CN111439128B - Electric vehicle remaining mileage estimation method and device and computer equipment - Google Patents
Electric vehicle remaining mileage estimation method and device and computer equipment Download PDFInfo
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- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
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Abstract
The application relates to a method and a device for estimating remaining mileage of an electric vehicle, computer equipment and a storage medium. The method comprises the following steps: acquiring a battery energy state and a predicted value of remaining mileage of the electric vehicle; the predicted value of the remaining mileage comprises an ideal predicted value and a theoretical predicted value; determining an ideal confidence coefficient of the ideal predicted value and a theoretical confidence coefficient of the theoretical predicted value according to the energy state of the battery; and weighting the ideal predicted value and the theoretical predicted value according to the ideal confidence coefficient and the theoretical confidence coefficient to obtain the estimated value of the remaining mileage. By adopting the method, the accuracy and the stability of the estimation of the remaining mileage of the electric vehicle can be improved.
Description
Technical Field
The present application relates to the field of automotive technologies, and in particular, to a method and an apparatus for estimating remaining mileage of an electric vehicle, a computer device, and a storage medium.
Background
The remaining mileage, namely the remaining driving range of the vehicle, can provide reference for a vehicle owner to plan a trip, avoid the situation of insufficient energy supply in the driving process of the vehicle, play an important role in improving user experience and relieving range anxiety, and the accurate estimation of the remaining mileage becomes one of the research focuses of pure electric vehicles along with the development of new energy automobile technology.
Currently, the remaining mileage of the electric-only vehicle is estimated mainly by obtaining the Energy State (SOE) of the power battery and dividing the Energy State by the average Energy consumption of the vehicle. However, the SOE result output by the power battery management system usually has errors, which easily results in inaccuracy of the remaining distance estimation, and in addition, when the vehicle driving condition, the accessory power, and the vehicle state change greatly, or when the vehicle just enters the driving state and the energy consumption sample points are few, the average energy consumption of the vehicle fluctuates greatly, which easily results in instability of the remaining distance estimation result.
Therefore, the existing pure electric vehicle remaining mileage estimation method has the problems of inaccurate and unstable estimation results.
Disclosure of Invention
In view of the above, it is necessary to provide a method, an apparatus, a computer device and a storage medium for estimating remaining mileage of an electric vehicle with high accuracy and stability.
An electric vehicle remaining mileage estimation method comprising:
acquiring a battery energy state and a residual mileage predicted value of the electric vehicle; the residual mileage predicted value comprises an ideal predicted value and a theoretical predicted value;
determining an ideal confidence coefficient of the ideal predicted value and a theoretical confidence coefficient of the theoretical predicted value according to the battery energy state;
and weighting the ideal predicted value and the theoretical predicted value according to the ideal confidence coefficient and the theoretical confidence coefficient to obtain a remaining mileage estimated value.
In one embodiment, the determining the ideal confidence of the ideal predicted value and the theoretical confidence of the theoretical predicted value according to the battery energy state includes:
determining a prediction error variance of the predicted value of the remaining mileage according to the battery energy state; the prediction error variance comprises an ideal prediction error variance corresponding to the ideal prediction value;
obtaining the prediction error covariance of the ideal predicted value according to the ideal prediction error variance;
calculating the filter gains of the ideal predicted value and the theoretical predicted value according to the prediction error covariance;
and obtaining the ideal confidence coefficient of the ideal predicted value and the theoretical confidence coefficient of the theoretical predicted value according to the filtering gain.
In one embodiment, the prediction error covariance includes a first covariance corresponding to a current time and a second covariance corresponding to a next time; the determining an ideal confidence degree of the ideal predicted value and a theoretical confidence degree of the theoretical predicted value according to the battery energy state further includes:
obtaining an estimation error covariance according to the filter gain and the first covariance; the estimation error covariance corresponds to the next time instant;
and obtaining the second covariance according to the estimation error covariance and the ideal prediction error variance.
In one embodiment, the obtaining the battery energy state and the remaining mileage predicted value of the electric vehicle includes:
acquiring the running speed, the running time and the initial remaining mileage of the electric vehicle;
obtaining the driving mileage of the electric vehicle according to the driving speed and the driving time;
and obtaining the ideal predicted value of the remaining mileage according to the initial remaining mileage and the driving mileage.
In one embodiment, the obtaining the battery energy state and the predicted remaining mileage value of the electric vehicle further includes:
obtaining the residual electric quantity of the battery and the average electric quantity consumed by the electric vehicle for driving a unit mileage according to the energy state of the battery;
and obtaining the theoretical predicted value of the remaining mileage according to the remaining electric quantity and the average electric quantity.
In one embodiment, the obtaining the remaining capacity of the battery and the average consumed capacity of the electric vehicle per mileage according to the battery energy state includes:
obtaining the total power consumption of the electric vehicle according to the energy state of the battery;
obtaining the average electric quantity before filtering consumed by the electric vehicle for driving unit mileage by dividing the total electric consumption quantity by the total mileage of the electric vehicle;
and performing first-order lag filtering on the average electric quantity before filtering to obtain the average electric quantity consumed by the unit mileage of the electric vehicle.
In one embodiment, the total power consumption amount includes a first total power consumption amount corresponding to a previous time and a second total power consumption amount corresponding to a current time; the obtaining of the total power consumption of the electric vehicle according to the battery energy state includes:
acquiring a time interval between the current moment and the last moment, and acquiring the voltage and the current of the battery at the current moment according to the energy state of the battery;
obtaining interval power consumption corresponding to the time interval by multiplying the voltage, the current and the time interval;
obtaining the second total power consumption amount by adding the first total power consumption amount and the interval power consumption amount.
An electric vehicle remaining mileage estimation device, comprising:
the input module is used for acquiring the battery energy state and the predicted value of the remaining mileage of the electric vehicle; the residual mileage predicted value comprises an ideal predicted value and a theoretical predicted value;
the confidence coefficient calculation module is used for determining an ideal confidence coefficient of the ideal predicted value and a theoretical confidence coefficient of the theoretical predicted value according to the battery energy state;
and the estimation module is used for weighting the ideal predicted value and the theoretical predicted value according to the ideal confidence coefficient and the theoretical confidence coefficient to obtain a remaining mileage estimation value.
A computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:
acquiring a battery energy state and a residual mileage predicted value of the electric vehicle; the residual mileage predicted value comprises an ideal predicted value and a theoretical predicted value;
determining an ideal confidence coefficient of the ideal predicted value and a theoretical confidence coefficient of the theoretical predicted value according to the battery energy state;
and weighting the ideal predicted value and the theoretical predicted value according to the ideal confidence coefficient and the theoretical confidence coefficient to obtain a remaining mileage estimated value.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:
acquiring a battery energy state and a residual mileage predicted value of the electric vehicle; the residual mileage predicted value comprises an ideal predicted value and a theoretical predicted value;
determining an ideal confidence coefficient of the ideal predicted value and a theoretical confidence coefficient of the theoretical predicted value according to the battery energy state;
and weighting the ideal predicted value and the theoretical predicted value according to the ideal confidence coefficient and the theoretical confidence coefficient to obtain a remaining mileage estimated value.
According to the method, the device, the computer equipment and the storage medium for estimating the remaining mileage of the electric vehicle, the smoothly-descending ideal predicted value and the theoretical predicted value closer to the actual driving range can be obtained by obtaining the predicted value of the remaining mileage of the electric vehicle, the ideal confidence coefficient and the theoretical confidence coefficient are determined according to the battery energy state by obtaining the battery energy state, data fusion can be carried out on the ideal predicted value and the theoretical predicted value according to the ideal confidence coefficient and the theoretical confidence coefficient, the ideal predicted value and the theoretical predicted value are weighted according to the ideal confidence coefficient and the theoretical confidence coefficient, the estimated value of the remaining mileage is obtained, and the accuracy and the stability of the estimation of the remaining mileage of the electric vehicle can be improved.
Drawings
FIG. 1 is a schematic flow chart diagram of a remaining mileage estimation method for an electric vehicle according to an embodiment;
FIG. 2 is a diagram illustrating the variance of the ideal prediction error as a function of the state of energy of the battery in one embodiment;
FIG. 3 is a schematic diagram of the variance of an ideal prediction error over time in one embodiment;
FIG. 4 is a flowchart illustrating a remaining mileage estimation method of an electric vehicle according to another embodiment;
FIG. 5 is a block diagram showing the construction of a remaining mileage estimating apparatus for an electric vehicle according to an embodiment;
FIG. 6 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
In one embodiment, as shown in fig. 1, there is provided a remaining mileage estimation method for an electric vehicle, which is described by taking a controller of an electric-only vehicle as an example, and includes the following steps:
and step S110, acquiring the battery energy state and the predicted value of the remaining mileage of the electric vehicle.
The battery energy state is the residual electric energy of the power battery of the pure electric vehicle.
The predicted value of the remaining mileage of the pure electric vehicle comprises an ideal predicted value and a theoretical predicted value.
In specific implementation, a battery management system of the pure electric vehicle can obtain the residual electric energy according to the state estimation of the power battery, and obtain the battery energy state. In order to obtain an ideal predicted value of the remaining mileage, an initial time may be preset, and the remaining mileage at the initial time may be obtained as an initial remaining mileage, for example, the vehicle starting time may be used as the initial time, marked as time 0, and the remaining mileage displayed by the vehicle interior instrument at the starting time may be used as the initial remaining mileage. After a period of travel time Δ TkThen, the driving speed of the vehicle in the driving time is obtained, for example, the driving speed in the driving time can be represented by V (k), wherein k ∈ [0, Δ T ∈ isk]By integrating the speed of travel over the period of travel time, or by integrating the periodThe running time is multiplied by the running speed, and the running time delta T of the pure electric vehicle can be calculatedkAnd subtracting the actual driving mileage from the initial remaining mileage to obtain an ideal predicted value of the remaining mileage. In order to obtain the theoretical predicted value of the remaining mileage, the total mileage and the total energy consumption of the vehicle from the initial time to the current time can be obtained, the average energy consumption of the unit mileage of the vehicle in the current running can be obtained by dividing the total energy consumption by the total mileage, and the theoretical predicted value of the remaining mileage can be obtained by dividing the remaining energy of the power battery of the pure electric vehicle by the average energy consumption of the unit mileage.
In one embodiment, when the time step Δ T is greater than the threshold valuekWhen smaller, the ideal prediction value can be calculated using the following formula:
Mid(ΔTk)＝Mid(0)-V(k)ΔTk。
wherein M isid(ΔTk) Is DeltaTkIdeal predicted value of remaining mileage at that moment, Mid(0) For initial remaining range, V (k) is Δ TkThe vehicle speed at the moment.
In another embodiment, when the time step Δ T is greater than the threshold valuekWhen smaller, the following formula can also be used to calculate the ideal predicted value:
Mid(k)＝Mid(k-1)-V(k)ΔTk。 (1)
where k denotes the current time, k-1 denotes the previous time, Mid(k) Is an ideal predicted value of the remaining mileage at the current moment, Mid(k-1) is an ideal predicted value of the remaining mileage at the previous moment, V (k) is the vehicle speed at the current moment, and Delta TkThe time interval between time k and time k-1.
In another embodiment, the theoretical prediction value of the remaining mileage can be calculated by using the following formula:
wherein M isthe(k) Is a theoretical predicted value, Q, of the remaining mileage at the current momentbat(k) The residual electric energy of the battery at the current moment can be estimated by a battery management system according to the state of the power battery, Qm(k) Average energy consumption per mileage for vehicle driving, e.g. Qm(k) May be the average energy consumption per kilometer.
In another embodiment, when the time step Δ T is greater than the threshold valuekWhen smaller, the following formula can be used to calculate the average energy consumption per mileage traveled by the vehicle:
wherein Q iscon(k) Total power consumption, Q, for a vehicle travelling from an initial time to a current timecon(k-1) Total Power consumption from the initial time to the previous time of the vehicle, Mh(k) Is the total driving range of the vehicle from the initial time to the current time, Mh(k-1) is the total driving range of the vehicle from the initial time to the previous time, Ubat(k) And Ibat(k) The voltage and the current of the power battery at the current moment are respectively.
In another embodiment, to avoid the calculated average energy consumption Qm(k) Frequently jittering for a period of time after the vehicle is powered on can be applied to Qm(k) Performing first-order lag filtering, wherein the specific filtering method can be expressed as formula
Qm_s(k)＝aQm(k)+(1-a)Qm_s(k-1)
Wherein Q ism_s(k) For the average energy consumption per kilometer at the current moment after filtering, Qm_s(k-1) is the average energy consumption per kilometer at the last moment after filtering, a is a filter coefficient, and the value range is that a belongs to [0,1 ]]。
Wherein, the average energy consumption per kilometer after filtering is as initial value Qm_s(0) The value of the filter coefficient a is to ensure the average energy consumption Q per kilometer after the vehicle is electrified and drivenm_sNo sharp jitter occurs. Wherein the standard workerThe conditions include NEDC (New European Driving Cycle), WLTP (World Light Vehicle Test Procedure), and CLTC (China Light Vehicle Test Cycle).
