A lithium battery cloud detection system for high-altitude pure electric heavy truck testing

Abstract

The application relates to the technical field of battery management, and discloses a lithium battery cloud detection system for high-altitude pure electric heavy truck testing, which comprises a vehicle-mounted data acquisition module, an edge computing gateway and a cloud intelligent analysis platform.The vehicle-mounted data acquisition module is used for collecting electric variable data, vehicle operation data and environmental data of a pure electric heavy truck lithium battery system in real time.The edge computing gateway is in communication connection with sensors of the vehicle-mounted data acquisition module at an input end, and is provided with a wireless communication unit at an output end, and is used for pre-processing, compressing and encrypting the collected data.The cloud intelligent analysis platform is in communication connection with the edge computing gateway through the wireless communication unit, is used for receiving and storing the processed data, and is used for analyzing the data to generate a battery health state evaluation, an energy consumption analysis report, a charging strategy suggestion and an infrastructure planning suggestion.The application significantly improves the testing depth, operation safety and economy of a pure electric heavy truck in an extreme environment.

Description

Technical Field

[0001] This invention relates to the field of battery management technology, and in particular to a cloud-based testing system for lithium batteries used in high-altitude pure electric heavy-duty truck testing. Background Technology

[0002] Existing pure electric heavy-duty trucks and their battery management systems are primarily designed for plains and normal temperature environments. When applied to harsh environments such as high altitudes and low temperatures, these conditions significantly and adversely affect the performance of lithium batteries. Low air pressure and low oxygen levels impair heat dissipation efficiency, while low temperatures directly increase electrolyte viscosity, internal resistance, and activity, leading to a sharp reduction in usable capacity, decreased charging efficiency, and severe range degradation. Traditional on-board battery management subsystems rely on a single data dimension, monitoring only basic battery electrical parameters. They lack deep integration with multi-dimensional information, including real-time vehicle load, operating altitude, and ambient temperature and humidity, making it impossible to accurately assess and provide early warnings about the battery's true condition under complex high-altitude and low-temperature conditions.

[0003] Secondly, high-altitude areas are often remote, with uneven mobile communication network coverage and weak, unstable signals. Traditional cloud-based monitoring systems, which rely on continuous and stable network connections, cannot guarantee the continuity and integrity of data uploads in such areas. A large amount of critical operational data in extreme environments is lost due to network interruptions, leading to distorted data analysis and failing to provide a reliable basis for technological improvements and operational decisions.

[0004] Finally, existing technologies lack sufficient intelligence and data analysis is lagging. Currently, most methods rely on manual recording and offline post-event analysis, which is inefficient and cannot provide real-time diagnosis and decision support. For example, it is impossible to accurately quantify the specific impact of low temperatures on charging power, analyze the abnormal degradation patterns of battery SOC during vehicle idling, compare the significant differences in energy consumption caused by different driver operating habits in real time, or provide data-driven solutions for the scientific planning of charging infrastructure.

[0005] Therefore, this invention proposes a cloud-based testing system for lithium batteries used in high-altitude pure electric heavy-duty truck testing. Summary of the Invention

[0006] The purpose of this invention is to solve the problems of single data acquisition dimension, easy transmission interruption, delayed analysis and lack of basis for infrastructure planning in the existing technology, and to propose a cloud-based testing system for lithium batteries for high-altitude pure electric heavy trucks.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: a cloud-based testing system for lithium batteries used in high-altitude pure electric heavy-duty truck testing, comprising:

[0008] The on-board data acquisition module is used to collect real-time electrical variable data, vehicle operation data, and environmental data of the lithium battery system of pure electric heavy-duty trucks;

[0009] The edge computing gateway has an input end that communicates with the sensors of the vehicle data acquisition module, and an output end equipped with a wireless communication unit for preprocessing, compressing and encrypting the acquired data.

[0010] The cloud-based intelligent analysis platform establishes a communication connection with the edge computing gateway through a wireless communication unit. It receives and stores processed data, and analyzes the data to generate battery health status assessments, energy consumption analysis reports, charging strategy recommendations, and infrastructure planning recommendations.

[0011] Furthermore, the vehicle-mounted data acquisition module includes:

[0012] The battery management subsystem sensor unit is integrated inside the vehicle battery pack and is used to collect electrical variable data, including the total voltage of the battery pack, the total current, the voltage of each individual cell, the internal temperature of the battery pack, the state of charge, and the estimated state of health.

[0013] The vehicle CAN bus interface unit is connected to the vehicle controller local area network bus and is used to obtain vehicle operation data from the vehicle network. The vehicle operation data includes the real-time output power of the motor, vehicle speed, load data provided by the vehicle load sensor, air pump working status and brake pedal opening signal.

