CN109733406A - Policy control method is travelled based on the pure electric automobile of fuzzy control and Dynamic Programming - Google Patents
Policy control method is travelled based on the pure electric automobile of fuzzy control and Dynamic Programming Download PDFInfo
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Abstract
Policy control method is travelled based on the pure electric automobile of fuzzy control and Dynamic Programming the invention discloses a kind of, the following steps are included: obtaining accelerator pedal aperture, accelerator pedal aperture change rate, battery dump energy value and torque supplements load coefficient, using accelerator pedal aperture, accelerator pedal aperture change rate and battery dump energy value as input variable, fuzzy control rule is established using torque supplement load coefficient as output variable；Driving mode, including dynamic mode, comfort mode and energy-saving mode are divided, the torque supplement load coefficient under Three models is obtained according to fuzzy control rule；Load coefficient is supplemented according to torque and current loads coefficient obtains target load coefficient, and target motor load torque is obtained by target load coefficient；Motor current torque is adjusted according to target motor load torque for dynamic mode and comfort mode；For energy-saving mode, torque outgoing route is planned using dynamic programming algorithm integration objective speed and target load coefficient.
Description
Technical field
The present invention relates to electric automobile whole control field, specifically disclose a kind of based on fuzzy control and Dynamic Programming
Pure electric automobile travels policy control method.
Background technique
As global energy crisis and air pollution problems inherent are increasingly serious, pure electric automobile develops into the solution energy
The task of top priority of pollution.Three the most key big technologies of pure electric automobile, i.e. motor, battery, electronic control technology at present, are pure electric vehicles
Automobile breaks through efficiency the very corn of a subject technology.Most important part first is that its whole-control system is set in Development of Electric Vehicles
Meter.Whole-control system has been related to power-on and power-off management, signal processing, driver intention parsing, vehicle mode management, gear pipe
Reason, drive control, Brake energy recovery, battery energy management, accessory system control, course continuation mileage calculating, fault diagnosis and place
The functions such as reason, condition monitoring and display.The wherein dynamic property and economy in drive control strategy regulation vehicle driving process is right
The raising of vehicle performance plays a significant role.
Pure electric automobile drive control strategy mainly has knowing based on driver intention for Datong District, the Qin, University Of Chongqing et al. proposition
The variation feelings according to accelerator pedal that other dynamic property of pure electric automobile drive control strategy, Hunan University Zheng Chaoxiong et al. are proposed
The intelligent mode control strategy of the accelerating ability of condition dynamic adjustment vehicle.Knowledge of these methods primarily directed to driver intention
Not, the dynamic response for improving vehicle considers vehicle traveling economy insufficient.And with the development of pure electric automobile, power
Property has met the demand of production and living, but course continuation mileage deficiency is current urgent problem to be solved.
Summary of the invention
The object of the invention provide it is a kind of based on the pure electric automobile of fuzzy control and Dynamic Programming travel policy control side
Method, to solve technological deficiency existing in the prior art.
To achieve the above object, it is travelled the present invention provides a kind of based on the pure electric automobile of fuzzy control and Dynamic Programming
Policy control method, comprising the following steps:
S1: accelerator pedal aperture, accelerator pedal aperture change rate, battery dump energy value and torque are obtained and supplements load
Coefficient is supplemented using accelerator pedal aperture, accelerator pedal aperture change rate and battery dump energy value as input variable with torque
Load coefficient establishes fuzzy control rule as output variable；
S2: driving mode, including dynamic mode, comfort mode and energy-saving mode are divided, is obtained according to fuzzy control rule
Torque under to Three models supplements load coefficient；
S3: load coefficient is supplemented according to torque and current loads coefficient obtains target load coefficient, and is negative by target
Lotus coefficient obtains target motor load torque；
S4: motor current torque is adjusted according to target motor load torque for dynamic mode and comfort mode；For section
Energy mode utilizes dynamic programming algorithm integration objective speed and target load coefficient to plan torque outgoing route.
