Storage battery charging system and method of gas-electric hybrid vehicle

Abstract

The invention belongs to the technical field of gas-electric hybrid vehicles, particularly relates to a storage battery charging method of a gas-electric hybrid vehicle, and provides the following scheme: step 1, a data acquisition step; step 2, based on the collected information, determining whether to start power supply by calculating a dynamically changing power supply trigger threshold value, and executing predicted energy management in combination with future driving path information of the vehicle; 3, after it is judged that electricity supplementing is needed, scoring is conducted on multiple predefined electricity supplementing modes, and the mode with the highest comprehensive score is selected as the optimal electricity supplementing mode; and step 4, performing power supply execution and switching. Continuous and stable high-power output can be provided, the power requirement of the vehicle is met, and the situation that the follow-up electricity supplementing cost is increased due to over-discharge of the power battery is avoided.

Description

Technical Field

[0001] This invention relates to the field of gas-electric hybrid vehicle technology, and in particular to a battery charging system and method for gas-electric hybrid vehicles. Background Technology

[0002] Hybrid vehicles, such as those using hydrogen fuel cells or natural gas engines paired with power batteries as the hybrid power source, rely on 12V or 24V low-voltage batteries for their conventional low-voltage electrical systems. The energy of these batteries usually needs to be supplemented by a high-voltage energy source via a DC-DC converter.

[0003] In existing technologies, the battery charging of gas-electric hybrid vehicles mainly relies on the engine directly driving the generator or a single power battery power supply mode. The timing of charging is not intelligent, and there is a lack of an adaptive switching mechanism for multiple charging modes, resulting in energy waste. Summary of the Invention

[0004] Based on the technical problems in the background art, the present invention proposes a battery charging system and method for gas-electric hybrid vehicles.

[0005] A battery charging system for a gas-electric hybrid vehicle includes: a data acquisition module for executing step one, which includes various vehicle status sensors, a positioning and mapping module, and a vehicle-to-infrastructure communication module; a decision control module, which is based on a vehicle controller and is used to execute steps two and three; and an execution control module for executing step four, which includes an engine controller, a power battery management system, a DC-DC voltage converter controller, and an integrated starter-generator controller.

[0006] The present invention proposes a method for charging the battery of a gas-electric hybrid vehicle, comprising the following steps: Step 1: Data Acquisition Steps. Real-time data collection of vehicle operating status, energy reserves, and environmental information.

[0007] Step 2, Intelligent Charging Timing Decision Step: Based on the collected information, the system calculates the dynamically changing charging trigger threshold to determine whether to initiate charging, and performs predictive energy management by combining the vehicle's future driving path information. The calculation of the dynamically changing charging trigger threshold specifically includes: setting a basic trigger threshold; dynamically correcting the basic trigger threshold based on one or more of the following influencing factors: driver's driving style, current road conditions, weather conditions, real-time traffic conditions, vehicle energy reserve status, and road slope information based on navigation prediction; and limiting the corrected threshold to a preset safety range as the final dynamic threshold for determining whether to trigger charging. Predictive energy management includes: when navigation information predicts that the vehicle will enter a long uphill section ahead, if the current state of charge of the battery is lower than a preset value, the vehicle will be controlled to recharge in advance; when navigation information predicts that the vehicle will enter a long downhill section ahead, if the current state of charge of the battery is higher than a preset value, the vehicle will be controlled to actively consume some electrical energy to reserve space for subsequent braking energy recovery.

[0008] Step 3: Adaptive power replenishment mode decision-making step. After determining that power replenishment is needed, multiple predefined power replenishment modes are scored, and the mode with the highest comprehensive score is selected as the optimal power replenishment mode. Multiple charging modes include at least: a mode that uses the power battery to directly charge the storage battery, a mode that uses a hydrogen or natural gas engine to generate electricity to charge the storage battery, and a mode that uses energy recovery during vehicle braking or downhill driving to charge the storage battery. The factors used to score each charging mode include: the energy conversion efficiency of the mode under the current operating conditions, the impact of the mode on the vehicle's power performance, and the energy consumption or cost generated by the operation of the mode; the weight of the score can be adaptively adjusted according to the driving mode or real-time road conditions.

[0009] Step 4: Power replenishment execution and switching steps. Execute the selected optimal power replenishment mode, and when switching between different power replenishment modes, use a power gradual change method to achieve a smooth transition. A smooth transition is achieved by using a power gradient method. Specifically, during the process of switching from the current power supply mode to the target power supply mode, the output power of the current power supply is gradually reduced at fixed time intervals, while the output power of the target power supply is gradually increased at the same time intervals until the switch is completed. During mode switching, the battery voltage is monitored in real time. If the voltage fluctuation exceeds the allowable range, the switching process is paused and the current operating mode is maintained.

