CN102717797B - Energy management method and system of hybrid vehicle - Google Patents

Abstract

The invention discloses an energy management method and system of a hybrid vehicle. The energy management system comprises a server and a whole hybrid vehicle controller, wherein the whole hybrid vehicle controller acquires actual control parameters of the vehicle on a target line through a CAN (Controller Area Network) bus, and sends the acquired actual control parameters to the server by virtue of a GPRS (General Packet Radio Service) module through a network; the server establishes a driver demand power transition probability matrix; an energy management state transition equation is established on the basis of a stochastic dynamic programming algorithm; and the server completes parameter calibration of the whole hybrid vehicle controller, the whole hybrid vehicle controller sends a control parameter, namely demanded motor torque, to the bus according the current state of the vehicle, and a motor controller receives information through the CAN bus and outputs the motor torque, wherein the value of the torque determines the working mode and the fuel economy of the hybrid vehicle.

Description

A kind of hybrid vehicle energy management method and energy management system

Technical field

The present invention relates to a kind of hybrid vehicle energy management method and energy management system, particularly relate to a kind of hybrid vehicle energy management method and energy management system also with long-range floor data collection and analysis and parameter calibration function except thering is car load energy distribution function.

Background technology

At present, the common problem existing in motor vehicle driven by mixed power evolution is both at home and abroad, in actual track operational process, oil-saving effect is not clearly, and this has deviated from the original intention of research hybrid power.The theoretical operating mode that wherein energy management strategy Development process adopts and the inconsistency of motor vehicle driven by mixed power actual operating mode are that various energy management strategies can not reach a theoretical optimum major reason.Current known hybrid power energy management strategy adopts static logic thresholding control policy in addition, this strategy mainly relies on engineering experience that logic threshold parameter is set, and these static logic thresholding parameters can not adapt to the dynamic change of vehicle actual condition, cannot guarantee that Vehicle Economy is optimum, thereby cannot make Full Vehicle System reach maximal efficiency.All the time, energy management strategy is all that the gordian technique of hybrid power is the emphasis of studying both at home and abroad, and the stochastic dynamic programming energy management strategy of known excellent performance is because the inconsistency of the operating mode adopting in development process and actual condition can not get practical application always.

Summary of the invention

Object of the present invention cannot adapt to the deficiency of vehicle actual condition dynamic change just in order to overcome hybrid vehicle energy management strategy in prior art, thereby provide a kind of hybrid vehicle energy management method, the method is regarded chaufeur power demand as a Markov process, by motor vehicle driven by mixed power entire car controller (HCU), to described vehicle, the actual condition in target line carries out vehicle speed data collection, then according to gathered vehicle speed data, pass through vehicle dynamics formula, try to achieve each instantaneous power demand, obtain the transition probability matrix of chaufeur demand power, set up the markov probabilistic model of chaufeur demand power, then based on stochastic dynamic programming algorithm, set up energy management problem, be specially chaufeur power demand, motor output torque, battery SOC and speed scattering turn to finite space, with SOC value of battery, the speed of a motor vehicle, gear, chaufeur demand power is state variable, form state space X, using motor output torque as decision variable G, with fuel oil consumption, driving engine discharge, SOC value of battery is cost function J, with the speed of a motor vehicle, battery SOC, the maximum of motor output torque and minimum value are as constraint, it is the boundary condition of solution procedure, set up the state transition equation of chaufeur demand power, and using modified policy iteration method carries out iterative, the best decision variable G of each step combines and forms the energy management strategy that is applicable to this circuit, concrete form is: (T _m(k))=π (SOC (k), ω _w(k), g (k), T _dem(k)), T in formula _m(k) represent the motor torque of demand, SOC (k) represents battery charge state, ω _w(k) represent the speed of a motor vehicle, g (k) represents automobile gear level, T _dem(k) represent the torque of chaufeur demand, this energy management strategy is motor booster type, by regulating the output of motor torque to make driving engine be operated in as far as possible efficient district, the control parameter of method by long-range demarcation to HCU again, according to current vehicle-state (SOC (k), ω _w(k), g (k), T _dem(k)) definite motor output torque, upgrades in the mode of Function Fitting or data sheet, and HCU is dealt into the value of this torque in CAN bus, to complete the control to motor.

