AI-controlled multi-fuel solid fuel cell range extender system for electric vehicle

Abstract

The invention relates to the technical field of prolonging the endurance mileage of a battery electric vehicle, and discloses an AI-controlled multi-fuel solid fuel battery range extender system for an electric vehicle, which continuously receives and processes real-time data from a vehicle system, carries out segmented energy consumption simulation on the residual stroke by using a prediction algorithm based on the obtained data, and obtains the endurance mileage of the battery electric vehicle. And performing voltage monitoring and current monitoring on the battery, judging whether an abnormal condition exists or not, analyzing the internal resistance of the battery and the consistency of single batteries, evaluating the aging degree and the potential fault risk, and dynamically limiting the charging power of the SOFC or performing emergency turn-off in danger according to the safety state of the battery. After a future charging demand point is recognized, the system starts the heating cycle of the SOFC in advance according to the known preheating time of the SOFC, after the SOFC reaches the working temperature, the AI controller instructs the SOFC to output power at constant and efficient power to generate electricity, and the system captures high-quality waste heat generated by the SOFC and reuses the high-quality waste heat. And introducing the waste gas discharged from the anode of the SOFC into a vehicle-mounted carbon capture and storage unit for treatment.

Description

Technical Field

[0001] This application relates to the technical field of extending the driving range of battery electric vehicles, and in particular to an AI-controlled multi-fuel solid fuel cell range extender system for electric vehicles. Background Technology

[0002] Battery electric vehicles (BEVs) offer a promising solution for reducing emissions in the transportation sector. However, consumer "range anxiety"—the fear that a vehicle's battery will run out before reaching its destination or a charging station—hinders their widespread adoption. Despite continuous advancements in BEV technology, battery capacity remains a limiting factor in terms of cost, weight, and charging time.

[0003] One proposed solution is to use an onboard auxiliary power unit (APU), or range extender, to charge the battery while driving. Internal combustion engines have been used for this purpose, but they undermine the zero-emission advantage of electric vehicles. Proton exchange membrane fuel cells (PEMFCs) are another option, but they typically require high-purity hydrogen as fuel, and the associated hydrogen refueling infrastructure is lacking.

[0004] Solid oxide fuel cells (SOFCs) are known for their high power generation efficiency and fuel flexibility. However, their high operating temperatures result in long start-up times and an inability to quickly respond to fluctuations in power demand during direct vehicle drive, making them generally unsuitable as primary automotive power sources.

[0005] This invention addresses these limitations by proposing a novel system and operating method that uniquely leverages the advantages of SOFC in range extender applications. Summary of the Invention

[0006] The purpose of this invention is to provide an AI-controlled multi-fuel solid fuel cell range extender system for electric vehicles, which solves at least one of the above problems.

[0007] This invention provides an AI-controlled multi-fuel solid fuel cell range extender system for electric vehicles, comprising: Step S1: Continuously receive and process real-time data from the vehicle system; Step S2: Based on the obtained data, use a prediction algorithm to simulate the segmented energy consumption of the remaining journey; Step S3: Monitor battery status in real time and perform battery health analysis; Step S4: After identifying future charging demand points, the system starts the SOFC heating cycle in advance based on the known preheating time of the SOFC. In step S5, after the SOFC reaches its operating temperature, the AI ​​controller instructs it to generate electricity with constant and efficient power output. Step S6: The system captures the high-quality waste heat generated by the SOFC, reuses the high-quality waste heat, and introduces the exhaust gas from the SOFC anode into the on-board carbon capture and storage unit for treatment.

[0008] As a further technical solution, the real-time data includes: core vehicle status data, route and environment data, and driver and assistance system data; The core vehicle status data includes battery state of charge, instantaneous energy consumption, vehicle speed and acceleration, and odometer readings. The route and environmental data include GPS coordinates and planned route, terrain data, real-time traffic conditions and environmental conditions; the terrain data includes the elevation and slope information of the road segment the vehicle is about to travel on; the environmental conditions include external temperature, precipitation and wind speed. The driver and assistance system data includes driver behavior characteristics and assistance load status.

