Hybrid semi-trailer battery management system and multi-mode equalization control method

Abstract

The present application relates to the technical field of battery management of hybrid vehicles, in particular to a hybrid semi-trailer battery management system and a multi-mode equalization control method, comprising a data acquisition module, a main control module, an equalization execution module, a thermal management module, a communication interface module and a safety protection module. The present application realizes accurate estimation of battery state and dynamic updating of parameters through advanced algorithms and models, provides decision basis for equalization and thermal management, adopts active and passive hybrid architecture and intelligent topology switching strategy, efficiently realizes energy management between cells, avoids energy waste, and uses reinforcement learning to dynamically regulate wind speed and direction to maintain cell temperature consistency with low energy consumption, significantly improves the energy utilization efficiency, state control accuracy and operation safety of the battery management system, effectively prolongs the service life of the battery and guarantees stable operation of the system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of battery management of hybrid vehicles, in particular to a hybrid semi-trailer battery management system and a multi-mode equalization control method. BACKGROUND

[0002] As a new type of freight vehicle that combines traditional fuel power and electric power, the hybrid semi-trailer significantly improves energy utilization efficiency and reduces emissions through dual power cooperation. Its core competitiveness depends on an efficient energy management system, especially the battery management system (BMS) that needs to accurately regulate battery status, balance energy distribution, and ensure safe operation. In the prior art, the current mainstream hybrid semi-trailer battery management technology still has the problems of single equalization mode, significant energy waste, insufficient state estimation accuracy, fixed thermal management strategy, and serious energy waste caused by insufficient state estimation accuracy, low decision reliability, fixed thermal management strategy, energy consumption and temperature control imbalance, and lagging safety protection, resulting in poor system stability.

[0003] Therefore, the present application provides a hybrid semi-trailer battery management system and a multi-mode equalization control method to solve the above technical problems. SUMMARY

[0004] The present application aims to provide a hybrid semi-trailer battery management system and a multi-mode equalization control method. The present application uses advanced algorithms and models to accurately estimate battery status and dynamically update parameters, providing decision-making basis for equalization and thermal management. It uses an active and passive hybrid architecture and an intelligent topology switching strategy to efficiently manage energy between cells, avoid energy waste, and use reinforcement learning to dynamically control wind speed and direction to maintain cell temperature consistency with low energy consumption. This significantly improves the energy utilization efficiency, state control accuracy, and operational safety of the battery management system, effectively extends the service life of the battery, and ensures stable operation of the system.

[0005] To achieve the above purpose, the present application provides the following technical solutions:

[0006] The present application provides a hybrid semi-trailer battery management system, which includes a data acquisition module, a main control module, an equalization execution module, a thermal management module, a communication interface module, and a safety protection module. Among them:

[0007] The data acquisition module is responsible for real-time monitoring of the key parameters of voltage, current, and temperature of each cell in the battery module, and transmitting them to the main control module through an isolation circuit.

[0008] The master module is used for receiving data from the data acquisition module, jointly estimating the state of charge and the state of health of the battery through an electrochemical-equivalent circuit hybrid model and an adaptive extended Kalman filtering algorithm, and dynamically updating the battery model parameters in combination with an online parameter identification mechanism;

[0009] The equalization execution module is used for realizing inter-cell energy management through a hybrid architecture of dynamically switching active equalization and passive equalization according to the instructions of the master module, wherein the active equalization reuses the excess energy through an energy transfer or feedback type topology, the passive equalization dissipates energy through a resistor, and the switching of the equalization mode is based on a difference threshold of the cell state parameters.

[0010] The thermal management module is used for dynamically adjusting the wind speed and flow direction of the air cooling system through reinforcement learning to maintain the inter-cell temperature difference within a set range.

[0011] The communication interface module is used for interacting data with the vehicle VCU and the charger through the CAN bus, and supports remote monitoring.

[0012] The safety protection module is used for monitoring overvoltage, undervoltage, overcurrent and temperature abnormalities in real time, and triggering graded protection.

[0013] The data acquisition module includes a voltage acquisition unit, a current acquisition unit, a temperature acquisition unit, an isolation transmission unit and a signal conditioning unit, wherein:

[0014] The voltage acquisition unit is used for acquiring the voltage signal of each cell terminal in real time, and the precision can reach the mV level.

[0015] The current acquisition unit is used for acquiring the charging and discharging current using a Hall sensor, and supports bidirectional current detection.

[0016] The temperature acquisition unit acquires the cell surface and environmental temperature through a thermistor or a digital temperature sensor.

[0017] The isolation transmission unit is used for electrically isolating the high-voltage and low-voltage circuits through magnetic coupling.

