New energy automobile charging process inspection, diagnosis and charging integrated system

Abstract

The invention discloses a new energy automobile charging process inspection, diagnosis and charging integrated system, and relates to the technical field of new energy automobile charging control. The system comprises a central analysis module and a plurality of chargers connected with the central analysis module. The method comprises the following steps: collecting environmental data in real time through a charger, combining the environmental data with a battery state by a battery management module, and matching a power correction coefficient based on a lookup table or an artificial intelligence model to determine a target power upper limit; meanwhile, the central analysis module groups the chargers according to target power upper limit change and distributes the surplus capacity of the power station in proportion; according to the method, power is corrected through two dimensions of environment and battery state, limitation of single parameter correction is avoided, safety risks of coupling of an extreme environment and battery aging are effectively avoided, and charging safety is guaranteed; power is distributed according to dynamic grouping and proportion, the problem of capacity waste or overload of traditional unified distribution is avoided, and the capacity utilization rate of the charging station is improved.

Description

Technical Field

[0001] This invention belongs to the field of new energy vehicle charging control technology, specifically a new energy vehicle charging process detection and integrated charging system. Background Technology

[0002] With the increasing number of new energy vehicles, the rationality of the capacity allocation of charging stations, as core energy replenishment facilities, directly affects charging efficiency and system stability. Current charging station capacity allocation schemes have obvious technical limitations: traditional models mostly adopt "uniform power allocation" or "static priority allocation," without taking into account the dynamic changes in the power demand of the corresponding batteries for differentiated processing.

[0003] The uniform power allocation scheme does not differentiate between the urgency of charger power adjustments. When some chargers need to reduce power due to battery status (such as increased SOC or abnormal temperature), while others need to increase power, the scheme still allocates capacity according to a fixed ratio. This can easily lead to chargers that need to reduce power urgently causing battery overvoltage and overheating risks due to excessive capacity usage, while chargers that need to increase power have extended charging time due to insufficient capacity. This results in both wasted charging station capacity and safety hazards.

[0004] Static priority allocation schemes (such as preset priorities based on vehicle type and charging duration) cannot respond in real time to dynamic changes in the battery target power limit. For example, newly connected vehicles or vehicles with sudden changes in battery status are difficult to be quickly included in the capacity allocation system, resulting in low capacity utilization of charging stations.

[0005] This invention provides an integrated system for detecting and diagnosing the charging process of new energy vehicles, in order to solve the above-mentioned technical problems. Summary of the Invention

[0006] This invention aims to solve at least one of the technical problems existing in the prior art. To this end, this invention proposes an integrated charging and diagnostic system for new energy vehicles. This system corrects power through a dual-dimensional approach of "environment + battery status," avoiding the limitations of single-parameter correction and effectively mitigating the safety risks associated with the coupling of extreme environments and battery aging, thus ensuring charging safety. It also dynamically groups and proportionally allocates power, avoiding the capacity waste or overload problems of traditional uniform allocation and improving the capacity utilization rate of charging stations. Furthermore, it diagnoses battery fault types based on actual charging parameters, improving fault identification accuracy and providing reliable support for charging safety control and battery maintenance.

[0007] To achieve the above objectives, a first aspect of the present invention provides an integrated system for detecting and diagnosing the charging process of new energy vehicles, including a central analysis module and a plurality of chargers connected to the central analysis module; Charger: Used to collect environmental data in real time during charging, and receive the target power limit obtained by the battery management module in the new energy vehicle based on the environmental data correction; and, Used to extract charging parameter sequences of new energy vehicles; to identify battery fault types of new energy vehicles based on the charging parameter sequences; wherein, the charging parameter sequences include target output power, charging current, charging voltage, battery temperature, battery health and battery SOC; Central Analysis Module: Used to classify charging units operating within a charging station into Charging Unit 1 and Charging Unit 2 based on changes in the target power upper limit; and, It is used to determine the remaining capacity of the power station based on the target power limit of charging unit one and the capacity of the charging station, and to allocate the target output power of each charger in charging unit two based on the remaining capacity of the power station.

[0008] Preferably, before charging a new energy vehicle, the charger determines the initial power limit through message interaction, including: Establish a connection with the battery management module in new energy vehicles through message exchange; Send charging capability parameters and receive the initial power limit determined by the battery management module based on the charging capability parameters; wherein, the charging capability parameters are used to represent the charging capability that the charger can support.

