CN109556729B - Electric automobile battery management system capable of automatically correcting temperature and humidity along with use - Google Patents
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Abstract
In order to better control the battery temperature of the hybrid electric vehicle, the invention provides an electric vehicle battery management system capable of automatically correcting temperature and humidity along with use from the perspective of infrared temperature detection. The system solves the problem that the batteries of the hybrid electric vehicle are arranged in a distributed mode in the prior art, particularly the problem that the fuel auxiliary driving unit does not have real-time reference value for the temperature of the electric auxiliary driving unit on the four-wheel drive hybrid electric vehicle with the batteries arranged in a distributed mode, obtains correction parameters for correcting the control of the batteries by the battery management system through the digital processing of infrared images, and improves the working stability and service life of the batteries.
Description
Technical Field
The invention belongs to the technical field of automobile battery control, and particularly relates to an electric automobile battery management system capable of automatically correcting temperature and humidity along with use.
Background
At present, most of battery management systems for electric vehicles are arranged in a centralized structure at the front or the rear of the vehicle. However, with the development of electric vehicles, hybrid vehicles have been increasingly recognized to have advantages in terms of energy saving, driving convenience, and the like, compared to electric vehicles. For the driving system of the hybrid electric vehicle which is widely used at present, a fuel auxiliary driving unit (usually a gasoline engine or a diesel engine) and an electric auxiliary driving unit (usually a battery) are arranged, and the fuel auxiliary driving unit and the electric auxiliary driving unit cooperate with each other to drive the hybrid electric vehicle.
The hybrid vehicle requires the electric auxiliary driving unit to discharge large current when accelerating or climbing a slope, and requires the electric auxiliary driving unit to be rapidly charged to recover braking energy when decelerating or descending the slope, which requires the electric auxiliary driving unit to have excellent high-rate rapid charging and discharging characteristics and a relatively long service life. For example, chinese patent application No. CN201711441888.2 discloses a four-wheel drive hybrid system based on parallel connection of a super capacitor and a storage battery, which includes a four-wheel drive transmission system driven by an engine unit and a rear drive transmission system driven by a motor in an auxiliary manner, where the rear drive transmission system includes a motor drive unit and an auxiliary power drive unit provided in a matching manner; the auxiliary power driving unit comprises a storage battery unit and a super capacitor unit which are arranged in parallel; the battery unit provides electric energy for the motor driving unit; when the vehicle accelerates or climbs a slope, the super capacitor unit discharges to provide assistance to the motor drive unit; the electric motor drive unit charges the supercapacitor unit when the vehicle is decelerating or descending.
Meanwhile, in the currently used electric auxiliary driving unit mainly including a storage battery, the working temperature of the fuel auxiliary driving unit affects the charging speed and the working state of the battery in the process of driving the automobile to charge the battery. Therefore, in order to ensure normal charging and discharging of the battery in the automobile, the battery management system needs to perform correction according to the temperature of the fuel assist drive unit. In the prior art, a temperature sensor is usually adopted in the correction process to acquire the temperature of the battery management system and the ambient temperature, however, the temperature sensor has instantaneity, and the propagation of the temperature has a certain delay, especially when the automobile is in some working conditions (such as just starting, long-term low-gear slow speed, and the like), the temperature of the fuel auxiliary driving unit may change slowly due to slow conduction, so that the temperature acquisition process of the fuel auxiliary driving unit needs to be continuously corrected by the ambient temperature, which causes the working time of the temperature sensor at the approximate temperature to be longer, and the sensitivity of the temperature sensor to be easily reduced. In addition, a certain time is required for heat exchange between the ambient temperature of the automobile, the engine and the battery and the temperature of the engine and the battery, and an accurate and rapid model is difficult to be established between the temperature value detected by the temperature sensor and the battery working state such as how to control charging or discharging of the battery by the battery management system even after the ambient temperature is corrected.
In addition, similar problems exist with four-wheel drive hybrid electric vehicles.
