CN102195101B - Power battery management system and method thereof - Google Patents
Power battery management system and method thereof Download PDFInfo
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- CN102195101B CN102195101B CN201010219571.6A CN201010219571A CN102195101B CN 102195101 B CN102195101 B CN 102195101B CN 201010219571 A CN201010219571 A CN 201010219571A CN 102195101 B CN102195101 B CN 102195101B
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Abstract
The invention relates to an electrical design field and discloses a power battery management system and a method thereof. Estimation algorithm of cell state of charge (SOC), a heat management technology, a safety management and other new technologies are researched and implemented. The system in the invention comprises a safety management subsystem, a heat management subsystem, an optimization subsystem and the like. The safety management subsystem is used for monitoring charged states of cell monomers; determining whether the cell monomers are in discharge end states according to charged states of cell monomers and terminating cell discharges if the cell monomers are in discharge end states. The heat management subsystem is used for detecting temperatures of cell monomer surfaces; determining upper limits of temperature ranges in cell monomers according to temperature history information of cell monomer surfaces; determining temperature distribution information of a cell box consisting of all cell monomers; controlling an operating temperature of the battery according to temperature distribution information so that the operating temperature is in a range of preset temperatures. The optimization subsystem is respectively connected with the safety management subsystem and the heat management subsystem, and is used for controlling working states of the safety management subsystem and the heat management subsystem. In the invention, the safety management, the heat management and the like are applied in a management field of a power battery, which can improve safety performance, prolong service life, raise usage efficiency, give fully play to cell potential and guarantee the safety of using.
Description
Technical field
The present invention relates to electrical design field, relate in particular to a kind of power battery pack management system and method.
Background technology
Along with highlighting of energy problem, electric automobile becomes a kind of new environment-friendly type power tool.And power battery pack is a key components and parts of electric automobile, the quality of its performance directly affects dynamic property and the economy of car load.Battery management system is the contact bridge between electrokinetic cell and car load, and setting battery management system can improve the Performance And Reliability of electrokinetic cell.
Battery management technology is the requisite technical guarantee of electric automobile power battery use procedure, to guaranteeing that battery is used safely, gives full play to performance, life-saving, the raising service efficiency of existing battery, thereby meet electric automobile safe and efficient work significant to promoting electric vehicle industrialization process.Battery management system is processed the problems such as measurement, prediction, state demonstration and comprehensive management of accumulator of electric car.
Battery management system is an important subsystem of EMS, and it processes the problems such as measurement, prediction, state demonstration and comprehensive management of accumulator of electric car.Battery management technology is the requisite technical guarantee of electric automobile power battery use procedure, to guaranteeing that battery is used safely, gives full play to performance, life-saving, the raising service efficiency of existing battery, thereby meet electric automobile safe and efficient work significant to promoting electric vehicle industrialization process.Battery management technology is a platform technology, and, as military field, aviation field etc., there is the application space of battery set management technology in the field of every use power battery pack.So the application space of battery set management technology is boundless, economic benefit and social agency's highly significant.
In battery management system, the dump energy of battery (characterizing SOC:State of Charge with state-of-charge SOC) estimates it is one of key technology.Due to battery SOC, be subject to the impact of the factors such as ambient temperature, self discharge, periodic duty number of times, battery pack inconsistency, the difficulty of accurately estimating is very large, therefore that application prior art is carried out the accuracy of SOC estimation is not high, can not effectively manage system.
Summary of the invention
The present invention's the first object is to provide a kind of power battery pack management system, and it to the management of automobile batteries group more effectively, reliably.
The present invention's the second object is to provide a kind of power battery group management method, and it to the management of automobile batteries group more effectively, reliably.
A kind of power battery pack management system that the embodiment of the present invention provides, comprise: security management subsystem, for monitoring the state-of-charge of each battery cell, according to the state-of-charge of described each battery cell, determine that described each battery cell, whether in discharge off state, if it is stops described battery discharge;
Thermal management subsystem, for surveying the temperature on described each battery cell surface, according to the temperature history information on described each battery cell surface, determine the temperature upper limit of described each battery cell inside, determine the temperature distribution information of the battery case being formed by all described battery cells, according to described temperature distribution information, control the working temperature of described battery pack, described working temperature is in the temperature range of setting;
Optimize subsystem subsystem, be electrically connected to described security management subsystem, thermal management subsystem respectively, for controlling the work of described thermal management subsystem, security management subsystem.
Alternatively, described security management subsystem comprises:
Initialization module, state-of-charge initial value SOC0, the predicting covariance battle array initial value P of each battery cell described in determining for initialization
0, measuring noise square difference Q, filter and amplification multiple α;
Sampling module, for for k=1, each sampling instant of 2..., obtains the measurement noise v that intends each battery cell of sampling instant
k, current signal I
k;
The state-of-charge estimation block of battery, is electrically connected to described initialization module, sampling module respectively, for determining the state-of-charge of described each battery cell:
The state-of-charge equation of battery: make x
k=SOC
k, wherein said k is natural number,
According to following formula, determine the load voltage amount y (k) of described each battery cell:
Y (k)=f (I
k, x
k)+v
k=K
0-RI
k-K
1/ x
k-K
2x
k+ K
3ln (x
k)+K
4ln (1-x
k)+v
k, wherein, v
kfor measurement noise, described v
kvariance be Q,
Order measures battle array
The Kalman filter equation of the state-of-charge of described each battery cell prediction is:
The state-of-charge prediction of described each battery cell
Calculate described measurement battle array: C
k=K
1/ (x
k)
2-K
2+ K
3/ x
3-K
4/ (1-x
k),
Determine filter gain:
Determine prediction mean square deviation: P
k/k-1=P
k-1/k-1,
Determine and estimate mean square deviation: P
k/k=(I-K
kc
k) P
k/k-1,
Obtain the estimated value of the state-of-charge of described each battery cell: x
k/k=x
k/k-1+ aK
k[y
k-f (I
k, x
k/k-1)];
Electric discharge, by module, is electrically connected to the state-of-charge estimation block of described each battery cell, for make described battery cut-off electric discharge according to the estimated value of the state-of-charge of described each battery cell.
Alternatively, described thermal management subsystem comprises: temperature sensor, is arranged in the battery case for accommodating described each battery cell, for surveying the temperature on described each battery cell surface;
Memory module, is electrically connected to described temperature sensor, for storing the temperature history information on described each battery cell surface;
Monomer internal temperature upper limit determination module, is electrically connected to respectively with described temperature sensor and memory module, for according to the current temperature of described each battery cell and temperature history information, determines the temperature upper limit of described each battery cell inside;
Temperature Distribution analysis module, is electrically connected to described monomer internal temperature upper limit determination module, for according to the temperature upper limit of described each battery cell inside, determines the temperature distribution information of the battery case consisting of all described battery cells;
Temperature control modules, is electrically connected to described Temperature Distribution analysis module, for control the work of the heat dissipation equipment of described each battery case according to described temperature distribution information, make described each battery cell respectively working temperature be in the temperature range of setting;
Heat dissipation equipment, is arranged in described each battery case.