[0014] The environmental sensor unit, fixed to the outside of the vehicle, integrates a temperature sensor, a humidity sensor, and a barometer. It is used to collect temperature and humidity data of the external environment of the vehicle and to calculate the real-time altitude data of the vehicle by using the air pressure value measured by the barometer.

[0015] Furthermore, the edge computing gateway is an embedded hardware device installed in the vehicle's driver's cab, including a microprocessor, memory, and wireless communication unit;

[0016] The edge computing gateway has embedded edge computing programs to realize data preprocessing, data compression and encryption, and breakpoint resume management;

[0017] The data preprocessing is used to clean and filter the raw data received from the vehicle data acquisition module, and remove outliers and noise interference.

[0018] The data compression and encryption are used to convert the data into binary format and compress it, and to encrypt it using an encryption algorithm;

[0019] The breakpoint resume management is used to continuously monitor network signal strength. When the signal strength is lower than a preset threshold, the data is automatically cached in the memory. After the network connection is restored, the connection is automatically re-established and the cached data is uploaded.

[0020] Furthermore, the cloud-based intelligent analysis platform includes:

[0021] A data storage unit is used to realize the classified storage and query of heterogeneous data, including time-series data and structured data;

[0022] The battery health assessment unit is used to take historical sequence data as input and output the predicted value of battery SOH and the confidence interval of remaining life, so as to realize battery life warning.

[0023] The energy consumption analysis unit has a built-in multiple linear regression model, which is used to construct a power consumption prediction model with load, altitude difference, and ambient temperature as independent variables, and calculate the deviation.

[0024] The charging strategy optimization unit has a rule-based intelligent decision engine, which receives real-time battery temperature data and dynamically outputs different charging power commands based on temperature thresholds.

[0025] The infrastructure planning unit integrates Geographic Information System (GIS) technology; it is used to spatially visualize and render the low SOC location information of vehicles, generate heat maps, and combine algorithms to calculate the site coordinates and service radius of charging stations.

[0026] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0027] This invention, by deploying an onboard data acquisition module that integrates battery management subsystem sensors, vehicle CAN bus interface, and environmental sensors, enables synchronous and real-time acquisition of data across all dimensions of "battery-vehicle-environment," providing a comprehensive and reliable data foundation for accurately analyzing the true performance of batteries under complex coupled conditions of high altitude and low temperature.

[0028] This invention, by deploying an edge computing gateway with data preprocessing, compression encryption, and breakpoint resume functions on the vehicle, can effectively address the problem of unstable network signals in high-altitude areas, ensuring that all critical test data can be uploaded to the cloud completely and reliably, thus completely solving the problem of data loss.

[0029] This invention constructs a cloud-based intelligent analysis platform and sets up functional units for battery health assessment, energy consumption analysis, charging strategy optimization, and infrastructure planning. It can perform in-depth mining and intelligent analysis of massive amounts of data, realizing the transformation from raw data to intelligent decision-making. It outputs data such as battery life prediction, driver behavior scores, optimal charging strategies, and scientific site selection suggestions for charging stations, greatly improving the intelligence level of testing and decision-making efficiency. Attached Figure Description

[0030] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 This is a schematic diagram of the system configuration provided in an embodiment of the present invention. Detailed Implementation

[0032] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a cloud-based lithium battery testing system for high-altitude pure electric heavy-duty trucks proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0033] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0034] The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0035] The following description, in conjunction with the accompanying drawings, details the specific solution of the cloud-based testing system for lithium batteries used in high-altitude pure electric heavy-duty trucks provided by this invention.

[0036] Example

[0037] Please see Figure 1 The diagram illustrates a system configuration of a cloud-based testing system for lithium batteries used in high-altitude pure electric heavy-duty trucks, according to an embodiment of the present invention, comprising:

[0038] I. Vehicle-mounted data acquisition module

[0039] The on-board data acquisition module is used to collect real-time electrical variable data, vehicle operation data, and environmental data of the lithium battery system of pure electric heavy-duty trucks;

[0040] The vehicle data acquisition module includes a battery management subsystem sensor unit, a vehicle CAN bus interface unit, and an environmental sensor unit;

[0041] The battery management subsystem sensor unit is integrated inside the vehicle battery pack and is used to collect electrical variable data, including the total voltage and total current of the battery pack, the voltage of each individual cell, the internal temperature of the battery pack, the state of charge, and the estimated state of health.

[0042] The vehicle CAN bus interface unit connects to the vehicle controller local area network bus and is used to obtain vehicle operation data from the vehicle network. The vehicle operation data includes the real-time output power of the motor, vehicle speed, load data provided by the vehicle load sensor, air pump working status and brake pedal opening signal.

