Issue 
Int. J. Metrol. Qual. Eng.
Volume 10, 2019



Article Number  16  
Number of page(s)  6  
DOI  https://doi.org/10.1051/ijmqe/2019015  
Published online  12 December 2019 
Research Article
Integrated control performance of drivebywire independent drive electric vehicle
^{1}
School of Mechanical Engineering, Henan Polytechnic Institute, Nanyang, Henan 473009, PR China
^{2}
School of Electromechanical Automation, Henan Polytechnic Institute, Nanyang, Henan 473009, PR China
^{*} Corresponding author: yllingyu@yeah.net
Received:
10
October
2019
Accepted:
9
November
2019
In order to improve the stability and safety of vehicles, it is necessary to control them. In this study, the integrated control method of drivebywire independent drive electric vehicle was studied. Firstly, the reference model of electric vehicle was established. Then, an integrated control method of acceleration slip regulation (ARS) and direct yaw moment control (DYC) was designed for controlling the nonlinearity of tyre, and the simulation experiment was carried out under the environment of MATLAB/SIMULINK. The results showed that the vehicle lost its stability when it was uncontrolled; under the control of a single DYC controller, r and β values got some control, but the vehicle stability was still low; under the integrated control of ARS+DYC, the vehicle stability was significantly improved; under the integrated control method, the overshoot, regulation time and steadystate error of the system were all small. Under the simulation of extreme conditions, the integrated control method also showed excellent performance, which suggested the method was reliable. The experimental results suggests the effectiveness of the integrated control method, which makes some contributions to the further research of the integrated control of electric vehicles.
Key words: Independent drive electric vehicle / linear control / direct yaw moment control / integrated control
© L. Yu and S. Yuan, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
1 Introduction
Electric vehicles are vehicles driven by electricity [1], including fourwheel independent drive (4WID), fourwheel independent steering (4WIS), fourwheel independent braking (4WIB), etc. The whole vehicle system adopts wire control technology, which can reduce the load of drivers. In order to further improve the stability and safety of electric vehicles, vehicle stability control has been widely studied. At present, there are two main directions: one is to control the wheel angle, including Active Front Steering (AFS) [2], Active RearWheel Steering (ARS), etc., and the other is to improve the tire adhesion to the ground or to correct the vehicle deficiencies under extreme conditions, including Acceleration Slip Regulation (ASR), Direct Yaw Moment Control (DYC), etc [3]. But these control methods all have some shortcomings. In order to achieve higher performance, different control methods are integrated. Integrated control of vehicles becomes a new choice of electric vehicle control. Integrated control can achieve the optimization of vehicle performance very well, which has attracted extensive attention of researchers. Wu et al. [4] proposed an integrated control strategy combining AFS and DYC, and developed a twolane transformation (DLC) simulation, and found through simulation that the method had high control performance. Zhao et al. [5] established a 14DOF vehicle model, then studied the coordinated control among active suspension system (ASS), AFS and DYC and found that the method could effectively improve the lateral and longitudinal dynamics of vehicles. Shen et al. [6] combined fourwheel steering (4WS) with DYC to ensure vehicle stability by generating appropriate 4WS angle and correcting yaw moment based on sliding mode control and verified the effectiveness of the method through experiments. In this study, an integrated control method combining ARS with DYC was designed for 4WID to improve the control effect when the tyre was in the nonlinear state. The simulation results proved the control performance of the method, which is beneficial to improve vehicle stability and safety. This work provides some theoretical bases for improving the control performance of electric vehicles.