[0009] As a further technical solution, the prediction algorithm includes: For each trip the vehicle takes, the AI ​​vehicle manager records the vehicle data and the actual energy consumption. The obtained data is processed into the structural format required by the machine learning model; The prediction model is trained using a historical labeled dataset, which takes a feature set from past trips as input to predict energy consumption and compares the predicted value with the actual energy consumption recorded for the trip. The prediction results and process are iterated to obtain the relationship between various driving conditions and energy consumption. Before each deployment, the trained model is tested. If the prediction accuracy is verified, the trained model will be deployed to the vehicle's AI vehicle manager.

[0010] As a further technical solution, the segmented energy consumption simulation includes: After receiving the destination from the navigation system, the controller divides the entire route into a series of short segments that are equidistant and continuous. Calculate the predicted total energy consumption for each individual short-circuit segment; Starting from the current power level, subtract the predicted energy consumption for each road segment in turn to generate the power consumption curve for the entire journey; The predicted power curve is compared with a preset threshold to identify the future time point when the range extender needs to be activated.

[0011] As a further technical solution, the method for calculating and predicting comprehensive energy consumption includes:

[0012] in, For each independent short-circuit segment, the predicted total energy consumption is calculated. This refers to the driving energy consumption of a car during operation. The energy consumption caused by each acceleration and deceleration of a car. For the total mass of the car, This refers to the vertical change in height during vehicle movement. For the efficiency of the car's motor, For the efficiency of car batteries, This refers to the number of times a car accelerates or decelerates within a given road segment. The current temperature of the car. For reference temperature, The energy generated when turning on the car's air conditioning... The time to turn on the car air conditioning This is a reference coefficient for the temperature at which the car is located.

[0013] As a further technical solution, the working process of step S3 includes: Step S31: Monitor the battery voltage and current to determine if there are any abnormal conditions; Step S32: Analyze the battery internal resistance and cell consistency to assess the degree of aging and potential failure risks; Step S33: Dynamically limit SOFC charging power or shut down in case of danger, depending on the battery safety status.

[0014] As a further technical solution, the working process of step S3 includes: Step S41: Accurately calculate the future time point when the battery's state of charge is expected to drop to the charging threshold and record it as the time when SOFC needs to start generating electricity. Step S42: The system calculates the earliest start-up time that must begin heating by working backwards from the start of power generation by the calibration preheating time of an SOFC. Step S43: When the real-time clock reaches the earliest start time, the AI ​​vehicle manager immediately sends a start signal to the SOFC system to trigger it to perform a preheating cycle and continuously monitors the temperature and heating status of the SOFC. In step S44, when the internal temperature sensor of the SOFC system confirms that the SOFC has reached the preset optimal operating temperature range, the system will enter the standby state.

[0015] As a further technical solution, the reuse of high-quality waste heat includes: The system captures the high-quality waste heat generated by the SOFC during operation. The waste heat at the highest temperature is directly converted into electrical energy through a thermoelectric generator to supplement the vehicle's low-voltage power grid or to preheat the battery pack. After initial power generation, the waste heat from the medium-temperature section is directed to the reformer to provide the necessary thermal energy for the fuel reforming process, thereby reducing the system's own energy consumption. The waste heat in the low-temperature range is used for the heating system of the passenger compartment or to provide thermal management support for the vehicle's power battery pack, ensuring that it operates within the optimal temperature window.

[0016] As a further technical solution, the on-board carbon capture and storage unit's treatment of exhaust gas includes: The high-temperature exhaust gas discharged from the SOFC anode is pretreated to condense and separate most of the water vapor before being discharged. The dehydrated exhaust gas is then transported to the carbon capture and storage unit. The dehydrated waste gas comes into full contact with the carbon capture and storage unit. Specific substances in the carbon capture and storage unit react chemically with carbon dioxide, capturing it from the waste gas. Finally, the remaining inert gas is safely discharged. The specific substance that has adsorbed carbon dioxide is further heated to raise its temperature to a preset temperature, and carbon dioxide is released from the specific substance to form a carbon dioxide gas flow. At the same time, the cooled specific substance is recycled back to the carbon capture and storage unit for reuse. The released carbon dioxide gas is pressurized and stored in a dedicated high-pressure storage tank on the vehicle. The system monitors the pressure and capacity levels of the high-pressure storage tank in real time. When the tank is close to full capacity, the system issues a prompt to the driver through the vehicle-mounted human-machine interface.