In described hybrid vehicle energy management method, described motor vehicle driven by mixed power entire car controller (HCU) gathers vehicle speed data by CAN bus, and by GPRS module, gathered vehicle speed data is sent to server by network.

In described hybrid vehicle energy management method, the ICP/IP protocol module that described server by utilizing LABVIEW software inhouse is integrated, receives described vehicle speed data, and completes demonstration and storage work.

In described hybrid vehicle energy management method, the speed of a motor vehicle time history of LABVIEW software storage, by statistical analysis, obtains the actual condition data of described vehicle in described target line described in described server by utilizing MATLAB software transfer.

In described hybrid vehicle energy management method, described in described server by utilizing LABVIEW by the strategy of generation with form or the good function representation of matching and be updated in described motor vehicle driven by mixed power entire car controller (HCU) by network.

The present invention also provides a kind of hybrid vehicle energy management system, this system comprises server and motor vehicle driven by mixed power entire car controller (HCU), described motor vehicle driven by mixed power entire car controller (HCU) gathers the speed information of described vehicle in target line by CAN bus, and by GPRS module, gathered described working control parameter is sent to described server by network; The integrated ICP/IP protocol module of described server by utilizing LABVIEW software inhouse receives the described working control parameter of storage; Described in described server by utilizing MATLAB software transfer, LABVIEW software receives the described working control parameter of storage, by vehicle dynamics Formula chaufeur demand power transition probability matrix; Then based on stochastic dynamic programming algorithm, set up energy management state transition equation, the SDP tool box of applying in described MATLAB software carries out iterative; The strategy that described in described server by utilizing, LABVIEW generates iterative is with form or the good function representation of matching and be updated in described motor vehicle driven by mixed power entire car controller (HCU) by network remote, complete the parameter calibration of described motor vehicle driven by mixed power entire car controller (HCU), motor vehicle driven by mixed power entire car controller (HCU) sends control parameter, the i.e. motor torque of demand according to vehicle current state in bus.

The invention has the advantages that:

1, by the method for long-range demarcation, the control parameter of HCU is upgraded, HCU completes tactful realization.This fuel economy and its discharge of reduction raising China to the hybrid power bus of fixed line has very large practical significance.

2, realized " line one strategy ", can improve in fact the fuel economy of hybrid power bus long-time running, and can consider its emission behavior when design energy problem of management.

Accompanying drawing explanation

Fig. 1 is hybrid vehicle energy management system schematic of the present invention;

Fig. 2 is the structural representation of the motor vehicle driven by mixed power entire car controller HCU in hybrid vehicle energy management system of the present invention.

The specific embodiment

Below in conjunction with the drawings and specific embodiments, the present invention will be further described.

The system principle of paper Markov process:

The basic conception of Markov process is " transfer " of system " state " and state.When system is described by the variable-value of definition status completely, the system of can saying is in a state.If the description variable of system changes to the particular value of another state from the particular value of a state, at this moment, we just say that system realizes state transitions.

Vehicle driver's behavior is a very typical Markov process, and the corresponding power demand of driving behavior of chaufeur is exactly a state, from a driving behavior to another driving behavior, is state transitions.

Chaufeur power demand P _{dr_dem}span can discretely be the set of limited value, that is:

$P_{\mathrm{dr}\_\mathrm{dem}}\∈\{P_{\mathrm{dr}\_\mathrm{dem}}^1,P_{\mathrm{dr}\_\mathrm{dem}}^2,...,P_{\mathrm{dr}\_\mathrm{dem}}^{N_s}\}---\left(1\right)$

Similar with chaufeur power demand, vehicle velocity V _velthe set of limited value of span discretization, that is:

$V_{\mathrm{vel}}\∈\{V_{\mathrm{vel}}^1,V_{\mathrm{vel}}^2,...,V_{\mathrm{vel}}^{N_w}\}---\left(2\right)$

Chaufeur power demand transition probability p _{ij, k}, that is:

$\mathrm{Pr}\left\{P_{\mathrm{dr}\_\mathrm{dem}}^m\right|P_{\mathrm{dr}\_\mathrm{dem}}^i,V_{\mathrm{vel}}^j\}=p_{\mathrm{ij},k},\underset{k=1}{\overset{N_s}{\mathrm{\Σ}}}{p}_{\mathrm{ij},k}=1,$ i,m＝1,2,...,N _s,j＝1,2,...,N _w (3)

In formula, N _s, N _wrespectively chaufeur power demand and speed scattering number,

-time t _ktime power demand and the speed of a motor vehicle,

-time t _k+1time power demand.

