Self-adaptive current regulation electric vehicle charging method

Abstract

The invention relates to the technical field of electric vehicle charging, in particular to a self-adaptive current regulation electric vehicle charging method, which comprises the steps of monitoring battery parameters of an electric vehicle in real time, analyzing the health state of a battery based on the monitored battery parameter data, and predicting the optimal charging current demand of the battery by utilizing a machine learning algorithm. Generating a charging current curve adaptive to the current battery state, dynamically adjusting the charging current according to the predicted charging current curve, starting a cooling or heating system according to the temperature change condition in the charging process, continuously monitoring the voltage, temperature and current change conditions of the battery in the charging process, and starting the cooling or heating system when the battery reaches a preset charging upper limit. And the charging current is gradually reduced, and a charging report is generated after charging is completed. The charging efficiency is improved, the service life of the battery is prolonged, the safety of the charging process is enhanced, detailed charging information is provided for a user, and monitoring and maintenance of the battery are facilitated.

Description

Technical Field

[0001] The invention relates to the technical field of electric vehicle charging, and in particular to an electric vehicle charging method with adaptive current regulation. Background Art

[0002] With the popularity of electric vehicles, the research and development of battery charging technology has become increasingly important. The battery charging process of electric vehicles directly affects the battery life and the performance of the entire vehicle. In order to improve the endurance and service life of electric vehicles, the development of efficient and safe charging methods has become the focus of the industry.

[0003] However, there are still some technical problems with existing electric vehicle charging technology. Traditional charging methods usually use fixed charging current and voltage, which cannot adapt to the needs of batteries in different charging states and easily lead to overcharging or undercharging of the battery. In addition, the existing charging system lacks real-time monitoring and intelligent adjustment capabilities, and cannot be dynamically adjusted according to the actual state of the battery, resulting in low charging efficiency and shortened battery life. The temperature management during the charging process is also imperfect, and fails to effectively prevent the battery from overheating or overcooling, thereby increasing safety risks. Therefore, there is an urgent need for an intelligent charging method that can monitor the battery status in real time and dynamically adjust the charging current and temperature control system to solve these problems in the prior art. Summary of the invention

[0004] Based on the above purpose, the present invention provides an electric vehicle charging method with adaptive current regulation.

[0005] The electric vehicle charging method with adaptive current regulation comprises the following steps:

[0006] S1, battery parameter monitoring: real-time monitoring of electric vehicle battery parameters, including voltage, temperature, internal impedance and current charging current, through the preset data acquisition module in the charging equipment;

[0007] S2, battery health status assessment: Based on the monitored battery parameter data, the battery health status is analyzed through a preset health assessment algorithm;

[0008] S3, charging demand prediction: Based on the battery health status and real-time battery parameters, the machine learning algorithm is used to predict the optimal charging current demand of the battery and generate a charging current curve that adapts to the current battery status;

[0009] S4, dynamic current regulation: through the current regulation module preset in the charging device, the charging current is dynamically adjusted according to the predicted charging current curve to ensure that the charging process matches the battery status;

[0010] S5, thermal management strategy application: During the charging process, the battery temperature is monitored in real time, and the cooling or heating system is activated according to the temperature changes to maintain the battery within the optimal temperature range to prevent overheating or overcooling;

[0011] S6, charging safety monitoring: continuously monitor the battery voltage, temperature and current changes during the entire charging process;

[0012] S7, charging completion and report generation: When the battery reaches the preset charging upper limit, the charging current is gradually reduced until charging is completed. After charging is completed, a charging report is generated, including the charging time, charging current changes, battery temperature changes and battery health status.

[0013] Optionally, the data acquisition module preset in S1 specifically includes:

[0014] Voltage sensor: A high-precision voltage sensor is installed in the charging device to measure the voltage on the battery terminals and obtain voltage data. The voltage sensor is directly connected to the positive and negative poles of the battery and can continuously monitor the voltage changes of the battery during the charging process.

[0015] Temperature sensor: Use a thermocouple, thermistor or infrared temperature sensor to measure the battery temperature. The temperature sensor needs to be placed on the surface or inside the battery to obtain battery temperature data;

[0016] Impedance measurement device: Using AC impedance measurement technology, a small AC current is applied at a specific frequency, the response voltage is measured, the impedance value is calculated, and the internal impedance data of the battery is obtained;

[0017] Current sensor: Use Hall effect current sensor or shunt to measure charging current and obtain battery current data.

