Abstract
Recently, battery-powered electric vehicle (EV) has received wide attention due to less pollution during use, low noise, and high energy efficiency and is highly expected to improve urban air quality and then mitigate energy and environmental pressure. However, the widespread use of EV is still hindered by limited battery capacity and relatively short cruising range. This paper aims to propose a state of charge (SOC) estimation method for EV’s battery necessary for route planning and dynamic route guidance, which can help EV drivers to search for the optimal energy-efficient routes and to reduce the risk of running out of electricity before arriving at the destination or charging station. Firstly, by analyzing the variation characteristics of power consumption rate with initial SOC and microscopic driving parameters (instantaneous speed and acceleration), a set of energy consumption rate models are established according to different operation modes. Then, the SOC estimation model is proposed based on the presented EV power consumption model. Finally, by comparing the estimated SOC with the measured SOC, the proposed SOC estimation method is proved to be highly accurate and effective, which can be well used in EV route planning and navigation systems.
Highlights
In recent years, energy and environment have suffered from heavy pressure caused by rapidly increased gasoline and diesel powered vehicles, and the growing concern about energy reserves and environmental quality of cities has called for sustainable transportation technologies and motivated active research on vehicles with alternative energy sources [1, 2]
In order to evaluate the accuracy of the proposed power consumption rate estimation models, the measured power consumption rate data are compared with the regression models estimation through
The paper presents a novel state of charge (SOC) estimation approach based on the data collected by a chassis dynamometer test with New European Driving Cycle (NEDC), which is characterized by fully considering the impacts of microscopic driving parameters on SOC
Summary
Energy and environment have suffered from heavy pressure caused by rapidly increased gasoline and diesel powered vehicles, and the growing concern about energy reserves and environmental quality of cities has called for sustainable transportation technologies and motivated active research on vehicles with alternative energy sources [1, 2]. The battery management system (BMS) has been developed as one of the EV’s key technologies, and the accurate estimation of SOC of battery is viewed as a critical part of BMS. Since the battery is a strong nonlinear and time-variability system for its complicated electrochemical process [5, 6], the SOC is affected by many factors such as open-circuit voltage, self-rescue effect, temperature, charge and discharge efficiency, and circle life. A great many SOC estimation methods have been explored by researchers, the accurate estimation of SOC is very difficult and complex due to limited battery models and parametric uncertainties [7]
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