Intelligent charging management system and method based on battery state of health

Abstract

The application discloses an intelligent charging management system and method based on a battery health state, which is used for managing an on-board battery on a new energy electric vehicle and comprises a battery health monitoring module, an intelligent decision module, a charging execution module and a thermal management module. The application corrects a battery health state SOH evaluation value through the battery health monitoring module, cooperates with the intelligent decision module based on the SOH evaluation value, dynamically outputs an optimal charging curve, integrates pulse charging through the charging execution module to reduce polarization and recycle charging waste heat, and can timely start a battery balancing function to delay the attenuation speed of the battery life, reduce the risk of thermal runaway caused by fast charging, reduce charging loss to improve the energy utilization rate, realize real-time monitoring and dynamic regulation, and achieve the effect of optimizing the charging efficiency, safety and battery life.

Description

Technical Field

[0001] This invention relates to the field of vehicle battery charging control technology, and more particularly to an intelligent charging management system based on battery health status, and also to a management method that utilizes this system to achieve intelligent battery charging management. Background Technology

[0002] New energy vehicles include pure electric vehicles, range-extended electric vehicles, hybrid electric vehicles, fuel cell electric vehicles, and hydrogen engine vehicles. All new energy electric vehicles are equipped with onboard batteries for storing and releasing electrical energy. These batteries require a corresponding battery management system to perform charging and discharging tasks. Currently, existing new energy vehicle charging systems generally use a fixed current or fixed voltage charging mode, failing to dynamically adjust charging strategies based on the battery's state of health (SOH). This leads to problems such as rapid battery pack lifespan degradation, increased risk of thermal runaway during fast charging, and reduced energy utilization efficiency. Specifically, without dynamic adjustment of charging strategies based on SOH, the average annual capacity loss of the battery pack exceeds 15%, causing rapid lifespan degradation; the rate of battery thermal runaway accidents caused by fast charging increases to 43% of battery charging failures; and energy loss due to charging reaches 20%-30%.

[0003] Therefore, it is necessary to further improve the charging management of on-board batteries in new energy vehicles in order to eliminate the above-mentioned defects. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide an intelligent charging management system based on battery health status that can achieve triple optimization of charging efficiency, safety and battery life through real-time monitoring and dynamic control.

[0005] To solve the above-mentioned technical problems, the technical solution of the present invention is: an intelligent charging management system based on battery health status, used for managing the on-board battery in new energy electric vehicles, including a battery health monitoring module, an intelligent decision-making module, a charging execution module, and a thermal management module. The battery health monitoring module includes a multi-physics sensor array for signal acquisition and an electrochemical impedance spectroscopy (EIS) detection unit. Both the multi-physics sensor array and the EIS detection unit are connected to a main control unit (MCU). The MCU contains a pre-set battery health status assessment model. The multi-physics sensor array detects the temperature, voltage, current, vibration, and pressure signals of the vehicle battery. The EIS detection unit performs timed internal resistance scans of the vehicle battery. The battery health status assessment model corrects the SOH (State of Health) assessment value by fusing the ampere-hour integral method and the Kalman filter algorithm. The intelligent decision-making module is connected to the main control unit (MCU) and includes a charging strategy generation unit and a multi-objective optimization algorithm unit. The charging strategy generation unit dynamically outputs the optimal charging curve by constructing a battery aging prediction model. The multi-objective optimization algorithm unit performs Pareto optimal decision-making based on charging speed, battery life, and energy efficiency. The charging execution module is connected to the main control unit MCU and is equipped with a bidirectional DC-DC converter, a pulse charging controller and a waste heat recovery unit. Under the coordinated control of the battery health monitoring module and the intelligent decision module, it implements voltage balancing, charging and waste heat recovery of the vehicle battery during the charging process. The thermal management module is equipped with a liquid-cooled / air-cooled composite heat dissipation unit and a temperature field equalization control unit. The liquid-cooled / air-cooled composite heat dissipation unit automatically switches between air-cooled and liquid-cooled heat dissipation modes according to the charging rate to balance the voltage of the vehicle battery and dissipate heat during charging. The temperature field equalization control unit works in conjunction with the liquid-cooled / air-cooled composite heat dissipation unit to control the temperature difference between the individual battery cells in the vehicle battery.

[0006] As a preferred technical solution, the electrochemical impedance spectroscopy detection unit performs an internal resistance scan of the vehicle battery every 10 seconds, with a scanning accuracy of 0.1 mΩ.