Disclosure of Invention
In order to better control the battery temperature of the hybrid electric vehicle (the hybrid electric vehicle has a four-wheel drive hybrid electric vehicle with batteries distributed, in particular, the batteries distributed), the invention provides an electric vehicle battery management system automatically corrected along with the use of temperature and humidity from the perspective of infrared temperature detection, which comprises:
a detection parameter set obtaining module, configured to collect, through the temperature sensor, detection parameter sets of N distributively-arranged batteries at times t1, t2, t3, t4, t5, t6, t7, and t8, where each set corresponds to a position of each arranged battery, and each set includes: temperature T1 of fuel-assisted drive UnitFuelBattery temperature T1Electric powerAnd the ambient humidity WRing (C)Ambient temperature T1Ring (C)Wherein N is an integer greater than 5;
the system further comprises:
an infrared picture obtaining module for photographing and outputting the temperature T2 of the fuel auxiliary driving unit with respect to the N distributively arranged cells through the infrared detector during the time T1 to the time T8FuelBattery temperature T2Electric powerObtaining N infrared pictures;
an automatic correction module for when WRing (C)And when the temperature exceeds the preset humidity early warning value, performing multipoint temperature self-correction type correction on the temperature of the battery based on the detection parameter set and the infrared picture.
Further, the auto-calibration module comprises:
the algebraic submodule is used for carrying out algebraic representation on the N infrared pictures respectively so as to obtain N check values;
a geometric mean calculation submodule for calculating the battery temperature T1 of the N positions during the period from the time T1 to the time T8Electric powerThe geometric mean of (a);
and the corresponding correction submodule is used for correcting the N geometric mean values in a one-to-one correspondence mode according to each of the N check values.
Further, the algebraic submodule comprises:
a normalization processing unit for normalizing the temperature T2 of the fuel auxiliary driving unit corresponding to the detection parameter set corresponding to the position where one of the batteries is arranged and the infrared pictureFuelBattery temperature T2Electric powerUsing T1Ring (C)For T2FuelCarrying out normalization processing;
a digital processing unit, configured to perform compression conversion on the infrared picture, generate a color picture Img with a resolution of at least 256 × 256 pixels, and construct a blue picture Img2 with different grays, where the blue picture Img2 with different grays is a corresponding picture of the picture Img at different grays, and the grayscale value g of the blue picture Img2 is linearly expressed by a color space as:
g＝αrIr+αgIg+αbIb
wherein alpha isr≥0，αg≥0，αb≥0，αr+αg+αb＝1
In the formula of alphar，αg，αbFor the parameter to be determined, Ir，Ig，IbIs the color channel value of picture Img;
the following function was constructed:
N＝lg(1-p)×ming∑x，y∈I′(gx-gy-x，y)2
in the formula, x and y are pixel points, l' is the set of all pixels of the picture Img, and gx，gyThe gray values of x and y are respectively, x and y are Euclidean measurement of x and y pixel points of the color model space converted from the picture Img, and p is gx，gyAnd taking the reciprocal of p when the absolute value of p is greater than 1;
by pixel points x, y andx，ythe following objective function is set:
wherein, Δ gx，y＝gx-gyσ is a scale factor and is a preset value, gx，yRepresenting the gray value at the pixel point (x, y);
calculating the parameter alpha when the objective function E (g) is at the maximumr，αg，αb；
The blue picture obtained by processing the gray levels of the blue pictures with different gray levels through the GAUSS moving average satisfies the following distribution G (x, y, sigma), and the L function is constructed as follows:
L(x，y，σ，ρ)＝ρ·I(x，y)·G(x，y，σ)
wherein, (x, y) represents the pixel points of the blue image, the gray value of each pixel point is represented as the quotient between the gray value itself and the maximum module value max of E (g), ρ is the scaling empirical factor and is equal to α when the objective function E (g) is the maximum valuer，αg，αbI (x, y) is the color temperature of the blue picture;
establishing a contrast extension function, namely:
wherein c is a contrast extension center, the center is one of the pixel points expressed by the (x, y), and λ is a preset contrast extension slope and is equal to ρ/max; calculating the autocorrelation matrix of each pixel point of the blue picture by using a Harris matrix:
wherein x and y are pixel coordinates, and N is picture resolution, the contrast extension picture characteristic response function is as follows:
R(X，y，c)＝detA(x，y，fc)-k(traceA(x，y，fc))2
wherein k is a constant factor, the det () function represents a function for calculating the value of the determinant of the square matrix A, and the trace () function represents a function for solving the trace of the matrix;
and (x, y) is taken as a variable, the value of the constant integral of the function R during the period when x and y respectively change between 0 and 255 is calculated, the values are accumulated to obtain an accumulated sum, and the accumulated sum is taken as the characteristic value Rt of the infrared picture.
