Energy management method for hybrid power heavy truck

Abstract

The invention relates to the technical field of energy management methods, in particular to an energy management method for a hybrid power heavy truck, which comprises the following steps of: 1, initializing a system and sensing a state; a second step of performing distributed prediction and problem construction; 3, performing online optimization solution and coordination; 4, control instruction distribution and execution; and 5, carrying out real-time iteration. According to the method, through alternate use of the off-line map and the on-line map, in energy management of the hybrid power heavy truck, energy consumption control can be remarkably optimized, the system robustness can be improved, and the comprehensive operation cost can be reduced.

Description

Technical Field

[0001] This invention relates to the technical field of energy management methods, and in particular to an energy management method for hybrid heavy-duty trucks. Background Technology

[0002] With the continuous progress of society, the transportation industry has developed rapidly. Heavy-duty trucks are a crucial component of this industry. Currently, most heavy-duty trucks utilize parallel hybrid power systems. In practical applications, the coupling of mechanical, electrical, and thermal energy flows introduces a large number of state and control variables, making collaborative optimization control extremely complex. Traditional single-optimization algorithms suffer from enormous computational burdens and are prone to getting trapped in local optima, exhibiting the following drawbacks: Insufficient utilization of predictive information: Existing rule-based or instantaneous optimization strategies (such as the Equivalent Fuel Consumption Minimization Strategy - ECMS) lack foresight regarding global road condition information (such as gradient and speed limits), leading to suboptimal energy allocation. Poor real-time computation: Traditional dynamic programming (DP) and model predictive control (MPC) have excessive computational burdens in long-distance scenarios, making real-time onboard applications difficult. Lack of multi-timescale coordination: A single control layer cannot simultaneously respond to second-level power allocation and minute-level road condition changes, resulting in deep battery charging and discharging or frequent engine start-stop. Insufficient robustness to uncertain operating conditions: Fixed-rule strategies are prone to failure when actual road conditions (traffic flow, gradient error) fluctuate.

[0003] CN116394914A discloses a predictive energy management method for hybrid electric vehicles considering the State of Charge (SOC) trajectory, belonging to the field of plug-in hybrid electric vehicle control. First, the global target SOC trajectory is linearly solved based on the vehicle's driving path and target energy consumption. In the prediction time domain, a dynamic programming algorithm is used to solve for the optimal torque allocation to obtain a reference SOC trajectory. Then, with the goal of optimizing SOC following effect and engine fuel consumption, a linear quadratic output tracking controller is used to solve for the optimal engine power. Finally, the torque demand of each power source is calculated based on rules, and the control quantity is sent to the corresponding controller to achieve torque allocation. The purpose of this invention is to simultaneously solve for the optimal torque allocation in the prediction time domain and control the energy consumption during the vehicle's driving path, that is, to fully utilize the predicted information of future operating conditions to improve vehicle economy while controlling target energy consumption. The above method has drawbacks: it relies entirely on the prediction of the global driving path, making it difficult to accurately obtain dynamic traffic information in practical applications; the dynamic programming computation is complex, limiting real-time performance.

[0004] CN118219935A discloses a slope prediction-based intelligent energy management method for hybrid electric vehicles (HEVs), comprising the following steps: establishing models of each energy source and the entire vehicle in the HEV; predicting the total power demand using a long short-term memory network based on the vehicle's current slope and speed information; employing a fuzzy control-based adaptive low-pass filter and a strategy to minimize equivalent consumption for hierarchical power processing; constructing a slope prediction-based HEV energy management system; using the total equivalent hydrogen consumption of each energy source, the lifespan of the fuel cell, and the SOC of the energy storage system under the predicted slope as learning objectives; and designing an efficient hybrid experience replay technique to update the network in the intelligent algorithm. This invention can optimize and adjust the energy management of HEVs online by considering road slope information, and better update the optimal strategy in complex and random driving environments by predicting road slope in real time. The shortcomings of the above management method are that slope prediction relies on high-precision map data, resulting in poor applicability in mountainous or unmapped areas; and the fuel cell lifespan model does not consider actual degradation.

[0005] CN119773724A discloses a method, device, and commercial vehicle for energy management of a hybrid truck, belonging to the field of commercial vehicle energy management technology. During vehicle operation, the vehicle controller acquires the commercial vehicle's operating status information and transmits it to the hybrid truck control module. The hybrid truck control module inputs the commercial vehicle's operating status information into the hybrid truck model. The hybrid truck model calculates the current engine operating point based on engine speed and torque, and estimates the boost parameters of the electronic supercharger based on exhaust temperature and exhaust gas flow. Based on the vehicle's driving resistance and the driver's operating intentions, it determines the motor torque distribution and the battery charging and discharging state. This rational allocation of motor torque assistance achieves a balance between power and energy consumption, improving fuel economy and power performance. It also enhances engine power output and improves the efficiency and reliability of the electronic supercharger. The shortcomings of the above device and method are that it lacks integrated traffic information prediction functionality, resulting in weak predictive control capabilities; and the engine operating point optimization is based solely on the current state. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to overcome the shortcomings of the prior art and provide a hybrid heavy-duty truck energy management method. In the energy management of heavy-duty trucks, the alternating use of offline and online maps can significantly optimize energy consumption control, improve system robustness, and reduce overall operating costs.