The implication of above-mentioned formula is: at moment k, chaufeur demand power is the speed of a motor vehicle is condition under, chaufeur demand power is at t _k+1constantly transfer to probability.

M-speed of a motor vehicle floor data during according to vehicle, uses vehicle dynamics formula:

$P_{\mathrm{dr}\_\mathrm{dem}}=\frac{V_{\mathrm{vel}}}{360{0\mathrm{\η}}_T}(\mathrm{Gf}\cos \mathrm{\α}+G\sin \mathrm{\α}+\frac{C_D{\mathrm{AV}}_{\mathrm{vel}}^2}{21.15}+\mathrm{\δ}\frac{G}{g}\frac{{\mathrm{dV}}_{\mathrm{vel}}}{\mathrm{dt}})---\left(4\right)$

In formula, P _{dr_dem}-Vehicle Driving Cycle power, V _vel-the speed of a motor vehicle, f-coefficient of rolling resistance, A-wind area, C _d-aerodynamic drag factor, α-road grade angle, G-vehicle gravity, g-acceleration due to gravity, δ-vehicle rotary mass conversion coefficient,

-running car acceleration/accel, η _tmechanical efficiency of power transmission) calculate current time t _kpower demand is P _{dr_rdem}, the speed of a motor vehicle is V _velunder condition, next is t constantly _k+1power demand, a step transition probability of chaufeur power demand can be expressed as by maximal possibility estimation

${\hat{p}}_{\mathrm{ij},k}\stackrel{\mathrm{\Δ}}{=}\frac{n_{\mathrm{ij},m}}{n_{\mathrm{ij}}}---\left(5\right)$

N in formula _{ij, m}represent chaufeur power demand from

transfer to

number of times,

represent

shift total degree, all transition probabilities form transition probability matrix P.

Consider a stochastic dynamic programming M=(X, G, P, L) problem, there is finite state space X, limited action space G, this is above-mentioned transition probability matrix P for cost function L:X * G → L and transitionmatrix P().At each constantly, system is in the some state X in finite state space.The finite aggregate G that has the behavior that a system can take for each the state x ∈ X in state space.System is evolved according to state transition probability matrix P, and P (x, G, x ') expression system has taked to transfer to after behavior G the probability of state x ' under state x.Cost function is by L (x, G, x ') expression, and system takes behavior G to transfer to the cost that state x ' pays from state x.Strategy π is the sequence that state mapping arrives behavior, and it has pointed out to shift constantly at each, the behavior that system should be taked for current residing state.Value function J has defined the accumulated value of following cost function that each state x expects under certain tactful π.And optimum value function J is defined as accumulated value average of the minimum following cost function of each state.According to Bellman principle of optimality, write out recurrence relation:

$J_{\mathrm{\π}}^{s+1}\left(x\right)=\underset{G}{\min }\underset{x^{\′}}{\mathrm{\Σ}}{p}_{\mathrm{ij},k}(L(x.G,x^{\′})+J_{\mathrm{\π}}^s\left(x^{\′}\right))---\left(6\right)$

In formula, s-iterations, the state that x '-system is new.

Had the definition of optimal value function, system is selected optimum behavior according to minimization expected value principle, selects to make the behavior of expectation value function minimum of each state as optimum behavior

${\mathrm{\π}}^{*}\left(x\right)=\underset{\mathrm{\μ}\∈G}{\arg }\min \underset{x^{\′}}{\mathrm{\Σ}}{p}_{\mathrm{ij},k}(L(x,G,x^{\′})+J_{\mathrm{\π}}^s\left(x^{\′}\right))---\left(7\right)$

For all states, J in formula _πrepresent the cost function that stragetic innovation process obtains, after a new strategy obtains, need to upgrade cost function, until J _πconverging to this iterative process of predetermined value finishes.