[0018] The signal conditioning unit is used for filtering, amplifying and denoising the original signal.

[0019] The master module includes an SOC estimation unit, an SOH estimation unit, a parameter identification unit, an equalization decision unit, a fault diagnosis unit and a control output unit, wherein:

[0020] The SOC estimation unit estimates the state of charge of the battery based on an electrochemical-equivalent circuit hybrid model and an adaptive extended Kalman filtering algorithm.

[0021] The SOH estimation unit is configured to estimate the battery health state by combining an aging model and historical data.

[0022] The parameter identification unit is configured to identify the key parameters of the battery online and dynamically update the model.

[0023] The balancing decision unit is configured to determine whether to start balancing and select a balancing mode according to the SOC and voltage difference between the battery cells.

[0024] The fault diagnosis unit is configured to monitor abnormal behavior and provide early warning of the risk of battery cell failure.

[0025] The control output unit is configured to generate control instructions and send them to the balancing execution module and the thermal management module.

[0026] The SOC estimation unit estimates the battery state of charge based on an electrochemical-equivalent circuit hybrid model and an adaptive extended Kalman filter algorithm, and the specific operation is as follows:

[0027] A1: Dynamically adjust the weight coefficients of the electrochemical model and the equivalent circuit model according to the battery temperature interval;

[0028] A2: Optimize the convergence of the EKF algorithm by updating the process noise covariance matrix and the observation noise covariance matrix in real time;

[0029] A3: Correct the initial SOC value under low temperature conditions based on the open circuit voltage-temperature mapping table.

[0030] The balancing execution module includes a balancing mode switching unit, an active balancing unit, a passive balancing unit, a balancing drive unit, and a balancing feedback unit, wherein:

[0031] The balancing mode switching unit is configured to automatically select an active or passive balancing mode according to the battery cell difference threshold;

[0032] The active balancing unit is configured to perform efficient energy transfer or feedback to the power grid through a DC / DC converter or a capacitor transfer topology;

[0033] The passive balancing unit is configured to dissipate energy from high-energy battery cells by connecting a discharge resistor through a MOSFET switch;

[0034] The balancing drive unit is configured to drive the power devices in the balancing circuit;

[0035] The balancing feedback unit is configured to feedback the current and voltage changes during the balancing process.

[0036] The active balancing unit performs efficient energy transfer or feedback to the power grid through a DC / DC converter or a capacitor transfer topology, and the specific operation is as follows:

[0037] B1: Automatically select Buck, Boost or Buck-Boost type DC / DC converter topology according to battery voltage difference;

[0038] B2: Feedback excess power to vehicle low-voltage system or power grid through bidirectional AC / DC converter;

[0039] B3: Real-time monitor conversion efficiency and dynamically adjust switching frequency.

[0040] The thermal management module includes a wind speed control unit, a wind direction adjustment unit, a temperature monitoring unit, and a reinforcement learning decision unit, wherein:

[0041] The wind speed control unit is used to control the fan speed and adjust the cooling intensity according to the current temperature difference and target temperature;

[0042] The wind direction adjustment unit is used to control the air duct baffle or fan direction to optimize air distribution;

[0043] The temperature monitoring unit is used to continuously monitor the battery temperature distribution and provide input for the control strategy;

[0044] The reinforcement learning decision unit dynamically adjusts the cooling strategy based on reinforcement learning algorithm to maintain temperature control target with minimum energy consumption.

[0045] The communication interface module includes a CAN communication unit and a protocol analysis unit, wherein:

[0046] The CAN communication unit is used to support CAN 2.0B protocol, data transmission rate is 500kbps, and communicates with vehicle controller VCU and charger through shielded twisted pair;

[0047] The protocol analysis unit performs CRC check and data frame analysis on received data packets based on SAE J1939 protocol standard, and sends control instructions encapsulated as UDS diagnostic protocol frame conforming to ISO 15765-2 standard.

[0048] The safety protection module includes a voltage monitoring unit, a current monitoring unit, a temperature and battery monitoring unit, and a hierarchical response unit, wherein:

[0049] The voltage monitoring unit is used to detect overvoltage and undervoltage events in real time and trigger protection action;

[0050] The current monitoring unit is used to monitor overcurrent and short circuit conditions and cut off the circuit in time;

[0051] The temperature and battery monitoring unit is used to monitor battery and environmental temperature;

[0052] The hierarchical response unit is used for a three-level response mechanism of alarm, power limiting and power-off.

[0053] The application further provides a multi-mode equalization control method of the hybrid semi-trailer battery management system.