[0010] Preferably, after step three, a degradation power replenishment decision step is also included, specifically including: If all charging modes fail to meet the requirements under the standard scoring rules, the first step is to check whether the vehicle has any faults or conditions that involve core safety, i.e., whether all preset safety constraints are met. If all safety constraints are met, the mode with the highest score among all power replenishment modes is selected as the candidate mode, and the key non-safety constraints that cause the mode to fail to meet the requirements are identified. While ensuring safety, temporarily relax key non-safety constraints and re-evaluate the candidate mode; If the candidate mode meets the requirements after reassessment, the vehicle is controlled to perform the corresponding power replenishment operation in a downgraded power replenishment mode.

[0011] During the implementation of the downgraded charging mode, the vehicle status related to the relaxed critical non-safety constraints is continuously monitored; when the vehicle status is detected to have recovered to meet the original critical non-safety constraints, the downgraded charging mode is automatically exited and the standard charging mode decision-making process is resumed.

[0012] Key non-safety constraints include: the minimum permissible state of charge threshold set to protect the high-voltage power battery, and the minimum operating speed or power threshold set to optimize engine efficiency.

[0013] The beneficial effects of this invention are as follows: 1. In this invention, the four modes defined are direct battery charging, hydrogen engine power generation, regenerative braking, and hybrid charging. Each mode has its applicable scenarios and high efficiency range. In congested urban traffic conditions, where vehicles travel at low speeds and frequently start and stop, the direct battery charging mode is prioritized due to its 95% high efficiency. It directly uses the power battery to charge the storage battery without starting the hydrogen engine, thus reducing fuel consumption. In high-speed driving and high-power demand conditions, the hydrogen engine power generation mode intervenes in a timely manner. Although its efficiency is slightly lower than that of direct battery charging, it can provide a continuous and stable high-power output to meet the vehicle's power needs and avoid excessive discharge of the power battery, which would increase subsequent charging costs. Attached Figure Description

[0014] Figure 1 This is an overall block diagram of a battery charging system for a gas-electric hybrid vehicle proposed in this invention. Figure 2 This is an overall flowchart of a battery charging method for a gas-electric hybrid vehicle proposed in this invention. Figure 3 This is a schematic diagram of the adaptive charging mode scoring and decision-making process for a battery charging method for a gas-electric hybrid vehicle proposed in this invention. Figure 4 This is a schematic diagram of the downgraded charging decision process in Embodiment 2 of the battery charging method for a gas-electric hybrid vehicle proposed in this invention. Detailed Implementation

[0015] Example 1: Refer to Figure 1 A battery charging system for a gas-electric hybrid vehicle includes a data acquisition layer, a decision control layer, and an execution layer.

[0016] Data Acquisition Layer: Responsible for comprehensively and in real-time collecting information on vehicle operating status, energy reserves, and external environment; specifically including: Vehicle speed sensor: Hall effect type, resolution 0.1 km / h, used to detect real-time vehicle speed; Accelerator pedal position sensor: linear potentiometer, resolution 0.5%; Brake pressure sensor: piezoresistive type, range 0-10MPa; accelerator pedal position sensor and brake pressure sensor are used to determine the driver's operating intention; Driving mode selection switch: retrieves the driver's settings for driving modes such as Eco, Standard, and Sport; Battery SOC sensor: Accuracy ±1%, accurately monitors the state of charge of low-voltage batteries; Power battery SOC sensor: accuracy ±1%, monitors the state of charge of high-voltage power battery pack; Hydrogen or natural gas storage sensor: pressure type, range 0-70MPa, monitors fuel balance; High-precision GPS positioning module: Centimeter-level high-precision positioning, providing vehicle location information; Built-in high-precision map: provides information on the slope, curvature, and speed limit of the path ahead of the vehicle; V2X vehicle-road cooperative communication module: used to receive information such as real-time traffic light status and road congestion warnings ahead; Environmental sensors: temperature sensor, light sensor, rain and snow sensor.

[0017] Decision Control Layer: The core of this layer is the Vehicle Controller Unit (VCU), which runs the core software algorithm module of this invention - the Battery Charging Strategy Manager. This manager receives and integrates all information from the data acquisition layer and includes multiple sub-functional modules such as a driving behavior analysis module, a road condition prediction and energy consumption calculation module, and a battery charging mode decision module. Its core responsibility is to perform in-depth data processing and intelligent decision-making, and finally output precise control commands on when to start battery charging and what mode to use for battery charging.