Specific to hybrid vehicle energy management problem of the present invention, be about to chaufeur power demand, motor output torque, battery SOC and speed scattering turn to finite space, with SOC value of battery, the speed of a motor vehicle, gear, chaufeur demand power is state variable, form state space X, using motor output torque as decision variable G, with fuel oil consumption, driving engine discharge, SOC value of battery be weighted to cost function J, with the speed of a motor vehicle, battery SOC, the maximum of motor output torque and minimum value are as constraint, it is the boundary condition of solution procedure, set up the state transition equation (6) of chaufeur demand power, and using modified policy iteration method (the SDP tool box in MATLAB) carries out iterative, the best decision variable G of each step of all cost function J minimums is combined and form the energy management strategy π that is applicable to this circuit ^*, concrete form is: (T _m(k))=π (SOC (k), ω _w(k), g (k), T _dem(k)) (T in formula _m(k) represent the motor torque of demand, SOC (k) represents battery charge state, ω _w(k) represent the speed of a motor vehicle, T _dem(k) represent the torque of chaufeur demand), this energy management strategy is motor booster type, by regulating the output of motor torque to make driving engine be operated in as far as possible efficient district, the control parameter of method by long-range demarcation to HCU again, HCU is according to current vehicle-state (SOC (k), ω _w(k), g (k), T _dem(k)) definite motor output torque, upgrades in the mode of Function Fitting or data sheet, and HCU is dealt into the value of this torque in CAN bus, to complete the control to motor.According to the concrete principle of hybrid vehicle energy management system of the present invention and implementation procedure, be:

First, regard chaufeur power demand as a Markov process, the road condition to hybrid-power bus in target line schedules to last the vehicle speed data collection of about approximately to two weeks, obtains the actual condition of this circuit;

Secondly, based on this floor data, by above-mentioned vehicle dynamics formula (4), try to achieve each instantaneous chaufeur power demand, and then according to above-mentioned formula (1) (2) (3) (4) (5), obtain the transition probability matrix of chaufeur power demand Markov process;

Again, with Stochastic Dynamic Programming Method, try to achieve the energy management strategy that is applicable to this circuit.

Finally, by the method for long-range demarcation, the control parameter of HCU is upgraded, HCU completes tactful realization.

This fuel economy and its discharge of reduction raising China to the hybrid power bus of fixed line has very large practical significance.

System architecture

This system comprises hybrid power whole vehicle controller (HCU) and server software two parts, as Fig. 1.

(1) hardware configuration

HCU structure is as Fig. 2,32 PowerPC series monolithic MPC5644A that main control chip MCU adopts Freescale company to develop for power drive system specially, by CAN interface circuit, be connected in CAN bus, the information that MCU mainly obtains from bus has SOC value of battery, the current torque of driving engine, the current torque of motor, current gear, current vehicle speed, and the information sending in CAN bus has motor torque, motor torque and accelerator open degree.Main control chip is connected with GPRS module by serial communication interface circuit, MCU is to control parameter from the energy distribution of far-end server by the information of the long-range reception of GPRS, and by GPRS, sending to the information of far-end server is the speed of a motor vehicle-time history of hybrid power bus on certain public bus network.The acceleration pedal signal of chaufeur is issued MCU through modulate circuit, and the minimum system circuit of power circuit, crystal oscillating circuit, reset circuit composition control device guarantees the reliability service of hardware.

(2) server software structure

Server software designs based on LABVIEW and MATLAB Mixed-Programming Technology, LABVIEW is foreground display layer, radical function is the telecommunication of being responsible for realization and HCU, complete the transmission of floor data and control parameter, possesses data Storage & Display function, can be by controlling beginning and the end of data acquisition with HCU telecommunication, and can be by interfacing (ActiveX, DDE, Mathscript) or interface facility bag SIT control, check the operation of model in MATLAB/SIMULINK.MATLAB is operation layer, receive the vehicle speed data that LABVIEW gathers, obtain after treatment the speed of a motor vehicle time history of the hybrid power bus of this circuit, with statistical method, calculate transition probability matrix, then based on Stochastic Dynamic Programming Method, set up and solve energy management problem

Workflow

1) first hybrid power bus in the operation of one to two week real-world operation circuit enterprising behavior phase, now in HCU, the control parameter of energy management is one group of static threshold parameter based on engineering experience, HCU can by CAN bus with the sampling frequency online acquisition bus of 1Hz the speed of a motor vehicle on real-world operation route, the GPRS module that serial ports connects sends to server by these data by network.

2) the integrated ICP/IP protocol module of server end LABVIEW software inhouse, utilizes the vehicle speed data on the software collection network interface card of writing out, completes and shows and storage work.