2 4WID model establishment
4WID electric vehicle [7] was taken as the research subject in this study. The vehicle adopts full drivebywire technology, including drivebywire driving, steering and braking, which can effectively reduce the driver's burden and realize intelligent response of vehicles. It can realize motion modes such as frontwheel steering, rearwheel steering, oblique motion and crab motion, suggesting excellent performance. Before designing the integrated control method, the reference model of 4WID needs to be designed. In this study, a linear twodegreeoffreedom model was used, and the differential equations are:$$mv\left({\displaystyle \dot{\beta}}+r\right)=\left({c}_{f}+{c}_{r}\right)\beta \frac{{c}_{f}a{c}_{r}b}{v}r+{c}_{f}{\delta}_{f}+{c}_{r}\delta r\text{,}$$(1) $${I}_{z}{\displaystyle \dot{r}}=\left({c}_{r}b{c}_{f}a\right)\beta \frac{{c}_{f}{a}^{2}+{c}_{r}{b}^{2}}{v}+{c}_{f}a{\delta}_{f}{c}_{r}b{\delta}_{r}\text{,}$$(2)where: m: total weight of vehicle, v: centroid speed of vehicle, β: sideslip angle of vehicle centroid, r: angular velocity of vehicle, c_{f}: cornering stiffness of front axle, c_{r}: lateral stiffness of back axle, a: distance from centroid to the front axle, b: distance from centroid to the rear axis, δ_{f}: front wheel steering angle, δ_{r}: rear wheel steering angle, I_{z}: yaw rotational inertia of vehicle.
According to state quantity X = [β r]^{ T } and input vector u = [δ_{f} δ_{r}]^{ T }, the above equation is converted to state equation, then$$\dot{X}}=AX+Bu$$(3)
where$$A=\left[\begin{array}{c}\hfill \frac{{c}_{f}+{c}_{r}}{mv}\hfill \\ \hfill \frac{{c}_{f}a{c}_{r}b}{{I}_{z}}\hfill \end{array}\text{}\begin{array}{c}\hfill 1\frac{{c}_{f}a{c}_{r}b}{m{v}_{2}}\hfill \\ \hfill \frac{{c}_{f}{a}^{2}+{c}_{r}{b}^{2}}{{I}_{z}v}\hfill \end{array}\text{}\right]\text{.}$$(4)and$$B=\left[\begin{array}{l}\frac{{c}_{f}}{mv}\hfill \\ \frac{{c}_{f}a}{{I}_{z}}\hfill \end{array}\text{}\begin{array}{c}\hfill \frac{{c}_{r}}{mv}\hfill \\ \hfill \frac{{c}_{r}b}{{I}_{z}}\hfill \end{array}\right]\text{.}$$(5)
Vehicle reference model is to design expected yaw velocity r_{d} and expected centroid sideslip angle β_{d}. The steady state response of vehicle yaw angular speed r to front wheel steering angle δ_{f} can be expressed as:$$r=\frac{v}{l+{K}_{v}{v}^{2}}\frac{{\theta}_{sw}}{{n}_{sw}}=\frac{v}{l+{K}_{v}{v}^{2}}{\delta}_{f}\text{,}$$(6) $${K}_{v}=\frac{m}{l}\left(\frac{b}{{c}_{f}}\frac{a}{{c}_{r}}\right)\text{,}$$(7)where l stands for front and rear axle distance, K_{v} stands for vehicle understeering gradient, θ_{sw} stands for steering angle of steering wheel, and n_{sw} stands for transmission ratio of steering wheel to front wheel.
The maximum lateral acceleration of vehicle will be limited by the ground adhesion coefficient: ${a}_{y}={{\displaystyle \dot{v}}}_{y}+{v}_{x}r$, a_{y} ≤ μg; therefore, the peak expected yaw velocity needs to be limited: $\left{r}_{d}\right\le \left{r}_{\mathrm{max}}\right\le 0.85\frac{ug}{v}$. r_{d} can be expressed as:$${r}_{d}=\mathrm{min}\left\{\left\frac{v}{l+{K}_{v}{v}^{2}}{\delta}_{f}\right,\left0.85\frac{\mu g}{v}\right\right\}\mathrm{sgn}\left({\delta}_{f}\right)\frac{1}{1+{\tau}_{r}s}\text{,}$$(8)where a_{y} stands for lateral acceleration, μ stands for nominal ground adhesion coefficient, g stands for gravity acceleration, τ_{r} stands for firstorder delay time, between 0.1 and 0.25 s, and sgn stands for symbolic function.
