Vehicle battery energy-saving control system of high-strength impact-resistant scooter

Abstract

The invention 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 bin, a battery energy-saving control system and a battery energy-saving control system, wherein the impact-resistant battery bin comprises a multi-layer composite shell, a vibration sensor and a filling layer; the energy-saving control module comprises an intelligent detection unit, a central controller and a power optimizer; the power optimizer is used for adjusting the PWM duty ratio of the motor driving circuit according to the power mapping table; the energy recovery module is used for receiving brake force, a slope angle, a motor rotating speed and a vehicle mass estimation value, calculating an optimal recovery current through a Kalman filter, and injecting the recovery current into a battery pack by adopting a bidirectional Buck-Boost circuit; and the safety cooperative controller executes a hierarchical protection strategy, and a data bus is connected with each module to realize data interaction. Therefore, the problems that in the prior art, an electric scooter is insufficient in battery endurance, low in energy recovery efficiency, weak in impact resistance, poor in collaboration and the like are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric scooters, and in particular to a battery energy-saving control system for a high-strength, impact-resistant scooter. Background Art

[0002] With the acceleration of urbanization and the dramatic increase in demand for short-distance travel, electric scooters, thanks to their flexibility, portability, low carbon footprint, and environmental friendliness, have become an indispensable component of urban transportation systems. However, current electric scooter battery systems still face numerous technical bottlenecks: The battery life is insufficient to meet long-distance travel needs, and energy consumption fluctuates significantly under complex road conditions. The energy recovery mechanism is rudimentary, with recovery efficiencies generally below 30%, resulting in significant wasted braking energy. The battery compartment's simple impact-resistant design makes it susceptible to damage from vibration and compression during bumpy roads or unexpected collisions, potentially posing safety risks. The limitations of traditional battery management systems further exacerbate the above problems: the protection mechanism mostly relies on a single threshold trigger and cannot dynamically adjust the protection strategy according to the impact intensity and riding scenario. For example, in the scenarios of sudden braking on a steep slope and light collision on flat ground, the use of the same protection logic can easily lead to over-protection or under-protection; low-speed communication protocols are used between functional modules, and the data interaction delay exceeds 100ms, resulting in extremely poor coordination between energy-saving regulation, energy recovery and safety protection. When abnormal battery temperature is detected, the energy recovery module often cannot stop working in time, and it is difficult to synchronously cut off the motor power output when safety protection is activated. This not only affects the battery life, but also poses a serious threat to riding safety. Summary of the Invention

[0003] The present application provides a vehicle battery energy-saving control system for a high-strength impact-resistant scooter to solve the problems of insufficient battery life, low energy recovery efficiency, weak impact resistance and poor coordination in the prior art electric scooters.

[0004] The first embodiment of the present application provides a high-strength impact-resistant scooter battery energy-saving control system, including: an impact-resistant battery compartment, an energy-saving control module, an energy recovery module, a safety collaborative controller and a data bus; wherein, 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 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; and 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 is used to receive brake force, slope angle, motor speed, and vehicle mass estimation, calculate the optimal recovery current through a Kalman filter, and inject the recovery current into the battery pack using a bidirectional Buck-Boost circuit; The safety collaborative controller implements a hierarchical protection strategy, wherein when the longitudinal acceleration is greater than or equal to a first preset threshold, the first level of protection is activated; when the longitudinal acceleration is greater than or equal to a second preset threshold, the second level of protection is activated; when the vibration sensor detects an impact frequency greater than a preset frequency, the third level of protection is triggered; The data bus connects the impact-resistant battery compartment, the energy-saving control module, the energy recovery module and the safety collaborative controller to achieve real-time data interaction.

[0005] Preferably, the central controller is also configured with a machine learning module, which generates a personalized power adjustment curve by continuously learning the user's riding parameters in different time periods and road conditions.

[0006] Preferably, the adaptive energy-saving algorithm includes a road condition identification submodule and a dynamic efficiency optimization submodule, wherein: The road condition recognition submodule is used to classify the road surface roughness level according to the real-time image; The dynamic efficiency optimization submodule is used to adjust the optimal working range of motor efficiency according to the road grade and slope angle.

[0007] 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.

[0008] 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 collaborative controller; the filling layer is a gradient density polyurethane foam, which is located between the battery pack and the multi-layer composite shell.

[0009] Preferably, the data bus adopts the CAN FD protocol, and the transmission rate is ≥5Mbps.

[0010] Preferably, the vehicle mass estimation value is calculated by the starting acceleration and motor output torque collected by the intelligent detection unit, and the calculation formula is: Estimated vehicle mass = motor output torque ÷ (starting acceleration × wheel radius).

[0011] Preferably, 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.

[0012] Preferably, it also includes a wireless communication module, which supports Bluetooth 5.0 and Wi-Fi connections and can synchronize battery health status, energy consumption data and fault information to the user's mobile terminal in real time.

