with the battery status data. Based on the impact signal and acceleration, a progressive safety response is triggered, which is linked to the PWM duty cycle and energy recovery module.

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