Further, the corresponding syndrome module includes an absolute value obtaining unit, configured to perform a difference between each of the N pairs of one-to-one corresponding check values and each of the N geometric mean values, respectively, to obtain an absolute value of the difference; when the absolute value of a certain position where the battery is set is larger than a preset threshold value, reducing the charging current for the position, and detecting the charging current to ensure that the value of the charging current is smaller than a preset charging current value; and conversely, when the absolute value of the position where the battery is set is smaller than the preset threshold value, increasing the charging current for the position, and detecting the charging current to ensure that the value of the charging current is larger than the preset charging current value.
The invention has the following beneficial effects:
the invention solves the problem that the hybrid electric vehicle is provided with batteries in a distributed mode in the prior art, in particular to a four-wheel drive hybrid electric vehicle provided with batteries in a distributed mode, the fuel auxiliary driving unit does not have real-time reference value for the temperature of the electric auxiliary driving unit, and the correction parameters for correcting the battery control of the battery management system are obtained through the digital processing of the infrared image, so that the working stability and the service life of the battery are improved.
Drawings
FIG. 1 is a block diagram of the system of the present invention.
Detailed Description
As shown in fig. 1, according to a preferred embodiment of the present invention, there is provided an electric vehicle battery management system automatically correcting temperature and humidity according to usage, including:
a detection parameter set obtaining module, configured to collect, through the temperature sensor, detection parameter sets of N distributively-arranged batteries at times t1, t2, t3, t4, t5, t6, t7, and t8, where each set corresponds to a position of each arranged battery, and each set includes: temperature T1 of fuel-assisted drive UnitFuelBattery temperature T1Electric powerAnd the ambient humidity WRing (C)Ambient temperature T1Ring (C)Wherein N is an integer greater than 5;
the system further comprises:
an infrared picture obtaining module for photographing and outputting the temperature T2 of the fuel auxiliary driving unit with respect to the N distributively arranged cells through the infrared detector during the time T1 to the time T8FuelBattery temperature T2Electric powerObtaining N infrared pictures;
an automatic correction module for when WRing (C)And when the temperature exceeds the preset humidity early warning value, performing multipoint temperature self-correction type correction on the temperature of the battery based on the detection parameter set and the infrared picture.
Preferably, the automatic correction module comprises:
the algebraic submodule is used for carrying out algebraic representation on the N infrared pictures respectively so as to obtain N check values;
a geometric mean calculation submodule for calculating the battery temperature T1 of the N positions during the period from the time T1 to the time T8Electric powerThe geometric mean of (a);
and the corresponding correction submodule is used for correcting the N geometric mean values in a one-to-one correspondence mode according to each of the N check values.