[0007] As a preferred technical solution, the bidirectional DC-DC converter supports a wide current adjustment range of 0-150A; The pulse output of the pulse charging controller is 5-500Hz; The waste heat recovery unit is a phase change material (PCM) coupled thermoelectric generator (TEG).

[0008] As a preferred technical solution, the battery aging prediction model constructed by the charging strategy generation unit is based on an LSTM neural network.

[0009] As a preferred technical solution, the liquid-cooled / air-cooled composite heat dissipation unit includes an air-cooled subunit and a liquid-cooled subunit; The air-cooled subunit includes two air ducts that are respectively connected to the inside of the vehicle battery. A centrifugal fan and an electric air duct baffle are connected in series on the air duct. A PWM speed control module is installed on the centrifugal fan for use. The liquid cooling subunit includes a plate heat exchanger used in conjunction with the vehicle battery, and a liquid cooling pipe is connected to the plate heat exchanger. A refrigerant storage device, a centrifugal pump and a solenoid valve are sequentially connected in series on the liquid cooling pipe. The temperature field equalization control unit includes a heat dissipation controller, and the heat dissipation controller is connected to the main control unit MCU. The centrifugal fan, the electric air duct baffle, the PWM speed control module, the centrifugal pump and the solenoid valve are respectively connected to the heat dissipation controller.

[0010] As a preferred technical solution, the refrigerant storage device stores a 50% ethylene glycol solution, which is used as the refrigerant for the vehicle battery.

