Mine heavy truck large-multiple ultra-charging power domain system and collaborative control method thereof

Abstract

The application discloses a mine heavy truck large-multiple-rate super-charging power domain system and a collaborative control method thereof. The system comprises a high-power charger, a power battery pack, an integrated thermal management system, an electric drive system and a power domain collaborative controller. The power domain collaborative controller executes a set of collaborative control strategies based on multi-target real-time optimization, programmable multi-source energy flow arbitration rules, dynamic priority scheduling and distribution of grid power, battery charging demand, thermal management demand and vehicle auxiliary load; according to the predicted driving condition, the battery pre-heating / pre-cooling is actively started at the end of the charging stage, so that the battery is in the most suitable temperature window for subsequent high-power discharge or safe storage at the end of the charging. The application solves the problems of thermal runaway risk, low system energy utilization efficiency and component life loss during large-multiple-rate super-charging of the mine heavy truck, and realizes safe, efficient and intelligent super-charging.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle technology, specifically to a high-rate supercharging power domain system for mining heavy trucks and its collaborative control method. Background Technology

[0002] Mining heavy-duty trucks, as core equipment for mine transportation, are rapidly evolving towards electrification. To shorten charging time and improve operational efficiency, high-rate overcharging technology has become an inevitable choice. However, high-rate charging brings serious challenges:

[0003] Thermal runaway risk: High current causes a surge in battery heat generation, which can easily lead to thermal runaway if heat dissipation is not timely.

[0004] Inefficient system: During charging, energy consumption from battery thermal management and cab air conditioning increases the burden on the power grid, resulting in uneconomical energy utilization;

[0005] Component lifespan reduction: Abrupt charging without coordination accelerates the performance degradation of critical components in the battery and electric drive system.

[0006] In existing technologies, the charging, thermal management, and electric drive subsystems of electric heavy-duty trucks are typically managed independently by their respective controllers, lacking unified scheduling. For example, the charger only performs constant current and constant voltage charging according to the basic instructions of the battery management system; the thermal management system passively responds to battery temperature; and the energy flow between each system is isolated. This siloed approach cannot cope with the systemic thermal and energy management challenges brought about by high-rate overcharging, poses safety hazards, and cannot simultaneously achieve both efficiency and lifespan. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this invention aims to overcome these shortcomings by providing a high-rate overcharging power domain system for mining heavy-duty trucks and its collaborative control method, in order to solve the problems of thermal safety, energy efficiency, and system lifespan under high-rate overcharging.

[0008] To achieve the above objectives, the present invention adopts the following technical solution:

[0009] In a first aspect, the present invention provides a high-rate supercharging power domain system for mining heavy-duty trucks, comprising:

[0010] High-power chargers are used to connect to the external power grid and provide charging power for vehicles;

[0011] The power battery pack is used to store electrical energy;

[0012] An integrated thermal management system is used to heat or cool the power battery pack, electric drive system, and cab.

[0013] An electric drive system is used to drive the vehicle and provide regenerative braking.

[0014] The power domain collaborative controller is communicatively connected to the high-power charger, power battery pack, integrated thermal management system, and electric drive system, respectively.

[0015] The power domain cooperative controller is configured to execute a cooperative control strategy in charging mode, the strategy including charging control based on dynamic charging map and thermal budget, multi-source energy flow arbitration scheduling, and battery temperature pre-adjustment based on driving condition prediction.

[0016] The Charging Pressure Index (CSI) calculation module is integrated into the power domain co-controller.

[0017] The CSI historical database is used to store historical charging CSI values ​​and related parameters;

[0018] The safety boundary prediction module predicts the safety boundary for the next charge based on historical CSI data.

[0019] Preferably, the dynamic charging map is a model that integrates the temperature, temperature difference, battery health status, and real-time maximum acceptable charging power of each monitoring point in the battery pack; the thermal budget is a threshold of total heat generation that can be withstood during this charging process, calculated based on the dynamic charging map, while ensuring battery thermal safety.

[0020] Preferably, the multi-source energy flow arbitration rule defines the power allocation priority under different scenarios; in charging mode, the priority order is: battery safety boundary > battery safety temperature control requirements > battery charging requirements > cab comfort temperature control requirements > other auxiliary load requirements.