In order to ensure the highspeed stability of vehicle, suppose β_{d} = 0.
In the reference model, the input is θ_{sw} or δ_{f}, and the outputs are r_{d} and β_{d}.
3 Integrated control method
3.1 ARS controller
ARS means to control the active steering of the rear wheel of a vehicle [8] to reduce the cornering angle and improve vehicle stability, but when the tire is nonlinear, its control effect will be weakened. ARS controller is designed with linear sliding mode controller. Its sliding mode surface can be expressed as:$${S}_{1}={\displaystyle \dot{e}}+{c}_{0}e+{c}_{1}\int edt\text{,}$$(9)where $\dot{e}$ stands for the tracking error, $\dot{e}}=r{r}_{d$, and c_{0} and c_{1} are undetermined coefficients. Then$${{\displaystyle \dot{S}}}_{1}={\displaystyle \dot{r}}{{\displaystyle \dot{r}}}_{d}+{c}_{0}{\displaystyle \dot{e}}+{c}_{1}e\text{.}$$(10)
Suppose ${{\displaystyle \dot{S}}}_{1}=K\mathrm{sgn}\left({S}_{1}\right)$ and K as the parameter of controller, then:$${\delta}_{r}=\frac{{I}_{z}}{{c}_{r}{l}_{r}}\left[\frac{{c}_{r}{l}_{r}{c}_{f}{l}_{f}}{{I}_{z}}\beta \frac{{c}_{f}{{l}_{f}}^{2}+{c}_{r}{{l}_{r}}^{2}}{{I}_{z}v}r+\frac{{c}_{f}{l}_{f}}{{I}_{z}{\delta}_{f}}{{\displaystyle \dot{r}}}_{d}+{c}_{0}{\displaystyle \dot{e}}+{c}_{1}e+K\mathrm{sgn}\left({S}_{1}\right)\right]\text{.}$$(11)
To reduce chattering, sgn (S_{1}) is replaced by saturation function sat (S_{1}), then:$${\delta}_{r}=\frac{{I}_{z}}{{c}_{r}{l}_{r}}\left[\frac{{c}_{r}{l}_{r}{c}_{f}{l}_{f}}{{I}_{z}}\beta \frac{{c}_{f}{{l}_{f}}^{2}+{c}_{r}{{l}_{r}}^{2}}{{I}_{z}v}r+\frac{{c}_{f}{l}_{f}}{{I}_{z}{\delta}_{f}}{{\displaystyle \dot{r}}}_{d}+{c}_{0}{\displaystyle \dot{e}}+{c}_{1}e+K\mathrm{s}\mathrm{a}\mathrm{t}\left({S}_{1}\right)\right]\text{,}$$(12)where l_{r} stands for rear axle distance and l_{f} stands for front axle distance.
According to the above equation, the inputs of ARS controller are r, β, δ_{f} and r_{d}, and the output is δ_{r}.
3.2 DYC controller
DYC refers to the control of wheel braking when tyres are nonlinear, in details, generating torque to change yaw angular speed. It can improve vehicle yaw stability [9,10] and prevent vehicle from losing control in the nonlinear area, but DYC will force to reduce vehicle speed, which is not applicable in some cases where deceleration is not allowed.
