Customized battery replacement system and method for bus

Abstract

The invention relates to the technical field of electric vehicles, and discloses a customized battery replacement system and method for a bus, and the method comprises the steps: collecting the whole system historical operation parameters of a specific bus in advance as a design basis, generating new battery design parameters which are in ecological deep coupling with an electromechanical system of the bus in a constraining manner, and carrying out the installation and adaptation of the new battery design parameters; through collaborative debugging including a feedback optimization mechanism, a complete data closed loop is formed, systematic mismatching and potential operation risks caused by mismatching of standards and performance of non-original batteries are solved, deep coupling of new and old systems is achieved in the design stage, and the design efficiency is improved. And the high adaptability and the system cooperation stability after battery replacement are ensured.

Description

Technical Field

[0001] This invention relates to a customized battery swapping system and method for public buses, belonging to the field of electric vehicle battery swapping technology. Background Technology

[0002] In urban public transportation systems, trolleybuses have become an important component. Their core power source, the power battery, needs to be replaced regularly due to cycle life, technological iteration, or sudden failures. This is a basic maintenance task to ensure the normal operation of the fleet. Currently, the common practice in the industry is to replace the battery pack with a new one after the battery performance degrades to a preset threshold in order to restore the vehicle's range and operating efficiency. This approach is direct and effective in the early stages when battery technology standards are unified and development is slow.

[0003] However, with the rapid development of battery technology, new technical challenges have emerged. The pace of battery innovation has far exceeded the replacement cycle of the entire vehicle, resulting in a large number of new batteries on the market that are incompatible with the original vehicle design in terms of energy density, charge / discharge rate, thermal management characteristics, and even communication protocols. When operators attempt to replace older vehicles with these more advanced batteries, contradictions become apparent: the vehicle's power system, drive system, electronic control system, energy recovery system, and cooling system are a precise whole that has been deeply bound to and calibrated in coordination with the specific performance parameters of the original battery from the initial stage of vehicle design. Its control logic and operating strategy are highly targeted. Simply physically installing the new battery and making basic electrical connections, even if the compatibility issues of the installation interface and voltage level are resolved, cannot solve the problem of mismatch in dynamic operating characteristics between the old and new systems.

[0004] This mismatch is not simply a performance penalty, but rather triggers a series of potential operational problems. Specifically, existing technologies have the following shortcomings: 1. The charge-discharge curve of the new battery does not match the preset model of the vehicle's energy management system, leading to inaccurate energy estimation and reduced energy utilization efficiency; 2. Because the regenerative braking system cannot know the instantaneous maximum acceptable charging current of the new battery, its recovery strategy may be too conservative, resulting in energy waste, or too aggressive, posing a risk of battery damage; 3. The original cooling system was designed based on the heat generation model of the old battery. Faced with the new battery, whose energy density and rate characteristics have changed, its heat dissipation capacity may be mismatched, posing a safety hazard. Simply improving the performance of a certain system or modifying a single parameter often triggers a chain reaction in other systems, failing to solve this systemic coordination problem. Therefore, how to establish an effective technical method that, when replacing non-original batteries in in-service buses, goes beyond the physical and basic electrical compatibility levels, and delves into the data and control logic levels to achieve coordination and performance matching between the new battery and the vehicle's complex electromechanical system ecosystem has become the technical challenge that this invention aims to solve. Summary of the Invention

[0005] This invention provides a customized battery swapping system and method for public buses. Its main purpose is to solve the problem of how to achieve deep synergy and performance matching between the new battery and the vehicle's inherent electromechanical system ecosystem when replacing non-original batteries in in-service public buses.

[0006] To achieve the above objectives, this invention provides a customized battery swapping system for public buses. The system establishes an operational procedure that couples non-original batteries with the electromechanical system ecosystem of a specific in-service public bus in a data-driven manner. The system includes: The data acquisition module specifically acquires historical operating parameters of five systems of the trolleybus, including the power system, drive system, electronic control system, energy recovery system and cooling system, through the vehicle diagnostic interface, and simultaneously extracts the communication protocol data of the original battery installed in the trolleybus. The customized battery design module has its input end connected to the output end of the data acquisition module. Specifically, it generates design parameters for a new battery, including its structural dimensions, physical interface type, rated energy density, and charge / discharge characteristic curve, based on historical operating parameters and communication protocol data. The values ​​of the design parameters are limited to a numerical solution space composed of historical operating parameters. The installation and adaptation module specifically performs the physical installation of the new battery according to the design parameters and establishes a communication link between the new battery and the vehicle control system of the bus. When incompatibility of communication protocol data is detected, the communication repeater is activated to perform real-time protocol conversion on the data signals in the communication link. The collaborative debugging and verification module monitors and regulates the collaborative operation of the new battery and five systems of the bus under preset test conditions after the communication link is established. During this collaborative operation, a set of real-time operating data is collected. When it is determined that the real-time operating data deviates from a benchmark data range calculated from historical operating parameters, a parameter optimization instruction is generated and fed back to the customized battery design module for updating the design parameters.

[0007] Preferably, the historical operating parameters acquired by the data acquisition module further include: the voltage and current fluctuation distribution of the power supply system, the torque response curve of the drive system, the command response delay of the electronic control system, the braking energy recovery power curve of the energy recovery system, and the correspondence between the heat dissipation power of the cooling system and the temperature rise of the battery cell.