[0007] The technical solution adopted by this invention to solve its technical problem is: an energy management method for hybrid heavy-duty trucks, comprising the following steps:

[0008] Step 1, System Initialization and Status Awareness: The vehicle controller collects real-time data from each subsystem controller via the CAN bus network. The system status includes the current vehicle speed, the state of charge of the power battery, the engine speed and torque, the motor speed and torque, the vehicle's required power, and the power requirements of the accessory systems.

[0009] Step 2, Distributed Prediction and Problem Construction: Based on multi-source map information fusion and processing, combining offline map data and online map information, global reference trajectory planning is performed to establish a vehicle speed prediction model and a SOC trajectory prediction model, generating a reference vehicle speed trajectory and a SOC target trajectory. The vehicle speed prediction model incorporates driver reaction delay and vehicle power lag effects, and the SOC trajectory prediction model dynamically adjusts the SOC target based on the travel distance. This includes the following steps:

[0010] Step 21, Vehicle Speed ​​Prediction: Establish a vehicle speed prediction model, incorporating driver reaction delay and vehicle power lag effects. The improved speed prediction equation is as follows:

[0011] (1)

[0012] The symbols in formula (1) represent the following meanings:

[0013] Indicates reaction time;

[0014] To indicate the distance traveled, take half the distance from the front of the vehicle.

[0015] x represents the prediction error of vehicle speed distribution within the next 30 minutes, and this error is no greater than 3 km / h;

[0016] q represents the mapping between distance and time;

[0017] t represents the travel time;

[0018] v indicates vehicle speed;

[0019] ρ represents the mapping between travel distance and time;

[0020] Step 3, Online Optimization and Coordination: The Distributed Model Predictive Control (DMPC) algorithm is used to decompose the global optimization problem into multiple local optimization sub-problems. Each agent solves its own optimal control sequence, and the system achieves overall coordination through information exchange. A rolling optimization mechanism is adopted, that is, within each control cycle, the optimal control sequence for a future finite time domain is solved.

[0021] Step 4, Control Command Allocation and Execution: The vehicle controller sends the optimal power allocation command obtained through optimization calculation to the corresponding subsystem controller via the CAN bus. Each controller executes the command to drive the vehicle.

[0022] Step 5, Real-time Iteration: Steps 1-4 are executed cyclically within a very short control cycle. Based on the latest state information, predictions, optimizations, and energy allocation are re-performed to achieve adaptive optimization control in response to changes in driving conditions and driver intentions.

[0023] To further define the above technical solution, the second step of distributed prediction and problem construction includes the following steps:

[0024] Step 22, SOC Trajectory Prediction: Establish an SOC reference trajectory prediction model, dynamically adjust the SOC target based on the driving distance to avoid sudden battery drop, and define the SOC reference trajectory. The calculation is as follows:

[0025] (2)

[0026] The symbols in formula (2) represent the following meanings:

[0027] Indicates the initial SOC value;

[0028] Indicates the reference SOC value;

[0029] Indicates the target SOC value;

[0030] L indicates the range on a full charge;

[0031] This represents the slope adaptive coefficient;

[0032] d represents the time derivative;

[0033] Through the objective function right Perform optimization calculations while ensuring While meeting the minimum energy consumption requirements of the vehicle, the thermal management safety of the power battery is controlled as a constraint; constraint conditions: Engine torque limit: ;

[0034] The symbols in the objective function represent the following meanings:

[0035] Indicates fuel consumption;

[0036] This represents the SOC value at time K;

[0037] Indicates engine torque;

[0038] Indicates engine speed;

[0039] These represent weighting coefficients, with values ​​of 0.7, 0.2, and 0.1 respectively.

[0040] △T eng Indicates the average time interval;

[0041] Safety requirements for power batteries: ;in, Indicates the motor current;

[0042] The safety constraints for power battery thermal management are described by the battery temperature rise model, as shown in the following formula:

[0043] (3)

[0044] The symbols in formula (3) represent the following meanings:

[0045] Indicates changes in battery temperature;

[0046] Indicates battery current;

[0047] when At that time, the discharge power will be forcibly limited by 20%.

