Lithium battery full life cycle health safety management platform

Abstract

The invention discloses a lithium battery full life cycle health safety management platform which comprises a data acquisition module, a capacitance test module, a deep discharge evaluation module, an internal resistance measurement module, a temperature control module, a graph line module, a model prediction module, an early warning module and an Internet of Things module. Through systematic monitoring and analysis, potential problems can be identified in time, the use of the battery is optimized, the service life of the battery is prolonged, the environmental influence is reduced, the safety and performance of the lithium battery can be managed more effectively, and the risk of accidents is reduced.

Description

Technical Field

[0001] The present invention belongs to the field of lithium battery supervision, and specifically, relates to a lithium battery full life cycle health and safety management platform. Background Art

[0002] Lithium batteries are a type of battery that uses lithium metal or lithium alloy as positive / negative electrode materials and a non-aqueous electrolyte solution. Lithium batteries can be roughly divided into two categories: lithium metal batteries and lithium-ion batteries. Lithium-ion batteries do not contain metallic lithium and are rechargeable.

[0003] The battery life cycle refers to the overall life of the battery, including charging and discharging cycles. In other words, the number of cycles a battery can go through before it starts to lose power is called the battery life cycle. Through life cycle management, the battery life cycle can be effectively monitored, problems can be discovered in time and measures can be taken to optimize the battery's efficiency and extend its life cycle. This not only helps to improve economic benefits, but also reduces environmental impact.

[0004] However, both lithium-ion batteries and lead-acid batteries are rechargeable batteries, but they have different advantages and disadvantages. The life cycles of lithium-ion batteries and lead-acid batteries are also different in characteristics, performance parameters and management requirements. Therefore, using the same life cycle management platform, the management and prediction efficiency is inaccurate. The life cycle monitoring method for lithium batteries should comprehensively consider its characteristics and application requirements, and should adopt high-precision monitoring equipment and intelligent management systems to ensure the safe, efficient and sustainable use of batteries. Summary of the invention

[0005] In view of the above defects, the present invention provides a lithium battery full life cycle health and safety management platform, including:

[0006] A data acquisition module for real-time data collection of lithium batteries and storage of data through a storage module;

[0007] Capacity test module and deep discharge assessment module for regular evaluation of lithium battery capacity;

[0008] Internal resistance measurement module for calculating and monitoring the internal resistance of lithium batteries;

[0009] Temperature control module for testing the temperature characteristics and thermal management of lithium batteries;

[0010] A graph module for visual analysis of capacity decay and internal resistance changes of lithium batteries;

[0011] Model prediction module for model prediction and model optimization of lithium battery SOH;

[0012] An early warning module for monitoring and reminding various thresholds of lithium batteries;

[0013] And an IoT module for cloud data communication.

[0014] Furthermore, the data acquisition module includes the acquisition of the voltage, current, temperature and charge and discharge state parameters of the lithium battery, and the acquisition frequency is once per minute;

[0015] The storage module adopts a cloud database.

[0016] Furthermore, the electric capacity test module integrates a standardized charge and discharge program to perform a charge and discharge cycle test on the lithium battery. At this time, the parameters collected by the data acquisition module include charging voltage, charging current, discharge voltage, discharge current, charging time, discharge time and actual capacity, and the relative capacity is calculated by the actual capacity. The calculation formula of the actual capacity is:

[0017] Actual capacity = discharge current * discharge time. The unit of actual capacity is Ah, the unit of discharge current is A, and the unit of discharge time is h.

[0018] Relative capacity = (actual capacity / rated capacity) * 100, the unit of relative capacity is %, the unit of actual capacity is Ah, and the unit of rated capacity is Ah;

[0019] The deep discharge evaluation module is used to perform multiple charge and discharge cycle tests at different deep discharge levels and set different deep discharge values. Each deep discharge performs a quantitative cycle and records the number of cycles, actual capacity and relative capacity.

