Remaining available energy prediction for lithium-ion batteries considering electrothermal effect and energy conversion efficiency

Accurate remaining available energy (E-RAE) prediction of lithium-ion batteries is still a challenging issue for electric vehicles, which is crucial for the prediction of remaining driving range. An approach for battery E-RAE prediction is proposed considering the electrothermal effect and energy-conversion-efficiency. Firstly, a novel definition of battery State-of-Energy (SOE) is proposed based on the first law of thermodynamics to reflect the battery remaining chemical energy (E-RCE) state. Secondly, due to the strong nonlinear characteristics of the battery, an adaptive Square-Root Unscented Kalman Filter is adopted to accurately estimate the battery model parameters and SOE. Finally, in order to extract E-RAE from E-RCE, the energy-conversion-efficiency (ECE) of the battery is studied. Since the prediction of battery SOE and ECE both depend on the future load, a Markov model is established to realize the future load prediction. To validate the proposed method, two different kinds of lithium-ion batteries are tested under dynamic conditions. The results indicate that the new method have high accuracy and good robustness. Even with 20% initial SOE error, the predicted battery SOE could quickly converge to the actual value in less than 1 min. The estimation error of battery SOE and E-RAE can both be controlled within 2% under dynamic conditions.