[0009] Preferably, the target power upper limit is adjusted based on environmental data feedback, including: The power correction factor of the battery is matched according to the environmental data; the current safe power limit is corrected based on the power correction factor to obtain the corrected safe power limit; The target power limit is determined by comparing the safe power limit with the charger's output power limit.

[0010] Preferably, the power correction factor for the battery is matched based on environmental data, including: Extract the battery's state characteristic data; the state characteristic data includes the number of battery cycles, battery health, and battery capacity; The power correction coefficient is obtained by matching battery characteristic data and environmental data; the matching of the power correction coefficient is based on a lookup table or an artificial intelligence model.

[0011] Preferably, based on the change in the target power upper limit, the chargers operating within the charging station are divided into charger group one and charger group two, including: Identify changes in the target power limit corresponding to a working charger; When the target power limit is lower than the previous target power limit, the corresponding charger will be assigned to charger group one; otherwise, the corresponding charger will be assigned to charger group two.

[0012] Preferably, determining the remaining capacity of the power station based on the target power limit of charging unit one and the capacity of the charging station includes: Extract the sum of the rated power of the charging station that can be used to charge new energy vehicles, and use it as the charging station capacity; The remaining capacity of the power station is obtained by subtracting the occupied capacity from the total capacity of the charging station; the occupied capacity includes the sum of the target power limits of each charger in the charging unit.

[0013] Preferably, the target output power of each charger in Charger Unit 2 is allocated based on the remaining capacity of the power station, including: Calculate the sum of the target power upper limits corresponding to all chargers in charger group two, and use it as the base power value; use the ratio of the target power upper limit corresponding to each charger to the base power value as the allocation ratio; The product of the remaining capacity of the power station and the allocation ratio is used as the target output power of the corresponding charger, so as to obtain the target output power of each charger in charger group two.

[0014] Preferably, before assigning the corresponding charger to charger group two, its power regulation frequency is verified, including: Extract the time difference between the charger's previous charging output power adjustment time and the current time; When the time difference is not less than the preset time difference, the charger is assigned to charger group two; when the time difference is less than the preset time difference, the charger is assigned to charger group three.

[0015] Preferably, the method for identifying battery fault types in new energy vehicles based on charging parameter sequences includes: The charging parameter sequence is converted into a fault identification sequence; the charging parameter sequence includes actual charging power, charging current, charging voltage, battery temperature, battery health, and battery SOC. The fault identification sequence is input into the fault identification model to obtain the battery fault type of the new energy vehicle; the fault identification model is built based on an artificial intelligence model.

[0016] Preferably, converting the charging parameter sequence into a fault identification sequence includes: The adjustment time of the actual charging power is determined from the charging parameter sequence, as well as the change characteristics of the charging current, charging voltage and battery temperature corresponding to the actual charging power. By integrating the actual charging power, battery health, battery SOC, and the changes in charging current, charging voltage, and battery temperature in the order of adjustment time, a fault identification sequence is obtained.

[0017] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention collects environmental data in real time through the charger. The battery management module combines the environmental data and battery status, and matches a power correction coefficient based on a lookup table or artificial intelligence model to determine the target power ceiling. Simultaneously, the central analysis module groups the chargers according to changes in the target power ceiling and allocates the remaining capacity of the power station proportionally. This invention corrects power through a dual-dimensional approach of "environment + battery status," avoiding the limitations of single-parameter correction and effectively mitigating the safety risks associated with the coupling of extreme environments and battery aging, thus ensuring charging safety. Dynamic grouping and proportional power allocation avoids the capacity waste or overload problems of traditional uniform allocation, improving the capacity utilization rate of the charging station. The central analysis module and chargers work together to diagnose battery fault types based on actual charging parameters, improving fault identification accuracy and providing reliable support for charging safety control and battery maintenance.