[0043] The environmental sensor unit, fixed to the outside of the vehicle, integrates a temperature sensor, a humidity sensor, and a barometer. It is used to collect temperature and humidity data of the external environment of the vehicle and to calculate the real-time altitude data of the vehicle by using the air pressure value measured by the barometer.

[0044] It should be noted that the battery management subsystem sensor unit is deeply integrated inside the vehicle battery pack, directly contacting the battery cells or connected via wiring harnesses, thereby enabling it to obtain the most direct and raw electrical variable data.

[0045] Total voltage and total current: used to calculate the overall power and energy throughput of the battery pack.

[0046] Individual cell voltage: Used to monitor the consistency between cells, it is a key indicator for judging the health status of the battery and whether a fault has occurred. Any abnormal voltage of any cell may lead to the risk of thermal runaway.

[0047] Battery pack internal temperature: Multiple temperature sensors are typically arranged to monitor the temperature at different locations of the cells, bus connection points, and coolant inlet and outlet. In high-altitude and low-temperature environments, internal temperature is a core parameter for judging battery activity, charge and discharge capacity, and the working efficiency of the thermal management system.

[0048] State of Charge (SOC) estimation: The percentage of remaining battery charge estimated by the battery management subsystem using the ampere-hour integral method, open-circuit voltage method, or Kalman filter algorithm is a direct reflection of the vehicle's driving range. Low temperatures can lead to inaccurate SOC estimation, so continuous monitoring is necessary.

[0049] Battery health status assessment: The battery management subsystem estimates the battery health percentage based on comprehensive data such as cycle count, internal resistance change, and capacity decay rate, reflecting the battery life; this system collects this data for more accurate long-term health prediction in the cloud.

[0050] Real-time output power of the motor: directly reflects the current energy consumption level of the vehicle and is the key to calculating instantaneous energy consumption and total energy consumption.

[0051] Vehicle speed: A basic operating parameter used to calculate mileage and average speed.

[0052] Load data provided by the vehicle weight sensor: This is one of the core variables in energy consumption analysis. The energy consumption of heavy trucks is strongly correlated with load. This invention provides the possibility of establishing an accurate energy consumption model by directly reading this data.

[0053] Air pump operating status: The air pump provides air to the braking system, and its operating frequency and power consumption indirectly reflect road conditions and driving behavior.

[0054] Brake pedal opening signal: used to determine driving behavior and, in conjunction with the energy recovery system status, analyze the impact of driving behavior on energy consumption.

[0055] Temperature and humidity sensors are used to monitor the ambient temperature and humidity outside the vehicle. Ambient temperature is the primary external factor that causes battery performance degradation and reduced driving range, and its data must be recorded accurately.

[0056] Barometer: By measuring atmospheric pressure and calculating the vehicle's real-time altitude data based on the correlation between air pressure and altitude, this is a key innovation of the invention. Altitude directly affects air density and heat dissipation efficiency. More importantly, the altitude difference of the route is another core variable affecting the energy consumption of heavy trucks. This system provides invaluable data support for subsequent analysis of the "altitude difference-energy consumption" relationship by collecting altitude data in real time.

[0057] II. Edge Computing Gateway

[0058] The edge computing gateway's input end is connected to the sensors of the vehicle-mounted data acquisition module, and its output end is equipped with a wireless communication unit for preprocessing, compressing, and encrypting the acquired data.

[0059] An edge computing gateway is an embedded hardware device installed in the driver's cab of a vehicle, which includes a microprocessor, memory, and wireless communication unit;

[0060] The edge computing gateway embeds edge computing programs to enable data preprocessing, data compression and encryption, and breakpoint resume management.

[0061] Data preprocessing is used to clean and filter the raw data received from the vehicle data acquisition module, and remove outliers and noise interference.

[0062] Data compression and encryption are used to convert data into binary format and compress it, and then encrypt it using encryption algorithms;

[0063] The breakpoint resume management is used to continuously monitor network signal strength. When the signal strength is lower than a preset threshold, the data is automatically cached in the storage. After the network connection is restored, the connection is automatically re-established and the cached data is uploaded.

[0064] It should be noted that the edge computing gateway is an embedded hardware device installed in the vehicle's driver's cab, and its physical components include:

[0065] Microprocessor: It is the "brain" of the gateway. It is a chip that integrates a central processing unit, memory, counter, and various serial / parallel interfaces. It is responsible for executing the fixed edge computing program and completing all computing tasks, including data preprocessing, protocol conversion, and logical judgment. In high-altitude and high-vibration vehicle environments, industrial-grade or automotive-grade processors with wide temperature ranges should be selected to ensure their operational stability and reliability.

[0066] Memory includes volatile memory such as RAM and non-volatile memory such as eMMC, SD card, and solid-state drive; RAM is used for program execution and temporary data caching; non-volatile memory is used to implement breakpoint resume functionality.