[0048] To further define the above technical solution, the second step of distributed prediction and problem construction includes the following steps:

[0049] Step 23: Generate the reference vehicle speed trajectory Vref(t) and the SOC target trajectory SOCref(t).

[0050] To further define the above technical solution, the second step of distributed prediction and problem construction includes the following steps:

[0051] Step 24: Within 10-30 seconds, based on the reference trajectory provided by the long-term planning layer and real-time environmental feedback, achieve efficient coordination of the power system and stable following of the vehicle platoon.

[0052] To further define the above technical solution, the controllers of each subsystem mainly include the battery management system (BMS), the engine controller (ECU), and the motor controller (MCU).

[0053] The beneficial effects of this invention are as follows: The energy management method for hybrid heavy-duty trucks proposed in this invention can significantly optimize energy consumption control, improve system robustness, and reduce overall operating costs by alternating between offline and online maps in heavy-duty truck energy management. Specifically, through deep fusion and dynamic switching of offline and online maps, combined with a hierarchical distributed predictive control architecture, a unified improvement in foresight, real-time performance, robustness, and economy is achieved.

[0054] I. Proactive Energy Planning: By utilizing global path information from offline maps and real-time traffic data from online maps, optimal vehicle speed and SOC reference trajectory are generated, enabling the power system to anticipate future road conditions, avoid frequent engine start-stop and deep battery charging and discharging, and optimize energy consumption from the source.

[0055] II. High-efficiency real-time optimization: The distributed model predictive control (DMPC) algorithm is adopted to decompose the complex global problem into local subproblems that can be solved in parallel. Through the rolling optimization mechanism, the decision is completed within the millisecond control cycle, which significantly reduces the computational burden and meets the real-time requirements of vehicle.

[0056] 3. Enhanced system robustness: The hybrid map dynamic switching mechanism ensures the continuity of control in areas with poor network signal (such as mountainous areas and tunnels); at the same time, the online optimization layer can quickly respond to fluctuations in operating conditions based on real-time sensor data, overcoming the failure problem of fixed rule strategies in uncertain environments.

[0057] IV. Reduced overall operating costs: Precise energy management directly improves fuel economy; offline computing reduces reliance on the cloud and continuous data traffic, lowering communication costs; in addition, smooth power distribution and battery thermal management constraints extend the lifespan of key components. Attached Figure Description

[0058] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0059] Figure 1 This is a schematic diagram of the hierarchical control framework in this invention;

[0060] Figure 2 This is a schematic diagram of the implementation process in this invention;

[0061] Figure 3 This is a schematic diagram of the core hardware module inside the controller in this invention and its connection relationship with all external key systems. Detailed Implementation

[0062] To make the technical problems solved, the technical solutions, and the beneficial effects of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0063] This application involves the fusion and processing of multi-source map information: integrating offline map data with online map information to achieve global planning; and setting triggering and switching conditions for multi-source maps.

[0064] The hybrid map dynamic switching mechanism is as follows: Triggering conditions: Switch to offline mode when network signal strength is ≤-90dBm; switch to online mode after signal recovery and stabilization for >10 seconds; offline data packets are forcibly pre-added for critical road sections (such as long steep slopes and tunnels). Data fusion strategy: Online data prioritizes over offline data; in case of conflict, real-time information prevails; offline data supplements online data blind spots (such as mountainous areas with no signal).

[0065] The global domain control layer (global planning) in this application generates a spatial domain reference trajectory (vehicle speed, SOC target) based on real-time online map navigation path, slope data and historical operating conditions; the path segmentation clustering divides the driving path into several segments according to slope and congestion status, and pre-calculates the SOC (State of Charge) reference trajectory for each segment.

[0066] The real-time domain control layer (real-time optimization) in this application adjusts power distribution in seconds based on the reference trajectory output by the global planning layer and combined with real-time sensor data (vehicle speed, acceleration, battery SOC). Model predictive control (MPC) optimizes the torque distribution between the engine and motor by minimizing tracking error and energy consumption targets.

[0067] See Figure 1 , Figure 2 and Figure 3 The present invention provides an energy management method for hybrid heavy-duty trucks, comprising the following steps:

[0068] Step 1, System Initialization and Status Awareness: The vehicle controller collects real-time data from each subsystem controller via the CAN bus network. The system status includes the current vehicle speed, the state of charge of the power battery, the engine speed and torque, the motor speed and torque, the vehicle's required power, and the power requirements of the accessory systems.