[0018] Execution layer: Receives instructions from the VCU and controls each actuator to complete the power replenishment operation; mainly includes: Hydrogen or natural gas engine ECU: Used to control the engine's start-stop, speed, and torque output; Battery Management System (BMS): Used to control the discharge of the power battery; DCDC converter controller: Used to regulate its output voltage and current to meet the charging needs of the battery; ISG Motor Controller: An integrated starter-generator unit used to control the switching of ISG motors between electric and generator modes.

[0019] Reference Figures 1-3A battery charging method for a gas-electric hybrid vehicle includes an intelligent charging timing decision method: the decision control layer (VCU) uses a composite method combining a dynamic threshold algorithm and a predictive charging strategy to intelligently determine the timing of charging initiation.

[0020] ① Dynamic threshold algorithm: This algorithm abandons the fixed trigger threshold. It first sets a basic trigger threshold, for example, setting it to consider charging when the low-voltage battery SOC drops to 30%. However, this threshold is not fixed, but will dynamically fluctuate upward or downward according to a series of correction factors that reflect the actual comprehensive working conditions. The final trigger threshold used for actual judgment is calculated and determined by the following formula: Final trigger threshold = max(15%, min(45%, basic threshold + ∑ correction factor)).

[0021] The ∑ correction factor in the formula is the algebraic sum of multiple influencing factors, each of which expresses its direction and degree of influence on the threshold as a percentage; for example: Driving style correction factor: If the system determines that the driving style is economical, 3% is subtracted from the threshold; if it determines that the driving style is aggressive, 5% is added. Real-time traffic correction factors: 8% increase for driving on mountain roads, 5% increase for urban congestion, and 2% decrease for driving on highways; Weather condition correction factor: Rain and snow weather increase by 3%; Real-time traffic information correction factor: 5% increase if congestion is detected ahead via V2X; Energy reserve status correction factor: Increase by 5% when fuel reserves are insufficient, and decrease by 3% when power battery charge is sufficient; Navigation-based predictive correction factors: Decrease by 5% if navigation indicates a long downhill section ahead, and increase by 8% if navigation indicates a long uphill section ahead.

[0022] Through this dynamic adjustment, the charging trigger point becomes highly intelligent, ensuring that the charging trigger point is within a safe range of 15%-45%, and can respond sensitively to various complex and changing driving conditions.

[0023] ②Predictive power replenishment strategy: The VCU not only uses the current state, but also actively uses data to predict trends in the short term, and performs forward-looking energy management accordingly.

[0024] On the one hand, it utilizes a large amount of historical driving data accumulated by vehicles, such as collecting 10,000 kilometers of driving data, to construct a Markov chain model and establish a driving behavior state transition matrix. The state is defined as: acceleration / deceleration / constant speed / coasting / stopping, thereby predicting the possible energy consumption trend in the future, such as the next 10 minutes.

[0025] On the other hand, it combines precise path information and real-time vehicle speed provided by high-precision maps to calculate the theoretical energy consumption of the predetermined driving path in real time using physical formulas. The calculation model includes: Slope resistance energy consumption: ; Energy consumption due to air resistance: ; Rolling resistance energy consumption: ; Overall resistance energy consumption: ; in For vehicle weight, It is the acceleration due to gravity. For slope, For vehicle speed, air density, This is the drag coefficient. For windward area, This is the rolling resistance coefficient.

[0026] Based on the above energy consumption prediction, the VCU will execute a forward-looking power replenishment strategy, as shown in the following example: Strategy before a long downhill section: When the navigation indicates that there is a long downhill section with a gradient of ≥5% 1km ahead: If the battery SOC is greater than 60%, some in-vehicle electrical devices will be turned on to consume some power, such as seat heating and increasing the power of the air conditioning fan. If the battery SOC is ≤60%, the current state will be maintained to reserve sufficient storage space for subsequent regenerative braking.

[0027] Strategy before a long uphill climb: When the navigation indicates a long uphill section with a gradient of ≥8% 2km ahead... If the battery's state of charge (SOC) is less than 50%, the hydrogen engine will be started in advance to replenish the battery, with a target SOC of 60%. If the battery SOC is greater than or equal to 50%, the current state will be maintained, and the power battery will be used as the auxiliary drive in priority.

[0028] Congested Road Strategy: When the V2X module indicates congestion 3km ahead: If the battery SOC is less than 40%, and if there is sufficient hydrogen, the hydrogen engine should be started first to replenish the battery. If the battery SOC is ≥40%, maintain the current state and avoid frequent engine start-stop in congested traffic to reduce energy consumption and emissions.