[0021] Preferably, the battery temperature pre-adjustment based on driving condition prediction refers to the following: at the end of the charging process, the power domain co-controller actively controls the integrated thermal management system to adjust the battery temperature to a target temperature window suitable for subsequent high-power discharge or safe storage, based on the input subsequent driving plan.

[0022] Construct a dynamic charging map and thermal budget: Integrate multi-dimensional battery status temperature, temperature difference, SOH, and maximum acceptable power in real time to form a dynamic charging map, and calculate the thermal budget for this charge as the core constraint for adjusting the charging power.

[0023] Preferably, the definition and calculation of the charging pressure index CSI are as follows:

[0024] The Charging Stress Index (CSI) is a dimensionless index that comprehensively quantifies the various stresses exerted on the battery during the charging process. Its value ranges from [0,1], where 0 represents no pressure and 1 represents reaching the preset maximum allowable pressure. The CSI is calculated by weighting four sub-indices:

[0025]

[0026] in, These are the weighting coefficients, and The calculation methods for each sub-index are as follows:

[0027] 1) Thermal stress index CSIthermal: reflects the impact of heat generation during charging on the battery.

[0028] ,

[0029] Where T is the average temperature during the charging process, Tpeak is the highest temperature, Tref is the reference temperature (e.g., 25℃), Tmax is the maximum allowable temperature (e.g., 55℃), dT / dtavg is the average temperature rise rate, and (dT / dt)max is the maximum allowable temperature rise rate.

[0030] 2) Rate Stress Index (CSIrate): Reflects the stress on the battery caused by the charging current.

[0031] ,

[0032] Where Cavg is the average charging rate, Cpeak is the peak charging rate, and Cmax is the battery's nominal maximum charging rate. This represents the charging time at high rates (e.g., >0.8Cmax), and ttotal represents the total charging time.

[0033] 3) SOC window stress sub-index : Reflects the impact of charging start and end SOC on battery life.

[0034] ,

[0035] Where SOCstart is the starting SOC for charging, and SOCend is the ending SOC for charging. The charging time during the high SOC range (e.g., >80%).

[0036] 4) Temperature gradient stress sub-index CSIΔT: reflects the temperature non-uniformity inside the battery.

[0037] ,

[0038] Where ΔTmax is the maximum temperature difference between cells, ΔT is the average temperature difference, and ΔTlimit is the maximum allowable temperature difference.

[0039] Preferably, the CSI historical database stores a complete record of each charge, including: charging timestamp, start SOC, end SOC, charging duration, average CSI, values ​​of each sub-index, highest temperature, maximum temperature difference, average rate of return, peak rate of return, etc. The database uses a circular storage structure to store the most recent N (e.g., 50-100) charging records. The database also calculates the following derived data:

[0040] The average of the most recent k CSI values;

[0041] CSI trend (rising, falling, stable);

[0042] Statistical characteristics of CSI (mean, variance, extreme values).

[0043] Preferably, the safety boundary prediction model, specifically the safety boundary prediction module, predicts the safety boundary parameters for the next charge based on historical CSI data. The prediction model can employ linear regression, exponential decay, or machine learning methods. Taking a linear model as an example:

[0044]

[0045]

[0046] ,

[0047] in, These are the predicted safe temperature, maximum current, and maximum voltage for the next charge, respectively. These are the initial calibration values; The average CSI for the most recent k charges; α, β, γ are the decay coefficients, calibrated experimentally.

[0048] 4. Adaptive adjustment mechanism for charging strategy

[0049] The power domain co-controller adaptively adjusts the charging strategy based on the CSI pressure level and its changing trend.

[0050] Pressure level classification:

[0051] Low pressure: CSI ≤ 0.4;

[0052] Medium pressure: 0.4 < CSI ≤ 0.7;

[0053] High pressure: CSI > 0.7.

[0054] Trend type identification:

[0055] Upward trend: The CSI has been rising continuously for the last three times, and the increment has exceeded a threshold such as 0.1;

[0056] Downward trend: The CSI has been declining for the last three consecutive times, and the reduction has exceeded the threshold;

[0057] Stable trend: CSI fluctuations are within the threshold range.