Alternatively, described heat dissipation equipment is blower fan.
Alternatively, described temperature control modules is also electrically connected with a display unit,
Described display unit is for showing control information on described temperature control modules and the warm distributed intelligence in each battery case.
Alternatively, described optimization subsystem adopts embedded system development to form.
A kind of power battery group management method that the embodiment of the present invention provides, comprising:
Initial value SOC0, the predicting covariance battle array initial value P of the state-of-charge of each battery cell determined in initialization
0, measuring noise square difference Q, filter and amplification multiple α;
For k=1, each sampling instant such as 2..., obtains the measurement noise v that intends each battery cell of sampling instant
k, current signal I
k;
Make x
k=SOC
k, according to the state-of-charge equation of each battery cell:
The state-of-charge of determining described each battery cell, wherein said k is natural number,
According to formula:
Y (k)=f (I
k, x
k)+v
k=K
0-RI
k-K
1/ x
k-K
2x
k+ K
3ln (x
k)+K
4ln (1-x
k)+v
k, the load voltage amount y (k) of each battery cell described in determining, wherein, v
kfor measurement noise, described v
kvariance be Q,
Order measures battle array C
k:
), according to Kalman filter equation:
Calculate described measurement battle array: C
k=K
1/ (x
k)
2-K
2+ K
3/ x
3-K
4/ (1-x
k);
Determine filter gain:
Determine prediction mean square deviation: P
k/k-1=P
k-1/k-1;
Determine and estimate mean square deviation: P
k/k=(I-K
kc
k) P
k/k-1;
Determine the estimated value of the state-of-charge of described each battery cell: x
k/k=x
k/k-1+ aK
k[y
k-f (I
k, x
k/k-1)];
According to the estimated value of the state-of-charge of described each battery cell, make described each battery cell cut-off electric discharge.
Alternatively, according to the estimated value of the state-of-charge of described each battery cell, make described each battery cell cut-off electric discharge, comprise the following steps:
1) initialization: the fixedly final voltage threshold value V of given each battery cell
0;
The threshold value WT of given load voltage signal extreme point after wavelet transformation
valve;
2), according to the estimated value of the state-of-charge of described each battery cell, obtain the load voltage signal V (t) of each battery cell;
3) judge that whether V (t) is lower than threshold value V
0, as V (t) < V
0time perform step 4), otherwise return to step 2);
4) V (t) is carried out to wavelet transformation by following formula:
}；
Wherein t is limit of integration, for the initial time that starts to carry out wavelet analysis is to the region between current sampling instant;
Above formula is abbreviated as: WT
ax (t)=x (t) * Ψ
a(t), wherein x (t)=V (t) a is scale factor, a=2
j, j is natural number, its scope is 2～5,
θ (t) is Gauss's lowpass function,
5) obtain the extreme point of figure signal: WT
max;
6) judgement WT
maxwhether surpass threshold value WT
valve, WT
valvespan is 0～2, works as WT
max> WT
valvetime, described battery cell stops electric discharge, otherwise returns to step 2).Therefore, the technical program is by battery SOC algorithm for estimating, according to the state-of-charge of each battery cell, the electric discharge of each battery cell is controlled, prevent that electric discharge excessively, by battery cell surface temperature and historical information, estimate inside battery maximum temperature simultaneously, analyze the characteristic distributions in temperature field in battery case, be conducive to optimize quantity and the position of battery case inner sensor, and control the work of the heat dissipation equipment of electrokinetic cell so that each battery cell respectively working temperature be within the scope of the preference temperature of setting.By optimizing module, thermal management technology and safety management technology optimisation technique are combined, be conducive to solve better power battery pack security performance, life-saving, raising service efficiency, reach the target of giving full play to battery potential and ensuring safety and use.
Accompanying drawing explanation
Accompanying drawing described herein is used to provide a further understanding of the present invention, forms the application's a part, does not form inappropriate limitation of the present invention, in the accompanying drawings:
A kind of power battery pack management system structural representation that Fig. 1 provides for the embodiment of the present invention 1;
The structural representation of the security management subsystem 0 that Fig. 2 provides for the embodiment of the present invention 1;
The structural representation of a kind of power battery pack thermal management subsystem 1 in the power battery pack management system that Fig. 3 provides for the embodiment of the present invention 1;
The enforcement structural representation of a kind of power battery pack management system that Fig. 4 provides for the embodiment of the present invention 1 in power vehicle;
The structural representation that the air-cooled serial heat radiation of battery pack in a kind of power battery pack thermal management subsystem 1 in the power battery pack management system that Fig. 5 provides for inventive embodiments 1 arranges;
The structural representation that the air-cooled parallel heat radiation of battery pack in a kind of power battery pack thermal management subsystem 1 in the power battery pack management system that Fig. 6 provides for inventive embodiments 1 arranges;
The method of estimation schematic flow sheet of a kind of electric power storage electrical automobile battery SOC based on improved EKF that Fig. 7 provides for inventive embodiments 1;
Current signal I (t) the waveform schematic diagram of gained in the method for estimation implementation process of a kind of electric power storage electrical automobile battery SOC based on improved EKF that Fig. 8 provides for inventive embodiments 1;
Voltage signal V (t) the waveform schematic diagram of gained in the method for estimation implementation process of a kind of electric power storage electrical automobile battery SOC based on improved EKF that Fig. 9 provides for inventive embodiments 1;
Gained SOC estimated result waveform schematic diagram in when SOC exists initial error in the method for estimation implementation process of a kind of electric power storage electrical automobile battery SOC based on improved EKF that Figure 10 provides for inventive embodiments 1;
The SOC estimated result waveform schematic diagram that naturally discharges that in the method for estimation of a kind of electric power storage electrical automobile battery SOC based on improved EKF that Figure 11 provides for inventive embodiments 1, implementation process hypothesis exists;
The decision method schematic flow sheet of a kind of battery of electric vehicle discharge off state based on wavelet transformation that Figure 12 provides for inventive embodiments 1;
In the decision method implementation process of a kind of battery of electric vehicle discharge off state based on wavelet transformation that Figure 13 provides for inventive embodiments 1 when discharging current is 80A discharge voltage signal V (t) waveform schematic diagram;
Wavelet transformation result (yardstick a=8) the waveform schematic diagram of discharge voltage signal V (t) when discharging current is 80A in the decision method implementation process of a kind of battery of electric vehicle discharge off state based on wavelet transformation that Figure 14 provides for inventive embodiments 1;
Figure 15 is the battery management system frame diagram of method shown in Figure 13 in implementation process;
A kind of thermal management technology design cycle schematic diagram that Figure 16 provides for inventive embodiments 1.
Embodiment
Below in conjunction with accompanying drawing and specific embodiment, describe the present invention in detail, in this illustrative examples of the present invention and explanation, be used for explaining the present invention, but not as a limitation of the invention.