[0011] This invention also relates to a management method for the aforementioned intelligent charging management system based on battery health status, comprising the following steps: Real-time monitoring The multi-physics sensor array is used to collect the temperature, voltage, current, vibration and pressure signals of the vehicle battery in real time, and the collected signals are sent to the main control unit MCU. The internal resistance of the vehicle battery is scanned every 10 seconds using the electrochemical impedance spectroscopy detection unit, and the scan data is transmitted to the main control unit MCU. Health assessment The battery health monitoring module, based on data from real-time monitoring steps, uses a fusion of the ampere-hour integral method and the Kalman filter algorithm to obtain the battery health state assessment model used to correct the battery health state (SOH) assessment value. First, the initial state of health (SOH) of the battery is calculated using the ampere-hour integration method. The calculation formula is as follows: SOH init =Q current / Q rated ×100% In the formula, Q current The current maximum usable capacity of the vehicle battery is calculated using ∫I·η·dt, where I is the charging current, η is the dynamically corrected coulombic efficiency, and Q is the current. rated The rated capacity of the vehicle battery; Then, based on the state equation and the observation equation, the battery health state (SOH) value is iteratively corrected using a Kalman filter algorithm, with a corrected error rate of <3%. The state equation is... In the formula, x k Let x be the state of health (SOH) assessment value of the battery at time k. k-1 I represents the State of Health (SOH) assessment value of the battery at time k-1. k Let η be the charging current at time k. k For dynamically corrected Coulomb efficiency, ω k This refers to process noise in the state equations. The observation equation is z k=OCV(SOH) k T k )+R k ·I k +v k In the formula, z k Let OCV be the observed battery terminal voltage at time k, and SOH be the open-circuit voltage. k Let T be the state of health (SOH) value of the battery at time k. k Let R be the battery temperature at time k. k Let I be the battery internal resistance at time k. k Let v be the charging current at time k. k The observation noise in the observation equation; The battery health status assessment model is trained based on historical data to dynamically calculate the remaining battery capacity (RC), equivalent circuit parameters R0, and R... ct and C dl ; Strategy Generation A battery aging prediction model is constructed based on an LSTM neural network. The model input parameters include historical current, voltage, temperature, internal resistance, and cycle count. The cycle count is set to 50 times, i.e., 50 sampling points in the time window. A 64-neuron LSTM layer is used to capture temporal features, and the predicted state of health (SOH) of the battery after the next 50 cycles is output. The training uses 100 sets of battery SOH values, with the SOH range being 50-100%. MSE is used as the loss function, and the parameters are updated online every 50 charge-discharge cycles, dynamically outputting the optimal charging curve. Pareto optimal decision-making is performed based on charging speed, battery life, and energy efficiency as objective functions. The NSGA-II algorithm is used to solve the Pareto solution set. Weights are dynamically allocated according to the battery health state (SOH) to dynamically adjust charging parameters and control the charging speed. The charging speed formula is F1 = 1 / t charge In the formula t charge Time taken to charge; The battery life formula is: In the formula, E a The activation energy reflects temperature sensitivity, and the unit is J / mol. R is the gas constant (8.314 J / (mol•K)), T+273 is the absolute temperature, and I is the battery current. The energy efficiency formula is F3 = Q output / Q input ×100%, where Q output Q represents the total energy output during the discharge phase. input The total energy input during the charging phase; Implementation of regulation The charging execution module is used to implement multi-mode collaborative charging and intelligent balancing as needed; Multi-mode collaborative charging is, When the battery's state of health (SOH) is ≥ 90%, the charging rate is limited to 0.8C or higher. When 80% ≤ State of Health (SOH) < 90%, the charging rate is limited to less than 0.8 and greater than or equal to 0.5C. When the battery health status (SOH) is less than 80%, the charging rate is limited to less than or equal to 0.5C and intelligent balancing is implemented. Intelligent balancing is, When the voltage difference of a battery cell is greater than 5mV, the pulse charging controller and the bidirectional DC-DC converter work together to transfer energy from the high-voltage battery cell to the low-voltage battery cell within a response time of less than 10ms, thus completing voltage equalization; when the battery health state (SOH) is less than 80%, the equalization current is increased by 20%. The thermal management module automatically switches between air cooling and liquid cooling modes based on the charging rate. When the charging rate is ≤0.5C and the vehicle battery temperature is 25-35℃, the liquid-cooled / air-cooled composite heat dissipation unit is controlled to implement low-level air-cooling heat dissipation. When the temperature is >35℃ or the temperature difference between individual battery cells is >2℃, it switches to high-level air-cooling heat dissipation. When the charging rate is >0.8C and the vehicle battery temperature is 25-35℃, the liquid-cooled / air-cooled composite heat dissipation unit is controlled to implement low-flow liquid cooling heat dissipation. When the temperature is >35℃ or the temperature difference between individual battery cells is >2℃, it switches to high-flow liquid cooling heat dissipation. The liquid-cooled / air-cooled composite heat dissipation unit is controlled by a PID algorithm to ensure that the battery temperature fluctuation is <1℃. When 0.5C < charging rate ≤ 0.8C and the vehicle battery temperature is 25-35℃, the liquid cooling / air cooling composite heat dissipation unit is controlled to implement low-level air cooling and low-flow liquid cooling for coordinated heat dissipation; when the temperature is > 35℃ or the temperature difference of the battery cells is > 2℃, it switches to high-level air cooling and high-flow liquid cooling for coordinated heat dissipation. The temperature field equalization control unit works in conjunction with the liquid cooling / air cooling composite heat dissipation unit to control the temperature difference between individual battery cells in the vehicle battery to be less than 2℃.

[0012] As an improvement to the above technical solution, the charging execution module receives battery health status (SOH) difference data between individual battery cells obtained through the battery health monitoring module. When the difference is greater than 10%, the equalization priority is increased. When the charging execution module performs intelligent balancing during the charging process, it automatically reduces the charging current by 10%-20%. After equalization is completed, the consistency data of individual battery cells is fed back to the intelligent decision-making module to optimize the charging curve; The heat generated during the intelligent balancing process is simultaneously dissipated by the thermal management module.

[0013] As an improvement to the above technical solution, the process of increasing the equalization priority is as follows: pause the current charging process, raise the response priority of intelligent equalization to the highest level, shorten the voltage difference detection interval, and increase the equalization current to 1.5 times the normal value until the SOH difference between battery cells is ≤5%, then resume the original charging process.

[0014] By adopting the above technical solution, the present invention has the following beneficial effects: the battery health monitoring module corrects the battery health status (SOH) assessment value, and based on this, in conjunction with the intelligent decision-making module, dynamically outputs the optimal charging curve; the charging execution module integrates pulse charging to reduce polarization and recover charging waste heat, and can promptly activate the battery balancing function to slow down the rate of battery life decay, reduce the risk of thermal runaway caused by fast charging, and reduce charging loss to improve energy utilization. Through real-time monitoring and dynamic control, the present invention optimizes charging efficiency, safety, and battery life. Attached Figure Description