[0058] Secondly, the present invention provides a cooperative control method for the above-mentioned system, characterized by comprising the following steps:

[0059] S1: After the charging connection is established, the power domain co-controller obtains battery status, thermal management system status and driving plan information;

[0060] S2: Based on battery status information, build or update a dynamic charging map in real time and calculate the thermal budget for the current charging stage;

[0061] S3: Based on the dynamic charging map, thermal budget, and multi-source energy flow arbitration rules, collaboratively make decisions and allocate the total power from the high-power charger, and issue instructions to the charger, integrated thermal management system, and other loads respectively;

[0062] S4: Real-time monitoring of battery thermal status and the status of various system components, calculation of charging pressure index (CSI), and dynamic adjustment of power distribution in step S3.

[0063] S5: Before the charging process reaches the preset end condition, start the battery temperature pre-adjustment according to the driving plan so that the battery reaches the target temperature when the charging ends.

[0064] Preferably, in step S2, the formula for calculating the thermal budget is:

[0065] Among them, Cp and These are the average specific heat capacity and mass of the battery pack, respectively. For the allowable temperature rise budget, λ is the penalty coefficient, representing the allowable temperature difference budget.

[0066] Preferably, in step S3, the collaborative decision-making is achieved by solving the following optimization problem:

[0067] Optimization variable: Charging current Cooling power ,

[0068] Objective function: Maximize ,

[0069] The constraints include: cell temperature constraint, temperature difference constraint, thermal budget constraint, current capability constraint, and cooling power constraint.

[0070] Preferably, in step S3, the multi-source energy flow arbitration adopts a dynamic weight allocation algorithm to assign dynamic weights to battery charging demand, battery thermal management demand, cab thermal management demand, and other load demands. ,in Battery temperature The function of deviation from the optimal temperature range, when When the safety threshold is exceeded, Approaching infinity.

[0071] Preferably, in step S5, the target temperature window Based on the predicted power demand under driving conditions Determined, specifically calculated using the following formula:

[0072] ,

[0073] in, denoted as peak driving power of the vehicle, and k is the adjustment coefficient.

[0074] Preferably, the predicted power demand under driving conditions Calculated using the following formula:

[0075] ,

[0076] in, For the total mass of the vehicle. It is the acceleration due to gravity. The average slope The rolling resistance coefficient, air density, This is the drag coefficient. To predict vehicle speed, To drive system efficiency.

[0077] This invention transforms the charging process into a continuous mathematical optimization problem, from open-loop preset to closed-loop real-time optimization, and solves for the optimal safe path in each control cycle.

[0078] Innovation in resource management from single-constraint to multi-constraint coupled management: Introducing the concept of thermal budget, which quantifies thermal security as a dynamically consumable global resource.

[0079] Innovations from post-charge response to pre-charge prediction and coordinated preheating: A mathematical formula is used to link the final charging temperature with the predicted subsequent driving power demand, achieving coordination across time scales. Implementation of battery temperature pre-regulation based on operating condition prediction: At the end of the charging process, based on the vehicle's upcoming task, such as heavy-load uphill driving, the battery temperature is proactively adjusted to the optimal range for subsequent high-power discharge, such as 25-35°C, instead of being allowed to cool naturally, thereby improving the vehicle's immediate power performance after charging is complete.

[0080] This invention relates to a high-rate supercharging power domain system for mining heavy-duty trucks and its cooperative control method. It possesses the following beneficial effects:

[0081] 1) Regarding charging control methods, existing technologies generally employ open-loop preset constant current-constant voltage (CC-CV) charging curves, which is a passive protection mode. This means that protective actions such as derating or interruption are only taken when battery parameters (such as temperature and voltage) reach preset thresholds. This invention proposes closed-loop real-time multi-objective optimization control. By dynamically solving the optimization problem in each control cycle (e.g., 100 milliseconds) using a power domain co-controller, the system can actively and in real-time find and track the optimal charging trajectory within strict thermal safety boundaries. This shift from passively avoiding risks to actively seeking the optimal safe path reduces charging time by 10-15% under the same thermal safety boundary conditions, achieving both safety and efficiency.