Embodiment 1:
The structural representation of a kind of power battery pack management system that Fig. 1 provides for the present embodiment, referring to diagram, this system comprises: security management subsystem 0, thermal management subsystem 1, optimize subsystem 2.Its operation principle is as follows:
For monitoring the electric current and voltage of battery, whether surpass restriction, if it is stop described battery discharge, thereby prevent battery over-discharge.In order to make full use of the energy content of battery, prevent again arbitrary battery over-discharge in battery pack, must be in good time and accurately to the judgement of discharging ending status of battery.Therefore prevent that battery over-discharge from being core technology, the decision method of discharge off state is the key of this research.
Thermal management subsystem 1, for controlling the working temperature of described battery pack, is in the temperature range of setting described working temperature.Battery pack will be given full play to good performance, and its working temperature must be limited to a smaller scope.For the battery using under high power discharge and hot conditions, the heat management of battery is particularly necessary.The research of heat management system mainly improves three aspects: by battery thermal model, the design of battery pack heat management system and battery heat dispersion and forms.
Optimize subsystem 2, be electrically connected to security management subsystem 0, thermal management subsystem 1 respectively, for management system software and hardware is optimized, control the work of thermal management subsystem 1, security management subsystem 0, improving the work of security management subsystem 0 and the work of thermal management subsystem 1 provides good coordination, promotes mutually.Be such as but not limited to improve: the accuracy of module voltage, current measurement, real-time, for SOC algorithm for estimating and the correlated performance research of battery provides accurate test environment; Employing, in application programming technology, improves the maintainability of system, is convenient to through engineering approaches and the industrialization of system.
Therefore, native system is in conjunction with security management subsystem 0 and thermal management subsystem 1, the technology such as safety management and heat management are applied to the management domain of electrokinetic cell, be conducive to solve better power battery pack security performance, life-saving, raising service efficiency, reach the target of giving full play to battery potential and ensuring safety and use.
The structural representation of the security management subsystem 0 in a kind of power battery pack management system that Fig. 2 provides for the present embodiment, referring to diagram, this system comprises: the state-of-charge estimation block 003 of initialization module 000, sampling module 001, battery, electric discharge are by module 004.Wherein,
Initialization module 000, sampling module 001 are electrically connected to the state-of-charge estimation block 003 of battery respectively, the parameter input of estimating as the state-of-charge estimation block 003 of each battery cell, electric discharge is connected with the state-of-charge estimation block 003 of battery by module 004.Its operation principle is as follows:
Initial value SOC0, the predicting covariance battle array initial value P of the state-of-charge (SOC) of each battery cell determined in initialization module 000 initialization
0, measuring noise square difference Q, filter and amplification multiple α.Sampling module 001 is for k=1, and the measurement noise v of Buddhist nun's sampling instant battery is obtained in each sampling instant of 2...
k, current signal I
k.The state-of-charge estimation block 003 of battery is for determining the estimated value of the state-of-charge of each battery cell.Electric discharge is discharged for make each battery cell end according to the estimated value of the state-of-charge of each battery cell by module 004.
The state-of-charge estimation block 003 of battery is determined and can be adopted following method to estimate the state-of-charge value of each battery cell:
The state-of-charge equation of battery: make x
k=SOC
k, wherein said k is natural number,
According to following formula, determine the load voltage amount y (k) of each battery cell:
Y (k)=f (I
k, x
k)+v
k=K
0-RI
k-K
1/ x
k-K
2x
k+ K
3ln (x
k)+K
4ln (1-x
k)+v
k,, wherein, v
kfor measurement noise, described v
kvariance be Q,
Order measures battle array
The Kalman filter equation of the state-of-charge of each battery list prediction is:
The state-of-charge prediction of each battery cell:
Calculate described measurement battle array: C
k=K
1/ (x
k)
2-K
2+ K
3/ x
3-K
4/ (1-x
k),
Determine filter gain:
Determine prediction mean square deviation: P
k/k-1=P
k-1/k-1,
Determine and estimate mean square deviation: P
k/k=(I-K
kc
k) P
k/k-1,
Obtain the estimated value of the state-of-charge of each battery cell: x
k/k=x
k/k-1+ aK
k[y
k-f (I
k, x
k/k-1)].
Fig. 3 has implemented the structural representation of a kind of thermal management subsystem 1 of providing for this, visible, this thermal management subsystem 1 mainly comprises with lower member: temperature sensor 301, memory module 302, monomer internal temperature upper limit determination module 303, Temperature Distribution analysis module 304, temperature control modules 305, heat dissipation equipment 306.Annexation is as follows:
Temperature sensor 301 is arranged in the battery case for accommodating each battery cell, memory module 302 is electrically connected to temperature sensor 301, monomer internal temperature upper limit determination module 303 is electrically connected to respectively with temperature sensor 301 and memory module 302, Temperature Distribution analysis module 304 is electrically connected to monomer internal temperature upper limit determination module 303, and temperature control modules 305 is electrically connected to Temperature Distribution analysis module 304.Its operation principle is as follows:
Be arranged on the temperature on temperature sensor 301 each battery cell surfaces of detection in battery case, and temperature is stored in memory module 302, therefore store the temperature history information on each battery cell surface in memory module 302, the current temperature of monomer internal temperature upper limit determination module 303 bases, and the temperature history information on each battery cell surface of storing in memory module 302 is determined the temperature upper limit (being maximum temperature value) of each battery cell inside, Temperature Distribution analysis module 304 is according to the temperature upper limit of described each battery cell inside, determine the temperature distribution information of the battery case being formed by all battery cells, temperature control modules 305 is controlled the work of the heat dissipation equipment 306 of described each battery case according to temperature distribution information, make each battery cell respectively working temperature be in the temperature range of setting.Wherein this heat dissipation equipment 306 can be, but not limited to as blower fan.
Temperature control modules 305 according to temperature distribution information, control described each battery case heat dissipation equipment 306 work mainly:
A: under battery low-temperature condition, the Measurement accuracy of battery temperature and monitoring;
B: effectively loose wind and ventilation when battery pack temperature is too high;
C: the Fast Heating under cryogenic conditions, battery pack can normally be worked;
D: available ventilation when pernicious gas produces;
E: guarantee being uniformly distributed of battery pack temperature field.
In the present embodiment, consider that temperature sensor likely lost efficacy, the quantity of the temperature sensor that each battery cell is corresponding can not very little, be at least two again.
Monitoring for the ease of user, can also on temperature control modules 305, connect a display unit 307, display unit 307 can distribute for the temperature in the control information in displays temperature control module 305 and each battery case, so that user monitoring improves user's use and experiences.
In addition, the temperature distribution information of the battery case that people can also form according to all battery cells when carrying out power battery pack design is optimized quantity and the position of battery case inner sensor, thereby by the heat management function of battery management system, combine with administrating system optimisation technique, make to make the Temperature Distribution of electrokinetic cell more even when design, be further conducive to guarantee work and the safe handling of power battery pack.