[0013] A second embodiment of the present application provides a method for controlling energy-saving of a battery for a high-strength impact-resistant scooter, comprising: obtaining braking force, slope angle, motor speed, road condition data, impact signal, and acceleration; Dynamically generate a power strategy based on the road condition data, slope angle, battery status, and user riding habits, and adjust the PWM duty cycle according to the power strategy; Based on the braking force, slope angle, and motor speed, combined with the vehicle mass estimate and the PWM duty cycle, a Kalman filter outputs an optimal recovery plan, using supercapacitors and bidirectional circuits for recharging, linked to battery status data; According to the impact signal and acceleration, a progressive safety response linked with the PWM duty cycle and energy recovery module is triggered.

[0014] Therefore, this application has the following beneficial effects: In the embodiment of this application, 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 to accurately match the motor output power with actual demand, reducing ineffective energy consumption. This dynamic regulation based on real-time road conditions and user habits significantly reduces battery energy waste and effectively improves the electric scooter's endurance. The energy recovery module receives multiple parameters and calculates the optimal regenerative current using a Kalman filter. The supercapacitor buffer unit properly handles peak regenerative current during braking, while the bidirectional Buck-Boost circuit ensures that the regenerative current is properly injected into the battery pack. This multi-parameter collaborative calculation, combined with efficient circuit design, significantly improves energy recovery efficiency, effectively utilizing energy that would otherwise be wasted, such as from braking. The impact-resistant battery compartment utilizes a multi-layer composite shell. An outer layer of carbon fiber reinforced polymer and an inner layer of aluminum alloy honeycomb structure provide solid structural support. A gradient density polyurethane foam padding further cushions impacts. Vibration sensors detect impacts and transmit them to the safety coordination controller. This multi-layered impact-resistant design significantly enhances the 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 transfer between the impact-resistant battery compartment, energy-saving control module, energy recovery module, and the safety collaborative controller. The safety collaborative controller implements a hierarchical protection strategy that coordinates other modules to implement appropriate protective measures based on different impact scenarios. This ensures close coordination among modules in safety and protection, improving the overall system's operational coordination and reliability.

[0015] This solves the problems of insufficient battery life, low energy recovery efficiency, weak impact resistance and poor coordination of electric scooters in the prior art.

[0016] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which: Figure 1 This is a schematic structural diagram of a battery energy-saving control system for a high-strength, impact-resistant scooter provided in accordance with an embodiment of the present application; Figure 2 A structural diagram of an impact-resistant battery compartment provided according to an embodiment of the present application; Figure 3 This is a flow chart of a method for controlling energy saving of a battery for a high-strength impact-resistant scooter according to one embodiment of the present application; Figure 4 A schematic diagram of the structure of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0019] A vehicle battery energy-saving control system of a high-strength impact-resistant scooter is described below with reference to the accompanying drawings. In view of the insufficient battery endurance problem mentioned in the background art, the present application provides a vehicle battery energy-saving control system of a high-strength impact-resistant scooter. In this system, the energy-saving control module collects multi-dimensional data through the intelligent detection unit, the central controller runs the adaptive energy-saving algorithm and dynamically generates the motor power mapping table in combination with the user's riding habits, and the power optimizer adjusts the PWM duty cycle accordingly to accurately match the motor output power with the actual demand, thereby reducing invalid energy consumption. This dynamic regulation based on real-time road conditions and user habits significantly reduces battery energy waste and effectively improves the endurance of electric scooters. The energy recovery module receives multiple parameters and calculates the optimal recovery current through the Kalman filter, the super capacitor buffer unit can properly handle the peak recovery current at the moment of braking, and the bidirectional Buck-Boost circuit ensures that the recovery current is injected into the battery pack in the right way. The combination of multi-parameter collaborative calculation and efficient circuit design significantly improves the efficiency of energy recovery, making the otherwise wasted braking energy effectively utilized; the impact-resistant battery compartment adopts a multi-layer composite shell, the outer layer of carbon fiber reinforced polymer and the inner layer of aluminum alloy honeycomb structure provide solid structural support, and the gradient density polyurethane foam filling layer further buffers the impact. The vibration sensor can detect the impact in time and transmit it to the safety coordination controller. This multi-level impact-resistant design greatly enhances the battery compartment's resistance to external impact and effectively protects the battery pack; the data bus uses the CAN FD protocol to realize real-time data interaction between modules, ensuring timely and efficient information transmission between the impact-resistant battery compartment, the energy-saving control module, the energy recovery module, and the safety coordination controller. The hierarchical protection strategy implemented by the safety coordination controller can take appropriate protective measures in conjunction with other modules according to different impact situations, achieving close coordination among modules in terms of safety protection and other aspects, and improving the work coordination and reliability of the entire system. Thus, the problems of insufficient battery endurance, low energy recovery efficiency, weak impact resistance, and poor coordination in the prior art are solved.

[0020] Figure 1 A structural diagram of a vehicle battery energy-saving control system of a high-strength impact-resistant scooter provided by the embodiments of the present application.