[0082] 2) Regarding thermal management strategies, existing solutions are mostly passive responses, relying on a single temperature threshold to trigger the cooling system, lacking global and predictive capabilities. This invention creatively introduces the concept of thermal budget management and a dynamic weighted arbitration mechanism. The system quantifies the total allowable heat generation during the entire charging process into a dynamically monitorable and allocable budget, and assigns dynamic weights to battery thermal management requirements that vary exponentially with temperature deviation. When the battery temperature approaches the safety boundary, the thermal management weight increases sharply, ensuring it receives power priority. This proactive thermal management strategy constructs a robust safety defense at the system level, and analysis and simulation verification have shown that it can reduce the risk of thermal runaway by more than 90%.

[0083] 3) Regarding energy allocation strategies, traditional systems often employ fixed-priority competition or simple sequential allocation modes among subsystems such as charging, battery thermal management, and cab air conditioning, resulting in rigidity and inefficiency. This invention achieves millisecond-level intelligent energy scheduling based on dynamic weights. The power domain co-controller calculates and dynamically adjusts the demand weights of each electronic system in real time based on multiple factors such as battery status, environmental conditions, and user settings, and intelligently proportionally allocates the total power input from the power grid. This refined scheduling capability ensures that energy is always allocated to the most needed components, thereby improving the overall energy utilization efficiency of the entire charging system by 5-8%.

[0084] 4) Regarding the coordination between charging and vehicle use, traditional charging processes end when the battery reaches the target charge level, and the final battery temperature is a natural consequence of the charging process, disconnected from subsequent vehicle tasks. This invention achieves battery temperature pre-regulation based on operating condition prediction. At the end of the charging process, the system predicts power demand based on the upcoming vehicle tasks, such as heavy-load uphill driving or high-speed travel, using a built-in model, and proactively adjusts the battery temperature to the optimal performance window for that task. This allows the vehicle to deliver optimal power performance immediately after charging, solving the problem of insufficient power in low temperatures. Tests have shown that this can increase the vehicle's initial peak power output capability under extreme conditions by approximately 20%.

[0085] 5) In terms of system lifecycle management, existing technologies rarely consider the impact of charging strategies on the long-term lifespan of core components such as batteries. This invention incorporates a component lifespan degradation model into a collaborative decision-making algorithm. When optimizing charging curves and allocating power, it considers not only instantaneous speed and temperature but also long-term lifespan factors such as battery health status and equivalent cycle count as part of the optimization objectives, proactively selecting the charging path most beneficial to the battery. This lifecycle-oriented design philosophy can effectively mitigate battery degradation, and is expected to extend the battery's cycle life by 15-20%, significantly reducing the vehicle's total lifecycle operating costs. Attached Figure Description

[0086] none Detailed Implementation

[0087] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0088] To achieve the above objectives, the present invention adopts the following technical solution:

[0089] In a first aspect, the present invention provides a high-rate supercharging power domain system for mining heavy-duty trucks, comprising:

[0090] High-power chargers are used to connect to the external power grid and provide charging power for vehicles;

[0091] The power battery pack is used to store electrical energy;

[0092] An integrated thermal management system is used to heat or cool the power battery pack, electric drive system, and cab.

[0093] An electric drive system is used to drive the vehicle and provide regenerative braking.

[0094] The power domain collaborative controller is communicatively connected to the high-power charger, power battery pack, integrated thermal management system, and electric drive system, respectively.

[0095] The power domain cooperative controller is configured to execute a cooperative control strategy in charging mode, the strategy including charging control based on a dynamic charging map and thermal budget, multi-source energy flow arbitration scheduling, and battery temperature pre-adjustment based on driving condition prediction.

[0096] The Charging Pressure Index (CSI) calculation module is integrated into the power domain co-controller.

[0097] The CSI historical database is used to store historical charging CSI values ​​and related parameters;

[0098] The safety boundary prediction module predicts the safety boundary for the next charge based on historical CSI data.

[0099] Preferably, the dynamic charging map is a model that integrates the temperature, temperature difference, battery health status, and real-time maximum acceptable charging power of each monitoring point in the battery pack; the thermal budget is a threshold of total heat generation that can be withstood during this charging process, calculated based on the dynamic charging map, while ensuring battery thermal safety.

[0100] Preferably, the multi-source energy flow arbitration rule defines the power allocation priority under different scenarios; in charging mode, the priority order is: battery safety boundary > battery safety temperature control requirements > battery charging requirements > cab comfort temperature control requirements > other auxiliary load requirements.