The enforcement structural representation of a kind of power battery pack management system that Fig. 4 provides for the present embodiment in power vehicle.
Shown in Figure 4, whole power battery pack management system is three layers of pyramid structure, be that data acquisition daughter board, data acquisition motherboard and CPU CPU are controlled by separate CPU layering, piecemeal, adopt secondary CAN field bus technique realize mutual information communication and with the information communication of car load turn-key system.System is by building distributed frame, and bottom adopts low side microprocessor and host computer selects the high-end microprocessor of 16 bit or DSP to solve, thereby guarantees system-computed ability, to evade the potential problem of the system of exempting from.
The exploitation of this power battery pack management system is preferably based on submersible system, and submersible systematically analyzes the strategy of SOC value estimation, has provided voltage acquisition and and the data such as temperature control.
Therefore, in power vehicle, according to mode shown in Fig. 4, this power battery pack management system is arranged in power vehicle, when realizing, whole system is three layers of pyramid structure, the bottom is detecting element, intermediate layer is each bottom CPU 102 and battery pack bottom monitoring unit 105, and intermediate layer is connected with the upper data processing equipment 106 of top layer by CAN-Bus109.In the thermal control of battery pack, realize the mode of layering, piecemeal control, realize modularization, blocking control, make the heat management of battery pack in order not disorderly, guarantee the validity of heat management and the convenience of safeguarding.
In addition, in the present embodiment, adopt secondary CAN field bus technique realize each bottom CPU 102 in intermediate layer and battery pack bottom monitoring unit 105 and upper data processing equipment 106 mutual information communication and with the information communication of car load turn-key system, guarantee its high speed information transmission, and made communication there is high noise immunity.
In addition, monitoring for the ease of user, can also on upper data processing equipment 106, connect a display unit 111, display unit 111 can be for showing control information on upper data processing equipment 106 and temperature conditions and the Voltage-output situation of each battery pack.Be convenient to user monitoring, improve user's use impression.
Further, in order to improve operability and the Man machine interaction of native system, can also on upper data processing equipment 106, connect a PC110, this PC110 can be connected with upper data processing equipment 106 by RS232 interface, and this PC110 is for arranging, monitor the work of described upper data processing equipment 106 for user.Improve the man-machine interaction of native system, be convenient to user management.
In the present embodiment, this heat dissipation equipment 306 can be existing various heat dissipation equipments 306, such as the cooling 3 kinds of modes of air cooling, liquid cooling and phase-change material.Air is cooling is plain mode, only need allow air flow through battery surface.Liquid cools is divided into direct contact and two kinds of modes of non-direct contact.Mineral oil can be used as direct contact heat transfer medium, and water or anti-icing fluid can be used as typical non-direct contact heat transfer medium.Liquid cooling must could be carried out battery by heat exchange facilities such as water jackets cooling, and this has reduced heat exchange efficiency to a certain extent.The factor analysis such as form, flow velocity, fluid density and thermal conductivity that heat transfer rate between battery wall and fluid media (medium) and fluid are mobile.
The major advantage of air cooling mode has: (1) is simple in structure, and weight is relatively little; (2) there is not the possibility of leakage; (3) energy available ventilation when pernicious gas produces; (4) cost is lower.Shortcoming is that between itself and battery wall, heat exchange coefficient is low, cooling, firing rate is slow.
The major advantage of liquid cooling mode has: between (1) and battery wall, heat exchange coefficient is high, cooling, firing rate is fast; (2) small volume.Major defect has: the possibility that has leakage; Weight is relatively large; Maintenance and maintenance are complicated; Need the parts such as water jacket, heat exchanger, structure relative complex.
The battery pack of parallel type hybrid dynamic electric motor car is as auxiliary power component, and service conditions is not very severe, adopts air cooling mode just may reach instructions for use; For pure electric automobile and serial type hybrid automobile, battery pack is as main power component, and heat-dissipating amount is very large, wants to obtain reasonable thermal management effect, can consider to adopt the mode of liquid cooling.
Temperature contrast between the interior different battery modules of battery case 107, the inconsistency that can aggravate the internal resistance of cell and capacity, if long time integration can cause part battery overcharge or overdischarge, and then affects life-span and the performance of battery, and causes potential safety hazard.Temperature contrast and the battery pack of battery case 107 interior battery modules are furnished with much relations, and generally, the battery in centre position easily accumulates heat, and the battery radiating condition at edge is quite a lot of.So when carrying out battery pack structure layout and heat dissipation design, guarantee the uniformity of battery pack heat radiation as far as possible.
The air cooling heat radiation of take comes as example, and draft type generally has serial and walks abreast two kinds, as shown in Figure 5 and Figure 6.
Under the draft type of serial shown in Fig. 5, cold air blows people from left side and blows out from right side.Air is constantly heated in flow process, so the cooling effect on right side is poorer than left side in figure, the interior battery pack temperature of battery case 107 from left to right raises successively, has the defect of non-uniform temperature.
Shown in Fig. 6, the air inlet of described blower fan is arranged on an end of each battery being arranged side by side, the other end of each battery being arranged side by side described in the air outlet of blower fan is arranged on, the inlet channel 401 that air inlet is wedge shape, inlet channel 401 progressively narrows down along air intake direction; Air outlet is the exhaust passage 402 of wedge shape, exhaust passage, described place 402 progressively broadens along air-out direction, the inlet channel 401 of this wedge shape, exhaust passage 402 are consistent the upper and lower pressure differential in gap between different battery modules substantially, have guaranteed to blow over different battery moulds and have adopted the parallel draft type shown in Fig. 5 that air mass flow is distributed more equably between battery module.
In battery management system, the dump energy of battery (characterizing SOC:State of Charge with state-of-charge SOC) estimates it is one of key technology.Due to battery SOC, be subject to the impact of the factors such as ambient temperature, self discharge, periodic duty number of times, battery pack inconsistency, the difficulty of accurately estimating is very large.
The advanced batteries management system of developing is at present by research and the enforcement of the new technologies such as battery SOC algorithm for estimating, thermal management technology and safety management, the parameter of bringing for cell degradation changes, the two filtering algorithms of research, when estimating SOC, battery capacity is estimated, and in conjunction with change in voltage, carried out the health status forecasting procedure research of battery.The discharging ending status of battery that the inventor proposes to combine based on wavelet transformation and SOC technology is judged and management system optimisation technique, for different environments for use and different types of battery, can judge adaptively discharge off state, improve electric discharge fail safe and give full play to again the energy content of battery, unhealthy battery early prediction technology.
The inventor studies in conjunction with battery thermal model by practice, the algorithm of inside battery maximum temperature is estimated in proposition by battery cell surface temperature and historical information, analyze the characteristic distributions in temperature field in battery case, optimize quantity and the position of battery case inner sensor, thereby the heat management function administrating system optimisation technique of battery management system is combined.