[0029] Reference Figures 1-3 A battery charging method for a gas-electric hybrid vehicle also includes an adaptive charging mode switching mechanism: when the system determines that charging needs to be initiated, the decision control layer needs to select and switch to the optimal one from a variety of feasible energy supply methods.

[0030] 1. Definition of Power Supply Mode: This invention system manages and coordinates four core power supply energy source modes: 1.1 Mode 1, Direct Battery Charging Mode: Directly uses the electrical energy stored in the high-voltage power battery pack, and charges the low-voltage battery after being stepped down by the DC-DC converter; suitable for low-speed and stable driving scenarios with sufficient power battery charge, the advantage is high efficiency (about 95%) and no additional fuel consumption.

[0031] 1.2 Mode 2, Hydrogen / Natural Gas Engine Power Generation Mode: Start the vehicle's gas fuel engine, which drives the ISG motor or other form of generator to generate electricity. The generated electricity is then converted to charge the battery. This mode is suitable for high-speed, high-power demand or scenarios with extremely low battery power. Its advantages are that it can provide high power (up to 15kW) and fast response.

[0032] 1.3 Mode 3, Braking Energy Recovery Mode: When the vehicle decelerates, goes downhill, or coasts, the drive motor (or ISG motor) is switched to generator mode to convert the vehicle's kinetic or potential energy into electrical energy, which is then used to charge the battery directly or after conversion. This mode is suitable for scenarios involving vehicle deceleration, going downhill, and coasting. Its advantage is zero additional energy consumption, converting braking energy into electrical energy with a recovery efficiency of up to 70%.

[0033] 1.4 Mode 4, Hybrid Power Supply Mode: When facing complex driving conditions such as mountain roads and frequent start-stop, two or more of the above power supply energy sources are used in a coordinated manner. The overall energy efficiency of the system is optimized through optimized control. For example, when climbing a hill, the engine can generate electricity and the power battery can supply power at the same time to meet the instantaneous high power demand.

[0034] 2. Mode Decision Algorithm: The VCU has a built-in multi-dimensional scoring decision algorithm to calculate a comprehensive utility score in real time for each available power replenishment mode.

[0035] The scoring process takes into account multiple dimensions, such as the estimated energy conversion efficiency of the mode under the current operating conditions, the potential impact of the mode on the current power output performance of the vehicle after it is activated, the fuel consumption or cost caused by the mode, and the impact on vehicle emissions and NVH (noise, vibration and harshness) levels. Each dimension is assigned a different weight coefficient according to its importance.

[0036] Exemplary scoring logic explanation: 2.1 Decision-making on the direct subsidy model for power batteries: Available conditions: Power battery SOC > 30% and vehicle speed < 60km / h; Scoring logic: Score = Efficiency (value 95%) × 0.6 - Dynamic impact (value 0) × 0.4 = 57 points; Priority scenarios: When driving at low speeds in the city and when the battery is fully charged, it should be activated first to avoid starting the engine and increasing energy consumption.

[0037] 2.2 Hydrogen Engine Power Generation Mode Decision: Available conditions: Hydrogen storage > 15% and (vehicle speed > 80 km / h or energy demand > 10 kW); Scoring logic: Score = Efficiency (value 85%) × 0.5 - Fuel consumption impact (value 10) × 0.5 = 37.5 points; Priority scenarios: High-power demand scenarios such as high-speed cruising and hill climbing, or forced start when the battery is extremely low.

[0038] 2.3 Braking Energy Recovery Mode Decision: Available under the following conditions: vehicle acceleration < -0.5 m / s² or road condition is downhill; Scoring logic: Score = Efficiency (value 70%) × 0.7 + Braking energy saving (value -5) × 0.3 = 47 points; Priority scenarios: Automatic activation during deceleration and downhill sections, zero-energy recovery of energy, saving wear on the braking system.

[0039] 2.4 Hybrid Power Supply Mode Decision: Available conditions: (aggressive driving + mountain road conditions) or (congested road conditions + battery SOC < 30%). Scoring logic: Score = Overall efficiency (value 88%) × 0.5 - System complexity (value 5) × 0.5 = 41.5 points; Priority scenario: Multi-source coordinated power replenishment under complex operating conditions, such as when frequently accelerating and decelerating on mountain roads, the engine and battery can be used simultaneously for power replenishment.