[0015] The following figures are intended only to illustrate and explain the present invention and do not limit the scope of the invention. Wherein: Figure 1 This is a simplified block diagram of the liquid-cooled / air-cooled composite heat dissipation unit according to an embodiment of the present invention; Figure 2 This is a simplified flowchart of the multi-mode collaborative charging method according to an embodiment of the present invention; In the diagram: 1-Vehicle battery; 2-Air duct; 3-Centrifugal fan; 4-Electric air duct baffle; 5-PWM speed control module; 6-Plate heat exchanger; 7-Liquid cooling pipe; 8-Refrigerant storage; 9-Centrifugal pump; 10-Solenoid valve; 11-Heat dissipation controller. Detailed Implementation

[0016] The present invention will be further described below with reference to the accompanying drawings and embodiments. In the following detailed description, only certain exemplary embodiments of the invention are described by way of illustration. Undoubtedly, those skilled in the art will recognize that various modifications can be made to the described embodiments without departing from the spirit and scope of the invention. Therefore, the drawings and description are illustrative in nature and not intended to limit the scope of the claims.

[0017] The intelligent charging management system based on battery health status is used to manage the on-board batteries of new energy electric vehicles. Through real-time monitoring and dynamic adjustment, it optimizes charging efficiency, safety, and battery life, realizing the safe and energy-saving use of the on-board batteries. Specifically, it is accomplished through the cooperation of a battery health monitoring module, an intelligent decision-making module, a charging execution module, and a thermal management module.

[0018] The battery health monitoring module is based on battery health status quantitative assessment technology. This module includes a multiphysics sensor array for signal acquisition and an electrochemical impedance spectroscopy (EIS) detection unit. The multiphysics sensor array and the EIS detection unit are respectively connected to a main control unit (MCU). The MCU has a pre-set battery health status assessment model. Based on the detection signals from the multiphysics sensor array and the EIS detection unit, the MCU processes the data to obtain the battery health status (SOH) and corrects it to ensure an error rate of <3%. The specific process for obtaining the battery health status (SOH) is well-known to those skilled in the art and will not be described in detail here.

[0019] The multiphysics sensor array in the battery health monitoring module is used to detect the temperature, voltage, current, vibration, and pressure signals of the vehicle battery. For example, it employs a temperature sensor from TE Connectivity (accuracy within ±0.1℃) and a MAX471 current sensor from MAXIM (accuracy within ±0.5%). The vibration signal is used to monitor mechanical anomalies such as loose battery cell connections and casing deformation. A preset threshold can be set in the main control unit (MCU) as a reference parameter for control. This is the first time vibration signals have been introduced into the battery health monitoring module. The vibration signal can be used as one of the input parameters of the model, participating in fusion calculations along with temperature, voltage, current, pressure signals, and internal resistance data acquired by the electrochemical impedance spectroscopy detection unit.

[0020] Specifically, after the amplitude and frequency characteristics of the vibration signal are quantized, they are input into the battery health status assessment model preset in the main control unit MCU. This model is used to correct the error coefficients in the fusion process of the ampere-hour integral method and the Kalman filter algorithm. For example, when the vibration signal is within the normal range (e.g., amplitude ≤ 0.3g, where g is the acceleration due to gravity), the model assigns it a lower weight. When the vibration signal exhibits abnormal fluctuations (e.g., amplitude > 0.3g and duration > 1s), the model increases the weight of this signal to enhance the correction of the battery health status (SOH) assessment value, thereby reflecting the impact of mechanical vibration on the stability of the battery's internal structure and ultimately improving the accuracy of the SOH assessment.

[0021] This helps improve early fault warning capabilities. The electrochemical impedance spectroscopy (EIS) detection unit performs timed internal resistance scans on the vehicle battery, such as scanning the internal resistance of the vehicle battery every 10 seconds, with a scanning accuracy of 0.1 mΩ. This high accuracy ensures a low error rate in the battery state of health (SOH) correction assessment. Furthermore, the battery state of health assessment model corrects the battery SOH assessment value by integrating the ampere-hour integral method and the Kalman filter algorithm, thus solving the problem of large errors in traditional battery SOH assessments.