Preferably, the algebraic submodule comprises:
a normalization processing unit for normalizing the temperature T2 of the fuel auxiliary driving unit corresponding to the detection parameter set corresponding to the position where one of the batteries is arranged and the infrared pictureFuelBattery temperature T2Electric powerUsing T1Ring (C)For T2FuelCarrying out normalization processing;
a digital processing unit, configured to perform compression conversion on the infrared picture, generate a color picture Img with a resolution of at least 256 × 256 pixels, and construct a blue picture Img2 with different grays, where the blue picture Img2 with different grays is a corresponding picture of the picture Img at different grays, and the grayscale value g of the blue picture Img2 is linearly expressed by a color space as:
g＝αrIr+αgIg+αbIb
wherein alpha isr≥0，αg≥0，αb≥0，αr+αg+αb＝1
In the formula of alphar，αg，αbFor the parameter to be determined, Ir，Ig，IbIs the color channel value of picture Img;
the following function was constructed:
N＝lg(1-p)×ming∑x，y∈I′(gx-gy-x，y)
in the formula, x and y are pixel points, l' is the set of all pixels of the picture Img, and gx，gyThe gray values for x and y respectively,x，yconverting picture Img into Euclidean measurement of x, y pixel points of color model space, wherein p is gx，gyAnd taking the reciprocal of p when the absolute value of p is greater than 1;
by pixel points x, y andx，ythe following objective function is set:
wherein, Δ gx，y＝gx-gyσ is a scale factor and is a preset value, gx，yRepresenting the gray value at the pixel point (x, y);
calculating the parameter alpha when the objective function E (g) is at the maximumr，αg，αb；
The blue picture obtained by processing the gray levels of the blue pictures with different gray levels through the GAUSS moving average satisfies the following distribution G (x, y, sigma), and the L function is constructed as follows:
L(x，y，σ，ρ)＝ρ·I(x，y)·G(x，y，σ)
wherein, (x, y) represents the pixel points of the blue image, the gray value of each pixel point is represented as the quotient between the gray value itself and the maximum module value max of E (g), ρ is the scaling empirical factor and is equal to α when the objective function E (g) is the maximum valuer，αg，αbI' (x, y) is the color temperature of the blue picture;
establishing a contrast extension function, namely:
wherein c is a contrast extension center, the center is one of the pixel points expressed by the (x, y), and λ is a preset contrast extension slope and is equal to ρ/max; calculating the autocorrelation matrix of each pixel point of the blue picture by using a Harris matrix:
wherein x and y are pixel coordinates, and N is picture resolution, the contrast extension picture characteristic response function is as follows:
R(x，y，c)＝detA(x，y，fc)-k(traceA(x，y，fc))2
wherein k is a constant factor, the det () function represents a function for calculating the value of the determinant of the square matrix A, and the trace () function represents a function for solving the trace of the matrix;
and (x, y) is taken as a variable, the value of the constant integral of the function R during the period when x and y respectively change between 0 and 255 is calculated, the values are accumulated to obtain an accumulated sum, and the accumulated sum is taken as the characteristic value Rt of the infrared picture.
Preferably, the corresponding syndrome module includes an absolute value obtaining unit, configured to perform difference between each of the N pairs of one-to-one corresponding check values and each of the N geometric mean values, respectively, to obtain an absolute value of the difference; when the absolute value of a certain position where the battery is set is larger than a preset threshold value, reducing the charging current for the position, and detecting the charging current to ensure that the value of the charging current is smaller than a preset charging current value; and conversely, when the absolute value of the position where the battery is set is smaller than the preset threshold value, increasing the charging current for the position, and detecting the charging current to ensure that the value of the charging current is larger than the preset charging current value.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (1)
1. An electric vehicle battery management system capable of automatically correcting temperature and humidity along with use comprises a detection parameter set acquisition module, an infrared picture acquisition module and an automatic correction module;
a detection parameter set obtaining module, configured to acquire, through the temperature sensor, detection parameters of the N distributively-arranged batteries at times t1, t2, t3, t4, t5, t6, t7, and t8, respectively, to obtain N detection parameter sets that are one-to-one corresponding to positions of the N distributively-arranged batteries, where each set includes: temperature T1 of fuel-assisted drive UnitFuelBattery temperature T1Electric powerAnd the ambient humidity WRing (C)Ambient temperature T1Ring (C)Wherein N is an integer greater than 5;
an infrared picture obtaining module for photographing and outputting the temperature T2 of the fuel auxiliary driving unit with respect to the N distributively arranged cells through the infrared detector during the time T1 to the time T8FuelBattery temperature T2Electric powerObtaining N infrared pictures;
an automatic correction module for when WRing (C)When the temperature of the battery exceeds a preset humidity early warning value, performing multi-point temperature self-correction type correction on the temperature of the battery based on the detection parameter set and the infrared picture;
