Battery parallel connection method, device and equipment based on vehicle working conditions and medium

Abstract

The invention provides a battery parallel connection method, device and equipment based on vehicle working conditions and a medium, and belongs to the technical field of batteries. The method comprises the steps that a plurality of driving parameters, driving environment parameters and environment temperature of a vehicle are obtained, and the driving parameters represent historical driving parameters in a set time period; generating state description information according to each driving parameter, wherein the state description information is used for driving states of the vehicle in different time periods; the driving duration is determined according to the driving speed and the driving environment parameters, and the vehicle energy consumption is calculated according to the environment temperature, the driving duration and the state description information; the vehicle power demand is determined according to the vehicle energy consumption, battery parallel information is determined according to the vehicle power demand and a power demand threshold value, and the battery parallel information comprises whether parallel connection exists or not and the number of battery clusters connected in parallel. The battery parallel information accuracy can be effectively improved, and the vehicle energy consumption is reduced.

Description

Technical Field

[0001] This application belongs to the field of battery technology, and in particular relates to a method, apparatus, device and medium for parallel connection of batteries based on vehicle operating conditions. Background Technology

[0002] With the increasing frequency of scientific expeditions and resource exploration in polar and high-temperature regions, special vehicles have become crucial mobile platforms for the successful implementation of these missions. These special vehicles, such as polar all-terrain transport vehicles and plateau special-purpose vehicles, operate in environments characterized by consistently low temperatures and complex road conditions. In low-temperature environments, vehicles not only need to overcome conventional driving resistance but also face severe challenges posed by extreme weather. Low temperatures have a significant impact on vehicle energy consumption. First, low temperatures increase air resistance; second, they increase lubricant viscosity, thus increasing energy consumption. Furthermore, to ensure the normal operation of special equipment and maintain a suitable internal temperature, the insulation system is also a vital component of special vehicle energy consumption. Therefore, controlling energy consumption in low-temperature environments is particularly important for special vehicles, and a scientifically sound configuration of the number of parallel batteries in special vehicles can effectively reduce battery energy consumption.

[0003] In related technologies, the vehicle's load is monitored in real time, and the power required for the vehicle's load is calculated based on the load. The power requirement corresponding to the vehicle's power is determined based on the correspondence between power and wattage, and the number of battery packs is matched according to the power requirement. However, in the actual production and operation of vehicles, the vehicle's power requirement is affected by multiple factors. It can be seen that the accuracy of determining the number of battery packs in parallel based solely on the vehicle's load is poor. Summary of the Invention

[0004] To address the aforementioned technical problems, this application provides a battery parallel connection method, apparatus, device, and medium based on vehicle operating conditions.

[0005] Firstly, this application provides a battery parallel connection method based on vehicle operating conditions, employing the following technical solution: A battery parallel connection method based on vehicle operating conditions includes: The vehicle acquires multiple driving parameters, driving environment parameters, and ambient temperature. The driving parameters represent historical driving parameters within a set time period. Status description information is generated based on various driving parameters. This status description information is used to describe the driving status of the vehicle at different times. The driving time is determined based on driving speed and driving environment parameters, and the vehicle energy consumption is calculated based on ambient temperature, driving time and state description information. The vehicle power demand is determined based on the vehicle's energy consumption, and the battery parallel connection information is determined based on the vehicle power demand and the power demand threshold. The battery parallel connection information includes whether the battery is connected in parallel and the number of battery clusters connected in parallel.

[0006] In one embodiment, the vehicle state includes a first state, a second state, a third state, a fourth state, and a fifth state. The first state represents an empty vehicle traveling on a dry, flat road; the second state represents an empty vehicle traveling on a dry, uphill road; the third state represents a loaded vehicle traveling on a dry, downhill road; the fourth state represents a loaded vehicle traveling on a muddy, flat road; and the fifth state represents an empty vehicle in a waiting state.

[0007] In one embodiment, state description information is generated based on various driving parameters, including: Acquire multiple historical operation data of vehicles within a set time period. The historical operation data includes multiple road gradients and the historical energy consumption value corresponding to each road gradient. Based on the slope of each road and the historical energy consumption value corresponding to each road slope, weighted information is determined. The weighted information includes a first weighted value corresponding to the slope and a second weighted value corresponding to the mud level. For each state, calculate the weighted sum of the transitions from state to other states; Determine the sample size of the target state and the first non-target state, and calculate the transition probability based on the sample size and the sum of weighted values; A state matrix is ​​generated based on the elements of each target state, and the state matrix is ​​used as the state description information.

[0008] In one embodiment, weighted information is determined based on each road slope and the corresponding historical energy consumption value, including: For each state transition pair, each road slope is divided into multiple slope intervals, and each slope interval includes multiple road slopes. For each slope interval, obtain the energy consumption value and the number of state transitions corresponding to the slope interval, and determine the average energy consumption value corresponding to the slope interval based on the energy consumption value and the number of state transitions. Obtain the preset energy consumption baseline value, and calculate the energy consumption sensitivity corresponding to each slope range based on the preset energy consumption baseline value and the average energy consumption value; Based on energy consumption sensitivity, the average energy consumption for each slope range is normalized, and the normalized average energy consumption is used as the weighted information.

[0009] In one embodiment, vehicle energy consumption is calculated based on ambient temperature, driving time, and state description information, including: Based on the driving time and the preset calculation step size, the number of iterations with the state description information is obtained, and the state description information is iteratively calculated based on the number of iterations. For each iteration of the state description information, obtain the vehicle energy consumption corresponding to each driving state, and calculate the initial comprehensive energy consumption based on the vehicle energy consumption and the probability of each state. Compare the ambient temperature with the preset ambient temperature threshold; If the ambient temperature is not greater than the preset ambient temperature threshold, the initial comprehensive energy consumption will be determined as the vehicle energy consumption. If the ambient temperature is greater than the preset ambient temperature threshold, the target energy consumption correction value corresponding to the ambient temperature is determined based on the relationship between the ambient temperature and the energy consumption correction value. The initial combined energy consumption is corrected using the target energy consumption correction value to obtain the vehicle energy consumption.