[0008] Preferably, the collaborative debugging and verification module, in regulating the coordinated operation of the new battery and the energy recovery system, specifically performs the following operations: during vehicle braking, it monitors the battery pack temperature of the new battery in real time. Based on a braking energy recovery intensity adjustment rule, the recovery current of the energy recovery system is adjusted. Adjustments were made, including the recovery of the current. The instantaneous target value is determined by the following formula: ,in, This is a reference recovery current set based on the braking energy recovery power curve from historical operating parameters. This is an upper limit for charging temperature determined based on the new battery material system. This is a coefficient calibrated based on battery test data to characterize the effect of temperature on charge acceptance.

[0009] Preferably, the customized battery design module specifically generates a three-dimensional structural model, including the internal cell layout and thermal management flow channel, based on design parameters to guide battery production, as well as a target charge-discharge strategy curve adapted to the control logic of the bus energy management system; the installation and adaptation module includes a communication repeater, which specifically performs real-time bidirectional conversion of message identifiers, data formats, and transmission rates of the controller local area network bus communication protocol between the battery management system of the new battery and the vehicle controller of the bus.

[0010] Preferably, in the collaborative debugging and verification module, the specific rule for determining whether real-time operating data deviates from the reference data range is as follows: the measured range of the new battery under the standard range test cycle is compared with a theoretical range calculated based on design parameters. When the absolute value of the deviation between the two is greater than 5% of the theoretical range, it is determined to be a deviation; or the highest temperature rise rate of the new battery is compared with the upper limit of the temperature rise rate set in the reference data range. When the highest temperature rise rate is greater than the upper limit, it is determined to be a deviation.

[0011] Preferably, the collaborative debugging and verification module performs collaborative debugging in the following order: first, power-on and communication checks are performed on the low-voltage system; then, pre-charging and insulation status checks are performed on the high-voltage system; and finally, charge-discharge collaborative debugging is performed under both static load and dynamic simulated load conditions.

[0012] Preferably, the system also includes a fleet data management module. The fleet data management module is an independent module connected to the collaborative debugging and verification module. Specifically, it stores the final design parameters and real-time operation data of any bus after the collaborative debugging and verification module confirms that the collaborative operation is qualified in the database. When performing battery swapping services for other buses of the same model, the final design parameters are called as the initial design parameters of the customized battery design module.

[0013] Preferably, the collaborative debugging and verification module, when regulating the collaborative operation of the new battery and the cooling system, specifically involves: acquiring real-time temperature data of a group of cell areas through multiple temperature sensors deployed inside the new battery, calculating the maximum temperature difference between the group of cell areas, and controlling the maximum temperature difference to be below 5 degrees Celsius by adjusting the speed of the cooling medium circulation pump or the speed of the cooling fan in the cooling system.

[0014] Preferably, the preset test conditions adopted by the collaborative debugging and verification module include two or more high-load operation scenarios. The high-load operation scenarios are specifically the long-distance continuous uphill operation scenario under full load and the high-frequency acceleration and braking scenario under urban congestion conditions, in order to verify the collaborative stability of the new battery and all existing systems of the bus under extreme conditions.

[0015] A customized battery swapping method for public buses and trolleybuses is proposed. The method establishes an operational procedure that couples non-original batteries with the electromechanical system ecosystem of a specific in-service public bus and trolleybus in a data-driven manner. The method includes the following steps: Step a, data acquisition: Through the vehicle diagnostic interface, obtain a set of historical operating parameters of five systems of the bus, including the power system, drive system, electronic control system, energy recovery system and cooling system, and simultaneously extract the communication protocol data of the original battery installed in the bus. Step b, customized battery design: Based on the historical operating parameters and communication protocol data obtained in the data acquisition step, design parameters for the structural dimensions, physical interface type, rated energy density, and charge-discharge characteristic curve of a new battery are generated. The values ​​of the design parameters are limited to a numerical solution space composed of historical operating parameters. Step c, Installation and Adaptation: Based on the design parameters generated in the customized battery design step, the physical installation of the new battery is performed, and a communication link is established between the new battery and the vehicle control system of the bus. When incompatibility of communication protocol data is detected, the communication repeater is activated to perform real-time protocol conversion on the data signals in the communication link. Step d, Collaborative Debugging and Verification: After the communication link is established, monitor and regulate the collaborative operation of the five systems of the new battery and the bus under the preset test conditions in step a. During this collaborative operation, collect a set of real-time operating data. When it is determined that the real-time operating data deviates from a benchmark data range calculated from historical operating parameters, generate parameter optimization instructions and feed them back to the customized battery design step for updating the design parameters.

[0016] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention establishes a technical path that enables the new battery to form a pre-emptive structural coupling with the existing system ecosystem of a specific vehicle at the physical and electrical performance level. This approach transforms the inter-system collaboration after battery installation from a passive adaptation based on general standards to a targeted matching based on the historical operating data of a specific vehicle. This avoids system function degradation or potential operational risks caused by inconsistent standards or mismatched performance parameters.