[0067] Wireless communication unit: refers to a mobile communication module that supports 4G LTE or 5G NR technology, and is the channel through which the gateway establishes a connection with the cloud platform.

[0068] The edge computing program is stored in the gateway's memory and runs automatically upon power-up, performing the following core functions:

[0069] Data preprocessing: refers to the preliminary cleaning and processing of data locally before uploading. Specific operations include:

[0070] Data cleaning: Remove outliers that clearly do not conform to physical laws, such as instantaneous current values ​​exceeding the physical limits of the battery or drastic temperature changes. These outliers may originate from instantaneous sensor errors or electromagnetic interference.

[0071] Data filtering: Software algorithms are used to smooth the raw data, filter out high-frequency noise interference, retain the true data change trend, and improve data quality. Software algorithms include Kalman filtering and moving average filtering.

[0072] Data compression and encryption: The amount of raw data collected is huge. Compression can significantly reduce the network transmission load and time. Encryption prevents vehicle operation data and location information from being stolen during transmission, thus meeting the requirements of data security.

[0073] Compression: Using algorithms to convert data into a more compact binary format, reducing the size of data packets.

[0074] Encryption: Using encryption algorithms and keys, data is converted into ciphertext to ensure confidentiality during transmission.

[0075] Resumable download management: A network communication mechanism that automatically saves the state of transmitted and untransmitted data when the network connection is interrupted; when the network is restored, it can resume transmission from where it left off without retransmitting all data; it is a core function designed to address network pain points in high-altitude areas, and its workflow is as follows:

[0076] 1. Continuous monitoring: The program monitors the network signal strength of the wireless communication unit in real time.

[0077] 2. Judgment and caching: When the signal strength is lower than the preset threshold, such as -110 dBm, the program determines that the network is unavailable and then automatically caches the subsequently received, pre-processed data packets in the local non-volatile memory.

[0078] 3. Waiting and recovery: The program continuously attempts to reconnect, and once it detects that the network signal has been restored and stabilized, it automatically re-establishes the TCP / IP connection with the cloud.

[0079] 4. Resume transmission: Read the cached data packets from the memory and continue uploading from the first data packet after the last successful transmission, ensuring the integrity and continuity of the data sequence.

[0080] III. Cloud-based Intelligent Analysis Platform

[0081] The cloud-based intelligent analytics platform establishes a communication connection with the edge computing gateway through a wireless communication unit. This connection is used to receive and store processed data, and to analyze the data to generate battery health status assessments, energy consumption analysis reports, charging strategy recommendations, and infrastructure planning suggestions.

[0082] The cloud-based intelligent analysis platform includes a data storage unit, a battery health assessment unit, an energy consumption analysis unit, a charging strategy optimization unit, and an infrastructure planning unit.

[0083] 1. Data storage unit

[0084] The data storage unit is used to realize the classified storage and query of heterogeneous data, including time-series data and structured data;

[0085] The data storage unit includes a data classification subunit, a time-series database read / write subunit, and a relational database read / write subunit;

[0086] The data classification subunit automatically classifies received data packets into time-series data and structured data based on their source and type.

[0087] The time-series database read / write subunit is used to write timestamped sensor stream data into the time-series database and provides a query interface based on time range.

[0088] The relational database read-write sub-unit is used to write structured data into a relational database and provide an associated query interface.

[0089] It should be noted that time-series data: usually refers to those streaming data that are strongly related to time, continuously generated by sensors, and have a large amount of data. The characteristics are that data points carry timestamps and are rarely updated after being written. It is mainly used to describe the state of a device at a certain time point; it includes data such as battery voltage, current, temperature, vehicle speed, and GPS coordinates collected multiple times per second.

[0090] Structured data: refers to those data with clear relationships, fixed formats, and usually from business operations. The characteristics are that there are logical associations between data, and frequent associated queries and updates are required; for example: vehicle information table (license plate number, VIN code, model), driver information table (driver ID, name), task work order information (task ID, departure place, destination, planned load).

[0091] Write into the time-series database: Continuously write these sensor stream data with timestamps into the time-series database in a highly optimized manner. The time-series database is a database specially designed to process this kind of time-series data, with extremely fast writing speed and extremely high compression efficiency, and is very suitable for storing a large amount of vehicle sensor data.

[0092] Time-range-based query interface: This sub-unit provides a fast query interface based on time range to the upper-layer application, which means that the analysis module can easily query all the running data of a certain vehicle within a specific time period. For example, "Query all the battery temperature data of the vehicle with license plate number Tibetan A12345 from 14:00 to 15:00 on January 10, 2025". The execution efficiency of this kind of query in the time-series database is much higher than that of traditional relational databases.