[0018] Optionally, the S2 specifically includes:

[0019] S21, data preprocessing: obtaining the monitored battery parameters and performing preprocessing, including data cleaning, data smoothing, data calibration and data normalization, and extracting key features, including voltage change rate, temperature change rate, internal impedance change and charging current change;

[0020] S22, health status assessment: a hybrid model based on physical models and data-driven is used to assess the battery health status and predict the remaining battery life;

[0021] S23, health status report: Generate a health status report, including the current battery health status and the predicted remaining battery life.

[0022] Optionally, in S3, according to the health status of the battery and the real-time battery parameters, a machine learning algorithm is used to predict the optimal charging current requirement of the battery, specifically including:

[0023] S31, data preparation: collecting historical battery parameters, real-time monitored battery parameters and corresponding battery health status;

[0024] S32, data preprocessing: preprocessing the collected data, including data cleaning and data normalization, and extracting key features that affect the charging current, including voltage, temperature, internal impedance, current charging current, voltage change rate, temperature change rate, internal impedance change and battery health status;

[0025] S33, model selection and training: using random forest algorithm to establish charging current prediction model;

[0026] S34, real-time prediction: using the battery parameters monitored in real time and the trained charging current prediction model to predict the charging current.

[0027] Optionally, generating a charging current curve adapted to the current battery state in S3 specifically includes:

[0028] S35, Charging stage: Electric vehicle battery charging is divided into constant current charging stage, constant voltage charging stage and floating charging stage;

[0029] Constant current charging stage: In the constant current charging stage, the current remains constant until the battery voltage reaches the set value, expressed as:

[0030] I charging (t) = I optimal ;

[0031] Constant voltage charging stage: In the constant voltage charging stage, the voltage remains constant and the charging current gradually decreases. The formula is:

[0032]

[0033] Among them, I charging (t) is the charging current at time t, I optimal is the optimal charging current predicted by the model, α is the attenuation coefficient determined according to the battery characteristics, and t0 is the starting time of the constant voltage stage;

[0034] Floating charge stage: During the floating charge stage, the current is kept at a minimum constant value to maintain the battery voltage within a safe range, expressed as:

[0035] I charging (t) = I float ;

[0036] Among them, I floatIt is the current during the floating charge phase, which is generally a smaller value to maintain the battery charge;

[0037] S36, generating a charging current curve: combining the charging current formulas of the constant current charging stage, the constant voltage charging stage and the floating charging stage, generating a complete charging current curve, expressed as:

[0038]

[0039] Among them, V set is the set voltage of the constant voltage stage, and t0 is the starting time of the constant voltage stage.

[0040] Optionally, the current regulation module preset in S4 includes a control algorithm unit and a current regulator, and the charging current is dynamically adjusted according to the predicted charging current curve in S4, specifically including:

[0041] S41, charging current calculation: The control algorithm unit dynamically calculates the required charging current based on the real-time battery parameters and the predicted charging current curve, expressed as:

[0042]

[0043] Among them, V set is the set voltage in the constant voltage stage, V max is the maximum charging voltage of the battery, α is the attenuation coefficient determined according to the battery characteristics, t0 is the starting time of the constant voltage stage, I optimal is the predicted optimum charging current, I float is the charging current during the floating charge phase;

[0044] S42, real-time adjustment of charging current: the current regulator adjusts the charging current in real time according to the output of the control algorithm unit;

[0045] S43, real-time monitoring and dynamic adjustment: During the charging process, the battery parameters and charging current are continuously monitored, and the charging current is dynamically adjusted according to the actual situation to ensure a safe and efficient charging process.

[0046] Optionally, the S5 specifically includes:

[0047] S51, monitoring battery temperature: monitoring the battery temperature in real time through a temperature sensor;

[0048] S52, data analysis: analyzing the real-time monitored temperature data, calculating the temperature change rate to evaluate the temperature change trend;

[0049] S53, set temperature range: set the optimal temperature range of the battery, the battery temperature should be maintained within this range to ensure safety and optimal performance;

[0050] S54, dynamic adjustment: dynamically adjust the cooling or heating system according to the real-time monitored temperature data and temperature changes;

[0051] S55, real-time monitoring and adjustment: During the charging process, the battery temperature is continuously monitored and dynamically adjusted according to the actual situation to ensure that the battery temperature remains within the optimal range;

[0052] S56, protection mechanism: set upper and lower temperature limit protection. When the temperature exceeds the limit, take emergency measures (such as suspending charging) to protect the battery safety.