[0022] The intelligent decision-making module is connected to the main control unit (MCU) and is used to dynamically output the optimal charging curve. This module includes a charging strategy generation unit and a multi-objective optimization algorithm unit. The charging strategy generation unit dynamically outputs the optimal charging curve by constructing a battery aging prediction model. The multi-objective optimization algorithm unit performs Pareto optimal decisions based on charging speed, battery life, and energy efficiency. The main control unit (MCU) is based on the NXP S32K3 series microcontroller and integrates a neural network acceleration unit (NNA) for constructing the battery aging prediction model. The charging strategy generation unit is based on an LSTM neural network, and with the cooperation of the neural network acceleration unit (NNA), it completes the construction of the battery aging prediction model.

[0023] The charging execution module is connected to the main control unit (MCU) and works with the InfineonHybridPACK™ Drive 2 module installed on the vehicle body. It supports a high power density of 1200V / 300A and is equipped with a bidirectional DC-DC converter, a pulse charging controller, and a waste heat recovery unit. Under the coordinated control of the battery health monitoring module and the intelligent decision-making module, it performs voltage balancing, charging, and waste heat recovery during the charging process of the vehicle battery. The bidirectional DC-DC converter supports a wide current adjustment range of 0-150A and has an efficiency of ≥98.5%. The pulse output of the pulse charging controller is a high-frequency pulse of 5-500Hz to reduce polarization effects.

[0024] The waste heat recovery unit recovers charging waste heat by coupling a phase change material (PCM) to a thermoelectric generator (TEG), improving the heat recovery efficiency to over 18%. Specifically, the waste heat recovery unit can be applied in the following ways: In low-temperature environments (battery temperature <15℃), the heat stored in the PCM is used to preheat the battery to reduce low-temperature charging losses; the electrical energy generated by the TEG, after DC-DC conversion, supplements the power supply to the vehicle's low-voltage system (such as headlights and central control screen), reducing the main battery's energy consumption.

[0025] The thermal management module is equipped with a liquid-cooled / air-cooled composite heat dissipation unit and a temperature field equalization control unit. The liquid-cooled / air-cooled composite heat dissipation unit automatically switches between air-cooled and liquid-cooled heat dissipation modes according to the charging rate to balance the voltage of the vehicle battery and dissipate heat during charging. The temperature field equalization control unit works in conjunction with the liquid-cooled / air-cooled composite heat dissipation unit to control the temperature difference between the individual battery cells in the vehicle battery.

[0026] like Figure 1 As shown, the liquid-cooled / air-cooled composite heat dissipation unit includes an air-cooled subunit and a liquid-cooled subunit, which form two independent heat dissipation channels that can be used in combination.

[0027] The air-cooling subunit includes two air ducts 2, each with its own port connected to the interior of the vehicle battery 1. The air ducts 2 can be constructed of aluminum alloy tubing. A centrifugal fan 3 and an electric air duct baffle 4 are connected in series on the air ducts 2. A PWM speed control module 5 is installed on the centrifugal fan 3 for coordinated use. The electric air duct baffle 4 controls the flow and shut-off of the air ducts 2. The centrifugal fan 3 accelerates the airflow speed between the vehicle battery 1 and the air ducts 2 to adjust the heat dissipation efficiency. The PWM speed control module 5 controls the power of the centrifugal fan 3, allowing for real-time adjustment based on heat dissipation efficiency requirements. The centrifugal fan 3 can be adjusted within an airflow range of 0-1500 m³ / h, which is sufficient to meet the heat dissipation needs of the vehicle battery 1.

[0028] The liquid-cooled subunit includes a plate heat exchanger 6 used in conjunction with the vehicle battery 1. The plate heat exchanger 6 is located inside the vehicle battery 1 and surrounds the outer periphery of each battery cell; this is a conventional structure in this technical field and will not be described in detail here. A liquid-cooling pipe 7 is connected to the plate heat exchanger 6. A refrigerant storage tank 8, a centrifugal pump 9, and a solenoid valve 10 are sequentially connected in series on the liquid-cooling pipe 7. The centrifugal pump 9 and the solenoid valve 10 allow the refrigerant to circulate between the refrigerant storage tank 8, the liquid-cooling pipe 7, and the plate heat exchanger 6. A flow regulating valve can also be connected in series on the liquid-cooling pipe 7 to improve the flexibility of flow regulation. In this embodiment, heat dissipation of the vehicle battery 1 is achieved through heat exchange between the plate heat exchanger 6 and the battery cells, with a heat exchange efficiency ≥95%, and heat energy is recovered through the refrigerant. The refrigerant storage tank 8 stores a 50% ethylene glycol solution, which is used as the refrigerant for the vehicle battery 1.

