A method and system for estimating the state of charge of a power battery

Abstract

The application provides a power battery SOC estimation method and system, the method comprises the following steps: based on the running state of the power battery, selecting the SOC estimation method, determining the target SOC estimation algorithm; according to the target SOC estimation algorithm, estimating the battery SOC value, and correcting the estimation result based on the target correction coefficient to obtain the real-time SOC value; based on the real-time SOC value, combining the actual travel data of the vehicle, determining the external power supply critical point of the vehicle, and when reaching the external power supply critical point, generating an end warning and interrupting the external power supply; the real-time SOC value of the new energy vehicle power battery is calculated by the open circuit voltage-ampere hour integral method estimation method, and according to the real-time SOC value, combining the actual travel data corresponding to the outgoing time of the driver and the passenger, the external power supply critical point of the power battery is determined, and when reaching the external power supply critical point, the external power supply is timely warned and interrupted, so that the vehicle has enough power to return safely.

Description

Technical Field

[0001] This invention relates to the field of battery SOC estimation technology, and in particular to a method and system for estimating the SOC of a power battery. Background Technology

[0002] As oil resources become increasingly scarce, the demand for new energy sources is growing. Electric vehicles, as a major type of new energy vehicle, are gaining popularity and have become one of the primary choices for travel. With the development of electronic technology, more and more portable electronic devices have emerged, increasing the demand for power supply when out and about. The batteries inside electric vehicles, their main power source, can act as mobile power sources, leading to the development of many external devices compatible with electric vehicles. However, excessive reliance on outdoor power sources (e.g., powering outdoor lights for extended periods) and the inability to replenish the battery in time can cause electric vehicles to be unable to complete their journey home. Summary of the Invention

[0003] This invention provides a method and system for estimating the State of Charge (SOC) of a power battery. The method calculates the real-time SOC value of a power battery in a new energy vehicle using the open-circuit voltage-ampere-hour integral method. Based on the real-time SOC value and the actual travel data corresponding to the driver's and passengers' journey, the method determines the external power supply critical point of the power battery. When the external power supply critical point is reached, the method promptly issues an early warning to interrupt the external power supply, ensuring that the vehicle has sufficient power to safely return home.

[0004] This invention provides a method for estimating the state of charge (SOC) of a power battery, comprising:

[0005] Step 1: Based on the operating status of the power battery, select the SOC estimation method and determine the target SOC estimation method;

[0006] Step 2: Estimate the battery SOC value according to the target SOC estimation method, and correct the estimation result based on the target correction coefficient to obtain the real-time SOC value;

[0007] Step 3: Based on the real-time SOC value and the vehicle's actual travel data, determine the vehicle's external power supply critical point, and generate an end warning and interrupt external power supply when the external power supply critical point is reached.

[0008] Preferably, in a power battery SOC estimation method, step 1 includes:

[0009] When the power battery is in a static state, the open-circuit voltage method is used as the target SOC estimation method.

[0010] When the power battery is in an open-circuit state, the ampere-hour integral method is used as the target SOC estimation method.

[0011] Preferably, in a power battery SOC estimation method, step 2 includes:

[0012] Input the current ambient temperature and charge / discharge rate, and determine the required parameters corresponding to the target correction factor based on the target SOC estimation method;

[0013] The target correction coefficient is obtained by calculating the required parameters based on the preset ambient temperature correction coefficient algorithm or charge / discharge rate correction coefficient algorithm.

[0014] Obtain the initial SOC value, and estimate the battery SOC value based on the target SOC estimation method and the initial SOC value to obtain the estimated SOC value;

[0015] Based on the target correction factor, the estimated SOC value is corrected to obtain the real-time SOC value.

[0016] Preferably, in a power battery SOC estimation method, step 3 includes:

[0017] Obtain the vehicle's actual travel data, and based on the actual travel data, determine the vehicle's round-trip route and its road conditions;

[0018] By comparing the road conditions of the outbound and return routes, the driving differences are obtained. Based on the actual power consumption of the vehicle when it arrived, and combined with the driving differences, the preliminary predicted power consumption of the return route is determined.

[0019] Based on the cloud database corresponding to the vehicle and the trip type corresponding to the current trip, multiple similar travel data are obtained. The sunrise and sunset times and arrival times of the multiple similar travel data are marked. Based on the marking results, the return time is extracted to obtain the return time features.

[0020] Based on the return time characteristics, combined with the sunrise and sunset times corresponding to the current date and the arrival time, the predicted return time of the current trip is obtained.

[0021] Based on the predicted return time of the current itinerary, the road conditions on the return journey are predicted to obtain the estimated return time. The estimated return time is then compared with the actual time taken to arrive to obtain the travel time difference.

[0022] Based on the difference in travel time and the average power consumption during congestion, the power consumption difference between the outbound and return routes is obtained. The preliminary power consumption is then corrected based on the power consumption difference between the outbound and return routes to obtain the estimated power consumption for the return journey.

[0023] Based on the real-time SOC value, the vehicle's current actual power storage capacity is determined. Based on the actual power storage capacity and the expected power consumption during the return journey, the vehicle's external power supply critical point is determined.