[0053] Optionally, the S6 specifically includes:

[0054] S61, data receiving: receiving the vehicle battery parameters monitored by the data acquisition module in real time, including voltage, temperature and current charging current;

[0055] S62, data processing: processing the real-time received automobile battery parameters, and calculating the voltage change rate, temperature change rate and current change rate;

[0056] S63, safety range setting: setting the safety range of battery parameters, including voltage safety range, temperature safety range and current safety range;

[0057] S64, dynamically adjust charging strategy: When overheating, overvoltage or overcurrent risks are detected, reduce charging current or suspend charging to mitigate safety risks.

[0058] Optionally, the S7 specifically includes:

[0059] S71, setting the upper limit of charging: setting the upper limit of the charging voltage of the battery and the upper limit of the charging current of the battery;

[0060] S72, gradually reducing the charging current: when the battery voltage reaches a preset upper charging limit, gradually reducing the charging current;

[0061] S73, charging completed: when the charging current drops to the set minimum value, charging is considered completed;

[0062] S74, generate a charging report: after charging is completed, generate a charging report including charging time, charging current change, battery temperature change and battery health status.

[0063] Beneficial effects of the present invention:

[0064] The present invention uses a machine learning algorithm to predict the optimal charging current requirement by real-time monitoring of key parameters of the battery, such as voltage, temperature, and current, and dynamically adjusts the charging current to ensure that the charging process always matches the battery status. By adopting a strategy of gradually reducing the charging current, when the battery reaches the preset upper charging limit, the damage caused by overcharging is avoided, the charging efficiency is significantly improved, and the battery life is extended. At the same time, the charging current and temperature are dynamically adjusted to prevent the battery from overheating or overcooling during the charging process, further protecting the battery and extending its service life.

[0065] The present invention continuously monitors the changes in the voltage, temperature and current of the battery, and promptly detects potential safety risks, such as overheating, overvoltage and overcurrent. When these risks are detected, protective measures can be quickly taken, such as reducing the charging current or suspending charging, to ensure the safety of the charging process. In addition, by setting the upper and lower temperature limit protection mechanism, when the temperature exceeds the limit, charging is stopped immediately to avoid the occurrence of safety accidents. Through these safety measures, the safety of the electric vehicle charging process is significantly improved.

[0066] In the present invention, after charging is completed, the system generates a detailed charging report, including information such as charging time, charging current changes, battery temperature changes, and battery health status. Users can fully understand every detail of the charging process through these reports, and timely grasp the health status of the battery and the charging effect. This not only helps users to perform daily maintenance of the battery, but also helps users optimize charging habits and improve the overall performance and life of the battery. The detailed charging report provides users with valuable data support, making battery management more scientific and efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0068] Figure 1 Schematic diagram of specific steps of an embodiment of the present invention;

[0069] Figure 2 Schematic diagram of step S3 of an embodiment of the present invention. DETAILED DESCRIPTION

[0070] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0071] It should be noted that the references to "one embodiment", "an embodiment", "an exemplary embodiment", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0072] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0073] like Figure 1-Figure 2 As shown, the electric vehicle charging method with adaptive current regulation includes the following steps:

[0074] S1, battery parameter monitoring: real-time monitoring of electric vehicle battery parameters, including voltage, temperature, internal impedance and current charging current, through the preset data acquisition module in the charging equipment;

[0075] S2, battery health status assessment: Based on the monitored battery parameter data, the battery health status is analyzed through a preset health assessment algorithm;

[0076] S3, charging demand prediction: Based on the battery health status and real-time battery parameters, the machine learning algorithm is used to predict the optimal charging current demand of the battery and generate a charging current curve that adapts to the current battery status;

[0077] S4, dynamic current regulation: through the current regulation module preset in the charging device, the charging current is dynamically adjusted according to the predicted charging current curve to ensure that the charging process matches the battery status;

[0078] S5, thermal management strategy application: During the charging process, the battery temperature is monitored in real time, and the cooling or heating system is activated according to the temperature changes to maintain the battery within the optimal temperature range to prevent overheating or overcooling;

[0079] S6, charging safety monitoring: continuously monitor the battery voltage, temperature and current changes during the entire charging process;

[0080] S7, charging completion and report generation: When the battery reaches the preset charging upper limit, the charging current is gradually reduced until charging is completed. After charging is completed, a charging report is generated, including the charging time, charging current changes, battery temperature changes and battery health status.

