Two-level optimization strategy for vehicle speed and battery thermal management in connected and automated EVs

Yan Ma, Qian Ma, Yongqin Liu, Jinwu Gao and Hong Chen

Applied Energy, 2024, vol. 361, issue C, No S0306261924003118

Abstract: The performance of the battery is affected by temperature, and the battery thermal management (BTM) system consumes considerable energy to maintain the temperature in the suitable range. The unnecessary acceleration and deceleration of electric vehicles (EVs) during driving causes higher energy consumption in the powertrain. The emergence of connected and automated vehicle (CAV) technology provides an opportunity for predictive control of thermal and energy management. To explore the coordination optimization between battery thermal and vehicle energy management, this article proposes a two-level optimization framework for the speed and BTM of EVs, which improves energy efficiency and battery safety. Each level consists of a sequential optimization of speed and battery thermal. In the upper layer, speed planning based on iterative dynamic programming (IDP) is first proposed to reduce powertrain energy consumption using intelligent traffic information. Then, based on the BTM system and the battery thermodynamics features, the long-term optimal trajectory of the battery temperature is derived according to optimized speed. In the lower layer, the model predictive controllers (MPC) are designed to track reference speed and temperature trajectories in real-time and enforce energy saving. Meanwhile, to improve the prediction accuracy of the system model, we integrate the Gaussian process (GP) model in the MPC and build the learning-based MPC strategy. Simulation results verify the performance of the proposed method which reduces the powertrain energy consumption by 20.95%. In the high and low temperature environment, compared with normal MPC, PID-based and Rule-based, it reduces BTM energy consumption by up to 15.69%, 29.68% and 38.73%.

Keywords: Connected and automated vehicle; Battery thermal management; Iterative dynamic programming; Reference trajectory planning; Learning-based MPC (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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DOI: 10.1016/j.apenergy.2024.122928

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