A Real-Time Robust Ecological-Adaptive Cruise Control Strategy for Battery Electric Vehicles

Abstract

This work addresses the ecological-adaptive cruise control problem for connected electric vehicles by a computationally efficient robust control strategy. The problem is formulated in the space-domain with a realistic description of the nonlinear electric powertrain model and motion dynamics to yield a convex optimal control problem (OCP). The OCP is approached by a novel robust model predictive control (RMPC) method handling various disturbances due to modelling mismatch and inaccurate leading vehicle information. The RMPC problem is solved by semi-definite programming relaxation and single linear matrix inequality (sLMI) techniques for further enhanced computational efficiency. The performance of the proposed real-time robust ecological-adaptive cruise control (REACC) method is evaluated using an experimentally collected driving cycle. Its robustness is verified by comparison with a nominal MPC which is shown to result in speed-limit constraint violations. The energy economy of the proposed method outperforms a state-of-the-art time-domain RMPC scheme, as a more precisely fitted convex powertrain model can be integrated into the space-domain scheme. The additional comparison with a traditional constant distance following strategy (CDFS) further verifies the effectiveness of the proposed REACC. Finally, it is verified that the REACC can be potentially implemented in real-time owing to the sLMI and resulting convex algorithm.

Figures (13)

• Figure 1: Scheme of EACC paradigm with V2I and V2V Communications.
• Figure 2: Block diagram of the battery electric vehicle powertrain with a DC-DC converter, a DC-AC converter (an inverter), a PMS motor (a generator), and a mechanical transmission set. Green and blue arrows represent electrical and mechanical power flows, respectively.
• Figure 3: Battery electric vehicle power consumption, $P_{b}$ (shown in Fig. \ref{['fig:drive_powertrain_map']}) and powertrain efficiency, $\eta_{p}$ (shown in Fig. \ref{['fig:drive_powertrain_eff']}), associated with powertrain driving force, $F_t$ and vehicle velocity, $v$. Positive force means the battery is discharging while negative force indicates the battery is recharging. Solid lines in Fig. \ref{['fig:drive_powertrain_map']} and Fig. \ref{['fig:drive_powertrain_eff']} are battery power consumption and efficiency contour lines, respectively. Black dashed lines are motor operational bounds. Red dashed rectangles denote the feasible overall vehicle powertrain operating range which is determined by the minimum and maximum powertrain driving forces ($F_{t,\min}$ and $F_{t,\max}$), as well as the minimum and maximum velocities ($v_{\min}$ and $v_{\max}$, where for illustration purposes the highest value of $v_{leg}$ is shown for $v_{\max}$ in Fig. \ref{['fig:drive_powertrain']}).
• Figure 4: Electric vehicle battery power fitting map by the quadratic function in \ref{['eq:powertrain_fit']}. Nonlinear regression of the battery-drawn power data represented by the blue regression surface, calculated based on the power consumption map shown in Fig. \ref{['fig:drive_powertrain_map']}, with the coefficient of determination, $R^2= 0.995$.
• Figure 5: $18.7$ km route in London UK selected for the velocity profile of the leading vehicle in the numerical simulations. (https://goo.gl/maps/2CTCW7smdCkGCsKv5)

Theorems & Definitions (1)

• Remark 1