[0021] The embodiments of the present application provide a vehicle battery energy-saving control system of a high-strength impact-resistant scooter, which comprises 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.

[0022] The anti-impact battery compartment 100 comprises a multi-layer composite shell, a vibration sensor and a filling layer; the energy-saving control module 200 comprises an intelligent detection unit, a central controller and a power optimizer, wherein the intelligent detection unit is used to collect the driving speed, the motor current, the battery temperature, the slope angle and the real-time road image; the central controller is used to run an adaptive energy-saving algorithm, and dynamically generate a motor power mapping table based on the battery health state, the road condition prediction and the user's 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 300 is used to receive the brake force, the slope angle, the motor speed and the vehicle mass estimation value, calculate the optimal recovery current through the Kalman filter, and inject the recovery current into the battery pack through the bidirectional Buck-Boost circuit; the safety cooperative controller 400 executes a hierarchical protection strategy, wherein when the longitudinal acceleration is greater than or equal to a first preset threshold, the first level protection is started; when the longitudinal acceleration is greater than or equal to a second preset threshold, the second level protection is started; when the vibration sensor detects that the impact frequency is greater than a preset frequency, the third level protection is triggered; the data bus connects the anti-impact battery compartment 100, the energy-saving control module 200, the energy recovery module 300 and the safety cooperative controller 400, and realizes real-time data interaction.

[0023] It can be understood that in the embodiments of the application, the anti-impact battery compartment enhances the anti-impact capability of the battery through the multi-layer composite shell, the vibration sensor and the filling layer; the energy-saving control module realizes energy saving and prolongs the endurance by means of intelligent detection and dynamic power regulation; the energy recovery module improves the energy recovery efficiency by using multi-parameter calculation and high-efficiency circuit; the safety cooperative controller guarantees safety through the hierarchical protection strategy; the data bus realizes real-time interaction of each module, cooperative work, effectively improves the safety, energy saving, endurance and energy recovery efficiency of the battery of the electric scooter, and optimizes the comprehensive performance.

[0024] Specifically, when the user rides the electric scooter on a bumpy road, the multi-layer composite shell and the filling layer of the anti-impact battery compartment will buffer the impact caused by the road bumps, the vibration sensor will detect the impact in real time and transmit the data to the safety cooperative controller through the data bus, and if the impact does not reach the threshold, no special protection is triggered, ensuring the stability of the battery in complex road conditions. When the user rides to a steep uphill section, the intelligent detection unit of the energy-saving control module collects data such as slope angle and motor current, the central controller generates a motor power mapping table suitable for the current slope in combination with the battery health state and the user's past uphill riding habits, and the power optimizer adjusts the PWM duty cycle accordingly, so that the motor outputs appropriate power, avoiding excessive power consumption of the battery and prolonging the endurance. When the user brakes during the process of riding on a flat road, the energy recovery module receives data such as braking force, motor speed, and the like, and estimates the vehicle mass to calculate the optimal recovery current through a Kalman filter. The super capacitor buffer unit first stores the peak current at the braking moment, and then injects the current into the battery pack through a bidirectional Buck-Boost circuit to improve the energy recovery efficiency. If an emergency occurs during riding, the vehicle longitudinal acceleration reaches a first preset threshold, the safety coordination controller starts the first level protection to limit the motor peak power to the rated value; if the acceleration further increases to reach a second preset threshold, the second level protection is started, the motor drive is cut off, and the maximum braking strength of the energy recovery module is activated; if the vehicle is subjected to a large impact, the impact frequency detected by the vibration sensor exceeds the preset frequency, triggering the third level protection, the battery main relay is disconnected, and the hub motor is locked, thereby comprehensively ensuring the safety of the user and the vehicle.

[0025] It should be noted that the first preset threshold can be specifically calibrated, such as 1.5g, etc., and the second preset threshold can be specifically calibrated, such as 3g, etc.

[0026] In the embodiments of the present application, the central controller is also configured with a machine learning module, which generates a personalized power adjustment curve by continuously learning the riding parameters of the user at different time periods and road conditions.

[0027] It can be understood that the machine learning module configured by the central controller of the embodiments of the present application can continuously collect and analyze the riding parameters of the user at different time periods (such as weekday morning rush hours, weekend afternoon) and different road conditions (such as congested streets, open asphalt roads, uphill sections), including commonly used speed ranges, acceleration habits (such as sudden acceleration or smooth acceleration), and brake frequency. Through continuous learning of these data, the module can accurately capture the riding preferences and habit patterns of the user, and then generate a power adjustment curve that meets the personalized needs of the user. The personalized power adjustment curve can make the motor output power highly match the user's riding habits, avoiding the problem of power excess or deficiency that may occur with a general power strategy. For example, for users who are used to riding smoothly, the curve will make the power of the motor rise smoothly when accelerating, reducing unnecessary energy consumption; and for users who prefer fast start, the curve will reasonably increase the power at the start-up stage to meet the demand, while avoiding power waste, effectively improving the utilization efficiency of battery energy. On the other hand, the power adjustment based on user habits makes the riding process smoother and more comfortable, and the user does not need to manually adjust frequently. The system can "predict" the user's riding needs, improve the overall riding experience, and make the energy-saving control more intelligent and humanized.