[0081] The data acquisition modules preset in S1 specifically include:

[0082] Voltage sensor: A high-precision voltage sensor is installed in the charging device to measure the voltage on the battery terminals and obtain voltage data. The voltage sensor is directly connected to the positive and negative poles of the battery and can continuously monitor the voltage changes of the battery during the charging process.

[0083] Temperature sensor: Use a thermocouple, thermistor or infrared temperature sensor to measure the battery temperature. The temperature sensor needs to be placed on the surface or inside the battery to obtain battery temperature data;

[0084] Impedance measurement device: Using AC impedance measurement technology, a small AC current is applied at a specific frequency, the response voltage is measured, the impedance value is calculated, and the internal impedance data of the battery is obtained;

[0085] Current sensor: Use Hall effect current sensor or shunt to measure charging current and obtain battery current data. Hall effect current sensor can measure current contactlessly. Shunt calculates current value by detecting voltage drop across resistor. Current sensor is installed in charging line to measure charging current through battery in real time.

[0086] S2 specifically includes:

[0087] S21, data preprocessing: obtain the monitored battery parameters and perform preprocessing, including data cleaning (remove noise and outliers in the monitoring data to ensure data accuracy), data smoothing (use filters to smooth the data to reduce the impact of data fluctuations on subsequent analysis), data calibration (according to the calibration parameters of the sensor, calibrate the raw data to eliminate system errors) and data normalization, and extract key features, including voltage change rate, temperature change rate, internal impedance change and charging current change;

[0088] S22, Health Status Assessment: A hybrid model based on physical models and data-driven is used to assess the battery health status and predict the remaining battery life, including:

[0089] (1) Physical model evaluation: The state of health (SOH) of a battery is evaluated by measuring its capacity decay. Typically, SOH is defined as the ratio of the current battery capacity to the new battery capacity, expressed as:

[0090] Among them, C current is the actual capacity of the current battery, C rated is the rated capacity of the battery, the actual capacity C current It can be obtained through constant current charge and discharge test;

[0091] (2) Data-driven model evaluation: Using historical battery parameters and real-time monitored battery parameters, machine learning algorithms are used to perform health status assessment and remaining life prediction, including:

[0092] (2.1) Health status assessment: Support vector machine is used to assess health status and a model is established, which is expressed as:

[0093] SOH = f(V, T, R, I), where f is a mapping function trained by a support vector machine, the input is the monitored voltage, temperature, internal impedance and current, and the output is the state of health (SOH);

[0094] In the specific implementation, the health status assessment model is refined as follows:

[0095]

[0096] Among them, α1, α2, …, α8 are weight coefficients obtained through SVM training.

[0097] (2.1) Remaining life prediction (RUL): Use random forest to predict the remaining life and build a model, which is expressed as:

[0098] RUL = g(V, T, R, I), where g is a mapping function obtained through random forest training, the input is the monitored voltage, temperature, internal impedance and current, and the output is the remaining life;

[0099] In the specific implementation, the remaining life prediction model is refined as follows:

[0100]

[0101] Among them, β1, β2, …, β8 are weight coefficients obtained through random forest training;

[0102] (2.3) The weighted average method is used to weight the health status assessment results of the physical model and the data-driven model to improve the accuracy and robustness of the health assessment, which is expressed as:

[0103] SOH combined =w1·SOH physical +w2·SOH data-driven , where w1 and w2 are weight coefficients that can be optimized through experimental data, SOH data-driven It is the health status assessed by the data-driven model;

[0104] S23, health status report: generating a health status report including the current battery health status and the predicted remaining battery life;

[0105] Through the above methods and calculation formulas, a comprehensive analysis of the battery health status and an accurate prediction of the remaining life can be achieved, ensuring the efficiency, safety and intelligence of the charging process.

