How to develop an optimization-based, causal, cascaded, map-based energy management strategy for hybrid electric vehicles with multiple control variables?
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An optimization-based, causal, cascaded, map-based energy management strategy for hybrid electric vehicles with multiple control variables can be developed by combining various approaches proposed in the literature. One approach is to use deep reinforcement learning (DRL) based methods, such as the supplementary learning controller (SLC) proposed by Hu et al. This SLC works alongside an existing rule-based EMS to search for an optimal solution for the action, reducing uncertainty and improving convergence speed. Another approach is the hierarchical control strategy of predictive energy management (PEM) proposed by Li et al. This strategy includes two layers of algorithms for speed planning and energy distribution, using predictive cruise control and dynamic programming to optimize fuel consumption. Additionally, an energy management strategy based on driving pattern recognition, driving condition prediction, and model predictive control is proposed by Hao et al. This strategy improves fuel consumption while maintaining battery state of charge through the use of discretization, linearization, and quadratic programming. By combining these approaches, an optimization-based, causal, cascaded, map-based energy management strategy can be developed for hybrid electric vehicles with multiple control variables.
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| The provided paper does not discuss the development of an optimization-based, causal, cascaded, map-based energy management strategy for hybrid electric vehicles with multiple control variables. | |
| The provided paper does not mention the development of an optimization-based, causal, cascaded, map-based energy management strategy for hybrid electric vehicles with multiple control variables. |
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