This paper presents a model-based algorithm designed for electric vehicles to estimate, control and optimize their range. By utilizing both short-term and long-term energy consumption data, the algorithm accurately predicts the range based on the current riding pattern. To achieve the desired range, the algorithm incorporates Hamilton-Jacobi-Bellman (HJB) optimization, which optimizes a cost function. The algorithm leverages short-term energy consumption patterns to smoothen the real-time watt-hour consumption for accurate range estimation. Simultaneously, it monitors long-term energy consumption patterns to account for factors such as vehicle aging, wear, terrain dynamics, and initial wh/km calculation. A comprehensive cost function, considering parameters like wh/km, rider demand, and terrain requirements, ensures optimal range without compromising the overall ride experience. The algorithm employs HJB optimization to dynamically control the range using parameters such as battery DC-current, throttle tuning, and torque control.
Implemented in the Matlab/Simulink environment, the proposed algorithm undergoes rigorous testing and validation through on-road trials involving various riders and riding modes. The results exhibit enhanced accuracy in range estimation, empowering drivers with a clearer understanding of their vehicle's remaining range. Furthermore, the optimization algorithm effectively manages energy consumption by restricting inefficient zones and strategically shifting operating points to regions of higher efficiency, thereby improving performance characteristics.