1,1 native system has been set safety management function:
The function of safety management comprises that whether the electric current and voltage of monitoring battery surpasses restriction, prevents battery over-discharge etc.In order to make full use of the energy content of battery, prevent again arbitrary battery over-discharge in battery pack, must be in good time and accurately to the judgement of discharging ending status of battery.
The method of the judgement discharge off state that current driving force battery is used mainly contains: fixedly final voltage method, discharge curve Slope Method and capacity accumulative.
Wherein, although fixedly final voltage method is simple, under different discharging currents, final discharging voltage is different, the standard that neither one is unified, and the discharging current of actual electrokinetic cell is change at random, there is no certain rule.If set unified final discharging voltage, for preventing battery overdischarge under any operating mode, this voltage must be tending towards conservative (higher), can affect making full use of of the energy content of battery.
Discharge curve Slope Method is used a kind of maximum methods while being constant-current discharge test.From battery discharge curve, can see in the battery discharge later stage, obviously there is the slope catastrophe point (being commonly called as " flex point ") of a discharge curve, the capacity that battery can be emitted after this flex point seldom, and the use in this electric discharge for battery and future is also very uneconomical, this flex point can be decided to be to the terminating point of battery discharge.Generally flex point is decided to be to its slope equals the point that 10 times of voltage-time curve plateau slopes are located now.Battery is when low discharging current, and the slope of the plateau of its voltage drop is very little, and in discharge curve, slope is the obvious and actual discharge off point wide apart in the plateau place of 10 times.And during heavy-current discharge, the slope of the plateau of its voltage drop is larger, in discharge curve, slope is the plateau place of 10 times, and it is very fast that voltage drop obtains, and surpassed the stop value of actual discharge.The impact that noise is measured in the calculating of discharge curve slope is simultaneously very large.
Capacity accumulative records the electric weight that battery is filled with and emits, and when in a charge and discharge cycles, both equate, think that battery discharge stops.This method constantly records discharging and recharging historical data of battery except need, also will carry out to capacity the corrections such as discharging current, cell degradation, self discharge, and process is complicated, generally seldom uses.
This inventor has proposed a kind of decision method of the discharging ending status of battery based on wavelet transformation, for different environments for use and different types of battery, can judge adaptively discharge off state, there is adaptivity, can on the basis that guarantees fail safe, give full play to battery efficiency, thereby making the SOC obtaining is an overall target, be conducive to further improve the reliability of the method, the method and SOC can be combined and carry out discharge off condition judgement.
1, the technology such as 2 heat managements
Battery pack will be given full play to good performance, and its working temperature must be limited to a smaller scope.For the battery using under high power discharge and hot conditions, the heat management of battery is particularly necessary.The research of thermal management subsystem 1 mainly improves three aspects: by battery thermal model, 1 design of battery pack thermal management subsystem and battery heat dispersion and forms.Wherein battery thermal model is research core, for thermal management subsystem 1 design and the improvement of battery heat dispersion provide theoretical foundation.About battery thermal model, foreign study mechanism has carried out more research, and domestic research work is in the starting stage.1979, U.S. Argonne National Laboratory set up Two-Dimensional Heat model for the first time; Battery was divided into kernel in 86 years and two area three-dimensional thermal models of shell are studied; University of Texas is considering inside battery material under anisotropic condition aspect heat conduction and is dynamically setting up the Two-Dimensional Heat model of lithium battery under industrial and mineral.Research about thermal model is gradually improved, and the design of carrying out thermal management subsystem 1 for this project lays the foundation.But also exist, battery is divided into kernel and two regions of shell, considers that inner other parts are as collector etc.; Most models all adopts Bernardi heat-dissipating speed, due to the difference of battery variety and model, need to study the applicability of Bernardi heat-dissipating speed etc.Therefore need to study in conjunction with thermal model, to battery case interior flow field and battery heat-dissipating analysis of heat transfer, the method of the inner maximum temperature of battery pack is estimated in research by battery cell surface temperature in battery pack and historical information, grasp the characteristic distributions in temperature field in battery case, optimize quantity and the position of temperature sensor in battery case, improve the heat management level of battery management system.
Battery pack thermal management subsystem 1 of the present invention is from user's angle, is used for guaranteeing that battery pack is operated in the whole system of preference temperature scope, comprises the parts such as battery case, heat transfer medium, monitoring equipment.The feature of battery pack thermal management subsystem 1 and major function:
Under a, battery low-temperature condition, the Measurement accuracy of battery temperature and monitoring;
Effectively loose wind and ventilation when b, battery pack temperature are too high;
Fast Heating under c, cryogenic conditions, battery pack can normally be worked; Available ventilation when d, pernicious gas produce;
Being uniformly distributed of e, assurance battery pack temperature field.
Adopt the good battery pack thermal management subsystem 1 of systematized method for designing design performance, the design of native system, on the Research foundation in early stage, sum up repeatedly the achievement of Systematic Design, consider temperature on the impact in battery performance and useful life to determine the selection of the optimum operating temperature range of battery, the prediction of battery thermal field accounting temperature, heat transfer medium, the key technologies such as selection of thermal management subsystem 1 heat radiation consequence devised, blower fan and point for measuring temperature.
In addition, management system software and hardware is optimized, improves module voltage, the accuracy of current measurement, real-time, for SOC algorithm for estimating and the correlated performance research of battery provides accurate test environment; Employing, in application programming technology, improves the maintainability of system, is convenient to through engineering approaches and the industrialization of system.
Therefore native system is attached most importance to the strong tracking Kalman filter method based on neural network model, its major technique innovative point is as follows:
(1), under the inhomogeneous condition of monomer characteristic, battery pack SOC estimates and the SOC algorithm for estimating based on strong tracking Kalman filter;
(2), battery discharge is proposed by condition judging method, raising electric discharge fail safe;
(3), battery system heat-dissipating is analyzed and management system optimisation technique.
The commercialization design of native system: Engineering Design, standard interface design, Light Electrical converter ic plate and the power stability compensating circuit feedback integrated circuit (IC) design etc. that comprise product.Make native system possess following features:
(1) communication and control interconnection;
(2) embedded μ C/OS and driving exploitation;
(3) SOC that location (RTLS) algorithm utilizes Kalman filtering to carry out battery pack in real time estimates;
(4) data-storage system adopts the method that embedded database and JAVAAapplet technology combine;
(5) miniaturization.
(6) low power dissipation design etc.
The result of study of native system based on battery cell, based on battery pack SOC estimation and the SOC algorithm for estimating based on strong tracking Kalman filter under the inhomogeneous condition of monomer characteristic, utilize Kalman filter to realize the Minimum Mean Square Error estimation of SOC, realized the estimation to battery charge state, and the error of initial value is had to very strong correcting action, noise is had to very strong inhibitory action, can improve the reliability of SOC algorithm, make algorithm for estimating there is stronger mutation status follow-up control, cell health state forecast is provided, be applicable to the violent actual condition of curent change, thereby meet electric automobile when work to battery security and give full play to the requirement of battery effective utilization, significant to promoting electric vehicle industrialization process.