As the further supplement to the above method:
Preferably, the method for the torque supplement load coefficient under Three models is obtained in S2 are as follows: according to fuzzy control rule,
Supplement load coefficient is exported using the driving intention of Mamdani immediate reasoning method reasoning driver, and according to driving intention.
Preferably, in S3 target motor load torque calculation are as follows:
Lend=L+Lcompensate
Wherein, LendFor target machine load torque, L is motor present load torque, LcompensateLoad system is supplemented for torque
Number.
Preferably, dynamic programming algorithm is utilized to integrate the vehicle energy consumption under unit mileage and acceleration time to target electricity in S4
Machine load torque be adjusted to obtain desired motor load torque the following steps are included:
S41: being control variable with motor torque, motor speed is state variable, establishes optimum control according to demand is accelerated
Problem, and the system state equation of optimal control problem is obtained according to vehicle overall design model；
S42: the constraint equation of state variable is obtained according to target vehicle speed and system state equation；According to target load coefficient
And extending space needed for torque search obtains the constraint equation of control variable；
S43: performance indicator equation is established according to the constraint equation of state variable and the constraint equation for controlling variable；
S44: the optimal control sequence of performance indicator equation is solved according to dynamic programming algorithm；
S45: torque outgoing route is planned according to optimal control sequence.
Preferably, the vehicle overall design model in S41 are as follows:
Wherein, TmIt (t) is motor output torque, nmIt (t) is motor speed, i0For final driver ratio, ηTFor power train
Mechanical efficiency, m are car mass, and r is tire radius, CDFor coefficient of air resistance, A is front face area, and G is automobile gravity, and f is
Coefficient of rolling resistance, i are road grade.
Preferably, the optimal control problem expression formula in S41 are as follows:
J=min { ∑ L [x (k), u (k), k] }
X (k+1)=f (x (k), u (k))
x∈X
u∈U。
Preferably, the constraint equation of the constraint equation of state variable and control variable is respectively as follows:
Wherein, nnowFor motor current rotating speed, ndemFor desired motor speed, TminCurrent vehicle speed is balanced most for motor
Small torque, Tmax(Lend+ δ) it is motor motor maximum output torque under this extending space and current vehicle speed.
Preferably, performance indicator equation are as follows:
Wherein, taccFor acceleration time, PbatIt (k) is cell output, Dis (k) is the distance of single-order vehicle driving, λ1
And λ2For weight factor, ωtAnd ωeleFor normalization factor, JtFor the performance indicator for considering the acceleration time, JeleTo consider unit
The performance indicator of vehicle energy consumption under mileage.
The invention has the following advantages:
1, the present invention has divided three dynamic mode, comfort mode, energy-saving mode modes, in the base for ensuring driving dynamics
On plinth, dynamic driving intention under three driving modes of pure electric automobile is identified and exported by intelligent mode control strategy
Compensate load coefficient, the driving status of vehicle made to be more in line with the driving intention of driver, and ensure that the dynamic property of vehicle with
Economy.
2, the present invention carries out driving torque optimization using fuzzy control strategy, can be not against mathematical model, strong robustness.
3, the present invention comprehensively utilizes dynamic programming algorithm for energy-saving mode to the higher requirement of vehicle cost-effectiveness requirement,
The vehicle energy consumption and the optimization of the output torque of acceleration time considered under unit mileage is proposed, guarantees vehicle in certain power demand
On the basis of realize vehicle economy maximization.
Below with reference to accompanying drawings, the present invention is described in further detail.
Detailed description of the invention
The attached drawing constituted part of this application is used to provide further understanding of the present invention, schematic reality of the invention
It applies example and its explanation is used to explain the present invention, do not constitute improper limitations of the present invention.In the accompanying drawings:
Fig. 1 provides a kind of based on the pure electric automobile of fuzzy control and Dynamic Programming traveling for the preferred embodiment of the present invention
Policy control method flow chart；
Fig. 2 is the graph of relation of preferred embodiment of the present invention motor torque load coefficient and accelerator pedal aperture.