[0010] In one embodiment, determining battery parallel connection information based on vehicle power demand and a power demand threshold includes: Compare vehicle power requirements with power requirement thresholds; If the vehicle's power demand is less than the power demand threshold, then the battery parallel connection information is determined to be not required. If the vehicle's power demand is not less than the power demand threshold, then the battery parallel connection information is determined to be required to be connected in parallel. Based on the correspondence between vehicle power demand, vehicle power demand and correction value, determine the target correction value corresponding to vehicle power demand; Obtain the single capacity value, calculate the number of parallel connections based on the single capacity value, vehicle power requirements, and target correction value, and determine the number of parallel connections as battery parallel connection information.

[0011] Secondly, this application provides a battery parallel connection device based on vehicle operating conditions, employing the following technical solution: A battery parallel connection device based on vehicle operating conditions includes: The acquisition module is used to acquire multiple driving parameters, driving environment parameters, and ambient temperature of the vehicle. The driving parameters represent historical driving parameters within a set time period. The driving status generation module is used to generate status description information based on various driving parameters. The status description information is used to describe the driving status of the vehicle in different time periods. The vehicle energy consumption determination module is used to determine the driving time based on driving speed and driving environment parameters, and to calculate the vehicle energy consumption based on ambient temperature, driving time and status description information. The parallel connection information determination module is used to determine the vehicle power demand based on the vehicle energy consumption, and to determine the battery parallel connection information based on the vehicle power demand and the power demand threshold. The battery parallel connection information includes whether the battery is connected in parallel and the number of battery clusters connected in parallel.

[0012] Thirdly, this application provides an electronic device that adopts the following technical solution: At least one processor; Memory; At least one application, stored in memory, when executed by at least one processor, causes at least one processor to execute a battery parallel method based on vehicle operating conditions, as described in any of the first aspects.

[0013] Fourthly, this application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform a battery parallel connection method based on vehicle operating conditions, as described in any of the first aspects.

[0014] In summary, this application includes the following beneficial technical effects: This invention acquires multiple driving parameters, a second driving parameter, and driving environment parameters of the vehicle, and generates state description information based on each driving parameter to accurately describe the multiple states existing during vehicle operation. The energy consumption of the vehicle varies under different driving states, thus requiring prediction of the vehicle's driving state. The driving time is then determined based on driving speed and driving environment parameters. As the driving time increases, the types and number of vehicle driving states increase, and the corresponding vehicle energy consumption also changes. Therefore, it is necessary to determine the driving time to obtain accurate vehicle energy consumption by combining the driving time with the vehicle state as a reference. The vehicle power demand is then determined based on the vehicle energy consumption, and battery parallel connection information is determined based on the accurate vehicle power demand and power demand threshold. This effectively improves the accuracy of determining battery parallel connection information. Compared to related technologies that directly determine battery parallel connection information based on battery energy consumption, this application obtains accurate power demand by referring to the actual driving scenario of the vehicle and determines battery parallel connection information based on the accurate power demand, effectively improving the accuracy of battery parallel connection information determination. Attached Figure Description

[0015] Figure 1 A schematic flowchart illustrating a battery parallel connection method based on vehicle operating conditions provided in an embodiment of this application; Figure 2 This is a schematic diagram of the parallel structure inside a vehicle provided in an embodiment of this application; Figure 3 A schematic diagram of a battery parallel connection device based on vehicle operating conditions provided in an embodiment of this application; Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0016] The following is in conjunction with the appendix Figure 1 To be continued Figure 4 This application will be described in further detail.

[0017] This specific embodiment is merely an explanation of this application and is not intended to limit it. After reading this specification, those skilled in the art can make modifications to this embodiment without contributing any inventive step, but such modifications are protected by patent law as long as they are within the scope of this application.

[0018] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions in the embodiments of this application are described clearly and completely. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0019] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0020] The embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0021] Low temperatures have a comprehensive impact on vehicle energy consumption. In actual operation and production, firstly, low ambient temperatures increase air density, thus increasing vehicle drag; secondly, low temperatures alter vehicle operating conditions and lead to increased lubricant viscosity, resulting in increased wear on conventional systems. Furthermore, to ensure a suitable internal temperature for specialized vehicles, the vehicle insulation system is also a crucial component of vehicle energy consumption. Therefore, compared to conventional vehicles, vehicles in low-temperature environments consume more energy. To reduce vehicle energy consumption, it is necessary to rationally configure the number of batteries connected in parallel. Against this backdrop, electric or hybrid special-purpose vehicles are increasingly widely used due to their excellent environmental adaptability and control precision. However, the vehicle's power source—the battery pack—also experiences degradation in low-temperature environments. Therefore, it is essential to accurately calculate the energy consumption of special-purpose vehicles in low-temperature environments and rationally configure the battery system based on the accurate energy consumption data.

[0022] Based on this, this application provides a technical solution that obtains multiple driving parameters, a second driving parameter, and driving environment parameters of a vehicle, generates state description information based on the driving parameters to predict the driving state of the vehicle, determines the driving time based on the driving speed and driving environment parameters, calculates the vehicle energy consumption based on the driving time and driving state, and configures the battery system according to the corresponding vehicle energy consumption, thereby effectively reducing vehicle energy consumption and ensuring the normal operation of special vehicles.