[0018] 2. Before dividing the charging station into two groups, this invention verifies the time difference between the last power adjustment of the charger and the current time. If the time difference is less than a preset time difference, the charger is assigned to the third charging station and the power adjustment is delayed. When calculating the remaining capacity of the power station, the power occupied by the third charging station is deducted. This invention avoids the battery current surge caused by traditional unrestricted adjustment through power adjustment frequency verification and the division into three charging stations, effectively reducing the capacity decay rate of the battery caused by frequent power fluctuations. At the same time, it accurately calculates the remaining capacity of the power station, preventing power distribution overload problems caused by not deducting the power occupied by the third charging station. It takes into account both battery life and charging station operation stability, and is especially suitable for home slow charging pile scenarios that require long-term charging. Attached Figure Description

[0019] To more clearly illustrate the technical solutions 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.

[0020] Figure 1 This is a schematic diagram of the integrated diagnostic and filling system in Embodiment 1 of the present invention.

[0021] Figure 2 This is a schematic diagram of the working method of the integrated examination and filling system in Embodiment 1 of the present invention. Detailed Implementation

[0022] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] Example 1: Please see Figures 1-2 The first aspect of the present invention provides an integrated system for detecting and diagnosing the charging process of a new energy vehicle, including a central analysis module and a plurality of chargers connected to the central analysis module; Charger: Used to collect environmental data in real time during charging, and receive the target power limit obtained by the battery management module in the new energy vehicle based on the environmental data correction; and, Used to extract charging parameter sequences of new energy vehicles; to identify battery fault types of new energy vehicles based on the charging parameter sequences; wherein, the charging parameter sequences include target output power, charging current, charging voltage, battery temperature, battery health and battery SOC; Central Analysis Module: Used to classify charging units operating within a charging station into Charging Unit 1 and Charging Unit 2 based on changes in the target power upper limit; and, It is used to determine the remaining capacity of the power station based on the target power limit of charging unit one and the capacity of the charging station, and to allocate the target output power of each charger in charging unit two based on the remaining capacity of the power station.

[0024] The central analysis module is deployed within the charging station, communicating and / or electrically connecting with the chargers within the station to coordinate and control the operation of each charger. The charger is a core component of the charging pile. A charging pile is a complete charging equipment system, providing charging interfaces, human-machine interaction, communication services, etc., including the charger, and also requiring a casing, cables, control board, protective devices, etc. The charger is the core equipment of the charging pile, converting AC power from the grid into DC power required by new energy vehicles, and precisely controlling voltage and current (such as the DC charger in a fast-charging pile), or simply distributing power (such as the AC charger in a slow-charging pile).

[0025] During the charging process of a new energy vehicle, the charger collects environmental data about the vehicle's location in real time. Based on this environmental data, the safe power limit given by the battery management module in conjunction with the battery status is corrected to obtain the target power limit. Generally, the target power limit is lower than the safe power limit. After receiving the target power limit sent by each charger, the central analysis module groups the chargers and allocates power based on the target power limit. This embodiment of the invention comprehensively determines the safe charging power limit for new energy vehicles by integrating environmental data, and then allocates charging output power to each charger according to this power limit, thus determining the balance of charging power for the new energy vehicle. Simultaneously, charging parameters are recorded during the charging process, and these charging parameters are combined with an artificial intelligence model to identify battery fault types. Since charging parameters are unpredictable, this improves the accuracy of battery fault type identification.

[0026] Before a charger can charge a new energy vehicle, a connection needs to be established between the vehicle and the charger. After the connection is established, the vehicle's battery management module and the charger negotiate to determine the initial power limit. Charging is then initiated based on this initial power limit.

[0027] Before charging a new energy vehicle, the charger determines the initial power limit through message exchange, including: Establish a connection with the battery management module in new energy vehicles through message exchange; Send charging capability parameters and receive the initial power limit determined by the battery management module based on the charging capability parameters; wherein, the charging capability parameters are used to represent the charging capability that the charger can support.

[0028] The battery management module (BMS) inside a new energy vehicle will respond to the handshake message sent by the charger and report the parameters. The reported parameters mainly include key parameters such as the maximum allowable charging voltage of the battery and the battery type. If necessary, it can also set power limiting instructions based on whether the battery is aging or has abnormal temperature.

[0029] Before charging begins, the battery management module interacts with the charger via messages to complete parameter configuration. The battery management module reports the real-time status of the battery to the charger, and the charger provides feedback on its own capabilities. The battery management module determines the initial power limit based on the battery requirements and the charger's capabilities.