[0020] Furthermore, the internal resistance measurement module performs measurement by a direct current internal resistance measurement method, calculates an internal resistance value according to the measured data, and compares and evaluates the calculated internal resistance value with a rated internal resistance value.

[0021] Furthermore, the temperature control module is used to measure the capacity and internal resistance of the lithium battery at different temperatures during the charge and discharge cycle test, and is also used to control the active temperature control device to maintain a monitoring temperature of 0°C to 45°C.

[0022] Furthermore, the graph module generates a capacity decay curve graph and an internal resistance change graph for the data of the capacitance test module through an integrated statistical analysis tool.

[0023] Furthermore, the model prediction module constructs a prediction model for the SOH of the lithium battery through a random forest model, and adopts k-fold cross validation.

[0024] Furthermore, the thresholds of the early warning module include but are not limited to the internal resistance value, temperature value, and voltage value during the charge and discharge cycle test, and are also integrated with fault tree analysis software for finding out the cause of the fault and generating corresponding preventive measures to control the temperature control module.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] 1. Through systematic monitoring and analysis, potential problems can be identified in a timely manner, battery use can be optimized, battery life can be extended, and environmental impact can be reduced. The safety and performance of lithium batteries can be managed more effectively, and the risk of accidents can be reduced;

[0027] 2. Compared with the relatively relaxed working conditions of lead-acid batteries, this technical solution aims at the health and safety of the life cycle of lithium batteries. It can realize automatic charge and discharge management, automatically adjust the charging strategy according to the battery status, and avoid overcharging and over-discharging; it can actively issue an alarm to remind users to perform maintenance or replace the battery, thereby extending the battery life and ensuring safety; through comprehensive evaluation of multi-dimensional data such as internal resistance, temperature, SOC (state of charge), SOH (health state), etc., it provides more comprehensive safety management; use machine learning to predict the battery status and identify potential faults and risks in advance;

[0028] 3. Lithium batteries are more prone to thermal runaway, so the temperature control module is used to detect and prevent overheating, and the data acquisition module can quickly respond to real-time monitoring data, while the traditional lead-acid battery platform only relies on periodic detection; at the same time, the modular design facilitates the subsequent addition of new functions or updating of algorithms to adapt to technological development. DETAILED DESCRIPTION

[0029] In order to facilitate the understanding of the present invention, the device of the present invention will be described more comprehensively below. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0030] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "set" should be understood in a broad sense, for example, they can be fixedly connected or set, or detachably connected or set, or integrally connected or set. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0031] Example

[0032] This embodiment provides a lithium battery life cycle health and safety management platform, including a data acquisition module, a capacitance test module, a depth of discharge assessment module, an internal resistance measurement module, a temperature control module, a graph module, a model prediction module, an early warning module and an Internet of Things module;

[0033] In detail, the data acquisition module is used to collect lithium battery data in real time, and store it in the cloud through an integrated storage module (AWSRDS cloud database is used in this embodiment). The collected data specifically includes the voltage, current, temperature and charge and discharge state parameters of the lithium battery, and the collection frequency is once per minute;

[0034] The capacity test module is used to regularly evaluate the capacity of lithium batteries. Specifically, it integrates standardized charge and discharge procedures to perform charge and discharge cycle tests on lithium batteries:

[0035] This embodiment adopts CC-CV (constant current-constant voltage) charging mode. Charging stage: Based on constant current (e.g. C / 10, i.e., charging the battery in 10 hours) to the maximum voltage of the battery (usually 4.2V). Constant voltage stage: When the battery voltage reaches 4.2V, continue to charge at a constant voltage until the charging current drops to the set cut-off current (e.g., 0.05C). Discharging process: After charging is completed, the battery is discharged to a preset minimum safety voltage (usually 3.0V) using constant current discharge. The discharge rate used can be set to C / 10 or other suitable rates to ensure the consistency and repeatability of the results.

