Torque vectoring to maximize straight-line efficiency in an all-electric vehicle with independent rear motor control

BEVs are a critical pathway towards achieving energy independence and meeting greenhouse and pollutant gas reduction goals in the current and future transportation sector [1]. Automotive manufacturers are increasingly investing in the refinement of electric vehicles as they are becoming an increasingly popular response to the global need for reduced transportation emissions. Therefore, there is a desire to extract the most fuel economy from a vehicle as possible. Some areas that manufacturers spend much effort on include minimizing the vehicle’s mass, body drag coefficient, and drag within the powertrain. When these values are defined or unchangeable, interest is driven to other areas such as investigating the control strategy of the powertrain. If two or more electric motors are present in an electric vehicle, Torque Vectoring (TV) strategies are an option to further increase the fuel economy of electric vehicles. Most of the torque vectoring strategies in literature focus exclusively on enhancing the vehicle stability and dynamics with few approaches that consider efficiency or energy consumption. The limited research on TV that addresses system efficiency have been done on a small number of vehicle architectures, such as four independent motors, and are distributing torque front/rear instead of left/right which would not induce any yaw moment. The proposed research aims to address these deficiencies in the current literature. First, by implementing an efficiency-optimized TV strategy for a rear-wheel drive, dual-motor vehicle under straight-line driving as would be experienced in during the EPA drive cycle tests. Second, by characterizing the yaw moment and implementing strategies to mitigate any undesired yaw motion. The application of the proposed research directly impacts dual-motor architectures in a way that improves overall efficiency which also drives an increase in fuel economy. Increased fuel economy increases the range of electric vehicles and reduces the energy demand from an electrical source that may be of non-renewable origin such as coal

A novel torque vectoring algorithm with regenerative braking capabilities

[Resumen] Los sistemas inteligentes de transporte (ITS) son actualmente una de las áreas de investigación más activas, siendo los vehículos eléctricos (EV) y la mejora de su comportamiento dinámico temas clave. Para este propósito, es necesario el desarrollo de sistemas avanzados de asistencia a la conducci ón (ADAS) y sistemas avanzados de control para la dinámica vehicular. Convencionalmente, estos sistemas se han centrado en aumentar la estabilidad del vehículo en escenarios críticos. Sin embargo, los vehículos eléctricos permiten incluir también la eficiencia, haciendo uso del frenado regenerativo como una variable de control. Para poder diseñar sistemas de control tan sofisticados, es necesario implementar técnicas de control capaces de gestionar tanto la estabilidad como la eficiencia. En este sentido, las técnicas de control inteligente han demostrado ser una de las mejores alternativas. En este trabajo se presenta un algoritmo de distribución de par, Torque Vectoring (TV), basado en técnicas de control inteligente y con capacidades de frenado regenerativo. El algoritmo de TV presentado ha sido implementado en un sistema embebido y validado en un entorno de "Hardware in the Loop" (HiL). Los resultados muestran que el sistema presentado no solo es capaz de mejorar el comportamiento dinámico del vehículo en una maniobra de emergencia desafiante, sino también aumentar su eficiencia.[Abstract] Intelligent Transportation Systems (ITS) is currently one of the most active research areas, being electric vehicles (EVs) and their vehicle dynamics enhancement key topics. For this purpose, the development of optimal Advanced Driver-Assistance Systems (ADAS) and Advanced Vehicle Dynamics Control Systems (AVDC) is required. Conventionally, these systems have been focused on increasing the stability of the vehicle in critical scenarios. However, EVs enable the possibility of including also the eficiency by making use of the regenerative braking as a control variable. In order to be able to design such sophisticated control systems, it is necessary to implement control techniques capable to manage both stability and eficiency. In this sense, intelligent control techniques have demonstrated to be one of the best alternatives. In this work a Torque Vectoring (TV) algorithm based on intelligent control techniques and with regenerative braking capabilities is presented. The presented TV approach has been implemented in a embedded platform and tested in a Hardware in the Loop (HiL) setup. Results show that the presented approach is able to not only enhance the dynamics vehicle behaviour in a challenging emergency manoeuvre, but also to increase its overall eficiency.ECSEL HIPERFORM; 78317