[0069] Step 2, Distributed Prediction and Problem Construction: Based on the fusion and processing of multi-source map information, combined with offline map data and online map information, global reference trajectory planning is performed to establish a vehicle speed prediction model and a SOC trajectory prediction model, generating a reference vehicle speed trajectory and a SOC target trajectory; the vehicle speed prediction model incorporates driver reaction delay and vehicle power lag effect, and the SOC trajectory prediction model dynamically adjusts the SOC target based on the driving distance;

[0070] Step 3, Online Optimization and Coordination: The Distributed Model Predictive Control (DMPC) algorithm is used to decompose the global optimization problem into multiple local optimization sub-problems. Each agent solves its own optimal control sequence, and the system achieves overall coordination through information exchange. A rolling optimization mechanism is adopted, that is, within each control cycle, the optimal control sequence for a future finite time domain is solved.

[0071] Step 4, Control Command Allocation and Execution: The vehicle controller sends the optimal power allocation command obtained through optimization calculation to the corresponding subsystem controller via the CAN bus. Each controller executes the command to drive the vehicle.

[0072] Step 5, Real-time Iteration: Steps 1-4 are executed iteratively within an extremely short control cycle. Based on the latest state information, predictions, optimizations, and energy allocation are re-performed to achieve adaptive optimization control in response to changes in driving conditions and driver intentions. This process is repeated continuously within a very short control cycle. After each control cycle, the system re-performs predictions, optimizations, and energy allocation based on the latest state information, thereby achieving adaptive optimization control for constantly changing driving conditions and driver intentions.

[0073] The main subsystem controllers include the battery management system (BMS), the engine controller (ECU), and the motor controller (MCU).

[0074] The second step, distributed prediction and problem construction, includes the following steps:

[0075] Step 21, Vehicle Speed ​​Prediction Model: Introducing driver reaction delay and vehicle power lag effect, the improved speed prediction equation is as follows:

[0076] (1)

[0077] The symbols in formula (1) represent the following meanings:

[0078] Indicates reaction time;

[0079] To indicate the distance traveled, take half the distance from the front of the vehicle.

[0080] x represents the prediction error of vehicle speed distribution within the next 30 minutes, and this error is no greater than 3 km / h;

[0081] q represents the mapping between distance and time;

[0082] t represents the travel time;

[0083] v indicates vehicle speed;

[0084] ρ represents the mapping between travel distance and time.

[0085] Step 22, SOC Trajectory Prediction: Establish an SOC reference trajectory prediction model, dynamically adjust the SOC target based on driving distance to avoid sudden battery drop, and define the SOC reference trajectory. The calculation is as follows:

[0086] (2)

[0087] The symbols in formula (2) represent the following meanings:

[0088] Indicates the initial SOC value;

[0089] Indicates the reference SOC value;

[0090] Indicates the target SOC value;

[0091] L indicates the range on a full charge;

[0092] This represents the slope adaptive coefficient;

[0093] d represents the time derivative.

[0094] Through the objective function right Perform optimization calculations while ensuring While meeting the minimum energy consumption requirements of the vehicle, the thermal management safety of the power battery is controlled as a constraint; constraint conditions: Engine torque limit: ;

[0095] The symbols in the objective function represent the following meanings:

[0096] Indicates fuel consumption;

[0097] This represents the SOC value at time K;

[0098] Indicates engine torque;

[0099] Indicates engine speed;

[0100] These represent weighting coefficients, with values ​​of 0.7, 0.2, and 0.1 respectively.

[0101] △T eng This represents the average time interval.

[0102] Safety requirements for power batteries: ;in, Indicates the motor current;

[0103] The safety constraints for power battery thermal management are described by the battery temperature rise model, as shown in the following formula:

[0104] (3)

[0105] The symbols in formula (3) represent the following meanings:

[0106] Indicates changes in battery temperature;

[0107] Indicates battery current;

[0108] when At that time, the discharge power will be forcibly limited by 20%.

[0109] Step 23: Reference vehicle speed trajectory Vref(t), SOC target trajectory SOCref(t);

[0110] Step 24: Within 10-30 seconds, based on the reference trajectory provided by the long-term planning layer and real-time environmental feedback, achieve efficient coordination of the power system and stable following of the vehicle platoon.

[0111] The online optimization and coordination in step 3 includes the following steps:

[0112] Step 31, Distributed Solution: DMPC decomposes the complex global optimization problem into multiple more manageable local optimization problems; each agent independently solves its own optimal control sequence and coordinates with other agents through information exchange to ensure the overall consistency of the system;

[0113] Step 32, Rolling Optimization: The controller solves for the optimal control sequence in a future finite time domain and applies the control command at the current moment to the system; at the next time step, the whole process will be repeated based on the new system state, forming a "rolling optimization" mechanism.

[0114] I. System Architecture Design of the Invention

[0115] 1. Information perception module: Real-time collection of vehicle status (vehicle speed, battery SOC, motor speed) and environmental data (slope, traffic signals); obtains road topology and traffic flow information 3km ahead via V2X.