[0028] In the embodiment of the present application, the adaptive energy-saving algorithm comprises a road condition recognition submodule and a dynamic efficiency optimization submodule, wherein the road condition recognition submodule is configured to classify the road flatness level according to real-time images; and the dynamic efficiency optimization submodule is configured to adjust the motor efficiency optimal working interval according to the road level and the slope angle.

[0029] It can be understood that, in the embodiment of the present application, the road condition recognition submodule of the adaptive energy-saving algorithm classifies the road flatness level according to real-time images, thereby providing basic road condition information for the dynamic efficiency optimization submodule; and the dynamic efficiency optimization submodule adjusts the motor efficiency optimal working interval in combination with the road level and the slope angle, thereby ensuring that the motor can operate efficiently under different road conditions, effectively reducing the battery energy consumption, and improving the endurance of the scooter.

[0030] Specifically, when the electric scooter travels on a flat urban asphalt road, the road condition recognition submodule analyzes the real-time images collected by the intelligent detection unit, and identifies that the road flatness level is “excellent”. Then, the dynamic efficiency optimization submodule receives this road level information, and simultaneously obtains that the slope angle of the current road section is 0° (flat road), and adjusts the motor efficiency optimal working interval to the middle-low power section (for example, PWM duty ratio 40%-50%). At this time, the motor output power is moderate, which can ensure the normal travel speed and will not cause excessive energy consumption, thereby realizing efficient energy saving.

[0031] If the vehicle enters a section of the cement road with a small number of potholes, the road condition recognition submodule updates the road flatness level to “medium” through image recognition. The dynamic efficiency optimization submodule combines the level and the slope angle of 0°, and fine-tunes the motor efficiency optimal working interval to the middle power section (PWM duty ratio 50%-60%) to avoid increasing the energy consumption due to the frequent power fluctuations of the motor caused by the bumpy road, thereby ensuring that the motor outputs sufficient power to maintain the travel stability, and controls the energy consumption within a reasonable range.

[0032] When the vehicle travels to an uphill road section with a slope angle of 8° and a sandstone road with a road flatness level of “medium”, after the road condition recognition submodule determines the road level, the dynamic efficiency optimization submodule combines the road level and the slope angle information, and adjusts the motor efficiency optimal working interval to the middle-high power section (PWM duty ratio 60%-75%). In this interval, the motor can output sufficient power to overcome the slope and road resistance, and ensure smooth uphill, and compared with blindly increasing the power, the interval can make the motor still maintain high efficiency under high load, thereby reducing unnecessary energy waste.

[0033] In the embodiment of the present application, the first-level protection is to limit the motor peak power to the rated value, the second-level protection is to cut off the motor drive and activate the maximum braking strength of the energy recovery module, and the third-level protection is to disconnect the battery main relay and lock the hub motor In the embodiment of the present application, as Figure 2 As shown, the outer layer of the multi-layer composite shell is 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 collaborative controller; the filling layer is gradient density polyurethane foam, which is located between the battery pack and the multi-layer composite shell.

[0034] It can be understood that 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 in the embodiment of the present application form a solid protective barrier that can effectively resist external impact force; the vibration sensor detects the impact acceleration in real time and transmits it to the safety coordination controller to provide a basis for safety protection; the gradient density polyurethane foam filling layer acts as a buffer between the battery pack and the shell.

[0035] It should be noted that the inner layer of gradient density polyurethane foam is 300kg / m 3 , Middle layer 200kg / m 3 、Outer layer 80kg / m 3 .

[0036] In the embodiment of the present application, the data bus adopts the CAN FD protocol, and the transmission rate is ≥5Mbps.

[0037] In the embodiment of the present application, the vehicle mass estimation value is calculated by the starting acceleration and motor output torque collected by the intelligent detection unit, and the calculation formula is: Estimated vehicle mass = motor output torque ÷ (starting acceleration × wheel radius).

[0038] Specifically, when the user starts the electric scooter, the intelligent detection unit will collect two key parameters in real time: one is the acceleration at the moment of starting (measured by the acceleration sensor, for example, 2m / s 2 ), and the second is the output torque of the motor at the same time (obtained by the motor controller, for example, 15N·m). At the same time, the wheel radius of the scooter is a known fixed parameter (for example, 0.15m).