the automatic correction module comprises an algebraic submodule, a geometric mean calculation submodule and a corresponding correction submodule;
the algebraic submodule is used for carrying out algebraic representation on the N infrared pictures respectively so as to obtain N check values;
a geometric mean calculation submodule for calculating the cell temperatures T1 of the N distributively arranged cells during the period from the time T1 to the time T8Electric powerObtaining N geometric mean values;
the corresponding correction submodule is used for correcting the N geometric mean values in a one-to-one correspondence mode according to each of the N check values;
the algebraic submodule comprises a normalization processing unit and a numeralization processing unit,
a normalization processing unit for normalizing the temperature T2 of the fuel auxiliary driving unit corresponding to the detection parameter set corresponding to the position where one of the batteries is arranged and the infrared pictureFuelBattery temperature T2Electric powerUsing T1Ring (C)For T2FuelCarrying out normalization processing;
a digital processing unit, configured to perform compression conversion on an infrared picture corresponding to a position where the certain battery is set, generate a color picture Img with a resolution of at least 256 × 256 pixels, and construct a first blue picture Img2 with different grays, where the first blue picture Img2 with different grays is a corresponding picture of the picture Img at different grays, and a grayscale value g of the first blue picture Img2 is linearly expressed by a color space:
g＝αrIr+αgIg+αbIb
wherein alpha isr≥0，αg≥0，αb≥0，αr+αg+αb＝1
In the formula of alphar，αg，αbFor the parameter to be determined, Ir，Ig，IbIs the color channel value of picture Img;
the following function was constructed:
in the formula, x and y are coordinates of pixel points (x and y) of the picture Img, I' is a set of all pixels of the picture Img, and gx，gyThe gray values of the pixel points (x, y) in the x direction and the y direction respectively,x，yconverting picture Img into Euclidean measurement of pixel point (x, y) of color model space, and p is gx，gyAnd taking the reciprocal of p when the absolute value of p is greater than 1;
by pixel points (x, y) andx，ythe following objective function is set:
wherein, Δ gx，y＝gx-gyσ is a scale factor and is a preset value;
calculating the parameter alpha when the objective function E (g) is at the maximumr，αg，αb；
The second blue picture obtained by processing the gray levels of the first blue picture Img2 with different gray levels through the GAUSS moving average meets the following distribution G (x)1,y1σ), and construct the L function as follows:
L(x1，y1，σ，ρ)＝ρ·I’(x1，y1)·G(x1，y1，σ)
in the formula (x)1，y1) Representing the pixels of the second blue picture, the gray value of each pixel is represented as the quotient between its own gray value and the maximum modulus max of E (g), ρ is a scaling empirical factor and is equal to α when the objective function E (g) is at the maximumr，αg，αbSum of squares, I' (x)1，y1) The color temperature of the second blue picture is obtained;
establishing a contrast extension function, namely:
wherein c is the center of contrast elongation and the center is the above (x)1，y1) Lambda is a preset contrast extension slope and is equal to rho/max; calculating the autocorrelation matrix of each pixel point of the second blue picture by using a Harris matrix:
wherein x1，y1If the pixel point coordinates of the second blue picture are obtained, the characteristic response function of the contrast extension picture is as follows:
R(x1，y1，c)＝detA(x1，y1，fc)-k(traceA(x1，y1，fc))2
wherein k is a constant factor, the det () function represents a function for calculating the value of the determinant of the autocorrelation matrix a, and the trace () function represents a function for solving the trace of the autocorrelation matrix;
with (x)1，y1) For variables, the constant integral of the function R is calculated at x1And y1Each at a value during a period varying between 0 and 255, and integrating the function R at x1And y1Accumulating the values of the batteries in the changing period of 0-255 to obtain an accumulated sum, and taking the accumulated sum as a characteristic value Rt of the infrared picture corresponding to the position where one battery is arranged;
the corresponding syndrome module comprises an absolute value obtaining unit, a calculating unit and a calculating unit, wherein the absolute value obtaining unit is used for respectively calculating the geometric mean values of the N check values and the N check values in a one-to-one correspondence manner to obtain the absolute value of the difference value; when the absolute value of a certain position where the battery is set is larger than a preset threshold value, reducing the charging current of the battery at the position where the absolute value is larger than the preset threshold value, and detecting the charging current to ensure that the value of the charging current is smaller than a preset charging current value; and conversely, when the absolute value of a certain position where the battery is set is smaller than the preset threshold, increasing the charging current of the battery at the position corresponding to the position where the absolute value is smaller than the preset threshold, and detecting the charging current to ensure that the value of the charging current is larger than the preset charging current value.
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