In order to be further convenient to the public, system of the present invention is had to further connection, estimation technique and the implementation method of the storage battery charge state (SOC) of native system is below described:
From giving full play to the ability of battery and improving two angles of fail safe, the accurate estimation of SOC is the key factor that realizes battery-efficient management; The estimated accuracy of SOC is also the basis of carrying out EnergyPolicy research simultaneously, is one of key technology of electric automobile.The cell row electricity condition equation of this method based on Ah counting method, and the state space equation of the battery that forms of the measurement equation of cell load voltage, then with improved EKF equation, calculate to obtain the state-of-charge of battery.Fig. 7 is the method for estimation flow chart of the electric power storage electrical automobile battery SOC based on improved EKF, and shown in Figure 7, the method comprises the following steps:
Step 501: initialization: the initial value SOC of the SOC of given battery
0(be following x
0);
Given predicting covariance battle array initial value P
0;
Given measuring noise square difference Q:
Given filter and amplification multiple α;
For k=1,2..., waits each sampling instant, calculates as follows respectively:
Step 502: obtain the measurement noise v that intends sampling instant battery
k, current signal I
k;
Step 503: determine battery model: battery model is comprised of following state-of-charge equation and load voltage measurement equation:
The state-of-charge equation of battery: make x
k=SOC
k
The load voltage measurement equation of battery:
y(k)＝f(I
k，x
k)+v
k＝K
0-RI
k-K
1/x
k-K
2x
k+K
3ln(x
k)+K
4ln(1-x
k)+v
k，
Wherein, v
kfor measurement noise, its variance is Q.
Order measures battle array
) Kalman filter equation be status predication:
Calculate and measure battle array: C
k=K
1/ (x
k)
2-K
2+ K
3/ x
3-K
4/ (1-x
k);
Filter gain:
Prediction mean square deviation: P
k/k-1=P
k-1/k-1;
Estimate mean square deviation: P
k/k=(I-K
kc
k) P
k/k-1;
State estimation: x
k/k=x
k/k-1+ aK
k[y
k-f (I
k, x
k/k-1)];
For k=1,2..., waits each sampling instant, and the computational process of circulation 2～3 can obtain each SOC estimated value of battery constantly.
Gained SOC estimated result in when wherein, the current signal I (t) of experiment measuring gained, voltage signal V (t) SOC exist initial error in implementation process, suppose the SOC estimated result that naturally discharges that exists waveform schematic diagram respectively as shown in Fig. 8-11.
By above experimental results show that, method proposed by the invention has very strong adaptivity, for different environments for use and different types of battery the method, can estimate more exactly the SOC of battery, importantly insensitive for initial value error, can partly correct the error that electric discharge brings.Reached the object of expection.
(3) safety management technology (decision method of the discharging ending status of battery based on wavelet transformation).
Referring to Figure 12, safety management technology is that battery discharge is studied by condition judging method, improves electric discharge fail safe, in conjunction with SOC technology, and the discharging ending status of battery decision method that research is merged with SOC technology mutually based on wavelet transformation.The parameter of bringing for cell degradation changes, and the two filtering algorithms of research are estimated battery capacity, and in conjunction with change in voltage, carried out the health status forecasting procedure research of battery when estimating SOC.The discharging ending status of battery decision method that proposition is merged with SOC technology mutually based on wavelet transformation, can judge discharge off state adaptively for different environments for use and different types of battery, improves electric discharge fail safe and gives full play to again the energy content of battery., unhealthy battery early prediction technology
The decision method of the battery of electric vehicle discharge off state based on wavelet transformation relates to electric automobile intelligent information processing technology field.Fixedly final voltage method and discharge curve Slope Method carry out discharge off judgement in its comprehensive utilization, when load voltage starts wavelet analysis module during lower than fixing final voltage, utilize wavelet transformation to analyze the load voltage information of battery, term extracts the slope catastrophe point of voltage signal after level and smooth, just stops electric discharge when there is slope catastrophe point.This method has very strong adaptability, for different environments for use and dissimilar battery, all can adaptive judgement discharge off state, and can effectively overcome the impact of measurement noise simultaneously.
Referring to Figure 12, the following steps that the decision method of the battery of electric vehicle discharge off state based on wavelet transformation contains the central processing unit controlling run in battery management system:
Step 601: initialization: the fixedly final voltage threshold value V of given battery
0;
The threshold value WT of given load voltage signal extreme point after wavelet transformation
valve;
Step 602: the load voltage signal V (t) that obtains battery cell;
Step 603: judge that whether the load voltage signal V (t) of battery cell is lower than the threshold value V setting
0, as V (t) < V
0shi Ze carries out wavelet transformation, otherwise continues step 602;
Step 604: V (t) is carried out to wavelet transformation by following formula:
,
Wherein t is limit of integration, for the initial time that starts to carry out wavelet analysis is to the region between current sampling instant;
Above formula is abbreviated as: WT
ax (t)=x (t) * Ψ
a(t);
X (t)=V (t), a is scale factor, a=2
j, j is natural number, its scope is 2～5;
Wherein:
Wherein, θ (t) is Gauss's lowpass function,
Step 605: the extreme point that obtains figure signal: WT
max.
Step 606: judgement extreme point WT
maxwhether surpass the extreme point threshold value WT setting
valve, WT
valvescope is 0～2, works as WT
max> knows WT
valveas time perform step 607: battery stops electric discharge, otherwise continues step 602.
Step 607: battery stops electric discharge.
Wavelet transformation result (yardstick a=8) the waveform schematic diagram of discharging current be 80A when load voltage signal V (t), the load voltage signal V (t) of the decision method that Figure 13,14 is respectively the above-mentioned battery of electric vehicle discharge off state based on wavelet transformation in implementation process.
Figure 15 is the battery management system frame diagram of method shown in Figure 12 in implementation process.
The function of safety management comprises that whether the electric current and voltage of monitoring battery surpasses restriction, prevents battery over-discharge etc.In order to make full use of the energy content of battery, prevent again arbitrary battery over-discharge in battery pack, to the judgement of discharging ending status of battery in good time and accurately.
Therefore, adopt the discharging ending status of battery decision method based on wavelet transformation of the present embodiment, for different environments for use and different types of battery, can judge adaptively discharge off state, on the basis that guarantees electric discharge fail safe, give full play to the energy content of battery.
(4) function of heat management and design cycle:
Figure 16 is the thermal management technology design flow diagram in this power battery pack management system.This battery pack thermal management subsystem 1, from user's angle, is used for guaranteeing that battery pack is operated in the whole system of preference temperature scope, comprises the parts such as battery case, heat transfer medium, monitoring equipment.The feature of battery pack thermal management subsystem 1 and major function:
Under a, battery low-temperature condition, the Measurement accuracy of battery temperature and monitoring;
Effectively loose wind and ventilation when b, battery pack temperature are too high;
Fast Heating under c, cryogenic conditions, battery pack can normally be worked;
Available ventilation when d, pernicious gas produce;
Being uniformly distributed of e, assurance battery pack temperature field.