When β is small, the motion state of vehicle is determined by r. Suppose e = r − r_{d} . Sliding mode surface is expressed as:$$\begin{array}{l}{S}_{2}={\displaystyle \dot{e}}+e=\left({\displaystyle \dot{r}}{{\displaystyle \dot{r}}}_{f}\right)+\left(r{r}_{d}\right)\\ =\left[\frac{2\left(a{k}_{1}b{k}_{2}\right)}{{I}_{z}}\beta +\left(\frac{2\left({a}^{2}{k}_{1}+{b}^{2}{k}_{2}\right)}{{v}_{x}{I}_{z}}+1\right)r\frac{2a{k}_{1}}{{I}_{z}}{\delta}_{f}+\frac{2b{k}_{2}}{{I}_{z}}{\delta}_{r}{r}_{d}{{\displaystyle \dot{r}}}_{d}\right]+\frac{\mathrm{\Delta}{M}_{r}}{{I}_{z}}\end{array}$$(13)there is$${{\displaystyle \dot{S}}}_{2}={\displaystyle \stackrel{\xb7}{e}}+{\displaystyle \dot{e}}=A+\frac{\mathrm{\Delta}{{\displaystyle \dot{M}}}_{r}}{{I}_{z}}$$(14)
after derivation, where$$\begin{array}{l}A=\frac{2\left(a{k}_{1}b{k}_{2}\right)}{{I}_{z}}\left[\frac{2\left(a+b\right)}{m{v}_{x}}\beta +\left(\frac{2\left(a{k}_{1}b{k}_{1}\right)}{m{v}_{x}^{2}}1\right)r\frac{2a}{m{v}_{x}}{\delta}_{f}\frac{2b}{m{v}_{x}}{\delta}_{r}\right]+\left(\frac{2\left({a}^{2}{k}_{1}+{b}^{2}{k}_{2}\right)}{{v}_{x}{I}_{z}}+1\right){\displaystyle \dot{r}}\\ \frac{2a{k}_{1}}{{I}_{z}}{{\displaystyle \dot{\delta}}}_{f}+\frac{2b{k}_{2}}{{I}_{z}}{{\displaystyle \dot{\delta}}}_{r}{{\displaystyle \dot{r}}}_{d}{{\displaystyle \stackrel{\xb7}{r}}}_{d}.\end{array}$$(15)
Suppose ${{\displaystyle \dot{S}}}_{2}={K}_{r}\mathrm{sgn}\left(S\right)$_{,}
then$$\mathrm{\Delta}{\displaystyle \dot{M}}={I}_{z}\left[{K}_{r}\mathrm{sgn}\left({S}_{2}\right)+A\right]\text{,}$$(16) where k_{1} stands for roll stiffness of front suspension and k_{2} stands for roll stiffness of rear suspension. Additional yaw moment ΔM_{r} needed by DYC can be obtained by integrating $\mathrm{\Delta}{{\displaystyle \dot{M}}}_{r}$.
When μ is small, β needs to be limited to ensure the stability of vehicle. Suppose e = β − β_{d}. The sliding mode surface is expressed as:$$\begin{array}{l}{S}_{3}={\displaystyle \dot{e}}+e=\left({\displaystyle \dot{\beta}}{{\displaystyle \dot{\beta}}}_{d}\right)+\left(\beta {\beta}_{d}\right)\\ =\left(\frac{2\left({k}_{1}+{k}_{2}\right)}{m{v}_{x}}+1\right)\beta +\left(\frac{2\left(a{k}_{1}+b{k}_{2}\right)}{m{v}_{x}^{2}}1\right)r\frac{2{k}_{1}}{m{v}_{x}}{\delta}_{f}\frac{2{k}_{2}}{m{v}_{x}}{\delta}_{r}{{\displaystyle \dot{\beta}}}_{d}{\beta}_{d},\end{array}$$(17)there is