[0023] This application provides a battery parallel connection method based on vehicle operating conditions, executed by an electronic device. This electronic device can be a server or a terminal device. The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smartphone, tablet, laptop, desktop computer, etc., but is not limited to these. The terminal device and the server can be directly or indirectly connected via wired or wireless communication. This application does not impose any limitations on this connection. Figure 1 As shown, the method includes steps S1, S2, S3, and S4, wherein: Step S1: Obtain multiple driving parameters, multiple secondary driving parameters, and driving environment parameters of the vehicle. The driving parameters represent the historical driving parameters within a set time period.

[0024] Specifically, the vehicle is a special vehicle that operates in low-temperature environments, such as a polar research vehicle, a low-temperature refrigerated transport vehicle, or an emergency rescue vehicle for high-altitude and cold regions; this application embodiment does not limit this to a specific type. The second driving parameter describes the vehicle's current driving status. Driving environment parameters include ambient temperature and driving distance.

[0025] The electronic device integrates a monitoring program to monitor triggering behaviors. Once a request is detected, the acquisition operation is executed. Driving parameters, secondary driving parameters, and driving environment parameters can be retrieved from a database. The secondary driving parameters and driving environment parameters are monitored in real-time by corresponding sensors and uploaded to the electronic device. It is understandable that special vehicles operating in low-temperature environments are more sensitive to driving conditions and parameters. Specifically, when the ambient temperature is lower or road conditions are worse, the vehicle needs to overcome more resistance to ensure normal transport operations, thus consuming more energy.

[0026] Step S2: Generate status description information based on driving parameters. The status description information is used to describe the driving status of the vehicle in different time periods.

[0027] Specifically, state description information can be generated based on multiple historical operation data, historical energy consumption values, and driving parameters. The specific generation process can be referred to in the following embodiments. The state description information describes the probability of a vehicle transitioning from its current state to the next state. It is also understood that in this embodiment, the driving states include a first driving state, a second driving state, a third driving state, a fourth driving state, and a fifth driving state. The first driving state represents an empty vehicle driving on a dry, flat road; the second driving state represents an empty vehicle driving on a dry, uphill road; the third driving state represents a loaded vehicle driving on a dry, downhill road; the fourth driving state represents a loaded vehicle driving on a muddy, flat road; and the fifth state represents an empty vehicle in a waiting state.

[0028] It is understandable that the energy consumption of a vehicle varies depending on its driving state. For example, the energy consumption of a vehicle in the first driving state is much lower than that in the fourth driving state. When a vehicle is in the fourth driving state, it needs to consume more energy due to increased road friction and the weight of the cargo. Therefore, it is necessary to predict the driving state of the vehicle and quantify the dynamic characteristics of the vehicle based on the driving state to obtain a more accurate energy consumption.

[0029] Step S3: Determine the driving time based on the driving speed and driving environment parameters, and calculate the vehicle energy consumption based on the driving time and status description information.

[0030] Specifically, the process of determining the driving time based on driving speed and environmental parameters includes: calculating the initial driving time based on driving speed and distance; determining whether the current driving environment affects vehicle driving based on the ambient temperature and a preset ambient temperature; if the ambient temperature is lower than the preset ambient temperature, the current driving environment affects vehicle driving; if the ambient temperature is not lower than the preset ambient temperature, the current driving environment does not affect vehicle driving; if the current driving environment affects vehicle driving, the environmental impact coefficient corresponding to the environmental information is obtained, and the initial driving time is corrected according to the correction formula and the environmental impact coefficient. The correction formula is: Target driving time = Initial driving time (1 + Environmental impact coefficient). It is understandable that when a vehicle is in severe weather, the vehicle speed decreases, the driving time increases, and the driving energy consumption increases, thus requiring correction based on the actual environment. The environmental impact coefficient corresponding to the environmental information is pre-input into the electronic device by technicians. As the severity of the weather increases, the environmental impact coefficient increases accordingly. For example, the environmental impact coefficient for snowy days is greater than that for rainy days, thus the driving time for a vehicle on a snowy day is higher than that on a rainy day. Furthermore, the specific process of calculating vehicle energy consumption based on driving time and status description information can be found in the following embodiment.

[0031] Step S4: Determine the vehicle power demand based on the vehicle energy consumption, and determine the battery parallel connection information based on the vehicle power demand and the power demand threshold. The battery parallel connection information includes whether to connect in parallel and the number of battery clusters connected in parallel.

[0032] Specifically, in one feasible approach, vehicle power demand can be determined based on a correspondence. The correspondence between vehicle energy consumption and vehicle power demand is used to determine the target vehicle power demand corresponding to the vehicle energy consumption. Further, vehicle parallel connection information is determined based on the target vehicle power demand and a power demand threshold. The specific process for determining battery parallel connection information based on vehicle power demand and the power demand threshold can be found in the following embodiments.

[0033] Based on the above embodiments, multiple driving parameters, second driving parameters, and driving environment parameters of the vehicle are obtained, and state description information is generated according to each driving parameter to accurately describe the multiple states existing during vehicle driving. The energy consumption of the vehicle is different under different driving states, so it is necessary to predict the driving state of the vehicle. Then, the driving time is determined according to the driving speed and driving environment parameters. As the driving time increases, the types and number of vehicle driving states increase, and the corresponding vehicle energy consumption also changes. Therefore, it is necessary to determine the driving time so as to obtain accurate vehicle energy consumption by combining the driving time with the vehicle state. Then, the vehicle power demand is determined according to the vehicle energy consumption, and the battery parallel connection information is determined according to the accurate vehicle power demand and power demand threshold, thereby effectively improving the accuracy of battery parallel connection information determination. Compared with the related technology that directly determines battery parallel connection information based on energy consumption, this application obtains accurate power demand by referring to the actual driving scenario of the vehicle, and determines battery parallel connection information based on accurate power demand, which effectively improves the accuracy of battery parallel connection information determination.