Specific embodiment
The embodiment of the present invention is described in detail below in conjunction with attached drawing, but the present invention can be defined by the claims
Implement with the multitude of different ways of covering.
Embodiment 1: strategy control is travelled based on the pure electric automobile of fuzzy control and Dynamic Programming the present invention provides a kind of
Method processed, referring to Fig. 1, comprising the following steps:
S1: accelerator pedal aperture, accelerator pedal aperture change rate, battery dump energy value and torque are obtained and supplements load
Coefficient is supplemented using accelerator pedal aperture, accelerator pedal aperture change rate and battery dump energy value as input variable with torque
Load coefficient establishes fuzzy control rule as output variable；
S2: driving mode, including dynamic mode, comfort mode and energy-saving mode are divided, is obtained according to fuzzy control rule
Torque under to Three models supplements load coefficient；
S3: load coefficient is supplemented according to torque and current loads coefficient obtains target load coefficient, and is negative by target
Lotus coefficient obtains target motor load torque；
S4: motor current torque is adjusted according to target motor load torque for dynamic mode and comfort mode；For section
Energy mode utilizes dynamic programming algorithm integration objective speed and target load coefficient to plan torque outgoing route.
Torque outgoing route, that is, Motor torque with motor speed change torque output trajectory.
Embodiment 2: the principle of pure electric automobile normally travel be electric system receive entire car controller torque instruction after,
Actual torque is exported, wheel is driven by transmission device.So determining that the demand torque characteristics of driving motor is research driving control
Make the key of strategy.Accelerator pedal aperture is able to reflect driver for the size of driving motor torque-demand, motor speed energy
Enough characterize the fan-out capability of motor torque capacity under current rotating speed.In order to clearly determine accelerator pedal aperture and drive
The relationship of member's demand level of torque, defines the relationship of motor torque load coefficient L and accelerator pedal aperture P_acc are as follows:
L=f (P_acc)
Referring to fig. 2, according to the division of vehicle operating mode, three kinds of motor torque load coefficient L and acceleration have been formulated respectively
The relation curve of pedal opening P_acc, dynamic mode, comfort mode, energy-saving mode respectively correspond curve A, B, C.
Wherein the relation curve of the motor torque load coefficient L of curve A and accelerator pedal aperture P_acc are convex, note
Ponderomotive force, accelerating ability are preferably suitble to work between high load region；The motor torque load coefficient L and accelerator pedal of curve C
The relation curve of aperture P_acc is concave, focuses on economy, and accelerating ability is partially soft, is suitble to work between low load region；Curve
B then between curve A and curve C, takes into account dynamic property and economy.
Pedal control strategy mainly influences the uniformity coefficient of motor torque external characteristics, in order to make different accelerator pedal apertures
Corresponding power of motor output is more uniform and smooth, the motor demand determined according to motor torque load coefficient and motor speed
The mathematic(al) representation of torque is as follows:
Wherein, TmaxFor the peak torque of motor, n0For the rated speed of motor, n is motor current rotating speed.
According to the demand of driving procedure, the present invention designs three kinds of drive modes, respectively dynamic mode, comfort mode, section
Energy mode etc..Dynamic mode main target is to improve the dynamic property of pure electric automobile, in the big acceleration such as starting and anxious acceleration
Under demand, the sensitive of motor output torque is improved；Energy-saving mode main target is the course continuation mileage for increasing pure electric automobile traveling,
Improve the economy of vehicle；Comfort mode then uses linear motor torque load coefficient and accelerator pedal opening curve, torque
Output is steady, and driving experience is comfortable.