${{\displaystyle \dot{S}}}_{3}={\displaystyle \ddot{e}}+{\displaystyle \dot{e}}=B+\left(\frac{2\left(a{k}_{1}+b{k}_{2}\right)}{m{v}_{x}^{2}}1\right)\frac{\mathrm{\Delta}{M}_{\beta}}{{I}_{z}}$(18)after derivation, where$$\begin{array}{l}B=\left(\frac{2\left({k}_{1}+{k}_{2}\right)}{m{v}_{x}}+1\right)\left[\frac{2\left({k}_{1}+{k}_{2}\right)}{m{v}_{x}}\beta +\left(\frac{2\left(a{k}_{1}+b{k}_{2}\right)}{m{v}_{x}^{2}}1\right)\beta \frac{2{k}_{1}}{m{v}_{x}}{\delta}_{f}\frac{2{k}_{2}}{m{v}_{x}}{\delta}_{r}\right]+\left(\frac{2\left(a{k}_{1}+b{k}_{2}\right)}{m{v}_{x}^{2}}1\right)\\ \left[\frac{2\left(a{k}_{1}b{k}_{2}\right)}{{I}_{z}}\beta +\left(\frac{2\left({a}^{2}{k}_{1}+{b}^{2}{k}_{2}\right)}{{v}_{x}{I}_{z}}\right)r\frac{2a{k}_{1}}{{I}_{z}}{\delta}_{f}+\frac{2b{k}_{2}}{{I}_{z}}{\delta}_{r}\right]\frac{2{k}_{1}}{m{v}_{x}}{{\displaystyle \dot{\delta}}}_{f}\frac{2{k}_{2}}{m{v}_{x}}{{\displaystyle \dot{\delta}}}_{x}{{\displaystyle \stackrel{\xb7}{\beta}}}_{d}{{\displaystyle \dot{\beta}}}_{d}.\end{array}$$(19)
Suppose$${{\displaystyle \dot{S}}}_{3}={K}_{\beta}\mathrm{sgn}\left({S}_{3}\right)\text{,}$$(20)
then$$\mathrm{\Delta}{M}_{\beta}={I}_{z}\left[B+{K}_{\beta}\mathrm{sgn}\left({S}_{3}\right)\right]{\left(1\frac{2\left(a{k}_{1}+b{k}_{2}\right)}{m{v}_{x}^{2}}\right)}^{1}\text{.}$$(21)
In summary, the final output of the DYC controller is:$$\mathrm{\Delta}M=K\mathrm{\Delta}{M}_{r}+\left(1K\right)\mathrm{\Delta}{M}_{\beta}\text{.}$$(22)When β is relatively small, r dominates; when β is large, β dominates. In this way, the influence of both r and β on the lateral stability of vehicle can be taken into account.
3.3 Integration of ARS and DYC
According to Hurwitz stability judgment, the vehicle stability conditions are:$${c}_{f}{c}_{r}{l}^{2}+m{u}^{2}\left({c}_{r}{l}_{r}{c}_{f}{l}_{f}\right)>0$$(23) $${u}_{ch}^{2}=\frac{{c}_{f}{c}_{r}{l}^{2}}{m\left({c}_{r}{l}_{r}{c}_{f}{l}_{f}\right)}$$(24) $$1+\frac{{u}^{2}}{{u}_{ch}^{2}}>0$$(25)where u represents the longitudinal speed and u_{ch} stands for the characteristic speed. The method of determining the stability of vehicle is:$$\{\begin{array}{l}1+\frac{{u}^{2}}{{u}_{ch}^{2}}<0,\mathrm{u}\mathrm{n}\mathrm{s}\mathrm{t}\mathrm{a}\mathrm{b}\mathrm{l}\mathrm{e}\hfill \\ 1+\frac{{u}^{2}}{{u}_{ch}^{2}}<0,\mathrm{c}\mathrm{r}\mathrm{i}\mathrm{t}\mathrm{i}\mathrm{c}\mathrm{a}\mathrm{l}\text{}\mathrm{s}\mathrm{t}\mathrm{a}\mathrm{b}\mathrm{l}\mathrm{e}\hfill \\ 1+\frac{{u}^{2}}{{u}_{ch}^{2}}<0,\mathrm{s}\mathrm{t}\mathrm{a}\mathrm{b}\mathrm{l}\mathrm{e}\hfill \end{array}$$(26)
Under the integrated control of ARS and DYC, the specific strategy is shown in Figure 1.