[0093] Write into the relational database: Write vehicle static information, driver identity information, and task work order information into the relational database. The relational database stores data in the form of tables, supports transaction operations, and can ensure the consistency and integrity of data.

[0094] Associated query interface: Provide a powerful associated query interface to the upper-layer application, which means that the system can execute complex queries. For example, "Query what are the three routes with the highest average energy consumption in all transportation tasks of driver Zhang San in January 2025". This kind of query requires associating the driver table, task table, and energy consumption data table.

[0095] 2. Battery health assessment unit

[0096] The battery health assessment unit is used to take historical sequence data as input, output the predicted value of battery SOH and the confidence interval of the remaining life, and realize battery life warning;

[0097] The battery health assessment unit includes a data sequence construction subunit, a model inference subunit, and a lifespan early warning subunit;

[0098] The data sequence construction subunit is used to extract historical voltage, current, temperature and SOC data of a specified vehicle from the time series database and process them into an equally spaced time series.

[0099] The model inference subunit is used to input the time series data into a pre-trained time series prediction model and output the predicted value of the battery health state (SOH).

[0100] The lifespan warning subunit is used to compare the predicted SOH value with a preset threshold. When the predicted value is lower than the threshold, a battery lifespan warning message is generated.

[0101] It should be noted that, according to the evaluation instructions, the data sequence construction subunit extracts the original data of the specified vehicle within a historical period specified by the user or preset by the system from the time-series database of the data storage unit. The data is a sequence that changes over time, including the total battery voltage, total current, temperature, and state of charge.

[0102] Subsequently, the data sequence construction sub-unit performs data normalization. Since the data may have slight intervals due to network transmission, this sub-unit will resample the data into an evenly spaced time series, such as one data point per minute, through an interpolation algorithm. This process eliminates irregularities on the time axis.

[0103] The model inference subunit receives equally spaced time series data processed by the data sequence construction subunit. It loads a pre-trained time series prediction model, which is a deep recurrent neural network, such as a long short-term memory network. The model has been trained in the cloud using a large amount of historical battery data and has learned to capture subtle features of battery performance degradation from the change patterns of voltage, current, temperature, and SOC.

[0104] The model inference subunit inputs new time series data into the trained model. The model calculates and outputs a predicted value about the battery's state of health. This value is a percentage, representing the percentage of the battery's maximum usable capacity under current usage conditions relative to its rated capacity when it was brand new. For example, a predicted state of health (SOH) of 80 percent means that the battery can currently only be charged to a maximum of 80 percent of its original capacity when it was brand new.

[0105] The lifespan warning subunit receives the SOH prediction value calculated by the model inference subunit. It has a preset operation and maintenance strategy threshold, which is usually set by battery experts based on battery characteristics and operational needs. For example, it may be set that when the SOH is below 70%, the battery is no longer suitable for the current heavy-load transportation task.

[0106] The lifespan warning subunit compares the real-time predicted value with the preset threshold. Once it detects that the SOH predicted value is continuously lower than the safety threshold, it automatically triggers the alarm process and generates a structured battery lifespan warning message. This message includes the vehicle identification number, the current SOH predicted value, the threshold, the predicted remaining effective cycle count or remaining usage days confidence interval, and suggests battery inspection or maintenance.

[0107] 3. Energy Consumption Analysis Unit

[0108] The energy consumption analysis unit has a built-in multiple linear regression model, which is used to construct a power consumption prediction model with load, altitude difference, and ambient temperature as independent variables, and to calculate the deviation.

[0109] The energy consumption analysis unit includes a baseline model calculation subunit, a deviation analysis subunit, and a driving behavior correlation subunit;

[0110] The baseline model calculation subunit is used to call the multiple linear regression model to calculate the expected power consumption of the current task based on the real-time load, cumulative altitude difference and average ambient temperature.

[0111] The deviation analysis subunit is used to obtain the actual power consumption from the data storage module and calculate the deviation from the expected power consumption.

[0112] The driving behavior association subunit is used to retrieve the frequency of vehicle rapid acceleration and deceleration events within the time period corresponding to the deviation, and generate a driver behavior score report that includes deviation cause analysis and optimization suggestions.

[0113] It should be noted that the core of the benchmark model's calculation subunit is a pre-trained multiple linear regression model. This mathematical model characterizes the quantitative relationship between the energy consumption of pure electric heavy-duty trucks and three core influencing factors:

[0114] Real-time load: This is the most important influencing factor. The greater the load, the more energy is required to overcome rolling resistance and inertia.

[0115] Cumulative elevation difference: This is a key innovative consideration for transportation in high-altitude mountainous areas. The higher the cumulative elevation gain, the more work is done to overcome gravity, resulting in a significant increase in energy consumption. This data comes from the elevation calculated by the environmental sensor unit.