[0106] S3 uses machine learning algorithms to predict the optimal charging current requirements of the battery based on the battery's health status and real-time battery parameters, including:

[0107] S31, data preparation: collecting historical battery parameters, real-time monitored battery parameters and corresponding battery health status;

[0108] S32, data preprocessing: preprocessing the collected data, including data cleaning and data normalization, and extracting key features that affect the charging current, including voltage, temperature, internal impedance, current charging current, voltage change rate, temperature change rate, internal impedance change and battery health status;

[0109] S33, model selection and training: Use the random forest algorithm to establish a charging current prediction model, including:

[0110] (1) Data division: Divide the collected data into training set and test set, using 80% of the data as the training set and 20% of the data as the test set;

[0111] (2) Model training: Use the training set data to train the random forest model, adjust the hyperparameters of the random forest (such as the number of trees, maximum depth, etc.) through cross-validation to obtain the best model, and use the optimized hyperparameters to train the final model. The specific training process is expressed as follows: optimal = g(X);

[0112] Where X = [V, T, R, I, ΔV / Δt, ΔT / Δt, ΔR, SOH] is the input feature vector, and g is the mapping function obtained by random forest training;

[0113] (3) Model evaluation: Use the test set data to evaluate the model performance and calculate the prediction accuracy and error of the model. The performance indicators include mean square error, mean absolute error and coefficient of determination. The formula is as follows:

[0114] Mean Squared Error (MSE):

[0115] Mean Absolute Error (MAE):

[0116] Coefficient of determination (R 2 ):

[0117] Among them, y i is the actual value, is the predicted value, is the average of the actual values, and n is the number of samples;

[0118] S34, real-time prediction: Use the real-time monitored battery parameters and the trained charging current prediction model to predict the charging current. The steps are as follows:

[0119] (1) Real-time data input: The model inputs real-time battery parameters, expressed as:

[0120] X real-time =[V real-time ,T real-time ,R real-time ,I real-time ,ΔV / Δt real-time ,ΔT / Δt real-time ,ΔR real-time ,SOH real-time ];

[0121] Among them, V real-time is the voltage monitored in real time, T real-time is the real-time monitored temperature, R real-time is the internal impedance monitored in real time, I real-time is the current charging current monitored in real time, ΔV / Δt real-time is the voltage change rate calculated in real time, ΔT / Δt real-time is the temperature change rate calculated in real time, ΔR real-time is the internal impedance change calculated in real time, SOH real-time It is a real-time assessment of the battery health status;

[0122] (2) Model prediction: Use the trained charging current prediction model to predict the optimal charging current requirement, expressed as: I optimal =g(X real-time );

[0123] Among them, g is the mapping function obtained by training the random forest algorithm, X real-time is the battery parameter vector obtained in real time, I optimal is the optimal charging current requirement predicted by the model.

[0124] S3 generates a charging current curve adapted to the current battery state, including:

[0125] S35, Charging stage: Electric vehicle battery charging is divided into constant current charging stage, constant voltage charging stage and floating charging stage;

[0126] Constant current charging stage: In the constant current charging stage, the current remains constant until the battery voltage reaches the set value. The optimal charging current is determined by the prediction model and is expressed as:

[0127] I charging (t) = I optimal ;

[0128] Constant voltage charging stage: In the constant voltage charging stage, the voltage remains constant and the charging current gradually decreases. The formula is:

[0129]

[0130] Among them, I charging (t) is the charging current at time t, I optimal is the optimal charging current predicted by the model, α is the attenuation coefficient determined according to the battery characteristics, and t0 is the starting time of the constant voltage stage;

[0131] Floating charge stage: During the floating charge stage, the current is kept at a minimum constant value to maintain the battery voltage within a safe range, expressed as:

[0132] I charging (t) = I float ;

[0133] Among them, I float It is the current during the floating charge phase, which is generally a smaller value to maintain the battery charge;

[0134] S36, generating a charging current curve: combining the charging current formulas of the constant current charging stage, the constant voltage charging stage and the floating charging stage, generating a complete charging current curve, expressed as:

[0135]

[0136] Among them, V set is the set voltage of the constant voltage stage, and t0 is the starting time of the constant voltage stage.

