With the rapid adoption of electric vehicles (EVs), the need to optimize energy efficiency and battery longevity has intensified. While extensive research has focused on technological advancements such as battery chemistry and powertrain design, driver behavior remains a relatively underexplored yet major determinant of energy consumption and battery State of Health (SOH). This systematic review synthesizes peer-reviewed studies published between 2010 and 2025 to examine the relationship between driving behavior, energy demand, and battery degradation in battery electric vehicles (BEVs). The synthesis shows that aggressive driving—characterized by high acceleration, repetitive deceleration, and elevated Relative Positive Acceleration (RPA)—can drive energy consumption variation of up to 30–50% over comparable journey distances, whereas eco-driving practices can extend range by approximately 4–11% in real-world conditions. Real-time adaptive energy prediction algorithms that incorporate behavioral correction achieve mean absolute percentage errors below 5%. Aggressive acceleration also elevates thermal stress above the safe operating threshold (>40 °C), accelerating capacity degradation and shortening battery lifespan. Importantly, a trade-off emerges: driving habits that lower per-kilometer energy use can simultaneously prolong exposure to suboptimal operating conditions for the battery, indicating that energy-minimization and battery-longevity objectives must be balanced rather than treated as equivalent. Longitudinal evidence further indicates that drivers stabilize their behavior after roughly 3–5 months of EV acclimation. Building on these findings, this review (i) maps driver-behavior effects to four research questions covering energy variation, battery health, predictive modeling, and longitudinal adaptation; (ii) clarifies the trade-off between immediate efficiency and long-run durability; and (iii) outlines a behavior-aware research agenda for intelligent Battery Management System (BMS) architectures and predictive control.