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As a global optimization method, dynamic programming (DP) can be employed to seek the optimal velocity with minimum energy consumption for EV on given driving cycles. Due to its terrible computational burden, conventional DP is not suitable for real-time implementation especially with higher dimensions. In this paper, we propose an iterative dynamic programming (IDP) approach to reduce computing time firstly. The IDP can obtain the optimal control laws alike the conventional DP by converging the optimal control strategy iteratively and save considerable computing time. Second, the developed IDP and model predictive control (MPC) are combined to establish a real-time cruising controller called IDP-MPC for an EV with terrain preview. In the predictive controller, we use the IDP to solve a constrained finite horizon nonlinear optimization problem.

In order to reflect the actual power consumption of logistics electric vehicles in a city, sample real vehicle road data. After preprocessing, the short-stroke analysis method is used to divide it into working blocks of no less than 20 seconds. Based on principal component analysis, three of the 12 characteristic parameters were selected as the most expressive. K-means clustering algorithm is adopted to obtain the proportions of various short strokes, according to the proportion, select the short stroke with small deviation degree to combine, and construct the driving cycle, it has the characteristics of low average speed, high idle speed ratio and short driving distance. AVL-cruise software builds the vehicle model and runs the driving cycle of urban logistic EV. Compared with WLTC, the difference in power consumption is 34.3%, which is closer to the actual power consumption, the areas with the highest motor speed utilization are concentrated only in the idle area.

In this advanced technological era, lightweight design for fuel efficiency and environmental friendliness is essential for both conventional and hybrid electric vehicles (HEVs), without sacrificing the durability which is an important design factor for vehicle safety. To achieve these objectives, reduction of the structural mass of the full vehicle plays a vital role. The scope of this paper is to describe design methodologies for the vehicle differential case applied to achieve light weight and to ensure product life. The focus of this paper includes two tasks. The topology optimization and fatigue analysis of a vehicle differential case are conducted. Finite element analysis (FEA) is used to simulate the stress with constraint. After that, optimization parameters (design variables, responses, objective functions and constraints) of a vehicle differential case are selected for lightweight design by solid isotropic microstructures with penalization (SIMP) method.

This paper presents a novel torque distribution strategy (TDS) and a modified regenerative braking strategy (MRBS) for distributed-drive electric vehicle (DDEV) considering energy saving and brake stability. The presented TDS minimizes the energy consumption from battery in driving process. In order to overcome the shortcomings by using polynomial approximation for motor efficiency and the local minima problem, an exhaustive search method (ESM) is proposed to obtain the optimal front-rear torque distribution ratio. First, the power summation of four in-wheel motors is selected as the cost function of the optimization problem. Second, the ESM is designed to obtain the optimal torque distribution ratio according to current torque demand and motor speed based on motor efficiency map. Maximum motor torque and tire-road conditions are taken as constraints. Third, a MRBS is proposed to improve energy recovery performance by take ECE R13 and motor efficiency into account.