[0054] S1: Real-time acquisition of voltage, current and temperature parameters of each battery cell through a multi-channel isolation acquisition system, and signal denoising processing;

[0055] S2: Parallel calculation of the SOC difference and SOH aging difference of the battery cell by using a hybrid model algorithm, and dynamic updating of the battery model parameters;

[0056] S3: Dynamic selection of active or passive equalization mode according to the SOC difference threshold and the SOH difference threshold;

[0057] S4: Automatic selection of the optimal energy transfer topology in the active equalization mode, and graded activation of discharge according to the health state of the battery cell in the passive equalization mode;

[0058] S5: Dynamic adjustment of the next equalization trigger threshold based on the historical equalization efficiency, and updating of the reinforcement learning strategy matrix.

[0059] Compared with the prior art, the application has the beneficial effects that:

[0060] The application realizes accurate battery state estimation and dynamic parameter updating through advanced algorithms and models, provides decision basis for equalization and thermal management, adopts active and passive hybrid architecture and intelligent topology switching strategy, efficiently realizes energy management between battery cells, avoids energy waste, dynamically controls wind speed and direction by using reinforcement learning, maintains the consistency of battery cell temperature at low energy consumption, significantly improves the energy utilization efficiency, state control accuracy and operation safety of the battery management system, effectively prolongs the service life of the battery and guarantees stable operation of the system. BRIEF DESCRIPTION OF DRAWINGS

[0061] Figure 1 The application is a hybrid semi-trailer battery management system and a multi-mode equalization control method.

[0062] Figure 2 The application is a hybrid semi-trailer battery management system and a multi-mode equalization control method.

[0063] BRIEF DESCRIPTION OF DRAWINGS

[0064] 100, data acquisition module; 101, voltage acquisition unit; 102, current acquisition unit; 103, temperature acquisition unit; 104, isolation transmission unit; 105, signal conditioning unit; 200, main control module; 201, SOC estimation unit; 202, SOH estimation unit; 203, parameter identification unit; 204, balancing decision unit; 205, fault diagnosis unit; 206, control output unit; 300, balancing execution module; 301, balancing mode switching unit; 302, active balancing unit; 303, passive balancing unit; 304, balancing driving unit; 305, balancing feedback unit; 400, thermal management module; 401, wind speed control unit; 402, wind direction adjustment unit; 403, temperature monitoring unit; 404, reinforcement learning decision unit; 500, communication interface module; 501, CAN communication unit; 502, protocol analysis unit; 600, safety protection module; 601, voltage monitoring unit; 602, current monitoring unit; 603, temperature and cell monitoring unit; 604, hierarchical response unit. DETAILED DESCRIPTION

[0065] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application.

[0066] Embodiment 1:

[0067] As Figure 1As shown, the embodiment provides a hybrid semi-trailer battery management system, comprising a data acquisition module 100, a main control module 200, an equalization execution module 300, a thermal management module 400, a communication interface module 500, and a safety protection module 600, wherein: the data acquisition module 100 is used to monitor the key parameters of the voltage, current, and temperature of each battery cell in real time and transmit the parameters to the main control module 200 through an isolation circuit; the main control module 200 is used to receive the data from the data acquisition module 100, jointly estimate the state of charge and the state of health of the battery through an electrochemical-equivalent circuit hybrid model and an adaptive extended Kalman filter algorithm, and dynamically update the battery model parameters in combination with an online parameter identification mechanism; the equalization execution module 300 is used to realize energy management among the battery cells through a hybrid architecture of dynamically switching active equalization and passive equalization according to the instructions of the main control module 200, wherein the active equalization reuses the excess energy through an energy transfer or feedback topology, the passive equalization dissipates energy through a resistor, and the switching of the equalization mode is based on the difference threshold of the state parameters of the battery cells; the thermal management module 400 is used to dynamically adjust the wind speed and flow direction of the air cooling system through reinforcement learning to maintain the temperature difference among the battery cells within a set range; the communication interface module 500 is used to interact with the vehicle VCU and the charger through a CAN bus to support remote monitoring; and the safety protection module 600 is used to monitor the overvoltage, undervoltage, overcurrent, and temperature abnormalities in real time and trigger graded protection.

[0068] It should be noted that the data acquisition module 100 monitors the battery parameters in real time and transmits the parameters to the main control module 200, the main control module 200 generates equalization instructions and temperature control strategies through algorithm analysis, drives the equalization execution module 300 and the thermal management module 400 to perform corresponding operations, interacts with the vehicle system through the communication interface module 500, and is monitored by the safety protection module 600 throughout the system safety state.