[0116] 2. Hierarchical control framework, see details. Figure 1 .

[0117] Upper layer (global planning layer): Employs dynamic programming algorithm, based on 3km road topology information (slope, traffic signals, etc.) obtained from V2X, to generate 10-30 minute reference speed and SOC trajectory with the goal of minimizing fuel consumption throughout the journey; Lower layer (real-time execution layer): Employs model predictive control (MPC) to continuously optimize torque distribution and gear selection for the next 5-10 seconds.

[0118] (1) Environmental perception and data preprocessing

[0119] Multi-source data input: 1) Road topology data: Obtain slope, curvature, and speed limit information for the next 3-5km through offline maps, and correct position deviations by combining real-time GPS positioning. 2) Dynamic traffic information: Integrate V2X signals (traffic light phases, vehicle density ahead, congestion prediction) through online maps. 3) Vehicle status monitoring: Real-time collection of battery SOC, vehicle speed, motor torque, and load quality.

[0120] (2) Data filtering and fusion

[0121] (3) Data processing process: 1) Kalman filtering is used to eliminate sensor noise; 2) Spatiotemporal alignment: V2X data is matched with the vehicle coordinate system to establish SLT (Spatial-Longitudinal-Temporal) three-dimensional decision space.

[0122] (4) Multi-source map information fusion and processing: 1) Offline map data: Stores static road network topology, historical slope curves, fixed-point speed limits, etc. Compression technology is used to reduce storage space, and the data is managed by road segment; 2) Online map information: Real-time acquisition of traffic congestion index, sudden weather, temporary construction information, and dynamic vehicle speed limits. Updated every 5-10 seconds via V2X communication, with a delay of <500ms; 3) Hybrid map dynamic switching mechanism: Triggering conditions: Switch to offline mode when network signal strength ≤-90dBm; Switch to online mode after signal recovery and stabilization for >10 seconds; Offline data packets are forcibly added to key road segments (such as long steep slopes, tunnels). Data fusion strategy: Online data prioritizes offline data, and real-time information takes precedence in case of conflict; Offline data supplements online data blind spots (such as mountainous areas without signal).

[0123] (5) Global Reference Trajectory Planning Layer (Time Scale: 10-30 minutes): Responsible for global path planning and energy allocation. Its core logic lies in generating the optimal reference trajectory for vehicle speed and power battery SOC through spatial domain optimization. This trajectory is used to guide the actual operation of the vehicle and provide macro-level decision-making basis for the real-time optimization layer.

[0124] (6) Operating Condition Identification and Classification: Collect historical driving data (vehicle speed, acceleration, gradient) and divide typical operating condition segments (high-speed cruising / city start-stop / mountain climbing) through k-means clustering; 1) High-speed cruising: Maintain the engine's high-efficiency range (1200-2000rpm). 2) City start-stop: Predict the timing of brake energy recovery (dynamically increase the SOC target value by 5%). 3) Long uphill sections: Discharge in advance (reduce the SOC target value by 8%) to avoid the engine's inefficient zone.

[0125] (7) Reference Trajectory Planning: Inputs: Road topology (slope, curvature) in high-precision map, V2X traffic flow data (traffic light phases, congestion prediction), initial battery SOC. Reference Trajectory Planning: 1) Vehicle Speed ​​Prediction Model: Introducing driver reaction delay and vehicle power lag effect to improve the speed prediction equation: ,in, This refers to the reaction time (approximately 1-2 seconds). The distance traveled is half the distance from the front of the vehicle; x represents the speed distribution prediction error within the next 30 minutes, which is no greater than 3 km / h; 2) SOC trajectory prediction model: dynamically adjust the SOC target based on the travel distance to avoid sudden battery drop, and use the SOC reference trajectory. The calculation is as follows: ,in, This is the initial SOC value; For reference SOC value; Target SOC value; L is the range on a full charge; For slope adaptation coefficients (to reserve more power on steep slopes); the following objective function is used to... Perform optimization calculations to ensure The reference trajectory can meet the requirement of minimizing the energy consumption of the entire vehicle, while controlling it with the thermal management safety of the power battery as a constraint. Constraints: Engine torque limit ,in, Fuel consumption; SOC at time K; This refers to engine torque; Engine speed; The weighting coefficients are 0.7, 0.2, and 0.1 respectively (dynamically adjusted to 0.4 for high-temperature conditions). Power battery safety requirements: ,in, Motor current; power battery thermal management safety constraints, battery temperature rise model; ,in, For battery temperature changes; For battery current; when The discharge power is forcibly limited to 20%. Output: reference vehicle speed trajectory Vref(t), SOC target trajectory SOCref(t). The real-time domain control layer (time scale: 5-10 seconds) is the key link connecting the global planning layer and the execution layer. Its main goal is to achieve efficient coordination of the power system and stable following of the vehicle platoon within a limited prediction time domain (usually 10 to 30 seconds), based on the reference trajectory provided by the global planning layer and real-time environmental feedback.