The function of battery pack thermal management subsystem 1, the battery pack thermal management subsystem 1 that design performance is good, to adopt systematized method for designing, the design of native system, on the Research foundation in early stage, sum up repeatedly the achievement of Systematic Design, consider temperature on the impact in battery performance and useful life to determine the selection of the optimum operating temperature range of battery, the prediction of battery thermal field accounting temperature, heat transfer medium, the key technologies such as selection of thermal management subsystem 1 heat radiation consequence devised, blower fan and point for measuring temperature.
To sum up, based on battery pack SOC estimation and the SOC algorithm for estimating based on strong tracking Kalman filter under the inhomogeneous condition of monomer characteristic, utilize Kalman filter to realize the Minimum Mean Square Error estimation of SOC, realized the estimation to battery charge state, and the error of initial value is had to very strong correcting action, noise is had to very strong inhibitory action, can improve the reliability of SOC algorithm, make algorithm for estimating there is stronger mutation status follow-up control, cell health state forecast is provided, be applicable to the violent actual condition of curent change, thereby meet electric automobile when work to battery security and give full play to the requirement of battery effective utilization, significant to promoting electric vehicle industrialization process.
The technical scheme above embodiment of the present invention being provided is described in detail, applied specific case herein the principle of the embodiment of the present invention and execution mode are set forth, the explanation of above embodiment is only applicable to help to understand the principle of the embodiment of the present invention; , for one of ordinary skill in the art, according to the embodiment of the present invention, in embodiment and range of application, all will change, in sum, this description should not be construed as limitation of the present invention meanwhile.
Claims (7)
1. a power battery pack management system, is characterized in that, comprising:
Security management subsystem, for monitoring the state-of-charge of each battery cell, determines that according to the state-of-charge of described each battery cell described each battery cell, whether in discharge off state, if it is stops described battery discharge;
Thermal management subsystem, for surveying the temperature on described each battery cell surface, according to the temperature history information on described each battery cell surface, determine the temperature upper limit of described each battery cell inside, determine the temperature distribution information of the battery case being formed by all described battery cells, according to described temperature distribution information, control the working temperature of described battery pack, described working temperature is in the temperature range of setting;
Optimize subsystem, be electrically connected to described security management subsystem, thermal management subsystem respectively, for controlling the work of described thermal management subsystem, security management subsystem;
Described security management subsystem comprises:
Initialization module, state-of-charge initial value SOC0, the predicting covariance battle array initial value P of each battery cell described in determining for initialization
0, measuring noise square difference Q, filter and amplification multiple α;
Sampling module, for for k=1, each sampling instant of 2..., obtains the measurement noise v that intends each battery cell of sampling instant
k, current signal I
k;
The state-of-charge estimation block of battery, is electrically connected to described initialization module, sampling module respectively, for determining the state-of-charge of described each battery cell:
The state-of-charge equation of battery: make x
k=SOC
k, wherein said k is natural number,
According to following formula, determine the load voltage amount y (k) of described each battery cell:
Y (k)=f (I
k, x
k)+v
k=K
0-RI
k-K
1/ x
k-K
2x
k+ K
3ln (x
k)+K
4ln (1-x
k)+v
k, wherein, v
kfor measurement noise, described v
kvariance be Q,
Order measures battle array
the Kalman filter equation of the state-of-charge of described each battery cell prediction is:
The state-of-charge prediction of described each battery cell
Calculate described measurement battle array: C
k=K
1/ (x
k)
2-K
2+ K
3/ x
3-K
4/ (1-x
k),
Determine filter gain:
Determine prediction mean square deviation: P
k/k-1=P
k-1/k-1,
Determine and estimate mean square deviation: P
k/k=(I-K
kc
k) P
k/k-1,
Obtain the estimated value of the state-of-charge of described each battery cell: x
k/k=x
k/k-1+ aK
k[y
k-f (I
k, x
k/k-1)];
Electric discharge, by module, is electrically connected to the state-of-charge estimation block of described each battery cell, for make described battery cut-off electric discharge according to the estimated value of the state-of-charge of described each battery cell.
2. power battery pack management system according to claim 1, is characterized in that, described thermal management subsystem comprises: temperature sensor, is arranged in the battery case for accommodating described each battery cell, for surveying the temperature on described each battery cell surface;
Memory module, is electrically connected to described temperature sensor, for storing the temperature history information on described each battery cell surface;
Monomer internal temperature upper limit determination module, is electrically connected to respectively with described temperature sensor and memory module, for according to the current temperature of described each battery cell and temperature history information, determines the temperature upper limit of described each battery cell inside;
Temperature Distribution analysis module, is electrically connected to described monomer internal temperature upper limit determination module, for according to the temperature upper limit of described each battery cell inside, determines the temperature distribution information of the battery case consisting of all described battery cells;
Temperature control modules, is electrically connected to described Temperature Distribution analysis module, for control the work of the heat dissipation equipment of described each battery case according to described temperature distribution information, described each battery cell working temperature is in respectively in the temperature range of setting;
Heat dissipation equipment, is arranged in described each battery case.
3. power battery pack management system according to claim 2, is characterized in that,
Described heat dissipation equipment is blower fan.
4. power battery pack management system according to claim 3, is characterized in that,
Described temperature control modules is also electrically connected with a display unit,
Described display unit is for showing control information on described temperature control modules and the temperature distribution information in each battery case.
5. power battery pack management system according to claim 1, is characterized in that,
Described optimization subsystem adopts embedded system development to form.
6. a management method of utilizing the power battery pack management system described in claim 1, is characterized in that, comprising:
Utilize security management subsystem initialization to determine the initial value SOC0 of the state-of-charge of each battery cell, predicting covariance battle array initial value P
0, measuring noise square difference Q, filter and amplification multiple α;
For k=1, each sampling instant such as 2..., security management subsystem obtains the measurement noise v that intends each battery cell of sampling instant
k, current signal I
k;
Make x
k=SOC
k, according to the state-of-charge equation of each battery cell:
Security management subsystem is determined the state-of-charge of described each battery cell, and wherein said k is natural number, according to formula:
Y (k)=f (I
k, x
k)+v
k=K
0-RI
k-K
1/ x
k-K
2x
k+ K
3ln (x
k)+K
4ln (1-x
k)+v
k, the load voltage amount y (k) of each battery cell described in determining, wherein, v
kfor measurement noise, described v
kvariance be Q;
Order measures battle array C
k:
according to Kalman filter equation:
the state-of-charge of described each battery cell of security management subsystem prediction;
And calculate described measurement battle array: C
k=K
1/ (x
k)
2-K
2+ K
3/ x
3-K
4/ (1-x
k);
Determine filter gain:
Determine prediction mean square deviation: P
k/k-1=P
k-1/k-1;
Determine and estimate mean square deviation: P
k/k=(I-K
kc
k) P
k/k-1;
Determine the estimated value of the state-of-charge of described each battery cell: x
k/k=x
k/k-1+ aK
k[y
k-f (I
k, x
k/k-1)];
According to the estimated value of the state-of-charge of described each battery cell, make described each battery cell cut-off electric discharge.