[0069] In the embodiment, it should be further noted that the data acquisition module 100 comprises a voltage acquisition unit 101, a current acquisition unit 102, a temperature acquisition unit 103, an isolation transmission unit 104, and a signal conditioning unit 105, wherein: the voltage acquisition unit 101 is used to acquire the voltage signal of each battery cell terminal in real time, with an accuracy of mV level; the current acquisition unit 102 is used to acquire the charging and discharging current using a Hall sensor, supporting bidirectional current detection; the temperature acquisition unit 103 acquires the surface temperature of the battery cell and the environmental temperature through a thermistor or a digital temperature sensor; the isolation transmission unit 104 is used to perform electrical isolation of high and low voltage circuits through magnetic coupling; and the signal conditioning unit 105 is used to filter, amplify, and denoise the original signal.

[0070] It should be noted that the voltage acquisition unit 101, the current acquisition unit 102 and the temperature acquisition unit 103 realize the synchronous acquisition of multiple parameters in parallel, and after the electrical isolation is completed by the isolation transmission unit 104, the unified signal standardization processing is performed by the signal conditioning unit 105.

[0071] Further, it should be noted that the voltage acquisition unit 101 realizes the mV level precision sampling by using a 24-bit ADC chip (such as ADS1263); the current acquisition unit 102 selects a closed-loop Hall sensor (such as ACS758) with a measurement range of ±200A; the isolation transmission unit 104 adopts a magnetic coupling isolation chip (such as ADuM1401) with an isolation voltage of 2500Vrms. The signal conditioning unit 105 suppresses high-frequency noise through a second-order Butterworth low-pass filter (cutoff frequency 100Hz) and realizes signal amplification by using an instrument amplifier AD8221.

[0072] In the embodiment, it should also be noted that the main control module 200 includes an SOC estimation unit 201, an SOH estimation unit 202, a parameter identification unit 203, an equalization decision unit 204, a fault diagnosis unit 205, and a control output unit 206, wherein: the SOC estimation unit 201 estimates the battery state of charge based on an electrochemical-equivalent circuit hybrid model and an adaptive extended Kalman filter algorithm; the specific operation is as follows: A1: dynamically adjusting the weight coefficients of the electrochemical model and the equivalent circuit model according to the battery temperature interval; A2: optimizing the convergence of the EKF algorithm by updating the process noise covariance matrix and the observation noise covariance matrix in real time; A3: correcting the SOC initial value under low temperature conditions based on the open circuit voltage-temperature mapping table. The SOH estimation unit 202 is used to estimate the battery health state by combining the aging model and the historical data; the parameter identification unit 203 is used to identify the key parameters of the battery online and dynamically update the model; the equalization decision unit 204 is used to determine whether to start equalization and select the equalization mode according to the SOC and voltage difference between the cells; the fault diagnosis unit 205 is used to monitor abnormal behavior and early warn the risk of cell failure; and the control output unit 206 is used to generate control instructions and send them to the equalization execution module 300 and the thermal management module 400.

[0073] It should be noted that the SOC estimation unit 201 and the SOH estimation unit 202 output the battery state evaluation results in parallel, the parameter identification unit 203 updates the model parameters in real time to support state calculation, the equalization decision unit 204 generates an equalization strategy by comprehensively considering the state differences, the fault diagnosis unit 205 monitors system abnormalities throughout the process, and finally the control output unit 206 integrates all information to generate an execution instruction.

[0074] Furthermore, it should be noted that the SOC estimation unit 201 employs a hybrid model synergy: under low-temperature conditions (T<0℃), the electrochemical model accounts for 80% of the weight, focusing on lithium-ion diffusion kinetics calculations; under normal-temperature conditions (0℃≤T≤40℃), the equivalent circuit model accounts for 70% of the weight, rapidly responding to voltage changes through a third-order RC network. The fault diagnosis unit 205 extracts high-frequency components of the voltage signal based on wavelet transform; when the high-frequency energy proportion exceeds 30%, it is determined to be an internal short-circuit risk and triggers an early warning. The basic formula for adaptive extended Kalman filtering and the state prediction equation are as follows: ,in, It is the predicted state value at time k. This is the state transition function. yes State estimate at time 10:00 It is the input quantity. It's process noise. Covariance prediction equation: ,in, It is the prediction error covariance matrix. It is the state transition matrix. yes The covariance matrix of the estimation error at time t. This is the process noise covariance matrix. Kalman gain calculation: ,in, It is Kalman gain. It is the observation matrix. This is the observation noise covariance matrix. State update equation: ,in, It is the state estimate after time k. These are the observed values, and h is the observation function. Covariance update equation: Where I is the identity matrix. The formula for correcting the initial SOC value under low-temperature conditions: Let the open-circuit voltage be OCV, and the corrected value... for: ,in, This is the original estimate. It is a correction value obtained from the open-circuit voltage-temperature mapping table.