In order to adjust the accelerating ability of vehicle according to the situation of change of accelerator pedal dynamic, make the driving status of vehicle
It is more in line with the driving intention of driver, the present invention uses pedal current loads coefficient+compensation load coefficient method, realizes root
According to driving intention dynamic adjustment torque output.Torque output formula is as follows:
Lend=L+Lcompensate
Wherein, LendFor final pedal load coefficient value, LcompensateFor the compensation load coefficient after driving intention identification
Value.
Pedal current loads coefficient is according to the relation curve of motor torque load coefficient L and accelerator pedal aperture P_acc
It can obtain.Compensation load coefficient is obtained using fuzzy control method, because classical control theory is difficult to reflect driving intention and vehicle
State, and fuzzy control method is not against mathematical model, strong robustness.
It is driven in dynamic mode and comfort mode design using accelerator pedal aperture and accelerator pedal aperture change rate
Intention assessment, accelerator pedal aperture P_acc represent the expectation of driver's acceleration and deceleration intention, accelerator pedal aperture change rate
Represent the desired speed of acceleration-deceleration.Accelerator pedal aperture P_acc, accelerator pedal aperture change rate are utilized in economic model designAnd battery dump energy value SOC carries out driving intention identification.Compensation is gradually decreased according to the reduction of residual electric quantity
The value of load coefficient achievees the purpose that improve vehicle economy.
Design is with accelerator pedal aperture P_acc, accelerator pedal aperture change rateAnd battery dump energy value SOC
For input variable, LcompensateCompensation load coefficient after driving intention identification is that output variable establishes fuzzy controller, and selects
Select subordinating degree function of the triangular function as input/output variable.
The universe of fuzzy sets of accelerator pedal aperture P_acc is defined as 0~100, linguistic variable setting are as follows: and minimum ZS, it is small
S, middle M, big B, very big ZB }.
Accelerator pedal aperture change rateUniverse of fuzzy sets be defined as 0~200, linguistic variable setting are as follows: { pole
Small ZS, small S, middle M, big B, very big ZB }.
The universe of fuzzy sets of battery dump energy value SOC is defined as 0~200, linguistic variable setting are as follows: and low L, middle L,
High H }.
Compensate load coefficient LcompensateUniverse of fuzzy sets be defined as 0~0.2, linguistic variable setting are as follows: { minimum
ZS, small S, middle M, big B, very big ZB }.
Fuzzy control rule be as obtained from summary of experience and refinement, expression-form be " if ... (condition) that
... (conclusion) ".By many experiments, the dynamic mode as shown in the following table 1,2 and 3, comfort mode and energy saving mould are established
The fuzzy control rule of formula.
Fuzzy control rule table under 1 dynamic mode of table
Fuzzy control rule table under 2 dynamic mode of table
Fuzzy control rule table under 3 economic model of table
According to the fuzzy control rule under the subordinating degree function of input and output and each mode, directly pushed away using Mamdani
Logos reasoning driver's driving intention, and export compensation load coefficient Lcompensate.It is negative according to pedal current loads coefficient+compensation
Lotus coefficient obtains final pedal load coefficient value, and calculates motor load torque and export.
Travel the design requirement of Three models according to vehicle, dynamic mode and comfort mode to vehicle cost-effectiveness requirement compared with
It is small, mainly meet the dynamic property and comfort of driving.Energy-saving mode is higher to vehicle cost-effectiveness requirement, only passes through fuzzy control
The maximization that method cannot achieve vehicle economy improves, and this section utilizes dynamic programming algorithm, considers the vehicle under unit mileage
Energy consumption and acceleration time are performance indicator, torque outgoing route optimal in accelerator are cooked up, in certain power demand
On the basis of realize vehicle economy maximization.