[0017] As a further technical solution, an AI-controlled multi-fuel solid fuel cell range extender system for electric vehicles is characterized by comprising: a solid oxide fuel cell, an AI vehicle manager, a combined heat and power unit, and a carbon capture and storage unit. The solid oxide fuel cell includes a reformer and a multi-fuel tank, the multi-fuel tank storing a variety of conventional fuels, and the reformer processing the various fuels from the multi-fuel tank; The AI ​​vehicle manager communicates with various systems of the vehicle, receives key data, integrates prediction algorithms and machine learning models, and makes intelligent decisions on SOFC startup, power generation, and overall system linkage. The combined heat and power unit captures the high-quality waste heat generated during SOFC operation and intelligently distributes this heat energy to the cockpit for heating or the power battery for preheating. The carbon capture and storage unit includes a specific adsorbent and absorbent liquid to process and store carbon dioxide in the SOFC anode exhaust.

[0018] In summary, this application includes at least one of the following beneficial technical effects: 1. Through AI predictive management, the slow-starting solid oxide fuel cell can be preheated in advance and generate electricity on demand, continuously replenishing the battery and achieving a driving range far exceeding that of pure battery capacity, fundamentally solving users' range anxiety problem.

[0019] 2. The system can utilize a variety of common fuels, eliminating the dependence on dedicated hydrogen refueling stations; at the same time, waste heat is recovered through the combined heat and power unit for cabin heating or battery preheating, which greatly improves the overall energy utilization efficiency of the vehicle.

[0020] 3. With the carbon capture and storage unit, the system can capture and store the carbon dioxide produced by the fuel cell in the vehicle, enabling the vehicle to operate with near-zero carbon emissions even when using carbon-based fuels. This is an environmental advantage that surpasses traditional internal combustion engines and even some grid-powered electric vehicles. Attached Figure Description

[0021] Figure 1 This is a flowchart of the workflow of an AI-controlled multi-fuel solid fuel cell range extender system for electric vehicles.

[0022] Figure 2 This is a schematic block diagram of the architecture of a multi-fuel SOFC range extender system with intelligent management within a battery electric vehicle. Detailed Implementation

[0023] The embodiments of this application are described in detail below, and examples of the embodiments are shown in the accompanying drawings.

[0024] In the description of this specification, the references to "certain embodiments," "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples" refer to specific features, structures, materials, or characteristics described in connection with the described embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0025] This invention discloses an AI-controlled multi-fuel solid fuel cell range extender system for electric vehicles. Please refer to [link / reference]. Figure 1 As shown, it includes: Step S1: Continuously receive and process real-time data from the vehicle system; Step S2: Based on the obtained data, use a prediction algorithm to simulate the segmented energy consumption of the remaining journey; Step S3: Monitor battery status in real time and perform battery health analysis; Step S4: After identifying future charging demand points, the system starts the SOFC heating cycle in advance based on the known preheating time of the SOFC. In step S5, after the SOFC reaches its operating temperature, the AI ​​controller instructs it to generate electricity with constant and efficient power output. Step S6: The system captures the high-quality waste heat generated by the SOFC, reuses the high-quality waste heat, and introduces the exhaust gas from the SOFC anode into the on-board carbon capture and storage unit for treatment.

[0026] In this embodiment, the real-time data includes: core vehicle status data, route and environment data, and driver and assistance system data; The core vehicle status data includes battery state of charge, instantaneous energy consumption, vehicle speed and acceleration, and odometer readings. The route and environmental data include GPS coordinates and planned route, terrain data, real-time traffic conditions and environmental conditions; the terrain data includes the elevation and slope information of the road segment the vehicle is about to travel on; the environmental conditions include external temperature, precipitation and wind speed. The driver and assistance system data includes driver behavior characteristics and assistance load status.

[0027] In this embodiment, the prediction algorithm includes: For each trip the vehicle takes, the AI ​​vehicle manager records the vehicle data and the actual energy consumption. The obtained data is processed into the structural format required by the machine learning model; The prediction model is trained using a historical labeled dataset, which takes a feature set from past trips as input to predict energy consumption and compares the predicted value with the actual energy consumption recorded for the trip. The prediction results and process are iterated to obtain the relationship between various driving conditions and energy consumption. Before each deployment, the trained model is tested. If the prediction accuracy is verified, the trained model will be deployed to the vehicle's AI vehicle manager.

