Real-time range maximisation of electric vehicles through active cell balancing using model-predictive control

One of the factors limiting the range of electric vehicles is cell imbalance. This means that the condition of a single cell can cause the entire pack to be shut down, thus missing out on energy in the other cells. Active cell balancing can be used to overcome cell imbalance, but a balancing strategy is required that maximises the range. This paper proposes a real-time active-cell-balancing strategy based on model-predictive control. To prevent the need for large prediction horizons, several supplementary balancing objectives, such as voltage, SoC and a charge-based quantity are considered. The controller performance is compared to a range benchmark, obtained using a method from previous work. Furthermore, the range increase is demonstrated on a scenario of realistic length and the influence of uncertainty in the future power demand is investigated. On average, this strategy can provide a range increase of approximately 5 percent and the controller is shown to be robust to uncertainties in the power demand, as long as a worst-case prediction is considered.