[0024] When a vehicle has multiple return routes, the power consumption of the return routes is predicted simultaneously for multiple return routes to obtain multiple estimated power consumptions for the return routes, and the maximum estimated power consumption for the return route is taken as the final estimated power consumption for the return route.

[0025] Preferably, in a power battery SOC estimation method, step 3 further includes:

[0026] Determine the vehicle's current available power based on the external power supply critical point;

[0027] Obtain the total output power of all external power supplies currently available for the vehicle, and based on the total output power and the current available power, obtain the duration for which external power can be supplied.

[0028] When the external power supply time is less than or equal to the preset value, an external power supply rejection notification is sent to the driver and passengers, and the power supply to the external device connection port is interrupted.

[0029] Otherwise, allow external devices to supply power through their connection ports and send reminders of the estimated power supply time to the driver and passengers.

[0030] Preferably, in a power battery SOC estimation method, when an external device connection port is allowed to supply power, it includes:

[0031] The real-time SOC value is compared with the threshold value corresponding to the external power supply threshold. When the current real-time SOC value is detected to be equal to the threshold value, the power supply to the external device connection port is immediately interrupted, and an end warning is generated and broadcast via voice.

[0032] Preferably, in a power battery SOC estimation method, the road conditions corresponding to the outbound and return routes are compared to obtain the driving differences. Based on the actual power consumption of the vehicle when it arrives, and combined with the driving differences, a preliminary predicted power consumption corresponding to the return route is determined, including:

[0033] Obtain the return route that is the same as the route taken from the beginning as the first target route, and use the remaining return routes as the second target routes;

[0034] Based on the actual driving data when arriving, the actual road conditions for each road segment are determined, and the actual road conditions are flipped accordingly to obtain the return road conditions.

[0035] By comparing the actual road conditions with the return road conditions, we can determine the cost-saving road sections and toll-free road sections on the return journey, as well as the toll-free road sections and cost-saving road sections on the way here.

[0036] The power consumption difference length is obtained by comparing the power consumption point section on the return journey with the power consumption point section on the way there, and the power saving difference length is obtained by comparing the power saving point section on the return journey with the power saving point section on the way there.

[0037] Based on the vehicle's preset average uphill power consumption and power consumption difference length, as well as the preset average downhill power consumption and power saving difference length, the predicted power consumption difference between the route taken when arriving and the first target route is calculated.

[0038] Obtain the actual power consumption of the vehicle when it arrives, and based on the difference between the actual power consumption and the predicted power consumption, obtain the preliminary predicted power consumption of the first target route.

[0039] Obtain the maximum speed limit value for each segment of the first and second target routes respectively;

[0040] Based on the preset average power consumption of the vehicle in each speed range, and combined with the maximum speed limit value corresponding to each road segment, the predicted power consumption of each road segment is calculated respectively.

[0041] Based on the predicted power consumption of all road segments corresponding to the first target route, the predicted power consumption at the first speed of the first target route is calculated. The predicted power consumption at the first speed is compared with the preliminary predicted power consumption to obtain the prediction error rate.

[0042] The predicted power consumption of all road segments corresponding to different second target routes is obtained respectively, and the predicted power consumption of the second speed corresponding to different second target routes is calculated.

[0043] The predicted power consumption for the second speed is corrected based on the prediction error rate to obtain the preliminary predicted power consumption for the second target route.

[0044] Preferably, in a power battery SOC estimation method, feature extraction is performed based on the return time corresponding to the labeling results to obtain return time features, including:

[0045] Based on the sunrise and sunset times corresponding to the return dates of multiple similar travel data, multiple travel timelines are generated, and different arrival times and return times are marked on the corresponding travel timelines;

[0046] The time difference between each arrival time and return time is calculated, and based on the time difference, the outdoor travel duration characteristics of drivers and passengers are extracted.

[0047] Simultaneously, based on sunrise and sunset times, the solar altitude corresponding to different return times is determined, and based on the solar altitude, the return node features of drivers and passengers are extracted.

[0048] Based on the characteristics of outdoor travel duration and return travel node, return travel time characteristics are generated.

[0049] This invention provides a power battery SOC estimation system, comprising:

[0050] The estimation method determination module is used to select the SOC estimation method based on the operating state of the power battery and determine the target SOC estimation method.

[0051] The real-time SOC value calculation module is used to estimate the battery SOC value according to the target SOC estimation method, and to correct the estimation result based on the target correction coefficient to obtain the real-time SOC value.

[0052] The external power supply warning module is used to determine the vehicle's external power supply critical point based on the real-time SOC value and the vehicle's actual travel data. When the external power supply critical point is reached, an end warning is generated and the external power supply is interrupted.

