Abstract
The problem of energy recovery in braking of an electric vehicle is solved here, which ensures high quality blended deceleration using electrical and friction brakes. A model reference controller is offered, capable to meet the conflicting requirements of intensive and gradual braking scenarios at changing road surfaces. In this study, the neural network controller provides torque gradient control without a tire model, resulting in the return of maximal energy to the hybrid energy storage during braking. The torque allocation algorithm determines how to share the driver’s request between the friction and electrical brakes in such a way as to enable regeneration for all braking modes, except when the battery state of charge and voltage levels are saturated, and a solo friction brake has to be used. The simulation demonstrates the effectiveness of the proposed coupled two-layer neural network capable of capturing various dynamic behaviors that could not be included in the simplified physics-based model. A comparison of the simulation and experimental results demonstrates that the velocity, slip, and torque responses confirm the proper car performance, while the system successfully copes with the strong nonlinearity and instability of the vehicle dynamics.
Highlights
A common paradox of contemporary energy converters is that while their specific performance improves with each passing year, the overall conversion performance is getting worse
The hybrid energy storage (HES) block consumes the electrical fraction of the energy kJ generated by physics-based vehicle model (PBEV), while its value does not exceed the permissible state of charge (SOC), voltage, and curren
electric vehicle (EV) atDuring such an inten braking, the HES block consumes the electrical fraction of the energy kJ generated by the to meet the driver’s reference TB*, on the one hand, and avoid wheel skidding cour
Summary
A common paradox of contemporary energy converters is that while their specific performance improves with each passing year, the overall conversion performance is getting worse. In [45], the HES-based braking system implements automatic control of the EV regeneration torque of the motor to improve driver’s comfort and energy efficiency To apply this system, an accurate prediction of the vehicle deceleration states was produced. To reflect the nonlinear time-varying characteristics of the longitudinal dynamics, the NNC is designed based on a sliding mode controller and a single-neuron PIDC, which provides deceleration in emergency braking conditions. The main contribution of the paper is in the design of the NNC capable to meet the conflicting requirements of different braking modes and road surfaces This is the first study where the NN provides the torque gradient control without a tire model resulting in the return of maximal energy to the vehicle HES during the braking process.
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