And step S120, determining an ideal confidence coefficient of the ideal predicted value and a theoretical confidence coefficient of the theoretical predicted value according to the battery energy state.
The ideal confidence coefficient is the weight of an ideal predicted value in the data fusion process, and the theoretical confidence coefficient is the weight of a theoretical predicted value in the data fusion process.
In specific implementation, the pure electric vehicle can determine the variance x (k) of the error between the ideal predicted value and the remaining mileage real value at the current moment according to the battery energy state, and record the variance x (k) as the ideal predicted error variance, and the variance r (k) of the error between the theoretical predicted value and the remaining mileage real value at the current moment as the theoretical predicted error variance. The controller acquires the covariance P (k-1| k-1) of the error between the estimated value of the remaining mileage and the true value of the remaining mileage in advance at the previous moment, and sums the covariance P (k-1| k-1) and the ideal prediction error variance X (k) to obtain the covariance P (k | k-1) of the error between the ideal predicted value and the true value of the remaining mileage at the current moment. According to the covariance P (k | k-1) and the theoretical prediction error variance R (k), the Kalman filtering gain K (k) at the current moment can be calculated, and the ideal confidence coefficient of the ideal predicted value and the theoretical confidence coefficient of the theoretical predicted value can be obtained through the Kalman filtering gain. In the method, by adjusting x (k) and r (k), the kalman filter gain k (k) can be adjusted, and further the degree of influence of the ideal predicted value and the theoretical predicted value of the remaining mileage on the final remaining mileage estimated value can be adjusted.
In one embodiment, 1-k (k) may be used as the ideal confidence of the ideal predicted value, and k (k) may be used as the theoretical confidence of the theoretical predicted value. When X (k) is larger, the ideal predicted value and the true value of the remaining mileage are indicated to have larger errors, and in order to ensure the accuracy of the estimated value of the remaining mileage, the ideal confidence coefficient 1-K (k) needs to be reduced, namely the Kalman filtering gain K (k) needs to be increased; when X (k) is smaller, the ideal confidence coefficient 1-K (k) can be increased to show that a smaller error exists between the ideal predicted value and the true value of the remaining mileage, namely the Kalman filtering gain K (k) can be reduced; when R (k) is larger, a larger error exists between the theoretical predicted value and the true value of the remaining mileage, and in order to ensure the accuracy of the estimated value of the remaining mileage, the theoretical confidence coefficient K (k) needs to be reduced, namely the Kalman filtering gain K (k) needs to be reduced; when R (k) is smaller, it indicates that there is a smaller error between the theoretical predicted value and the true value of the remaining mileage, the theoretical confidence K (k) may be increased, that is, the Kalman filtering gain K (k) may be increased. Thus, the Kalman filter gain K (k) is proportional to X (k) and inversely proportional to R (k).
In one embodiment, a schematic of the variance of the ideal prediction error as a function of the state of energy of the battery is provided, as shown in FIG. 2. By presetting a lookup table and recording the mapping relation between a plurality of SOC values and ideal prediction error variance in the table, the residual electric energy Q of the battery can be obtainedbat(k) Looking up the table to obtain specific values of X (k), wherein the table look-up result can be XSOCAnd (4) showing. Because the ideal predicted value of the remaining mileage has the advantage of smooth decline, and the theoretical predicted value can be closer to the actual driving range, when the SOC value of the power battery is higher, the ideal confidence of the ideal predicted value can be improved in order to ensure the smooth change of the estimated value of the remaining mileage, and when the ideal confidence is higher, the corresponding ideal prediction error variance X (k) is required to be smaller; when the battery SOC value is low, in order to avoid deviation of the remaining mileage estimation value, which is not beneficial to user trip planning, the theoretical confidence of the theoretical prediction value can be improved, and when the theoretical confidence is high, the corresponding theoretical prediction error variance needs to be small, i.e. the ideal prediction error variance x (k) is large. Therefore, the Q of the power batterybat(k) The value is inversely related to the ideal prediction error variance x (k).
In another embodiment, a graphical representation of the variance of the ideal prediction error over time is provided, as shown in FIG. 3. In order to prevent the remaining mileage estimation value displayed on the meter from suddenly changing for a certain period of time after the vehicle starts to run, the initial time may be setThe ideal prediction error variance X (0) is set to a small value and maintained at that value for a period of time, followed by a gradual transition to XSOCThe influence of the theoretical predicted value on the residual mileage estimated value is reduced, and fluctuation is reduced.
In another specific embodiment, the system state equation can be established according to the calculation formula (1) of the ideal predicted value, the observation equation can be established according to the calculation formula (2) of the theoretical predicted value, and the equation set can be obtained
Wherein wkFor systematic equation of state error, vkTo observe the equation error. The calculation formula of the ideal predicted value of the remaining mileage at the current moment is Mest(k|k-1)＝Mest(k-1|k-1)-V(k)ΔTk，
Wherein M isest(k | k-1) is an ideal predicted value of the remaining mileage at the current moment, Mest(k-1| k-1) is the last-time remaining mileage estimation value. The covariance of the error between the ideal predicted value of the remaining mileage and the true value of the remaining mileage at the current time can be calculated by the following formula:
P(k|k-1)＝P(k-1|k-1)+X(k)，
wherein P (k | k-1) is the covariance of the error between the ideal predicted value and the actual value of the remaining mileage at the current moment, P (k-1| k-1) is the covariance of the error between the estimated value of the remaining mileage and the actual value of the remaining mileage at the previous moment, and X (k) is the variance of the error between the ideal predicted value and the actual value of the remaining mileage at the current moment. Further, the Kalman filter gain at the present time may be calculated as
Wherein, k (k) is the kalman filtering gain at the current moment, and r (k) is the variance of the error between the theoretical predicted value and the true value of the remaining mileage at the current moment. 1-K (k) may be taken as the ideal confidence of the ideal predicted value, and K (k) may be taken as the theoretical confidence of the theoretical predicted value.
And S130, weighting the ideal predicted value and the theoretical predicted value according to the ideal confidence coefficient and the theoretical confidence coefficient to obtain a remaining mileage estimated value.
