State Estimation of Drive-by-Wire Chassis Vehicle Based on Dual Unscented Particle Filter Algorithm

Accurate vehicle dynamic information plays an important role in vehicle driving safety.However,due to the characteristics of high mobility and multiple controllable degrees of freedom of drive-by-wire chassis vehicles,the current mature application of traditional vehicle state estimation algorithms can not meet the requirements of drive-by-wire chassis vehicle state estimation.This paper proposes a state estimation method for drive-by-wire chassis vehicle based on the dual unscented particle filter algorithm,which make full use of the known advantages of the four-wheel drive torque and steer angle parameters of the drive-by-wire chassis vehicle.In the dual unscented particle filter algorithm,two unscented particle filter transfer information to each other,observe the vehicle state information and the tire force parameter information of the four wheels respectively,which reduce the influence of parameter uncertainty and model parameter changes on the estimation accuracy during driving.The performance with the dual unscented particle filter algorithm,which is analyzed in terms of the time-average square error,is superior of the unscented Kalman filter algorithm.The effectiveness of the algorithm is further verified by driving simulator test.In this paper,a vehicle state estimator based on dual unscented particle filter algorithm was proposed for the first time to improve the estimation accuracy of vehicle parameters and states.

Vehicle Dynamic State Estimation： State of the Art Schemes and Perspectives

Next-generation vehicle control and future autonomous driving require further advances in vehicle dynamic state estimation. This article provides a concise review, along with the perspectives, of the recent developments in the estimation of vehicle dynamic states. The definitions used in vehicle dynamic state estimation are first introduced, and alternative estimation structures are presented. Then, the sensor configuration schemes used to estimate vehicle velocity, sideslip angle, yaw rate and roll angle are presented. The vehicle models used for vehicle dynamic state estimation are further summarized, and representative estimation approaches are discussed. Future concerns and perspectives for vehicle dynamic state estimation are also discussed.

Estimation Method of State-of-Charge For Lithium-ion Battery Used in Hybrid Electric Vehicles Based on Variable Structure Extended Kalman Filter

Since the main power source of hybrid electric vehicle（HEV） is supplied by the power battery,the predicted performance of power battery,especially the state-of-charge（SOC） estimation has attracted great attention in the area of HEV.However,the value of SOC estimation could not be greatly precise so that the running performance of HEV is greatly affected.A variable structure extended kalman filter（VSEKF）-based estimation method,which could be used to analyze the SOC of lithium-ion battery in the fixed driving condition,is presented.First,the general lower-order battery equivalent circuit model（GLM）,which includes column accumulation model,open circuit voltage model and the SOC output model,is established,and the off-line and online model parameters are calculated with hybrid pulse power characteristics（HPPC） test data.Next,a VSEKF estimation method of SOC,which integrates the ampere-hour（Ah） integration method and the extended Kalman filter（EKF） method,is executed with different adaptive weighting coefficients,which are determined according to the different values of open-circuit voltage obtained in the corresponding charging or discharging processes.According to the experimental analysis,the faster convergence speed and more accurate simulating results could be obtained using the VSEKF method in the running performance of HEV.The error rate of SOC estimation with the VSEKF method is focused in the range of 5% to 10% comparing with the range of 20% to 30% using the EKF method and the Ah integration method.In Summary,the accuracy of the SOC estimation in the lithium-ion battery cell and the pack of lithium-ion battery system,which is obtained utilizing the VSEKF method has been significantly improved comparing with the Ah integration method and the EKF method.The VSEKF method utilizing in the SOC estimation in the lithium-ion pack of HEV can be widely used in practical driving conditions.

Vehicle State and Bias Estimation Based on Unscented Kalman Filter with Vehicle Hybrid Kinematics and Dynamics Models

In recent years,vehicle state estimation methods incorporating different vehicle models have received extensive attention.When the vehicle is disturbed by external forces not considered in traditional vehicle models(for example,a certain slope,or wind resistance different from theoretical calculation),the problem of model mismatch will occur,which leads to the inaccurate estimation of the vehicle states.To solve this problem,an Unscented Kalman Filter(UKF)algorithm is used to fuse inertial navigation data with the vehicle hybrid model in this paper.The hybrid model introduces a switching strategy that fuses the vehicle kinematics and the dynamics models while augmenting biases that need to be estimated in the vehicle states.The switching strategy resolves the integration divergence problem of vehicle dynamics models at low speeds and the inaccurate estimation of vehicle kinematics models at high speeds.Simulation experiments demonstrate that the proposed method can accurately estimate biases induced by external forces,enhancing the accuracy and confidence of states by eliminating errors caused by these biases.The robustness of the method is validated in vehicle verification platform experiments,where errors in vehicle lateral speed and yaw rate are reduced by 9.7 cm/s and 0.012°/s,respectively,under large curvature maneuvers,and 9.6 cm/s and 0.004°/s under quarter-turn maneuvers.The proposed method significantly improves lateral speed and vehicle position accuracies.