[0028] In this embodiment, the segmented energy consumption simulation includes: After receiving the destination from the navigation system, the controller divides the entire route into a series of short, continuous segments of equal distance, each segment being 1 km long; Calculate the predicted total energy consumption for each individual short-circuit segment; Starting from the current power level, subtract the predicted energy consumption for each road segment in turn to generate the power consumption curve for the entire journey; The predicted battery level curve is compared with a preset threshold of 25% to identify the future time point when the range extender needs to be activated.

[0029] In this embodiment, the method for calculating and predicting comprehensive energy consumption includes:

[0030] in, For each independent short-circuit segment, the predicted total energy consumption is calculated. This refers to the driving energy consumption of a car during operation. The energy consumption caused by each acceleration and deceleration of a car. For the total mass of the car, This refers to the vertical change in height during vehicle movement. For the efficiency of the car's motor, For the efficiency of car batteries, This refers to the number of times a car accelerates or decelerates within a given road segment. The current temperature of the car. For reference temperature, The energy generated when turning on the car's air conditioning... The time to turn on the car air conditioning This serves as a reference coefficient for the temperature at which the car is located. in, =1kWh / 100km =90%, =85%, =0.05, =25°, dimensionless operations are performed between the terms.

[0031] In this embodiment, the working process of step S3 includes: Step S31: Monitor the battery voltage and current to determine if there are any abnormal conditions; Step S32: Analyze the battery internal resistance and cell consistency to assess the degree of aging and potential failure risks; Step S33: Dynamically limit SOFC charging power or shut down in case of danger, depending on the battery safety status.

[0032] In this embodiment, the working process of step S3 includes: Step S41: Accurately calculate the future time point when the battery's state of charge is expected to drop to 25% of the charging threshold, and record it as the time when SOFC needs to start generating electricity. Step S42: The system calculates the earliest start-up time that must begin heating by working backward from the start of power generation by 15 minutes, which is the calibration preheating time of an SOFC. Step S43: When the real-time clock reaches the earliest start time, the AI ​​vehicle manager immediately sends a start signal to the SOFC system to trigger it to perform a preheating cycle and continuously monitors the temperature and heating status of the SOFC. In step S44, when the internal temperature sensor of the SOFC system confirms that the SOFC has reached the preset optimal operating temperature range (100°, 120°), the system will enter the standby state.

[0033] In this embodiment, the reuse of high-quality waste heat includes: The system captures the high-quality waste heat generated by the SOFC during operation. The waste heat at the highest temperature is directly converted into electrical energy through a thermoelectric generator to supplement the vehicle's low-voltage power grid or to preheat the battery pack. After initial power generation, the waste heat from the medium-temperature section is directed to the reformer to provide the necessary thermal energy for the fuel reforming process, thereby reducing the system's own energy consumption. The waste heat in the low-temperature range is used for the heating system of the passenger compartment or to provide thermal management support for the vehicle's power battery pack, ensuring that it operates within the optimal temperature window.

[0034] In this embodiment, the on-board carbon capture and storage unit's treatment of exhaust gas includes: The high-temperature exhaust gas discharged from the SOFC anode is pretreated to condense and separate most of the water vapor before being discharged. The dehydrated exhaust gas is then transported to the carbon capture and storage unit. The dehydrated waste gas comes into full contact with the carbon capture and storage unit. The absorbent in the carbon capture and storage unit reacts chemically with the carbon dioxide to capture it from the waste gas. Finally, the remaining inert gas is safely discharged. The absorbent liquid that has adsorbed carbon dioxide is further heated to raise its temperature to a preset temperature. Carbon dioxide is released from the absorbent liquid to form a carbon dioxide gas flow. At the same time, the cooled absorbent liquid is recycled back to the carbon capture and storage unit for reuse. The released carbon dioxide gas is pressurized and stored in a dedicated high-pressure storage tank on the vehicle. The system monitors the pressure and capacity levels of the high-pressure storage tank in real time. When the tank approaches 95% of its full capacity, the system issues a prompt to the driver through the vehicle's human-machine interface.

