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With the increasing demand for battery electric buses, the dual-motor coupling powertrain (DMCP) shows great advantages, but it makes the energy optimization problem more complex. To solve the hybrid system optimization problem, a stochastic model predictive control (SMPC) method is proposed to exploit the potential performance of DMCP, where the most critical issue is to improve the prediction accuracy and handle the uncertainties. After analyzing the typical velocity profiles, statistical properties are used to develop a novel Signed Markov Chain Monte Carlo (SMCMC) method that can enhance the accuracy of velocity prediction by more than 50%, compared to conventional Markov Chain methods. Next, considering the uncertainties present in various driving scenarios, the development of driving cycle recognition model based on fuzzy logic control (FLC) is introduced; this method permits to identify the current category of driving cycle rapidly. Then, dynamic programming (DP) is adopted to solve the rolling optimization problems in each finite horizon online, including necessary constraints of dynamic response. Finally, simulation results demonstrate that the proposed energy management strategy can address various daily driving cycles well, and can improve the energy performance by 6% under a generalized combination of driving conditions compared to preliminary rule-based control.

Considering the regular characteristics of city bus routines, dynamic programming (DP) is much better than rule-based strategy in developing the global optimal control strategy for a dual-motor coaxial coupling propulsion electric bus (DMCEB). However, its complexity and computational burden hinder the implementation capability. To solve the trade-off problem between optimum and practicability, a novel systematic extraction method of energy management strategy is proposed in this paper. Globally, DP is applied offline to find optimal operating points of propulsion components. Explicit shift schedule lines are designed by utilizing nonlinear support vector machine (SVM) with a new efficient factor tuning method. Torque split ratio (TSR) is extracted statistically in quadratic functions by using recursive k-means clustering and piecewise polynomial fitting methods. Then integrated rule-based strategy can be developed systematically and implemented online with proper operating range and limits. Eventually, hardware-in-the-loop (HIL) experiment results demonstrate that the proposed method can be executed through a 16-bit onboard processor in real-time. The online performance of the improved strategy is highly consistent with simulation results, which can reduce energy consumption by 21.06% compared to the preliminary heuristic strategy. Moreover, the applicability of the proposed methodology is further verified over different typical driving cycles.

Energy Management strategy is indispensable for the power allocation control and energy-saving performance of a dual-motor coupling propulsion electric bus (DMCEB). Considering the constraints and instantaneous optimization, a strategy based on model predictive control is proposed. Firstly, the stationary feature of the driving cycle trend is analyzed and a novel acceleration sign prediction process is explored based on auto-regressive moving average (ARMA) method. Secondly, two specific state transition probability matrices are established according to the acceleration sign and piecewise Markov chain model is utilized to predict velocity sequences in the 5-second horizon followed by prior process. The fluctuations are significantly moderated under the effect of acceleration sign prediction and the RMSE can be controlled within around 1.4119 km/h. At last, dynamic programming (DP) is adopted as the online rolling optimization part of the model predictive control (MPC) based strategy and DP-based results are used as the benchmark to evaluate the control effect. The simulation results show that, the energy economy based on the proposed strategy decreased by 21.4% comparing with the preliminary rule-based strategy and is only 6.8% worse than that based on DP. With the 85.75 kWh/100 km energy consumption performance and low mode switch frequency, the strategy is reasonable and suitable for electric bus.

A practical dual-motor coaxial coupling propulsion system for electric city-buses and an extraction method for optimal energy management strategy are proposed. The powertrain configuration of the dual-motor coaxial coupling propulsion bus (DMCEB) is illustrated. To research the energy-saving strategy of DMCEB, the comprehensive state and cost functions are established. Then dynamic programming (DP) algorithm is adopted to find the global optimal energy management strategy including the transmission shift schedule, the torque split ratio and the operating points of the two motors. Since the DP-based strategy is hardly implemented in real vehicle directly, a novel application-oriented optimal rule-based strategy is extracted from the DP results by utilizing non-linear support vector machine (SVM) classifier, K-means clustering and piecewise polynomial fitting methods. The extracted strategy is used to redesign and improve the preliminary rule-based strategy, which can decouple the online calculation and offline application parts. The simulation results demonstrate that the extracted strategy is only around 10% worse than that based on DP results. However, it can be executed online easily due to its simplified polynomial form.