[0017] 2. This invention integrates the customized parameter design of the battery, the communication protocol adaptation during installation, and the multi-system collaborative debugging after installation into a streamlined process. The battery charging and discharging characteristics preset based on vehicle data provide a clear benchmark for subsequent energy management system debugging, while the application of the communication repeater establishes a basic channel for command interaction between the battery management system and the vehicle's electronic control system. This close integration of design, installation, and debugging transforms the traditional scattered, piecemeal debugging work after battery swapping into a well-defined and streamlined process of fine-tuning system performance, reducing the time and technical uncertainties required for the vehicle to enter a stable operating state after battery swapping.

[0018] 3. This invention expands the battery swapping process from a one-time hardware replacement operation into an iterative data closed-loop process by constructing a verification and optimization process that includes full-scenario operation testing. The test data under simulated different road conditions and loads is not only used to verify the current system's collaborative stability in battery swapping, but the results will also be fed back to optimize the battery design parameters and debugging strategies for subsequent similar vehicles. This forms a mechanism of continuous accumulation and self-optimization in the actual engineering scenario of mass battery swapping for bus fleets, so that each battery swapping operation can draw on past experience, and the overall energy system performance of the fleet can be systematically maintained and improved as the battery swapping service progresses. Attached Figure Description

[0019] Figure 1 This is a schematic diagram illustrating the workflow of a customized battery swapping system and method for public buses according to the present invention. Figure 2 This is a comparison curve of temperature changes between the experimental group battery and the control group battery under simulated working conditions in this invention; Figure 3 This is a schematic diagram of the data closed-loop and feedback optimization mechanism of a customized battery swapping system for public buses according to the present invention.

[0020] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0022] This application provides a customized battery swapping system and method for public buses. The system establishes a data-driven operational procedure that couples non-original batteries with the electromechanical system ecosystem of a specific in-service public bus. The technical solution involves the sequential and closed-loop collaborative work of a data acquisition module, a customized battery design module, an installation and adaptation module, and a collaborative debugging and verification module. This enables full-process control from data acquisition and analysis to customized battery design, and then to multi-system collaborative debugging, verification, and optimization. The system's workflow begins with the data acquisition module acquiring the historical operating parameters of the entire system of a specific public bus and inputting these parameters as constraints into the customized battery design module to generate new battery design parameters compatible with the vehicle's electromechanical system. Subsequently, the installation and adaptation module completes the physical assembly of the new battery and establishes the communication link. Finally, the collaborative debugging and verification module tests and calibrates the vehicle's performance under preset operating conditions and compares the measured data with historical data through a feedback optimization mechanism. If a deviation is detected, optimization instructions are fed back to the design module, thus forming an iterative data closed loop.

[0023] In scenarios where buses need to replace their batteries with non-original ones due to battery aging, a common technical problem is that the dynamic charge and discharge characteristics of the new battery do not match the vehicle's inherent energy management system's preset model. This may lead to mismatch issues such as inaccurate energy estimation and reduced energy utilization efficiency. To address this challenge, the data acquisition module in this invention is configured to acquire a set of historical operating parameters for five systems of the bus, including the power system, drive system, electronic control system, energy recovery system, and cooling system, through a vehicle diagnostic interface, such as connecting to the vehicle's controller area network bus. Simultaneously, it extracts the communication protocol data of the original battery. It should be noted that these historical operating parameters are not simply static nominal values, but rather include data from the vehicle's long-term operation. The dynamic characteristic data accumulated during actual operation further includes: the voltage and current fluctuation distribution of the power supply system, such as the probability density function of the high-voltage bus voltage in the range of 580V to 650V within a standard operating day; the torque response curve of the drive system, i.e., the average response time and overshoot of the motor controller when it receives an acceleration command and outputs 90% of the peak torque; the command response delay of the electronic control system, i.e., the end-to-end average delay from the vehicle controller issuing the command to the execution of actions by each subsystem, for example, not exceeding 100ms; the braking energy recovery power curve of the energy recovery system, which records the average recovery power value at different vehicle speeds and braking depths; and the relationship between the cooling system's heat dissipation power and the cell temperature rise, for example, at an external ambient temperature of 35°C. The average temperature rise rate of the battery pack when the coolant circulation pump is running at 50% of its rated speed is collected. By collecting these dynamic data, which reflect the driver's habits and the characteristics of specific operating routes, the system can construct a numerical solution space that can quantitatively characterize the electromechanical system ecology of the specific vehicle, providing targeted engineering basis for subsequent customized battery design.

[0024] Given the differences in energy density, rate performance, and other parameters among products from different battery manufacturers, direct replacement may lead to incompatibility with the vehicle's powertrain. The customized battery design module's operating procedure addresses this issue. Its input is connected to the output of the data acquisition module. Its function is to generate design parameters for a new battery, including its structural dimensions, physical interface type, rated energy density, and charge / discharge characteristic curves, based on acquired historical operating parameters and communication protocol data. This generation process is a constrained optimization process, limiting the design parameters of the new battery to a numerical solution space composed of historical operating parameters. For example, based on the characteristic of the drive system frequently operating in the high torque range in historical operating parameters, the system will correspondingly increase the weight of the instantaneous discharge rate performance of the new battery in the design parameters and generate a target charge / discharge strategy curve that adapts to the vehicle's existing energy management system control logic. Simultaneously, based on the design parameters, the module further generates a three-dimensional structural model, including internal cell layout and thermal management channels, to guide battery production. This ensures that the new battery is pre-fitted to the existing system ecosystem of a specific vehicle at both the physical performance and control logic levels.