[0101] Preferably, the battery temperature pre-adjustment based on driving condition prediction refers to the following: at the end of the charging process, the power domain co-controller actively controls the integrated thermal management system to adjust the battery temperature to a target temperature window suitable for subsequent high-power discharge or safe storage, based on the input subsequent driving plan.

[0102] Construct a dynamic charging map and thermal budget: Integrate multi-dimensional battery status (temperature, temperature difference, SOH, maximum acceptable power) in real time to form a dynamic charging map, and calculate the thermal budget for this charge as the core constraint for adjusting the charging power.

[0103] Secondly, the present invention provides a cooperative control method for the above-mentioned system, characterized by comprising the following steps:

[0104] S1: After the charging connection is established, the power domain co-controller obtains battery status, thermal management system status and driving plan information;

[0105] S2: Based on battery status information, build or update a dynamic charging map in real time and calculate the thermal budget for the current charging stage;

[0106] S3: Based on the dynamic charging map, thermal budget, and multi-source energy flow arbitration rules, collaboratively make decisions and allocate the total power from the high-power charger, and issue instructions to the charger, integrated thermal management system, and other loads respectively;

[0107] S4: Real-time monitoring of battery thermal status and the status of various system components, calculation of charging pressure index (CSI), and dynamic adjustment of power distribution in step S3.

[0108] S5: Before the charging process reaches the preset end condition, start the battery temperature pre-adjustment according to the driving plan so that the battery reaches the target temperature when the charging ends.

[0109] Preferably, in step S2, the formula for calculating the thermal budget is:

[0110] Among them, Cp and These are the average specific heat capacity and mass of the battery pack, respectively. For the allowable temperature rise budget, For the allowable temperature difference budget, This is the penalty coefficient.

[0111] Preferably, in step S3, the collaborative decision-making is achieved by solving the following optimization problem:

[0112] Optimization variable: Charging current Cooling power ,

[0113] Objective function: Maximize ,

[0114] The constraints include: cell temperature constraint, temperature difference constraint, thermal budget constraint, current capability constraint, and cooling power constraint.

[0115] Preferably, in step S3, the multi-source energy flow arbitration adopts a dynamic weight allocation algorithm to assign dynamic weights to battery charging demand, battery thermal management demand, cab thermal management demand, and other load demands. ,in Battery temperature The function of deviation from the optimal temperature range, when When the safety threshold is exceeded, Approaching infinity.

[0116] Preferably, in step S5, the target temperature window Based on the predicted power demand under driving conditions Determined, specifically calculated using the following formula:

[0117] ,

[0118] in, denoted as peak driving power of the vehicle, and k is the adjustment coefficient.

[0119] Preferably, the predicted power demand under driving conditions Calculated using the following formula:

[0120] ,

[0121] in, For the total mass of the vehicle. It is the acceleration due to gravity. The average slope The rolling resistance coefficient, air density, This is the drag coefficient. To predict vehicle speed, To drive system efficiency.

[0122] 1. Specific dynamic charging map and thermal budget model:

[0123] At the start of charging, the power domain co-controller calculates the total thermal budget for this charging cycle based on the initial state of the battery. The budget is not only a total value, but also broken down into allowable temperature rise budgets. and allowable temperature difference budget .

[0124] , where Cp and These are the average specific heat capacity and mass of the battery pack, respectively. For the allowable temperature rise budget, For the allowable temperature difference budget, This is a penalty coefficient. Used to limit internal temperature differences.

[0125] The controller maintains a simplified battery thermo-electric coupling model in real time to predict different charging currents. The rate of temperature rise at the following conditions :

[0126] ,

[0127] in, This refers to the battery's internal resistance (which varies with SOC and temperature). For other heat sources, For ambient temperature, This represents the system's thermal resistance.

[0128] 2. Collaborative decision-making algorithm based on optimization problem

[0129] In each control cycle (e.g., 100ms), the controller solves a rolling time-domain optimization problem to determine the optimal charging current for the next cycle. Power request from thermal management system .

