High−precision and robust localization is critical for intelligent vehicle and transportation systems, while the sensor signal loss or variance could dramatically affect the localization performance. The vehicle localization problem in an environment with Global Navigation Satellite System (GNSS) signal errors is investigated in this study. The error state Kalman filtering (ESKF) and Rauch–Tung–Striebel (RTS) smoother are integrated using the data from Inertial Measurement Unit (IMU) and GNSS sensors. A segmented RTS smoothing algorithm is proposed in order to estimate the error state, which is typically close to zero and mostly linear, which allows more accurate linearization and improved state estimation accuracy. The proposed algorithm is evaluated using simulated GNSS signals with and without signal errors. The simulation results demonstrate its superior accuracy and stability for state estimation. The designed ESKF algorithm yielded an approximate 3% improvement in long straight line and turning scenarios compared to classical EKF algorithm. Additionally, the ESKF−RTS algorithm exhibited a 10% increase in the localization accuracy compared to the ESKF algorithm. In the double turning scenarios, the ESKF algorithm resulted in an improvement of about 50% in comparison to the EKF algorithm, while the ESKF−RTS algorithm improved by about 50% compared to the ESKF algorithm. These results indicated that the proposed ESKF−RTS algorithm is more robust and provides more accurate localization.

The use of regenerative braking systems is an important approach for improving the travel mileage of electric vehicles, and the use of an auxiliary hydraulic braking energy recovery system can improve the efficiency of the braking energy recovery process. In this paper, we present an algorithm for optimizing the energy recovery efficiency of a hydraulic regenerative braking system (HRBS) based on fuzzy Q-Learning (FQL). First, we built a test bench, which was used to verify the accuracy of the hydraulic regenerative braking simulation model. Second, we combined the HRBS with the electric vehicle in ADVISOR. Third, we modified the regenerative braking control strategy by introducing the FQL algorithm and comparing it with a fuzzy-control-based energy recovery strategy. The simulation results showed that the power savings of the vehicle optimized by the FQL algorithm were improved by about 9.62% and 8.91% after 1015 cycles and under urban dynamometer driving schedule (UDDS) cycle conditions compared with a vehicle based on fuzzy control and the dynamic programming (DP) algorithm. The regenerative braking control strategy optimized by the fuzzy reinforcement learning method is more efficient in terms of energy recovery than the fuzzy control strategy.

To enhance the braking stability of electric vehicles and maximize braking energy recovery, this paper proposes a regenerative braking force distribution strategy based on Electro-Mechanical Braking (EMB) in a single-pedal mode. The braking intention during single-pedal operation is identified using an Adaptive Neuro-Fuzzy Inference System (ANFIS), with the effectiveness of this method validated through data collection and analysis on a six-degree-of-freedom test rig, showing significant improvement in intention recognition accuracy. An innovative distribution method for front and rear axle braking forces is developed, and a fuzzy controller is designed with battery State of Charge ( SOC ), vehicle velocity ( v ), braking intensity ( z ), and braking intention ( I ) as inputs, and the regenerative braking ratio coefficient ( k ) as the output. The controller is optimized using the Sparrow Search Algorithm (SSA), further enhancing braking energy recovery efficiency. Co-simulation with Simulink and AVL Cruise software demonstrates the strategy’s effectiveness. Results indicate that under the Worldwide Harmonized Light Vehicles Test Cycle (WLTC) and China Light-Duty Vehicle Test Cycle (CLTC) conditions, the braking energy recovery efficiency of the proposed strategy reaches 21.54% and 25.39%, respectively. These findings confirm that the EMB-based single-pedal regenerative braking force distribution strategy significantly improves both braking stability and energy recovery efficiency in electric vehicles, offering valuable insights for future braking strategy development.

This research delves deeply into the traffic light intersection control issue for connected autonomous vehicles (CAVs). Firstly, we employed the vehicle dynamics simulation software, CarSim, to model the vehicle, and utilized the intelligent driving simulation software, PreScan, to establish a road environment model. Subsequently, we designed a rule-based traffic light crossing controller (RB-TLCC) for CAVs in Matlab/Simulink and implemented a co-simulation with CarSim and PreScan. Furthermore, we conducted driver-in-the-loop studies using the Logitech G27 driving simulator kit and compared the experimental results with those from RB-TLCC. Our findings indicate that the RB-TLCC enhances regenerative braking energy by 23.5%, improves traffic efficiency by 17.9%, and increases driving smoothness by 50.7%. Additionally, based on Markov Chain theory, we proposed a multi-objective optimization model (MO-OM) for CAVs traffic light intersection crossing using the Proximal Policy Optimization algorithm (PPO). A comparison was conducted under two different operating conditions between RB-TLCC and dynamic programming algorithms (DP). The results indicate that the MO-OM proposed in this paper exhibits the best comprehensive control performance among the three control methods. While adhering to traffic regulations, it enhances the recuperation of braking energy, the efficiency of vehicle passage, and the smoothness of travel at traffic light intersections. This study offers effective methodologies for enhancing the performance of CAVs in traffic light intersection control and holds significant reference value for the advancement of future intelligent transportation systems (ITS).

In this paper, an optimization algorithm of energy recovery efficiency is proposed for parallel hydraulic hybrid systems (PHHS) using dynamic programming (DP). Global optimal solution of pump displacement and transmission ratio under the known urban drive cycles is obtained by using the DP approach, where the total amount of energy recovery is defined as the cost function, and the pump displacement and the transmission ratio of the torque coupler are defined as the deciding variables. Two major steps are involved in verifying the proposed approach. Firstly, a PHHS Simulink model is accurately obtained by repeated comparison with the bench test. Subsequently, we derive a parallel hydraulic hybrid vehicle (PHHV) from adding a hydraulic hybrid system to an electric vehicle in ADVISOR (advanced vehicle simulator). This vehicle is used to validate the effectiveness of the proposed method in energy recovery efficiency.

In order to reduce the energy consumption caused by the frequent braking of vehicles at signalized intersections, an optimized speed trajectory control method is proposed, based on braking energy recovery efficiency (BERE) in connection with an automated system for vehicle real-time interaction with roadside facilities and regional central control. Our objectives were as follows; firstly, to establish the simulation model of the hybrid energy regenerative braking system (HERBS) and to verify it by bench test. Secondly, to build up the genetic algorithm (GA) optimization model for the deceleration stopping of the HERBS. Then, to obtain signal light status and timing information to be the constraints; the BERE is to be the optimized objective, resulting in optimization for the speed trajectory under the deceleration stopping condition of a single signalized intersection. Finally, vehicle simulations in ADVISOR software are utilized to validate the optimization results. The results show that the BERE during deceleration stopping at a single signalized intersection after the speed trajectory optimization is 36.21% higher than that of inexperienced drivers, and 7.82% higher than that of experienced drivers.