Research on electric vehicle (EV) driving range prediction method based on PSO-LSSVM

Electric vehicle (EV) driving range directly reflects EVs' performance, safety, reliability and economy. EV has gained wide attention in recent years. However, most of researches are carried out under ideal conditions and the existing methods have numerous drawbacks. This paper presents a novel prediction method based on a least squares support vector machine (LSSVM) model with parameters γ and σ² optimized by particle swarm optimization (PSO). The main parameters which cannot be obtained directly by drivers such as days, temperature, depth of discharge (DOD) of battery pack are used for training model to predict EV driving range. Furthermore, the performance of PSO-LSSVM model is illustrated by statistical parameters (RE and AARE). AARE of training data and testing data is 1.99% and 5.99% respectively. The results suggest that the model has a stability, generalization ability and reliable predictive performance to predict EV driving range. Meanwhile, the results can also provide a guidance for drivers to grasp and manage their EVs' health conditions and predict the driving range.