Abstract
With the impact of serious environmental pollution in our cities combined with the ongoing depletion of oil resources, electric vehicles are becoming highly favored as means of transport. Not only for the advantage of low noise, but for their high energy efficiency and zero pollution. The Power battery is used as the energy source of electric vehicles. However, it does currently still have a few shortcomings, noticeably the low energy density, with high costs and short cycle life results in limited mileage compared with conventional passenger vehicles. There is great difference in vehicle energy consumption rate under different environment and driving conditions. Estimation error of current driving range is relatively large due to without considering the effects of environmental temperature and driving conditions. The development of a driving range estimation method will have a great impact on the electric vehicles. A new driving range estimation model based on the combination of driving cycle identification and prediction is proposed and investigated. This model can effectively eliminate mileage errors and has good convergence with added robustness. Initially the identification of the driving cycle is based on Kernel Principal Component feature parameters and fuzzy C referring to clustering algorithm. Secondly, a fuzzy rule between the characteristic parameters and energy consumption is established under MATLAB/Simulink environment. Furthermore the Markov algorithm and BP(Back Propagation) neural network method is utilized to predict the future driving conditions to improve the accuracy of the remaining range estimation. Finally, driving range estimation method is carried out under the ECE 15 condition by using the rotary drum test bench, and the experimental results are compared with the estimation results. Results now show that the proposed driving range estimation method can not only estimate the remaining mileage, but also eliminate the fluctuation of the residual range under different driving conditions.
Highlights
Electric vehicles are gaining ever increasing amount of attention because of their advantages of environmental protection, energy saving to name just a few
This paper focus on the factors that affect the driving range of electric vehicles, such as speed, driving conditions, vehicle parameters, SOC, SOH, etc
The estimation of residual driving range is achieved by combining driving condition identification and prediction, two important problems during the process of estimation are energy consumption modeling after driving condition identification and real-time prediction of future driving conditions
Summary
Electric vehicles are gaining ever increasing amount of attention because of their advantages of environmental protection, energy saving to name just a few. It is found that the accuracy of the electric vehicles remaining driving range estimation decreased without unit energy consumption optimization and prediction. B.Rosca and H.J.Bergveld use Internet technology to transmit the power battery parameters to the terminal computer in real time,[2] these parameters are identified in real time including an estimate of the remaining battery energy in real time, so as to improve the estimation accuracy of mileage This technology is mainly to calculate the residual energy of the battery accurately, and it does not predict the future energy consumption accurately. The vehicle track is detected by the positioning system, and the energy consumption of the vehicle is determined under certain conditions according to current vehicle speed, data processing platform predicts the remaining mileage.
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