[0034] In one possible implementation of this application, the vehicle state includes a first state, a second state, a third state, a fourth state, and a fifth state. The first state represents an empty vehicle traveling on a dry, flat road; the second state represents an empty vehicle traveling on a dry, uphill road; the third state represents a loaded vehicle traveling on a dry, downhill road; the fourth state represents a loaded vehicle traveling on a muddy, flat road; and the fifth state represents an empty vehicle in a waiting state.

[0035] Specifically, under normal circumstances, the vehicle's operating route is a fixed route. The vehicle starts from its parking position, travels along a flat road or uphill to the loading area to wait for loading, and after loading, it is considered to be in a heavily loaded state. It then travels downhill or along a flat road to the unloading point, and after unloading, it returns empty to its parking position. When the vehicle is not loaded, its load state is empty; otherwise, it is heavily loaded. In this embodiment, the vehicle's load state includes empty and heavily loaded, the road conditions include flat roads, uphill, and downhill, and the road surface conditions include dry and muddy. It is understood that, combined with the actual production and operation environment, based on special vehicle operations, process constraints, low-temperature impact focus, and actual production principles, the first, second, third, fourth, and fifth states can accurately and comprehensively cover all production and operation scenarios of special vehicles and constitute a complete operational cycle.

[0036] One possible implementation of this application embodiment involves generating state description information based on driving parameters, including: Acquire multiple historical operation data of vehicles within a set time period. The historical operation data includes multiple road gradients and the historical energy consumption value corresponding to each road gradient. Based on the slope of each road and the historical energy consumption value corresponding to each road slope, weighted information is determined. The weighted information includes a first weighted value corresponding to the slope and a second weighted value corresponding to the mud level. For each state, calculate the weighted sum of the transitions from state to other states; Determine the sample size of the target state and the first non-target state, and calculate the transition probability based on the sample size and the sum of weighted values; A state matrix is ​​generated based on the elements of each target state, and the state matrix is ​​used as the state description information.

[0037] Specifically, historical operation data can be obtained from a historical information database. This historical operation data is generated by multiple sensors in the vehicle and uploaded to the electronic device in real time. This application embodiment does not limit the sensors. The set time period can be three months or two months. The process of determining the historical energy consumption value corresponding to each road slope includes: for each road slope, obtaining multiple historical energy consumption values ​​corresponding to each road slope; determining the average energy consumption value corresponding to each road slope based on all historical energy consumption values ​​and the number of historical energy consumption values; and determining the average energy consumption value as the historical energy consumption value corresponding to the road slope.

[0038] Specifically, the process of determining weighted information based on road slope and corresponding historical energy consumption values ​​can be found in the following embodiment. It is understood that using weighted values ​​as a reference allows for assigning different weights to different influencing factors, highlighting factors with a greater degree of influence, and also enabling the probability to have stronger scenario variability. That is, the state transition probability changes as the vehicle scenario changes, and obtaining the energy consumption value based on this state transition probability can effectively improve the accuracy of the energy consumption value.

[0039] Specifically, the process of calculating the weighted sum of other states for each state includes: calculating the probability that the next time step after the first state is the second state, where the slopes corresponding to the second state are G1, G2, G3, and G4, respectively, with each representing a different slope and a corresponding weight of a1, a2, a3, and a4. The road states at the transition to the second state are E1 and E2, with corresponding weights of a5 and a6. When the first state transitions to the second state (G1E1), the corresponding weight is a5. 1* a5, the weight value corresponding to the transition from the first state to the second state (G2E2) is a. 2* a6, the weight value corresponding to the transition from the first state to the second state (G1E2) is a. 1* Then, the weighted values ​​of all transitions from the first state to the second state are summed to obtain the total weighted value. Based on the total weighted value of the transition from the first state to the first state, the total weighted value of the transition from the first state to the second state, the total weighted value of the transition from the first state to the third state, the total weighted value of the transition from the first state to the fourth state, and the total weighted value of the data volume of the transition from the first state to the fifth state, the total weighted value of the transition from the first state to the second state is calculated and the proportion of the total weighted value is obtained. The proportion is determined as the transition probability of the transition from the first state to the second state. Then, the transition probabilities between all states are obtained, and a state matrix is ​​generated based on the transition probabilities between all states.

[0040] Based on the above embodiments, multiple historical operation data of vehicles within a set time period are obtained, and a first weighting value and a second weighting value are determined according to the road slope and the energy consumption value of each road slope. In order to clarify the impact of different road slopes and mud levels on vehicle energy consumption through weighting values, the sum of weighted values ​​for state transition to other states is calculated, the sample size of the target state and the first non-target state is determined, and the transition probability is calculated based on the sample size and the sum of weighted values. Then, a state matrix is ​​generated based on the elements of each target state, and the state matrix is ​​determined as state description information, thereby obtaining accurate state description information.

[0041] One possible implementation of this application embodiment determines weighted information based on each road slope and the corresponding historical energy consumption value, including: For each state transition pair, each road slope is divided into multiple slope regions, and the slope region includes multiple road slopes; For each slope interval, obtain the energy consumption value and the number of state transitions corresponding to the slope interval, and determine the average energy consumption value corresponding to the slope interval based on the energy consumption value and the number of state transitions. Obtain the preset energy consumption baseline value, and calculate the energy consumption sensitivity corresponding to each slope range based on the preset energy consumption baseline value and the average energy consumption value; Based on energy consumption sensitivity, the average energy consumption for each slope range is normalized, and the normalized average energy consumption is used as the weighted information.