[0036] At this time, the parameters collected by the data acquisition module include charging voltage (battery voltage at each time point during charging), charging current (current change during charging), discharge voltage (battery voltage at each time point during discharge), discharge current (current change during discharge), charging time, discharge time and actual capacity (actual discharge capacity of lithium battery at the end of discharge), and the relative capacity is calculated by the actual capacity. The calculation formula of the actual capacity is:

[0037] Actual capacity = discharge current * discharge time. The unit of actual capacity is Ah, the unit of discharge current is A, and the unit of discharge time is h.

[0038] Relative capacity = (actual capacity / rated capacity) * 100, the unit of relative capacity is %, the unit of actual capacity is Ah, and the unit of rated capacity is Ah;

[0039] The deep discharge evaluation module is used to perform multiple charge and discharge cycle tests at different deep discharge levels to evaluate the impact of DOD on battery capacity. Different deep discharge values ​​(20%, 50%, 80%) are set. Each deep discharge is quantitatively cycled and the number of cycles, actual capacity and relative capacity are recorded, as shown in the following table:

[0040]

[0041]

[0042] The capacity decay curve is generated by the graph module through the integrated statistical analysis tool (Matplotlib is used in this embodiment), which is convenient for analyzing the impact of DOD on battery life, that is, as DOD increases, the relative capacity of the battery will gradually decrease;

[0043] The internal resistance measurement module measures the internal resistance of the lithium battery by using the DC internal resistance measurement method, calculates the internal resistance value based on the measured data, compares and evaluates the calculated internal resistance value with the rated internal resistance value, and generates an internal resistance change graph through the integrated statistical analysis tool of the graph module;

[0044] The steps of the DC internal resistance measurement method are as follows:

[0045] S1. Charging preparation: Make sure the battery is in a stable state. It is best to measure after it is fully charged.

[0046] S2, apply load: Use a known load resistance (R load ) Connect the battery and record the battery voltage before discharge (V open );

[0047] S3. Measure voltage using an AC impedance analyzer: After applying the load, record the load current (I load ) and the battery voltage after discharge (V load );

[0048] S4. Calculate the internal resistance according to the following formula:

[0049]

[0050] Where V open is the open circuit voltage before the load is applied, V load is the voltage after the load is applied, I load is the load current;

[0051] Compare the measured internal resistance with the rated internal resistance of the battery to evaluate the battery's health status, as shown in the following table:

[0052] Measurement conditions <![CDATA[V open (V)]]> <![CDATA[V loud (V)]]> <![CDATA[I loud (V)]]> Internal resistance(mΩ) Test1 3.7 3.5 1.0 200 Test2 3.7 3.6 1.5 67

[0053] Since the internal resistance will change with temperature, that is, the internal resistance will increase at low temperature, and may decrease at high temperature, the temperature control module is needed to monitor the capacity and internal resistance of the lithium battery at different temperatures (in order to reduce the workload, it can be recorded through the storage module at three temperature values ​​of 0℃, 25℃ and 45℃) during the charge and discharge cycle test (and can also record the charging and discharging time). It can also be used to control the active temperature control equipment to maintain the monitoring temperature of 0℃ to 45℃ (active temperature control equipment, automatic temperature control equipment, automatic constant temperature equipment, etc. for automatic control at 0℃ to 45℃ are all available, which will not be elaborated here);

[0054] The model prediction module predicts the SOH (health state) of lithium batteries by using the random forest model. The input parameters are the number of charge times, internal resistance, temperature, and capacity. The model is defined as:

[0055]

[0056] In the formula, X is one of the parameters including the number of charging times, internal resistance, temperature and capacity, T k is k decision trees;

[0057] Then each decision tree T k (X) will output a predicted value

[0058]

[0059] The final predicted value for:

[0060]

[0061] Cross-validation is then performed and the model evaluation formula is:

[0062]

[0063] Where L is the loss function, For the model D i The prediction of i is the true value, M is the average performance (the smaller the value, the better the model);

[0064] The early warning module is used to detect the internal resistance value (greater than 25mΩ), temperature value (greater than 45℃), and voltage value (lower than 3V) during the charge and discharge cycle test. It also integrates the function of finding the cause of the fault and generating corresponding preventive measures. That is, when the temperature exceeds or falls below the threshold, the information is transmitted to the fault tree analysis software of the temperature control module to control the active temperature control equipment to maintain the temperature between 0℃ and 45℃.