[0040] 2.5 Adaptive Weight Adjustment: When driving aggressively, the weight of the hydrogen engine mode is increased by 10% to prioritize power; when driving economically, the weight of the regenerative braking mode is increased by 15% to maximize energy recovery; in mountainous areas, the weight of the hybrid mode is increased by 20%; in congested areas, the weight of the engine mode is reduced by 15% to avoid frequent start-stop.

[0041] 2.6 Predictive switching mechanism: Long downhill prediction: When the navigation indicates a downhill slope 1km ahead, reduce engine power 30 seconds in advance to reserve energy storage space for regenerative braking mode; Hydrogen refueling station navigation: When the distance to a hydrogen refueling station is less than 10km, if the hydrogen storage is greater than 30%, the engine charging priority will be temporarily increased to ensure that the battery is fully charged when arriving at the station.

[0042] 2.7 Anomaly Handling and Security Mechanisms: No available mode handling: When all mode scores are <0, output "no_charging" and trigger backup plans, such as restricting non-essential power consumption. Conflict resolution during switching: If the score difference between the two modes is less than 5 points, the "lowest energy consumption" mode shall be selected first, such as choosing the former when comparing battery direct compensation and pneumatic power generation; Real-time monitoring and correction: The score is re-evaluated every 10 seconds during the power replenishment process. If the score of the current mode is more than 10 points lower than that of the suboptimal mode, a forced switch is triggered.

[0043] 3. Smooth Switching Control Strategy: To avoid significant voltage fluctuations in the low-voltage electrical system or abrupt power delivery during switching between different charging modes due to sudden changes in energy sources, the system employs active power smoothing transition control; specific control example: Power balance control: When switching modes, a linear interpolation algorithm is used for a smooth transition. For example, when switching from a power battery to a hydrogen engine, the DC-DC power decreases by 10% every 0.5 seconds, while the engine power increases by 10% every 0.5 seconds. Status monitoring and protection: During the switching process, the battery voltage and temperature are monitored in real time. If an abnormality occurs (such as voltage fluctuation > ±0.5V), the switching is paused and the current mode is maintained. Predictive switching: Based on road condition predictions, prepare to switch 30 seconds in advance. For example, start reducing engine power 30 seconds before going downhill to prepare for regenerative braking mode.

[0044] In this embodiment, in the economic driving mode, the threshold is reduced by 3% to avoid premature charging when the battery is fully charged; while before a long uphill climb, the threshold is increased by 8% to reserve power in advance for high-power demands.

[0045] In this embodiment, regarding road conditions, mountain roads have steep slopes and many curves. Vehicles need high power output when climbing hills and have great potential for brake energy recovery when going downhill. When the algorithm detects a long uphill ahead, it increases the threshold by 8% and starts the hydrogen engine in advance to replenish the battery and reserve sufficient power for climbing. When going downhill for a long time, the threshold is reduced by 5% to reserve more power for brake energy recovery and avoid wasting recovered energy due to battery saturation.

[0046] In this embodiment, four modes are defined: direct battery charging, hydrogen engine power generation, regenerative braking, and hybrid charging. Each mode has its applicable scenarios and high efficiency range. In congested urban traffic, where vehicles travel at low speeds and frequently start and stop, the direct battery charging mode is prioritized due to its 95% efficiency advantage. It directly uses the power battery to charge the storage battery without starting the hydrogen engine, thus reducing fuel consumption. In high-speed driving and high-power demand conditions, such as overtaking and climbing hills, the hydrogen engine power generation mode intervenes promptly. Although its efficiency is slightly lower than that of direct battery charging (approximately 85%), it can provide a continuous and stable high-power output to meet the vehicle's power needs and avoid excessive discharge of the power battery, which would increase subsequent charging costs. The regenerative braking mode automatically activates when the vehicle decelerates or goes downhill, converting the wasted braking energy into electrical energy with an efficiency of up to 70%. For example, when a vehicle decelerates from a highway into a toll station, this mode immediately starts, and the recovered electrical energy is directly replenished to the battery, reducing the frequency of other charging modes and improving energy utilization.