[0030] The charger establishes a connection with the battery management module in the new energy vehicle through message exchange. The specific process is as follows: First, the charger actively sends a charger handshake message (CHM) and receives a BMS handshake message (BHM) in response from the battery management module. Then, the charger sends a charger identification message to the battery management module. The battery management module determines the protocol version based on the charger identification message (CRM) and then sends a BMS vehicle identification message (BRM). The two determine the communication protocol version through this negotiation process. Next, the battery management module reports a battery charging parameter message (BCP), and the charger responds with a charger output capability message (CML). The battery management module determines the initial power limit based on the intersection of the battery charging parameter message and the charger output capability message. After the charger confirms this initial power limit, charging can begin.

[0031] Generally, the initial power limit negotiated between the battery management module and the charger can be used as the upper limit of the charger's output power. However, during the negotiation process, the battery management module mainly considers the battery status, such as battery capacity, battery health, and battery temperature. Nevertheless, the charging environment of new energy vehicles can also affect the battery charging process, and failure to adjust the charger's output power in a timely manner can easily lead to safety risks.

[0032] Taking ambient temperature as an example: When the ambient temperature is very low (such as less than 5°C), the viscosity of the battery electrolyte increases and the ion migration speed slows down. If high-power charging is forced, it is easy to cause "lithium dendrite precipitation" (piercing the separator and causing a short circuit), which will accelerate battery degradation in the long term. When the ambient temperature is high (such as greater than 35°C), the battery charging itself will generate heat. After the high temperature is added, the heat is difficult to dissipate, which can easily trigger thermal runaway of the battery cell (such as bulging and fire).

[0033] Therefore, the charger needs to collect environmental data in real time and send the collected environmental data to the battery management module of the new energy vehicle so that the battery management module can determine the target power limit in a timely manner.

[0034] In the charger, the target power limit is adjusted based on environmental data feedback, including: The power correction factor of the battery is matched according to the environmental data; the current safe power limit is corrected based on the power correction factor to obtain the corrected safe power limit; The target power limit is determined by comparing the safe power limit with the charger's output power limit.

[0035] After determining the initial power limit, the charger charges the battery of the new energy vehicle based on this initial power limit. During the charging process, the charger collects environmental data in real time and feeds the collected environmental data back to the battery management module. The battery management module dynamically adjusts the safe power limit based on the environmental data, compares it with the charger's output capacity to obtain the target power limit, and controls the charger's charging output power based on the target power limit.

[0036] The target power limit adjusted by the battery management module is the battery's charging red line; that is, the current charger's output power must not exceed the target power limit, otherwise it is highly likely to cause safety risks. Of course, when the charging station capacity allows, the charging efficiency is highest when the charger uses the target power limit as its charging output power to charge new energy vehicles.

[0037] It should also be noted that charging stations generally allow multiple new energy vehicles to charge at the same time. During the charging process, the chargers and new energy vehicles will continuously dissipate heat, which will cause the temperature in some areas of the charging station to rise significantly. Therefore, each charger should be equipped with corresponding data sensors to collect environmental data of the location of the corresponding new energy vehicle. Data sensors such as temperature sensors and humidity sensors are also required.

[0038] In this example, charging stations include not only commercial charging stations but also charging stations comprised of privately owned charging stations. For instance, if multiple homeowners in a residential building's underground parking garage have installed private charging stations, these stations can be combined to form a single charging station.

[0039] In the charger, the battery's power correction factor is matched based on environmental data, including: Extract the battery's state characteristic data; the state characteristic data includes the number of battery cycles, battery health, and battery capacity; The power correction coefficient is obtained by matching battery characteristic data and environmental data; the matching of the power correction coefficient is based on a lookup table or an artificial intelligence model.

[0040] Power correction coefficients can be matched based on lookup tables. Before matching power correction coefficients using lookup tables, a full-range charging test is required. Specifically, batteries with different health levels, capacities, and cycle counts are prepared, and the charging process of these batteries is simulated within a full range of environments. The safe power limit of the battery changes with the environment, and then the power correction coefficient can be calculated. The full-range environment mainly includes the full-range temperature range and the full-range humidity range. The full-range temperature range is [-40℃, +60℃], and the full-range humidity range is [0, 100%]. A lookup table is established based on battery health level, battery capacity, battery cycle count, ambient temperature, ambient humidity, and the corresponding power correction coefficient. During matching, the power correction coefficient is obtained directly from the table by looking up the relevant data.

