A battery management system (BMS) is responsible for protecting the battery from damage, predicting battery life, and maintaining the battery in an operational condition. In this paper, we propose an efficient way of predicting the power requirements of electric vehicles (EVs) based on a history of their power consumption, speed, and acceleration, as well as the road information from a pre-downloaded map. The predicted power requirement is then used by the BMS to prevent the damage of battery cells that might result from high discharge rates. This prediction also helps BMS efficiently schedule and allocate battery cells in real time to meet an EV's power demands. For accurate prediction of power requirements, we need an accurate model for the power requirement of each given application. We generate this model in real time by collecting and using historical data of power consumption, speed, acceleration, and road information such as slope and speed limit. By using this information and the operator's driving pattern, the model extracts the vehicle's history of speed and acceleration, which, in turn, enables the prediction of the vehicle's (immediate) future power requirements. That is, the power requirement prediction is achieved by combining a real-time power requirement model and the estimation of the vehicle's acceleration and speed. The proposed approach predicts closer to the actual required power than a widely-used heuristic approach that uses measured power demand, by up to 69.2%.
One method is the model-based approach, based on vehicle dynamics to model the consumption behavior of electric vehicles REF .
Real-time prediction of battery power requirements for electric vehicles
ICCPS '13
Computer Science