[0137] The current regulation module preset in S4 includes a control algorithm unit and a current regulator. S4 dynamically adjusts the charging current according to the predicted charging current curve, specifically including:

[0138] S41, charging current calculation: The control algorithm unit dynamically calculates the required charging current based on the real-time battery parameters and the predicted charging current curve, expressed as:

[0139]

[0140] Among them, V set is the set voltage in the constant voltage stage, V max is the maximum charging voltage of the battery, α is the attenuation coefficient determined according to the battery characteristics, t0 is the starting time of the constant voltage stage, I optimal is the predicted optimum charging current, I float is the charging current during the floating charge phase;

[0141] S42, real-time adjustment of charging current: the current regulator adjusts the charging current in real time according to the output of the control algorithm unit;

[0142] The current regulation process is as follows:

[0143] Current setting: According to the calculation results, set the output current I of the current regulator set ;

[0144] Anti-price control: through real-time monitoring of current I(t) and set value I set Make a comparison and adjust the current regulator output to make the actual charging current consistent with the set value;

[0145] S43, real-time monitoring and dynamic adjustment: During the charging process, the battery parameters and charging current are continuously monitored, and the charging current is dynamically adjusted according to the actual situation to ensure a safe and efficient charging process.

[0146] S5 specifically includes:

[0147] S51, monitoring battery temperature: monitoring the battery temperature in real time through a temperature sensor;

[0148] S52, data analysis: analyzing the real-time monitored temperature data, calculating the temperature change rate to evaluate the temperature change trend;

[0149] S53, set temperature range: set the optimal temperature range of the battery [T min , T max ], the battery temperature should be maintained within this range to ensure safety and optimal performance;

[0150] S54, dynamic adjustment: dynamically adjust the cooling or heating system according to the real-time monitored temperature data and temperature changes;

[0151] Cooling system control: When the temperature exceeds the upper limit T max When , the cooling system is started, which is expressed as:

[0152] ifT(t)>T max , then start cooling system;

[0153] Heating system control: When the temperature is lower than the lower limit T min When , the heating system is started, which is expressed as:

[0154] ifT(t) <T min , then start the heating system;

[0155] Regulation formula: Use the proportional control (P control) algorithm to regulate the power output of the cooling or heating system;

[0156] P cooling =K c ·(T(t)-T max );

[0157] P heating =K h ·(T min -T(t));

[0158] Among them, K c and K h are the proportionality factors for cooling and heating systems, respectively;

[0159] S55, real-time monitoring and adjustment: During the charging process, the battery temperature is continuously monitored and dynamically adjusted according to the actual situation to ensure that the battery temperature remains within the optimal range;

[0160] S56, protection mechanism: set upper and lower temperature limit protection. When the temperature exceeds the limit, take emergency measures (such as suspending charging) to protect the battery safety;

[0161] Through the above steps and methods, the battery temperature can be monitored in real time during the charging process, and the cooling or heating system can be dynamically started and adjusted according to the temperature changes to maintain the battery within the optimal temperature range, prevent overheating or overcooling, and ensure the safety of the charging process and extend the battery life.

[0162] S6 specifically includes:

[0163] S61, data receiving: receiving the vehicle battery parameters monitored by the data acquisition module in real time, including voltage, temperature and current charging current;

[0164] S62, data processing: processing the real-time received automobile battery parameters, and calculating the voltage change rate, temperature change rate and current change rate;

[0165] S63, safety range setting: Set the safety range of battery parameters, including voltage safety range [V min V max ]、Temperature safety range [T min T max ] and current safety range [I min I max ];

[0166] Use the following formula to detect potential security risks:

[0167] if V(t)>V max or V(t) <V min ;

[0168] then trigger overvoltage / undervoltage protection;

[0169] ifT(t)>T max or T(t) <T min ;

[0170] then trigger overtemperature / undertemperature protec;

[0171] ifI(t)>I max or I(t) min ;

[0172] then trigger overcurrent / undercurrent protection;

[0173] S64, dynamically adjust charging strategy: when overheating, overvoltage or overcurrent risks are detected, reduce charging current or suspend charging to mitigate safety risks;

[0174] The formula for reducing the charging current is: I new (t) = k·I(t);

[0175] Wherein, k is a regulation coefficient less than 1, which is set according to specific circumstances, and is usually taken as 0.5≤k≤0.8, and I(t) is the current charging current.