Vehicle is in the accelerator of energy-saving mode, according to current vehicle speed VnowWith target vehicle speed Vdem, according to Dynamic Programming
The optimization of algorithm realization output torque.Current vehicle speed VnowIt can be calculated by wheel speed sensors, target vehicle speed VdemThen according to vapour
The motor demand torque that vehicle Longitudinal Dynamic Model combines final pedal load coefficient value to obtain is calculated.Wherein automobile longitudinal
Kinetic model is as follows:
Wherein, TmIt (t) is motor output torque, nmIt (t) is motor speed, i0For final driver ratio, ηTFor power train
Mechanical efficiency, m are car mass, and r is tire radius, CDFor coefficient of air resistance, A is front face area, and G is automobile gravity, and f is
Coefficient of rolling resistance, i are road grade.
It is control variable with motor torque, motor speed is state variable, establishes pure electric automobile and adds according to demand is accelerated
The optimal control problem of fast process torque optimization:
J=min { ∑ L [x (k), u (k), k] }
X (k+1)=f (x (k), u (k))
x∈X
u∈U。
According to vehicle overall design model, the state equation of this paper optimal control system is obtained:
The inequality constraints equation of this paper optimal control system is according to target vehicle speed Vdem, obtain the constraint side of state variable
Journey；And the constraint side of control variable is obtained according to extending space needed for final pedal load coefficient value and torque search
Journey:
Wherein, nnowFor motor current rotating speed, ndemFor desired motor speed, TminCurrent vehicle speed is balanced most for motor
Small torque, Tmax(Lend+ δ) it is motor motor maximum output torque under this extending space and current vehicle speed.
Consider that vehicle energy consumption and acceleration time under unit mileage establish following performance indicator J.
Consider the performance indicator of acceleration time:
Consider the performance indicator of the vehicle energy consumption under unit mileage:
Multi-objective problem is converted into single-goal function, the i.e. objective function of optimization problem by weigthed sums approach are as follows:
Wherein, taccIt (k) is acceleration time, Pbat(k) be cell output, Dis (k) be single-order vehicle driving away from
From λ1And λ2For weight factor, ωtAnd ωeleFor normalization factor.
It is bottom-up according to dynamic programming algorithm, by calculating, solve the best decision of single step problem.And turned by state
The state value in equation calculation next stage is moved, is constantly iterated to calculate, more each section of performance indicator, piecewise decision has gradually extended
At, optimal control sequence is finally obtained,Pure electric automobile section is completed according to resulting control sequence
The optimum control of accelerator under energy mode, it is maximized to improve vehicle continuation of the journey on the basis of guarantee vehicle certain dynamic property
Mileage.
Specific embodiment:
The first step formulates three kinds of motor torques according to the drive demand of dynamic mode, comfort mode and energy-saving mode respectively
The relation curve of load coefficient and accelerator pedal aperture；
Second step, in order to keep the corresponding power of motor output of different accelerator pedal apertures more uniform and smooth, according to
The mathematic(al) representation for the motor demand torque that motor torque load coefficient and motor speed determine；
Third step, design are input with accelerator pedal aperture, accelerator pedal aperture change rate and battery dump energy value
Variable, the compensation load coefficient after driving intention identification are that output variable establishes fuzzy controller, and triangular function is selected to make
For the subordinating degree function of input/output variable；
4th step establishes dynamic mode, comfort mode and energy-saving mode according to many experiments and design experiences respectively
Fuzzy control rule.Using Mamdani immediate reasoning method reasoning driver's driving intention, and export compensation load coefficient.According to
Pedal current loads coefficient+compensation load coefficient obtains final pedal load coefficient value, and calculates motor load torque and export；
5th step considers demand of the energy-saving mode to vehicle economy, is obtained herein according to vehicle overall design model
The state equation of optimal control system, and establish the inequality constraints equation of optimal control system；
6th step considers that vehicle energy consumption and acceleration time under unit mileage establish the performance indicator of multiple-objection optimization；
7th step obtains current vehicle speed according to wheel speed sensors, and acquires accelerator pedal signal and obtain the torsion of target output
Square calculates the target vehicle speed of running car using Longitudinal Dynamic Model, if the two compares target vehicle speed and is greater than currently
Accelerator is divided into N number of process by speed；
8th step, it is bottom-up using dynamic programming algorithm, by calculating, solve the best decision of single step problem.And by
State transition equation calculates the state value in next stage, constantly iterates to calculate, more each section of performance indicator, piecewise decision is gradually
Extend and complete, finally obtain optimal control sequence, according to this control strategy, realizes the maximized mesh for improving vehicle course continuation mileage
Mark；
Step 9: integrate the control strategy under dynamic mode, comfort mode and economic model, obtain based on fuzzy control with
The integrated vehicle control tactics of dynamic programming algorithm.
