To promote the real-time application of the advanced energy management system in hybrid electric vehicles (HEVs), this article proposes an adaptive hierarchical energy management strategy for a plug-in HEV. In this work, deep learning (DL) and genetic algorithm (GA) are synthesized to derive the power split controls between the battery and internal combustion engine (ICE). First, the architecture of the multimode powertrain is founded, wherein the particular control actions, state variables, and optimization objective are explained. Then, the hierarchical framework for control actions generation is introduced. GA is utilized to search the global optimal controls based on the powertrain model pro-vided in Matlab/Simulink. DL is applied to train the neural net-work model that is connecting the inputs and control actions. Finally, the effectiveness of the presented integrated energy management strategy is validated via comparing with the original charge depleting/charge sustaining (CD/CS) policy. Simulation results indicate that the proposed technique can highly improve the fuel economy. Furthermore, a hardware-in-the-loop (HIL) is conducted to evaluate the adaptive and real-time characteristics of the designed energy management system.

An online energy management controller is presented in this paper for a plug-in hybrid electric vehicle (PHEV), which is based on driving conditions recognition and genetic algorithm (GA). The proposed controller can be used in the real-time ap-plication. First, the studied multimode PHEV is modeled and four traction operation modes are introduced in detail. Second, the principal component analysis (PCA) algorithm is utilized to classify the real historical driving conditions data. Four types of driving conditions are constructed to describe the representative scenarios. Then, GA is applied to search the optimal values for seven control actions offline. These parameters for different driving conditions are preserved and can be activated online. Finally, the driving condition is identified online and the corre-sponding control actions are loaded and adopted. Simulation results indicate that the proposed approach is close to the glob-ally optimal method, dynamic programming (DP) and is superi-or to the charge depleting/charge sustaining (CD/CS) technique. Also, hardware in the loop (HIL) experiment is built to validate the real-time characteristic of the proposed strategy.

An adaptive potential field is designed to adapt the acceleration/deceleration and mass of the obstacle. The potential fields are established in a transformed road coordinate system to improve the feasibility and robustness. A path planning method is proposed based on the designed adaptive potential field to improve the driving safety and the ride comfort of autonomous vehicles in complex driving scenarios, which including the cut-in, emergency braking, obstacle sudden accelerating during overtaking and the curve road driving scenarios. The effectiveness of the proposed method is validated by simulations with constructed and real data, respectively. The TTCs (Time-to-Collision) and the maximum lateral accelerations are used to evaluate the improvements on safety and ride comfort. The results show that both the driving safety and ride comfort are efficiently improved by using the proposed approach in emergency braking and accelerating scenarios. Meanwhile, the proposed method can be well applied in a curve road driving environment.

Hybrid energy storage systems that combine lithium-ion batteries and supercapacitors are considered as an attractive solution to overcome the drawbacks of battery-only energy storage systems, such as high cost, low power density, and short cycle life, which hinder the popularity of electric vehicles. A properly sized hybrid energy storage system and an implementable real-time power management system are of great importance to achieve satisfactory driving mileage and batterycyclelife.However,dimensioningandpowermanagement problems are quite complicated and challenging in practice. To address these challenges, this work proposes a Bi-level multi-objective design and control framework with the nondominated sorting genetic algorithm-II and fuzzy logic control as key components, to obtain an optimal sized hybrid energy storage system and the corresponding optimal real-time power management system based on fuzzy logic control simultaneously. In particular, a vectorized fuzzy inference system is devised, which allows large-scale fuzzy logic controllers to run in parallel, thereby improving optimization efficiency. Pareto optimal results of different hybrid energy storage systems incorporating both optimal design and control parameters are obtained efficiently thanks to the vectorization. An example solution chosen from the Pareto front shows that the proposed method can achieve a competitive number of covered laps while improving the battery cycle life significantly.

Left turning at unsignalized intersectionis one of the most challenging tasks for urban automated driving, due to the various shapes of intersections and rapidly changing nature of the driving scenarios. This article addresses the challenges of decision making at highly uncertain intersections of different shapes by proposing a generalized critical turning point (CTP)-based hierarchical decision-making and planning method. The high-level planner takes the road map information and generates CTPs using a parameterized extraction model that is proposed and verified by naturalistic driving data. The CTPs are used to generate behavior-oriented paths that could be adapted to various intersections. These modifications help to assure high searching efficiency of the planning process. The low-level planner makes real-time, 2-D planning using a partially observable Markov decision process solver, which can handle the uncertainties of the intersections and make less conservative yet safe actions. With proper modifications, our proposed method can make commute-efficient 2-D planning decisions at unsignalized intersections of various shapes in real time.

A 4-independent wheel driving (4-IWD) electric vehicle has distinctive advantages with both enhanced dynamic and energy efficiency performances since this configuration provides more flexibilities from both the design and control aspects. However, it is difficult to achieve the optimal performances of a 4-IWD electric vehicle with conventional design and control approaches. This work is dedicated to investigating the vehicular optimal design and control approaches, with a 4-IWD electric race car aiming at minimizing the lap time on a given circuit as a case study. A 14-DOF vehicle model that can fully evaluate the influences of the unsprung mass is developed based on Lagrangian dynamics. The 14-DOF vehicle model implemented with the reprogrammed Magic Formula tire model and a timeefficient suspension model supports metric operations and parallel computing, which can dramatically improve the computational efficiency. The optimal design and control problems with design parameters of the motor, transmission, mass center, anti-roll bar and the suspension of the race car are successively formulated. The formulated problems are subsequently solved by directly transcribing the original problems into large scale nonlinear optimization problems based on trapezoidal approach. The influences of the mounting positions of the propulsion system, the mass and inertia of the unsprung masses, the anti-roll bars and suspensions on the lap time are analyzed and compared quantitatively for the first time. Some interesting findings that are different from the “already known facts” are presented.