[0041] It should be noted that the power correction factor is the ratio of the difference between the recorded safe power limit and the reference safe power to the reference safe power. The reference safe power is the safe power limit calculated by the battery management module without taking environmental data into account.

[0042] Alternatively, power correction coefficients can be matched based on an artificial intelligence model. The battery health, battery capacity, battery cycle count, and environmental data from the aforementioned lookup table are standardized and used as model input data. The corresponding power correction coefficients are then standardized to obtain model output data. The artificial intelligence model is trained using the input and output data, and the trained model is saved and can be directly called upon when matching is needed. This artificial intelligence model can be built based on a backpropagation (BP) neural network, which includes an input layer, hidden layers, and an output layer.

[0043] It should be noted that the lookup table or artificial intelligence model is constructed through the central analysis module, and the charger can directly retrieve it from the central analysis module when in use.

[0044] After determining the target power ceiling for new energy vehicles, it is necessary to determine whether the charging station capacity meets the target power ceiling of the current number of new energy vehicles. That is, it is necessary to determine whether the sum of the target power ceilings of all new energy vehicles with charging needs in the current charging station is greater than the charging station capacity. If it meets the requirement, the target power ceiling can be used as the charging output power of the charger; if it does not meet the requirement, the chargers corresponding to the new energy vehicles need to be adjusted separately.

[0045] After obtaining the target power limit for the battery in a new energy vehicle, if this target power limit is lower than the previous target power limit, it indicates that the previous target power limit was too high compared to the current battery condition, and the charger's charging output power needs to be adjusted promptly based on the newly determined target power limit. Conversely, if the target power limit is higher than the previous target power limit, continuing to charge based on the previous target power limit will not pose a danger to the battery, but may reduce battery health.

[0046] The comparison shows that if the target power limit is lower than the previous target power limit, the charging output power of the corresponding charger needs to be adjusted promptly; however, if the target power limit is not lower than the previous target power limit, delayed adjustment will not pose a danger to the battery. Therefore, based on these two situations, the chargers in the charging station are divided into two groups: one group needs to be adjusted promptly, while the other group can be adjusted later.

[0047] In the central analysis module, based on the change in the target power upper limit, the chargers operating within the charging station are divided into Charger Group 1 and Charger Group 2, including: Identify changes in the target power limit corresponding to a working charger; When the target power limit is lower than the previous target power limit, the corresponding charger will be assigned to charger group one; otherwise, the corresponding charger will be assigned to charger group two.

[0048] When the target power limit is lower than the previous target power limit, the corresponding charger is assigned to Charger Group 1, the remaining working chargers are assigned to Charger Group 2, and inactive chargers are not grouped. If a charger disconnects from charging, it will not participate in the next grouping; if a charger starts charging, it will participate in the next grouping. This not only dynamically updates the charging station capacity, ensuring efficient utilization, but also allows newly added new energy vehicles to be included in the adjustment scope, improving charging safety.

[0049] It should be noted that the initial power limit is equivalent to the first target power limit. When grouping chargers according to the target power limit in subsequent operations, the initial power limit can be used as the "previous target power limit". Moreover, the grouping and power allocation of chargers in the charging station can be performed periodically, such as every minute or thirty seconds, identifying the changes in the target power limit of the chargers in operation and grouping the chargers accordingly.

[0050] The charging output power of the chargers in Charger Unit 1 needs to be adjusted immediately. Furthermore, to ensure the charging efficiency for the corresponding new energy vehicles, the upper limit of the target power can be directly used as the charging output power of the corresponding charger. The remaining capacity of the power station will be allocated to several chargers in Charger Unit 2.

[0051] In the central analysis module, the remaining capacity of the power station is determined based on the target power limit of charging unit one and the capacity of the charging station, including: Extract the sum of the rated power of the charging station that can be used to charge new energy vehicles, and use it as the charging station capacity; The remaining capacity of the power station is obtained by subtracting the occupied capacity from the total capacity of the charging station; the occupied capacity includes the sum of the target power limits of each charger in the charging unit.