[0130] Optimize the problem model:

[0131] Objective function: ,

[0132] Constraints:

[0133] Predicted cell temperature: ,

[0134] Maximum cell temperature difference: ,

[0135] Remaining thermal budget: ,

[0136] Current constraint: ,

[0137] Cooling power constraints: .

[0138] Among them, coefficient It can be adjusted online according to electricity price and efficiency, reflecting a balance between economy and energy efficiency.

[0139] 3. Dynamic Weighted Energy Flow Arbitration Algorithm

[0140] The controller compares the total power demand with the grid power and uses dynamic weighting:

[0141] Total power demand:

[0142]

[0143] Dynamic weight calculation:

[0144] .

[0145] when When allocating, scale the distribution proportionally:

[0146] 4. Pre-conditioning algorithm based on prediction of battery temperature

[0147] Before the end of charging, when the State of Charge (SOC) reaches 95%, the system enters the pre-adjustment phase at the end of charging. Based on the predicted average gradient θ, load m, and vehicle speed v for the next stage of the journey, the average drive power requirement is estimated.

[0148] ,

[0149] Target temperature window Determined based on predicted power demand:

[0150] in, denoted as peak driving power of the vehicle, and k is the adjustment coefficient.

[0151] 5. Dynamic charging map technology:

[0152] The dynamic charging map described in this invention does not refer to a spatial geographic location map, but rather a multi-dimensional, time-varying dynamic data model or state space used to characterize the acceptable charging capability of the power battery pack in its current state. It is maintained and updated in real time by the power domain cooperative controller and is the core input to the cooperative control algorithm.

[0153] 1) Core Component Dimensions:

[0154] A dynamic charging map contains at least the following four interdependent dimensions:

[0155] Thermal safety dimension: This dimension focuses on the real-time temperature and maximum temperature difference at various monitoring points within the battery pack. It defines the instantaneous thermal safety boundary of the battery under the current temperature field distribution.

[0156] Electrical safety dimension: The real-time maximum acceptable charging current (or power) is calculated using a battery model based on the battery's real-time state of health (SOH), state of charge (SOC), and internal impedance. This value is not fixed but changes dynamically with SOC, temperature, and aging.

[0157] Lifetime degradation dimension (preferred): It can integrate battery cumulative stress models (such as the lifetime degradation coefficient of high SOC range, high temperature, and high current) to provide decision basis for optimization algorithms that consider long-term lifetime.

[0158] Historical and trend dimensions: These include recent temperature rise rates and historical current changes, used to predict short-term state evolution trends.

[0159] 2) The essential difference from existing technologies:

[0160] Traditional battery management systems (BMS) typically provide only a single, conservative maximum allowable charging current command to the charger. In contrast, the dynamic charging map of this invention is a high-dimensional state-space model, which offers the following advantages:

[0161] From scalar to vector: expanding from a single current value to a collection of multi-dimensional information including temperature field, health status, life cost, etc.

[0162] From static to dynamic: Map parameters are continuously refreshed during the charging process, rather than being determined only at the start of charging.

[0163] From isolation to correlation: The coupling relationship between parameters such as temperature, SOC, SOH, and current is clarified. For example, the map will reflect the upper limit of safe current at a certain SOC point under a certain temperature distribution.

[0164] 3) Construction and update methods:

[0165] The construction of dynamic charging maps relies on the vehicle sensor network (temperature sensor, voltage / current sensor) and the built-in battery electro-thermal coupling model.

[0166] Initialization: When charging begins, an initial map is generated by calling the model based on the battery's initial temperature, SOC, and SOH.

[0167] Real-time updates: In each control cycle, such as 100ms, the latest sensor data is received, and the predictive model is used to extrapolate the thermal-electric coupling model to predict the changes in various parameters of the map, especially temperature and maximum allowable current, under different charging current inputs in a short time domain in the future.

[0168] 4) Its role in collaborative control:

[0169] The dynamic charging map serves as a direct input and source of constraints for thermal budget calculations and real-time optimization solutions. For example, the constraint I_chg_max(t) in the optimization algorithm, T_cell_i(t) ≤ T_safe_max and I_chg ≤ I_chg_max(t), is the instantaneous safe current limit corresponding to the current temperature field and SOC, directly read from the dynamic charging map at the current moment.