[0042] Specifically, multiple road slopes can be divided into a preset number of slope regions. This application embodiment does not limit the preset number; users can set it themselves. It is understood that the span of each slope region is the same, all being 10°. The energy consumption value represents the energy consumption of a vehicle traveling on that slope segment; the state transition quantity is the amount of data transferred to that state within a preset time period. The energy consumption value and the state transition quantity can be obtained from an electronic database. The specific process of determining the average energy consumption based on the energy consumption value and the state transition quantity includes: calculating the sum of all energy consumption values, calculating the average value based on the state transition quantity and the sum of energy consumption values, and determining the average value as the average energy consumption corresponding to that slope interval.

[0043] The preset energy consumption benchmark value is pre-set and input into the electronic device by a technician. This application embodiment does not limit the preset energy consumption benchmark value. Further, it can be calculated according to a formula, which is: , where K G1 E represents the energy sensitivity corresponding to the first slope range. G E represents the energy consumption value for the first slope range. base G represents the average energy consumption in the first slope section. avg This represents the average slope of the first slope interval.

[0044] The specific process of normalizing the average energy consumption of each slope interval based on energy consumption sensitivity includes: selecting the maximum energy consumption sensitivity from the energy consumption sensitivity of each slope interval; calculating the weight of each slope interval based on the maximum energy consumption sensitivity, the energy consumption sensitivity of each slope interval, and the calculation formula, where the calculation formula is: , where K G1 K represents the energy consumption sensitivity in the first slope range. max For maximum energy consumption sensitivity, W G1Let M1 be the weight corresponding to the first slope interval, M2 be the minimum value of the weight interval, and 0.5 be a correction factor. In this embodiment, M1 is preferably 0.5 and M2 is 1.5 to normalize the weight values ​​corresponding to each slope interval into the weight interval. It can be understood that normalizing the weight values ​​of each slope interval can avoid significant differences or extreme values ​​between the weight values ​​of each slope interval, which facilitates subsequent calculations.

[0045] Based on the above embodiments, for each state transition pair, multiple road slopes are divided into multiple slope intervals. Then, the energy consumption value and the number of state transitions corresponding to the slope interval are obtained, and the average energy consumption is determined based on the energy consumption value and the number of state transitions, thereby obtaining the accurate average energy consumption for each slope interval. A preset energy consumption benchmark value is obtained, and the energy consumption sensitivity of each slope interval is obtained based on the preset energy consumption benchmark value and the average energy consumption value. In order to accurately reflect the influence of road slope and road mudliness on vehicle energy consumption through energy consumption sensitivity, the energy consumption sensitivity and the average energy consumption value are normalized to avoid the weight value being too large or too small, thereby controlling the energy consumption sensitivity within a reasonable data range, which is convenient for subsequent data calculation.

[0046] One possible implementation of this application embodiment calculates vehicle energy consumption based on ambient temperature, driving time, and state description information, including: Based on the driving time and the preset calculation step size, the number of iterations corresponding to the state description information is obtained, and the state description information is iteratively calculated based on the number of iterations. For each iteration of the state description information, obtain the vehicle energy consumption corresponding to each driving state, and calculate the initial comprehensive energy consumption based on the vehicle energy consumption and the probability of each state. Compare the ambient temperature with the preset ambient temperature threshold; If the ambient temperature is not greater than the preset ambient temperature threshold, the initial comprehensive energy consumption will be determined as the vehicle energy consumption. If the ambient temperature is greater than the preset ambient temperature threshold, the target energy consumption correction value corresponding to the ambient temperature is determined based on the correspondence between ambient temperature and energy consumption correction value. The initial combined energy consumption is corrected using the target energy consumption correction value to obtain the vehicle energy consumption.

[0047] Specifically, in this embodiment, the preset calculation step size is set by the technician in advance, preferably 10 minutes, so that the number of iterations corresponding to the state description information can be obtained, and the iterative calculation of the number of iterations of the state description information can be performed. This embodiment does not limit the specific iteration process, but you can refer to the iteration process of the Markov chain matrix.

[0048] Specifically, vehicle energy consumption for each driving state can be obtained from a vehicle energy consumption information database. The overall vehicle energy consumption corresponding to all state probabilities is calculated to obtain the initial overall energy consumption. This application embodiment does not limit the specific calculation process of the initial overall energy consumption. A preset ambient temperature threshold is set by technicians based on the vehicle's operating environment and input into the electronic device. If the ambient temperature is not greater than the preset ambient temperature threshold, it indicates that the vehicle does not need to consume additional electrical energy to ensure normal driving and interior temperature, and the initial overall energy consumption is directly determined as the vehicle energy consumption. If the ambient temperature is greater than the preset ambient temperature threshold, additional electrical energy is needed to ensure normal vehicle driving and interior temperature. The correspondence between ambient temperature and energy consumption correction value is set by technicians. In this application embodiment, as the ambient temperature decreases, the energy consumption correction value increases accordingly. The sum of the initial overall energy consumption and the target energy consumption correction value is calculated, and the overall energy consumption is determined as the vehicle energy consumption.

[0049] Based on the above embodiments, the number of iterations is calculated according to the driving time and the preset calculation step size, and the state description information is iteratively calculated according to the number of iterations. Then, for each iteration of the state description information, the vehicle energy consumption corresponding to each driving state is obtained, and the initial comprehensive energy consumption is calculated according to the vehicle energy consumption and the state probability. Then, the ambient temperature is compared with the preset ambient temperature threshold. When the ambient temperature is too low, more electrical energy needs to be consumed to ensure vehicle operation and heating. Therefore, it is necessary to compare the ambient temperature with the preset ambient temperature threshold. When the ambient temperature is not greater than the preset ambient temperature threshold, it indicates that the vehicle does not need to consume additional energy. When the ambient temperature is greater than the preset ambient temperature threshold, it indicates that additional energy needs to be consumed. The corresponding target energy consumption correction value is determined according to the ambient temperature, and the initial comprehensive energy consumption is corrected using the target energy consumption correction value. Then, the vehicle energy consumption is determined according to the corrected initial comprehensive energy consumption, thereby effectively improving the accuracy of vehicle energy consumption determination.