[0052] It should be noted that when calculating the remaining capacity of the power station, in addition to subtracting the sum of the target power limits of each charger in Charger Unit 1, other unallocated and occupied capacity should also be subtracted, such as the sum of the charging output power of each charger in Charger Unit 3 as described later.

[0053] When the remaining capacity of the power station is lower than the sum of the target power limits of all chargers in the second charging unit for new energy vehicles, the target output power of each charger needs to be allocated according to the target power limit, and the new energy vehicles are charged using the target output power.

[0054] In the central analysis module, the target output power of each charger in Charger Unit 2 is allocated based on the remaining capacity of the power station, including: Calculate the sum of the target power upper limits corresponding to all chargers in charger group two, and use it as the base power value; use the ratio of the target power upper limit corresponding to each charger to the base power value as the allocation ratio; The product of the remaining capacity of the power station and the allocation ratio is used as the target output power of the corresponding charger, so as to obtain the target output power of each charger in charger group two.

[0055] When calculating the target output power of the corresponding charger based on the target power limit, the remaining capacity of the power station is distributed proportionally to each charger. This ensures that the charging efficiency of new energy vehicles corresponding to several chargers is balanced.

[0056] It should also be noted that since the chargers in Charger Unit 1 are currently occupying part of the power station capacity, the chargers in Charger Unit 1 can be adjusted according to the safe power limit first. After the adjustment, some capacity will be released, and then the remaining capacity of the power station will be allocated to the chargers in Charger Unit 2.

[0057] In some other preferred embodiments, the target output power (charging output power) of each charger in Charger Group Two can be adjusted based on user needs. For example, if some chargers in Charger Group Two are for new energy vehicles that require priority in charging efficiency, then the upper limit of the target power for these chargers will be used as the target output power. This target output power will be subtracted from the remaining capacity of the power station before determining the target output power of the other chargers in Charger Group Two. User needs can be sent to the chargers during message exchange.

[0058] During the charging process, the charging parameters of the new energy vehicle are recorded continuously, including charging power, charging voltage, charging current, and battery temperature. Since the charging power of the new energy vehicle is dynamically adjusted due to external conditions, each adjustment corresponds to a charging power (the charging output power of the charger). By analyzing the changing trends of the above parameters after charging at the specified power, the battery can be diagnosed to determine if there are any faults.

[0059] In the charger, battery fault types in new energy vehicles are identified based on charging parameter sequences, including: The charging parameter sequence is converted into a fault identification sequence; the charging parameter sequence includes actual charging power, charging current, charging voltage, battery temperature, battery health, and battery SOC. The fault identification sequence is input into the fault identification model to obtain the battery fault type of the new energy vehicle; the fault identification model is built based on an artificial intelligence model.

[0060] In Example 1, the charging station allocates power to each charger based on its own charging station capacity and the target power limit of each new energy vehicle. During the allocation process, the charging power received by the new energy vehicle changes, and this change is not predictable. Using the charging parameters generated in this scenario to identify the battery fault type of the new energy vehicle can improve the identification accuracy.

[0061] Each new energy vehicle charging at a charging station corresponds to a charging parameter sequence, which records the changes in relevant parameters at various times. Battery health and battery SOC can be directly read through the battery management module, and battery health generally does not change significantly after a single charge. Actual charging power refers to the output power of the charger after power distribution in Example 1 (assuming the charger is not faulty); this actual charging power is a real power output value. Charging current, charging voltage, and battery temperature all change with the actual charging power, and these data are also key to identifying battery fault types.

[0062] The charging parameter sequence is converted into a fault identification sequence according to preset rules. This fault identification sequence is then input into a pre-trained fault identification model to obtain the battery fault type of the new energy vehicle. The fault identification model is built based on an artificial intelligence model and is used to determine the nonlinear mapping relationship between the fault identification sequence and the battery fault type.

[0063] The charging parameter sequence is converted into a fault identification sequence, including: The adjustment time of the actual charging power is determined from the charging parameter sequence, as well as the change characteristics of the charging current, charging voltage and battery temperature corresponding to the actual charging power. By integrating the actual charging power, battery health, battery SOC, and the changes in charging current, charging voltage, and battery temperature in the order of adjustment time, a fault identification sequence is obtained.