In specific implementation, after the Kalman filtering gain at the current moment is obtained through calculation, 1-K (k) is taken as an ideal confidence coefficient of an ideal predicted value, K (k) is taken as a theoretical confidence coefficient of a theoretical predicted value, and a remaining mileage estimated value at the current moment can be calculated as Mest(k|k)＝(1-K(k))Mest(k|k-1)+K(k)Mthe(k)，
The above formula is transformed, and the calculation formula of the estimated value of the remaining mileage at the current moment is Mest(k|k)＝Mest(k|k-1)+K(k)(Mthe(k)-Mest(k|k-1))，
Wherein M isestThe (k | k) is an estimated value of the remaining mileage at the present time, and can be output and applied to functions such as instrument display. After the calculated estimated value of the remaining mileage at the current moment, the covariance of the error between the estimated value of the remaining mileage and the actual value of the remaining mileage can be updated to obtain the covariance of the error between the estimated value of the remaining mileage and the actual value of the remaining mileage at the current moment, which is used for the iteration of the next moment, and the specific formula is P (k | k) ═ 1 (1-k (k))) P (k | k-1),
wherein P (k | k) is the covariance of the error between the estimated value of the remaining mileage and the true value of the remaining mileage at the present moment.
According to the method for estimating the remaining mileage of the electric vehicle, the smoothly-descending ideal predicted value and the theoretical predicted value closer to the actual driving range can be obtained by obtaining the predicted value of the remaining mileage of the electric vehicle, the ideal confidence coefficient and the theoretical confidence coefficient are determined according to the battery energy state by obtaining the battery energy state, data fusion can be carried out on the ideal predicted value and the theoretical predicted value according to the ideal confidence coefficient and the theoretical confidence coefficient, the ideal predicted value and the theoretical predicted value are weighted according to the ideal confidence coefficient and the theoretical confidence coefficient, the estimated value of the remaining mileage of the electric vehicle is obtained, and the accuracy and the stability of the estimation of the remaining mileage of the electric vehicle can be improved.
To facilitate understanding by those skilled in the art, another flow of the electric vehicle remaining range estimation method is provided as shown in fig. 4. Establishing a system state equation according to a calculation formula (1) of an ideal predicted value, establishing an observation equation according to a calculation formula (2) of a theoretical predicted value to obtain an equation set
Wherein wkFor systematic equation of state error, vkTo observe the equation error. Based on the system state equation and the observation equation, the following steps can be adopted to estimate the remaining mileage of the pure electric vehicle.
Firstly, calculating the predicted value of the remaining mileage at the current moment by using a formula Mest(k|k-1)＝Mest(k-1|k-1)-V(k)ΔTk，
Wherein M isest(k | k-1) is the predicted value of the remaining mileage at the current moment, Mest(k-1| k-1) is the last-time remaining mileage estimation value.
Secondly, calculating the covariance of the error between the predicted value and the true value of the remaining mileage, wherein the calculation formula is P (k | k-1) ═ P (k-1| k-1) + X (k),
wherein P (k | k-1) is the covariance of the error between the predicted value of the remaining mileage and the actual value of the remaining mileage at the current moment, P (k-1| k-1) is the covariance of the error between the estimated value of the remaining mileage and the actual value of the remaining mileage at the previous moment, and X (k) is the variance of the error between the ideal predicted value and the actual value of the remaining mileage at the current moment.
Thirdly, calculating Kalman gain according to the formula
Wherein K (k) is the Kalman filtering gain at the current moment, and R (k) is the variance of the error between the theoretical predicted value and the true value of the remaining mileage at the current moment.
Fourthly, calculating the estimated value of the remaining mileage at the current moment by the formula
Mest(k|k)＝Mest(k|k-1)+K(k)(Mthe(k)-Mest(k|k-1))，
Wherein M isestAnd (k | k) is the estimated value of the remaining mileage at the current time. And the residual mileage estimated value is used as output and is applied to functions such as instrument display and the like.
And fifthly, updating the covariance of the error between the estimated value of the remaining mileage and the true value of the remaining mileage at the current moment for the next iteration, wherein the calculation formula is P (k | k) ═ 1-K (k))) P (k | k-1),
wherein P (k | k) is the covariance of the error between the estimated value of the remaining mileage and the true value of the remaining mileage at the current time.
In an embodiment, the step S120 may specifically include: determining a prediction error variance of the predicted value of the remaining mileage according to the energy state of the battery; the prediction error variance comprises an ideal prediction error variance corresponding to the ideal prediction value; obtaining the prediction error covariance of the ideal predicted value according to the ideal prediction error variance; calculating the filtering gain of an ideal predicted value and a theoretical predicted value according to the prediction error covariance; and obtaining the ideal confidence coefficient of the ideal predicted value and the theoretical confidence coefficient of the theoretical predicted value according to the filtering gain.
The prediction error variance is the variance of the error between the predicted value and the true value of the remaining mileage, and the ideal prediction error variance is the variance of the error between the ideal predicted value and the true value of the remaining mileage; the prediction error covariance of the ideal predicted value is the covariance of the error between the ideal predicted value and the true value of the remaining mileage; the filter gain may be a kalman filter gain.
In specific implementation, the pure electric vehicle can determine the variance x (k) of the error between the ideal predicted value and the remaining mileage real value at the current moment according to the battery energy state, and record the variance x (k) as the ideal predicted error variance, and the variance r (k) of the error between the theoretical predicted value and the remaining mileage real value at the current moment as the theoretical predicted error variance. The controller acquires the covariance P (k-1| k-1) of the error between the estimated value of the remaining mileage and the true value of the remaining mileage in advance at the previous moment, and sums the covariance P (k-1| k-1) and the ideal prediction error variance X (k) to obtain the covariance P (k | k-1) of the error between the ideal predicted value and the true value of the remaining mileage at the current moment. According to the covariance P (k | k-1) and the theoretical prediction error variance R (k), the Kalman filtering gain K (k) at the current moment can be calculated, and the ideal confidence coefficient of the ideal predicted value and the theoretical confidence coefficient of the theoretical predicted value can be obtained through the Kalman filtering gain. In the method, by adjusting x (k) and r (k), the kalman filter gain k (k) can be adjusted, and further the degree of influence of the ideal predicted value and the theoretical predicted value of the remaining mileage on the final remaining mileage estimated value can be adjusted.
In practical application, a look-up table is preset, and the mapping relation between a plurality of SOC values and ideal prediction error variance is recorded in the table, so that the residual electric energy Q of the battery can be determinedbat(k) Looking up the table to obtain specific values of X (k), wherein the table look-up result can be XSOCAnd (4) showing. Because the ideal predicted value of the remaining mileage has the advantage of smooth decline, and the theoretical predicted value can be closer to the actual driving range, when the SOC value of the power battery is higher, the ideal confidence of the ideal predicted value can be improved in order to ensure the smooth change of the estimated value of the remaining mileage, and when the ideal confidence is higher, the corresponding ideal prediction error variance X (k) is required to be smaller; when the battery SOC value is low, in order to avoid deviation of the remaining mileage estimation value, which is not beneficial to user trip planning, the theoretical confidence of the theoretical prediction value can be improved, and when the theoretical confidence is high, the corresponding theoretical prediction error variance needs to be small, i.e. the ideal prediction error variance x (k) is large. Therefore, the Q of the power batterybat(k) The value is inversely related to the ideal prediction error variance x (k).