[0050] One possible implementation of this application embodiment determines battery parallel connection information based on vehicle power demand and a power demand threshold, including: Compare vehicle power requirements with power requirement thresholds; If the vehicle's power demand is less than the power demand threshold, then the battery parallel connection information is determined to be not required. If the vehicle's power demand is not less than the power demand threshold, then the battery parallel connection information is determined to be required to be connected in parallel. Based on the correspondence between vehicle power demand, vehicle power demand and correction value, determine the target correction value corresponding to vehicle power demand; Obtain the single capacity value, calculate the number of parallel connections based on the single capacity value, vehicle power requirements, and target correction value, and determine the number of parallel connections as battery parallel connection information.

[0051] Specifically, the power demand threshold is preset by the technician. In this embodiment, the power demand threshold is the maximum power demand threshold. When the vehicle power demand is less than the power demand threshold, it indicates that the current battery cluster can meet the vehicle's energy consumption. When the vehicle power demand is not less than the power demand threshold, it indicates that the battery cluster cannot meet the energy consumption during vehicle operation. To ensure the normal operation of the vehicle, the battery clusters need to be connected in parallel to provide more energy for the vehicle. Therefore, the battery parallel connection information is determined to be required. The single capacity value represents the capacity of each battery cluster and is preset by the technician. The correspondence between the vehicle power demand and the correction value is established by the technician based on multiple historical data. This embodiment does not limit the process of establishing the correspondence; users can set it themselves. The vehicle power demand and the correspondence are matched to obtain the corresponding number of parallel connections, thereby determining the battery parallel connection information. Furthermore, in the embodiments of this application, the battery clusters are connected in parallel. It is understood that in the actual production and operation of the vehicle, if the vehicle is connected in parallel with single batteries, any battery failure will affect the discharge of all batteries and cause a safety accident. However, connecting the battery clusters in parallel can quickly disconnect the faulty battery cluster when facing a battery failure, thereby avoiding affecting the charging and discharging function of other battery clusters and minimizing the risks in vehicle transportation.

[0052] Based on the above embodiments, in actual production and operation, vehicle operation and transportation have continuous characteristics. When the vehicle's power demand is less than the power demand threshold, it indicates that the current vehicle battery can meet the vehicle's actual needs. When the vehicle's power demand is not less than the power demand threshold, it indicates that the current vehicle battery cannot meet the vehicle's actual needs. Therefore, it is necessary to connect the batteries in parallel to meet the vehicle's needs. The target correction value is determined according to the correspondence between power demand and correction value. The single capacity value is obtained, and the number of parallel connections is calculated based on the single capacity value, vehicle power demand, and target correction value. The number of parallel connections is determined as the parallel connection information of the batteries, thereby obtaining accurate battery parallel connection information.

[0053] like Figure 2 The diagram shown is a schematic of the parallel structure inside a vehicle provided in an embodiment of this application. Multiple battery clusters are connected in parallel according to the vehicle's operating conditions. The battery clusters are first connected to diodes, and the diodes are then connected to [other components]. The load can be the vehicle's drive motor or working equipment. It is understood that if the voltage difference is too large during the parallel connection process, i.e., the instantaneous current is too large, it will burn out the power devices, wires, and copper busbars on the main circuit. By adding diodes, unidirectional isolation can be generated, avoiding the problem of not being able to achieve strong parallel connection due to voltage difference.

[0054] The above embodiments describe a battery parallel connection method based on vehicle operating conditions from the perspective of method flow. The following embodiments describe a battery parallel connection device based on vehicle operating conditions from the perspective of virtual modules or virtual units. For details, please refer to the following embodiments.

[0055] This application provides a battery parallel connection device based on vehicle operating conditions, such as... Figure 3 As shown, the battery parallel connection device based on vehicle operating conditions may specifically include: The acquisition module 201 is used to acquire multiple driving parameters, driving environment parameters and ambient temperature of the vehicle. The driving parameters represent the historical driving parameters within a set time period. The driving status generation module 202 is used to generate status description information based on various driving parameters. The status description information is used to describe the driving status of the vehicle in different time periods. The vehicle energy consumption determination module 203 is used to determine the driving time based on the driving speed and driving environment parameters, and to calculate the vehicle energy consumption based on the ambient temperature, driving time and state description information. The parallel connection information determination module 204 is used to determine the vehicle power demand based on the vehicle energy consumption, and to determine the battery parallel connection information based on the vehicle power demand and the power demand threshold. The battery parallel connection information includes whether the battery is connected in parallel and the number of battery clusters connected in parallel.

[0056] Based on the above embodiments, multiple driving parameters, a second driving parameter, and driving environment parameters of the vehicle are obtained. State description information is generated based on each driving parameter to accurately describe the multiple states existing during vehicle operation. The energy consumption of the vehicle varies under different driving states, thus requiring prediction of the vehicle's driving state. The driving time is then determined based on the driving speed and driving environment parameters. As the driving time increases, the types and number of vehicle driving states increase, and the corresponding vehicle energy consumption also changes. Therefore, it is necessary to determine the driving time so that accurate vehicle energy consumption can be obtained by combining the driving time as a reference with the vehicle state. Then, the vehicle power demand is determined based on the vehicle energy consumption, and the battery parallel connection information is determined based on the accurate vehicle power demand and power demand threshold. This effectively improves the accuracy of determining the battery parallel connection information. Compared to related technologies that directly determine battery parallel connection information based on battery energy consumption, this application obtains accurate power demand by referring to the actual driving scenario of the vehicle and determines battery parallel connection information based on the accurate power demand, effectively improving the accuracy of determining battery parallel connection information.