[0065] It should be noted that the structure described in the present invention can be implemented in a variety of different forms and is not limited to the described embodiments. Any equivalent transformations made by ordinary technicians in this field using the contents of the present invention specification, or directly or indirectly applied to other related technical fields, such as the loading and unloading of other items, are included in the protection scope of the present invention.

Claims

1. A lithium battery full life cycle health and safety management platform, characterized in that: include: A data acquisition module for real-time data collection of lithium batteries and storage of data through a storage module; Capacity test module and deep discharge assessment module for regular evaluation of lithium battery capacity; Internal resistance measurement module for calculating and monitoring the internal resistance of lithium batteries; Temperature control module for testing the temperature characteristics and thermal management of lithium batteries; A graph module for visual analysis of capacity decay and internal resistance changes of lithium batteries; Model prediction module for model prediction and model optimization of lithium battery SOH; An early warning module for monitoring and reminding various thresholds of lithium batteries; And an IoT module for cloud data communication.

2. A lithium battery full life cycle health and safety management platform as claimed in claim 1, characterized in that: The data acquisition module includes the acquisition of the voltage, current, temperature and charge and discharge state parameters of the lithium battery, and the acquisition frequency is once per minute; The storage module adopts a cloud database.

3. A lithium battery full life cycle health and safety management platform as claimed in claim 1, characterized in that: The electric capacity test module integrates a standardized charge and discharge program to perform a charge and discharge cycle test on a lithium battery. At this time, the parameters collected by the data acquisition module include charging voltage, charging current, discharging voltage, discharging current, charging time, discharging time and actual capacity, and the relative capacity is calculated by the actual capacity. The calculation formula of the actual capacity is: Actual capacity = discharge current * discharge time. The unit of actual capacity is Ah, the unit of discharge current is A, and the unit of discharge time is h. Relative capacity = (actual capacity / rated capacity) * 100, the unit of relative capacity is %, the unit of actual capacity is Ah, and the unit of rated capacity is Ah; The deep discharge evaluation module is used to perform multiple charge and discharge cycle tests at different deep discharge levels and set different deep discharge values. Each deep discharge performs a quantitative cycle and records the number of cycles, actual capacity and relative capacity.

4. A lithium battery full life cycle health and safety management platform as claimed in claim 1, characterized in that: The internal resistance measurement module performs measurement by a direct current internal resistance measurement method, calculates an internal resistance value according to the measured data, and compares and evaluates the calculated internal resistance value with the rated internal resistance value.

5. A lithium battery full life cycle health and safety management platform as claimed in claim 3, characterized in that: The temperature control module is used to measure the capacity and internal resistance of the lithium battery at different temperatures during the charge and discharge cycle test, and is also used to control the active temperature control device to maintain a monitoring temperature of 0°C to 45°C.

6. A lithium battery full life cycle health and safety management platform as claimed in claim 1, characterized in that: The graph module generates a capacity decay curve graph and an internal resistance change graph based on the data of the capacitance test module through an integrated statistical analysis tool.

7. A lithium battery full life cycle health and safety management platform as claimed in claim 1, characterized in that: The model prediction module constructs a prediction model for the SOH of a lithium battery through a random forest model and adopts k-fold cross validation.

8. A lithium battery full life cycle health and safety management platform as claimed in claim 5, characterized in that: The thresholds of the early warning module include but are not limited to the internal resistance value, temperature value, and voltage value during the charge and discharge cycle test, and are also integrated with fault tree analysis software for finding out the cause of the fault and generating corresponding preventive measures to control the temperature control module.

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