[0025] Due to advancements in battery technology, the communication protocol of new batteries may be incompatible with the vehicle's original controller protocol, preventing normal data exchange after battery swapping. After physically installing the new battery, the installation and adaptation module's primary task is to establish a communication link between the new battery's battery management system and the bus's vehicle controller, and to resolve potential protocol incompatibility issues. To achieve this, when the system detects communication protocol incompatibility, it activates a communication repeater to perform real-time protocol conversion on the data signals in the link. Specifically, this communication repeater acts as a bridge between the battery management system and the vehicle controller. The system enables real-time bidirectional conversion of message identifiers, data formats, and transmission rates in the Controller Area Network Bus (CAN) communication protocol. For example, if the battery management system of a new battery sends a message with identifier 0x18FF00F4 at a rate of 500kbps to report the total battery pack voltage, while the vehicle controller needs to receive a message with identifier 0xCF00400 at a rate of 250kbps, the repeater will buffer the former message, parse out the data fields, and then repackage it according to the latter's format and identifier and send it out at a rate of 250kbps, thereby establishing a data interaction channel for subsequent multi-system collaborative debugging.

[0026] After battery installation and communication adaptation are completed, the collaborative debugging and verification module intervenes to verify the stable operation of the entire vehicle as an organic whole. Within a data closed-loop framework, it monitors and regulates the collaborative operation of the new battery and the bus's five systems under preset test conditions. The debugging process is performed in sequence: first, the low-voltage system is powered on and its communication is checked to confirm that all controller nodes are online and communicating normally; then, pre-charging and insulation status checks are performed on the high-voltage system to confirm its safety; finally, charge and discharge collaborative debugging is performed under both static load and dynamic simulated load conditions. A key debugging step is regulating the collaboration between the new battery and the energy recovery system, especially during vehicle braking. To avoid damaging the battery due to excessive recovery current or wasting energy due to insufficient recovery, the system monitors the battery pack temperature of the new battery in real time. Based on a braking energy recovery intensity adjustment rule, the recovery current of the energy recovery system is adjusted. Dynamic adjustments are made, including the recovery current. The instantaneous target value is determined by the following formula: Here, It is a reference recovery current set based on the regenerative braking power curve in historical operating parameters, for example, 120A. It is an upper limit for charging temperature determined based on the new battery material system, such as 45°C. , This is a coefficient calibrated based on battery test data to characterize the effect of temperature on charge acceptance, for example, 0.04 / ;when 40 hour, , and when When the temperature rises to 50°C, This allows for control over the recovered energy.

[0027] During this collaborative operation, the module synchronously collects a set of real-time operating data and compares it with a baseline data range calculated from historical operating parameters. When the real-time operating data deviates from the baseline range, a parameter optimization instruction is generated and fed back to the customized battery design module to update the design parameters, thus forming a closed-loop optimization. The rules for determining deviations are definite and quantifiable. For example, the measured range of the new battery under standard range test cycles is compared with a theoretical range calculated based on design parameters. When the absolute value of the deviation is greater than 5% of the theoretical range, it is considered a deviation. Alternatively, the highest temperature rise rate of the new battery under full-load ramp conditions is compared with the upper limit of the temperature rise rate set in the baseline data range. When the highest temperature rise rate is greater than the upper limit, it is also considered a deviation. At the same time, the module also regulates the collaborative operation of the new battery and the cooling system through multiple [devices] deployed inside the battery pack. A temperature sensor acquires real-time temperature data for a set of battery cell areas and calculates the maximum temperature difference in this data set. A closed-loop control algorithm adjusts the speed of the cooling medium circulation pump or the cooling fan in the cooling system to actively control this maximum temperature difference below 5 degrees Celsius, ensuring the consistency of the battery system's operation. Furthermore, to verify the system's collaborative stability under extreme conditions, the preset test conditions include two or more high-load operating scenarios, such as long-distance continuous uphill operation under full load and high-frequency acceleration and braking in congested urban traffic. Finally, after the collaborative debugging and verification module confirms that the collaborative operation is qualified, the final version of the design parameters and real-time operating data will be stored in the database of an independent fleet data management module. This allows the final version of the design parameters to be used as the initial design parameters when performing battery swapping services for other buses of the same model, enabling the reuse of technical experience and improving efficiency.

[0028] Example 1: In a specific scenario of urban public transport operation, a fleet of in-service trolleybuses belonging to a public transport group faces battery replacement needs. These vehicles primarily serve an urban route with long, steep inclines. During summer operation, when the vehicles are fully loaded and climbing steep slopes, their power battery systems frequently trigger power limiting due to overheating of the battery cells, resulting in a decrease in speed. On downhill sections, to prevent battery overheating, the vehicle controller limits the power of the regenerative braking system, increasing the burden on the mechanical braking system and causing energy waste. This presents a technical challenge for electric commercial vehicles in extreme operating conditions, where power output and energy recovery are difficult to balance. To address this scenario, the customized battery swapping system for trolleybuses disclosed in the aforementioned specific implementation method is applied, using a data acquisition module... The module connects to the vehicle diagnostic interface of one of the buses and collects data for several consecutive operating days on that specific route. The acquired historical operating parameters depict the continuous high-current discharge curve of the bus during the climbing phase and the potential recovery energy distribution with instantaneous high-power characteristics during the descent phase. These data together constitute a numerical solution space that defines the specific operating condition. The customized battery design module performs a constrained design based on this numerical solution space. Instead of selecting the battery cell with the highest energy density on the market, it takes the instantaneous discharge rate performance and thermal management performance of the new battery as key design indicators and generates a new set of design parameters. These parameters guide the production of a new battery with optimized internal thermal management channels and charge-discharge characteristic curves that are adapted to the load characteristics of the route.

