10,716 research outputs found
Time-optimal Control Strategies for Electric Race Cars with Different Transmission Technologies
This paper presents models and optimization methods to rapidly compute the
achievable lap time of a race car equipped with a battery electric powertrain.
Specifically, we first derive a quasi-convex model of the electric powertrain,
including the battery, the electric machine, and two transmission technologies:
a single-speed fixed gear and a continuously variable transmission (CVT).
Second, assuming an expert driver, we formulate the time-optimal control
problem for a given driving path and solve it using an iterative convex
optimization algorithm. Finally, we showcase our framework by comparing the
performance achievable with a single-speed transmission and a CVT on the Le
Mans track. Our results show that a CVT can balance its lower efficiency and
higher weight with a higher-efficiency and more aggressive motor operation, and
significantly outperform a fixed single-gear transmission.Comment: 5 pages, 4 figures, submitted to the 2020 IEEE Vehicle Power and
Propulsion Conferenc
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Chapter 2 - Data-Driven Energy Efficient Driving Control in Connected Vehicle Environment
Control algorithms for e-car
Cílem práce byl návrh a implementace řídicích algoritmů pro optimalizaci spotřeby energie elektrického vozidla. Hlavním úkolem byla optimalizace rozložení energie mezi hlavním zdrojem energie (bateriemi) a super-kapacitory v průběhu jízdního cyklu. Jízdní výkonový profil je odhadován a předpovězen na základě 3D geografických souřadnic a matematického modelu vozidla. V první části jsou uvedeny komponenty vozidla a jejich modely. Poté jsou představeny algoritmy na základě klouzavého průměru a dynamického programování. Byly provedeny simulace a analýzy pro demostraci přínosů algoritmů. V poslední části je popsána Java implementace algoritmů a také aplikace pro operační systém Android.The aim of this work is to design and implement energy consumption optimization control algorithms for electric vehicle. The main objective is to optimize the power-split-ratio between the main power source (batteries) and the super-capacitors during the driving cycle. The driving power profile is estimated and predicted using 3D geographic data and vehicle model. In the first part, vehicle components modelling is introduced. Then, moving average based algorithm and dynamic programming algorithm are presented. Simulations and analysis are provided to show algorithms' benefits. In the last part, Java implementation and also Android operating system application are described.
Fuzzy logic control for energy saving in autonomous electric vehicles
Limited battery capacity and excessive battery dimensions have been two major limiting factors in the rapid advancement of electric vehicles. An alternative to increasing battery capacities is to use better: intelligent control techniques which save energy on-board while preserving the performance that will extend the range with the same or even smaller battery capacity and dimensions. In this paper, we present a Type-2 Fuzzy Logic Controller (Type-2 FLC) as the speed controller, acting as the Driver Model Controller (DMC) in Autonomous Electric Vehicles (AEV). The DMC is implemented using realtime control hardware and tested on a scaled down version of a back to back connected brushless DC motor setup where the actual vehicle dynamics are modelled with a Hardware-In-the-Loop (HIL) system. Using the minimization of the Integral Absolute Error (IAE) has been the control design criteria and the performance is compared against Type-1 Fuzzy Logic and Proportional Integral Derivative DMCs. Particle swarm optimization is used in the control design. Comparisons on energy consumption and maximum power demand have been carried out using HIL system for NEDC and ARTEMIS drive cycles. Experimental results show that Type-2 FLC saves energy by a substantial amount while simultaneously achieving the best IAE of the control strategies tested
Minimum Race-Time Planning-Strategy for an Autonomous Electric Racecar
Increasing attention to autonomous passenger vehicles has also attracted
interest in an autonomous racing series. Because of this, platforms such as
Roborace and the Indy Autonomous Challenge are currently evolving. Electric
racecars face the challenge of a limited amount of stored energy within their
batteries. Furthermore, the thermodynamical influence of an all-electric
powertrain on the race performance is crucial. Severe damage can occur to the
powertrain components when thermally overstressed. In this work we present a
race-time minimal control strategy deduced from an Optimal Control Problem
(OCP) that is transcribed into a Nonlinear Problem (NLP). Its optimization
variables stem from the driving dynamics as well as from a thermodynamical
description of the electric powertrain. We deduce the necessary first-order
Ordinary Differential Equations (ODE)s and form simplified loss models for the
implementation within the numerical optimization. The significant influence of
the powertrain behavior on the race strategy is shown.Comment: Accepted at The 23rd IEEE International Conference on Intelligent
Transportation Systems, September 20 - 23, 202
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