[0053] Compared with existing technologies, the present invention has the following beneficial effects: Based on the operating state of the power battery, the present invention selects a SOC estimation method, determines a target SOC estimation method, and combines multiple power battery SOC estimation methods to improve the accuracy of power battery SOC value estimation; then, the battery SOC value is estimated according to the target SOC estimation method, and the estimation result is corrected based on the target correction coefficient to obtain the real-time SOC value, effectively improving the accuracy of real-time SOC value estimation and providing a reliable foundation for the accurate determination of the external power supply critical point; finally, based on the real-time SOC value and combined with the actual travel data of the vehicle, the external power supply critical point of the vehicle is determined, and when the external power supply critical point is reached, an end warning is generated and the external power supply is interrupted, which can effectively prevent electric vehicles from over-supplying external devices during outdoor travel (e.g., camping), thus preventing the vehicle from being unable to return safely. This invention uses the open-circuit voltage-ampere-hour integral method to calculate the real-time SOC value of the power battery of new energy vehicles, thereby improving the accuracy of SOC value monitoring. Based on the real-time SOC value and combined with the actual travel data corresponding to the driver's and passengers' journey, the external power supply critical point of the power battery is determined. When the external power supply critical point is reached, an early warning is issued to interrupt the external power supply, ensuring that the vehicle has enough power to return safely.

[0054] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in this application.

[0055] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0056] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0057] Figure 1 This is a flowchart of a power battery SOC estimation method according to the present invention;

[0058] Figure 2 This is a flowchart of step 2 of the power battery SOC estimation method of the present invention;

[0059] Figure 3 This is a structural diagram of a power battery SOC estimation system according to the present invention. Detailed Implementation

[0060] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0061] Example 1:

[0062] This invention provides a method for estimating the state of charge (SOC) of a power battery, such as... Figure 1 As shown, it includes:

[0063] Step 1: Based on the operating status of the power battery, select the SOC estimation method and determine the target SOC estimation method;

[0064] Step 2: Estimate the battery SOC value according to the target SOC estimation method, and correct the estimation result based on the target correction coefficient to obtain the real-time SOC value;

[0065] Step 3: Based on the real-time SOC value and the vehicle's actual travel data, determine the vehicle's external power supply critical point, and generate an end warning and interrupt external power supply when the external power supply critical point is reached.

[0066] In this embodiment, the SOC estimation methods include two types: the open-circuit voltage method and the ampere-hour integration method.

[0067] The beneficial effects of the above technical solution are as follows: Based on the operating state of the power battery, this invention selects a SOC estimation method, determines a target SOC estimation method, and combines multiple power battery SOC estimation methods to improve the accuracy of power battery SOC value estimation. Then, the battery SOC value is estimated according to the target SOC estimation method, and the estimation result is corrected based on the target correction coefficient to obtain the real-time SOC value, effectively improving the accuracy of real-time SOC value estimation and providing a reliable foundation for the accurate determination of the external power supply critical point. Finally, based on the real-time SOC value and combined with the vehicle's actual travel data, the vehicle's external power supply critical point is determined, and when the external power supply critical point is reached, an end warning is generated and the external power supply is interrupted. This can effectively prevent electric vehicles from over-supplying external devices during outdoor travel (e.g., camping), which could lead to the vehicle being unable to return safely. This invention uses the open-circuit voltage-ampere-hour integral method to calculate the real-time SOC value of the power battery of new energy vehicles, thereby improving the accuracy of SOC value monitoring. Based on the real-time SOC value and combined with the actual travel data corresponding to the driver's and passengers' journey, the external power supply critical point of the power battery is determined. When the external power supply critical point is reached, an early warning is issued to interrupt the external power supply, ensuring that the vehicle has enough power to return safely.

[0068] Example 2:

[0069] Based on Example 1, step 1 includes:

[0070] When the power battery is in a static state, the open-circuit voltage method is used as the target SOC estimation method.

[0071] When the power battery is in an open-circuit state, the ampere-hour integral method is used as the target SOC estimation method.

[0072] The beneficial effects of the above technical solution are as follows: When the power battery is in a static state, the SOC value of the power battery is estimated using the open-circuit voltage method; when the power battery is in a charging and discharging state, the SOC value estimated by the open-circuit voltage method in the previous static state of the power battery is used as the initial SOC0, and then the charging and discharging amount during the time up to the previous static state is calculated using the ampere-hour integration method, finally obtaining the real-time SOC value. By combining the estimation method of open-circuit voltage method and ampere-hour integration method, the battery SOC value can be re-estimated using the open-circuit voltage method when the power battery is static, correcting the error that occurred in the ampere-hour integration method in the previous working period, effectively improving the estimation accuracy of the power battery SOC estimation system.

[0073] Example 3:

[0074] Based on Example 1, step 2 includes:

[0075] Step 201: Input the current ambient temperature and charge / discharge rate, and determine the required parameters corresponding to the target correction factor based on the target SOC estimation method;

[0076] Step 202: Calculate the required parameters according to the preset ambient temperature correction coefficient algorithm or charge / discharge rate correction coefficient algorithm to obtain the target correction coefficient;

[0077] Step 203: Obtain the initial SOC value. Based on the target SOC estimation method and the initial SOC value, estimate the battery SOC value to obtain the estimated SOC value.

[0078] Step 204: Based on the target correction coefficient, correct the estimated SOC value to obtain the real-time SOC value.