The foregoing is only a preferred embodiment of the present invention, is not intended to restrict the invention, for the skill of this field
For art personnel, the invention may be variously modified and varied.All within the spirits and principles of the present invention, made any to repair
Change, equivalent replacement, improvement etc., should all be included in the protection scope of the present invention.
Claims (8)
1. travelling policy control method based on the pure electric automobile of fuzzy control and Dynamic Programming, which is characterized in that including following
Step:
S1: accelerator pedal aperture, accelerator pedal aperture change rate, battery dump energy value and torque are obtained and supplements load system
Number, using the accelerator pedal aperture, the accelerator pedal aperture change rate and the battery dump energy value as input variable,
Fuzzy control rule is established using torque supplement load coefficient as output variable；
S2: driving mode, including dynamic mode, comfort mode and energy-saving mode are divided, obtains three according to fuzzy control rule
Torque under kind mode supplements load coefficient；
S3: load coefficient is supplemented according to torque and current loads coefficient obtains target load coefficient, and passes through target load system
Number obtains target motor load torque；
S4: motor current torque is adjusted according to target motor load torque for the dynamic mode and the comfort mode；It is right
In the energy-saving mode, torque outgoing route is planned using dynamic programming algorithm integration objective speed and target load coefficient.
2. according to claim 1 travel policy control method based on the pure electric automobile of fuzzy control and Dynamic Programming,
It is characterized in that, the method for obtaining the torque supplement load coefficient under Three models in the S2 are as follows: according to fuzzy control rule,
Supplement load coefficient is exported using the driving intention of Mamdani immediate reasoning method reasoning driver, and according to the driving intention.
3. according to claim 1 travel policy control method based on the pure electric automobile of fuzzy control and Dynamic Programming,
It is characterized in that, in the S3 target motor load torque calculation are as follows:
Lend=L+Lcompensate
Wherein, LendFor target motor load torque, L is motor present load torque, LcompensateLoad coefficient is supplemented for torque.
4. according to claim 1 travel policy control method based on the pure electric automobile of fuzzy control and Dynamic Programming,
It is characterized in that, dynamic programming algorithm is utilized to integrate the vehicle energy consumption under unit mileage and acceleration time to target electricity in the S4
Machine load torque be adjusted to obtain desired motor load torque the following steps are included:
S41: being control variable with motor torque, motor speed is state variable, establishes optimum control and asks according to demand is accelerated
Topic, and the system state equation of optimal control problem is obtained according to vehicle overall design model；
S42: the constraint equation of state variable is obtained according to target vehicle speed and system state equation；According to target load coefficient and
Extending space needed for torque search obtains the constraint equation of control variable；
S43: performance indicator equation is established according to the constraint equation of state variable and the constraint equation for controlling variable；
S44: the optimal control sequence of the performance indicator equation is solved according to dynamic programming algorithm；
S45: torque outgoing route is planned according to optimal control sequence.
5. according to claim 4 travel policy control method based on the pure electric automobile of fuzzy control and Dynamic Programming,
It is characterized in that, the vehicle overall design model in the S41 are as follows:
Wherein, TmIt (t) is motor output torque, nmIt (t) is motor speed, i0For final driver ratio, ηTFor power train machinery
Efficiency, m are car mass, and r is tire radius, CDFor coefficient of air resistance, A is front face area, and G is automobile gravity, and f is to roll
Resistance coefficient, i are road grade.