[0029] The temperature field equalization control unit includes a heat dissipation controller 11, which is connected to the main control unit MCU. The centrifugal fan 3, the electric air duct baffle 4, the PWM speed control module 5, the centrifugal pump 9, and the solenoid valve 10 are respectively connected to the heat dissipation controller 11. The heat dissipation controller 11 can control the automatic start / stop or opening degree of the above components to meet the automatic switching between air cooling mode and liquid cooling mode, as well as the control of their combined use, so that the heat dissipation method of the vehicle battery 1 is flexible and easy to adjust.

[0030] This embodiment also relates to a management method for an intelligent charging management system based on battery health status, including the following steps: Real-time monitoring The multi-physics sensor array is used to collect the temperature, voltage, current, vibration and pressure signals of the vehicle battery in real time, and the collected signals are sent to the main control unit MCU so that the main control unit MCU can obtain the battery health status (SOH) assessment value.

[0031] The internal resistance of the vehicle battery is scanned every 10 seconds using the electrochemical impedance spectroscopy (EIS) detection unit, and the scan data is transmitted to the main control unit MCU so that the main control unit MCU can obtain the battery health status (SOH) assessment value.

[0032] Health assessment The battery health monitoring module, based on data from real-time monitoring steps, uses a fusion of the ampere-hour integral method and the Kalman filter algorithm to obtain the battery health state assessment model used to correct the battery health state (SOH) assessment value. Specifically, it first calculates the initial battery health state (SOH) using the ampere-hour integral method, with the following formula: SOH init =Q current / Q rated ×100% In the formula, Q current The current maximum usable capacity of the vehicle battery is calculated using ∫I·η·dt, where I is the charging current and η is the dynamically corrected coulombic efficiency, which can be calibrated by the vehicle battery manufacturer. rated This refers to the rated capacity of the vehicle battery.

[0033] Then, based on the state equation and the observation equation, the battery health state (SOH) value is iteratively corrected using a Kalman filter algorithm, with a correction error rate of <3%. The state equation is: In the formula, x k Let x be the state of health (SOH) assessment value of the battery at time k. k-1I represents the State of Health (SOH) assessment value of the battery at time k-1. k Let η be the charging current at time k. k For dynamically corrected Coulomb efficiency, ω k The process noise of the state equation (follows a Gaussian distribution with a mean of 0).

[0034] The observation equation is z k =OCV(SOH) k T k )+R k ·I k +v k In the formula, z k Let OCV be the observed battery terminal voltage at time k, and SOH be the open-circuit voltage. k Let T be the state of health (SOH) value of the battery at time k. k Let R be the battery temperature at time k. k Let I be the battery internal resistance at time k. k Let v be the charging current at time k. k The observation noise of the observation equation (following a Gaussian distribution with a mean of 0); The battery health status assessment model is trained based on historical data to dynamically calculate the remaining battery capacity (RC), equivalent circuit parameters R0, and R... ct and C dl .

[0035] Strategy Generation A battery aging prediction model is constructed based on an LSTM neural network. The model input parameters include historical current, voltage, temperature, internal resistance, and cycle count. The cycle count is set to 50 times, i.e., 50 sampling points in the time window. A single 64-neuron LSTM layer captures temporal features and outputs the predicted state of health (SOH) value of the battery after the next 50 cycles. The training uses 100 sets of battery SOH values, with the SOH range being 50-100%. The loss function is MSE. The parameters are updated online every 50 charge-discharge cycles, and the optimal charging curve is dynamically output. The specific model algorithm is as follows: Python: class BatteryHealthModel(tf.keras.Model): def __init__(self): super().__init__() self.lstm=tf.keras.layers.LSTM(64,return_sequences=True) self.dense = tf.keras.layers.Dense(1) def call(self, inputs): x = self.lstm(inputs) return self.dense(x) Pareto optimal decision-making is performed based on charging speed, battery life, and energy efficiency as objective functions. The NSGA-II algorithm is used to solve the Pareto solution set. The weights are dynamically allocated according to the battery health state (SOH) to dynamically adjust the charging parameters and control the charging speed. For example, when the battery health state (SOH) is ≥ 90%, the F1 weight is set to 40%.

[0036] The charging speed formula is F1 = 1 / t charge In the formula t charge The shorter the charging time, the faster the charging speed.