[0079] In this embodiment, the required parameters include the current ambient temperature and the charge / discharge rate. When the open-circuit voltage method is used to estimate the SOC value, the required parameter is the current ambient temperature; when the ampere-hour integration method is used to estimate the SOC value, the required parameters are the current ambient temperature and the charge / discharge rate.

[0080] In this embodiment, the algorithm for the ambient temperature correction coefficient is as follows:

[0081] K T =6.683×10 -8 T 4 +2.753×10 -7 T 3 -4.394×10 -4 T 2 +2.01×10 -2 T+0.75

[0082] Among them, K T This is the ambient temperature correction factor, where T is the ambient temperature.

[0083] In this embodiment, the algorithm for the charge / discharge ratio correction coefficient is as follows:

[0084] K I = -2.133 × 10 -5 I 4 -1.353×10 -2 I 3 +1.033×10 -2 I 2 -6.870×10 -3 I+1.003

[0085] Among them, K I is the charge / discharge rate correction factor, and I is the battery discharge current.

[0086] The beneficial effects of the above technical solution are as follows: This invention corrects the SOC value estimated by the open-circuit voltage method based on the ambient temperature correction coefficient to obtain the initial SOC value estimated by the ampere-hour integration method, thereby compensating for the impact of excessively high or low ambient temperatures on the open-circuit voltage. Then, based on the ambient temperature and charge / discharge rate, the corresponding correction coefficients are obtained to further correct the SOC estimated by the ampere-hour integration method to obtain the real-time SOC value. This compensates for the impact of temperature and charge / discharge rate on battery capacity, effectively improving the accuracy of power battery SOC value estimation. It provides drivers and passengers with a more accurate estimate of the remaining electric vehicle power, facilitating trip planning and improving the accuracy of determining the critical point for subsequent external power supply.

[0087] Example 4:

[0088] Based on Example 1, step 3 includes:

[0089] Obtain the vehicle's actual travel data, and based on the actual travel data, determine the vehicle's round-trip route and its road conditions;

[0090] By comparing the road conditions of the outbound and return routes, the driving differences are obtained. Based on the actual power consumption of the vehicle when it arrived, and combined with the driving differences, the preliminary predicted power consumption of the return route is determined.

[0091] Based on the cloud database corresponding to the vehicle and the trip type corresponding to the current trip, multiple similar travel data are obtained. The sunrise and sunset times and arrival times of the multiple similar travel data are marked. Based on the marking results, the return time is extracted to obtain the return time features.

[0092] Based on the return time characteristics, combined with the sunrise and sunset times corresponding to the current date and the arrival time, the predicted return time of the current trip is obtained.

[0093] Based on the predicted return time of the current itinerary, the road conditions on the return journey are predicted to obtain the estimated return time. The estimated return time is then compared with the actual time taken to arrive to obtain the travel time difference.

[0094] Based on the difference in travel time and the average power consumption during congestion, the power consumption difference between the outbound and return routes is obtained. The preliminary power consumption is then corrected based on the power consumption difference between the outbound and return routes to obtain the estimated power consumption for the return journey.

[0095] Based on the real-time SOC value, the vehicle's current actual power storage capacity is determined. Based on the actual power storage capacity and the expected power consumption during the return journey, the vehicle's external power supply critical point is determined.

[0096] When a vehicle has multiple return routes, the power consumption of the return routes is predicted simultaneously for multiple return routes to obtain multiple estimated power consumptions for the return routes, and the maximum estimated power consumption for the return route is taken as the final estimated power consumption for the return route.

[0097] In this embodiment, the actual trip data refers to the road conditions when the vehicle arrives and the vehicle's operating data (including but not limited to power consumption and speed of each road segment).

[0098] In this embodiment, the round-trip route includes the actual route taken when arriving and the navigation-predicted return route, wherein there may be one or more return routes.

[0099] In this embodiment, the driving difference refers to the difference between the circuit-saving segment and the circuit-costing segment in the round trip route.

[0100] In this embodiment, road conditions refer to road surface conditions, such as uphill, downhill, and flat.

[0101] In this embodiment, the preliminary power consumption prediction refers to predicting the power consumption on the return journey without considering the traffic conditions on the return route.

[0102] In this embodiment, similar travel data refers to travel data that is the same as the current travel category (e.g., short trips, camping, and other outdoor sports travel).

[0103] In this embodiment, the arrival time refers to the time it takes to reach the outdoor operating destination.

[0104] In this embodiment, the sunrise and sunset times corresponding to the current date are obtained from the vehicle-related weather forecast.

[0105] In this embodiment, the average power consumption during congestion refers to the average power consumption calculated based on the power consumption data of vehicles during their waiting time in congested road sections.

[0106] In this embodiment, the road conditions on the way home refer to the driving conditions on each route home.

[0107] In this embodiment, the travel time difference refers to the time difference between the actual travel time of the outbound route and the estimated travel time corresponding to the navigation-predicted return route.

[0108] The difference in power consumption between the outbound and return routes refers to the product of the difference in travel time and the average power consumption during congestion.