[0039] Substituting these data into the formula "Estimated vehicle mass = Motor output torque ÷ (Starting acceleration × Wheel radius)", we can obtain: Estimated vehicle mass = 15N・m ÷ (2m / s 2 × 0.15m) = 15 ÷ 0.3 = 50kg. This result reflects the combined mass of the scooter and the user's weight. During energy recovery, 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 regenerative current to match the inertial kinetic energy. During power regulation, the central controller also uses this value to optimize power output, ensuring that the motor's driving force matches the actual load, avoiding power waste or insufficient power. By dynamically updating the vehicle's mass estimate, the system can more accurately adapt to different users (weight differences) and load variations (such as those carried by passengers), improving overall control accuracy.

[0040] In an embodiment of the present 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 a constant current into the battery pack through a bidirectional Buck-Boost circuit.

[0041] It can be understood that the supercapacitor buffer unit of the energy recovery module in the embodiment of the present application quickly stores the peak recovery current at the moment of braking, avoiding large current directly impacting the battery pack, and then converts the current into a constant value through the bidirectional Buck-Boost circuit and injects it into the battery, 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. In the embodiment of the present application, a wireless communication module is also included.

[0042] Among them, the wireless communication module supports Bluetooth 5.0 and Wi-Fi connections, and can synchronize battery health status, energy consumption data and fault information to the user's mobile terminal in real time.

[0043] It can be understood that the wireless communication module in the embodiment of the present application supports Bluetooth 5.0 and Wi-Fi connections, and synchronizes the battery health status, energy consumption data and fault information to the user's mobile terminal in real time, so that the user can grasp the device status at any time, charge, maintain or handle faults in time, improve the convenience and safety of use, and enhance the user's sense of control over the device. This embodiment of the present application proposes a battery energy-saving control system 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, accurately matching the motor output power to actual demand and reducing inefficient energy consumption. This dynamic control based on real-time road conditions and user habits significantly reduces battery energy waste and effectively improves the electric scooter's range. The energy recovery module receives multiple parameters and calculates the optimal recovery current using a Kalman filter. The supercapacitor buffer unit properly handles the peak recovery current during braking, and the bidirectional Buck-Boost circuit ensures that the recovered current is appropriately injected into the battery pack. The combination of multi-parameter collaborative calculation and efficient circuit design significantly improves energy recovery efficiency, effectively utilizing energy that would otherwise be wasted, such as braking. The impact-resistant battery compartment utilizes a multi-layer composite shell, with an outer layer of carbon fiber reinforced polymer and an inner layer of aluminum alloy honeycomb structure providing solid structural support. A gradient density polyurethane foam filling layer further cushions impacts, and a vibration sensor promptly detects impacts and transmits them to the safety collaborative controller. This multi-level impact-resistant design greatly enhances the battery compartment's ability to withstand external impacts and effectively protects 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 collaborative controller. The hierarchical protection strategy implemented by the safety collaborative controller can link other modules to take corresponding protection measures according to different impact situations, achieving close coordination among various modules in terms of safety protection, and improving the coordination and reliability of the entire system. This solves the problems of insufficient battery life, low energy recovery efficiency, weak impact resistance, and poor coordination in the existing technology of electric scooters.

[0044] The following describes a battery energy-saving control system for a high-strength impact-resistant scooter through a specific embodiment, including: User Xiao Wang uses this high-strength impact-resistant scooter to commute. From home to work, he has to pass through a city main road, an uphill section and a bumpy road after construction. At 7 o'clock in the morning, Xiao Wang started the scooter. The intelligent detection unit immediately recorded the acceleration at the moment of starting, which was 2.5m / s. 2 The motor output torque is 18 N·m. Given a wheel radius of 0.16 m, substituting this into the formula yields an estimated vehicle mass of 18 ÷ (2.5 × 0.16) = 45 kg (including Xiao Wang's weight and the scooter's own mass). The central controller's machine learning module records Xiao Wang's morning rush hour starting habits and uses this information to optimize power regulation. While driving on a smooth asphalt road on a city main road, the road condition recognition submodule uses real-time images to determine the road surface smoothness is "Excellent." The current slope angle is 0°, and the dynamic efficiency optimization submodule adjusts the motor's optimal operating range to a PWM duty cycle of 40%-50%. The intelligent detection unit records a driving speed of 18 km / h, a motor current of 7A, and a battery temperature of 28°C. The central controller, combining the battery health status (90% charge) and Xiao Wang's riding habits on flat roads (maintaining a moderate and steady speed), generates a power mapping table. The power optimizer stabilizes the PWM duty cycle at 45%, ensuring moderate motor output power and stable energy consumption. When the driver reaches an uphill section with a gradient of 6°, the intelligent detection unit transmits the slope data to the central controller. The central controller then uses the machine learning module to record Xiao Wang's uphill climbing habits (preferring slightly higher power for faster passage), and generates a corresponding power mapping table based on the battery status. The power optimizer then adjusts the PWM duty cycle to 65%, ensuring the motor outputs the appropriate power, ensuring uphill momentum while avoiding excessive power consumption. At this point, the energy recovery module is in standby mode, with recovery not activated. When navigating bumpy roads following construction, the impact-resistant battery compartment comes into play. The outer layer of carbon fiber-reinforced polymer protects against roadside debris, while the inner layer of aluminum alloy honeycomb structure and gradient-density polyurethane foam cushions the impact of bumps and shocks. The vibration sensor detects an impact frequency of 50Hz (preset frequency: 100Hz), which falls below the threshold. This data is transmitted to the safety collaborative controller via the CAN FD protocol data bus (transmission rate: 5Mbps), without triggering special protection measures, and the battery pack remains stable during the bumpy ride. Nearing the office, Xiao Wang braked on a flat road to slow down. The energy recovery module received information about the braking force (medium), motor speed (25 revolutions per second), bank angle of 0°, and estimated vehicle mass of 45 kg. Using a Kalman filter, it calculated the optimal regenerative current to be 4A. The 8A peak current generated by braking was stored in the supercapacitor buffer unit and then injected into the battery pack at a constant current of 4A via the bidirectional Buck-Boost circuit, achieving energy recovery. Suddenly, a pedestrian rushed out from the intersection ahead, and Xiao Wang braked urgently. The vehicle's longitudinal acceleration instantly reached 3g (the second preset threshold is 3g). The safety collaborative controller immediately activated the secondary protection, cutting off the PWM output of the motor drive and activating the energy recovery module's maximum braking intensity at the same time. Emergency braking was achieved with the help of the recharging circuit to avoid a collision. Throughout the commute, the wireless communication module synchronizes real-time battery health status (75% remaining charge) and energy consumption data (average consumption 8Wh / km) to Xiao Wang's mobile app, allowing him to view it at any time. The system's modules work together to ensure cycling safety while effectively saving energy and recycling it, demonstrating excellent overall performance.