In the embodiment, the prediction error variance of the predicted value of the remaining mileage is determined according to the battery energy state, the prediction error covariance of the ideal predicted value is obtained according to the ideal prediction error variance, the filtering gain of the ideal predicted value and the filtering gain of the theoretical predicted value are calculated according to the prediction error covariance, the ideal confidence coefficient of the ideal predicted value and the theoretical confidence coefficient of the theoretical predicted value are obtained according to the filtering gain, the prediction error variance which is easier to set can be obtained according to the battery energy state, the filtering gain is obtained according to the prediction error variance, the ideal confidence coefficient and the theoretical confidence coefficient are adjusted to be more reasonable values according to the filtering gain, and the accuracy and the stability of the remaining mileage estimation are improved.
In an embodiment, the step S120 may further include: obtaining an estimation error covariance according to the filtering gain and the first covariance; and obtaining a second covariance according to the estimation error covariance and the ideal prediction error variance.
The first covariance is the prediction error covariance of the current moment, the second covariance is the prediction error covariance of the next moment, and the estimation error covariance is the covariance of the error between the estimated value of the remaining mileage and the actual value.
In a specific implementation, the filter gain may be a kalman filter gain k (k), the first covariance may be a covariance P (k | k-1) of an error between an ideal predicted value and a true value of the remaining mileage at the current time, and the covariance of the error between the estimated value and the actual value of the remaining mileage at the next time may be obtained according to the filter gain and the first covariance, where the specific formula is P (k | k) ═(1-k (k)) P (k | k-1),
wherein P (k | k) is the covariance of the error between the estimated value of the remaining mileage and the true value of the remaining mileage at the current time. The ideal prediction error variance at the next time may be X (k +1), and the prediction error covariance at the next time may be obtained from the estimation error covariance and the ideal prediction error variance, and may be represented by the following formula P (k +1| k) ═ P (k | k) + X (k +1),
wherein P (k +1| k) is the covariance of the error between the ideal predicted value and the actual value of the remaining mileage at the next moment, P (k | k) is the covariance of the error between the estimated value and the actual value of the remaining mileage at the current moment, and X (k +1) is the variance of the error between the ideal predicted value and the actual value of the remaining mileage at the next moment.
In the embodiment, the estimation error covariance is obtained according to the filter gain and the first covariance, the second covariance is obtained according to the estimation error covariance and the ideal prediction error covariance, the estimation error covariance can be updated in real time, the prediction error covariance is updated in real time, and the accuracy and the stability of the remaining mileage estimation are improved.
In an embodiment, the step S110 may specifically include: acquiring the running speed, the running time and the initial remaining mileage of the electric vehicle; obtaining the driving mileage of the electric vehicle according to the driving speed and the driving time; and obtaining an ideal predicted value of the remaining mileage according to the initial remaining mileage and the driving mileage.
The driving time is the time between two remaining mileage estimation moments, and the initial remaining mileage is the remaining mileage estimation value of the last remaining mileage estimation moment.
In a specific implementation, in order to obtain an ideal predicted value of the remaining mileage, an initial time may be preset, and the remaining mileage at the initial time may be obtained as an initial remaining mileage, for example, the vehicle starting time may be used as the initial time, marked as time 0, and the remaining mileage displayed by the vehicle interior instrument at the starting time may be used as the initial remaining mileage. After a period of travel time Δ TkThen, the driving speed of the vehicle in the driving time is obtained, for example, the driving speed in the driving time can be represented by V (k), wherein k ∈ [0, Δ T ∈ isk]The running time delta T of the pure electric vehicle can be calculated by performing integral operation on the running speed in the running time or multiplying the running time by the running speedkAnd subtracting the actual driving mileage from the initial remaining mileage to obtain an ideal predicted value of the remaining mileage.
In one embodiment, when the time step Δ T is greater than the threshold valuekWhen smaller, the ideal prediction value can be calculated using the following formula:
Mid(ΔTk)＝Mid(0)-V(k)ΔTk。
wherein M isid(ΔTk) Is DeltaTkIdeal predicted value of remaining mileage at that moment, Mid(0) For initial remaining range, V (k) is Δ TkThe vehicle speed at the moment.
In another embodiment, when the time step Δ T is greater than the threshold valuekWhen smaller, the following formula can also be used to calculate the ideal predicted value:
Mid(k)＝Mid(k-1)-V(k)ΔTk。
where k denotes the current time, k-1 denotes the previous time, Mid(k) Is an ideal predicted value of the remaining mileage at the current moment, Mid(k-1) is an ideal predicted value of the remaining mileage at the previous moment, V (k) is the vehicle speed at the current moment, and Delta TkThe time interval between time k and time k-1.
In the embodiment, the driving speed, the driving time and the initial remaining mileage of the electric vehicle are obtained, the driving mileage of the electric vehicle is obtained according to the driving speed and the driving time, the ideal predicted value of the remaining mileage is obtained according to the initial remaining mileage and the driving mileage, the ideal predicted value of the smoothly-reduced remaining mileage can be obtained, and the situation that the obtained theoretical predicted value of the remaining mileage is inaccurate due to estimation errors of an SOE result output by a power battery management system is avoided.
In an embodiment, the step S110 may further include: obtaining the residual electric quantity of the battery and the average electric quantity consumed by the unit mileage of the electric vehicle according to the energy state of the battery; and obtaining a theoretical predicted value of the remaining mileage according to the remaining electric quantity and the average electric quantity.
In the specific implementation, in order to obtain the theoretical predicted value of the remaining mileage, the total mileage and the total energy consumption of the vehicle from the initial time to the current time can be obtained, the average energy consumption of the unit mileage of the vehicle in the current running can be obtained by dividing the total energy consumption by the total mileage, and the theoretical predicted value of the remaining mileage can be obtained by dividing the remaining energy of the power battery of the pure electric vehicle by the average energy consumption of the unit mileage.
In one embodiment, the theoretical prediction value of the remaining mileage can be calculated by using the following formula:
wherein M isthe(k) Is a theoretical predicted value, Q, of the remaining mileage at the current momentbat(k) The residual energy of the battery at the current moment can be obtained by the battery management systemThe state of the power battery is estimated to obtain Qm(k) Average energy consumption per mileage for vehicle driving, e.g. Qm(k) May be the average energy consumption per kilometer.