[0109] The beneficial effects of the above technical solution are as follows: First, the present invention acquires the actual travel data of the vehicle. Based on this data, it determines the vehicle's outbound and return routes and their road conditions. It then compares the road conditions corresponding to the outbound and return routes to obtain driving differences. Based on the vehicle's actual power consumption on the outbound journey, and combined with these driving differences, it determines the preliminary predicted power consumption for the return route, thus achieving a preliminary prediction of return route power consumption. Next, based on the vehicle's cloud database and the current trip type, it obtains multiple similar travel data sets. These sets are then marked with sunrise / sunset times and arrival times. Based on the marking results, it extracts features corresponding to the return time to obtain return time features. Based on these return time features, and combined with the sunrise / sunset times and arrival / departure times for the current date, it predicts the predicted return time for the current trip. Finally, based on the predicted return time of the current trip, it predicts the return route road conditions to obtain the estimated return time. The estimated travel time is compared with the actual travel time to the destination to obtain the travel time difference. Based on the travel time difference and the average power consumption during congestion, the power consumption difference between the outbound and return routes is obtained. The preliminary power consumption prediction is then corrected based on the power consumption difference between the outbound and return routes to obtain the estimated power consumption for the return journey. Integrating actual driving conditions with environmental road conditions helps improve the accuracy of power consumption during the journey home, ensuring that the predicted power consumption for the return journey can fully meet the needs of drivers and passengers to arrive home safely. Finally, based on the real-time SOC value, the vehicle's current actual power storage is determined. Based on the actual power storage and the estimated power consumption for the return journey, the vehicle's external power supply threshold is determined, providing a basis for timely warnings of external power supply. When the vehicle has multiple return routes, power consumption predictions for multiple return routes are performed simultaneously to obtain multiple estimated power consumptions for the return journey. The maximum estimated power consumption for the return journey is used as the final estimated power consumption for the return journey, which can fully ensure that the vehicle's external power supply equipment will not affect the drivers' and passengers' home return plans.

[0110] Example 5:

[0111] Based on Example 4, step 3 further includes:

[0112] Determine the vehicle's current available power based on the external power supply critical point;

[0113] Obtain the total output power of all external power supplies currently available for the vehicle, and based on the total output power and the current available power, obtain the duration for which external power can be supplied.

[0114] When the external power supply time is less than or equal to the preset value, an external power supply rejection notification is sent to the driver and passengers, and the power supply to the external device connection port is interrupted.

[0115] Otherwise, allow external devices to supply power through their connection ports and send reminders of the estimated power supply time to the driver and passengers.

[0116] The beneficial effects of the above technical solution are as follows: This invention determines the current available power of the vehicle by the external power supply critical point; obtains the total output power of all external power supplies corresponding to the vehicle; and estimates the external power supply time based on the total output power and the current available power. When the external power supply time is less than or equal to a preset value, an external power supply rejection notification is sent to the driver and passengers, and the power transmission of the external device connection port is interrupted to avoid unnecessary device connection when the available power is too low and the power supply time is very short. When the external device connection port is allowed to supply power, a power supply estimate reminder is sent to the driver and passengers, which facilitates the driver and passengers to adjust their outdoor operation plans in advance.

[0117] Example 6:

[0118] Based on Example 5, when the external device connection port is allowed to supply power externally, the following is included:

[0119] The real-time SOC value is compared with the threshold value corresponding to the external power supply threshold. When the current real-time SOC value is detected to be equal to the threshold value, the power supply to the external device connection port is immediately interrupted, and an end warning is generated and broadcast via voice.

[0120] The beneficial effects of the above technical solution are as follows: This invention compares the real-time SOC value with the critical value corresponding to the external power supply threshold in real time. When the current real-time SOC value is detected to be equal to the threshold value, the power transmission of the external device connection port is immediately interrupted, and an end warning is generated and broadcast via voice. This avoids situations where drivers and passengers are unaware of the vehicle's actual power level or are unclear about the vehicle's power usage plan, which could lead to an inability to return smoothly. In outdoor environments where charging is inconvenient, this invention maximizes the guarantee that the vehicle has sufficient power for the return trip.

[0121] Example 7:

[0122] Based on Example 4, the feature is that the road conditions corresponding to the round trip routes are compared to obtain the driving differences. Based on the actual power consumption of the vehicle when it arrived, and combined with the driving differences, the preliminary predicted power consumption corresponding to the return route is determined, including:

[0123] Obtain the return route that is the same as the route taken from the beginning as the first target route, and use the remaining return routes as the second target routes;

[0124] Based on the actual driving data when arriving, the actual road conditions for each road segment are determined, and the actual road conditions are flipped accordingly to obtain the return road conditions.

[0125] By comparing the actual road conditions with the return road conditions, we can determine the cost-saving road sections and toll-free road sections on the return journey, as well as the toll-free road sections and cost-saving road sections on the way here.

[0126] The power consumption difference length is obtained by comparing the power consumption point section on the return journey with the power consumption point section on the way there, and the power saving difference length is obtained by comparing the power saving point section on the return journey with the power saving point section on the way there.

[0127] Based on the vehicle's preset average uphill power consumption and power consumption difference length, as well as the preset average downhill power consumption and power saving difference length, the predicted power consumption difference between the route taken when arriving and the first target route is calculated.

