Adaptive Energy Management of Electric and Hybrid Electric Vehicles

An adaptive algorithm based on weighted recursive least squares is derived and implemented. The generality of the approach is underscored by the application of the algorithm to a 42 V lead acid and a high-voltage (375 V) nickel metal hydride battery system. The algorithm is fully recursive in that the only variables required for on-line regression are those of the previous time step and the current time step. A time-weighting technique often referred to as exponential forgetting is employed to damp exponentially the influence of older data on the regression analysis. The output from the adaptive algorithm is the battery state of charge (remaining energy), state of health (relative to the battery's nominal rating), and power capability. Such algorithms are likely to play a critical role in optimal operation of hybrid electric vehicles and on-board diagnostics. The behavior of the algorithm in terms of convergence, accuracy, and robustness is examined.