In another embodiment, when the time step Δ T is greater than the threshold valuekWhen smaller, the following formula can be used to calculate the average energy consumption per mileage traveled by the vehicle:
wherein Q iscon(k) Total power consumption, Q, for a vehicle travelling from an initial time to a current timecon(k-1) Total Power consumption from the initial time to the previous time of the vehicle, Mh(k) Is the total driving range of the vehicle from the initial time to the current time, Mh(k-1) is the total driving range of the vehicle from the initial time to the previous time, Ubat(k) And Ibat(k) The voltage and the current of the power battery at the current moment are respectively.
In another embodiment, to avoid the calculated average energy consumption Qm(k) Frequently jittering for a period of time after the vehicle is powered on can be applied to Qm(k) Performing first-order lag filtering, wherein the specific filtering method can be expressed as formula
Qm_s(k)＝aQm(k)+(1-a)Qm_s(k-1)，
Wherein Q ism_s(k) For the average energy consumption per kilometer at the current moment after filtering, Qm_s(k-1) is the average energy consumption per kilometer at the last moment after filtering, a is a filter coefficient, and the value range is that a belongs to [0,1 ]]。
Wherein, the average energy consumption per kilometer after filtering is as initial value Qm_s(0) The value of the filter coefficient a is to ensure the average energy consumption Q per kilometer after the vehicle is electrified and drivenm_sNo sharp jitter occurs. The standard working conditions comprise NEDC, WLTP and CLTC.
In the embodiment, the residual electric quantity of the battery and the average electric quantity consumed by the mileage of the electric vehicle are obtained according to the energy state of the battery, and the theoretical predicted value of the residual mileage is obtained according to the residual electric quantity and the average electric quantity, so that the theoretical predicted value of the residual mileage closer to the actual driving range can be obtained, and the inaccuracy of estimating the residual mileage by using an ideal predicted value alone is avoided.
In an embodiment, the step S110 may further include: obtaining the total power consumption of the electric vehicle according to the energy state of the battery; the average electric quantity before filtering consumed by the unit mileage of the electric vehicle is obtained by dividing the total electric quantity consumed by the electric vehicle by the total mileage; and performing first-order lag filtering on the average electric quantity before filtering to obtain the average electric quantity consumed by the unit mileage of the electric vehicle.
In a specific implementation, the time step Δ TkWhen smaller, the following formula can be used to calculate the average energy consumption per mileage traveled by the vehicle:
wherein Q iscon(k) Is the total power consumption of the vehicle from the initial time to the current time, Qcon(k-1) Total Power consumption from the initial time to the previous time of the vehicle, Mh(k) The total mileage, M, of the vehicle from the initial time to the current timeh(k-1) is the total mileage, U, of the vehicle from the initial time to the previous timebat(k) And Ibat(k) The voltage and the current of the power battery at the current moment are respectively. To avoid calculated average energy consumption Qm(k) Frequently jittering for a period of time after the vehicle is powered on can be applied to Qm(k) Performing first-order lag filtering, wherein the specific filtering method can be expressed as formula
Qm_s(k)＝aQm(k)+(1-a)Qm_s(k-1)，
Wherein Q ism_s(k) For the average energy consumption per kilometer at the current moment after filtering, Qm_s(k-1) is the average energy consumption per kilometer at the last moment after filtering, a is a filter coefficient, and the value range is that a belongs to [0,1 ]]. It is composed ofIn, initial value Q of average energy consumption per kilometer after filteringm_s(0) The value of the filter coefficient a is to ensure the average energy consumption Q per kilometer after the vehicle is electrified and drivenm_sNo sharp jitter occurs. The standard working conditions comprise NEDC, WLTP and CLTC.
In this embodiment, the total power consumption of the electric vehicle is obtained according to the battery energy state, the average power consumption before filtering consumed by the unit mileage traveled by the electric vehicle is obtained by dividing the total power consumption by the total mileage of the electric vehicle, and the average power consumption consumed by the unit mileage traveled by the electric vehicle is obtained by performing first-order lag filtering on the average power consumption before filtering, so that the problem that the calculated average power consumption frequently jitters within a period of time after the vehicle is powered on and the estimation result of the remaining mileage is unstable when the vehicle just enters the driving state due to a small number of sample points of the power consumption can be avoided.
In an embodiment, the step S110 may further include: acquiring a time interval between the current moment and the last moment, and acquiring the voltage and the current of the battery at the current moment according to the energy state of the battery; obtaining interval power consumption corresponding to the time interval by multiplying the voltage, the current and the time interval; and adding the first total power consumption and the interval power consumption to obtain a second total power consumption.
The interval power consumption is the power consumption of the electric vehicle in the time interval of the previous moment and the current moment, the first total power consumption is the total power consumption of the previous moment, and the second total power consumption is the total power consumption of the current moment.
In specific implementation, the current time may be set to k, the previous time may be set to k-1, and a time interval Δ T between the current time and the previous time may be obtainedkObtaining the current voltage U of the battery according to the energy state of the batterybat(k) And current Ibat(k) The time interval Δ T can be obtained by multiplying the voltage, the current and the time intervalkInner interval power consumption Ubat(k)Ibat(k)ΔTkBy comparing the first total power consumption Q at the previous momentcon(k-1) and Interval Power consumption additionThe second total power consumption Q at the current moment can be obtainedcon(k) The concrete formula is
Qcon(k)＝Qcon(k-1)+Ubat(k)Ibat(k)ΔTk，
Wherein Q iscon(k) Total power consumption, Q, for a vehicle travelling from an initial time to a current timecon(k-1) Total Power consumption, U, from the initial time to the previous time of the vehiclebat(k) And Ibat(k) The voltage and the current of the power battery at the current moment are respectively.
In this embodiment, by obtaining the time interval between the current time and the previous time, and the voltage and the current of the battery at the current time, obtaining the interval power consumption corresponding to the time interval by multiplying the voltage, the current, and the time interval, and obtaining the second total power consumption by adding the first total power consumption and the interval power consumption, the total power consumption of the electric vehicle at the current time can be accurately obtained, and the accuracy of estimating the remaining mileage of the electric vehicle is improved.
It should be understood that although the various steps in the flowcharts of fig. 1 and 4 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 1 and 4 may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed in turn or alternately with other steps or at least some of the other steps.
In one embodiment, as shown in fig. 5, there is provided an electric vehicle remaining mileage estimation device 500 including: an input module 501, a confidence calculation module 502 and an estimation module 503, wherein:
an input module 501, configured to obtain a battery energy state and a predicted remaining mileage value of an electric vehicle; the predicted value of the remaining mileage comprises an ideal predicted value and a theoretical predicted value;
a confidence coefficient calculation module 502, configured to determine an ideal confidence coefficient of the ideal predicted value and a theoretical confidence coefficient of the theoretical predicted value according to the battery energy state;
and the estimation module 503 is configured to weight the ideal predicted value and the theoretical predicted value according to the ideal confidence coefficient and the theoretical confidence coefficient to obtain an estimated value of the remaining mileage.