[0128] Obtain the actual power consumption of the vehicle when it arrives, and based on the difference between the actual power consumption and the predicted power consumption, obtain the preliminary predicted power consumption of the first target route.

[0129] Obtain the maximum speed limit value for each segment of the first and second target routes respectively;

[0130] Based on the preset average power consumption of the vehicle in each speed range, and combined with the maximum speed limit value corresponding to each road segment, the predicted power consumption of each road segment is calculated respectively.

[0131] Based on the predicted power consumption of all road segments corresponding to the first target route, the predicted power consumption at the first speed of the first target route is calculated. The predicted power consumption at the first speed is compared with the preliminary predicted power consumption to obtain the prediction error rate.

[0132] The predicted power consumption of all road segments corresponding to different second target routes is obtained respectively, and the predicted power consumption of the second speed corresponding to different second target routes is calculated.

[0133] The predicted power consumption for the second speed is corrected based on the prediction error rate to obtain the preliminary predicted power consumption for the second target route.

[0134] In this embodiment, the remaining return route refers to other navigation-predicted return routes besides the same return route as the route taken from the beginning.

[0135] In this embodiment, "reversing the actual road conditions" means reversing the road surface conditions of each segment of the future route accordingly. For example, an uphill section is changed to a downhill section, and vice versa. If the road is flat, it remains unchanged.

[0136] In this embodiment, the power-saving section refers to a section of the route from which the power output can be reduced, such as a downhill section; the power-consuming section refers to a section of the route from which the power output needs to be increased, such as an uphill section.

[0137] In this embodiment, the power consumption difference length refers to the difference between the power consumption point segment on the return journey and the power consumption point segment on the arrival journey; the power saving difference length refers to the difference between the power saving point segment on the return journey and the power saving point segment on the arrival journey.

[0138] In this embodiment, the preset average power consumption uphill and the preset average power consumption downhill refer to the ideal power consumption data calculated based on the vehicle test data before the vehicle leaves the factory and stored in the database.

[0139] In this embodiment, the maximum speed limit value refers to the highest speed corresponding to each road segment.

[0140] In this embodiment, the average power consumption within each speed range is calculated based on vehicle test data before the vehicle leaves the factory. This data is pre-stored in a database.

[0141] In this embodiment, the power consumption of the first target route is predicted based on the maximum speed limit value of each section of the first target route.

[0142] In this embodiment, the power consumption of the second target route is predicted based on the maximum speed limit values ​​of each segment of the second target route.

[0143] In this embodiment, the driving difference refers to the difference in road conditions between the outbound route and the return route.

[0144] The beneficial effects of the above technical solution are as follows: This invention obtains the difference in road conditions during the journey by comparing the road conditions of the outbound route and the corresponding return route. Then, based on the difference in road conditions, combined with preset average uphill power consumption and preset average downhill power consumption, the predicted power consumption difference between the outbound route and the corresponding return route is predicted. Finally, based on the actual power consumption of the vehicle on the outbound route and the predicted power consumption difference, a preliminary predicted power consumption for the first target route is obtained. Then, based on the maximum speed limit values ​​of each section of the first target route, a false prediction is made of the power consumption of the first target route to obtain a first speed predicted power consumption. The first speed predicted power consumption is compared with the preliminary predicted power consumption to obtain the prediction error rate. Finally, based on the maximum speed limit values ​​of each section of the second target route, a true prediction is made of the power consumption of the second target route to obtain a second speed predicted power consumption. Then, based on the prediction error rate, the preliminary predicted power consumption of the remaining return routes is obtained from the second speed predicted power consumption. This effectively improves the accuracy of power consumption prediction for each return route.

[0145] Example 8:

[0146] Based on Example 3, feature extraction is performed on the return time corresponding to the marking results to obtain return time features, including:

[0147] Based on the sunrise and sunset times corresponding to the return dates of multiple similar travel data, multiple travel timelines are generated, and different arrival times and return times are marked on the corresponding travel timelines;

[0148] The time difference between each arrival time and return time is calculated, and based on the time difference, the outdoor travel duration characteristics of drivers and passengers are extracted.

[0149] Simultaneously, based on sunrise and sunset times, the solar altitude corresponding to different return times is determined, and based on the solar altitude, the return node features of drivers and passengers are extracted.

[0150] Based on the characteristics of outdoor travel duration and return travel node, return travel time characteristics are generated.

[0151] The beneficial effects of the above technical solution are as follows: The present invention extracts features corresponding to the return time based on the marked results, obtains return time features, and associates the outdoor activity habits of drivers and passengers (based on the duration of outdoor activities) with the natural environment, which helps to improve the accuracy of return time prediction.

[0152] Example 9:

[0153] This invention provides a power battery SOC estimation system, such as... Figure 3 As shown, it includes:

[0154] The estimation method determination module is used to select the SOC estimation method based on the operating state of the power battery and determine the target SOC estimation method.

[0155] The real-time SOC value calculation module is used to estimate the battery SOC value according to the target SOC estimation method, and to correct the estimation result based on the target correction coefficient to obtain the real-time SOC value.

