A stochastic optimal energy management strategy considering battery health for hybrid electric bus

Abstract

The problem of battery health coupled with energy management brings a considerable challenge to the hybrid electric bus. To address this challenge, three contributions are made to realize optimal energy management control while prolonging battery life. First, a semi-empirical aging model of lithium iron phosphate battery is built and identified by the data fitting method, based on the battery cycling test. Besides, a severity factor map is constructed by employing the proposed aging model to characterize the relative aging of the battery under different operating conditions. Second, to make the driver demand torque more appropriate for statistical prediction, a Markov chain is formulated to predict driving behavior and also a stochastic vehicle mass estimation method is proposed to assist the prediction of required torque. Then, a stochastic multi-objective optimization problem is formulated by taking the severity factor map as a battery degradation criterion, where minimized battery degradation and fuel consumption can be simultaneously realized. Finally, a stochastic model predictive control strategy that considers battery health is established. Both simulation and hardware-in-loop tests are performed. The results demonstrate that fuel economy and battery degradation can be improved by 16.73% and 13.8% compared with rule-based strategy, respectively.

Mining Electric Vehicle Adoption of Users

“…Based on our literature review, a gap in state of the art was identified: few articles [6,37,66,67] referred to the degradation of batteries from the point of view of electric vehicles user behavior. In most cases, articles only mentioned the point of view of Electrochemistry.…”

Energy Management of the Power-Split Hybrid Electric City Bus Based on the Stochastic Model Predictive Control

“…Compared with the rule-based control strategy, the fuel economy in this method improves by 16.73%. However, this method has high computational expenses so that the corresponding calculation speed is relatively slow [40]. Recently, Xie et al proposed a MPC method based on the optimal depth of discharge (DOD) of the battery.…”

Hybrid Electric Vehicle Energy Management Strategy with Consideration of Battery Aging

“…The above advantages of performance modeling, or called column counting or Ahthroughput battery aging method, has made it become very popular in literatures related in Optimal EMS of HEV with battery aging consideration [52][53][54][55][56][57][58][59][60][61][62][63][64]. Lithium-ion battery LiFePO4 will be studied in this brief due to its advantages in safety, and longer life characteristic [65].…”

“…The battery aging estimation model in Hu's work [57], Wu's work in [58], and Cui's work in [61], are directly estimating the damage of battery from the instantaneous Ah-throughput based on experimental data, then used the aging model in MPC as the EMS for HEV. Cai's work in [59] used the Ah-throughput model to indicate the battery health, and analyzed the impact of power split configuration on fuel consumption and battery capacity degradation in HEV city buses.…”

“…A prior knowledge of drive cycle is required because that one of the boundary conditions from the two-point boundary value problem is defined at the final time [68]. The current literatures for MPC [54][57][58] [61] are mostly implemented in service vehicles with typical drive cycles which the future drive cycle is known or with less variance within the pre-defined vehicle speed dataset. MPC is similar to DP, which can achieve the global optimization results.…”