[0064] When converting the charging parameter sequence into a fault identification sequence, battery health, battery SOC, actual charging power, and corresponding adjustment time are directly included in the data sequence. At the same time, the changes in charging current, charging voltage, and battery temperature are calculated after the adjustment time of the actual charging power and before the next adjustment of the actual charging power. These changes are then concatenated with the data in the above data sequence and standardized to obtain the fault identification sequence. This fault identification sequence is input into the fault identification model to obtain the battery fault type.

[0065] It should be noted that the variation characteristics can be the range of data fluctuations, the data mean, the data extreme values, or the data standard deviation; of course, it can also be multiple data points. For example, the mean and maximum values ​​of charging current, charging voltage, and battery temperature. Moreover, the variation characteristics can be the variation characteristics within a set time (such as the variation characteristics corresponding to half of the actual charging power) or the variation characteristics corresponding to the entire actual charging power over the entire time period.

[0066] It's also important to note that the fault identification model is built upon an artificial intelligence (AI) model. This AI model can be a backpropagation (BP) neural network or an RBF neural network, with a basic three-layer structure comprising an input layer, hidden layers, and an output layer. Before training the AI ​​model, various battery fault types and normal batteries are simulated for charging, and the charging parameter sequences during the charging process are recorded. These charging parameter sequences are then converted into fault identification sequences. These fault identification sequences are integrated with the corresponding battery fault types to form a fault training dataset. Several fault training datasets can be obtained through simulation. The fault identification sequences from the fault training dataset are used as the model input data, and the battery fault types are used as the model output data to train the AI ​​model. The trained AI model is then labeled as the fault identification model.

[0067] Example 2: Because environmental data is collected and fed back in real time, the battery management module may frequently determine different safe power limits based on this data. This, in turn, leads to frequent negotiations with the charger to determine the target power limit, which in turn may cause the charger to frequently adjust its charging output power. To avoid the charging output power negatively impacting battery health, a preliminary assessment is conducted regarding whether the charger should be assigned to Charger Group Two.

[0068] Therefore, before assigning the corresponding charger to charger group two, its power regulation frequency is verified, including: Extract the time difference between the charger's previous charging output power adjustment time and the current time; Determine if the time difference is not less than the preset time difference; if yes, assign the charger to charger group two; otherwise, assign the charger to charger group three.

[0069] Since the target power limit for each charger in Charger Group 1 is lower than the previous target power limit, the charging output power of each charger needs to be adjusted immediately. Therefore, it is not necessary to pre-evaluate these chargers. However, if a charger is not assigned to Charger Group 1, it means that its corresponding charging output power can be adjusted with a slight delay. Pre-evaluation can be used to ensure that it is not frequently adjusted.

[0070] Before assigning the charger to charger group two, determine the time difference between the last adjustment time of the charging output power and the current time. If the time difference is less than the preset time difference, it means that the charger does not need to be adjusted at present, so as to avoid frequent adjustments affecting the battery health.

[0071] Correspondingly, if some chargers are assigned to charger group three, these chargers will maintain their current charging output power and will not participate in this remaining capacity allocation. If the sum of the charging output power of the chargers in charger group three is taken as the sum of the occupied power, then the remaining capacity of the power station is the sum of the target power upper limit of each charger in charger group one and the sum of the occupied power, which is the sum of the target power upper limit and the occupied power of each charger in charger group one, minus the total capacity of the power station.

[0072] The above embodiments are only used to illustrate the technical methods 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 methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A new energy vehicle charging process detection and diagnosis integrated system, characterized in that, It includes a central analysis module and several chargers connected to the central analysis module; Charger: Used to collect environmental data in real time during the charging process and receive the target power limit obtained by the battery management module in the new energy vehicle based on the environmental data correction; as well as, Used to extract the charging parameter sequence of the new energy vehicle; to identify the battery fault type of the new energy vehicle based on the charging parameter sequence; wherein, the charging parameter sequence includes target output power, charging current, charging voltage, battery temperature, battery health and battery SOC; Central Analysis Module: Used to classify charging units operating within a charging station into Charging Unit 1 and Charging Unit 2 based on changes in the target power upper limit; and, It is used to determine the remaining capacity of the power station based on the target power limit of charging unit one and the capacity of the charging station, and to allocate the target output power of each charger in charging unit two based on the remaining capacity of the power station.