[0156] The external power supply warning module is used to determine the vehicle's external power supply critical point based on the real-time SOC value and the vehicle's actual travel data. When the external power supply critical point is reached, an end warning is generated and the external power supply is interrupted.

[0157] The beneficial effects of the above technical solution are as follows: This invention determines the SOC estimation method based on the operating state of the power battery through an estimation method module, selecting a target SOC estimation method and combining multiple power battery SOC estimation methods to improve the accuracy of power battery SOC value estimation. Then, a real-time SOC value calculation module estimates the battery SOC value according to the target SOC estimation method and corrects the estimation result based on a target correction coefficient to obtain a real-time SOC value, effectively improving the accuracy of real-time SOC value estimation and providing a reliable foundation for the accurate determination of the external power supply critical point. Finally, an external power supply early warning module determines the vehicle's external power supply critical point based on the real-time SOC value and the vehicle's actual travel data. Upon reaching the external power supply critical point, an end warning is generated and external power supply is interrupted, effectively preventing electric vehicles from over-supplying external devices during outdoor travel (e.g., camping), thus ensuring the vehicle's safe return journey. This invention uses the open-circuit voltage-ampere-hour integral method to calculate the real-time SOC value of the power battery of new energy vehicles, thereby improving the accuracy of SOC value monitoring. Based on the real-time SOC value and combined with the actual travel data corresponding to the driver's and passengers' journey, the external power supply critical point of the power battery is determined. When the external power supply critical point is reached, an early warning is issued to interrupt the external power supply, ensuring that the vehicle has enough power to return safely.

[0158] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for estimating the state of charge (SOC) of a power battery, characterized in that, include: Step 1: Based on the operating status of the power battery, select the SOC estimation method and determine the target SOC estimation method; Step 2: Estimate the battery SOC value according to the target SOC estimation method, and correct the estimation result based on the target correction coefficient to obtain the real-time SOC value; Step 3: Based on the real-time SOC value and the vehicle's actual travel data, determine the vehicle's external power supply critical point, and generate an end warning and interrupt external power supply when the external power supply critical point is reached; Step 3 includes: Obtain the vehicle's actual travel data, and based on the actual travel data, determine the vehicle's round-trip route and its road conditions; By comparing the road conditions of the outbound and return routes, the driving differences are obtained. Based on the actual power consumption of the vehicle when it arrived, and combined with the driving differences, the preliminary predicted power consumption of the return route is determined. Based on the cloud database corresponding to the vehicle and the trip type corresponding to the current trip, multiple similar travel data are obtained. The sunrise and sunset times and arrival times of the multiple similar travel data are marked. Based on the marking results, the return time is extracted to obtain the return time features. Based on the return time characteristics, combined with the sunrise and sunset times corresponding to the current date and the arrival time, the predicted return time of the current trip is obtained. Based on the predicted return time of the current itinerary, the road conditions on the return journey are predicted to obtain the estimated return time. The estimated return time is then compared with the actual time taken to arrive to obtain the travel time difference. Based on the difference in travel time and the average power consumption during congestion, the power consumption difference between the outbound and return routes is obtained. The preliminary power consumption is then corrected based on the power consumption difference between the outbound and return routes to obtain the estimated power consumption for the return journey. Based on the real-time SOC value, the vehicle's current actual power storage capacity is determined. Based on the actual power storage capacity and the expected power consumption during the return journey, the vehicle's external power supply critical point is determined.

2. The method for estimating the state of charge (SOC) of a power battery according to claim 1, characterized in that, Step 1 includes: When the power battery is in a static state, the open-circuit voltage method is used as the target SOC estimation method. When the power battery is in an open-circuit state, the ampere-hour integral method is used as the target SOC estimation method.

3. The method for estimating the SOC of a power battery according to claim 1, characterized in that, Step 2 includes: Input the current ambient temperature and charge / discharge rate, and determine the required parameters corresponding to the target correction factor based on the target SOC estimation method; The target correction coefficient is obtained by calculating the required parameters based on the preset ambient temperature correction coefficient algorithm or charge / discharge rate correction coefficient algorithm. Obtain the initial SOC value, and estimate the battery SOC value based on the target SOC estimation method and the initial SOC value to obtain the estimated SOC value; Based on the target correction factor, the estimated SOC value is corrected to obtain the real-time SOC value.

4. The method for estimating the SOC of a power battery according to claim 1, characterized in that: When a vehicle has multiple return routes, the power consumption of the return routes is predicted simultaneously for multiple return routes to obtain multiple estimated power consumptions for the return routes, and the maximum estimated power consumption for the return route is taken as the final estimated power consumption for the return route.

5. The method for estimating the SOC of a power battery according to claim 4, characterized in that, Step 3 also includes: Determine the vehicle's current available power based on the external power supply critical point; Obtain the total output power of all external power supplies currently available for the vehicle, and based on the total output power and the current available power, obtain the duration for which external power can be supplied. When the external power supply time is less than or equal to the preset value, an external power supply rejection notification is sent to the driver and passengers, and the power supply to the external device connection port is interrupted. Otherwise, allow external devices to supply power through their connection ports and send reminders of the estimated power supply time to the driver and passengers.