[0037] The battery life formula is: In the formula, E a The activation energy reflects temperature sensitivity, and the unit is J / mol. R is the gas constant (8.314 J / (mol•K)), T+273 is the absolute temperature, and I is the battery current.

[0038] The energy efficiency formula is F3 = Q output / Q input ×100%, where Q output Q represents the total energy output during the discharge phase (Wh). input The total energy input during the charging phase (Wh).

[0039] Implementation of regulation The charging execution module is used to implement multi-mode collaborative charging and intelligent balancing as needed.

[0040] like Figure 2 As shown, multi-mode collaborative charging determines the charging rate and heat dissipation method based on the battery health state (SOH). In this embodiment, when the battery health state (SOH) is ≥90%, the charging rate is limited to 0.8C or higher, fast charging is implemented, and heat dissipation is combined with the thermal management module; when 80% ≤ SOH < 90%, the charging rate is limited to less than 0.8C but greater than or equal to 0.5C, slow charging is implemented, and heat dissipation is combined with the thermal management module; when the battery health state (SOH) < 80%, the charging rate is limited to 0.5C or less, intelligent balancing is implemented, slow charging is performed, and heat dissipation is combined with the thermal management module.

[0041] Intelligent balancing involves transferring energy from the high-voltage battery cell to the low-voltage battery cell within a response time of less than 10ms when the voltage difference of a battery cell is greater than 5mV, through the cooperation of the pulse charging controller and the bidirectional DC-DC converter, thus completing voltage balancing. When the battery health state (SOH) is less than 80%, the balancing current is increased by 20% to balance the voltage values ​​between battery cells, and polarization is reduced by integrating pulse charging.

[0042] The thermal management module automatically switches between air cooling and liquid cooling modes based on the charging rate, ensuring the vehicle battery temperature remains between 25-35°C during charging. Specifically: When the charging rate is ≤0.5C and the vehicle battery temperature is 25-35℃, the liquid-cooled / air-cooled composite heat dissipation unit is controlled to implement low-level air-cooling to maintain the temperature. When the temperature is >35℃ or the temperature difference between individual battery cells is >2℃, it switches to high-level air-cooling to achieve cooling or temperature equalization. When the charging rate is >0.8C and the vehicle battery temperature is 25-35℃, the liquid-cooled / air-cooled composite heat dissipation unit is controlled to implement low-flow liquid cooling. When the temperature is >35℃ or the temperature difference between individual battery cells is >2℃, it switches to high-flow liquid cooling. The heat dissipation controller is equipped with a PID algorithm, which controls the liquid-cooled / air-cooled composite heat dissipation unit by controlling the speed of the centrifugal fan 3 and the flow rate of the solenoid valve 10. Through the cooperation of the two heat dissipation modes, the temperature fluctuation of the battery is kept <1℃, resulting in a smooth cooling of the vehicle battery 1.

[0043] When 0.5C < charging rate ≤ 0.8C and the vehicle battery temperature is 25-35℃, the liquid cooling / air cooling composite heat dissipation unit is controlled to implement low-level air cooling and low-flow liquid cooling for coordinated heat dissipation; when the temperature is > 35℃ or the temperature difference of the battery cells is > 2℃, it switches to high-level air cooling and high-flow liquid cooling for coordinated heat dissipation.

[0044] The temperature field equalization control unit works in conjunction with the liquid cooling / air cooling composite heat dissipation unit to control the temperature difference between individual battery cells in the vehicle battery to be less than 2℃, thereby achieving temperature equalization between individual battery cells.

[0045] The charging execution module in this embodiment can receive the State of Health (SOH) difference data between individual battery cells obtained by the battery health monitoring module. When the difference is greater than 10%, the equalization priority is increased to maintain the consistency of the state of each individual battery cell as much as possible. Specifically, the process of increasing the equalization priority is as follows: pausing the current charging process, raising the response priority of intelligent equalization to the highest level, shortening the voltage difference detection interval (from the usual 100ms to 50ms), and increasing the equalization current to 1.5 times the usual value, until the SOH difference between individual battery cells is ≤5%, then resuming the original charging process.

[0046] During the charging process executed by the charging execution module, intelligent balancing is simultaneously implemented, automatically reducing the charging current by 10%-20% to ensure safe and smooth balancing. After balancing is completed, battery cell consistency data is fed back to the intelligent decision module to optimize the charging curve. Based on the online learning algorithm of the intelligent decision module, the system can automatically update the charging curve every 50 charge-discharge cycles. The heat generated during intelligent balancing is simultaneously dissipated by the thermal management module.