Interacting multiple model-based ETUKF for efficient state estimation of connected vehicles with V2V communication

Accurate prediction of the motion state of the connected vehicles,especially the preceding vehicle(PV),would effectively improve the decision-making and path planning of intelligent vehicles.The evolution of vehicle-tovehicle(V2V)communication technology makes it possible to exchange data between vehicles.However,since V2V communication has a transmission interval,which will result in the host vehicle not receiving information from the PV within the time interval.Furthermore,V2V communication is a time-triggered system that may occupy more communication bandwidth than required.On the other hand,traditional estimation methods of the PV state based on individual models are usually not applicable to a wide range of driving conditions.To address these issues,an event-triggered unscented Kalman filter(ETUKF)is first employed to estimate the PV state to strike a balance between estimation accuracy and communication cost.Then,an interactive multi-model(IMM)approach is combined with ETUKF to form IMMETUKF to further improve the estimation accuracy and applicability.Finally,simulation experiments under different driving conditions are implemented to verify the effectiveness of IMMETUKF.The test results indicated that the IMMETUKF has high estimation accuracy even when the communication rate is reduced to 14.84%and the proposed algorithm is highly adaptable to different driving conditions.

Development and Verification of the Equilibrium Strategy for Batteries in Electric Vehicles

The inconsistency of the cells in a battery pack can affect its lifespan,safety and reliability in the electric vehicles. The balanced system is an effective technique to reduce its inconsistency and improve the operating performance. A hybrid equilibrium strategy based on decision combing battery state-of-charge（ SOC） and voltage has been proposed. The battery SOC is estimated through an improved least squares method. An equalization hardware in loop（ HIL） platform has been constructed. Based on this HIL platform,equilibrium strategy has been verified under the constant-current-constant-voltage（ CCCV） and dynamicstresstest（ DST） conditions. Experimental results indicate that the proposed hybrid equalization strategy can achieve good balance effect and avoid the overcharge and over-discharge of the battery pack at the same time.

Feature mapping and state estimation for highly automated vehicles

The past decade has witnessed an acceleration of autonomous vehicle research and development,with technological advances contributed by academia,government,and the industrial and consumer sectors.These advancements hold the potential to improve society by enhancing transportation safety and throughput,where decreased congestion saves time and reduces vehicle emissions.Two of the key technologies to enable vehicle infrastructure interaction,advanced traffic management,and automated vehicles are automated roadway mapping and reliable vehicle state estimation.In this paper,we present an overview and new methods for the problems automated roadway mapping plus a discussion of the extension of these methods to the problem of vehicle state estimation.Results from the application of these methods to feature mapping and state estimation are presented.

Approximate Optimal Filter Design for Vehicle System through Actor‑Critic Reinforcement Learning

Precise state and parameter estimations are essential for identification,analysis and control of vehicle engineering problems,especially under significant model and measurement uncertainties.The widely used filtering/estimation algorithms,such as Kalman series like Kalman filter,extended Kalman filter,unscented Kalman filter,and particle filter,generally aim to approach the true state/parameter distribution via iteratively updating the filter gain at each time step.However,the optimal-ity of these filters would be deteriorated by unrealistic initial condition or significant model error.Alternatively,this paper proposes to approximate the optimal filter gain by considering the effect factors within infinite time horizon,on the basis of estimation-control duality.The proposed approximate optimal filter(AOF)problem is designed and subsequently solved by actor-critic reinforcement learning(RL)method.The AOF design transforms the traditional optimal filtering problem with the minimum expected mean square error into an optimal control problem with the minimum accumulated estimation error,in which the estimation error is used as the surrogate system state and the infinite-horizon filter gain is the control input.The estimation-control duality is proved to hold when certain conditions about initial vehicle state distributions and policy structure are maintained.In order to evaluate of the effectiveness of AOF,a vehicle state estimation problem is then demonstrated and compared with the steady-state Kalman filter.The results showed that the obtained filter policy via RL with different discount factors can converge to theoretical optimal gain with an error within 5%,and the average estimation errors of vehicle slip angle and yaw rate are less than 1.5×10–4.