6. according to claim 4 travel policy control method based on the pure electric automobile of fuzzy control and Dynamic Programming,
It is characterized in that, the optimal control problem expression formula in the S41 are as follows:
J=min { ∑ L [x (k), u (k), k] }
X (k+1)=f (x (k), u (k))
x∈X
u∈U。
7. according to claim 4 travel policy control method based on the pure electric automobile of fuzzy control and Dynamic Programming,
It is characterized in that, the constraint equation of the state variable and the constraint equation of control variable are respectively as follows:
Wherein, nnowFor motor current rotating speed, ndemFor desired motor speed, TminThe minimum for balancing current vehicle speed for motor turns
Square, Tmax(Lend+ δ) it is motor motor maximum output torque under this extending space and current vehicle speed.
8. according to claim 4 travel policy control method based on the pure electric automobile of fuzzy control and Dynamic Programming,
It is characterized in that, the performance indicator equation are as follows:
Wherein, taccFor acceleration time, PbatIt (k) is cell output, Dis (k) is the distance of single-order vehicle driving, λ1For power
Repeated factor, ωtAnd ωeleFor normalization factor, JtFor the performance indicator for considering the acceleration time, JeleTo consider under unit mileage
The performance indicator of vehicle energy consumption.
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| CN110356246A (en) * | 2019-06-14 | 2019-10-22 | 上海伊控动力系统有限公司 | A kind of Motor torque method of adjustment of the pure electric vehicle logistic car based on driving habit |
| CN112428982A (en) * | 2019-08-07 | 2021-03-02 | 纬湃科技投资(中国)有限公司 | Signal processing method for accelerator pedal of hybrid electric vehicle |
| CN112428982B (en) * | 2019-08-07 | 2022-02-01 | 纬湃科技投资(中国)有限公司 | Signal processing method for accelerator pedal of hybrid electric vehicle |
| CN113044032A (en) * | 2019-12-26 | 2021-06-29 | 北汽福田汽车股份有限公司 | Vehicle running power control method and device and vehicle |
| CN113044032B (en) * | 2019-12-26 | 2021-11-05 | 北汽福田汽车股份有限公司 | Vehicle running power control method and device and vehicle |
| CN111497848B (en) * | 2020-04-24 | 2021-09-21 | 上海元城汽车技术有限公司 | Method, device, server and medium for determining vehicle driving mode |
| CN111497848A (en) * | 2020-04-24 | 2020-08-07 | 上海元城汽车技术有限公司 | Method, device, server and medium for determining vehicle driving mode |
| CN111497857A (en) * | 2020-04-30 | 2020-08-07 | 智车优行科技（上海）有限公司 | Method and system for obtaining optimal efficiency of vehicle |
| CN113619558A (en) * | 2020-05-06 | 2021-11-09 | 上海汽车集团股份有限公司 | Torque distribution method and system for hybrid system vehicle |
| CN112638695A (en) * | 2020-10-31 | 2021-04-09 | 华为技术有限公司 | Torque control method, device and equipment of electric automobile and storage medium thereof |
| WO2022088154A1 (en) * | 2020-10-31 | 2022-05-05 | 华为技术有限公司 | Electric-vehicle torque control method, apparatus, device, and storage medium |
| CN113022577A (en) * | 2021-04-02 | 2021-06-25 | 中国第一汽车股份有限公司 | Driving mode switching method and device, vehicle and storage medium |
| CN113386768A (en) * | 2021-08-02 | 2021-09-14 | 合肥工业大学 | Single-pedal nonlinear model prediction control method for pure electric vehicle |
| CN114083995A (en) * | 2021-11-12 | 2022-02-25 | 东风越野车有限公司 | Method, system and medium for torque distribution of in-wheel motor vehicle |
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