In one embodiment, the confidence calculation module 502 is further configured to determine a prediction error variance of the predicted value of the remaining mileage according to the battery energy state; the prediction error variance comprises an ideal prediction error variance corresponding to the ideal prediction value; obtaining the prediction error covariance of the ideal predicted value according to the ideal prediction error variance; calculating the filtering gain of an ideal predicted value and a theoretical predicted value according to the prediction error covariance; and obtaining the ideal confidence coefficient of the ideal predicted value and the theoretical confidence coefficient of the theoretical predicted value according to the filtering gain.
In one embodiment, the confidence calculation module 502 is further configured to obtain an estimation error covariance according to the filter gain and the first covariance; the estimated error covariance corresponds to the next time instant; and obtaining a second covariance according to the estimation error covariance and the ideal prediction error variance.
In one embodiment, the input module 501 is further configured to obtain a driving speed, a driving time and an initial remaining mileage of the electric vehicle; obtaining the driving mileage of the electric vehicle according to the driving speed and the driving time; and obtaining an ideal predicted value of the remaining mileage according to the initial remaining mileage and the driving mileage.
In one embodiment, the input module 501 is further configured to obtain a remaining power of the battery and an average power consumed by the electric vehicle per mileage according to the energy state of the battery; and obtaining a theoretical predicted value of the remaining mileage according to the remaining electric quantity and the average electric quantity.
In one embodiment, the input module 501 is further configured to obtain a total power consumption of the electric vehicle according to the battery energy status; the average electric quantity before filtering consumed by the unit mileage of the electric vehicle is obtained by dividing the total electric quantity consumed by the electric vehicle by the total mileage; and performing first-order lag filtering on the average electric quantity before filtering to obtain the average electric quantity consumed by the unit mileage of the electric vehicle.
In one embodiment, the input module 501 is further configured to obtain a time interval between a current time and a previous time, and obtain a voltage and a current of the battery at the current time according to the battery energy state; obtaining interval power consumption corresponding to the time interval by multiplying the voltage, the current and the time interval; and adding the first total power consumption and the interval power consumption to obtain a second total power consumption.
For specific limitations of the electric vehicle remaining range estimation device, reference may be made to the above limitations of the electric vehicle remaining range estimation method, which will not be described herein again. The respective modules in the electric vehicle remaining range estimating apparatus described above may be wholly or partially implemented by software, hardware, and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in fig. 6. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing the remaining mileage estimation data of the electric vehicle. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement an electric vehicle remaining range estimation method.
Those skilled in the art will appreciate that the architecture shown in fig. 6 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program: acquiring a battery energy state and a predicted value of remaining mileage of the electric vehicle; the predicted value of the remaining mileage comprises an ideal predicted value and a theoretical predicted value; determining an ideal confidence coefficient of the ideal predicted value and a theoretical confidence coefficient of the theoretical predicted value according to the energy state of the battery; and weighting the ideal predicted value and the theoretical predicted value according to the ideal confidence coefficient and the theoretical confidence coefficient to obtain the estimated value of the remaining mileage.
In one embodiment, the processor, when executing the computer program, further performs the steps of: determining a prediction error variance of the predicted value of the remaining mileage according to the energy state of the battery; the prediction error variance comprises an ideal prediction error variance corresponding to the ideal prediction value; obtaining the prediction error covariance of the ideal predicted value according to the ideal prediction error variance; calculating the filtering gain of an ideal predicted value and a theoretical predicted value according to the prediction error covariance; and obtaining the ideal confidence coefficient of the ideal predicted value and the theoretical confidence coefficient of the theoretical predicted value according to the filtering gain.
In one embodiment, the processor, when executing the computer program, further performs the steps of: obtaining an estimation error covariance according to the filtering gain and the first covariance; the estimated error covariance corresponds to the next time instant; and obtaining a second covariance according to the estimation error covariance and the ideal prediction error variance.
In one embodiment, the processor, when executing the computer program, further performs the steps of: acquiring the running speed, the running time and the initial remaining mileage of the electric vehicle; obtaining the driving mileage of the electric vehicle according to the driving speed and the driving time; and obtaining an ideal predicted value of the remaining mileage according to the initial remaining mileage and the driving mileage.
In one embodiment, the processor, when executing the computer program, further performs the steps of: obtaining the residual electric quantity of the battery and the average electric quantity consumed by the unit mileage of the electric vehicle according to the energy state of the battery; and obtaining a theoretical predicted value of the remaining mileage according to the remaining electric quantity and the average electric quantity.
In one embodiment, the processor, when executing the computer program, further performs the steps of: obtaining the total power consumption of the electric vehicle according to the energy state of the battery; the average electric quantity before filtering consumed by the unit mileage of the electric vehicle is obtained by dividing the total electric quantity consumed by the electric vehicle by the total mileage; and performing first-order lag filtering on the average electric quantity before filtering to obtain the average electric quantity consumed by the unit mileage of the electric vehicle.
In one embodiment, the processor, when executing the computer program, further performs the steps of: acquiring a time interval between the current moment and the last moment, and acquiring the voltage and the current of the battery at the current moment according to the energy state of the battery; obtaining interval power consumption corresponding to the time interval by multiplying the voltage, the current and the time interval; and adding the first total power consumption and the interval power consumption to obtain a second total power consumption.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of: acquiring a battery energy state and a predicted value of remaining mileage of the electric vehicle; the predicted value of the remaining mileage comprises an ideal predicted value and a theoretical predicted value; determining an ideal confidence coefficient of the ideal predicted value and a theoretical confidence coefficient of the theoretical predicted value according to the energy state of the battery; and weighting the ideal predicted value and the theoretical predicted value according to the ideal confidence coefficient and the theoretical confidence coefficient to obtain the estimated value of the remaining mileage.
In one embodiment, the computer program when executed by the processor further performs the steps of: determining a prediction error variance of the predicted value of the remaining mileage according to the energy state of the battery; the prediction error variance comprises an ideal prediction error variance corresponding to the ideal prediction value; obtaining the prediction error covariance of the ideal predicted value according to the ideal prediction error variance; calculating the filtering gain of an ideal predicted value and a theoretical predicted value according to the prediction error covariance; and obtaining the ideal confidence coefficient of the ideal predicted value and the theoretical confidence coefficient of the theoretical predicted value according to the filtering gain.