[0029] After the new battery was installed and a communication link was established with the vehicle controller using a communication repeater, the collaborative debugging and verification module began to intervene. Under preset test conditions including full-load uphill climbing and long-distance downhill driving, the vehicle's operating data showed that during the uphill phase, the vehicle did not exhibit power limitation because the design parameters of the new battery matched the torque response curve of the drive system. During downhill braking, the collaborative debugging and verification module monitored the battery pack temperature in real time. Through relational formulas Dynamically regulate the recovery current of the energy recovery system The coordinated operation here is as follows: the historical operating parameters acquired by the data acquisition module serve as the reference recovery current. The established settings provide a baseline, while the customized battery design module provides an upper limit for charging temperature. With coefficient The input values ​​provided by the system enable the energy recovery system to recover more braking energy without exceeding the battery temperature threshold. After collaborative debugging and verification, the real-time operation data collected showed that the absolute value of the deviation between the vehicle's measured range and the theoretical range calculated based on the design parameters was less than 5%, and the maximum temperature rise rate was within the set upper limit. This set of verified final design parameters and real-time operation data were stored in the fleet data management module, providing directly callable initial design parameters for battery swapping services of other buses of the same model in the fleet. The original operational contradiction of limited climbing power and insufficient energy recovery on downhill slopes was alleviated under the new system architecture, allowing the vehicle to operate stably on this specific route.

[0030] Example 2: To objectively verify the technical effectiveness of the customized battery swapping system for buses of the present invention in improving the coordinated stability of non-original batteries and the vehicle system, a comparative experiment was conducted. The test platform was a hardware-in-the-loop simulation test bench, which consisted of a programmable DC power supply capable of simulating motor load characteristics, a real-time controller with a built-in vehicle dynamics model and energy management strategy, and a constant temperature chamber with a settable ambient temperature. The data acquisition system included multiple measurement accuracies of 0.1. The test included a temperature sensor and a voltage and current sensor with a sampling frequency of 100Hz. Two battery packs were selected as test objects. The control group was a commercially available standard power battery pack with a rated capacity and voltage level that matched the target vehicle model. The test group was a customized battery pack generated using the technical solution of this invention. Its design parameters were generated based on a set of historical operating parameters corresponding to the scenario in Example 1, which were obtained through a data acquisition module.

[0031] The test procedure was as follows: the battery packs of the control group and the test group were connected sequentially to the calibrated hardware-in-the-loop simulation test bench, and the temperature of the constant temperature chamber was set to 35°C. Under certain conditions, a standardized and simulated urban road cycle, including long-distance continuous uphill climbing and high-intensity braking energy recovery, was loaded by a real-time controller. This cycle was designed to reproduce the electrothermal coupling effect of the vehicle under extreme load. The uphill phase corresponded to the continuous high-rate discharge of the battery pack, while the downhill phase corresponded to the battery pack's ability to accept high-current charging. During the test, the data acquisition system recorded in real-time temperature changes, terminal voltage, current, and total discharged and recovered energy at key measuring points inside both battery packs. During the test, the thermal behavior and energy efficiency of the two battery packs under the same cycle showed quantifiable differences. In the continuous high-current discharge phase of the simulated uphill climb, the highest temperature of the cells inside the control group battery pack reached 61.8°C. The highest cell temperature in the test group was 55.1°C. In the subsequent simulated downhill braking phase, the control group frequently triggered the charging current limit of its internal battery management system due to higher cell temperatures, resulting in a total recovered capacity of only 2.9 kWh during the entire downhill phase. In contrast, the experimental group, through the control logic of the collaborative debugging and verification module, dynamically adjusted the recovery current, maintaining a higher average recovery power, and achieving a total recovered capacity of 4.6 kWh. Furthermore, throughout the entire cycle test, the maximum cell temperature difference within the experimental group's battery pack was controlled at 4.7°C. The maximum cell temperature difference in the control group reached 8.2°C. .

[0032] Test data shows that by applying the technical solution of this invention, a collaborative working relationship is formed between the newly replaced battery pack and the vehicle's energy management and thermal management system. The lower maximum cell temperature of the test group's battery pack is because its design parameters are constrained by historical operating parameters that include the characteristics of high-load vehicle operation, and the design of its internal thermal management flow channel is enhanced in the high-heat-load region. The improvement in its energy recovery directly benefits from the energy recovery intensity adjustment rules established during the collaborative debugging phase, which are coupled with the real-time temperature state of the battery. These data verify the role of the technical solution of this invention in improving the vehicle's operational stability and energy efficiency under specific operating conditions.