[0075] In this embodiment, it is also necessary to point out that the equalization execution module 300 includes an equalization mode switching unit 301, an active equalization unit 302, a passive equalization unit 303, an equalization driving unit 304, and an equalization feedback unit 305, wherein: the equalization mode switching unit 301 is used to automatically select an active or passive equalization mode according to a battery difference threshold; the active equalization unit 302 is used to perform energy efficient transfer or feedback to a power grid through a DC / DC converter or a capacitor transfer topology; the specific operation is as follows: B1: automatically select a Buck, Boost, or Buck-Boost type DC / DC converter topology according to a battery voltage difference; B2: feedback excess energy to a vehicle low-voltage system or a power grid through a bidirectional AC / DC converter; B3: monitor conversion efficiency in real time and dynamically adjust a switching frequency. The passive equalization unit 303 is used to dissipate energy of a high-energy battery through a MOSFET switch connected to a discharge resistor; the equalization driving unit 304 is used to drive power devices in an equalization circuit; and the equalization feedback unit 305 is used to feedback current and voltage changes in an equalization process.

[0076] It is necessary to point out that the equalization mode switching unit 301 decides an equalization strategy according to a battery state difference, the active equalization unit 302 and the passive equalization unit 303 perform energy transfer or dissipation operations, respectively, the equalization driving unit 304 accurately controls power device actions, and the equalization feedback unit 305 monitors operating parameters in real time and forms a closed-loop feedback.

[0077] Further, it is necessary to point out that topology selection logic in the active equalization unit 302: when a battery voltage difference is greater than 0 and less than 10%, a bidirectional Buck-Boost topology is enabled; when the battery voltage difference is greater than 10% and less than 20%, a capacitor transfer topology (a capacitor value is 10 mF, and a switching frequency is 100 Hz) is adopted. Energy feedback control: a totem pole bridgeless PFC topology is adopted for a bidirectional AC / DC converter, a power factor is greater than or equal to 0.99, and excess energy is fed back to a vehicle 12V system. Buck converter: wherein D is a duty cycle, is an input voltage, is an output voltage. Boost converter: Buck-Boost converter: Energy transfer efficiency formula wherein, is an input power, is an output power.

[0078] ​In this embodiment, it also needs to be explained that the thermal management module 400 includes a wind speed control unit 401, a wind direction adjusting unit 402, a temperature monitoring unit 403, and a reinforcement learning decision unit 404, wherein: the wind speed control unit 401 is used to control the fan rotating speed, and adjust the cooling intensity according to the current temperature difference and the target temperature; the wind direction adjusting unit 402 is used to control the air duct baffle or the fan direction, and optimize the air flow distribution; the temperature monitoring unit 403 is used to continuously monitor the battery cell temperature distribution, and provide input for the control strategy; and the reinforcement learning decision unit 404 dynamically adjusts the cooling strategy based on the reinforcement learning algorithm, so as to maintain the temperature control target with minimum energy consumption.

[0079] It needs to be explained that the temperature monitoring unit 403 collects temperature distribution data in real time, the reinforcement learning decision unit 404 dynamically generates an optimal control strategy based on the monitoring data, and the wind speed control unit 401 and the wind direction adjusting unit 402 cooperatively execute the cooling instruction.

[0080] Further, it needs to be explained that the fan rotating speed (0~100% PWM), the air duct baffle angle (0~90°), and the temperature difference threshold (0~10℃) are adjustable parameters. Reinforcement learning reward function , wherein, is the maximum temperature difference between the battery cells, P is the fan power consumption, and are weight coefficients, and , .

[0081] In this embodiment, it also needs to be explained that the communication interface module 500 includes a CAN communication unit 501 and a protocol analysis unit 502, wherein: the CAN communication unit 501 is used to support the CAN 2.0B protocol, the data transmission rate is 500kbps, and the CAN communication unit 501 communicates with the vehicle controller VCU and the charger through shielded twisted pair; and the protocol analysis unit 502 is based on the SAE J1939 protocol standard, performs CRC check and data frame analysis on the received data packet, and sends the control instruction encapsulated as a UDS diagnostic protocol frame conforming to the ISO 15765-2 standard.

[0082] It needs to be explained that the CAN communication unit 501 is responsible for high-speed data transmission of the physical layer, and the protocol analysis unit 502 completes the application layer protocol conversion and data encapsulation.

[0083] In this embodiment, it also needs to be explained that the safety protection module 600 includes a voltage monitoring unit 601, a current monitoring unit 602, a temperature and cell monitoring unit 603, and a hierarchical response unit 604, wherein: the voltage monitoring unit 601 is used to detect overvoltage and undervoltage events in real time and trigger protection actions; the current monitoring unit 602 is used to monitor overcurrent and short circuit conditions and cut off the circuit in time; the temperature and cell monitoring unit 603 is used to monitor the cell and environmental temperature to prevent thermal runaway; and the hierarchical response unit 604 is used to divide the response mechanism into three levels of alarm, power limitation, and power-off.