[0047] The description of this invention is given for illustrative and descriptive purposes only and is not intended to be exhaustive or to limit the invention to the forms disclosed. Many modifications and variations will be apparent to those skilled in the art. The embodiments were chosen and described in order to better illustrate the principles and practical application of the invention and to enable those skilled in the art to understand the invention and to design various embodiments with various modifications suitable for a particular purpose.

Claims

1. An intelligent charging management system based on battery health status, used for managing onboard batteries in new energy electric vehicles, characterized by: It includes a battery health monitoring module, an intelligent decision-making module, a charging execution module, and a thermal management module. The battery health monitoring module includes a multi-physics sensor array for signal acquisition and an electrochemical impedance spectroscopy (EIS) detection unit. Both the multi-physics sensor array and the EIS detection unit are connected to a main control unit (MCU). The MCU contains a pre-set battery health status assessment model. The multi-physics sensor array detects the temperature, voltage, current, vibration, and pressure signals of the vehicle battery. The EIS detection unit performs timed internal resistance scans of the vehicle battery. The battery health status assessment model corrects the SOH (State of Health) assessment value by fusing the ampere-hour integral method and the Kalman filter algorithm. The intelligent decision-making module is connected to the main control unit (MCU) and includes a charging strategy generation unit and a multi-objective optimization algorithm unit. The charging strategy generation unit dynamically outputs the optimal charging curve by constructing a battery aging prediction model. The multi-objective optimization algorithm unit performs Pareto optimal decision-making based on charging speed, battery life, and energy efficiency. The charging execution module is connected to the main control unit MCU and is equipped with a bidirectional DC-DC converter, a pulse charging controller and a waste heat recovery unit. Under the coordinated control of the battery health monitoring module and the intelligent decision module, it implements voltage balancing, charging and waste heat recovery of the vehicle battery during the charging process. The thermal management module is equipped with a liquid-cooled / air-cooled composite heat dissipation unit and a temperature field equalization control unit. The liquid-cooled / air-cooled composite heat dissipation unit automatically switches between air-cooled and liquid-cooled heat dissipation modes according to the charging rate to balance the voltage of the vehicle battery and dissipate heat during charging. The temperature field equalization control unit works in conjunction with the liquid-cooled / air-cooled composite heat dissipation unit to control the temperature difference between the individual battery cells in the vehicle battery. The liquid-cooled / air-cooled composite heat dissipation unit includes an air-cooled subunit and a liquid-cooled subunit; The air-cooled subunit includes two air ducts that are respectively connected to the inside of the vehicle battery. A centrifugal fan and an electric air duct baffle are connected in series on the air duct. A PWM speed control module is installed on the centrifugal fan for use. The liquid cooling subunit includes a plate heat exchanger used in conjunction with the vehicle battery, and a liquid cooling pipe is connected to the plate heat exchanger. A refrigerant storage device, a centrifugal pump and a solenoid valve are sequentially connected in series on the liquid cooling pipe. The temperature field equalization control unit includes a heat dissipation controller, and the heat dissipation controller is connected to the main control unit MCU. The centrifugal fan, the electric air duct baffle, the PWM speed control module, the centrifugal pump and the solenoid valve are respectively connected to the heat dissipation controller.

2. The intelligent charging management system based on battery health status as described in claim 1, characterized in that: The electrochemical impedance spectroscopy detection unit performs an internal resistance scan of the vehicle battery every 10 seconds, with a scanning accuracy of 0.1 mΩ.

3. The intelligent charging management system based on battery health status as described in claim 1, characterized in that: The bidirectional DC-DC converter supports a wide current adjustment range of 0-150A; The pulse output of the pulse charging controller is 5-500Hz; The waste heat recovery unit is a phase change material (PCM) coupled thermoelectric generator (TEG).

4. The intelligent charging management system based on battery health status as described in claim 1, characterized in that: The battery aging prediction model constructed by the charging strategy generation unit is based on an LSTM neural network.

5. The intelligent charging management system based on battery health status as described in claim 1, characterized in that: The refrigerant storage device contains a 50% ethylene glycol solution, which is used as the refrigerant for the vehicle battery.

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