[0033] Example 3: Combination of Examples Figures 1 to 3 This paper describes the implementation of a customized battery swapping system and method for public buses. Figure 1 As shown, the process begins with the data acquisition phase, which obtains historical operating parameters, specifically five major system parameters plus the communication protocol, and inputs the acquired transmission parameters into the battery design phase. In the battery design phase, the system generates design parameters based on the input parameters. This generation process is strictly constrained within the numerical solution space, and the output parameters are used to guide the next phase. In the subsequent installation and adaptation phase, physical installation is performed based on the design parameters, and a communication link is established. During this process, if the system determines that the protocol is incompatible, a repeater is used to resolve the communication problem through an alternative path. After the link is established, the collaborative debugging phase begins, which is a three-stage test including low voltage, high voltage, and load. If the system's operating state deviates from the baseline during debugging, an alternative optimization feedback process is activated, sending instructions to the battery design phase to update the parameters. Only after the system runs successfully are the final generated parameters sent to the data management phase for parameter storage.

[0034] like Figure 2 As shown, the horizontal axis of the graph represents time (minutes), and the vertical axis represents temperature (...). The graph contains three curves: a dashed line with a circular marker represents the temperature change of the control group battery; a solid line with a triangle marker represents the temperature change of the experimental group battery; and another line is located at 60°C. The horizontal dashed line represents the preset temperature limit. As can be observed from the figure, under the same test conditions, the temperature rise rate of the test group battery is lower than that of the control group battery throughout the entire test cycle, and its maximum temperature is always maintained within a safe range far below the temperature limit. In contrast, the temperature of the control group battery rises in the later stage of the test and once exceeds the limit. This shows that the present invention can improve the thermal management performance of the battery and enhance its operational stability under extreme conditions through data-driven customized design and collaborative debugging.

[0035] like Figure 3 As shown, the starting point of the entire data link is the in-service public transport trolleybus. The raw operating signals generated by it are processed by the 1.0 acquisition and parsing operating parameter module, which outputs historical operating parameters and communication protocol data and stores them in the historical operating parameter database. The data in this database is used to generate a baseline data range model and as input to the 2.0 design parameter generation module to generate an initial set of new battery design parameters. During collaborative debugging, real-time operating data from the in-service public transport trolleybus is sent to the 3.0 comparison and deviation judgment module. This module calls the baseline data range model for comparison. If a deviation is judged, a parameter optimization instruction is generated and fed back to the 2.0 module to iteratively optimize the design parameters. After the debugging is qualified, the final version of the design parameters is sent to the 4.0 archive final parameter module and finally stored in the fleet data management database for subsequent use by vehicles of the same model.

[0036] Example 4: Before assembling the new battery into the target trolleybus and conducting on-road cooperative debugging, to ensure the validity and safety of the initial parameters of the energy recovery system control logic, the aforementioned relationship needs to be modified. The core parameters are calibrated offline. The purpose of this work is to combine the electrochemical characteristics of individual battery cells with historical operating data from a specific vehicle to provide a set of targeted and reproducible initial parameters for the control algorithm, thereby reducing uncertainties during the actual vehicle debugging phase. This embodiment discloses a systematic parameter calibration procedure, which is executed on a battery test bench equipped with a high-precision power supply and thermal management system; to determine the upper limit of charging temperature. Cell samples were extracted from newly manufactured, customized batteries and subjected to multi-temperature charging characteristic tests on a test bench, with ambient temperatures ranging from 25°C to 100°C. With 5 Ascend to 55 using step gradient Within each temperature range, the battery cell samples were charged with a constant current of 1C, and their voltage and temperature response curves were monitored. When the ambient temperature exceeded 45°C... Subsequently, the surface temperature rise rate of the battery cell during charging exhibits a non-linear inflection point. Based on this physical phenomenon, an upper limit for the charging temperature of the battery cell with this specific material system is determined. It was determined to be 45. .

[0037] To calibrate the coefficient of the effect of temperature on charge acceptance. In the already determined Based on this, charging acceptance capability tests were conducted; at ambient temperatures of 25°C and 25°C respectively. 30 35 and 40 Under these conditions, the battery cell samples were subjected to constant voltage charging, and the maximum charging current that the battery cell could stably accept at different temperatures was recorded, denoted as . ; at various temperatures With 25 The reference current is normalized, and a curve of the normalized current versus temperature is plotted, with coefficients... This represents the absolute value of the slope of the linear fit of the curve within the working interval. Through this experiment, the coefficients... The value is calibrated as 0.04 / To set the reference recovery current Then, the historical vehicle operating parameters already acquired by the data acquisition module are called; from this set of parameters, energy recovery power data under all braking conditions are extracted, and converted into time series data of recovery current based on the average voltage of the vehicle's high-voltage system; statistical analysis is performed on this current data series, and its 85th percentile value is taken as the benchmark recovery current. This approach leverages the fact that the 85th percentile value represents the high-intensity regenerative current levels that frequently occur during daily vehicle operation. Adjustments based on this benchmark cover most braking conditions while avoiding interference from a few extreme values. Through the aforementioned procedures, a set of parameters associated with a specific battery and a specific vehicle are established. , and This information was identified and loaded into the collaborative debugging and verification module as the initial configuration, providing a traceable data foundation for subsequent real-vehicle debugging.