[0057] In one possible implementation of this application, the vehicle state includes a first state, a second state, a third state, a fourth state, and a fifth state. The first state represents an empty vehicle traveling on a dry, flat road; the second state represents an empty vehicle traveling on a dry, uphill road; the third state represents a loaded vehicle traveling on a dry, downhill road; the fourth state represents a loaded vehicle traveling on a muddy, flat road; and the fifth state represents an empty vehicle in a waiting state.

[0058] In one possible implementation of this application embodiment, when the driving state generation module 202 generates state description information based on various driving parameters, it is used for: Acquire multiple historical operation data of vehicles within a set time period. The historical operation data includes multiple road gradients and the historical energy consumption value corresponding to each road gradient. Based on the slope of each road and the historical energy consumption value corresponding to each road slope, weighted information is determined. The weighted information includes a first weighted value corresponding to the slope and a second weighted value corresponding to the mud level. For each state, calculate the weighted sum of the transitions from state to other states; Determine the sample size of the target state and the first non-target state, and calculate the transition probability based on the sample size and the sum of weighted values; A state matrix is ​​generated based on the elements of each target state, and the state matrix is ​​used as the state description information.

[0059] In one possible implementation of this application embodiment, when the driving status generation module 202 determines weighted information based on each road slope and the historical energy consumption value corresponding to each road slope, it is used to: Acquire multiple historical operation data of vehicles within a set time period. The historical operation data includes multiple road gradients and the historical energy consumption value corresponding to each road gradient. Based on the slope of each road and the historical energy consumption value corresponding to each road slope, weighted information is determined. The weighted information includes a first weighted value corresponding to the slope and a second weighted value corresponding to the mud level. For each state, calculate the weighted sum of the transitions from state to other states; Determine the sample size of the target state and the first non-target state, and calculate the transition probability based on the sample size and the sum of weighted values; A state matrix is ​​generated based on the elements of each target state, and the state matrix is ​​used as the state description information.

[0060] In one possible implementation of this application embodiment, when the vehicle energy consumption determination module 203 calculates the vehicle energy consumption based on ambient temperature, driving time, and state description information, it is used to: Based on the driving time and the preset calculation step size, the number of iterations with the state description information is obtained, and the state description information is iteratively calculated based on the number of iterations. For each iteration of the state description information, obtain the vehicle energy consumption corresponding to each driving state, and calculate the initial comprehensive energy consumption based on the vehicle energy consumption and the probability of each state. Compare the ambient temperature with the preset ambient temperature threshold; If the ambient temperature is not greater than the preset ambient temperature threshold, the initial comprehensive energy consumption will be determined as the vehicle energy consumption. If the ambient temperature is greater than the preset ambient temperature threshold, the target energy consumption correction value corresponding to the ambient temperature is determined based on the relationship between the ambient temperature and the energy consumption correction value. The initial combined energy consumption is corrected using the target energy consumption correction value to obtain the vehicle energy consumption.

[0061] In one possible implementation of this application embodiment, the parallel information determination module 204, when determining battery parallel information based on vehicle power demand and power demand threshold, is used for: Compare vehicle power requirements with power requirement thresholds; If the vehicle's power demand is less than the power demand threshold, then the battery parallel connection information is determined to be not required. If the vehicle's power demand is not less than the power demand threshold, then the battery parallel connection information is determined to be required to be connected in parallel. Based on the correspondence between vehicle power demand, vehicle power demand and correction value, determine the target correction value corresponding to vehicle power demand; Obtain the single capacity value, calculate the number of parallel connections based on the single capacity value, vehicle power requirements, and target correction value, and determine the number of parallel connections as battery parallel connection information.

[0062] This application provides an electronic device, such as... Figure 4 As shown, Figure 4 The illustrated electronic device includes a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, for example, via a bus 302. Optionally, the electronic device may also include a transceiver 304. It should be noted that in practical applications, the transceiver 304 is not limited to one type, and the structure of this electronic device does not constitute a limitation on the embodiments of this application.

[0063] Processor 301 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 301 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0064] Bus 302 may include a pathway for transmitting information between the aforementioned components. Bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 302 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 4 The symbol is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0065] The memory 303 may be a ROM (Read Only Memory) or other type of static storage device capable of storing static information and instructions, RAM (Random Access Memory) or other type of dynamic storage device capable of storing information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0066] The memory 303 is used to store application code that executes the solution of this application, and its execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the content shown in the foregoing method embodiments.

[0067] Electronic devices include, but are not limited to: mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and in-vehicle terminals (such as in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Servers can also be included. Figure 4 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0068] This application provides a computer-readable storage medium storing a computer program. When the program is run on a computer, it enables the computer to execute the corresponding content in the aforementioned method embodiments. Compared to related technologies, this application obtains multiple driving parameters, a second driving parameter, and driving environment parameters of the vehicle, and generates state description information based on each driving parameter to accurately describe the multiple states existing during vehicle operation. The energy consumption of the vehicle varies under different driving states, thus requiring prediction of the vehicle's driving state. The driving time is then determined based on the driving speed and driving environment parameters. As the driving time increases, the types and number of vehicle driving states increase, and the corresponding vehicle energy consumption also changes. Therefore, it is necessary to determine the driving time so that an accurate vehicle energy consumption can be obtained by combining the driving time with the vehicle state. The vehicle power demand is then determined based on the vehicle energy consumption, and battery parallel connection information is determined based on the accurate vehicle power demand and power demand threshold. This effectively improves the accuracy of determining battery parallel connection information. Compared to related technologies that directly determine battery parallel connection information based on battery energy consumption, this application obtains an accurate power demand by referring to the actual driving scenario of the vehicle and determines battery parallel connection information based on the accurate power demand, effectively improving the accuracy of determining battery parallel connection information.