① $\leftr\right\le \frac{0.85\mu g}{u}$ and r − r_{d > Δω1 or $\leftr\right>\frac{0.85\mu g}{u}$ and Δω1 < r − rd ≤ Δω2;}
② $\leftr\right>\frac{0.85\mu g}{u}$ and r − r_{d} > Δω_{2};
③ other conditions except ① and ②.
Fig. 1 Integrated control method. 
4 Performance analysis of algorithm
4.1 Experimental parameters
The integrated control method designed in this study was simulated under the environment of MATLAB/SIMULINK. The simulated vehicle speed was set as 80 km/h. The vehicle parameters are shown in Table 1.
Vehicle parameters.
4.2 Comparison of control performance
When the ground adhesion coefficient was 0.2, the vehicle control results are shown in Figures 2 and 3 and Table 2.
Based on Figures 2 and 3, it was found that the stability of the vehicle was poor under uncontrolled condition, values of r and β had large changes, indicating that the safety of the vehicle during driving was low; under the control of single DYC controller, the stability of the vehicle was improved to a certain extent; under the integrated control of ARS+DYC, the stability of the vehicle was significantly improved, and changes of values of r and β are relatively stable. It was found from the comparison in Table 2 that the overshoot of the vehicle system under integrated control was smaller, the adjustment time was shorter, and the steadystate error was smaller. It showed that the control performance of the integrated control method was better and more stable than that of single control method, which is more conducive to the safe driving of vehicles.
Control performance.
Fig. 2 Yaw velocity response. 
Fig. 3 Centroid sideslip angle response. 
4.3 Control result under limit conditions
The extreme conditions with crosswind interference were simulated, and the applied crosswind action force is shown in Figure 4.
Under the influence of crosswind action force shown in Figure 4, the vehicle control results are shown in Figures 5 and 6.
It was found from Figures 5 and 6 that under the influence of crosswind action force, if the vehicle was not controlled, values of r and β had large changes, which seriously affected the safe driving of vehicles; under the integrated control of ARS+DYC, values of r and β tracked the reference values well, and the errors between the control values and reference values were very small, which showed that the driving condition of the vehicle was relatively stable at this time. It was concluded that the integrated control had excellent performance under extreme conditions.
Fig. 4 Crosswind action force. 
Fig. 5 Yaw velocity response. 
Fig. 6 Centroid side slip angle response. 
5 Discussion
With the development of economy, the use of vehicles has been more and more widespread, and the pollution is also more and more serious [11,12]. Moreover, as the main energy used by vehicles, the oil resource is decreasing and the oil price is rising, which makes vehicles have to seek other energy sources to replace oil. However, as far as the current situation is concerned, coal, natural gas and other resources still cannot be fully utilized because of some technical problems in cleaning and transportation. It is found that electric energy, as a secondary energy, can be well used as automotive energy. It has obvious advantages, such as less dependence on primary energy, easy acquisition and less pollution to the environment [13]. Compared with traditional vehicles, electric vehicles are environmentally friendly [14]. They can realize frontwheel steering, rearwheel steering, fourwheel steering and other modes with high flexibility and controllable freedom. With the increasing complexity of electric vehicles, traditional independent control methods have not been able to meet the needs of vehicles, so integrated control appears. Integrated control can achieve coordinated work among different control systems and achieve better control effect. The integrated control method of ARS and DYC was studied in this study.
According to the experimental results, if the vehicle was not controlled, the fluctuations of values of r and β would be large during driving, which could lead to instability of the vehicle and severely threaten the safe driving of the vehicle. In the comparison between single DYC and ARS+DYC, the DYC controller could promote the stability of the vehicle, but it could not meet the need of safe driving; under the integrated control, the DYC controller could improve the stability of the vehicle, the variation of values of r and β was small, which was close to the reference value, indicating that the stability of the vehicle was good. The comparison of the performance between the two methods suggested that the integrated control method had smaller overshoot, higher regulation speed, and smaller steadystate error, i.e., having better performance than the single control method. The simulation results of extreme conditions suggested that the integrated control also showed good performance under the influence of crosswind, and values of r and β tracked the reference value well, achieving stable driving of the vehicle. According to the experimental results, it is found that the integrated control method of ARS+DYC designed in this study plays a prominent role in achieving stable and safe driving of vehicles and it is worth further promotion and application in practice.
