Dynamic Cell Modeling for Accurate SOC Estimation in Autonomous Electric Vehicles

This paper presents findings on dynamic cell modeling for state-of-charge (SOC) estimation in an autonomous electric vehicle (AEV). The studied cells are Lithium-Ion Polymer-based with a nominal capacity of around 8 Ah, optimized for power-needy applications. The AEV operates in a harsh environment with rate requirements up to ±25C and highly dynamic rate profiles, unlike portable-electronic applications with constant power output and fractional C rates. SOC estimation methods effective in portable electronics may not suffice for the AEV. Accurate SOC estimation necessitates a precise cell model. The proposed SOC estimation method utilizes a detailed Kalman-filtering approach. The cell model must include SOC as a state in the model state vector. Multiple cell models are presented, starting with a simple one employing “Coulomb counting” as the state equation and Shepherd’s rule as the output equation, lacking prediction of cell relaxation dynamics. An improved model incorporates filter states to account for relaxation and other dynamics in closed-circuit cell voltage, yielding better performance. The best overall results are achieved with a method combining nonlinear autoregressive filtering and dynamic radial basis function networks. The paper includes lab test results comparing physical cells with model predictions. The most accurate models obtained have an RMS estimation error lower than the quantization noise floor expected in the battery-management-system design. Importantly, these models enable precise SOC estimation, allowing the vehicle controller to utilize the battery pack’s full operating range without overcharging or undercharging concerns.

AI Based Traffic Flow Prediction Model for Connected and Autonomous Electric Vehicles

There is a paradigm shift happening in automotive industry towards electric vehicles as environment and sustainability issues gainedmomentum in the recent years among potential users.Connected and Autonomous Electric Vehicle(CAEV)technologies are fascinating the automakers and inducing them to manufacture connected autonomous vehicles with self-driving features such as autopilot and self-parking.Therefore,Traffic Flow Prediction(TFP)is identified as a major issue in CAEV technologies which needs to be addressed with the help of Deep Learning(DL)techniques.In this view,the current research paper presents an artificial intelligence-based parallel autoencoder for TFP,abbreviated as AIPAE-TFP model in CAEV.The presented model involves two major processes namely,feature engineering and TFP.In feature engineering process,there are multiple stages involved such as feature construction,feature selection,and feature extraction.In addition to the above,a Support Vector Data Description(SVDD)model is also used in the filtration of anomaly points and smoothen the raw data.Finally,AIPAE model is applied to determine the predictive values of traffic flow.In order to illustrate the proficiency of the model’s predictive outcomes,a set of simulations was performed and the results were investigated under distinct aspects.The experimentation outcomes verified the effectual performance of the proposed AIPAE-TFP model over other methods.

Stable trajectory planning and energyefficience control allocation of lane change maneuver for autonomous electric vehicle

Purpose–The purpose of this paper is to investigate problems in performing stable lane changes and tofind a solution to reduce energy consumption of autonomous electric vehicles.Design/methodology/approach–An optimization algorithm,model predictive control(MPC)and Karush–Kuhn–Tucker(KKT)conditions are adopted to resolve the problems of obtaining optimal lane time,tracking dynamic reference and energy-efficient allocation.In this paper,the dynamic constraints of vehicles during lane change arefirst established based on the longitudinal and lateral force coupling characteristics and the nominal reference trajectory.Then,by optimizing the lane change time,the yaw rate and lateral acceleration that connect with the lane change time are limed.Furthermore,to assure the dynamic properties of autonomous vehicles,the real system inputs under the restraints are obtained by using the MPC method.Based on the gained inputs and the efficient map of brushless direct-current in-wheel motors(BLDC IWMs),the nonlinear cost function which combines vehicle dynamic and energy consumption is given and the KKT-based method is adopted.Findings–The effectiveness of the proposed control system is verified by numerical simulations.Consequently,the proposed control system can successfully achieve stable trajectory planning,which means that the yaw rate and longitudinal and lateral acceleration of vehicle are within stability boundaries,which accomplishes accurate tracking control and decreases obvious energy consumption.Originality/value–This paper proposes a solution to simultaneously satisfy stable lane change maneuvering and reduction of energy consumption for autonomous electric vehicles.Different from previous path planning researches in which only the geometric constraints are involved,this paper considers vehicle dynamics,and stability boundaries are established in path planning to ensure the feasibility of the generated reference path.

An adaptive cascade trajectory tracking control for over-actuated autonomous electric vehicles with input saturation

This paper presented a novel adaptive cascade nonlinear trajectory tracking control scheme of over-actuated autonomous electric vehicles involving input saturation. First, a nonlinear vehicle dynamic model with input saturation is established, which can accurately describe the features of uncertainties and coupling of autonomous electric vehicles, and the hyperbolic tangent function is designed to estimate the saturation function for dealing with the input saturation problem. Then, a novel adaptive cascade trajectory tracking control scheme is designed. An adaptive neural network-based terminal sliding control law is proposed for producing the generalized force/moment in real-time, the asymptotic stability of this adaptive control system is proven by Lyapunov theory, and a quasi-newton distribution law is designed to determine the optimum tire forces that guarantee the actual generalized forces/moment are close to the desired values. Finally, simulation results demonstrate the effectiveness of the proposed control scheme.