[0035] In this embodiment, please refer to Figure 2 As shown, an AI-controlled multi-fuel solid fuel cell range extender system for electric vehicles is characterized by comprising: a solid oxide fuel cell, an AI vehicle manager, a combined heat and power unit, and a carbon capture and storage unit. The solid oxide fuel cell includes a reformer and a multi-fuel tank, the multi-fuel tank storing a variety of conventional fuels, and the reformer processing the various fuels from the multi-fuel tank; The AI ​​vehicle manager communicates with various systems of the vehicle, receives key data, integrates prediction algorithms and machine learning models, and makes intelligent decisions on SOFC startup, power generation, and overall system linkage. The combined heat and power unit captures the high-quality waste heat generated during SOFC operation and intelligently distributes this heat energy to the cockpit for heating or the power battery for preheating. The carbon capture and storage unit includes a specific adsorbent and absorbent liquid to process and store carbon dioxide in the SOFC anode exhaust.

[0036] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. An AI-controlled multi-fuel solid fuel cell range extender system for electric vehicles, characterized in that, The system operation process includes: Step S1: Continuously receive and process real-time data from the vehicle system; Step S2: Based on the obtained data, use a prediction algorithm to simulate the segmented energy consumption of the remaining journey; Step S3: Monitor battery status in real time and perform battery health analysis; Step S4: After identifying future charging demand points, the system starts the SOFC heating cycle in advance based on the known preheating time of the SOFC. In step S5, after the SOFC reaches its operating temperature, the AI ​​controller instructs it to generate electricity with constant and efficient power output. Step S6: The system captures the high-quality waste heat generated by the SOFC, reuses the high-quality waste heat, and introduces the exhaust gas from the SOFC anode into the on-board carbon capture and storage unit for treatment.

2. The AI-controlled multi-fuel solid fuel cell range extender system for electric vehicles according to claim 1, characterized in that, The real-time data includes: core vehicle status data, route and environment data, and driver and assistance system data; The core vehicle status data includes battery state of charge, instantaneous energy consumption, vehicle speed and acceleration, and odometer readings. The route and environmental data include GPS coordinates and planned route, terrain data, real-time traffic conditions and environmental conditions; the terrain data includes the elevation and slope information of the road segment the vehicle is about to travel on; the environmental conditions include external temperature, precipitation and wind speed. The driver and assistance system data includes driver behavior characteristics and assistance load status.

3. The AI-controlled multi-fuel solid fuel cell range extender system for electric vehicles according to claim 2, characterized in that, The prediction algorithm includes: For each trip the vehicle takes, the AI ​​vehicle manager records the vehicle data and the actual energy consumption. The obtained data is processed into the structural format required by the machine learning model; The prediction model is trained using a historical labeled dataset, which takes a feature set from past trips as input to predict energy consumption and compares the predicted value with the actual energy consumption recorded for the trip. The prediction results and process are iterated to obtain the relationship between various driving conditions and energy consumption. Before each deployment, the trained model is tested. If the prediction accuracy is verified, the trained model will be deployed to the vehicle's AI vehicle manager.

4. The AI-controlled multi-fuel solid fuel cell range extender system for electric vehicles according to claim 1, characterized in that, The segmented energy consumption simulation includes: After receiving the destination from the navigation system, the controller divides the entire route into a series of short segments that are equidistant and continuous. Calculate the predicted total energy consumption for each individual short-circuit segment; Starting from the current power level, subtract the predicted energy consumption for each road segment in turn to generate the power consumption curve for the entire journey; The predicted power curve is compared with a preset threshold to identify the future time point when the range extender needs to be activated.

5. The AI-controlled multi-fuel solid fuel cell range extender system for electric vehicles according to claim 4, characterized in that, The method for calculating and predicting comprehensive energy consumption includes: ； in, For each independent short-circuit segment, the predicted total energy consumption is calculated. This refers to the driving energy consumption of a car during operation. The energy consumption caused by each acceleration and deceleration of a car. For the total mass of the car, This refers to the vertical change in height during vehicle movement. For the efficiency of the car's motor, For the efficiency of car batteries, This refers to the number of times a car accelerates or decelerates within a given road segment. The current temperature of the car. For reference temperature, The energy generated when turning on the car's air conditioning The time to turn on the car air conditioning This is a reference coefficient for the temperature at which the car is located.