[0116] Average ambient temperature: Low temperatures lead to increased battery internal resistance and increased energy consumption for air conditioning heating, which are important environmental factors affecting energy consumption.

[0117] After each task, the baseline model calculation subunit obtains the specific values ​​of the three independent variables for that task, substitutes them into the model, and calculates a theoretically expected power consumption value. This value represents the power consumption that a standard driving level should consume under the same load, the same mountain road, and the same temperature. It serves as a baseline for measuring the energy efficiency of this task.

[0118] The deviation analysis subunit obtains the total amount of electricity actually consumed by the vehicle in this mission from the data storage unit; then, it performs a simple calculation: the deviation is equal to the actual electricity consumption minus the expected electricity consumption, and then divided by the expected electricity consumption, usually expressed as a percentage.

[0119] A positive deviation indicates that the actual consumption is higher than the theoretical baseline, resulting in energy waste.

[0120] A negative deviation indicates that the actual consumption is lower than the theoretical baseline, demonstrating excellent energy efficiency.

[0121] This deviation percentage is a highly quantified, fair and objective performance indicator that eliminates the influence of objective factors such as route, load, and weather, making the energy consumption performance of different tasks, different vehicles, and different drivers comparable.

[0122] The driving behavior association subunit is activated when the deviation analysis subunit detects a significant positive deviation. It first retrieves vehicle operation data within the task time period corresponding to the deviation, focusing on signals that can reflect driving behavior, mainly the frequency and intensity of rapid acceleration and rapid deceleration events. Rapid acceleration will cause a huge instantaneous current and a sharp increase in energy consumption; rapid deceleration wastes the kinetic energy that could have been recovered.

[0123] Next, the driving behavior correlation subunit performs correlation analysis on the frequency and deviation of bad driving events. If a high positive correlation is found between the two, it can be determined that the main reason for the excessive energy consumption is improper driving operation.

[0124] Ultimately, the driving behavior association subunit can automatically generate a structured driver behavior score report. The report includes deviation data and will clearly indicate that "due to frequent rapid acceleration, energy consumption deviates from the benchmark by XX kWh" and provide specific optimization suggestions, such as "It is recommended to maintain smooth acceleration and anticipate road conditions to reduce unnecessary braking".

[0125] 4. Charging Strategy Optimization Unit

[0126] The charging strategy optimization unit has a rule-based intelligent decision engine that receives real-time battery temperature data and dynamically outputs different charging power commands based on temperature thresholds.

[0127] The charging strategy optimization unit includes a temperature monitoring subunit, a rule base, and an instruction issuance subunit;

[0128] The temperature monitoring subunit is used to receive and monitor the internal temperature data of the battery pack uploaded by the vehicle in real time.

[0129] The rule base is used to store preset charging power control rules. The rule is defined as follows: if the battery temperature is lower than the first temperature threshold, a first charging power command is output; if the battery temperature is higher than or equal to the second temperature threshold, a second charging power command is output. The second charging power is greater than the first charging power.

[0130] The instruction issuing subunit is used to issue charging power instructions generated according to the rule base to the vehicle terminal or charging pile controller.

[0131] It should be noted that the temperature monitoring subunit maintains a real-time connection with the time-series database in the data storage unit, continuously receiving and parsing the latest internal temperature data of the battery pack uploaded by the target vehicle.

[0132] The temperature monitoring subunit focuses not on historical temperatures, but on the current, real-time temperature value, especially the battery temperature when the vehicle is about to start charging or is already charging.

[0133] The temperature monitoring subunit aggregates data from multiple temperature measurement points within the battery pack, typically selecting the lowest or average temperature as the basis for decision-making to ensure a comprehensive reflection of the battery's overall thermal state. Its monitoring is high-frequency and continuous, providing the most timely and accurate data input for subsequent decision-making.

[0134] The rule base stores a series of predefined charging power control rules, which are jointly formulated based on electrochemical principles, battery manufacturer specifications, and experience verified in winter tests.

[0135] Explicitly defined rules include:

[0136] If the battery temperature is lower than the first temperature threshold, the system will output the first charging power command.

[0137] If the battery temperature is higher than or equal to the second temperature threshold, the system will output a second charging power command, and the value of the second charging power will be greater than the value of the first charging power.

[0138] The first temperature threshold is usually set near the low-temperature critical point where the battery can be fast charged, such as 0 or 5 degrees Celsius.

[0139] The initial charging power is a low power, typically tens of kilowatts, designed to preheat the battery with a small current to prevent lithium metal deposition that could damage the battery.

[0140] The second temperature threshold is a point at which the battery reaches its ideal operating temperature, such as 15 or 20 degrees Celsius.