[0176] S7 specifically includes:

[0177] S71, setting the upper limit of charging: setting the upper limit of the charging voltage of the battery and the upper limit of the charging current of the battery;​

[0178] S72, gradually reducing the charging current: when the battery voltage reaches a preset upper charging limit, gradually reducing the charging current;

[0179] The exponential decreasing function is used to gradually reduce the charging current, which can be expressed as:

[0180]

[0181] Among them, I charging (t) is the charging current at time t, β is the attenuation coefficient, which is set according to the battery characteristics, and t0 is the starting time for gradually reducing the charging current;

[0182] During the charging process, the battery voltage and temperature are continuously monitored and the charging current is dynamically adjusted, which is expressed as:

[0183]

[0184] S73, charging completed: When the charging current drops to the set minimum value, charging is considered completed, expressed as:

[0185] if I charging (t)≤I min , then stop charging;

[0186] S74, generate a charging report: after charging is completed, generate a charging report including charging time, charging current change, battery temperature change and battery health status.

[0187] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0188] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. An electric vehicle charging method with adaptive current regulation, characterized in that: The following steps are involved: S1, battery parameter monitoring: real-time monitoring of electric vehicle battery parameters, including voltage, temperature, internal impedance and current charging current, through the preset data acquisition module in the charging equipment; S2, battery health status assessment: Based on the monitored battery parameter data, the battery health status is analyzed through a preset health assessment algorithm; S3, charging demand prediction: Based on the battery health status and real-time battery parameters, the machine learning algorithm is used to predict the optimal charging current demand of the battery and generate a charging current curve that adapts to the current battery status; S4, dynamic current regulation: dynamically adjust the charging current according to the predicted charging current curve through the current regulation module preset in the charging device; S5, thermal management strategy application: During the charging process, the battery temperature is monitored in real time, and the cooling or heating system is activated according to the temperature changes to maintain the battery within the optimal temperature range to prevent overheating or overcooling; S6, charging safety monitoring: continuously monitor the battery voltage, temperature and current changes during the entire charging process; S7, charging completion and report generation: When the battery reaches the preset charging upper limit, the charging current is gradually reduced until charging is completed. After charging is completed, a charging report is generated, including the charging time, charging current changes, battery temperature changes and battery health status.

2. The electric vehicle charging method with adaptive current regulation according to claim 1, characterized in that: The data acquisition module preset in S1 specifically includes: Voltage sensor: A voltage sensor is installed in the charging device to measure the voltage on the battery terminal and obtain voltage data; Temperature sensor: Use a thermocouple, thermistor or infrared temperature sensor to measure the temperature of the battery and obtain battery temperature data; Impedance measurement device: Using AC impedance measurement technology, a small AC current is applied at a specific frequency, the response voltage is measured, the impedance value is calculated, and the internal impedance data of the battery is obtained; Current sensor: Use Hall effect current sensor or shunt to measure charging current and obtain battery current data.

3. The electric vehicle charging method with adaptive current regulation according to claim 2, characterized in that: The S2 specifically includes: S21, data preprocessing: obtaining the monitored battery parameters and performing preprocessing, including data cleaning, data smoothing, data calibration and data normalization, and extracting key features, including voltage change rate, temperature change rate, internal impedance change and charging current change; S22, health status assessment: a hybrid model based on physical models and data-driven is used to assess the battery health status and predict the remaining battery life; S23, health status report: Generate a health status report, including the current battery health status and the predicted remaining battery life.

4. The electric vehicle charging method with adaptive current regulation according to claim 3 is characterized in that: In S3, the optimal charging current requirement of the battery is predicted by using a machine learning algorithm according to the health status of the battery and the real-time battery parameters, specifically including: S31, data preparation: collecting historical battery parameters, real-time monitored battery parameters and corresponding battery health status; S32, data preprocessing: preprocessing the collected data, including data cleaning and data normalization, and extracting key features that affect the charging current, including voltage, temperature, internal impedance, current charging current, voltage change rate, temperature change rate, internal impedance change and battery health status; S33, model selection and training: using random forest algorithm to establish charging current prediction model; S34, real-time prediction: using the battery parameters monitored in real time and the trained charging current prediction model to predict the charging current.