3) MATLAB calls the speed of a motor vehicle time history of LABVIEW storage, bad point data are rejected, the bad point here refers to obviously do not possess representational data due to what a variety of causes (as vehicle trouble etc.) caused, rejecting is not these data is listed in statistics below, obtains many actual operating mode data of bus on this circuit.

4), for a large amount of vehicle speed datas that obtain, by vehicle dynamics formula, reverse obtains per moment chaufeur demand power, according to the flow processing of formula (1) (2) (3) (4) (5) in above-mentioned principle, obtains transition probability matrix.

5) then based on stochastic dynamic programming algorithm, set up energy management problem, be specially chaufeur power demand, motor output torque, battery SOC and speed scattering turn to finite space, with SOC value of battery, the speed of a motor vehicle, gear, chaufeur demand power is state variable, form state space X, using motor output torque as decision variable G, with fuel oil consumption, driving engine discharge, SOC value of battery be weighted to cost function J, with the speed of a motor vehicle, battery SOC, the maximum of motor output torque and minimum value are as constraint, it is the boundary condition of solution procedure, write out state transition equation (6), SDP tool box in application MATLAB carries out iterative.

6) finally by LABVIEW by the strategy of generation with form or the good function representation of matching and be updated in HCU by network, HCU finally realizes this energy management strategy.

Claims (6)

1. a hybrid vehicle energy management method, comprises the following steps:

(1) the method is regarded chaufeur demand power as a Markov process, and by motor vehicle driven by mixed power entire car controller (HCU), to described vehicle, the actual condition in target line carries out vehicle speed data collection;

(2) according to gathered vehicle speed data, by vehicle dynamics formula, try to achieve each instantaneous chaufeur demand power, the vehicle dynamics formula that wherein adopted is:

$P_{\mathrm{dr}\_\mathrm{dem}}=\frac{V_{\mathrm{vel}}}{3600{\mathrm{\η}}_T}(\mathrm{Gf}\cos \mathrm{\α}+G\sin \mathrm{\α}+\frac{C_D{\mathrm{AV}}_{\mathrm{vel}}^2}{21.15}+\mathrm{\δ}\frac{G}{g}\frac{d{V}_{\mathrm{vel}}}{\mathrm{dt}})$

Wherein, P _{dr_dem}for the chaufeur demand power of Vehicle Driving Cycle, V _velfor the speed of a motor vehicle, f is coefficient of rolling resistance, and A is wind area, C _dfor aerodynamic drag factor, α is road grade angle, and G is vehicle gravity, and g is acceleration due to gravity, and δ is vehicle rotary mass conversion coefficient,

for running car acceleration/accel, η _tfor vehicle transmission system mechanical efficiency;

One step transition probability of chaufeur demand power is expressed as by maximal possibility estimation:

n in formula _{ij, m}represent chaufeur demand power from transfer to

number of times,

represent

shift total degree, p _{ij, k}statement chaufeur demand power transition probability, its implication is: at moment k, chaufeur demand power is

the speed of a motor vehicle is

condition under, chaufeur demand power is at t _k+1constantly transfer to

probability;

All transition probabilities form transition probability matrix P, obtain the transition probability matrix of chaufeur demand power, set up the markov probabilistic model of chaufeur demand power;

(3) based on stochastic dynamic programming algorithm, set up the energy management problem of this motor vehicle driven by mixed power, described energy management problem is specially: chaufeur demand power, motor output torque, battery SOC and speed scattering are turned to finite space; Take SOC value of battery, the speed of a motor vehicle, gear, chaufeur demand power is state variable, forms state space X; Using motor output torque Tm as decision variable G; Take the discharge of fuel oil consumption, driving engine, SOC value of battery is cost function J; Using the maximum of the speed of a motor vehicle, battery SOC, motor output torque and minimum value as constraint, i.e. the boundary condition of solution procedure; Thereby set up the state transition equation of chaufeur demand power;

(4) using modified policy iteration method carries out iterative to the state transition equation of described chaufeur demand power, solve the best decision variable G of each step of all cost function J minimums of sening as an envoy to, these best decision variablees G combines and forms the energy management strategy π that is applicable to this circuit, and concrete form is: (T _m(k))=π (SOC (k), ω _w(k), g (k), T _dem(k)), T in formula _m(k) represent the motor output torque of demand, SOC (k) represents battery charge state, ω _w(k) represent the speed of a motor vehicle, g (k) represents automobile gear level, T _dem(k) represent the torque of chaufeur demand, this energy management strategy is motor booster type, by regulating the output of motor torque to make driving engine be operated in as far as possible efficient district;