[0069] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0070] The above are only some embodiments of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A battery parallel connection method based on vehicle operating conditions, characterized in that, include: The vehicle acquires multiple driving parameters, driving environment parameters, and ambient temperature. The driving parameters represent historical driving parameters within a set time period. Status description information is generated based on various driving parameters. This status description information is used to describe the driving status of the vehicle at different times. The driving time is determined based on driving speed and driving environment parameters, and the vehicle energy consumption is calculated based on ambient temperature, driving time and state description information. The vehicle power demand is determined based on the vehicle's energy consumption, and the battery parallel connection information is determined based on the vehicle power demand and the power demand threshold. The battery parallel connection information includes whether the battery is connected in parallel and the number of battery clusters connected in parallel.

2. The battery parallel connection method based on vehicle operating conditions as described in claim 1, characterized in that, The vehicle status includes a first state, a second state, a third state, a fourth state, and a fifth state. The first state represents an empty vehicle traveling on a dry, flat road; the second state represents an empty vehicle traveling on a dry, uphill road; the third state represents a loaded vehicle traveling on a dry, downhill road; the fourth state represents a loaded vehicle traveling on a muddy, flat road; and the fifth state represents an empty vehicle in a waiting state.

3. The battery parallel connection method based on vehicle operating conditions as described in claim 1 or 2, characterized in that, Status description information is generated based on various driving parameters, including: Acquire multiple historical operation data of vehicles within a set time period. The historical operation data includes multiple road gradients and the historical energy consumption value corresponding to each road gradient. Based on the slope of each road and the historical energy consumption value corresponding to each road slope, weighted information is determined. The weighted information includes a first weighted value corresponding to the slope and a second weighted value corresponding to the mud level. For each state, calculate the weighted sum of the transitions from state to other states; Determine the sample size of the target state and the first non-target state, and calculate the transition probability based on the sample size and the sum of weighted values; A state matrix is ​​generated based on the elements of each target state, and the state matrix is ​​used as the state description information.

4. The battery parallel connection method based on vehicle operating conditions as described in claim 3, characterized in that, Based on the slope of each road and the corresponding historical energy consumption values, weighted information is determined, including: For each state transition pair, each road slope is divided into multiple slope intervals, and each slope interval includes multiple road slopes. For each slope interval, obtain the energy consumption value and the number of state transitions corresponding to the slope interval, and determine the average energy consumption value corresponding to the slope interval based on the energy consumption value and the number of state transitions. Obtain the preset energy consumption baseline value, and calculate the energy consumption sensitivity corresponding to each slope range based on the preset energy consumption baseline value and the average energy consumption value; Based on energy consumption sensitivity, the average energy consumption for each slope range is normalized, and the normalized average energy consumption is used as the weighted information.

5. The battery parallel connection method based on vehicle operating conditions as described in claim 1, characterized in that, Vehicle energy consumption is calculated based on ambient temperature, driving time, and condition description information, including: Based on the driving time and the preset calculation step size, the number of iterations with the state description information is obtained, and the state description information is iteratively calculated based on the number of iterations. For each iteration of the state description information, obtain the vehicle energy consumption corresponding to each driving state, and calculate the initial comprehensive energy consumption based on the vehicle energy consumption and the probability of each state. Compare the ambient temperature with the preset ambient temperature threshold; If the ambient temperature is not greater than the preset ambient temperature threshold, the initial comprehensive energy consumption will be determined as the vehicle energy consumption. If the ambient temperature is greater than the preset ambient temperature threshold, the target energy consumption correction value corresponding to the ambient temperature is determined based on the relationship between the ambient temperature and the energy consumption correction value. The initial combined energy consumption is corrected using the target energy consumption correction value to obtain the vehicle energy consumption.

6. The battery parallel connection method based on vehicle operating conditions as described in claim 1, characterized in that, Battery parallel connection information is determined based on vehicle power demand and power demand threshold, including: Compare vehicle power requirements with power requirement thresholds; If the vehicle's power demand is less than the power demand threshold, then the battery parallel connection information is determined to be not required. If the vehicle's power demand is not less than the power demand threshold, then the battery parallel connection information is determined to be required to be connected in parallel. Based on the correspondence between vehicle power demand, vehicle power demand and correction value, determine the target correction value corresponding to vehicle power demand; Obtain the single capacity value, calculate the number of parallel connections based on the single capacity value, vehicle power requirements, and target correction value, and determine the number of parallel connections as battery parallel connection information.

7. A battery parallel connection device based on vehicle operating conditions, characterized in that, include: The acquisition module is used to acquire multiple driving parameters, driving environment parameters, and ambient temperature of the vehicle. The driving parameters represent historical driving parameters within a set time period. The driving status generation module is used to generate status description information based on various driving parameters. The status description information is used to describe the driving status of the vehicle in different time periods. The vehicle energy consumption determination module is used to determine the driving time based on driving speed and driving environment parameters, and to calculate the vehicle energy consumption based on ambient temperature, driving time and status description information. The parallel connection information determination module is used to determine the vehicle power demand based on the vehicle energy consumption, and to determine the battery parallel connection information based on the vehicle power demand and the power demand threshold. The battery parallel connection information includes whether the battery is connected in parallel and the number of battery clusters connected in parallel.

8. An electronic device, characterized in that, include: At least one processor; Memory; At least one application, stored in memory, when executed by at least one processor, causes at least one processor to execute the battery parallel connection method based on any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, It stores a computer program, which, when executed in the computer, causes the computer to perform the battery parallel connection method based on vehicle operating conditions according to any one of claims 1 to 6.

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