6. The AI-controlled multi-fuel solid fuel cell range extender system for electric vehicles according to claim 1, characterized in that, The working process of step S3 includes: Step S31: Monitor the battery voltage and current to determine if there are any abnormal conditions; Step S32: Analyze the battery internal resistance and cell consistency to assess the degree of aging and potential failure risks; Step S33: Dynamically limit SOFC charging power or shut down in case of danger, depending on the battery safety status.

7. The AI-controlled multi-fuel solid fuel cell range extender system for electric vehicles according to claim 1, characterized in that, The working process of step S3 includes: Step S41: Accurately calculate the future time point when the battery's state of charge is expected to drop to the charging threshold and record it as the time when SOFC needs to start generating electricity. Step S42: The system calculates the earliest start-up time that must begin heating by working backwards from the start of power generation by the calibration preheating time of an SOFC. Step S43: When the real-time clock reaches the earliest start time, the AI ​​vehicle manager immediately sends a start signal to the SOFC system to trigger it to perform a preheating cycle and continuously monitors the temperature and heating status of the SOFC. In step S44, when the internal temperature sensor of the SOFC system confirms that the SOFC has reached the preset optimal operating temperature range, the system will enter the standby state.

8. The AI-controlled multi-fuel solid fuel cell range extender system for electric vehicles according to claim 1, characterized in that, The reuse of high-quality waste heat includes: The system captures the high-quality waste heat generated by the SOFC during operation. The waste heat at the highest temperature is directly converted into electrical energy through a thermoelectric generator to supplement the vehicle's low-voltage power grid or to preheat the battery pack. After initial power generation, the waste heat from the medium-temperature section is directed to the reformer to provide the necessary thermal energy for the fuel reforming process, thereby reducing the system's own energy consumption. The waste heat in the low-temperature range is used for the heating system of the passenger compartment or to provide thermal management support for the vehicle's power battery pack, ensuring that it operates within the optimal temperature window.

9. The AI-controlled multi-fuel solid fuel cell range extender system for electric vehicles according to claim 1, characterized in that, The on-board carbon capture and storage unit's treatment of exhaust gas includes: The high-temperature exhaust gas discharged from the SOFC anode is pretreated to condense and separate most of the water vapor before being discharged. The dehydrated exhaust gas is then transported to the carbon capture and storage unit. The dehydrated waste gas comes into full contact with the carbon capture and storage unit. Specific substances in the carbon capture and storage unit react chemically with carbon dioxide, capturing it from the waste gas. Finally, the remaining inert gas is safely discharged. The specific substance that has adsorbed carbon dioxide is further heated to raise its temperature to a preset temperature, and carbon dioxide is released from the specific substance to form a carbon dioxide gas flow. At the same time, the cooled specific substance is recycled back to the carbon capture and storage unit for reuse. The released carbon dioxide gas is pressurized and stored in a dedicated high-pressure storage tank on the vehicle. The system monitors the pressure and capacity levels of the high-pressure storage tank in real time. When the tank is close to full capacity, the system issues a prompt to the driver through the vehicle-mounted human-machine interface.

10. An AI-controlled multi-fuel solid fuel cell range extender system for electric vehicles, characterized in that, include: Solid oxide fuel cells, AI vehicle manager, combined heat and power unit, and carbon capture and storage unit; The solid oxide fuel cell includes a reformer and a multi-fuel tank, the multi-fuel tank storing a variety of conventional fuels, and the reformer processing the various fuels from the multi-fuel tank; The AI ​​vehicle manager communicates with various systems of the vehicle, receives key data, integrates prediction algorithms and machine learning models, and makes intelligent decisions on SOFC startup, power generation, and overall system linkage. The combined heat and power unit captures the high-quality waste heat generated during SOFC operation and intelligently distributes this heat energy to the cockpit for heating or the power battery for preheating. The carbon capture and storage unit includes a specific adsorbent and absorbent liquid to process and store carbon dioxide in the SOFC anode exhaust.

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