(5) by long-range demarcation the control parameter of motor vehicle driven by mixed power entire car controller (HCU), according to current vehicle-state (SOC (k), ω _w(k), g (k), T _dem(k)) definite motor output torque, mode with Function Fitting or data sheet is updated in motor vehicle driven by mixed power entire car controller (HCU), and motor vehicle driven by mixed power entire car controller (HCU) is dealt into the value of this motor output torque in CAN bus, to complete the control to motor.

2. hybrid vehicle energy management method according to claim 1, wherein said motor vehicle driven by mixed power entire car controller (HCU) gathers vehicle speed data by CAN bus, and by GPRS module, gathered vehicle speed data is sent to server by network.

3. hybrid vehicle energy management method according to claim 2, the ICP/IP protocol module that wherein said server by utilizing LABVIEW software inhouse is integrated, receives described vehicle speed data, and completes and show and storage work.

4. hybrid vehicle energy management method according to claim 3, the vehicle speed data of LABVIEW software storage described in wherein said server by utilizing MATLAB software transfer, by statistical analysis, obtain the actual condition data of described vehicle in described target line.

5. hybrid vehicle energy management method according to claim 4, described in wherein said server by utilizing LABVIEW by the control parameter of generation with data sheet or the good function representation of matching and be updated in described motor vehicle driven by mixed power entire car controller (HCU) by network.

6. a hybrid vehicle energy management system, this system comprises server and motor vehicle driven by mixed power entire car controller (HCU), described motor vehicle driven by mixed power entire car controller (HCU) gathers the speed information of described vehicle in target line by CAN bus, and by GPRS module, gathered described speed information is sent to described server by network; The integrated ICP/IP protocol module of described server by utilizing LABVIEW software inhouse receives the described speed information of storage; Described in described server by utilizing MATLAB software transfer, LABVIEW software receives the described speed information of storage, and try to achieve each instantaneous chaufeur demand power by vehicle dynamics formula, obtain the transition probability matrix of chaufeur demand power, set up the markov probabilistic model of chaufeur demand power; Then described server sets up energy management problem based on stochastic dynamic programming algorithm, and described energy management problem is specially: chaufeur demand power, motor output torque, battery SOC and speed scattering are turned to finite space; Take SOC value of battery, the speed of a motor vehicle, gear, chaufeur demand power is state variable, forms state space X; Using motor output torque Tm as decision variable G; Take the discharge of fuel oil consumption, driving engine, SOC value of battery is cost function J; Using the maximum of the speed of a motor vehicle, battery SOC, motor output torque and minimum value as constraint, i.e. the boundary condition of solution procedure; Thereby set up the state transition equation of chaufeur demand power, and the SDP tool box of applying in described MATLAB software carries out iterative to the state transition equation of chaufeur demand power, solve the best decision variable G of each step of all cost function J minimums of sening as an envoy to, these best decision variablees G combines and forms the energy management strategy π that is applicable to this circuit, and concrete form is: (T _m(k))=π (SOC (k), ω _w(k), g (k), T _dem(k)), T in formula _m(k) represent the motor output torque of demand, SOC (k) represents battery charge state, ω _w(k) represent the speed of a motor vehicle, g (k) represents automobile gear level, T _dem(k) represent the torque of chaufeur demand, this energy management strategy is motor booster type, by regulating the output of motor torque to make driving engine be operated in as far as possible efficient district; The control parameter of the motor vehicle driven by mixed power entire car controller (HCU) obtaining is according to current vehicle-state (SOC (k), ω _w(k), g (k), T _dem(k)) definite motor output torque; Data sheet or the good function representation of matching for control parameter that described in described server by utilizing, LABVIEW generates iterative, and be updated in described motor vehicle driven by mixed power entire car controller (HCU) by network remote, complete the control parameter calibration of described motor vehicle driven by mixed power entire car controller (HCU), described motor vehicle driven by mixed power entire car controller (HCU) thus the value of described vehicular electric machine output torque is dealt into and in CAN bus, completes the control to motor.

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