Remaining driving range estimation for electric vehicles based on an advanced battery residual energy model

نویسندگان

  • Guangming Liu
  • Minggao Ouyang
  • Languang Lu
  • Jianqiu Li
چکیده

The inaccuracy in remaining driving range (RDR) estimation lowers the consumers’ confidence on electric vehicles (EVs), while one important reason is the inaccuracy in battery residual usable energy (RUE) prediction. Traditionally, RUE is directly estimated based on state of charge (SOC) which could not reflect the energy variation in complex real-world applications. This paper introduces an RDR estimation method through an advanced RUE calculation model (AECM). In AECM, a number of RUE-influencing factors are specifically considered to describe the present energy loss and the available discharge capacity precisely. The future average discharge voltage is real-timely predicted, and the current influence on future energy loss is also considered. To obtain an accurate RUE result in various environmental conditions throughout EV lifetime, the AECM takes into account the influence of state of health (SOH), temperature, and cell inconsistency in battery pack. An accurate RUE value as well as a satisfying RDR could hence be obtained in real-world application. Embedded in a pure electric vehicle, the performance is validated under a set of vehicle experiments on a dynamometer test system. Results show that our RDR method could provide stable and convergent estimation results under different Beijing urban driving conditions, and the estimation error could be rapidly limited to ±5%.

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تاریخ انتشار 2015