Claims

1. A high-rate supercharging power domain system for mining heavy-duty trucks, characterized in that, include: High-power chargers are used to connect to the external power grid and provide charging power for vehicles; The power battery pack is used to store electrical energy; An integrated thermal management system is used to heat or cool the power battery pack, electric drive system, and cab. An electric drive system is used to drive the vehicle and provide regenerative braking. The power domain collaborative controller is communicatively connected to the high-power charger, power battery pack, integrated thermal management system, and electric drive system, respectively. The power domain cooperative controller is configured to execute a cooperative control strategy in charging mode, the strategy including charging control based on dynamic charging map and thermal budget, multi-source energy flow arbitration scheduling, and battery temperature pre-adjustment based on driving condition prediction. The Charging Pressure Index (CSI) calculation module is integrated into the power domain co-controller. The CSI historical database is used to store historical charging CSI values ​​and related parameters; The safety boundary prediction module predicts the safety boundary for the next charge based on historical CSI data.

2. The system according to claim 1, characterized in that, The dynamic charging map is a model that integrates the temperature, temperature difference, battery health status, and real-time maximum acceptable charging power of each monitoring point in the battery pack; the thermal budget is a threshold of total heat generation that can be withstood during this charging process, calculated based on the dynamic charging map, while ensuring battery thermal safety.

3. The system according to claim 1 or 2, characterized in that, The multi-source energy flow arbitration rule defines the power allocation priority under different scenarios; in charging mode, the priority order is: charging safety boundary > battery safety temperature control requirements > battery charging requirements > cab comfort temperature control requirements > other auxiliary load requirements.

4. The system according to claim 1, characterized in that, The battery temperature pre-adjustment based on driving condition prediction refers to the power domain co-controller actively controlling the integrated thermal management system to adjust the battery temperature to a target temperature window suitable for subsequent high-power discharge or safe storage at the end of the charging process, according to the input subsequent driving plan.

5. A method for coordinated control of the high-rate supercharging power domain of a mining heavy-duty truck applied to the system described in any one of claims 1-4, characterized in that, Includes the following steps: S1: After the charging connection is established, the power domain co-controller obtains battery status, thermal management system status and driving plan information; S2: Based on battery status information, build or update a dynamic charging map in real time and calculate the thermal budget for the current charging stage; S3: Based on the dynamic charging map, thermal budget, and multi-source energy flow arbitration rules, collaboratively make decisions and allocate the total power from the high-power charger, and issue instructions to the charger, integrated thermal management system, and other loads respectively; S4: Real-time monitoring of battery thermal status and the status of various system components, calculation of charging pressure index (CSI), and dynamic adjustment of power distribution in step S3. S5: Before the charging process reaches the preset end condition, start the battery temperature pre-adjustment according to the driving plan so that the battery reaches the target temperature when the charging ends.

6. The method according to claim 5, characterized in that, In step S2, the formula for calculating the thermal budget is: ,in, and These are the average specific heat capacity and mass of the battery pack, respectively. For the allowable temperature rise budget, For the allowable temperature difference budget, This is the penalty coefficient.

7. The method according to claim 5, characterized in that, In step S3, the collaborative decision-making is achieved by solving the following optimization problem: Optimization variable: charging current Cooling power , Objective function: Maximize , The constraints include: cell temperature constraint, temperature difference constraint, thermal budget constraint, current capability constraint, and cooling power constraint.

8. The method according to claim 5, characterized in that, In step S3, the multi-source energy flow arbitration adopts a dynamic weight allocation algorithm to assign dynamic weights to battery charging demand, battery thermal management demand, cab thermal management demand, and other load demands. ,in Battery temperature The function of deviation from the optimal temperature range, when When the safety threshold is exceeded, Approaching infinity.

9. The method according to claim 5, characterized in that, In step S5, the target temperature window Based on the predicted power demand under driving conditions Determined, specifically calculated using the following formula: ,in, This refers to the vehicle's peak drive power. This is the adjustment coefficient.

10. The method according to claim 9, characterized in that, The predicted power demand under driving conditions Calculated using the following formula: ,in, For the total mass of the vehicle. It is the acceleration due to gravity. The average slope The rolling resistance coefficient, air density, This is the drag coefficient. To predict vehicle speed, To drive system efficiency.

Images