[0045] Next, a method for controlling energy-saving of a battery of a high-strength, impact-resistant scooter according to an embodiment of the present application will be described with reference to the accompanying drawings.

[0046] like Figure 3 As shown, the energy-saving control of the vehicle battery of the high-strength impact-resistant scooter includes the following steps: In step S101 , the braking force, slope angle, motor speed, road condition data, impact signal and acceleration are obtained.

[0047] It can be understood that the embodiments of the present application 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 collaborative controller to execute the hierarchical protection strategy by obtaining braking force, slope angle, motor speed, road condition data, impact signal and acceleration.

[0048] 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.

[0049] Among them, PWM duty cycle refers to the ratio of the time that the pulse signal is at a high level in one cycle to the entire cycle time in pulse width modulation technology, usually expressed as a percentage.

[0050] It can be understood that the embodiment of the present application dynamically generates a power strategy by combining road condition data, slope angle, battery status and user riding habits, and then adjusts the PWM duty cycle according to the strategy, so that the motor output power can be accurately matched with the actual driving needs, thereby avoiding energy waste caused by excess power and preventing insufficient power from affecting the riding experience, thereby improving battery energy utilization efficiency, extending cruising range, and enhancing the smoothness and adaptability of riding.

[0051] The power optimizer controls the output voltage and current of the motor drive circuit by adjusting the PWM duty cycle, thereby varying the motor's output power. For example, a 45% PWM duty cycle means the motor drive circuit is on 45% of the time within a cycle, outputting a corresponding proportion of voltage to ensure the motor receives the appropriate power. A higher duty cycle results in a higher average voltage and greater output power, while a lower duty cycle results in lower power. By dynamically adjusting the PWM duty cycle, motor power can be precisely controlled to suit varying road conditions and riding requirements, achieving a balance between energy conservation and efficient driving.

[0052] In step S103, based on the braking force, slope angle and motor speed, combined with the vehicle mass estimation value and PWM duty cycle, the optimal recovery plan is output through the Kalman filter, and recharging is carried out with the help of supercapacitors and bidirectional circuits, which is linked to the battery status data.

[0053] It can be understood that the embodiment of the present application achieves precise and efficient energy recovery through multi-parameter fusion and Kalman filter output of the optimal recovery plan, with the help of supercapacitors and bidirectional circuits to recharge and link battery status data, which can not only avoid the damage of large current to the battery, but also adapt the recovered energy to the battery status, thereby improving energy utilization and battery safety, and enhancing the overall coordination of the system.

[0054] Specifically, the state equation is: in, is the state transition matrix, is the control input matrix, is the process noise, is the state vector at time k, For The state vector at time t, is the control input; The observation equation is: in, is the observation vector, H is the observation matrix, is the observation noise, Prediction based on Kalman filter: in, is the prior state estimate, is the a priori estimated covariance, is the posterior estimate of the previous moment, is the estimated covariance at the previous moment, is the process noise covariance matrix; The updated formula is: in, is the Kalman gain matrix, is the posterior estimated covariance, is the posterior state estimate, is the observation noise covariance matrix.

[0055] In step S104 , a progressive safety response linked to the PWM duty cycle and energy recovery module is triggered according to the impact signal and acceleration.

