Abstract: Mining trucks have fixed operating routes and no traffic constraints, but they often operate on complex terrains and endure largely varying loads. When driving on a long downhill slope with heavy loads, traditional adaptive equivalent consumption minimization strategy(A-ECMS) cannot fully capitalize on regenerative braking since battery state of charge(SOC) may be at a high level. To solve this problem, a predictive equivalent fuel consumption minimum strategy(P-ECMS) that combines slope information prediction and vehicle mass estimation is proposed. GPS topographical data is used to realize road slope prediction, and a recursive least squares method is used to estimate the vehicle mass after loading operation. A braking energy recovery estimation model is established to predict the target SOC values before the downhill driving with full and empty loads, and the reference SOC trajectory is further obtained based on the weighted sum of these two SOC values. A traditional A-ECMS algorithm is adopted to track the reference SOC trajectory while realizing power-split optimization instantaneously. Comprehensive hardware-in-the-loop(HIL) simulations are conducted and the results show that the proposed P-ECMS algorithm is computationally efficient and can be implemented in real-time in an automotive controller and improve the fuel economy up to 7.21% compared with the traditional A-ECMS.

Key words: mining trucks, recursive least squares, vehicle mass estimation, braking energy recovery, predictive ECMS