[0126] (8) Real-time dynamic optimization model: Input: 1) Real-time data synchronization from onboard sensors: Camera: captures the position of vehicles ahead, lane lines, and traffic signs (sampling rate ≥ 20Hz), outputting image sequences. Millimeter-wave radar: detects relative speed, distance (accuracy ± 0.1m), and azimuth angle (accuracy ± 0.5°), with a coverage range of 200 meters. Inertial measurement unit (IMU): collects vehicle yaw rate and longitudinal / lateral acceleration in real time (noise < 0.01g) for road curvature estimation. 2) Real-time dynamic trajectory planning, based on the establishment of a dynamic optimization model using DMPC: Hybrid heavy-duty trucks (heavy trucks) use distributed model predictive control (DMPC) for real-time energy management. Its core purpose is to coordinate multiple energy sources such as the engine, motor / generator, and battery to achieve optimal fuel economy while ensuring power performance. Its core calculation process is a closed-loop process of online rolling optimization.

[0127] Step 1: System Initialization and State Awareness: At the beginning of each control cycle, the Vehicle Management System (VMS) needs to collect real-time data from various subsystem controllers (such as the Battery Management System (BMS), Engine ECU, and Motor MCU) via the CAN bus network. Key state variables: These mainly include current vehicle speed, battery state of charge (SOC), engine speed and torque, motor speed and torque, vehicle power demand (calculated from the accelerator pedal and current driving resistance), and power demand from accessory systems. System Self-Check: Before acquiring data, a system self-check is usually performed to determine if there are any faults in each subsystem, ensuring that control commands can be executed on a healthy system.

[0128] Step Two: Distributed Prediction and Problem Construction: This is the core of DMPC. Based on the current state, the controller constructs a local optimization problem for each energy source (considered as an agent). Prediction Models: Dynamic mathematical models are established for key components such as the engine, motor, and battery. These models are used to predict the evolution of the system's future state (e.g., SOC changes, fuel consumption) under a given control input sequence. Objective Function: The objective of each local optimization problem is typically to minimize the total "cost" within the prediction time domain. This cost function (J) is usually a weighted sum of fuel consumption, energy consumption (converted to equivalent fuel consumption), and penalties for drastic changes in control commands. Constraints: Optimization must strictly adhere to various physical and safety constraints, such as the engine's maximum and minimum speeds and torques, the motor's maximum and minimum torques, the battery's upper and lower SOC limits, and the battery's maximum charge and discharge power.

[0129] Step 3: Online Optimization and Coordination: Each energy source agent, after considering the predicted information from neighboring agents (such as vehicle demand and the state of other energy sources), solves its own local optimization problem in parallel. Distributed Solution: DMPC decomposes the complex global optimization problem into multiple more manageable local optimization subproblems. Each agent (such as the engine controller and motor controller) independently solves its own optimal control sequence and exchanges necessary predicted information (such as future power demand prediction) with other agents through communication to achieve overall system coordination and consistency. Rolling Optimization: The controller does not solve for the optimal solution for the entire journey (that's the idea of ​​dynamic programming DP), but only solves for the optimal control sequence within a finite future time domain (prediction time domain), and only applies the control command at the current moment to the system. At the next time step, the entire process is repeated based on the new system state, forming a "rolling optimization" mechanism.

[0130] Step 4: Control Command Allocation and Execution: After solving the optimization problem, the Vehicle Management System (VMS) sends the calculated optimal power allocation commands (e.g., the power the engine should provide and the power the motor should provide / absorb) to the corresponding subsystem controllers (BMS, ECU, MCU) via the CAN bus. Each controller executes the commands to drive the vehicle.

[0131] Step 5: Real-time Iteration: The above steps are repeated continuously within a very short control cycle (usually milliseconds). After each control cycle, the system re-predicts, optimizes, and redistributes energy based on the latest state information, thereby achieving adaptive optimization control for dynamically changing driving conditions and driver intentions. Condition-Responsive Mode Switching: In the short-term layer, specific operating modes are triggered based on real-time torque demands from the power source; Start-up / Climbing: Dual power sources (engine + motor) provide hybrid power supply, delivering peak torque; Downhill: The motor switches to generator mode to recover braking energy; Platform Cruise: Pure electric / hybrid mode is determined based on the SOC threshold.

[0132] Hardware architecture: The core of this application lies in a powerful central computing unit (VMS), which receives prediction information at different time scales through multiple communication interfaces and coordinates and manages the entire powertrain.