[0084] It should be noted that the voltage monitoring unit 601, the current monitoring unit 602, and the temperature and cell monitoring unit 603 implement real-time monitoring of three parameters in parallel, and the hierarchical response unit 604 intelligently triggers three-level protection measures according to the abnormal level.

[0085] Further, it should be noted that the hierarchical response logic is as follows: first-level warning: when the voltage is greater than 4.3V or less than 2.8V, send fault code 0x123 to the instrument panel through the CAN bus and alarm the buzzer; second-level power limitation: when the current is greater than 150A or the temperature is greater than 55℃, the control output unit 206 limits the charge and discharge current to 0.5C; and third-level power-off: when a short circuit is detected or the temperature is greater than 65℃, the hierarchical response unit 604 triggers the solid-state relay to cut off the main circuit within 20μs.

[0086] Embodiment 2:

[0087] As shown in Figure 2 , in this embodiment, a multi-mode equalization control method for a hybrid tractor trailer battery management system is provided, which specifically includes the following steps:

[0088] S1. Data acquisition and transmission

[0089] The voltage (precision up to mV level), charge and discharge current (±200A range), and surface / environmental temperature data of each cell are synchronously collected at a sampling frequency of 10kHz;

[0090] The collected analog signals are subjected to 2500Vrms electrical isolation through a magnetic coupling isolation chip ADuM1401, then high-frequency noise is removed through a second-order Butterworth low-pass filter (cutoff frequency 100Hz), and the signals are amplified to the ADC receivable range by using an instrument amplifier AD8221;

[0091] S2. Battery state estimation

[0092] The weights of the electrochemical model and the equivalent circuit model are dynamically adjusted according to the real-time temperature of the battery;

[0093] When the temperature T is less than 0℃, the electrochemical model weight accounts for 80%, and the calculation of lithium ion diffusion dynamics is emphasized.

[0094] When 0℃≤T≤40℃, the equivalent circuit model weight accounts for 70%, and the three-order RC network quickly responds to voltage changes;

[0095] At the same time, the adaptive extended Kalman filter algorithm is used to update the process noise covariance matrix and the observation noise covariance matrix in real time, optimize the algorithm convergence speed, and correct the SOC initial value under low temperature conditions based on the open circuit voltage-temperature mapping table;

[0096] SOH and parameter identification: combined with the battery aging model (such as the capacity attenuation curve) and the historical charge and discharge cycle data, the battery health state is estimated;

[0097] Using recursive least squares method to identify key parameters such as battery internal resistance and polarization capacitance online, updating battery model to improve state estimation accuracy;

[0098] Output application: SOC and SOH estimation results, updated model parameters are transmitted to the equalization decision simultaneously, as the basis for equalization strategy decision;

[0099] S3. Equalization decision and mode selection

[0100] Decision logic: receive the SOC difference, voltage difference and temperature data between cells, and compare with the preset threshold: when the SOC difference between cells is >3%, the voltage difference is >0.05V and the temperature is between 20℃-45℃, select the active equalization mode; If the above conditions are not met, enable passive equalization mode;

[0101] Threshold design: threshold setting is based on battery characteristics and safety boundaries, for example, a 3% SOC difference threshold can effectively avoid cell capacity imbalance while reducing unnecessary equalization loss;

[0102] S4. Equalization execution

[0103] ① Active equalization mode:

[0104] Topology selection: automatically switch DC / DC converter topology according to cell voltage difference: when voltage difference ΔV>0.3V, enable bidirectional Buck-Boost topology to achieve high efficiency energy transfer; ΔV≤0.3V, use 10mF capacitor transfer topology to time-sharing multiplex energy storage capacitor at 100Hz frequency;

[0105] Energy feedback: excess energy is fed back to the vehicle 12V low-voltage system or power grid through a bidirectional AC / DC converter (totem column without bridge PFC topology, power factor ≥0.99);

[0106] Efficiency optimization: real-time monitoring of energy conversion efficiency, formula: , where, is the input power, is the output power, energy efficiency is improved by dynamically adjusting the switching frequency (range 50kHz-100kHz);

[0107] ②Passive balancing mode: through MOSFET switch connection 10Ω / 5W discharge resistance, the energy dissipation of the battery cell with voltage higher than the average voltage of the battery pack 0.1V, until the voltage difference is reduced to a safe range;

