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In this case, a solution of ACC with consideration of minimising energy consumption and maintaining string stability is provided. Then, to evaluate the performance of the proposed real‐time optimisation strategy on different traffic scenarios, the controller is applied to an adaptive cruise control (ACC) under connected environment. After that the optimisation problem is solved using sequential quadratic programming solver. Moreover, the multiple shooting algorithm is introduced to decouple the dynamic constraints with the ability of avoiding the strong non‐linearity while solving the optimisation problem. The designed controller aims to generate the optimal power split and gear ratio schedule with respect to minimise the energy consumption of fuel and electricity. First, a non‐linear optimal control problem under model predictive control scheme is formulated. The considered powertrain is built from a commercial HEV model. In this work, a real‐time energy management problem for a parallel hybrid electric vehicle (HEV) is proposed.
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Test results show optimal control effects in overall traffic situation levels and an enhanced energy recycling efficiency. The proposed strategy is verified in the co-simulation environment and field test respectively. Further, the coordinated control strategy arbitrates the control mode basing on the traffic situation level and distributes braking forces between the electronic hydraulic braking system and the cooperative regenerative auxiliary braking system. The intelligent electric vehicle framework with 4-wheel hub motors is established and the intention-aware longitudinal automated driving strategy for overall traffic situation levels is proposed. This research focuses on the longitudinal eco-driving considering the coordinated control for 4WD intelligent electric vehicles. Recent technological advances in most highly automated driving systems on electric vehicles with regenerative braking system not only enhance the safety and comfort level but also present a significant opportunity for automated eco-driving. Minimum energy consumption with maximum comfort driving experience defines the ideal human mobility. The validations of proposed strategy are conducted under various manoeuvres, yielding comprehensive improvements in terms of vehicle handling, lateral stability, and energy performance.
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In the coordinated torque allocator, a torque increment allocation problem is formulated and optimized to realize the desired forces, meanwhile based on VWF to minimize energy consumption and tire workload usage. In the energy efficiency controller, inter-axle torque distribution map is optimized for optimal vehicle energy economy. Then, a novel integral triple-steps method is proposed to calculate the proper direct yaw moment for the desired vehicle motion.
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In the handling-stability controller, a unified yaw rate reference of VWF is developed to simultaneously guarantee vehicle manoeuvrability and lateral stabilization. Subsequently, based on the feedback drive conditions and vehicle states, the identified boundary is dynamically quantified by the designed varying weight factor (VWF) in real time. In the dynamic control supervisor, firstly phase plane analysis is implemented to accurately define the vehicle stability boundary so that the look-up table of bounds can be established for online application. A supervisory control strategy, including dynamic control supervisor, handling-stability controller, energy efficiency controller, and coordinated torque allocator, is proposed for distributed drive electric vehicles to coordinate vehicle handling, lateral stability, and energy economy performance.