[0038] Example 5: After collecting historical operating parameters of a specific vehicle, to provide an objective and statistically significant evaluation benchmark for subsequent collaborative debugging and verification modules, the collected dynamic data needs to be preprocessed to generate a benchmark data range that can characterize the normal operating state of the vehicle. This procedure aims to transform the original time series data into a multi-dimensional, condition-specific benchmark model to avoid misjudgments due to random noise or condition jumps during real-time comparison. This example discloses a procedure for generating and applying a benchmark data range. This procedure first automatically divides the collected historical operating parameter time series, including the power system and drive system, into multiple standardized operating state segments based on vehicle state parameters such as vehicle speed, accelerator pedal opening, and brake pedal signal, covering acceleration, constant speed, braking, and idling. Subsequently, for each key performance parameter within each operating state segment, such as drive motor current, battery pack terminal voltage, and cell temperature rise rate, its statistical characteristics are calculated, specifically the mean value of the parameter under that state. with standard deviation Based on this, the baseline data range for this parameter under this operating state is determined to be [ , By performing this operation on all states and all key parameters within a closed interval, a multi-dimensional benchmark database is ultimately formed for subsequent real-time data evaluation.

[0039] During the operation of the collaborative debugging and verification module, the system retrieves the corresponding benchmark data range from the aforementioned benchmark database based on the vehicle's real-time operating status. When the module detects that five consecutive sampling points of a certain real-time operating data fall outside the benchmark data range for its corresponding operating condition, the system determines that the real-time operating data deviates from the benchmark. At this time, the collaborative debugging and verification module generates parameter optimization instructions and feeds them back to the customized battery design module for adjusting the initial design parameters. Through this procedure, the system performance verification after battery swapping is based on statistical deviation analysis of the vehicle's own historical operating patterns, rather than a simple fixed threshold judgment.

[0040] Example 6: After completing the physical installation and initial communication connection of the new battery for a specific trolleybus, a standardized pre-deployment calibration and verification procedure must be performed before it is officially put into operation. This procedure aims to ensure the accuracy of historical operating parameters that form the basis of the design and to verify the system's fault tolerance capability when facing boundary conditions such as communication anomalies. It is a necessary step to ensure the safety and stability of subsequent vehicle operation. The procedure first performs automated verification of the historical operating parameters acquired by the data acquisition module. The system compares key parameters in the dataset, such as battery pack voltage and cell temperature, with the preset physical limit range in the database of this vehicle model. Data points that exceed the range will be marked as abnormal. At the same time, the system performs correlation analysis on logically related parameter pairs, such as accelerator pedal opening and drive system output current. If the correlation coefficient is lower than the preset threshold of 0.8, the data consistency is questionable. When the cumulative proportion of data segments marked as abnormal or with questionable consistency in the dataset exceeds 2% of the total, the system will stop the subsequent process and generate a diagnostic report for manual intervention and investigation.

[0041] After confirming the validity of historical operating parameters, the procedure proceeds to the stress test phase of the online system. To verify the stability of the communication link, the collaborative debugging and verification module monitors the heartbeat messages sent by the communication repeater at 200ms intervals. If no valid heartbeat messages are received continuously within a 600ms window, the system determines that the communication link has failed and immediately executes the preset safety strategy, issuing an alarm to the driver via the instrument panel and limiting the output torque of the drive system to 30% of the rated value, putting the vehicle into a low-speed operating mode. Only after completing the above data verification and online fault tolerance tests, and confirming that all modules of the system are in normal working condition, is the vehicle's battery swapping process finally confirmed as qualified. After the vehicle has passed the battery swapping system debugging and been put into actual operation, in order to continuously maintain the internal temperature of the battery system... The system's built-in logic in the balanced, collaborative debugging and verification module performs real-time closed-loop control of the cooling system. The system continuously collects data from all temperature sensors deployed inside the new battery at a frequency of 1Hz, and calculates the maximum and minimum values ​​in the real-time temperature data within each sampling cycle to obtain the instantaneous maximum temperature difference. This temperature difference is then compared with a preset control threshold of 5 degrees Celsius. If the temperature difference exceeds 5 degrees Celsius, the system generates an adjustment command to increase the cooling system's heat dissipation power based on a graded gain control table. Specifically, this command increases the duty cycle of the pulse width modulation drive signal of the cooling medium circulation pump by a step, such as 5%, until the temperature difference falls back to the threshold range. Through this procedure, the system achieves proactive management of the temperature consistency inside the battery pack.

[0042] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0043] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A customized battery swapping system for public buses, characterized in that, The system includes: The data acquisition module specifically acquires historical operating parameters of five systems of the trolleybus, including the power system, drive system, electronic control system, energy recovery system and cooling system, through the vehicle diagnostic interface, and simultaneously extracts the communication protocol data of the original battery installed in the trolleybus. The customized battery design module has its input end connected to the output end of the data acquisition module. Specifically, it generates design parameters for a new battery, including its structural dimensions, physical interface type, rated energy density, and charge / discharge characteristic curve, based on historical operating parameters and communication protocol data. The values ​​of the design parameters are limited to a numerical solution space composed of historical operating parameters. The installation and adaptation module specifically performs the physical installation of the new battery according to the design parameters and establishes a communication link between the new battery and the vehicle control system of the bus. When incompatibility of communication protocol data is detected, the communication repeater is activated to perform real-time protocol conversion on the data signals in the communication link. The collaborative debugging and verification module monitors and regulates the collaborative operation of the new battery and five systems of the bus under preset test conditions after the communication link is established. During this collaborative operation, a set of real-time operating data is collected. When it is determined that the real-time operating data deviates from a benchmark data range calculated from historical operating parameters, parameter optimization instructions are generated and fed back to the customized battery design module.

