A regenerative braking system and hydraulic braking system are used in conjunction in the majority of electric vehicles worldwide. We propose a new regenerative braking distribution strategy that is based on multi-input fuzzy control logic while considering the influences of the battery’s state of charge, the brake strength and the motor speed. To verify the braking performance and recovery economy, this strategy was applied to a battery electric vehicle model and compared with two other improved regenerative braking strategies. The performance simulation was performed using standard driving cycles (NEDC, LA92, and JP1015) and a real-world-based urban cycle in China. The tested braking strategies satisfied the general safety requirements of Europe (as specified in ECE-13H), and the emergency braking scenario and economic potential were tested. The simulation results demonstrated the differences in the braking force distribution performance of these three regenerative braking strategies, the feasibility of the braking methods for the proposed driving cycles and the energy economic potential of the three strategies.

Agricultural tractors account for a substantial portion of greenhouse gas emissions in the farming sector, necessitating the development of sustainable machinery solutions. This study systematically reviews the latest advancements in electrification and smartification technologies for modern tractors, with a particular focus on algorithmic control strategies and their applications. Architecturally, the study provides a comparative analysis of four key configurations, pure electric, series hybrid, parallel hybrid, and series-parallel hybrid, detailing their respective advantages and challenges in energy efficiency and operational performance. From an algorithmic perspective, three primary methodologies—rule-based control strategies, optimization algorithms, and reinforcement learning—are examined for their applicability in energy management and control systems. The research further explores the integration of intelligent systems in unmanned farming scenarios, addressing critical challenges such as adaptive path planning in unstructured environments and multi-machine collaborative operations. A case study on battery-electric tractors demonstrates practical advancements in battery technology and energy management systems. Lifecycle cost analysis confirms the long-term economic viability of electrification, while outlining a forward-looking technological roadmap for sustainable and intelligent agricultural machinery.

Urban logistics holds the lifeline of global business and consumption demand. The amount of transport vehicles rises gradually but also leads to graver emission problem. Therefore, electrified vehicles are considered as better options to substitute traditional logistics vehicles. Extended range electric logistics van (ERELV) provides a solution by providing satisfactory driving range and lower production cost than battery electric logistics van (BELV). This work aims to present a thorough energy consumption and total cost of ownership (TCO) analysis for an extended range electric van to present its energy potential. Both the ERELV and BELV mathematical models are constructed and compared, and their long-term battery degradation comparison is studied for the first time. Dynamic programming (DP) is adopted in the energy management strategy for energy consumption optimization, and the global optimization result reveals the optimal energy consumption of the ERELV. Comparative results demonstrate that the ERELV has a relatively longer drive distance, slower battery aging trend, and cheaper TCO (6.6%) when comparing to the BELV. The adoption of ERELV will promisingly reduce operating cost as the alternative transportation solution for urban logistics.