4WID vehicle is the only route for the future development of advanced vehicles as it can effectively solve problems of energy and environment. Study on its integrate d control is of great values to improve the performance of vehicles. Therefore study on the integrated control of 4WID vehicles has broad prospect. Although some achievements have been made in the research of integrated control method, there are still many tasks to be further studied, such as:

vehicle suspension control is not taken into account;

the research is still at the level of simulation and needs to be tested in real vehicle;

the control algorithms such as chattering suppression need to be further explored;

the performance under other limited conditions of vehicles such as tyre pressure and ice snow covered pavement needs to be studied;

more integrated control systems can be considered.
6 Conclusion
This paper presented a vehicle integrated control method with ARS and DYC working in coordination. The control methods of ARS and DYC were introduced, and the coordinated control strategy was designed. The simulation results showed that the integrated control had better performance than single control and could achieve stable and safe driving of vehicles. It also showed excellent performance under extreme conditions. This work makes some contributions to the improvement of the control performance of electric vehicles and the further application of electric vehicles in the life.
References
 L.H. Liu, Key Eng. Mater. 93 , 8 (2016) [Google Scholar]
 Q. Meng et al., Int. J. Syst. Sci. 49, 1518–1528 (2018) [Google Scholar]
 Z.L. Liao et al., Acta. Armamentarii 38 , 833–842 (2017) [Google Scholar]
 J. Wu et al., Sci. China Technol. Sci. 58 , 75–85 (2015) [CrossRef] [Google Scholar]
 J. Zhao et al., Vehicle. Syst. Dyn. 55 , 72–103 (2017) [CrossRef] [Google Scholar]
 H. Shen, Y.S. Tan, Mod. Phys. Lett. B 31 , 19–21 (2017) [Google Scholar]
 S. Ding, J. Sun, Nonlinear. Dynam. 88 , 1–16 (2016) [Google Scholar]
 H. Shen et al., J. Comput. Theor. Nanos. 13 , 2043–2048 (2016) [CrossRef] [Google Scholar]
 H.Y. Ma, C.P. Li, Z.F. Wang, Energy Proc. 105 , 2310–2316 (2017) [CrossRef] [Google Scholar]
 T. Kobayashi et al., Vehicle Syst. Dyn. 56, 719–733 (2018) [CrossRef] [Google Scholar]
 K.M. Tan, V.K. Ramachandaramurthy, J.Y. Yong, Renew. Sust. Energ. Rev. 53 , 720–732 (2016) [CrossRef] [Google Scholar]
 M.A. Hannan et al., Renew. Sust. Energ. Rev. 78 , 834–854 (2017) [CrossRef] [Google Scholar]
 N. Andrenacci, R. Ragona, G. Valenti, Appl. Energ. 182 , 39–46 (2016) [CrossRef] [Google Scholar]
 Y. Cao et al., IEEE Commun. Mag. 56 , 150–156 (2018) [Google Scholar]
Cite this article as: Ling Yu, Sunan Yuan, Integrated control performance of drivebywire independent drive electric vehicle, Int. J. Metrol. Qual. Eng. 10, 16 (2019)
All Tables
All Figures
Fig. 1 Integrated control method. 

In the text 
Fig. 2 Yaw velocity response. 

In the text 
Fig. 3 Centroid sideslip angle response. 

In the text 
Fig. 4 Crosswind action force. 

In the text 
Fig. 5 Yaw velocity response. 

In the text 
Fig. 6 Centroid side slip angle response. 

In the text 
Current usage metrics show cumulative count of Article Views (fulltext article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 4896 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.