[0047] Example 2: Refer to Figures 1-4 A battery charging system and method for a gas-electric hybrid vehicle, based on Embodiment 1, has a potential logical processing scenario in its charging mode decision algorithm: when the system calculates based on multi-dimensional scoring rules and the comprehensive score of all selectable charging modes is below 0, the system's final decision output will be no available mode, i.e., no charging operation will be performed. In actual vehicle operation, the vehicle may experience a situation where the current SOC of the high-voltage power battery is slightly lower than the starting condition SOC>30% set for the direct charging mode. This condition is essentially an economic and durability constraint set to optimize battery life and maintain a certain redundancy. Simultaneously, the current operating condition does not meet the starting conditions of other charging modes, resulting in all mode scores being negative. Meanwhile, the actual SOC of the low-voltage battery may be... The current level is already quite low. If the system completely stops charging, the low-voltage battery will continue to consume power, posing a risk of limiting the functionality of the vehicle's low-voltage electrical system or even preventing the vehicle from starting or driving normally. Therefore, based on Example 1, a degraded charging decision mechanism based on conditional attribute differentiation and soft constraint relaxation is introduced: First, the activation conditions of each charging mode are finely classified to clearly distinguish between hard and soft constraints. Second, under the premise of satisfying all hard constraints (i.e., the safety baseline), when the conventional decision fails, the system is allowed to intelligently, temporarily, and conditionally relax one or more key soft constraints, thereby activating a degraded charging mode available under relaxed conditions. This maintains a minimum level of safe charging functionality in extreme situations, ensuring the vehicle's most basic power needs and operational safety.

[0048] The specific implementation steps and logic are as follows: 1. Definition and Classification of Startup Condition Attributes: During the system software initialization or parameter calibration phase, clearly define the attributes of each startup condition for each power-up mode, and classify them into two categories: The first category is hard constraints: These conditions involve the core operational safety of the vehicle, personal safety, or the physical limits of key components. They are rigid conditions that cannot be violated and are not allowed to be compromised in any way. Typical hard constraints include, but are not limited to: the low-voltage battery voltage being lower than the absolute safety threshold that ensures the minimum operating voltage of the controller, the high-voltage system reporting a serious insulation fault or short-circuit fault code, the presence of a leak alarm signal in the fuel supply line, and the failure of critical sensors.

[0049] The second category is soft constraints: These conditions mainly involve goals such as optimizing the economic operation of the system, improving energy efficiency, ensuring the long service life of components, or improving the driving experience. They are conditions set in pursuit of better performance. Typical soft constraints include, but are not limited to: "Power battery SOC > 30%" set in the direct power battery compensation mode, which aims to protect the battery from over-discharge; "Vehicle speed > 80km / h or instantaneous power demand > 10kW" set in the hydrogen engine power generation mode, which aims to ensure that the engine operates in a higher efficiency range; and "Avoid starting the engine if there is congestion ahead and the battery SOC > 40%" set to improve comfort in congested traffic.

[0050] 2. Conditions for triggering the degradation and power replenishment decision process: After the VCU executes the standard mode scoring algorithm described in Example 1, if the decision result is "all mode scores < 0", the system will not immediately jump to the final "no power replenishment (no_charging)" state and output it; instead, the system will first trigger a backup logic processing path for the degradation and power replenishment decision process.

[0051] 3. Downgrade process: Step 1: Hard constraint security verification and initial screening of pattern potential: a. Absolute safety verification: The system first strictly checks whether the current real-time status of the vehicle violates any condition defined as a hard constraint. If any hard constraint is detected to be violated, the entire downgrade process will be terminated immediately and unconditionally. The system will force the vehicle into a preset safety fault handling state. This state may include illuminating the fault warning light, recording fault codes, and possibly restricting certain vehicle functions. The system will never attempt any form of power replenishment. This step ensures that any power replenishment will not be performed under any circumstances that endanger safety.

[0052] b. Potential Pattern Recognition: If all hard constraints are confirmed to be met after inspection, it indicates that the vehicle is currently in a safe but poor operating condition. At this time, the system will focus on and analyze the one or two patterns with the highest scores in the standard scoring results, that is, the pattern with the smallest negative score, and identify which one or two key soft constraints are not met, thus causing the pattern to have a negative score.

[0053] Step 2: Intelligent relaxation and re-evaluation of soft constraints: For the potential patterns identified in the first step, the system will temporarily, with clear records and conditional restrictions, relax one or two of the key unmet soft constraints, provided that no safety issues are caused, such as the power battery being over-discharged and its health is damaged. For example, the conditions for the direct battery charging mode could be temporarily relaxed from "power battery SOC>30%" to "power battery SOC>25% and battery health is good"; or, the strict restrictions on minimum vehicle speed for engine power generation mode could be temporarily lifted, allowing it to start at low speeds but limiting its power generation capacity.