In one embodiment, the computer program when executed by the processor further performs the steps of: obtaining an estimation error covariance according to the filtering gain and the first covariance; the estimated error covariance corresponds to the next time instant; and obtaining a second covariance according to the estimation error covariance and the ideal prediction error variance.
In one embodiment, the computer program when executed by the processor further performs the steps of: acquiring the running speed, the running time and the initial remaining mileage of the electric vehicle; obtaining the driving mileage of the electric vehicle according to the driving speed and the driving time; and obtaining an ideal predicted value of the remaining mileage according to the initial remaining mileage and the driving mileage.
In one embodiment, the computer program when executed by the processor further performs the steps of: obtaining the residual electric quantity of the battery and the average electric quantity consumed by the unit mileage of the electric vehicle according to the energy state of the battery; and obtaining a theoretical predicted value of the remaining mileage according to the remaining electric quantity and the average electric quantity.
In one embodiment, the computer program when executed by the processor further performs the steps of: obtaining the total power consumption of the electric vehicle according to the energy state of the battery; the average electric quantity before filtering consumed by the unit mileage of the electric vehicle is obtained by dividing the total electric quantity consumed by the electric vehicle by the total mileage; and performing first-order lag filtering on the average electric quantity before filtering to obtain the average electric quantity consumed by the unit mileage of the electric vehicle.
In one embodiment, the computer program when executed by the processor further performs the steps of: acquiring a time interval between the current moment and the last moment, and acquiring the voltage and the current of the battery at the current moment according to the energy state of the battery; obtaining interval power consumption corresponding to the time interval by multiplying the voltage, the current and the time interval; and adding the first total power consumption and the interval power consumption to obtain a second total power consumption.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. An electric vehicle remaining mileage estimation method, comprising:
acquiring a battery energy state and a residual mileage predicted value of the electric vehicle; the residual mileage predicted value comprises an ideal predicted value and a theoretical predicted value;
determining an ideal confidence coefficient of the ideal predicted value and a theoretical confidence coefficient of the theoretical predicted value according to the battery energy state; the method specifically comprises the following steps: determining a prediction error variance of the predicted value of the remaining mileage according to the battery energy state; the prediction error variance comprises an ideal prediction error variance corresponding to the ideal prediction value; obtaining the prediction error covariance of the ideal predicted value according to the ideal prediction error variance; calculating the filter gains of the ideal predicted value and the theoretical predicted value according to the prediction error covariance; obtaining an ideal confidence coefficient of the ideal predicted value and a theoretical confidence coefficient of the theoretical predicted value according to the filtering gain;
and weighting the ideal predicted value and the theoretical predicted value according to the ideal confidence coefficient and the theoretical confidence coefficient to obtain a remaining mileage estimated value.
2. The method of claim 1, wherein the battery energy state is a remaining electrical energy of a power battery of a pure electric vehicle.
3. The method of claim 1, wherein the prediction error covariance comprises a first covariance and a second covariance, the first covariance corresponding to a current time instant, the second covariance corresponding to a next time instant; the determining an ideal confidence degree of the ideal predicted value and a theoretical confidence degree of the theoretical predicted value according to the battery energy state further includes:
obtaining an estimation error covariance according to the filter gain and the first covariance; the estimation error covariance corresponds to the next time instant;
and obtaining the second covariance according to the estimation error covariance and the ideal prediction error variance.
4. The method of claim 1, wherein the obtaining the battery energy status and the predicted remaining range value for the electric vehicle comprises:
acquiring the running speed, the running time and the initial remaining mileage of the electric vehicle;
obtaining the driving mileage of the electric vehicle according to the driving speed and the driving time;
and obtaining the ideal predicted value of the remaining mileage according to the initial remaining mileage and the driving mileage.
5. The method of claim 1, wherein the obtaining the battery energy status and the predicted remaining range value for the electric vehicle further comprises:
obtaining the residual electric quantity of the battery and the average electric quantity consumed by the electric vehicle for driving a unit mileage according to the energy state of the battery;
and obtaining the theoretical predicted value of the remaining mileage according to the remaining electric quantity and the average electric quantity.
6. The method of claim 5, wherein the obtaining the remaining capacity of the battery and the average consumed capacity of the electric vehicle per mileage based on the battery energy status comprises:
obtaining the total power consumption of the electric vehicle according to the energy state of the battery;
obtaining the average electric quantity before filtering consumed by the electric vehicle for driving unit mileage by dividing the total electric consumption quantity by the total mileage of the electric vehicle;
and performing first-order lag filtering on the average electric quantity before filtering to obtain the average electric quantity consumed by the unit mileage of the electric vehicle.
7. The method of claim 6, wherein the total power consumption amount comprises a first total power consumption amount corresponding to a previous time and a second total power consumption amount corresponding to a current time; the obtaining of the total power consumption of the electric vehicle according to the battery energy state includes:
acquiring a time interval between the current moment and the last moment, and acquiring the voltage and the current of the battery at the current moment according to the energy state of the battery;
obtaining interval power consumption corresponding to the time interval by multiplying the voltage, the current and the time interval;
obtaining the second total power consumption amount by adding the first total power consumption amount and the interval power consumption amount.
8. An electric vehicle remaining mileage estimation device, comprising:
the input module is used for acquiring the battery energy state and the predicted value of the remaining mileage of the electric vehicle; the residual mileage predicted value comprises an ideal predicted value and a theoretical predicted value;
the confidence coefficient calculation module is used for determining an ideal confidence coefficient of the ideal predicted value and a theoretical confidence coefficient of the theoretical predicted value according to the battery energy state; the method specifically comprises the following steps: determining a prediction error variance of the predicted value of the remaining mileage according to the battery energy state; the prediction error variance comprises an ideal prediction error variance corresponding to the ideal prediction value; obtaining the prediction error covariance of the ideal predicted value according to the ideal prediction error variance; calculating the filter gains of the ideal predicted value and the theoretical predicted value according to the prediction error covariance; obtaining an ideal confidence coefficient of the ideal predicted value and a theoretical confidence coefficient of the theoretical predicted value according to the filtering gain;
and the estimation module is used for weighting the ideal predicted value and the theoretical predicted value according to the ideal confidence coefficient and the theoretical confidence coefficient to obtain a remaining mileage estimation value.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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| CN112860782A (en) * | 2021-02-07 | 2021-05-28 | 吉林大学 | Pure electric vehicle driving range estimation method based on big data analysis |
| CN114347854B (en) * | 2021-05-24 | 2024-05-17 | 长城汽车股份有限公司 | Method and device for determining driving range of electric vehicle, controller, medium and vehicle |
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