[0141] The second charging power is the maximum fast charging power that the vehicle and charging station can support, such as 170 kilowatts or higher, designed to quickly restore the battery's charge after it reaches its optimal temperature.

[0142] The instruction issuing subunit receives the charging power instruction generated by the rule base based on the real-time temperature. Its core function is to translate this cloud-based decision into concrete action.

[0143] The instruction issuing subunit sends the instruction to the vehicle's onboard terminal display screen in a standardized data protocol format via a cloud communication link to prompt the driver; or it sends it directly to the charging pile's controller.

[0144] When the command reaches the charging pile controller, fully automatic intelligent charging management can be achieved: when the charging pile receives the "preheat" command, it will automatically start low-power charging, and when it receives the "fast charging" command, it will automatically switch to full-power output.

[0145] 5. Infrastructure Planning Unit

[0146] The infrastructure planning unit integrates Geographic Information System (GIS) technology to spatially visualize and render vehicle low SOC location information, generate heat maps, and combine algorithms to calculate the site coordinates and service radius of charging stations.

[0147] The infrastructure planning unit includes a data aggregation subunit, a heat map generation subunit, and a site selection calculation subunit;

[0148] The data aggregation subunit is used to aggregate and extract the geographical coordinates of a vehicle when its SOC is lower than a preset alarm value from historical vehicle data.

[0149] The heatmap generation sub-unit, based on GIS technology, calculates and renders the spatial density of geographic location coordinates on an electronic map to generate a low SOC hotspot distribution map.

[0150] The location calculation subunit is used to run the location optimization algorithm and calculate the candidate geographical coordinates and suggested service radius of one or more charging stations based on the low SOC hotspot distribution map.

[0151] It should be noted that the core task of the data aggregation subunit is to filter out low battery status data generated by all vehicles in all historical tasks from the data storage unit.

[0152] The data aggregation subunit has clear filtering criteria: it continuously scans historical data and extracts data records when the vehicle battery state of charge is lower than a preset alarm threshold, such as 20% or 15%.

[0153] Each such record signifies that the vehicle faced range anxiety at that time. The data aggregation subunit extracts two key pieces of information from these records: the vehicle identification number and the high-precision latitude and longitude geographic coordinates recorded at that time. Through long-term accumulation, a data set containing tens of thousands of "low battery location points" can be formed, with each point representing a real range anxiety event.

[0154] The heatmap generation subunit receives a massive dataset of geographic location coordinates provided by the data aggregation subunit and overlays these discrete coordinate points onto a high-precision electronic map base.

[0155] Subsequently, the heatmap generation sub-unit runs a spatial density calculation algorithm to calculate the density of points around each region on the map. The denser the points in a region, the darker the color, indicating that low battery events occur more frequently in that region; the sparser the points in a region, the lighter the color, indicating that fewer events occur.

[0156] Finally, the heatmap generation subunit renders and generates a low SOC hotspot distribution map. This colorful heatmap intuitively reveals the "hotspot areas" or "range black holes" where vehicles are most prone to range problems in the entire operating route network, making the abstract data clear at a glance.

[0157] The location calculation subunit runs a location optimization algorithm, such as the maximum coverage model algorithm. The optimization goal of the algorithm is to find one or more optimal locations so that after charging stations are built at these locations, they can cover as many low-power hotspot areas as possible.

[0158] The algorithm simulates and calculates the coverage effect of different candidate locations on an electronic map, and finally outputs the coordinates of one or more optimal candidate geographical locations. For each candidate location, the algorithm also calculates a suggested service radius based on the distribution range of its surrounding hotspot areas, clarifying the effective coverage range of the charging station, thereby providing a reference for planning the grid access capacity and the number of charging piles.