[0056] It can be understood that the embodiment of the present application triggers a progressive safety response linked to the PWM duty cycle and energy recovery module based on the impact signal and acceleration. It can limit the motor power or cut off the drive by adjusting the PWM duty cycle according to the severity of the impact and acceleration, and link the energy recovery module to enhance braking, thereby achieving graded protection from minor protection to emergency braking, comprehensively ensuring the safety of users and vehicles, and improving the safety and reliability of the system.

[0057] This embodiment of the present 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, precisely matching the motor output power to actual demand and reducing inefficient energy consumption. This dynamic control based on real-time road conditions and user habits significantly reduces battery energy waste and effectively improves the electric scooter's range. The energy recovery module receives multiple parameters and calculates the optimal recovery current using a Kalman filter. The supercapacitor buffer unit properly handles the peak recovery current during braking, and the bidirectional Buck-Boost circuit ensures that the recovered current is appropriately injected into the battery pack. The combination of multi-parameter collaborative calculation and efficient circuit design significantly improves energy recovery efficiency, effectively utilizing energy that would otherwise be wasted, such as braking. The impact-resistant battery compartment utilizes a multi-layer composite shell, with an outer layer of carbon fiber reinforced polymer and an inner layer of aluminum alloy honeycomb structure providing solid structural support. A gradient density polyurethane foam filling layer further cushions impacts. A vibration sensor promptly detects impacts and transmits them to the safety collaborative controller. This multi-level impact-resistant design greatly enhances the battery compartment's ability to withstand external impacts and effectively protects 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 collaborative controller. The hierarchical protection strategy implemented by the safety collaborative controller can link other modules to take corresponding protection measures according to different impact situations, achieving close coordination among various modules in terms of safety protection, and improving the coordination and reliability of the entire system. This solves the problems of insufficient battery life, low energy recovery efficiency, weak impact resistance, and poor coordination in the existing technology of electric scooters.

[0058] The following describes a method for controlling energy conservation of a battery for a high-strength impact-resistant scooter through a specific embodiment, including: User Xiao Li rode the scooter home from get off work, a total distance of 2.5 kilometers, covering urban auxiliary roads, a short uphill distance and a section of potholes that was temporarily repaired. Step S101: Data collection The intelligent detection unit collects various data in real time throughout the entire journey. When driving on urban auxiliary roads, it updates the vehicle's speed (20 km / h), motor current (6.8 A), and battery temperature (30°C) every second. The camera captures road conditions on a smooth asphalt road, with the slope sensor indicating a 0° slope. On a 5° uphill slope, the speed drops to 16 km / h, the motor current rises to 9.2 A, and the road image shows a concrete uphill slope. After entering a pothole-prone section, the speed drops to 12 km / h, the motor current fluctuates between 7.5 and 8.3 A, the vibration sensor detects a 35Hz impact signal, and the acceleration sensor indicates a longitudinal acceleration of 0.3g. Furthermore, during braking, the unit collects braking force (light braking corresponds to 20% pedal travel, heavy braking corresponds to 60%) and motor speed (28 rpm on flat roads, 22 rpm on uphill roads). Step S102: Power strategy generation and adjustment After receiving the data, the central controller dynamically generates a power strategy based on the battery status (82% remaining battery 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 excellent road conditions and 0° slope, a "medium-low power to maintain high speed" strategy is generated, and the power optimizer adjusts the PWM duty cycle to 48%, and the motor output power is stabilized at 800W; when going uphill, based on the 5° slope and acceleration habits, the strategy is updated to "medium-high power climbing", the PWM duty cycle is increased to 62%, and the power is increased to 1200W; when entering a bumpy section, combined with 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 not only avoids stalling due to insufficient power, but also prevents increased energy consumption due to excessive power. Step S103: Energy recovery When Xiao Li reached the flat road near the residential complex entrance, he lightly applied the brakes to slow down, prompting the system to initiate energy recovery. The system then determined a braking force of 20%, a bank angle of 0°, and a motor speed of 28 rpm. Combined with the estimated vehicle mass (Xiao Li's weight is 65 kg + the vehicle's weight is 15 kg = 80 kg, calculated using a formula) and the current PWM duty cycle of 48%, the Kalman filter calculated an optimal regenerative current of 3.5 A. The peak current of 7 A generated by braking was absorbed by the supercapacitor buffer unit, and the bidirectional Buck-Boost circuit then stabilized the current at 3.5 A and injected it into the battery for 1.5 seconds, raising the battery charge from 75% to 75.3%. Step S104: Security Response When passing a bumpy road, a piece of gravel hit the car body. The vibration sensor detected an impact signal frequency of 60Hz (the preset frequency threshold is 80Hz) and a longitudinal acceleration of 0.5g (the first preset threshold is 1.5g). The protection threshold was not reached and the system did not trigger a special response. When Xiao Li was almost home, he suddenly encountered a bicycle crossing the road. He braked suddenly and the longitudinal acceleration instantly reached 2g (exceeding the first preset threshold of 1.5g). The safety collaborative 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 linked to start the maximum braking intensity. The recovery current was instantly increased to 5A, and rapid braking was achieved with the help of recharge resistance to avoid collision. After the entire journey, the remaining battery power was 73%, and the APP showed that the energy recovery amount was 0.08kWh, which was about 15% lower than the energy consumption of ordinary scooters on the same road section. There were no battery abnormalities or safety hazards throughout the journey, which demonstrated the energy saving and safety of this control method.