[0133] See Figure 3 It details the core hardware modules inside the controller and their connections with all external key systems, demonstrating how it integrates long-term and short-term forecast information and enables energy management decisions.

[0134] Core Module Function Explanation: The main control processor (SoC / MPU) is the "brain" of the controller. To handle complex predictive models (such as MPC) and machine learning algorithms, a high-performance multi-core processor (such as the ARM Cortex-A series) is required, typically including: 1) Application Processor (AP): Responsible for running the upper-level operating system (such as Linux / AUTOSAR Adaptive) and core energy management algorithms. 2) Microcontroller (MCU): Responsible for high-real-time low-level tasks and communication management, conforming to the AUTOSAR Classic standard. 3) Communication Interface Module, Ethernet Switch: A high-speed backbone used to connect high-precision map positioning modules and remote information processing units (T-Boxes), with future expansion to connect to intelligent driving domain controllers. 4) Multi-channel CAN / CAN FD Controller: The absolute mainstay of the heavy-duty truck network. Connects all powertrain nodes such as BMS, ECU, MCU, and TCU, and collects vehicle sensor data. 5) V2X and Wireless Communication Interface (4G / 5G): Key to achieving long-term and short-term prediction. Responsible for receiving global traffic information (long-term prediction) from the cloud and real-time local road condition information (short-term prediction) from V2X devices. Digital / Analog I / O: Used to process hard-wired signals, such as wake-up signals, enable signals, relay control, etc. Storage Units (Flash & RAM): Large-capacity Flash: Used to store the operating system, applications, high-precision map data (cache), and fault logs. Large-capacity RAM is used for data storage and computation during algorithm runtime, especially for the rolling updates of large amounts of state data in the prediction time domain. Power Management and Hardware Security, Multi-channel Power Supply Regulation Circuit: Converts the 24V power supply from the vehicle battery to different voltages (such as 5V, 3.3V, 1.2V, etc.) required by various chips inside the controller, meeting stringent requirements for voltage fluctuations and transient immunity.

[0135] This application has the following advantages over traditional energy management methods for hybrid heavy-duty trucks:

[0136] Improved kinetic energy recovery efficiency: Energy recovery optimization based on slope prediction. Offline map-stored road slope data (such as digital elevation models) can be used to plan braking points in advance. Combined with the energy recovery system, this maximizes energy recovery on downhill sections, extending driving range by 15%-20%. Online maps dynamically adjust the timing of recovery based on real-time traffic flow data, avoiding ineffective braking due to congestion ahead.

[0137] Precise speed curve matching: Based on offline maps of historical road conditions, a steady-state speed curve is generated. Combined with online real-time road conditions (such as accident location) to correct predictions, the hybrid system operates in the optimal speed range, reducing ineffective engine start-stop and reducing fuel consumption by more than 7% in actual tests.

[0138] Network fault tolerance and cost control: Continuous management of no-signal areas: When the network is interrupted in mining areas or border sections, the offline map maintains basic navigation and energy strategy execution (such as pre-loaded fixed gradient routes) to ensure the continuous operation of the heavy truck SOC (State of Charge) balancing algorithm; after online recovery, it automatically switches to real-time mode to avoid the risk of abnormal fuel consumption.

[0139] Traffic and storage balance: Provincial offline maps require only 800MB of storage, avoiding traffic costs caused by continuous transmission of real-time data by 4G / 5G modules; online mode supplements dynamic information through incremental updates (only 10K-100K / 4 hours), significantly reducing fleet-level communication costs.

[0140] Enhanced security and reliability: Defense against cyberattacks: Offline mode reduces the time the vehicle system is exposed to public networks, lowering the risk of hackers tampering with navigation routes through online vulnerabilities (such as an unupdated system, which has a 27 times higher probability of being hijacked).

[0141] Hardware load optimization: Offline computing alleviates the pressure of cloud path planning, avoids control command delays caused by network latency, and ensures the torque response accuracy of heavy-duty vehicles in scenarios such as curves and steep slopes.

[0142] Parallel hybrid heavy-duty trucks encounter highly uncertain climate and driving conditions during operation, and the energy consumption coupling relationship between their power system and thermal management system is complex. As nonlinear time-varying systems with multiple state and control variables, revealing energy-saving control mechanisms using global optimization algorithms results in high computational complexity and low solution efficiency. One of the key issues this invention aims to address is how to improve the global optimization efficiency of energy flow collaborative management. To this end, a reference trajectory optimization algorithm for the state variables of the power system and thermal management system is proposed to achieve online and accurate tracking by the controller.