5. The electric vehicle charging method with adaptive current regulation according to claim 4, characterized in that: The step S3 generates a charging current curve adapted to the current battery state, specifically including: S35, Charging stage: Electric vehicle battery charging is divided into constant current charging stage, constant voltage charging stage and floating charging stage; Constant current charging stage: In the constant current charging stage, the current remains constant until the battery voltage reaches the set value, expressed as: I charging (t)=I optimal ; Constant voltage charging stage: In the constant voltage charging stage, the voltage remains constant and the charging current gradually decreases. The formula is: Among them, I charging (t) is the charging current at time t, I optimal is the optimal charging current predicted by the model, α is the attenuation coefficient determined according to the battery characteristics, and t0 is the starting time of the constant voltage stage; Floating charge stage: During the floating charge stage, the current is kept at a minimum constant value to maintain the battery voltage within a safe range, expressed as: I charging (t)=I float ; Among them, I float It is the current during the floating charge phase, which is generally a smaller value to maintain the battery charge; S36, generating a charging current curve: combining the charging current formulas of the constant current charging stage, the constant voltage charging stage and the floating charging stage, generating a complete charging current curve, expressed as: Among them, V set is the set voltage of the constant voltage stage, and t0 is the starting time of the constant voltage stage.

6. The electric vehicle charging method with adaptive current regulation according to claim 5, characterized in that: The current regulation module preset in S4 includes a control algorithm unit and a current regulator. The charging current is dynamically adjusted according to the predicted charging current curve in S4, specifically including: S41, charging current calculation: The control algorithm unit dynamically calculates the required charging current based on the real-time battery parameters and the predicted charging current curve, expressed as: Among them, V set is the set voltage in the constant voltage stage, V max is the maximum charging voltage of the battery, α is the attenuation coefficient determined according to the battery characteristics, t0 is the starting time of the constant voltage stage, I optimal is the predicted optimum charging current, I float is the charging current during the floating charge phase; S42, real-time adjustment of charging current: the current regulator adjusts the charging current in real time according to the output of the control algorithm unit; S43, real-time monitoring and dynamic adjustment: During the charging process, the battery parameters and charging current are continuously monitored, and the charging current is dynamically adjusted according to the actual situation.

7. The electric vehicle charging method with adaptive current regulation according to claim 6, characterized in that: The S5 specifically includes: S51, monitoring battery temperature: monitoring the battery temperature in real time through a temperature sensor; S52, data analysis: analyzing the real-time monitored temperature data, calculating the temperature change rate to evaluate the temperature change trend; S53, setting temperature range: setting the optimal temperature range of the battery; S54, dynamic adjustment: dynamically adjust the cooling or heating system according to the real-time monitored temperature data and temperature changes; S55, real-time monitoring and adjustment: During the charging process, the battery temperature is continuously monitored and dynamically adjusted according to the actual situation; S56, protection mechanism: set upper and lower temperature limit protection. When the temperature exceeds the limit, take emergency measures to protect the battery safety.

8. The electric vehicle charging method with adaptive current regulation according to claim 7, characterized in that: The S6 specifically includes: S61, data receiving: receiving the vehicle battery parameters monitored by the data acquisition module in real time, including voltage, temperature and current charging current; S62, data processing: processing the real-time received automobile battery parameters, and calculating the voltage change rate, temperature change rate and current change rate; S63, safety range setting: setting the safety range of battery parameters, including voltage safety range, temperature safety range and current safety range; S64, dynamically adjust charging strategy: When overheating, overvoltage or overcurrent risks are detected, reduce charging current or suspend charging to mitigate safety risks.

9. The electric vehicle charging method with adaptive current regulation according to claim 8, characterized in that: The S7 specifically includes: S71, setting the upper limit of charging: setting the upper limit of the charging voltage of the battery and the upper limit of the charging current of the battery; S72, gradually reducing the charging current: when the battery voltage reaches a preset upper charging limit, gradually reducing the charging current; S73, charging completed: when the charging current drops to the set minimum value, charging is considered completed; S74, generate a charging report: after charging is completed, generate a charging report including charging time, charging current change, battery temperature change and battery health status.

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