[0054] Step 3: Degradation Mode Scoring and Security Implementation: Using the relaxed new conditions, the system recalculates the utility score of the potential mode. Since the main obstacle soft constraint that caused its negative score has been removed, its new score has a high probability of turning positive. The system then uses this new positive score as the basis for decision-making and approves the execution of the power replenishment operation in the special state of the downgraded power replenishment mode. At the same time, the vehicle's human-machine interface will display clear prompts to the driver, such as the power replenishment system protection mode is running, to inform them that they are currently in a special operating state.

[0055] Step 4: Status Monitoring and Normal Recovery During the degraded charging mode operation, the system will continuously and closely monitor the state parameters related to the relaxed soft constraints. Once the vehicle operating environment is improved, allowing the original soft constraints to be met again, such as when the vehicle is parked and connected to a charging pile, and the power battery SOC is charged to 35%, the system will automatically and smoothly switch back to the standard optimal operating strategy corresponding to the charging mode, and at the same time remove the degraded mode prompt on the instrument panel, so that the system can fully restore the normal intelligent charging logic described in Example 1.

[0056] Specific application example: Consider a scenario where a hybrid electric vehicle has undergone a long period of low-speed urban commuting in severe winter: Due to the low temperature and continuous power supply to the low-voltage system, the SOC of the high-voltage power battery drops to 28%; while the SOC of the low-voltage battery also drops to 22% due to capacity reduction at low temperatures and load demands; at this moment, the system executes the standard mode decision: Mode 1 (Direct compensation for power battery): The soft constraint of power battery SOC>30% is not met, and the efficiency estimate at low temperature is too low, resulting in an overall score of -12.

[0057] Mode 2 (hydrogen engine power generation): Since the current vehicle speed is only 25km / h, it does not meet the efficiency soft constraint of vehicle speed >80km / h or demand >10kW. Moreover, the efficiency of starting the engine to replenish electricity at low temperature idling speed is extremely low and the emissions are severe. The overall score is -18.

[0058] Mode 3 (Brake Energy Recovery): The vehicle is in a constant speed following other vehicles, with no opportunity to brake or go downhill. It does not meet the applicable conditions, and the score is -25.

[0059] Mode 4 (Hybrid Mode): The current simple following condition does not meet the definition of a complex scenario, score -20.

[0060] If all mode standard scores are less than 0, and the system follows the basic logic of Example 1, it will not perform any power replenishment, and the low-voltage battery power may continue to drop to a dangerous level. According to the degradation mechanism of this embodiment, the system will trigger the backup process: First, check if there are any hard constraint alarms, such as no high voltage fault or fuel leak; then analyze and find that mode 1 is the highest potential mode with the highest score. Its key negative score is that the soft constraint of power battery SOC>30% is not met; the system assesses that the current power battery health status is good, the temperature is within the working range, and the low voltage battery SOC (22%) has reached the warning line that requires immediate charging. Therefore, the system temporarily relaxed the activation conditions of Mode 1 to power battery SOC > 25%; under this relaxed condition, the Mode 1 score was recalculated and became +8; then, the system activated the power battery direct replenishment in the downgraded replenishment mode to charge the low-voltage battery and alerted the driver. Once the vehicle's SOC recovers to above 30% through other means during subsequent driving, such as connecting to a charging station or recovering energy while driving, the system will automatically switch back to the standard direct battery charging mode and cancel the downgrade prompt.

[0061] In this embodiment, when conventional optimization decisions fail to yield feasible solutions due to multiple critical limits, the system no longer simply gives up. Instead, under the premise of strictly adhering to the safety bottom line, it explores and activates potential power replenishment capabilities through intelligent relaxation of soft constraints. This fundamentally prevents serious malfunctions such as the paralysis of the entire vehicle's low-voltage system or the inability to drive the vehicle due to the depletion of the low-voltage battery, significantly improving the vehicle's reliability and user experience.

[0062] By clearly distinguishing between hard and soft constraints, this mechanism constructs a clear safety boundary. All operations are carried out within the space that ensures that the absolute red line of hard constraints is not touched. Under this premise, the flexible and temporary adjustment of soft constraints reflects the system's intelligent trade-off ability in dealing with complex situations, and achieves maximum functionality under the premise of safety assurance. It is a refined energy management strategy.