[0108] S5. Balancing feedback and adjustment

[0109] Data acquisition: collect current and voltage data during the balancing process at 50ms intervals, including cell voltage changes, balancing current size, and DC / DC converter output parameters;

[0110] Closed-loop control: if the balancing efficiency is found to be less than 85% or the cell voltage difference rebounds more than the threshold, re-evaluate and adjust the balancing strategy, such as switching topology or extending the balancing time;

[0111] S5. Cooperative control

[0112] Thermal management coordination: according to the change of battery temperature during the balancing process, for example, when active balancing causes local temperature rise, dynamically adjust the fan speed and the angle of the air duct, to ensure that the temperature difference between cells is maintained within 2℃;

[0113] Safety protection linkage: continuous monitoring of voltage, current and temperature, once the overvoltage (>4.3V), overcurrent (>150A) or high temperature (>55℃) anomaly is triggered, the hierarchical response intervenes immediately: first warning (beeper alarm), second power limit (reduced to 0.5C) or third power-off (20μs to cut off the main circuit), while interrupting the balancing operation to ensure system safety.

[0114] In the description of the present specification, the description of the terms "one embodiment", "example", "specific example" and the like means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In the present specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0115] The preferred embodiments of the application disclosed above are only to facilitate the elucidation of the application. The preferred embodiments do not describe all the details of the application and limit the application to the specific embodiments described. Obviously, many modifications and variations can be made in light of the teachings above. The description is chosen and described in order to best explain the principles of the application and its practical application to thereby enable others skilled in the art to best utilize the application and get the best results from the application. The application is only limited by the claims and their full scope and equivalents.

Claims

1. A battery management system for a hybrid semi-trailer, characterized in that, It includes a data acquisition module (100), a main control module (200), a load balancing module (300), a thermal management module (400), a communication interface module (500), and a security protection module (600), wherein: The data acquisition module (100) is responsible for real-time monitoring of key parameters such as voltage, current and temperature of each cell in the battery module, and transmits them to the main control module (200) through an isolation circuit. The main control module (200) is used to receive data from the data acquisition module (100), jointly estimate the state of charge and health of the battery through the electrochemical-equivalent circuit hybrid model and the adaptive extended Kalman filter algorithm, and dynamically update the battery model parameters in conjunction with the online parameter identification mechanism. The equalization execution module (300) is used to realize energy management between cells by dynamically switching between a hybrid architecture of active equalization and passive equalization according to the instructions of the main control module (200). The active equalization uses energy transfer or feedback topology to reuse excess electrical energy, while the passive equalization dissipates energy through resistors. The switching of equalization mode is based on the difference threshold of cell state parameters. The thermal management module (400) is used to dynamically adjust the air speed and flow direction of the air-cooling system using reinforcement learning to maintain the temperature difference between the cells within a set range. The communication interface module (500) is used to exchange data with the vehicle VCU and charger via the CAN bus, and supports remote monitoring. The safety protection module (600) is used to monitor overvoltage, undervoltage, overcurrent and temperature abnormalities in real time and trigger graded protection. The main control module (200) includes a SOC estimation unit (201), a SOH estimation unit (202), a parameter identification unit (203), an equilibrium decision unit (204), a fault diagnosis unit (205), and a control output unit (206), wherein: The SOC estimation unit (201) estimates the battery state of charge based on an electrochemical-equivalent circuit hybrid model and an adaptive extended Kalman filter algorithm. The SOH estimation unit (202) is used to estimate the battery health status by combining the aging model with historical data. The parameter identification unit (203) is used to identify key battery parameters online and dynamically update the model. The equalization decision unit (204) is used to determine whether to start equalization and select the equalization mode based on the SOC and voltage differences between cells. The fault diagnosis unit (205) is used to monitor abnormal behavior and provide early warning of cell failure risks. The control output unit (206) is used to generate control commands and send them to the equalization execution module (300) and the thermal management module (400). The SOC estimation unit (201) estimates the battery state of charge based on a hybrid electrochemical-equivalent circuit model and an adaptive extended Kalman filter algorithm. The specific operation is as follows: A1: Dynamically adjust the weighting coefficients of the electrochemical model and the equivalent circuit model according to the battery temperature range; A2: Optimize the convergence of the EKF algorithm by updating the process noise covariance matrix and the observation noise covariance matrix in real time; A3: Correct the initial SOC value under low temperature conditions based on the open circuit voltage-temperature mapping table.

