Damon R. Frisch

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 batterys 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.

Modeling and estimation of Nickel Metal Hydride battery hysteresis for SOC estimation

Accurate estimation of battery state of charge (SOC) is essential for battery control and effective energy management of hybrid electric vehicles (HEV). Battery hysteresis poses a big challenge to the estimation of SOC. In the present battery state estimator (BSE), a rule-based hysteresis estimation method for nickel metal hydride (NiMH) batteries is used due to the lack of physical hysteresis models. The rule-based hysteresis estimation method gives only a coarse result with limited accuracy. To improve estimation accuracy and algorithm robustness, a new on-board algorithm, based on the Preisach operator, is developed to estimate battery hysteresis. The enhanced model-based algorithm is then integrated into the BSE algorithms for evaluation. Simulation and preliminary evaluation results for NiMH batteries show significant improvement in the accuracy and robustness of SOC estimation.