[0063] The above description is only 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. A method for charging the battery of a gas-electric hybrid vehicle, characterized in that, Includes the following steps: Step 1: Data Acquisition Steps. Real-time data collection of vehicle operating status, energy reserves, and environmental information. Step 2, Intelligent Charging Timing Decision: Based on the collected information, the system calculates the dynamically changing charging trigger threshold to determine whether to initiate charging, and combines this with the vehicle's future driving path information to perform predictive energy management. Step 3: Adaptive power replenishment mode decision-making step. After determining that power replenishment is needed, multiple predefined power replenishment modes are scored, and the mode with the highest comprehensive score is selected as the optimal power replenishment mode. Multiple charging modes include at least: a mode that uses the power battery to directly charge the storage battery, a mode that uses a hydrogen or natural gas engine to generate electricity to charge the storage battery, and a mode that uses energy recovery during vehicle braking or downhill driving to charge the storage battery. Step 4: Power replenishment execution and switching steps. Execute the selected optimal power replenishment mode, and when switching between different power replenishment modes, use a power gradual change method to achieve a smooth transition.

2. The battery charging method for a gas-electric hybrid vehicle according to claim 1, characterized in that, Step two, specifically calculating the dynamically changing power-up trigger threshold, includes: Set a basic trigger threshold; The base trigger threshold is dynamically adjusted based on one or more of the following influencing factors: driver's driving style, current road conditions, weather conditions, real-time traffic conditions, vehicle energy reserves, and road gradient information based on navigation prediction. The corrected threshold is limited to a preset safety range and used as the final dynamic threshold for determining whether to trigger a power replenishment.

3. The battery charging method for a gas-electric hybrid vehicle according to claim 2, characterized in that, Predictive energy management in step two includes: When the navigation information predicts that the vehicle will enter a long uphill section ahead, if the current state of charge of the battery is lower than a preset value, the vehicle will be controlled to recharge in advance. When navigation information predicts that the vehicle will enter a long downhill section, if the current state of charge of the battery is higher than a preset value, the vehicle will actively consume some electrical energy to reserve space for subsequent braking energy recovery.

4. The battery charging method for a gas-electric hybrid vehicle according to claim 1, characterized in that, In step three, the factors used to score each charging mode include: the energy conversion efficiency of the mode under the current operating conditions, the impact of the mode on the vehicle's power performance, and the energy consumption or cost generated by the operation of the mode. The weighting of the score can be adaptively adjusted based on driving mode or real-time road conditions.

5. A method for replenishing the battery of a gas-electric hybrid vehicle according to claim 4, characterized in that, In step four, a smooth transition is achieved by gradually changing the power output. Specifically, during the process of switching from the current power supply mode to the target power supply mode, the output power of the current power supply is gradually reduced at fixed time intervals, while the output power of the target power supply is gradually increased at the same time intervals until the switch is completed.

6. A method for replenishing the battery of a gas-electric hybrid vehicle according to claim 5, characterized in that, During the mode switching process in step four, the battery voltage is monitored in real time. If the voltage fluctuation exceeds the allowable range, the switching process is paused and the current operating mode is maintained.

7. A method for replenishing the battery of a gas-electric hybrid vehicle according to claim 1, characterized in that, Step three is followed by a degradation and power replenishment decision-making step, which specifically includes: If all charging modes fail to meet the requirements under the standard scoring rules, the first step is to check whether the vehicle has any faults or conditions that involve core safety, i.e., whether all preset safety constraints are met. If all safety constraints are met, the mode with the highest score among all power replenishment modes is selected as the candidate mode, and the key non-safety constraints that cause the mode to fail to meet the requirements are identified. While ensuring safety, temporarily relax key non-safety constraints and re-evaluate the candidate mode; If the candidate mode meets the requirements after reassessment, the vehicle is controlled to perform the corresponding power replenishment operation in a downgraded power replenishment mode.

8. A method for replenishing the battery of a gas-electric hybrid vehicle according to claim 7, characterized in that, During the implementation of the downgraded charging mode, the vehicle status related to the key non-safety constraints that have been relaxed will be continuously monitored; When the vehicle status is detected to have recovered to meet the original critical non-safety constraints, the degraded power replenishment mode is automatically exited, and the standard power replenishment mode decision-making process is resumed.

9. A method for replenishing the battery of a gas-electric hybrid vehicle according to claim 7, characterized in that, Key non-safety constraints include: the minimum permissible state of charge threshold set to protect the high-voltage power battery, and the minimum operating speed or power threshold set to optimize engine efficiency.

10. A battery charging system for a gas-electric hybrid vehicle, used to implement the battery charging method for a gas-electric hybrid vehicle as described in any one of claims 1 to 9, characterized in that, include: The data acquisition module is used to perform step one, and it includes various vehicle status sensors, a positioning and mapping module, and a vehicle-to-infrastructure communication module. The decision control module, with the vehicle controller at its core, is used to execute steps two and three. The execution control module, used to execute step four, includes an engine controller, a power battery management system, a DC voltage converter controller, and an integrated starter-generator controller.

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