[0059] In summary, when user Xiao Li rode his scooter home from get off work, the control method in this embodiment of the application operated in a sequential manner: Step S101 collected real-time data such as speed, current, and slope for each road section, providing a basis for subsequent control; Step S102 combined battery status and riding habits to generate a power strategy adapted to different road sections, achieving precise power control by adjusting the PWM duty cycle; Step S103 calculated the optimal regenerative current based on multiple parameters during braking, efficiently recharging energy using relevant components; and Step S104 triggered a progressive safety response based on impact and acceleration conditions. This reduced energy consumption by approximately 15% throughout the entire process while ensuring safety, fully demonstrating the advantages of this control method in terms of energy conservation, safety, and adaptability.

[0060] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include: Memory 401 , processor 402 , and computer programs stored in the memory 401 and executable on the processor 402 .

[0061] When the processor 402 executes the program, the energy-saving control method for a battery of a high-strength impact-resistant scooter provided in the above embodiment is implemented.

[0062] Furthermore, the electronic device further includes: The communication interface 403 is used for communication between the memory 401 and the processor 402 .

[0063] The memory 401 is used to store computer programs that can be run on the processor 402 .

[0064] The memory 401 may include a high-speed RAM (Random Access Memory) memory, and may also include a non-volatile memory, such as at least one disk memory.

[0065] If the memory 401, processor 402, and communication interface 403 are implemented independently, the communication interface 403, memory 401, and processor 402 can be connected to each other via a bus and communicate with each other. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0066] Optionally, in a specific implementation, if the memory 401, the processor 402 and the communication interface 403 are integrated on a chip, the memory 401, the processor 402 and the communication interface 403 can communicate with each other through an internal interface.

[0067] The processor 402 may be a CPU (Central Processing Unit), or an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present application.

[0068] In the description of this specification, reference to the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification, as well as features of different embodiments or examples, unless they are mutually inconsistent.

[0069] 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 being referred to. Thus, a feature specified as "first" or "second" may explicitly or implicitly include at least one such feature. Throughout the description of this application, "plurality" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0070] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application belong.

[0071] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having logic gate circuits for implementing logical functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0072] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0073] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A high-strength impact-resistant scooter battery energy-saving control system, characterized in that: include: Impact-resistant battery compartment, energy-saving control module, energy recovery module, safety collaborative controller and data bus; among them, 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 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; and 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 is used to receive brake force, slope angle, motor speed, and vehicle mass estimation, calculate the optimal recovery current through a Kalman filter, and inject the recovery current into the battery pack using a bidirectional Buck-Boost circuit; The safety collaborative controller implements a hierarchical protection strategy, wherein when the longitudinal acceleration is greater than or equal to a first preset threshold, the first level of protection is activated; when the longitudinal acceleration is greater than or equal to a second preset threshold, the second level of protection is activated; when the vibration sensor detects an impact frequency greater than a preset frequency, the third level of protection is triggered; The data bus connects the impact-resistant battery compartment, the energy-saving control module, the energy recovery module and the safety collaborative controller to achieve real-time data interaction.

2. The battery energy-saving control system for a high-strength impact-resistant scooter according to claim 1 is characterized in that: 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 in different time periods and road conditions.

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

4. The battery energy-saving control system for a high-strength impact-resistant scooter according to claim 1 is characterized in that: 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.

5. The battery energy-saving control system for a high-strength impact-resistant scooter according to claim 1 is characterized in that: 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 collaborative controller; the filling layer is a gradient density polyurethane foam, located between the battery pack and the multi-layer composite shell.

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

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

8. The battery energy-saving control system for a high-strength impact-resistant scooter according to claim 1, characterized in that: 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 the current into the battery pack at a constant current through a bidirectional Buck-Boost circuit.

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

10. A method for controlling an energy-saving battery system for a high-strength impact-resistant scooter according to any one of claims 1 to 9, characterized in that: The method comprises: Obtain brake force, slope angle, motor speed, road condition data, impact signal and acceleration; Dynamically generate a power strategy based on the road condition data, slope angle, battery status, and user riding habits, and adjust the PWM duty cycle according to the power strategy; Based on the braking force, slope angle, and motor speed, combined with the vehicle mass estimate and the PWM duty cycle, a Kalman filter outputs an optimal recovery plan, using supercapacitors and bidirectional circuits for recharging, linked to battery status data; According to the impact signal and acceleration, a progressive safety response linked with the PWM duty cycle and energy recovery module is triggered.

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