[0159] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A lithium battery cloud detection system for high-altitude pure electric heavy truck testing, characterized in that, The application relates to a pure electric heavy truck lithium battery system data collection method. The vehicle-mounted data collection module comprises a battery management subsystem sensor unit integrated in a vehicle battery pack for collecting electric variable data, the electric variable data comprising total voltage, total current, single battery voltage, battery pack internal temperature, state of charge (SOC) and state of health (SOH) evaluation of a battery pack; a vehicle controller area network (CAN) bus interface unit connected to a vehicle CAN bus for obtaining vehicle operation data from a vehicle network, the vehicle operation data comprising real-time motor output power, vehicle speed, load data provided by a vehicle load sensor, air pump working state and brake pedal opening degree signal; an environmental sensor unit fixed outside the vehicle and integrated with a temperature sensor, a humidity sensor and a barometer for collecting temperature and humidity data of an external environment of the vehicle and calculating real-time altitude data of the vehicle through a barometric pressure value measured by the barometer; an edge computing gateway with an input end in communication connection with sensors of the vehicle-mounted data collection module and a wireless communication unit arranged at an output end for pre-processing, compressing and encrypting collected data; the edge computing gateway is an embedded hardware device installed in a vehicle cab and comprises a microprocessor, a memory and a wireless communication unit; the edge computing gateway is solidified with an edge computing program for realizing data preprocessing, data compression and encryption and breakpoint resume transmission management; the data preprocessing is used for cleaning, filtering, eliminating abnormal values and noise interference of original data received from the vehicle-mounted data collection module; the data compression and encryption are used for converting data into a binary format, compressing the data and encrypting the data by using an encryption algorithm; and the breakpoint resume transmission management is used for continuously monitoring network signal strength, automatically caching data to the memory when the signal strength is lower than a preset threshold, automatically re-establishing a connection and uploading cached data after network connection is restored. ​ ​ ​ The cloud-based intelligent analysis platform establishes a communication connection with the edge computing gateway through a wireless communication unit. It receives and stores processed data, and analyzes the data to generate battery health status assessments, energy consumption analysis reports, charging strategy recommendations, and infrastructure planning recommendations. The cloud-based intelligent analysis platform includes: a data storage unit for classifying, storing, and querying heterogeneous data, including time-series data and structured data; a battery health assessment unit that takes historical sequence data as input and outputs predicted battery SOH values ​​and confidence intervals for remaining lifespan, enabling battery lifespan early warning; an energy consumption analysis unit with a built-in multiple linear regression model that uses load, altitude difference, and ambient temperature as independent variables to construct a power consumption prediction model and calculate the deviation; a charging strategy optimization unit with a rule-based intelligent decision engine that receives real-time battery temperature data and dynamically outputs different charging power commands based on temperature thresholds; and an infrastructure planning unit that integrates Geographic Information System (GIS) technology to spatially visualize and render vehicle low SOC location information, generate heat maps, and calculate the location coordinates and service radius of charging stations using algorithms. 2.The high-altitude pure electric heavy truck testing lithium battery cloud detection system according to claim 1, characterized in that, The data storage unit includes: The data classification subunit automatically classifies received data packets into time-series data and structured data based on their source and type. The time-series database read / write subunit is used to write timestamped sensor stream data into the time-series database and provides a query interface based on time range. The relational database read / write subunit is used to write structured data into a relational database and provides an interface for related queries. 3.The high-altitude pure electric heavy truck testing lithium battery cloud detection system according to claim 1, characterized in that, The battery health assessment unit includes: The data sequence construction subunit is used to extract historical voltage, current, temperature and SOC data of a specified vehicle from the time series database and process them into an equally spaced time series. The model inference subunit is used to input the time series data into a pre-trained time series prediction model and output the predicted value of the battery health state (SOH). The lifespan warning subunit is used to compare the predicted SOH value with a preset threshold. When the predicted value is lower than the threshold, a battery lifespan warning message is generated. 4.The high-altitude pure electric heavy truck testing lithium battery cloud detection system according to claim 1, characterized in that, The energy consumption analysis unit includes: The baseline model calculation subunit is used to call the multiple linear regression model to calculate the expected power consumption of the current task based on the real-time load, cumulative altitude difference and average ambient temperature. The deviation analysis subunit is used to obtain the actual power consumption from the data storage module and calculate the deviation from the expected power consumption. The driving behavior association subunit is used to retrieve the frequency of vehicle rapid acceleration and deceleration events within the time period corresponding to the deviation, and generate a driver behavior score report that includes deviation cause analysis and optimization suggestions. 5.The high-altitude pure electric heavy truck testing lithium battery cloud detection system according to claim 1, characterized in that, The charging strategy optimization unit includes: The temperature monitoring subunit is used to receive and monitor the internal temperature data of the battery pack uploaded by the vehicle in real time. A rule base is used to store preset charging power control rules. The rule is defined as follows: if the battery temperature is lower than a first temperature threshold, a first charging power command is output; if the battery temperature is higher than or equal to a second temperature threshold, a second charging power command is output. The second charging power is greater than the first charging power. The instruction issuing subunit is used to issue charging power instructions generated according to the rule base to the vehicle terminal or charging pile controller. 6.The high-altitude pure electric heavy truck testing lithium battery cloud detection system according to claim 1, characterized in that, The infrastructure planning unit includes: The data aggregation subunit is used to aggregate and extract the geographical coordinates of a vehicle when its SOC is lower than a preset alarm value from historical vehicle data. The heatmap generation sub-unit, based on GIS technology, calculates and renders the spatial density of geographic location coordinates on an electronic map to generate a low SOC hotspot distribution map. The location calculation subunit is used to run the location optimization algorithm and calculate the candidate geographical coordinates and suggested service radius of one or more charging stations based on the low SOC hotspot distribution map.

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