2. The integrated charging and diagnostic system for new energy vehicles according to claim 1, characterized in that, Before charging a new energy vehicle, the charger determines the initial power limit through message exchange, including: Establish a connection with the battery management module in new energy vehicles through message exchange; Send charging capability parameters and receive the initial power limit determined by the battery management module based on the charging capability parameters; wherein, the charging capability parameters are used to represent the charging capability that the charger can support.

3. The integrated charging and diagnostic system for new energy vehicles according to any one of claims 1, characterized in that, The target power ceiling is adjusted based on environmental data feedback, including: The power correction factor of the battery is matched according to the environmental data; the current safe power limit is corrected based on the power correction factor to obtain the corrected safe power limit; The target power limit is determined by comparing the safe power limit with the charger's output power limit.

4. The integrated charging and diagnostic system for new energy vehicles according to claim 3, characterized in that, The method of matching the battery power correction coefficient based on environmental data includes: Extract the battery's state characteristic data; the state characteristic data includes the number of battery cycles, battery health, and battery capacity; The power correction coefficient is obtained by matching the battery feature data and the environmental data; wherein the matching of the power correction coefficient is based on a lookup table or an artificial intelligence model.

5. The integrated charging and diagnostic system for new energy vehicles according to claim 1, characterized in that, Based on the change in the target power limit, the chargers operating within the charging station are divided into Charger Group 1 and Charger Group 2, including: Identify changes in the target power limit corresponding to a working charger; When the target power limit is lower than the previous target power limit, the corresponding charger will be assigned to charger group one; otherwise, the corresponding charger will be assigned to charger group two.

6. The integrated charging and diagnostic system for new energy vehicles according to claim 1, characterized in that, The remaining capacity of the power station is determined based on the target power limit of charging unit one and the capacity of the charging station, including: Extract the sum of the rated power of the charging stations that can be used for charging new energy vehicles, and use it as the charging station capacity; The remaining capacity of the power station is obtained by subtracting the occupied capacity from the total capacity of the charging station; wherein the occupied capacity includes the sum of the target power limits of each charger in the first charging unit.

7. The integrated charging and diagnostic system for new energy vehicles according to claim 1, characterized in that, Based on the remaining capacity of the power station, the target output power of each charger in Charger Unit 2 is allocated, including: Calculate the sum of the target power upper limits corresponding to all chargers in the second charging unit, and use it as the base power value; use the ratio of the target power upper limit corresponding to each charger to the base power value as the allocation ratio; The product of the remaining capacity of the power station and the allocation ratio is used as the target output power of the corresponding charger, so as to obtain the target output power of each charger in the second charger group.

8. A new energy vehicle charging process detection and integrated charging system according to any one of claims 5 to 7, characterized in that, Before assigning the corresponding charger to charger group two, verify its power regulation frequency, including: Extract the time difference between the previous charging output power adjustment time of the charger and the current time; When the time difference is not less than the preset time difference, the charger is assigned to charger group two; when the time difference is less than the preset time difference, the charger is assigned to charger group three.

9. A new energy vehicle charging process detection and integrated charging system according to claims 1 to 7, characterized in that, Identifying the battery fault type of the new energy vehicle based on the charging parameter sequence includes: The charging parameter sequence is converted into a fault identification sequence; wherein the charging parameter sequence includes actual charging power, charging current, charging voltage, battery temperature, battery health, and battery SOC; The fault identification sequence is input into the fault identification model to obtain the battery fault type of the new energy vehicle; wherein, the fault identification model is constructed based on an artificial intelligence model.

10. The integrated charging and diagnostic system for new energy vehicles according to claim 1, characterized in that, Converting the charging parameter sequence into a fault identification sequence includes: The adjustment time of the actual charging power is determined from the charging parameter sequence, as well as the change characteristics of the charging current, charging voltage and battery temperature corresponding to the actual charging power. By integrating the actual charging power, battery health, battery SOC, and the changes in charging current, charging voltage, and battery temperature in the order of adjustment time, a fault identification sequence is obtained.

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