6. The method for estimating the SOC of a power battery according to claim 5, characterized in that, When allowing external devices to connect to the port to supply power, this includes: The real-time SOC value is compared with the threshold value corresponding to the external power supply threshold. When the current real-time SOC value is detected to be equal to the threshold value, the power supply to the external device connection port is immediately interrupted, and an end warning is generated and broadcast via voice.

7. The method for estimating the SOC of a power battery according to claim 4, characterized in that, By comparing the road conditions of the outbound and return routes to obtain driving differences, and based on the actual power consumption of the vehicle on the outbound journey, combined with the driving differences, a preliminary predicted power consumption for the return route is determined, including: Obtain the return route that is the same as the route taken from the beginning as the first target route, and use the remaining return routes as the second target routes; Based on the actual driving data when arriving, the actual road conditions for each road segment are determined, and the actual road conditions are flipped accordingly to obtain the return road conditions. By comparing the actual road conditions with the return road conditions, we can determine the cost-saving road sections and toll-free road sections on the return journey, as well as the toll-free road sections and cost-saving road sections on the way here. The power consumption difference length is obtained by comparing the power consumption point section on the return journey with the power consumption point section on the way there, and the power saving difference length is obtained by comparing the power saving point section on the return journey with the power saving point section on the way there. Based on the vehicle's preset average uphill power consumption and power consumption difference length, as well as the preset average downhill power consumption and power saving difference length, the predicted power consumption difference between the route taken when arriving and the first target route is calculated. Obtain the actual power consumption of the vehicle when it arrives, and based on the difference between the actual power consumption and the predicted power consumption, obtain the preliminary predicted power consumption of the first target route. Obtain the maximum speed limit value for each segment of the first and second target routes respectively; Based on the preset average power consumption of the vehicle in each speed range, and combined with the maximum speed limit value corresponding to each road segment, the predicted power consumption of each road segment is calculated respectively. Based on the predicted power consumption of all road segments corresponding to the first target route, the predicted power consumption at the first speed of the first target route is calculated. The predicted power consumption at the first speed is compared with the preliminary predicted power consumption to obtain the prediction error rate. The predicted power consumption of all road segments corresponding to different second target routes is obtained respectively, and the predicted power consumption of the second speed corresponding to different second target routes is calculated. The predicted power consumption for the second speed is corrected based on the prediction error rate to obtain the preliminary predicted power consumption for the second target route.

8. The method for estimating the SOC of a power battery according to claim 3, characterized in that, Feature extraction is performed based on the return time corresponding to the labeling results to obtain return time features, including: Based on the sunrise and sunset times corresponding to the return dates of multiple similar travel data, multiple travel timelines are generated, and different arrival times and return times are marked on the corresponding travel timelines. The time difference between each arrival time and return time is calculated, and based on the time difference, the outdoor travel duration characteristics of drivers and passengers are extracted. Simultaneously, based on sunrise and sunset times, the solar altitude corresponding to different return times is determined, and based on the solar altitude, the return node features of drivers and passengers are extracted. Based on the characteristics of outdoor travel duration and return travel node, return travel time characteristics are generated.

9. A power battery SOC estimation system, characterized in that, include: The estimation method determination module is used to select the SOC estimation method based on the operating state of the power battery and determine the target SOC estimation method. The real-time SOC value calculation module is used to estimate the battery SOC value according to the target SOC estimation method, and to correct the estimation result based on the target correction coefficient to obtain the real-time SOC value. The external power supply warning module is used to determine the vehicle's external power supply critical point based on the real-time SOC value and the vehicle's actual travel data, and generate an end warning and interrupt the external power supply when the external power supply critical point is reached. The external power supply early warning module performs the following operations: Obtain the vehicle's actual travel data, and based on the actual travel data, determine the vehicle's round-trip route and its road conditions; By comparing the road conditions of the outbound and return routes, the driving differences are obtained. Based on the actual power consumption of the vehicle when it arrived, and combined with the driving differences, the preliminary predicted power consumption of the return route is determined. Based on the cloud database corresponding to the vehicle and the trip type corresponding to the current trip, multiple similar travel data are obtained. The sunrise and sunset times and arrival times of the multiple similar travel data are marked. Based on the marking results, the return time is extracted to obtain the return time features. Based on the return time characteristics, combined with the sunrise and sunset times corresponding to the current date and the arrival time, the predicted return time of the current trip is obtained. Based on the predicted return time of the current itinerary, the road conditions on the return journey are predicted to obtain the estimated return time. The estimated return time is then compared with the actual time taken to arrive to obtain the travel time difference. Based on the difference in travel time and the average power consumption during congestion, the power consumption difference between the outbound and return routes is obtained. The preliminary power consumption is then corrected based on the power consumption difference between the outbound and return routes to obtain the estimated power consumption for the return journey. Based on the real-time SOC value, the vehicle's current actual power storage capacity is determined. Based on the actual power storage capacity and the expected power consumption during the return journey, the vehicle's external power supply critical point is determined.

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