[0143] For parallel hybrid heavy-duty trucks, which exhibit fast powertrain response and significant thermal management system hysteresis, a single optimization algorithm cannot simultaneously consider long-term reference planning of state variables such as temperature and battery SOC, as well as short-term precise system control, making it difficult to achieve ideal global optimal energy consumption. By employing a hierarchical predictive control strategy and combining it with reference trajectory planning of system state variables, online precise tracking of the reference trajectory by the powertrain's actuators is achieved, thereby realizing the precise control objective of collaborative optimization.

[0144] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. An energy management method for hybrid heavy-duty trucks, characterized in that, Includes the following steps: Step 1, System Initialization and Status Awareness: The vehicle controller collects real-time data from each subsystem controller via the CAN bus network. The system status includes the current vehicle speed, the state of charge of the power battery, the engine speed and torque, the motor speed and torque, the vehicle's required power, and the power requirements of the accessory systems. Step 2, Distributed Prediction and Problem Construction: Based on multi-source map information fusion and processing, combining offline map data and online map information, global reference trajectory planning is performed to establish a vehicle speed prediction model and a SOC trajectory prediction model, generating a reference vehicle speed trajectory and a SOC target trajectory. The vehicle speed prediction model incorporates driver reaction delay and vehicle power lag effects, and the SOC trajectory prediction model dynamically adjusts the SOC target based on the travel distance. This includes the following steps: Step 21, Vehicle Speed ​​Prediction: Establish a vehicle speed prediction model, incorporating driver reaction delay and vehicle power lag effects. The improved speed prediction equation is as follows: （1） The symbols in formula (1) represent the following meanings: Indicates reaction time; To indicate the distance traveled, take half the distance from the front of the vehicle. x represents the prediction error of vehicle speed distribution within the next 30 minutes, and this error is no greater than 3 km / h; q represents the mapping between distance and time; t represents the travel time; v indicates vehicle speed; ρ represents the mapping between travel distance and time; Step 3, online optimization and coordination: The distributed model predictive control (DMPC) algorithm is adopted to decompose the global optimization problem into multiple local optimization sub-problems. Each agent solves its own optimal control sequence and achieves overall system coordination through information interaction; A rolling optimization mechanism is adopted, that is, within each control cycle, the optimal control sequence in the future finite time domain is solved; Step 4, Control Command Allocation and Execution: The vehicle controller sends the optimal power allocation command obtained through optimization calculation to the corresponding subsystem controller via the CAN bus. Each controller executes the command to drive the vehicle. Step 5, Real-time Iteration: Steps 1-4 are executed cyclically within a very short control cycle. Based on the latest state information, predictions, optimizations, and energy allocation are re-performed to achieve adaptive optimization control for changes in driving conditions and driver intentions.

2. The energy management method for a hybrid heavy-duty truck according to claim 1, characterized in that: The second step, distributed prediction and problem construction, includes the following steps: Step 22, SOC Trajectory Prediction: Establish an SOC reference trajectory prediction model, dynamically adjust the SOC target based on the driving distance to avoid sudden battery drop, and define the SOC reference trajectory. The calculation is as follows: （2） The symbols in formula (2) represent the following meanings: Indicates the initial SOC value; Indicates the reference SOC value; Indicates the target SOC value; L indicates the range on a full charge; This represents the slope adaptive coefficient; d represents the time derivative; Through the objective function right Perform optimization calculations while ensuring While meeting the minimum energy consumption requirements of the vehicle, the thermal management safety of the power battery is controlled as a constraint; constraint conditions: Engine torque limit: ; The symbols in the objective function represent the following meanings: Indicates fuel consumption; This represents the SOC value at time K; Indicates engine torque; Indicates engine speed; These represent weighting coefficients, with values ​​of 0.7, 0.2, and 0.1 respectively. △T eng Indicates the average time interval; Safety requirements for power batteries: ;in, Indicates the motor current; The safety constraints for power battery thermal management are described by the battery temperature rise model, as shown in the following formula: （3） The symbols in formula (3) represent the following meanings: Indicates changes in battery temperature; Indicates battery current; when At that time, the discharge power will be forcibly limited by 20%.

3. The energy management method for a hybrid heavy-duty truck according to claim 1, characterized in that: The second step, distributed prediction and problem construction, includes the following steps: Step 23: Generate the reference vehicle speed trajectory Vref(t) and the SOC target trajectory SOCref(t).

4. The energy management method for a hybrid heavy-duty truck according to claim 1, characterized in that: The second step, distributed prediction and problem construction, includes the following steps: Step 24: Within 10-30 seconds, based on the reference trajectory provided by the long-term planning layer and real-time environmental feedback, achieve efficient coordination of the power system and stable following of the vehicle platoon.

5. The energy management method for a hybrid heavy-duty truck according to claim 1, characterized in that: The main subsystem controllers include the battery management system (BMS), the engine controller (ECU), and the motor controller (MCU).

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