2. The customized battery swapping system for trolleybuses according to claim 1, characterized in that, The historical operating parameters acquired by the data acquisition module further include: the voltage and current fluctuation distribution of the power supply system, the torque response curve of the drive system, the command response delay of the electronic control system, the braking energy recovery power curve of the energy recovery system, and the corresponding relationship between the heat dissipation power of the cooling system and the temperature rise of the battery cells.

3. The customized battery swapping system for public buses according to claim 2, characterized in that, The collaborative debugging and verification module, in regulating the coordinated operation of the new battery and the energy recovery system, specifically performs the following operations: During vehicle braking, it monitors the battery pack temperature of the new battery in real time. ; Based on a braking energy recovery intensity adjustment rule, the recovery current of the energy recovery system is adjusted. Adjustments were made, including the recovery of the current. The instantaneous target value is determined by the following formula: ,in, This is a reference recovery current set based on the braking energy recovery power curve from historical operating parameters. This is an upper limit for charging temperature determined based on the new battery material system. This is a coefficient calibrated based on battery test data to characterize the effect of temperature on charge acceptance.

4. The customized battery swapping system for trolleybuses according to claim 1, characterized in that, The customized battery design module, specifically, generates a three-dimensional structural model based on design parameters, including the internal cell layout and thermal management flow channels, to guide battery production, as well as a target charge-discharge strategy curve adapted to the control logic of the bus energy management system; the installation and adaptation module includes a communication repeater, which performs real-time bidirectional conversion of message identifiers, data formats, and transmission rates of the controller local area network bus communication protocol between the battery management system of the new battery and the vehicle controller of the bus.

5. The customized battery swapping system for trolleybuses according to claim 1, characterized in that, In the collaborative debugging and verification module, the specific rule for determining whether real-time running data deviates from the baseline data range is as follows: compare the actual range of the new battery under the standard range test cycle with a theoretical range calculated based on the design parameters. When the absolute value of the deviation between the two is greater than 5% of the theoretical range, it is determined to be a deviation. Alternatively, the maximum temperature rise rate of the new battery can be compared with the upper limit of the temperature rise rate set in the reference data range. If the maximum temperature rise rate is greater than the upper limit, it is considered a deviation.

6. A customized battery swapping system for trolleybuses according to claim 1, characterized in that, The collaborative debugging and verification module performs collaborative debugging in the following order: first, it performs power-on and communication checks on the low-voltage system; then, it performs pre-charging and insulation status checks on the high-voltage system; and finally, it performs charge-discharge collaborative debugging under both static load and dynamic simulated load conditions.

7. A customized battery swapping system for trolleybuses according to claim 1, characterized in that, The system also includes a fleet data management module, which is an independent module connected to the collaborative debugging and verification module. Specifically, the fleet data management module stores the final design parameters and real-time operation data of any bus after the collaborative debugging and verification module confirms that the collaborative operation is qualified in the database. When performing battery swapping services for other buses of the same model, the final design parameters are called as the initial design parameters of the customized battery design module.

8. A customized battery swapping system for trolleybuses according to claim 1, characterized in that, The collaborative debugging and verification module regulates the coordinated operation of the new battery and the cooling system by: acquiring real-time temperature data of a group of cell areas through multiple temperature sensors deployed inside the new battery, calculating the maximum temperature difference between the cell areas, and controlling the maximum temperature difference to be below 5 degrees Celsius by adjusting the speed of the cooling medium circulation pump or the speed of the cooling fan in the cooling system.

9. A customized battery swapping system for public buses according to claim 1, characterized in that, The preset test conditions used by the collaborative debugging and verification module include two or more high-load operating scenarios. Specifically, the high-load operating scenarios are long-distance continuous uphill operation under full load and high-frequency acceleration and braking scenarios under urban traffic congestion.

10. A customized battery swapping method for trolleybuses, characterized in that, The method includes the following steps: Step a, data acquisition: Through the vehicle diagnostic interface, obtain a set of historical operating parameters of five systems of the bus, including the power system, drive system, electronic control system, energy recovery system and cooling system, and simultaneously extract the communication protocol data of the original battery installed in the bus. Step b, customized battery design: Based on the historical operating parameters and communication protocol data obtained in the data acquisition step, design parameters for the structural dimensions, physical interface type, rated energy density, and charge-discharge characteristic curve of a new battery are generated. The values ​​of the design parameters are limited to a numerical solution space composed of historical operating parameters. Step c, Installation and Adaptation: Based on the design parameters generated in the customized battery design step, the physical installation of the new battery is performed, and a communication link is established between the new battery and the vehicle control system of the bus. When incompatibility of communication protocol data is detected, the communication repeater is activated to perform real-time protocol conversion on the data signals in the communication link. Step d, Collaborative debugging and verification: After the communication link is established, monitor and regulate the collaborative operation of the five systems of the new battery and the bus under the preset test conditions in step a. During this collaborative operation, collect a set of real-time operating data. When it is determined that the real-time operating data deviates from a benchmark data range calculated from historical operating parameters, generate parameter optimization instructions and feed them back to the customized battery design step.

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