2. The hybrid semi-trailer battery management system according to claim 1, characterized in that, The data acquisition module (100) includes a voltage acquisition unit (101), a current acquisition unit (102), a temperature acquisition unit (103), an isolation transmission unit (104), and a signal conditioning unit (105), wherein: The voltage acquisition unit (101) is used to acquire the voltage signal of each cell in real time, with an accuracy of up to mV level; The current acquisition unit (102) is used to acquire charging and discharging current using a Hall sensor and supports bidirectional current detection. The temperature acquisition unit (103) acquires the surface temperature of the battery cell and the ambient temperature through a thermistor or a digital temperature sensor; The isolation transmission unit (104) is used for electrical isolation of high and low voltage circuits using magnetic coupling; The signal conditioning unit (105) is used to filter, amplify, and denoise the original signal.

3. The hybrid semi-trailer battery management system according to claim 1, characterized in that, The equalization execution module (300) includes an equalization mode switching unit (301), an active equalization unit (302), a passive equalization unit (303), an equalization drive unit (304), and an equalization feedback unit (305), wherein: The equalization mode switching unit (301) is used to automatically select active or passive equalization mode according to the cell difference threshold. The active balancing unit (302) is used to efficiently transfer or feed energy back to the grid through a topology including a DC / DC converter or a capacitor transfer topology. The passive equalization unit (303) is used to dissipate energy from high-capacity cells by connecting a discharge resistor through a MOSFET switch. The equalization drive unit (304) is used to drive the power devices in the equalization circuit. The equalization feedback unit (305) is used to provide feedback on current and voltage changes during the equalization process.

4. A hybrid semi-trailer battery management system according to claim 3, characterized in that, The active balancing unit (302) performs efficient energy transfer or feedback to the grid through a DC / DC converter or capacitor transfer topology, and the specific operation is as follows: B1: Automatically selects Buck, Boost, or Buck-Boost type DC / DC converter topology based on cell voltage difference; B2: Feed excess electrical energy back to the vehicle's low-voltage system or the power grid via a bidirectional AC / DC converter; B3: Real-time monitoring of conversion efficiency and dynamic adjustment of switching frequency.

5. A hybrid semi-trailer battery management system according to claim 1, characterized in that, The thermal management module (400) includes a wind speed control unit (401), a wind direction adjustment unit (402), a temperature monitoring unit (403), and a reinforcement learning decision-making unit (404), wherein: The wind speed control unit (401) is used to control the fan speed and adjust the cooling intensity according to the current temperature difference and the target temperature. The wind direction adjustment unit (402) is used to control the direction of the duct baffle or fan to optimize the airflow distribution. The temperature monitoring unit (403) is used to continuously monitor the temperature distribution of the battery cell and provide input for the control strategy. The reinforcement learning decision unit (404) dynamically adjusts the cooling strategy based on the reinforcement learning algorithm to maintain the temperature control target with minimum energy consumption.

6. A hybrid semi-trailer battery management system according to claim 1, characterized in that, The communication interface module (500) includes a CAN communication unit (501) and a protocol parsing unit (502), wherein: The CAN communication unit (501) is used to support the CAN 2.0B protocol, with a data transmission rate of 500kbps, and communicates with the vehicle controller (VCU) and charger via shielded twisted-pair cable. The protocol parsing unit (502) performs CRC check and data frame parsing on the received data packets based on the SAE J1939 protocol standard, and encapsulates the control commands into UDS diagnostic protocol frames conforming to the ISO 15765-2 standard for transmission.

7. A hybrid semi-trailer battery management system according to claim 1, characterized in that, The safety protection module (600) includes a voltage monitoring unit (601), a current monitoring unit (602), a temperature and cell monitoring unit (603), and a graded response unit (604), wherein: The voltage monitoring unit (601) is used to detect overvoltage and undervoltage events in real time and trigger protection actions. The current monitoring unit (602) is used to monitor overcurrent and short circuit conditions and cut off the circuit in a timely manner. The temperature and cell monitoring unit (603) is used to monitor the cell temperature and ambient temperature. The graded response unit (604) is used to divide the response mechanism into three levels: alarm, power limit, and power failure.

8. A multi-mode equalization control method for a hybrid semi-trailer battery management system, characterized in that: A hybrid semi-trailer battery management system using any one of claims 1-7 includes the following steps: S1: The voltage, current and temperature parameters of each battery cell are acquired in real time through a multi-channel isolation acquisition system, and the signal is denoised. S2: A hybrid model algorithm is used to calculate the cell SOC difference and SOH aging difference in parallel, and the battery model parameters are updated dynamically; S3: Dynamically select active or passive balancing mode based on the SOC difference threshold and SOH difference threshold; S4: In active balancing mode, the optimal energy transfer topology is automatically selected; in passive balancing mode, discharge is activated in stages according to the cell health status. S5: Dynamically adjust the next equilibrium trigger threshold based on historical equilibrium efficiency and update the reinforcement learning policy matrix.

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