7. the management method of power battery pack management system according to claim 6, is characterized in that,
According to the estimated value of the state-of-charge of described each battery cell, make described each battery cell cut-off electric discharge, comprise the following steps:
1) initialization: the fixedly final voltage threshold value V of given each battery cell
0;
The threshold value WT of given load voltage signal extreme point after wavelet transformation
valve;
2), according to the estimated value of the state-of-charge of described each battery cell, obtain the load voltage signal V (t) of each battery cell;
3) judge that whether V (t) is lower than threshold value V
0, as V (t) < V
0time perform step 4), otherwise return to step 2);
4) V (t) is carried out to wavelet transformation by following formula:
;
Wherein t is limit of integration, for the initial time that starts to carry out wavelet analysis is to the region between current sampling instant;
Above formula is abbreviated as: WT
ax (t)=x (t) * Ψ
a(t), x (t)=V (t) wherein, a is scale factor, a=2
j, j is natural number, its scope is 2～5,
θ (t) is Gauss's lowpass function,
5) obtain the extreme point of figure signal: WT
max;
6) judgement WT
maxwhether surpass threshold value WT
valve, WT
valvespan is 0～2, works as WT
max> WT
valvetime, described battery cell stops electric discharge, otherwise returns to step 2).
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Families Citing this family (27)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2013072927A2 (en) * | 2011-09-30 | 2013-05-23 | Kpit Cummins Infosystems Limited | System and method for battery monitoring |
| CN102709616B (en) * | 2012-05-31 | 2014-09-17 | 杭州万好万家新能源科技有限公司 | Distributed thermal management system for battery modules |
| CN103884985B (en) * | 2012-12-19 | 2017-05-24 | 北京创智信科科技股份有限公司 | Detection method of storage battery performance |
| CN103018682A (en) * | 2012-12-20 | 2013-04-03 | 惠州市亿能电子有限公司 | Thermal field testing and analytical processing method for battery system |
| CN103135065A (en) * | 2013-01-25 | 2013-06-05 | 文创太阳能(福建)科技有限公司 | Iron phosphate lithium battery electric quantity detecting method based on feature points |
| CN103413013B (en) * | 2013-09-10 | 2016-08-03 | 上海航天电源技术有限责任公司 | A kind of in-situ heat simulating analysis of battery cell in lithium ion battery pack system |
| CN104670031A (en) * | 2013-11-29 | 2015-06-03 | 国家电网公司 | Device and method for monitoring batteries of electric vehicle |
| CN103901294A (en) * | 2014-01-02 | 2014-07-02 | 智慧城市系统服务（中国）有限公司 | Super capacitor set charge state testing method and device |
| CN103792411B (en) * | 2014-01-22 | 2017-01-04 | 浙江精雷电器股份有限公司 | Electric vehicle power supply monitoring alarm |
| DE102014207395A1 (en) * | 2014-04-17 | 2015-10-22 | Robert Bosch Gmbh | Method for battery management and battery management system |
| CN104300188B (en) * | 2014-09-25 | 2017-01-11 | 惠州市亿能电子有限公司 | Heat radiation management method of battery pack |
| CN104600381B (en) * | 2015-01-27 | 2017-02-01 | 福州大学 | Optimization method for arrangement structures of lithium ion battery pack monomers |
| CN105355945A (en) * | 2015-11-18 | 2016-02-24 | 沈阳化工大学 | Microbiological fuel cell fault diagnosis method based on wavelet-transformation |
| CN106941268A (en) * | 2016-01-05 | 2017-07-11 | 陈生泰 | The battery system of monoblock battery is monitored and changed at any time |
| CN106026260B (en) * | 2016-06-24 | 2018-06-26 | 南京航空航天大学 | A kind of series winding battery pack SOC estimation method with equalizing circuit |
| CN107539137B (en) * | 2016-06-27 | 2020-05-22 | 比亚迪股份有限公司 | Temperature control system and method for power battery pack in electric automobile and electric automobile |
| CN107134604A (en) * | 2017-03-29 | 2017-09-05 | 南京航空航天大学 | A kind of power battery thermal management method based on working characteristicses |
| CN108091945B (en) * | 2017-12-04 | 2019-08-13 | 华中科技大学 | A kind of voltage monitoring method, method for estimating state and the device of liquid metal cell |
| CN108345327A (en) * | 2018-02-27 | 2018-07-31 | 上海瑞华(集团)有限公司 | A kind of cabin temperature gradient automatic control system of pure electric vehicle ship |
| CN108896922B (en) * | 2018-06-22 | 2020-10-30 | 江西江铃集团新能源汽车有限公司 | Electric automobile voltage platform determination method |
| CN109103522A (en) * | 2018-08-23 | 2018-12-28 | 江苏罗思韦尔电气有限公司 | A kind of battery management system |
| CN110244225A (en) * | 2019-06-27 | 2019-09-17 | 清华大学深圳研究生院 | A method of hot face temperature when prediction power battery electric discharge |
| CN110994053B (en) * | 2019-12-18 | 2021-04-09 | 北京理工大学 | Active management method and system for power battery performance |
| CN111211375B (en) * | 2020-01-10 | 2021-04-20 | 中国科学院广州能源研究所 | Lithium ion battery liquid cooling type heat pipe control method for frequency modulation and energy storage system |
| CN111923783B (en) * | 2020-06-19 | 2022-03-18 | 浙江大学 | Electric forklift low-temperature lithium battery management system and control method thereof |
| CN115332649B (en) * | 2022-07-05 | 2023-06-09 | 厦门宇电自动化科技有限公司 | Battery temperature control management method, device, equipment and readable storage medium |
| CN115659790B (en) * | 2022-10-13 | 2024-02-06 | 厦门宇电自动化科技有限公司 | Real-time temperature detection method for power battery pack |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1441510A (en) * | 2002-02-27 | 2003-09-10 | 合邦电子股份有限公司 | Intelligent secondary battery managing method and device |
| CN1625014A (en) * | 2003-12-04 | 2005-06-08 | 比亚迪股份有限公司 | Dynamic power supply managment system for electric vehicle |
| CN101022178A (en) * | 2007-03-09 | 2007-08-22 | 清华大学 | Method for estimating nickel-hydrogen power battery charged state based on standard battery model |
-
2010
- 2010-06-21 CN CN201010219571.6A patent/CN102195101B/en not_active Expired - Fee Related
Patent Citations (3)
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| CN1625014A (en) * | 2003-12-04 | 2005-06-08 | 比亚迪股份有限公司 | Dynamic power supply managment system for electric vehicle |
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